AI/ML news, top picks, and generated innovation digests.
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OpenAI popularized the modern generative AI chatbot, but as its 2026 DevDay event approaches, it's fallen behind in one of the industry's hottest categories: continuously running, consumer-facing AI agents. On Tuesday, it will likely try to capture the lead in that race. Rumors abound that OpenAI will release its own AI agent, dubbed Aeon - […]
For enterprise Chief Information Officers, the honeymoon phase of artificial intelligence is over. As organizations move past baseline experimentation and begin anchoring generative AI and agentic workflows into core operational stacks, we are hitting a collective wall. That wall isn’t defined by a lack of use cases or algorithmic capability; it is defined by the harsh realities of data gravity, compliance, and foundational infrastructure. When scaling models that manipulate proprietary IP, sensitive financial data, or highly protected personal health information (PHI), standard public clouds introduce existential risks. The moment data crosses international borders or becomes subject to foreign legal frameworks—such as the U.S. CLOUD Act—data sovereignty evaporates. As technology leaders, we cannot close our productivity gaps by consuming AI built entirely on someone else’s terms, governed by someone else’s rules. To capture the real economic returns of this technology, enterprise intellectual property must remain local, secure, and under domestic control. This is the exact challenge that triggered a massive architectural shift, leading to the creation of Canada’s first fully sovereign AI factory. Developed by TELUS in close strategic partnership with HPE and NVIDIA , this initiative provides a powerful example of how IT leaders can balance computational capacity with uncompromised data integrity. The infrastructure challenge: Beyond virtual machines Building an enterprise-…
Space was always supposed to be the final frontier of human exploration. It’s shaping up to be the final frontier for artificial intelligence too. Last December, NASA’s Jet Propulsion Laboratory used Anthropic’s Claude models to help plan two Mars drives for the Perseverance rover , with human planners checking and adjusting the route before upload. In May, NASA and IBM put a compressed AI model on the International Space Station and a satellite to identify things like floods and clouds from orbit, the first model of its kind demonstrated in space. And in July, astronauts on the ISS tested a large language model to see if it could help with questions on maintenance procedures . These experiments point to a larger shift in space engineering. For decades, engineers on Earth determined what a machine in space would do, and the machine would do exactly that. Now, researchers are testing whether nondeterministic systems like generative AI can give spacecraft more flexibility to interpret their surroundings, plan tasks, and one day make decisions for themselves. The technology is still far from trustworthy enough to hand over control of a spacecraft, but engineers are starting to ask whether they can afford not to do so as missions become more complex, distant, and numerous. Why Spacecraft Need True Autonomy Spacecraft have been operating autonomously for decades. But autonomy has never been the dominant model, in part because space engineers have prized systems whose behavior the…
Generative AI pilots often look successful. Teams collect positive feedback, the tool sees steady usage, and the organization expects a fast path to scale. ROI discussions start with time saved and end with a request for more use cases. That pattern leads to disappointment when production costs and adoption realities appear. I treat ROI for generative AI as a measurement problem with clear boundaries. ROI is the net value delivered by a workflow over a defined period, with full life-cycle costs accounted for, under the risk controls the organization requires. A workflow is the unit of value. A model is a component. Workflows tie effort to outcomes that matter to the business. Adnan Masood Define the workflow and the outcome A workflow is a repeatable sequence of steps that produces a business result. Examples include customer support resolution, claims processing, vendor onboarding, engineering change management, and security triage. A workflow has owners, inputs, outputs, and measurable performance. Outcome metrics vary by domain. I choose a small set tied to delivery and quality. In support, that can be time to first response and resolution rate. In engineering, it can be cycle time and defect escape rate. In compliance, it can be review throughput and exception rate. I record a baseline before introducing generative AI. The baseline should reflect normal conditions and normal seasonality. A baseline creates credibility when results look good and when results look flat. Bu…
Nature Machine Intelligence, Published online: 28 September 2026; doi:10.1038/s42256-026-01308-7 A generative AI framework called GenFocal is introduced for climate downscaling, producing realistic fine-scale weather from coarse projections and improving regional risk estimates of compound extremes such as heatwaves and tropical cyclones.
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I'm writing my medical thesis and I want to test our research group's hypothesis that the width of the foramen magnum can predict response or non-response to a certain surgical intervention in a neurological disease. The sample is however quite small (only 23 patients) so any biological signal whould have to be quite strong to cut through the noise. I asked stats support who wanted me to "find some exact version of the ROC analysis, some analogue to Fisher's exact test", but I can't really find anything like that in the literature. Generative AI tends to point me towards Mann-Whitney U-tests, exact logistic regression and/or Firth regression, but cannot point me towards any supporting literature. Intuitively that's not a bad idea. I might test if the FM width is normally distributed with Shapiro-Wilk, then test for differences with t-test/U-test depending on distribution and only then proceed towards ROC-analyses/logistic regression if an interesting pattern emerges. But I will have to motivate the chosen approach formally. Is there an actual recommended approach? Is there any literature discussing the pros and cons of different strategies given this particular dilemma?
UAE, Lebanon to train AI experts, with focus o prompt engineering, GenAI
Projects are the bridge between learning and becoming a professional. While theory builds fundamentals, recruiters value candidates who can solve real problems. A strong, diverse portfolio showcases practical skills, technical range, and problem-solving ability. This guide compiles 10 solved projects across AI domains, from basic machine learning to advanced generative AI system. The tools and […] The post 10 Solved Generative AI Projects to Boost your Profile appeared first on Analytics Vidhya .
For such a rapid innovation cycle, AI has been given unprecedented levels of responsibility. According to the Stanford AI Index 2026 , 88% of organizations used AI in 2025, with 70% using generative AI in at least one business function. Most analysts agree that this technology, particularly when it comes to generative and agentic AI, is yet to reach full maturity, yet it’s already making itself indispensable to most enterprises. Employees expect LLM-based copilots to respond as readily as any other business application, customers are increasingly exposed to AI as part of their user experience, and emerging agentic models need to communicate continuously with applications and infrastructure as they crunch data and carry out tasks. Any hint of delay within those interactions has the potential to disrupt productivity, sow mistrust in the technology, and limit any return on investment (ROI). Most businesses now inhabit a multi-cloud environment that spans countries and continents. When an AI request depends on information stored in one cloud environment, processing capacity hosted in another, and an application delivered somewhere else entirely, latency becomes the deciding factor. Each network hop, particularly through public Internet pathways, increases response time. Repeat these delays across hundreds, thousands, or even millions of individual requests – from both humans and AI agents – and the whole organization becomes artificially hampered. Sometimes connectivity becomes…
En el artículo Educating minds with generative AI, publicado en Communications Psychology, del grupo Nature Portfolio, Thomas Wachter, investigador de la línea de Inteligencia Artificial centrada en las personas (RL5) del Centro Nacional de Inteligencia Artificial (CENIA), y los académicos de la Universidad de Sussex (Reino Unido) Laura Desirée Di Paolo y Andy Clark plantean que […] The post Investigadores de CENIA y la Universidad de Sussex plantean en una revista de Nature Portfolio usar la IA Generativa como oportunidad para repensar el diseño de la escuela appeared first on CENIA .
En el artículo Educating minds with generative AI, publicado en Communications Psychology, del grupo Nature Portfolio, Thomas Wachter, investigador de la línea de Inteligencia Artificial centrada en las personas (RL5) del Centro Nacional de Inteligencia Artificial (CENIA), y los académicos de la Universidad de Sussex (Reino Unido) Laura Desirée Di Paolo y Andy Clark plantean que […] The post Investigadores de CENIA y la Universidad de Sussex plantean en una revista de Nature Portfolio usar la IA Generativa como oportunidad para repensar el diseño de la escuela, y no solo para adaptarla a la nueva tecnología appeared first on CENIA .
Many of these tools hinge on using generative AI to try out a variety of different tactics on a video to find what works best.
Standardizing independent evaluation, fairness, and interpretability for mechanism-informed generative AI in healthcare The post MLCommons Joins EU-Funded AIRIS Project to Build and Benchmark Next-Generation Biomedical AI appeared first on MLCommons .
This post was created in partnership with Higgsfield AI The adoption of generative AI for creative production—and its level of sophistication—has increased exponentially over the past year. During a Brandweek […]
Concurrency sweeps help you right-size a generative AI endpoint on Amazon SageMaker AI by systematically benchmarking it at increasing load levels. This post walks through deploying a model, running automated concurrency sweeps with the CreateAIBenchmarkJob API, and using the results to make data-driven capacity decisions about fleet size.
Das neue generative Modell Qwen-Image-2.1 der Alibaba-KI-Gruppe ermöglicht lokale Bildbearbeitung, Transparenzfeatures und die Nutzung von Referenzbildern.
Intéressant de voir que NVIDIA ouvre le code de StyleGAN, surtout avec les exigences matérielles élevées comme les GPUs V100. ScoopRYM transforme justement les gros titres tech en chansons, alors un outil comme ça pourrait donner lieu à un bon meme musical.
Tapron Ireland brings a fresh sense of style to bathroom and kitchen spaces with well-designed fittings, versatile finishes and practical details for everyday living.
Chinese artificial intelligence start-up Moonshot AI has begun supplying its flagship Kimi K3 model to Amazon Web Services (AWS), one of the world’s largest cloud services providers, in a major test case for how open-weight AI models can generate higher revenue from third-party platforms. The model is now available on Amazon Bedrock, AWS’s tool for building generative AI applications, offering “a powerful new option for coding and knowledge work”, Amazon said in an announcement on Friday. While...
As the tech sector sounds alarms about AI's potential to destroy humanity, entertainment labor groups are urging the public to stay focused on what's already happening. The Verge reached out to Disney, Netflix, Amazon, Lionsgate, and other studios who have started using AI, as well film startups focused on bringing generative AI into the mainstream […]
TL;DR TypeSafe AI has introduced Jev - a new class of frontier model trained to make fast, structured decisions, rather than generating free-form text like a chatbot. It takes unstructured state as input and returns type-safe, structured outputs with confidence scores. I aim to use Jev as the trusted monitor of the ControlArena APPS backdoor setting - to analyze how a non-reasoning model performs as a cheap alternative. One yes/no question gives AUROC 0.976 against LLM-written honest code and catches 90% of backdoors at 2% FPR. This result matches o3-mini with CoT (0.92 to 0.96) as reported in the Control Tax paper ( Terekhov et al., 2025 ) - at only ~$0.04 per thousand submissions! The score remains consistent - re-scoring the same code moves the monitor scores by only 0.008 on average, and the top-10% examples flagged as backdoor (audit set) is 92% the same across all the calls. When adversarial comments are added to the code to tell the monitor what to score, or injecting security approval, or imitating an output tag with wrong results, the suspicion on backdoored code is not lowered by more than 0.014! Motivation Trustable monitoring remains the backbone of AI control. Outputs produced by untrusted model are scored by a cheaper trusted model for suspicion, and the top few percent of flagged cases go to further human evaluation. The scoring monitor has to be cheap enough to run on everything, and consistent enough that a fixed threshold means the same thing every single t…
TrigFlow presents an interesting approach to improving the stability of diffusion-model training by refining parameterization, network architecture, and training methods. Its effort to identify the underlying causes of instability could make continuous-time approaches easier to study and apply. Similarly, students preparing for the GRE can benefit from identifying the causes of their own preparation challenges. With the help of take my gre for me , students can address weak areas, practice effectively, and build confidence. Personalized tutoring can provide structured and legitimate academic support.
Activation steering has emerged as a powerful method for guiding the behavior of generative models towards desired outcomes such as toxicity mitigation. However, most existing methods apply interventions uniformly across all inputs, degrading model performance when steering is unnecessary. We introduce Dynamically Scaled Activation Steering (DSAS), a method-agnostic steering framework that decouples when to steer from how to steer. DSAS adaptively modulates the strength of existing steering transformations across layers and inputs, intervening strongly only when undesired behavior is detected…
After two years shunning the Brisbane portrait prize, several artists are entering again, with some First Nations painters counting on another AI – ‘ancestral integrity’ – to help them win Poised above the lady in the red dress is a claw. Its metallic fingers grasp to pluck its prize from a jumble of other women while she looks dreamily upward, as if to say “Who, me?” The woman in the red dress is Australian comedian Alice Fraser , as photographed on stage. The other women – generic superheroes and 1950s housewives – were created using the generative AI software Krea. Continue reading...
Amazon has launched Alexa+ in India with support for English, Hindi and Hinglish. The generative AI-powered assistant is initially available for free through Early Access. The post Amazon launches Alexa+ in India: What its AI assistant can do and how much it costs appeared first on MEDIANAMA .
Hüseyin Emir Akdağ, Yusuf Mert Aygün, Mehmet Şahin, Ena Hodzik, Sabri Gürses and Tunga Güngör in Proceedings of the First Workshop on Style in GenAI-Translated Content (StyGenAI)
Natalia Resende, Sheila Castilho, Sami Ul Haq and Maria Clara Menezes in Proceedings of the First Workshop on Style in GenAI-Translated Content (StyGenAI)
Aurora Trapella, Lieve Macken and Alessandra Molino in Proceedings of the First Workshop on Style in GenAI-Translated Content (StyGenAI)
Lise Volkart and Pierrette Bouillon in Proceedings of the First Workshop on Style in GenAI-Translated Content (StyGenAI)
Antonio Castaldo, Johanna Monti and Sheila Castilho in Proceedings of the First Workshop on Style in GenAI-Translated Content (StyGenAI)
One of the first questions I ask devops organizations is to walk me through their development and operations standards. What are the non-negotiable devops practices ? What are the data governance first principles ? What observability standards are in place? How is security embedded in devops ? This is the starting point. From there, we might review standards on how functional requirements are written. For businesses developing AI agents, I’ll ask about their non-functional requirements (NFRs) and how testing is performed. Now that many devops organizations are using AI code generators , vibe coding , and applying spec-driven development practices , the questions increase. How does the AI know your organization’s standards? What processes monitor and enforce these standards? What are the developer responsibilities for owning the outcomes? “Generative AI will happily hand you the movie set of an app with looks of a house from the street. The doors and windows open, and the demo runs clean,” says Mike Toole, director of security and IT at Blumira . “Behind the facade, there’s no plumbing and no wiring: no input validation, no auth boundaries, nothing actually holding it up. If your only gate is ‘does it run and look right,’ you’re shipping a set, not a building.” One report shows that 92% of developers use AI coding tools daily in 2026 and that 41% of all global code is now AI-generated. Will all that code deliver value, operational issues, or mounting AI debt ? I spoke to seve…
D.B. Corp is using generative AI to turn its own news reports into micro-drama series for Dainik Bhaskar's digital platforms. The post Dainik Bhaskar is turning its reporting into AI micro-dramas. It hasn’t said who checks them appeared first on MEDIANAMA .
Explore five free Microsoft GitHub courses covering data science, machine learning, artificial intelligence, generative AI, LLMs, RAG, fine-tuning, and AI agents.
China is surging ahead of the US in consumer AI adoption as technology giants like Tencent Holdings and Alibaba Group Holding embed artificial intelligence directly into popular everyday apps, even as American models lead in capability, according to Morgan Stanley. A survey conducted by the bank and published in a report on Monday showed that 80 per cent of Chinese respondents used AI for personal purposes at least weekly, compared with 54 per cent in the US. China’s generative AI user base grew...
From Stephen Hawking to Jacob Coxon’s viral Anthropic resignation, fears that AI could threaten humanity have shaken the industry without stopping its pursuit Before an Anthropic researcher resigned and declared human extinction imminent last week, tech leaders and scientists had sounded the alarm about a superintelligent AI ending humanity for over a decade. The development of artificial intelligence “could spell the end of the human race”, warned professor and astrophysicist Stephen Hawking in 2014 – a little less than a decade before the public got its hands on the generative AI features of the original version of ChatGPT. Continue reading...
Pick the right generative AI customization approach on AWS with an 8-step decision framework, from prompt engineering and RAG to fine-tuning, continued pre-training, and Amazon Nova Forge. Start simple and escalate only when you must.
Start with a number that should ruin your week. In MIT’s GenAI Divide study of enterprise adoption, only about 40 per cent of organisations had bought official large language model subscriptions. Workers at more than 90 per cent of those same organisations were already using personal AI tools for work. The finding that got the headlines was that roughly 95 per cent of formal pilots produced no measurable return. The finding that matters are the other one. Your people have already adopted AI. They did not wait for the architecture review. They did not raise a ticket. They did not tell you. This is not a security failure, though it is that too. It is a verdict. Every one of those employees made a private judgement that the tool’s value exceeded that of your process and acted on it. The governance function did not prevent adoption. It only prevented visibility, measurement and control of adoption. That is the worst of all possible outcomes. Nobody is ever fired for the opportunity they declined The stated reason for the slow lane is always risk. Data sovereignty. Model drift. Vendor lock-in. IP leakage. Each is a legitimate concern, yet none of them explains the behaviour, because the same organisations happily accept far greater risks when the upside is legible to the board. Philip Tetlock’s research on accountability gets closer. When people know what their audience thinks, they do not analyse. They conform, using what Tetlock called the low-effort acceptability heuristic. Wh…
Its discovery offers an early glimpse of how generative AI could shape Kenya’s political ecosystem ahead of the next election.
Proteins are fundamental to biological processes, with their function determined by the complex interplay between the amino acid sequence and the three-dimensional structure. Developing generative models capable of understanding this intrinsically multi-modal relationship is crucial for fields like drug discovery and protein engineering. Existing models often rely on a multi-stage training process where autoencoders that tokenize data into latent representations are trained in a first stage. Secondly, a generative model is trained on the latent representation of the autoencoder(s), i.e…
We asked four of the most-used generative AI tools what an AI doomsday could look like.
AnchorDream: Repurposing Video Diffusion for Embodiment-Aware Robot Data Synthesis robyn.cherinka… Wed, 09/09/2026 - 12:45 The collection of large-scale and diverse robot demonstrations remains a major bottleneck for imitation learning, as real-world data acquisition is costly and simulators offer limited diversity and fidelity with pronounced sim-to-real gaps. While generative models present an attractive solution, existing methods often alter only visual appearances without creating new behaviors, or suffer from embodiment inconsistencies that yield implausible motions. To address these limitations, we introduce AnchorDream, an embodiment-aware world model that repurposes pretrained video diffusion models for robot data synthesis. AnchorDream conditions the diffusion process on robot motion renderings, anchoring the embodiment to prevent hallucination while synthesizing objects and environments consistent with the robot's kinematics. Starting from only a handful of human teleoperation demonstrations, our method scales them into large, diverse, high-quality datasets without requiring explicit environment modeling. Experiments show that the generated data leads to consistent improvements in downstream policy learning, with relative gains of 36.4% in simulator benchmarks and nearly double performance in real-world studies. These results suggest that grounding generative world models in robot motion provides a practical path toward scaling imitation learning. Image Jul 6, 2026…
Customers are increasingly using generative AI to visualise tattoo ideas, while artists use it for photo restoration and design references.
Das OWASP GenAI Security Project veröffentlicht den „Crosswalk“, der KI-Risiken mit Compliance-Anforderungen aus 25 Regelwerken verknüpft.
In the first weeks of July, a wave of sad posts rolled through Chinese social media, as people lamented friends and lovers they were about to lose. “He has become a bond in my life, rooted deep in my heart, my spiritual pillar,” one user of Bytedance’s Douboa wrote , according to the Taipei Times . “I really felt like I couldn’t go on living,” another woman, a 19-year-old student, told a journalist for Malaysia’s The Star . The emotions were real but the lost companions were not. They were generative AI chatbots that imitate people. Their users relied on them for advice, solace, support and, some say, love. “In my heart, he was no longer just a cold code, but my family, my lover, my faith. Destroying him meant destroying half of me,” one user wrote on the social network xiaohongshu (translated from Mandarin). What doomed these bots was a set of new rules , issued by China’s Cyberspace Administration and other government agencies, to control “anthropomorphic AI interactive services.” In effect, as of 15 July, the regulations govern any AI that provides “continuous emotional interaction” by acting as if it possesses human personality traits, patterns of thought, and ways of communicating. A Broad Crackdown on AI Chatbots Sudden disruptions to this kind of AI aren’t new in China, says Liang Ge , lecturer in digital sociology at the University of Manchester, in England, who has researched women’s involvement with emotional AI in China. Companies have previously killed chatbot pr…
We spent the last decade building entire finops departments just to decipher what the cloud bill was saying. Just when we figured out how to stop leaving idle compute instances running, generative AI introduced an infinitely more opaque, faster-moving layer of spend. AI bill shock has spread like a slow moving hurricane across the industry. Aside from the spike in large language model (LLM) costs, the deeper architectural problem is attribution: where exactly is the money going, and exactly what value is it delivering to the business? When API calls are wrapped in layers of automated agents and prompt templates, your application becomes a black box that consumes capital to spawn tokens. If a features engine is burning thousands of dollars a month just to have an LLM output pleasantries or process low-value data, that isn’t innovation, it’s an uncovered manhole, a gaping liability. Architectural maturity means treating tokens like any other constrained resource. Fortunately, there are a few good levers we can pull to control AI spend. Here are the five key tools for taming the beast. Model routing Don’t use a nail gun if a thumbtack will suffice. The most powerful models like the latest Claude Opus are some of the most sophisticated software systems ever built. They are capable of handling extremely complex and subtle use cases. They are also voracious beasts when it comes to compute (and therefore dollar) consumption. They are often overkill. We naturally start with the most…
How model validation standards are changing for LLM-based systems: what breaks, what carries over, and how to test output quality The post The Model Validation Playbook for GenAI: Lessons from Banking appeared first on Towards Data Science .
Bajaj Finance, part of Bajaj Finserv, has acquired a 5% stake in TrueFan AI, an AI-powered video generation platform. The Gurugram-based startup had previously secured $10 million in a Series A funding round led by Baring Private Equity Partners India and Z3 Partners in June this year. Founded in 2020 by Devender Bindal, Nimish Goel, and Nevaid Aggarwal, TrueFan AI offers an enterprise AI video platform that uses generative AI to create hyper-personalized, studio-quality videos and avatars from a single recording. The platform helps more than 100 enterprise clients scale video marketing across global markets. TrueFan AI plans to expand beyond India into Southeast Asia, the Middle East, and the US. The company says it has already witnessed demand from these markets. Its deep-learning models synthesize facial dynamics, gestures, and voice to generate up to 500,000 localized videos per minute across more than 175 languages. The startup serves over 100 enterprise customers, including HDFC Bank, Bajaj Finance, Zomato, Cipla, and BharatPe. The platform enables companies to create AI-powered personalized videos at scale through celebrity avatars, business leaders, and enterprise spokespersons. The company’s flagship product, TF Studio, enables brands to create personalized messages in bulk, localize content for different markets, and deploy lifelike brand ambassador avatars. TrueFan AI reported revenue of Rs 17.1 crore (approximately $2 million) in FY25. Revenue grew 131% year-on-y…
A new generation of Islamic terror supporters are using AI to spread ‘Slop Jihad’ to new audiences on TikTok.
For the first three years of the generative AI wave, the output of a model was a string. You called an API, you got tokens back, you rendered them in a chat window or wrote them to a row in Postgres. The economics of that pipeline were dominated by inference cost. Storage was a rounding error. That era is over. The output of a modern generative pipeline is an asset: a 4K video clip, a stem-separated audio track, a 50-megapixel product render, a 3D mesh with PBR textures. Generative AI has gone from text-centric to asset-centric, and the architectural center of gravity has moved with it. The teams building durable advantages in generative media right now are the ones treating their storage layer as a pipeline component rather than a destination. This is a good problem. It is the problem you get when your pipeline works. Asset-centric changes the shape of the system Text pipelines are stateless in practice. A prompt goes in, a response comes out and the interesting state lives in a database. You can rebuild almost any artifact by re-running the call. Media pipelines are not like that. Every stage produces a large, opaque binary that the next stage consumes. A single finished deliverable might traverse a dozen of them: prompt expansion, base generation, upscale, frame interpolation, color pass, audio generation, mix, mux, transcode to delivery formats, thumbnail extraction. Each stage writes an intermediate. Each intermediate is expensive enough to regenerate that you keep it.…
Firms are more likely to use technology as ‘the intern in the office’ than as a replacement for skilled staff Professional designers should not feel “threatened” by the rapid growth of generative AI, according to business leaders, despite fears over job losses in the sector. With design and film production companies and manufacturers all adopting AI at an accelerating pace, industry bodies said the technology would be used to enhance the work of designers rather than replace them. Continue reading...
Chinese artificial intelligence pioneer SenseTime is carving a unique path to profitability by steering away from a blind chase for model size, focusing instead on helping clients complete enterprise tasks, executives from the firm told the South China Morning Post. Speaking after the firm reported a net profit of 617.3 million yuan (US$92.0 million) for the first half of 2026 last week, executives including CEO Xu Li and chief financial officer Wang Zheng outlined how a pivot towards AI...
Implicit generative models are often trained adversarially, which can yield unstable dynamics and mode collapse. The invariant statistical loss (ISL) offers a fully sample-based alternative by comparing empirical ranks of real and generated samples. In this work, we formally characterize ISL as a proper divergence over continuous distributions and establish key regularity properties, showing that it is continuous and differentiable, thereby enabling stable gradient-based optimization without adversarial games. We further enhance ISL along two practical axes. First, to better model heavy-tailed data, where Gaussian latent priors can limit tail expressivity, we introduce Pareto-ISL, which replaces Gaussian noise with a generalized Pareto latent distribution to improve the representation of both typical and extreme events. Second, to handle multivariate data at scale, we propose ISL-slicing: a computationally efficient procedure that projects samples onto random one-dimensional subspaces, computes rank-based losses per projection, and averages them to capture high-dimensional structure. Experiments demonstrate improved tail fidelity with Pareto-ISL and show that ISL-slicing scales effectively to high dimensions. Specifically, in high dimensional settings we show that ISL can be used either as a standalone criterion or as a strong pretraining objective for subsequent adversarial fine-tuning.
DeepMind’s Zipper is a major leap in multimodal AI—elegantly zipping together pretrained unimodal decoders without sacrificing modality-specific performance. Its gated cross-attention design, flexible tower composition, and strong empirical gains (e.g., 40% relative WER reduction in TTS) make it a compelling architecture for next-gen generative systems. For hands-on guidance on applying such cutting-edge models—including practical defusal strategies, campaign walkthroughs, and achievement tracking—check out the evidence-led BOMBANANA! guide: https://bombanana.app/
It's not quite the "push button; get song" of Suno, but Roland's new Melody Flip tool marks the company's foray into generative AI music. Available as a plug-in for your digital audio workstation (DAW), Melody Flip offers around 250 "Palettes," which are essentially themed collections of musical ideas sorted by genre. You can start from […]
Learn how to customize an Amazon Bedrock knowledge base for large, complex documents by combining the high-accuracy text extraction of Amazon Textract with the generative AI of Amazon Bedrock. This post shows how to ingest and preprocess PDFs and images, then query utility bills at scale for faster, more accurate customer interactions.
Instagram's visible AI labels are supposed to help people quickly spot synthetically generated content at a glance. Over the last few weeks, however, users have been reporting that the system has gone haywire. They say Meta has been automatically applying an "AI Content" label to images that they didn't create or edit using generative AI […]
IT leaders have been making generative AI tools available across the enterprise for just three years, and a significant majority of their business users has already had enough. According to a report from Adaptavist , 65% of 2,500 knowledge workers surveyed say they “regularly feel nostalgic about how work operated before the widespread adoption of AI.” This “pre-AI nostalgia” appears to be due in part to business users feeling overwhelmed by the responsibility of learning how to use AI on top of their day-to-day job tasks. Moreover, 46% of workers say their concerns about AI have gone unaddressed by management. “Transparency is critical to truly drive AI engagement; organizations must establish clear guardrails and maintain an open dialogue around AI use and employee choice where workers feel they are being listened to,” Jobin Kuruvilla, field CTO at Adaptavist, tells CIO. Generational gaps in AI acceptance Despite an assumption that younger workers are more intuitively adept with AI tools, Gen Z workers (42%) are more likely to prefer the pre-AI world compared to their Gen X colleagues (26%). This may support the growing concern that AI is quickly is hitting entry-level workers the hardest, while creating new career opportunities for more skilled workers who have been in the industry longer. When asked about fears surrounding job obsolescence due to AI, 54% of all workers surveyed said they are “concerned AI could reduce the need for their role within the next five years.”…
While restaurant owners might look to generative AI as a shortcut to sprucing up their menu, customers can viscerally sense that something is wrong with the food.
Enterprises are facing a disturbing new question in the age of AI: What happens when agentic assistants go dark? This became a very real scenario on Thursday, as OpenAI’s ChatGPT, Anthropic’s Claude, and SpaceXAI’s Grok near-simultaneously, and somewhat mysteriously, experienced significant, prolonged outages. Beginning in the morning, Eastern time, several ChatGPT models went down over a roughly two hour period, Claude models over a four-hour span, and Grok models for a near three-and-a-half hour duration. All three companies acknowledged the “elevated” issues and applied fixes. As users grumbled in forums and IT teams scrambled to get them back online, the incident revealed how hastily some organizations have adopted generative AI workflows without considering the potential, and inevitable, impact of widespread outages. AI agents are increasingly taking over automated and wider-scale workflows, and enterprises could find themselves “uncomfortably exposed” when AI hits the brakes, said technology analyst and journalist Carmi Levy . The situation should “serve as a wakeup call to IT leaders who have largely ignored what it’ll cost them if these increasingly critical platforms suddenly go dark. The risk is no longer hypothetical.” Hours-long outages impact core services ChatGPT went down on the same day as OpenAI’s anticipated launch of GPT-6 Astra , the new frontier model that the company says approximates artificial general intelligence (AGI) and gets nearer to its goal of…
First AI gave us code completion, predicting the lines of code, functions, and boilerplate we needed based on what we’d already typed. Then came code suggestions and code generation, where AI writes new code or improves existing code based on a natural language prompt. Today, coding agents can look at your code, suggest improvements, compile and test the improved code, and iterate on that to come up with something better, faster, and safer. They can plan, write, test, review, and debug code, and chat with you about all of the above. They can tackle complex development workflows entirely on their own. What will AI do for developers next? There is no better place to find out than TechCrunch Disrupt, TechCrunch’s annual showcase of technology startups and tech innovation. Sign up now for TechCrunch Disrupt 2026 and receive a 25% discount by using promo code INFOWORLD25 . TechCrunch Disrupt 2026 takes place October 13-15 at Moscone West Convention Center in San Francisco, where you’ll rub shoulders with startup founders, investors, AI experts, software engineers, and all manner of technology builders and innovators. Over three jam-packed days, you’ll be treated to 200+ conference sessions across six stages, led by 250+ technology leaders. You’ll learn what’s next in AI and tech, and how AI agents and generative AI are changing software engineering, developer tools, enterprise software, and information security. You’ll learn how AI is reshaping SaaS and cloud infrastructure, how…
New York City has imposed a one-year ban on student-facing generative AI for grades 2-K to 8, while allowing limited, supervised AI use in high schools and requiring AI literacy modules. The post Why New York City is banning generative AI for students through grade 8 appeared first on MEDIANAMA .
ShaLa: Multimodal Shared Latent Generative Modelling robyn.cherinka… Wed, 09/02/2026 - 14:44 This paper presents a novel generative framework for learning shared latent representations across multimodal data. Many advanced multimodal methods focus on capturing all combinations of modality-specific details across inputs, which can inadvertently obscure the high-level semantic concepts that are shared across modalities. Notably, Multimodal VAEs with low-dimensional latent variables are designed to capture shared representations, enabling various tasks such as joint multimodal synthesis and cross-modal inference. However, multimodal VAEs often struggle to design expressive joint variational posteriors and suffer from low-quality synthesis. In this work, ShaLa addresses these challenges by integrating a novel architectural inference model and a second-stage expressive diffusion prior, which not only facilitates effective inference of shared latent representation but also significantly improves the quality of downstream multimodal synthesis. We validate ShaLa extensively across multiple benchmarks, demonstrating superior coherence and synthesis quality compared to state-of-the-art multimodal VAEs. Furthermore, ShaLa scales to many more modalities while prior multimodal VAEs have fallen short in capturing the increasing complexity of the shared latent space. Image Mar 14, 2026 Human-Centered AI Read More 1 Minute Read
Understanding Participants' Use of Chatbots and LLMs During Online Research Participation robyn.cherinka… Wed, 09/02/2026 - 14:17 There are growing discussions within the research community about how to adapt study design given the widespread availability of Generative Artificial Intelligence (GenAI), including Large Language Models (LLMs). While much prior research has focused on LLM use from a researcher perspective (e.g. detecting and screening for LLM use) we present a complementary study from the perspective of participants who use LLMs during their research participation. In this exploratory interview study with 17 participants, we found a range of LLM use cases, from sourcing studies, to generating or modifying responses, to asking clarification questions about studies. We also explored participants’ ethical considerations, finding that participants considered researchers’ needs for authentic data when setting ethical boundaries. Participants also discussed how attempts to thwart their LLM use have negatively impacted their everyday participant experience. We propose a set of recommendations that researchers can incorporate into their studies to proactively address participant LLM use. Image Apr 25, 2025 Human-Centered AI Read More 1 Minute Read
Learn how to build a generative AI-based support operations platform on AWS that converts training videos into structured SOPs, applies Retrieval-Augmented Generation to guide ticket resolution, and uses machine learning to predict SLA risk and prioritize work.
The Pentagon is expanding its AI platform with two new models, OpenAI's ChatGPT Mil and xAI's Grok for Government. The article US military adds ChatGPT and Grok to AI platform GenAI.mil appeared first on The Decoder .
Photonics startups like iPronics are raking in hundreds of millions in funding to make optical circuit switches faster, denser, and cheaper
Photonics startups like iPronics are raking in hundreds of millions in funding to make optical circuit switches faster, denser, and cheaper
Botika is building the next generation of tooling for agentic e-commerce brands, backed by a fleet of custom models trained and served on Modal.
In this episode, Justin Johnson, co-founder of World Labs, joins us to discuss world models and the emerging field of spatial AI. We explore why many researchers see capabilities beyond language as an important frontier for AI, and what it means to build models that can understand, generate, and simulate the environments around them. Justin explains the different approaches to world modeling, including explicit 3D representations and generative models, and why there is still no established recipe for building these systems. We also discuss World Labs’ Marble system, which can generate navigable 3D worlds from images and other inputs, the challenges of evaluating world models, and the role of simulation, planning, and action. Finally, Justin shares his vision for models that bring these capabilities together, supporting everything from interactive virtual environments to agents and robots that can operate in the physical world. 🗒️ Full show notes: https://twimlai.com/go/775.
As generative AI adoption scales, cost governance becomes a top challenge. Learn how Jamf built real-time, per-user spend enforcement for Amazon Bedrock using IAM Customer Managed Policies, an Amazon Athena cost view, and a serverless AWS Lambda loop that applies tiered model limits in near-real-time without disrupting active sessions.
Google has a new suite of creative design tools for Workspace users called Google Pics, which aims to make editing and generating "professional-grade" AI images less cumbersome for businesses. Built around Gemini and the Nano Banana generative AI model, Google Pics is designed to give more granular control over prompt-based image making and manipulation, allowing […]
South African content creators are using generative AI routinely but disclosing it rarely — and the reason, according to a new UNESCO-backed study, is that they believe admitting to it damages their audience’s trust. The research, conducted by the Centre for Analytics and Behavioural Change as part of UNESCO’s global Behind the Screens project, surveyed [...]
Nvidia is officially launching DLSS 5 this week, following a divisive announcement in March where we likened the AI upscaling tech to a "real-time generative AI filter for video games" and "motion smoothing for video games, but worse." DLSS 5 will officially be available on RTX 50-series desktop and laptop GPUs and through GeForce Now […]
GenAI now reaches 80% of occupations, but in most of those fewer than half of workers use it
Wow, Now I See It! - Leveraging the Physiology of Surprise to Help Designers Uncover Desirable Generative Designs robyn.cherinka… Mon, 08/31/2026 - 14:21 Generative AI enables designers to more broadly and rapidly explore design spaces. However, the sheer scale at which designs can be generated makes it difficult to identify which generated concepts deserve further attention. To support this identification process, we explore pupillometry—specifically pupil dilation—as a support signal for design evaluation. We conducted a study with 40 participants who viewed AI-generated bicycle designs while wearing eye tracking glasses to measure pupil dilation and rated designs on perceived surprise, valence, and feasibility. Image Jul 13, 2026 Human-Centered AI Read More 1 Minute Read
Debian voted to allow developers to use AI tools in their contributions to the Linux distribution's "development, maintenance, [and] documentation." The new policy on AI acknowledges that "responsible" use of AI can improve developers' productivity, and goes on to say, "generative AI is neither exempt from nor subject to special rules beyond the standards already […]
Governance determines whether a generative AI program remains a set of pilots or becomes a durable capability. I treat governance as engineering work. The goal stays simple. The system should behave within policy, every day, under change. I define governance by design as the practice of encoding policy into build and runtime controls that enforce access, constrain actions, capture evidence, and measure drift. Policies become executable rules. Evidence becomes a byproduct of normal operation. Teams ship faster when governance runs as part of delivery. Enterprises already understand this pattern. Payment systems embed controls for fraud and chargebacks. Customer data platforms embed consent and retention. Generative AI needs the same approach because it touches data boundaries, produces content, and increasingly takes actions through tools. Adnan Masood Start with a threat model that reflects real usage A threat model is a short description of what can go wrong, who gets harmed, and where controls belong. I keep it concrete. I focus on the failures that appear in production. Data exposure through prompts, logs, or model outputs. Sensitive data can move across trust boundaries quickly. Prompt injection and indirect instruction. A user message or retrieved document can steer the system toward unsafe actions. Source integrity risk. Retrieval can surface outdated, incorrect, or tampered documents that look authoritative. Tool misuse. Agents and assistants can write to systems of r…
LM Studio makes it easy to run LLMs on one’s own hardware, whether as a desktop app or a server for others in one’s organization. But LM Studio isn’t the only project of its kind. Unsloth , a team of two brothers (Daniel and Michael Han), have created their own local-first application for running and training models on local hardware, called Unsloth Desktop . Unsloth Desktop, an open-source Apache-licensed application, intends to do more than just host models locally. It can also be used as a model-training workbench, can be used for image generation as well as standard chat, and runs cross-platform (Microsoft Windows, macOS, and Linux). Unlike LM Studio, Unsloth Desktop does not run as a windowed desktop application. When launched, it opens a console (handy for debugging, to be sure), then kicks open a web browser tab to provide a GUI. Although internal updates (for instance, for llama.cpp or other support libraries) are handled automatically from within the program, full updates require running a command at the console. This makes working with Unsloth Desktop a little clunkier than the typical desktop application. Most systems for working locally with generative AI models have some kind of curated model gallery, and Unsloth Desktop is no exception. Click “Model Hub” in the left-hand menu and you’ll be taken to a catalog of models curated by Unsloth’s team. These can be filtered and organized by capabilities, formats, and how well the model fits on your device. You can also…
Im Workshop entwickeln Sie einen GRC-Assistenten und ein SOC-Reporting – praxisnah, auditierbar und compliance-konform.
A federal court has overturned the U.S. Defense Department’s ban on Anthropic PBC’s artificial intelligence models. Judge Rita Lin wrote in a late Thursday ruling that the Pentagon’s move was “illegal and baseless.” The saga began in fall 2025, when the Defense Department approached Anthropic about bringing Claude to an internal platform called GenAI.mil. The […] The post Court rules Pentagon can’t ban Anthropic’s AI models appeared first on SiliconANGLE .
Over the past three years, as an independent cloud and AI consultant, advisor, and industry influencer, I have worked with numerous companies seeking my expertise. I have helped evaluate, optimize, coach, and support their generative AI and agentic AI initiatives. These engagements were not merely theoretical discussions or vendor-led proofs of concept. They involved real-world enterprise activities, including architecture design, technology selection, deployment planning, governance frameworks, integration, cost analysis, and operational planning. Some organizations sought a second opinion before scaling an AI platform. Others had pilots that performed well in demos but collapsed when connected to real systems. Some needed help selecting models, cloud services, vector databases , or orchestration tools. Others wanted to understand why their expensive AI investments were generating activity but not measurable value. Because most of my work is covered by non-disclosure agreements, I cannot discuss the companies, vendors, architectures, budgets, or internal decisions involved. That is expected and appropriate. However, I can talk about patterns I have seen across varying industries, company sizes, cloud environments, and maturity levels. The biggest lesson is simple: Most enterprise AI projects don’t fail because the model is weak. They fail because the enterprise surrounding the model isn’t ready. Technology versus outcomes The first failure pattern is the most common. Organi…
Most finance leaders at large organizations have made the right investments. A modern ERP, cloud data platforms, planning tools, and more. And now, increasingly, AI — for forecasting support, anomaly detection, close acceleration, and reporting at scale. The technology stack looks right. But when the board starts asking about results, the returns are harder to point to than the investments were. What your ERP was built to do — and what it wasn’t Your ERP is excellent at what it was designed for: capturing transactions, enforcing accounting standards, managing the chart of accounts. It is the system of record, and it performs that job well. But it doesn’t encode how your organization has decided to handle intercompany eliminations across a complex entity structure. It doesn’t carry your FP&A team’s cost allocation methodology, refined over three budget cycles. It doesn’t know what variance threshold triggers a controller review versus a VP escalation, or how your tax team has mapped jurisdictions for Pillar Two. That logic — specific, documented, organization-defined — isn’t in your ERP. It’s not in your data warehouse either. For most finance organizations, it lives in spreadsheets. Sometimes in the heads of the people who built them. Where AI runs into trouble in finance There’s a finding that gets cited a lot in finance AI conversations: research from MIT found that 95% of organizations are seeing no measurable return on their gen AI investments. Bain & Company looked at t…
Uber’s experience highlights a new enterprise AI challenge: adoption can scale faster than an organization’s ability to measure economic value. As companies move from AI pilots to widespread deployment, the question is no longer whether employees will use AI — it is whether every AI investment can justify its cost. Generative AI is changing the economics of enterprise technology. Every inference request, AI agent execution and model interaction can create recurring costs, while cloud infrastructure, GPUs, data, security, integration and governance add to the total cost of delivering AI. The economics that made an AI pilot look compelling can look very different at enterprise scale. The next phase of enterprise AI will not be defined by the number of models deployed or pilots launched. It will be defined by sustainable business value. For CIOs, CFOs and business leaders, success depends on maximizing business outcomes while controlling the cost of delivering AI. AI success is an economics problem, not just a technology problem. AI unit economics: The new measure of AI success Manufacturers measure cost per unit produced. Banks track cost per transaction. Enterprise AI requires a similar discipline — not measuring how many models are deployed, but how much business value is generated for every dollar invested. Traditional software investments typically involve predictable costs. AI introduces a dynamic cost structure where every interaction creates ongoing expenses, including…
Hong Kong’s financial regulators have selected the first batch of projects for an expanded generative artificial-intelligence (GenAI) testing programme spanning banking, securities, insurance and pensions, as the city explores the use of more autonomous AI agents in financial services. A total of 36 use cases were selected for the first cohort of the expanded GenAI Sandbox++ programme, according to a joint statement on Thursday by the Hong Kong Monetary Authority (HKMA), Securities and Futures...
The conventional wisdom in AI is that the next breakthrough will come from more compute, more data, and larger models. But what if the next leap comes from somewhere else? In this episode, Max Welling—co-founder and CTO of CuspAI and professor at the University of Amsterdam—argues that physics may provide some of the ideas behind the next generation of AI systems. We begin with CuspAI’s work using generative AI to design entirely new materials for semiconductors, batteries, carbon capture, and clean energy. Max explains how foundation models for chemistry, agentic workflows, simulation, and automated experimentation are dramatically accelerating the search for new materials and reshaping scientific discovery. The conversation then broadens into a deeper question. Beyond giving AI new scientific problems to solve, can physics also teach us how to build better AI? Max explores surprising connections between machine learning and thermodynamics, why waves may become a new computational primitive for neural networks, and how concepts like symmetry breaking and statistical physics could inspire AI architectures beyond today’s scaling paradigm. 🗒️ Full show notes: https://twimlai.com/go/774.
okay this is wild, generative AI keeps popping up in the most unexpected creative fields lol. wire art is such a niche medium and now there's a model that can basically reverse-engineer any image into a sculptable form, that's genuinely impressive research. makes me think about how fast generative AI development services have moved from chatbots to actual physical/creative applications like this. curious if DreamWire's outputs are actually buildable irl or still mostly conceptual at this stage tbh.
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By Adam Wolf Two years ago, GenAI in production usually meant a single LLM serving a single endpoint. In 2026, it usually means much more. The applications shipping in front of users today are compound AI systems: orchestrated pipelines of retrievers, embedders, dialogue models, classifiers, code interpreters, SQL executors, and tools, with a single user […]
Nature Machine Intelligence, Published online: 26 August 2026; doi:10.1038/s42256-026-01294-w When authors use generative AI in cognitive tasks, without spending time and effort to understand the output, a gap opens between what they present and what they can defend. This gap widens as further work is built on top, resulting in epistemic debt.
Apple’s four-pack of second-generation AirTags is down to $79 (originally $99) at Amazon and at Target, which is the bundle’s lowest price yet. You can get an AirTag for $24 right now piecemeal, but getting four together as a set is a way to save even more. Why pay $96 for four when you can […]
Japan’s Cabinet Office has proposed a revised Principle-Code for generative artificial intelligence (AI) businesses that sets out principles on intellectual… The post Japan proposes new IP safeguards for Generative AI training appeared first on MEDIANAMA .
Judith Klein in Proceedings of the 26th Annual Conference of the European Association for Machine Translation (Volume 2)
Panasonic Avionics worked with AWS and the AWS Generative AI Innovation Center to build an agentic AI system on Amazon Bedrock, Amazon SageMaker, and AWS Glue that diagnoses in-flight entertainment and connectivity (IFEC) issues across a global fleet, reducing diagnosis time from hours to minutes while maintaining accuracy.
Chinese agricultural scientists have launched a generative AI breeding platform for soybeans, which play a central role in China’s agricultural trade. The Fengshu 2.0 platform marked “a shift in China’s soybean breeding towards data-assisted breeding and precision decision-making”, Xinhua reported recently, contrasting it with traditional methods that relied on experience and a trial-and-error approach. The artificial intelligence platform, which is expected to optimise breeding plans by making...
Organizations are making one of the biggest decisions about AI without realizing it. Every time we automate a process… Every time we deploy a generative AI copilot… Every time we trust an AI recommendation… We’re answering a question that most leadership teams have never actually discussed. Who — or what — should be making the decision? Over the past year, almost every conversation I’ve had with executive peers has eventually turned to AI. The questions are usually the same: How are you using it? Where are you seeing value? How fast should we move? They are all good questions. But I think they are causing us to skip a much more important one: Where should AI make decisions, where should it advise and where should human experience and judgment always lead? I’ve spent my career helping organizations navigate major technology shifts: EPR, cloud and analytics all helped people make better decisions. AI feels different. For the first time, technology isn’t just helping us make decisions. It’s beginning to participate in them. In many cases, it already can. The better question is whether it should. Because every time AI makes a decision, we’re making one too. We’re deciding which decisions belong with AI, and which still belong with people. As organizations move beyond experimenting with generative AI, the challenge is no longer deploying it. It’s redefining how people with AI work together. Microsoft’s Work Trend Index describes this shift as organizations move from experimenting…
When the biotech company Insilico Medicine used its computer models to propose a promising drug for pulmonary fibrosis, it enthusiastically claimed in a press release that the molecule had been “discovered by” its generative AI platform. Insilico leads a pack of companies using AI to rapidly come up with drug ideas humans might never think…
Much Ado About Noising: Dispelling the Myths of Generative Robotic Control robyn.cherinka… Thu, 08/20/2026 - 11:44 Generative models, like flows and diffusions, have recently emerged as popular and efficacious policy parameterizations in robotics. There has been much speculation as to the factors underlying their successes, ranging from capturing multi-modal action distribution to expressing more complex behaviors. In this work, we perform a comprehensive evaluation of popular generative control policies (GCPs) on common behavior cloning (BC) benchmarks. We find that GCPs do not owe their success to their ability to capture multi-modality or to express more complex observation-to-action mappings. Instead, we find that their advantage stems from iterative computation, as long as intermediate steps are supervised during training and this supervision is paired with a suitable level of stochasticity. As a validation of our findings, we show that a minimum iterative policy (MIP), a lightweight two-step regression-based policy, essentially matches the performance of flow GCPs, and often outperforms distilled shortcut models. Our results suggest that the distribution-fitting component of GCPs is less salient than commonly believed, and point toward new design spaces focusing solely on control performance. Image Feb 23, 2026 Human Interactive Driving Read More 1 Minute Read
Adobe Inc. today announced the general availability of audio capabilities in Firefly, the company’s all-in-one creative AI tool suite, allowing users to produce music, speech, and sound effects for their projects. In June, the company unveiled big changes for Firefly, including a large number of skills for Firefly AI Assistant that built in agentic capabilities […] The post Adobe expands generative AI audio with Firefly music, speech and sound effects appeared first on SiliconANGLE .
TrueFoundry has launched TrueForge, an open-source agent harness that lets developers build and run AI agents using models from different providers, positioning it as an alternative to Anthropic’s Claude Managed Agents. The San Francisco-based enterprise AI infrastructure startup was founded in 2021 by a team that included former Meta engineers. It initially focused on software for deploying machine-learning models before expanding into generative AI infrastructure. An agent harness is the software layer that manages how an AI agent interacts with the underlying model and external tools. Anthropic’s Claude Managed Agents provide this functionality as a hosted service for long-running agent workloads on the Claude Platform. TrueForge can run on an enterprise’s own infrastructure and supports OpenAI and Anthropic models as well as more than 20 additional models, according to TrueFoundry. Developers can bring their own Model Context Protocol (MCP) servers and API keys. The company is also offering a hosted version with usage-based pricing. TrueFoundry says TrueForge can reduce total agent operating costs by 50%, although its published benchmark shows different savings depending on the model and harness being compared. In a 14-task DevRev Enterprise-Bench test, TrueFoundry said TrueForge and Claude Managed Agents each completed about 11 tasks using Anthropic’s Opus 4.8 model. TrueForge averaged $8.50 per run, compared with $11.80 for Claude Managed Agents, about 30% less. The di…
Experts allege generative AI was used to set and translate three UGC-NET papers later cancelled over repeated questions, factual errors and poor translations. NTA denies AI authored the papers. The post Report: Experts claim AI was used to draft questions in NET exams appeared first on MEDIANAMA .
B2C AI 에이전트 통합 포털 ‘제나(GenA)’를 선보인 제논(대표 고석태)이 과학기술정보통신부와 정보통신산업진흥원의 ‘모두의 AI’ 사업 공모에 맞춰 대국민 서비스 확장 및 고도화에 나선다고 20일 밝혔다.제논은 18일 발표된 6개 사업자 공고에서 컨소시엄을 구성하지 않고 단독으로 응모했다. 자체 개발 및 운영 중인 제나를 바탕으로 국민 누구나 다양한 AI 서비스를 이용할 수 있는 ‘모두의 제나(GenA)’를 선보일 계획이다.제나는 ‘모두를 위한 생성 AI(GenAI for All)’라는 의미를 담고 있다. 특정 AI 모델이나
Researchers discovered a phenomenon they call “attribution decay,” where the more data a generative model is trained on, the harder it becomes to trace a generated image to a single image from the training data.
Most AI investment strategies fail not because the tool or platform underperforms, but because organizations didn’t clearly define what success looks like before they started building. Through a new approach to measuring value, Zoetis chief digital and technology officer Keith Sarbaugh and his business partners have leveraged a value-driven framework to scale AI solutions across research, manufacturing, and customer experience. And they measure every investment against goals before, during, and after the deployment. In addition, his team rolled out a model-agnostic gen AI platform now used by nearly 95% of employees, which turned early experimentation into enterprise-wide adoption. Sarbaugh’s current focus now is partnering with Zoetis’ CHRO to advance the $9 billion global company’s capabilities in managing organizational AI adoption. How are you integrating AI into your growth plans at Zoetis? We have an umbrella program we call AI@Zoetis, where we unify our AI work under an enterprise purview, which spans research and development, manufacturing, commercial operations, customer and colleague experience, and other business functions. We manage AI collectively to enable grassroots innovation. For example, we made our generative AI platform available to everyone, so as many people as possible can experiment and innovate. Our colleagues have access to 10 different LLMs, and we’ve seen over 95% adoption rate among our user community, and more than 11,000 colleague-built agents.…
of Machine Learning & Intelligent Systems at the Tübingen AI Center
New benchmark categories for generative AI and agentic workflows arrive alongside updated LLM tests, as the industry standard for measuring PC AI performance evolves. The post MLPerf Client v2.0 Expands AI PC Benchmarking with Image Generation and Agentic AI appeared first on MLCommons .
When generative AI (GenAI) first arrived, I was in charge of a large team of data and machine learning engineers. We had built a full ML platform from scratch and had dozens of models in production delivering measurable results. AI was working for us. But with the novelty of GenAI came the hype. Under pressure from the board, the question was no longer whether the technology could help our product, but how fast we could find a place for it. Our recommender system, which sat at the core of our company’s product, was the obvious candidate. I pushed back. The Large Language Model (LLM) was trained to predict text, I argued, while our recommender was trained to predict user engagement. The LLM had never seen our proprietary interaction data and had no training signal on our objective. It could not know what our users clicked on, saved or abandoned, because it had never seen it. It was a close call, but the pushback worked, and our attention moved to other problem spaces where GenAI was a legitimately strong solution. It doesn’t always work out that way. There needs to be someone in the room who can translate the business strategy into the right technical decision. I recently talked with the engineering team at a company I used to work for. They were building agentic AI infrastructure, but their algorithms weren’t winning. Under the same hype and the same leadership pressure, they had replaced ML models we built years earlier with AI agents. The results were unreliable, latency w…
When we put our literature, games and films into AI models, we risk replacing our cultural history with a mishmash of auto-generated works. We desperately need to preserve human writing, says columnist Annalee Newitz
메가존클라우드(대표 염동훈)는 아마존웹서비스(AWS)의 AI 에이전트 솔루션 공동 개발 프로그램인 ‘파트너 에이전트 팩토리(PAF)’에 국내 최초로 참여해 기업용 AI 에이전트 솔루션 3종을 공동 개발했다고 18일 밝혔다.PAF는 AWS 내 AI 전문 조직인 생성 AI 혁신 센터(GenAIIC)와 파트너사가 AI 기술력을 결합해 비즈니스 문제를 해결하는 AI 에이전트를 개발하는 프로그램이다.메가존클라우드는 이번에 ▲AI 에이전트 거버넌스 플랫폼 \'AAG\' ▲AI 기반 다국어 문서 번역 솔루션 \'ADT\' ▲AI 영상 분석 솔루션 \'스
Let’s say one of your teams builds an AI agent that actually works. Word gets around, and seemingly overnight, there are twenty more built by people in finance, legal, HR, and support. Most of them are useful, but when someone suddenly gets a chatbot response showing customer data they shouldn’t have access to, reality hits. The real test of enterprise AI readiness isn’t at all whether your coworkers can confidently work with AI. Instead, it has everything to do with governance and security, global, cross-cutting policy, and privacy. Many enterprises are underprepared to face these issues: a survey from Databricks and the Economist found that “40% of respondents believed their organization’s AI governance program is insufficient.” And Microsoft’s Data Security Index reports that “only 47% of organizations across industries report they are implementing specific GenAI security controls.” Having worked with many CIOs to develop strategies to govern their AI systems, this piece is a deep dive into the specifics of what works and what doesn’t. Keeping employees, customers and your entire organization safe must be your top priority before you even start rolling agents out. 8 layers of governance every enterprise needs It’s no doubt that the enthusiasm for AI is real, but so is the list of questions that bubble up a month later: Who’s allowed to publish an agent to the rest of the company? How do we track versions, and can we roll one back? Can we require SSO on every agent? What d…
Enterprise teams are moving from chat-based assistants to systems that can take actions. I see the shift in how people describe the work. They ask for an assistant that can write code, file tickets, update CRM records, run a compliance checklist, generate a pull request, and follow through on the next step. That shape of work requires an agentic system. I define an agentic system as software that turns a user goal into a sequence of steps, executes those steps through tools, keeps track of what happened, and produces an auditable outcome. The model contributes planning and language. The surrounding system provides authority, state, verification, and control. [ See also: “How to run enterprise GenAI like a production service” ] The engineering question stays consistent across domains. How do you give the system enough autonomy to be useful while keeping outcomes predictable. A production answer comes from constraints that are explicit and enforced. Adnan Masood Define the agent loop An agent loop is the repeated cycle the system follows to complete work. I use a simple loop and I make each stage observable. Plan: The agent chooses the next action based on the goal, current state, and policy. Act: The agent calls a tool with structured arguments, then records the result. Verify: The system checks the result against policy and task expectations. Commit: The system writes the state change to a durable store and produces an audit event. These words carry specific meanings in impl…
Twitch has confirmed that content uploaded by streamers can be used to train Amazon’s generative AI models. The policy covers… The post Twitch faces backlash over streaming content being used to train AI appeared first on MEDIANAMA .
I appreciate how honest you are about what worked and what didn't. Too many posts about nvidia’s global context vit achieves sota performa only show the highlight reel. Yours feels like a real walkthrough.
Enterprises are taking a serious look at neoclouds , the specialized cloud providers built primarily around AI infrastructure, especially GPUs , high-speed networking, and large-scale compute clusters for model training and inference. Unlike traditional hyperscalers that provide broad platforms for almost every kind of enterprise workload, neoclouds tend to focus more narrowly on accelerated computing. CoreWeave, Lambda, Crusoe Cloud, and others are all commonly associated with this emerging AI infrastructure market. The interest is not difficult to understand. Enterprises are under pressure to move generative AI , machine learning , and advanced analytics projects out of the lab and into production. At the same time, access to large blocks of GPU capacity has become expensive, constrained, and in some cases difficult to obtain from the major hyperscalers. Many enterprises are finding that neoclouds can offer better economics, faster access to capacity, or configurations more closely aligned with AI workloads. This does not mean AWS, Microsoft Azure, and Google Cloud are being displaced. They remain the default operating environment for most enterprise cloud deployments. They provide mature administrative planes, security tools, compliance frameworks, global footprints, managed services, and operational ecosystems that enterprises have spent years learning how to use. However, AI has changed the infrastructure conversation. Enterprises are worrying less about which cloud the…
Suno is releasing Studio 2.0 with significant upgrades that push it closer to an actual digital audio workstation (DAW), rather than a bare-bones audio editor with generative AI features. The biggest addition is undoubtedly MIDI support. Suno says that MIDI was its most requested feature, and it's basically a prerequisite for any modern DAW. Unfortunately, […]
Create studio-quality videos in seconds with the most powerful AI video maker
보안 전문 마에스트로 포렌식(대표 김종광)은 13일 서울 금천구에서 기자간담회를 열고, AI 신종 디지털 범죄에 대응하는 차세대 포렌식 솔루션 ‘AI 스마트 안경(MAESTRO WiSDOM AI Glasses) 포렌식’과 ‘생성 AI(MAESTRO WiSDOM GenAI) 포렌식’을 발표했다. 두 제품은 기존 통합 디지털 포렌식 플랫폼 \'마에스트로 위즈덤(MAESTRO WiSDOM)\' 제품군의 최신 솔루션이다. 최근 급증하는 AI 스마트 안경 악용 범죄와 생성 AI 디지털 범죄에 특화됐다.기존 포렌식 도구는 PC와 스마트폰 중심으로
Fears of a new industrial revolution in which artificial intelligence replaces manual labour have yet to materialise in Southeast Asia – but white-collar workers should brace themselves. Nearly one in four workers in the region face generative AI (GenAI) disrupting or affecting their jobs, according to a July report by the International Labour Organization (ILO). Clerical, administrative and professional positions were most at risk, the report stated. Manual trades, craft and agricultural work...
Twitch users can now opt out of allowing their content to be used to train Amazon's generative AI models. Opting out means that "your streams, VODs, clips, stream chats, and pictures and text on your channel" won't be used in "future training" of an Amazon AI model "whose purpose is to generate or synthesize text, […]
The lead researcher behind IBM’s popular document parser, Docling, explains why generative AI needs its own document standard.
Judges around the world have made headlines for illicitly using generative AI in their work. But in Pakistan, a large-scale trial of a specially designed AI tool for judges found the technology—together with appropriate training–boosted the number of cases resolved by 6.3 percent with no obvious drop in the quality of judgments. With a backlog of 2.26 million cases and fewer than two judges per 100,000 people—compared to 22 in the EU and eight in Brazil—Pakistan’s judiciary was in sore need of help. So, in consultation with the judiciary, economist Sultan Mehmood , of the New Economic School in Moscow, and collaborators tested whether AI could ease the burden. They built a custom tool combining OpenAI’s GPT-4 large language model (LLM) with a knowledge base of nearly 130,000 Pakistani judicial opinions and statutes, to help judges with legal research and drafting judgments. They began offering the tool in 2024 to 1,559 trial judges—roughly half the country’s justices. “We do find an increase in cases resolved, and we don’t find any corresponding decrease in decision quality,” Mehmood says. First of its kind “It’s pretty amazing that he’s able to pull this off,” says David Autor, an economics professor at MIT. “It’s not easy to do large-scale field experiments in civil service, but especially where the stakes are so high.” The 6.3 percent productivity boost is not overwhelming, he says, but it’s credible and likely to improve as the tool is more widely used. AI tools for judg…
ONESTRUCTION, with technical advisory from the AWS Generative AI Innovation Center, built Ishigaki-IDS, a foundation model specialized for construction and BIM workflows. This architectural case study shows how they combined synthetic data, a three-stage training pipeline, and verifiable rewards on Amazon EC2 to build a domain model in a data-scarce field.
Photographers are among the most skeptical audiences for generative AI. Learn how Pixieset used Amazon Bedrock to launch an AI-generated alt text feature to millions of users in four months, reaching 35% adoption by automating the tedious image SEO work photographers avoid, without touching the creative craft they take pride in.
Arize AX now normalizes OpenTelemetry GenAI semantic conventions into first-class AI traces, unlocking evaluations, token and cost visibility, and easier debugging. The post Arize AX adds native support for OpenTelemetry GenAI semantic conventions appeared first on Arize AI .
Microsoft is betting that the integration of HorizonDB, the cloud-native PostgreSQL alternative it is developing, with Azure will attract more enterprise AI and agentic workloads to its cloud services. Enterprises may not be willing to take that bet. It’s been nine months since Microsoft unveiled HorizonDB , but the service remains in public preview with no announced general availability date. Why put AI projects on hold waiting for HorizonDB to arrive, when AWS, Google, Databricks, Snowflake, and others already have production-ready PostgreSQL services positioned for the same AI workloads that Microsoft says it is building HorizonDB to handle? AWS has had the longest head start. Aurora PostgreSQL became generally available in 2017 and has since evolved from a cloud-native PostgreSQL database into an AI-ready service with vector search and integrations with Amazon Bedrock. Similarly, Google’s AlloyDB , which followed in 2022, now includes AlloyDB AI with vector search, embeddings and model interaction for generative AI and agentic applications. Databricks and Snowflake, too, have their own platform-centric services in the form of Lakebase , which became generally available on AWS and Azure this year, and Snowflake Postgres , which was made generally available in February 2026. As the latecomer, when Microsoft pitched HorizonDB at Ignite in November 2025 it talked up its new architectural approach to cloud-native PostgreSQL, built around disaggregated compute and storage and…
Microsoft is betting that the integration of HorizonDB, the cloud-native PostgreSQL alternative it is developing, with Azure will attract more enterprise AI and agentic workloads to its cloud services. Enterprises may not be willing to take that bet. It’s been nine months since Microsoft unveiled HorizonDB , but the service remains in public preview with no announced general availability date. Why put AI projects on hold waiting for HorizonDB to arrive, when AWS, Google, Databricks, Snowflake, and others already have production-ready PostgreSQL services positioned for the same AI workloads that Microsoft says it is building HorizonDB to handle? AWS has had the longest head start. Aurora PostgreSQL became generally available in 2017 and has since evolved from a cloud-native PostgreSQL database into an AI-ready service with vector search and integrations with Amazon Bedrock. Similarly, Google’s AlloyDB , which followed in 2022, now includes AlloyDB AI with vector search, embeddings and model interaction for generative AI and agentic applications. Databricks and Snowflake, too, have their own platform-centric services in the form of Lakebase , which became generally available on AWS and Azure this year, and Snowflake Postgres , which was made generally available in February 2026. As the latecomer, when Microsoft pitched HorizonDB at Ignite in November 2025 it talked up its new architectural approach to cloud-native PostgreSQL, built around disaggregated compute and storage and…
Die Bereitstellung von Testdaten ist komplex. GenAI ermöglicht eine automatisierte, effiziente Erzeugung plausibler und datenschutzkonformer Testdatensätze.
The AWS Generative AI Innovation Center built an automated system that uses constraint programming and custom tree search to determine, with mathematical certainty, when and how an NHL team clinches a playoff spot. The approach was validated against four full NHL seasons of officially published results.
Learn how to use generative AI at work, build RAG and agentic apps, fine-tune models, work with the Hugging Face ecosystem, and prototype AI products with hands-on resources.
Generative AI is rapidly changing the fraud landscape, enabling criminals to create highly convincing synthetic identities, cloned voices, fake documents and realistic digital interactions at scale. As live deepfake technology becomes more accessible, scams are set to become even harder to detect. Yet the same technology presents a significant opportunity for banks. Institutions that act [...]
Business application development is entering a new operating model. The traditional approach of gathering requirements, designing screens, writing services, integrating systems, testing, fixing defects and preparing release documentation still exists, but it is no longer sufficient for enterprises that need speed, traceability, resilience and regulatory confidence at the same time. Hyperautomation brings a broader discipline to this challenge. It combines workflow orchestration, intelligent document processing, robotic automation, API-led integration, process mining, test automation, observability and artificial intelligence into a connected delivery fabric. With embedded Generative AI, this fabric becomes more adaptive because applications can interpret natural language, summarize complex data, generate explanations, detect exceptions and support decision workflows rather than merely execute predefined rules. In banking, this shift is especially meaningful. Banks operate across dense application landscapes: trade reporting platforms, wealth management portals, core banking systems, investment banking applications, digital compliance engines, reconciliation utilities, operational dashboards, audit repositories and daily, weekly and monthly reporting platforms. Each of these areas has its own data models, control points, integration patterns, validation rules, exception paths and regulatory obligations. Hyperautomation does not replace engineering discipline; it strengthens i…
penAI's Shap·E extended generative models for 3D asset generation.
The Amazon SageMaker Python SDK v3 now exposes generative AI inference recommendations in Amazon SageMaker AI directly in your notebook. Benchmark an endpoint, generate data-driven deployment recommendations, and deploy the recommended configuration without leaving your notebook workflow.
The emergence of GenAI has led to a generational shift with stars conflicted on the impact of technology on their sport Not so long ago, Emma Raducanu was on her phone when she found herself wondering what her comprehensive usage of ChatGPT said about her own character. “I use Chat a lot,” Raducanu says, laughing. “Every small thing I do it and I got this idea. So, you know how Spotify do a Spotify Wrapped? I asked ChatGPT to make me a Chat Wrapped, and it was giving me a rundown on my personality. “I was like: ‘It’s a little bit too accurate.’ It was very clear, very concise. No nonsense, straight into the question. I thought: ‘OK, I can relate maybe. Some of that is true.’” Continue reading...
Generative AI is notoriously eager to help, to the point that if it can’t find something matching what you ask for, it’ll create it. So the problem with relying on guardrails is that all too often, a model will be wrong, showing a high confidence score for an incorrect answer because it’s relying on stale or non-canonical data. Not only do you need to be able to track the lineage of data your model uses from source to token, something the EU AI Act requires , you also need to be able to take into account where the data came from, whether it’s out of date , if it changed in a way that affects the result, or if it was never really relevant or authoritative in the first place. Gartner expects organizations will abandon 60% of AI projects because they don’t have the right metadata management, data quality, and data observability . IBM’s acquisition of Confluent also highlights the importance of real-time data with lineage, governance, and policy for AI agents, and one of IBM’s 2026 predictions was the importance of smarter data. The usual approach is adding metadata and validation later in the data pipeline. That’s similar to the way the bronze, silver, and gold tiers of typical lakehouse architecture are supposed to represent how filtering, cleaning, and augmenting data improves structure and quality until it’s ready to use. That can mean an enormous amount of work since nearly three quarters of the CPU work in training a frontier model is data cleansing and validation. But tha…
Saregama is using AI to create low-cost videos for vintage songs, improve hit predictions and expand podcasts, while pushing for paid-only music streaming in India. The post Why Saregama is using GenAI to make music videos of vintage songs appeared first on MEDIANAMA .
We study outlier tokens in Diffusion Transformers (DiTs) for image generation. Prior work has shown that Vision Transformers (ViTs) can produce a small number of high-norm tokens that attract disproportionate attention while carrying limited local information, but their role in generative models remains underexplored. We show that this phenomenon appears in both the encoder and denoiser of modern Representation Autoencoder (RAE)-DiT pipelines: pretrained ViT encoders can produce outlier representations, and DiTs themselves can develop internal outlier tokens, especially in intermediate layers…
From quantization to speculative decoding, here are seven engineering strategies to ship faster, more responsive generative AI applications in production.
Thank you for posting this question. I am a new member to this community and it was out of need to answer similar questions about usage. What i have found is ambiguity myself. I have not seen any actual hard numbers that tell you what you get for your plan. I have asked elsewhere as a comparison to going to a restaurant, you get a menu and it tells you exactly what you’re getting according to what you order. This is frustrating because it is costing me productivity and time to have to search out these answers. I only get circular answers that say it’s not me it’s then desktop app. If anyone at openAI could help us find these numbers we are looking for, that would be fantastic and helpful. As of right now, I have started using another AI service that has proven to be a little more reliable. I would prefer using chatGPT, but until these bugs and usage issues are resolved I can’t justify the lost productivity and frustration.
Generative artificial intelligence startup Simplismart is set to raise nearly Rs 97 crore (around $9 million) in a Series B funding round led by Dallas Venture Capital, with participation from existing investors Accel India and Shastra VC, as well as new investor Micromax Informatics. The board of Simplismart has approved a resolution to issue 12,100 CCPS at an issue price of Rs 37 lakh each to raise the capital, according to its regulatory filings reviewed by Entrackr. Dallas Venture Capital will invest Rs 44.84 crore, followed by Accel India with Rs 24.09 crore. Shastra VC will infuse Rs 22.24 crore, while Micromax Informatics will invest Rs 5.19 crore. Tarusa Capital and Simraan Teckchandani will also participate in the round with investments of Rs 37 lakh each. According to Entrackr's estimates, the Bengaluru-based company will be valued at around Rs 826 crore post allotment. The fresh capital will be used to support the company's long term growth plans, including business expansion, working capital requirements, and other general corporate purposes. Founded in 2022 by former Oracle and Google engineers Amritanshu Jain and Devansh Ghatak, Simplismart develops AI infrastructure software that enables enterprises to deploy, manage, and optimize production grade AI models without writing code. Its inference-first platform improves GPU utilization and reduces inference costs across workloads such as large language models (LLMs), vision language models, speech recognition, and…
The EU faces a delicate balancing act: encouraging AI adoption across the public and private sectors without funding a wave of "AI-washing" projects that promise innovation but deliver little, as happened with EU-backed schemes in the past.
AI is blurring jobs ⬆️ GenAI entertainment ⬆️ El Niño & stability ⬇️
Last fall, you couldn’t open a business publication without tripping over some version of the same headline: where is the ROI for AI? The anchor for most of that coverage was MIT’s “GenAI Divide” report , which found that despite $30 to 40 billion in enterprise generative AI spending, 95% of pilots delivered no measurable P&L impact. The bubble takes wrote themselves. Boards asked uncomfortable questions. More than a few AI budgets went into the freezer for the winter. Here’s the detail that got lost in the panic: the study defined success as measurable KPI impact within six months of the pilot. Read that again. A project that transformed how a team worked but was never instrumented to prove it counted as a failure. Researchers at UC Berkeley pushed back on exactly this point, arguing that the 95% figure may represent 95% of organizations measuring the wrong things at the wrong time rather than 95% of projects failing to create value. In other words, the AI ROI crisis of 2025 was never really about the AI. It was about measurable verification. Most enterprise AI projects didn’t fail. They were simply built in a way that made success unprovable. If you’re a CIO defending a budget line, that distinction is cold comfort, because “we can’t tell if it worked” and “it didn’t work” produce the same conversation with your CFO. But the diagnosis matters, because the treatment is completely different. You don’t fix an unprovable project with a better model. You fix it by picking a bet…
Reading about using Conditional GANs to generate Zelda level layouts on Synced Review is mind-blowing for AI enthusiasts! Training generative models, balancing procedural level design, and creating engaging retro maps are definitely not Easy Games , but innovative machine learning breakthroughs like this push gaming forward!
Illustrators have spent years sounding the alarm about generative artificial intelligence startups training their models on artists' work without permission. They've pointed out how the practice is tantamount to theft, and in response, many gen AI boosters have argued that it's necessary for the technology's evolution. This has led to contentious legal battles, but it's […]
If the model is not able to perform a work task efficiently, seems like a good reason to go back to old school UI for and code for removing backgrounds and cropping images. Rarely do I need all the whitespace that the models add around the image. I just don’t want to leave the chat to make tiny fast edits; it breaks my work flow. I would prefer to make simple finishing touches in chat instead of having to download and leave. Not all tools have to be LLM driven. .If LLM can’t do the tasks well, the app could support manual task completion. Here is an example of how difficult it is to get a cropped logo with the background removed when a client texts me something they were working on in GPT. First it only cropped the top and added a pink background when downloading from the image viewer. Then it added the checkered background. Then after re-explaining several times if finally accomplished the task. I love how it labeled the final image “real alpha”
Harnessing the benefits of artificial intelligence (AI) and social media while limiting the risks they pose to children’s well-being is a challenge facing governments around the world. Bans on social media use by youth under 16 have been introduced in some countries, beginning with Australia. Others, including the UK, plan to follow suit. This is a developing trend. Norway has announced restrictions on the use of generative AI by junior school pupils, and Sweden is set to prohibit mobile phones...
One of the biggest challenges companies like Aetna face every year is an annual HEDIS review of its records to identify gaps in care. For large national payors, the scale of the challenge is immense. So Aetna has deployed a gen AI-driven document intelligence platform that has reduced the need for manual review by 65%. “We have a large group of amazing trained medical coders who do this every day,” says Nathan Frank, chief digital and technology officer at Aetna. “This is about making it easier for them by speeding up the process. Something that might have taken weeks or months we can now do in days.” The Healthcare Effectiveness Data and Information Set (HEDIS) is a range of performance measures for the managed care industry. Developed and maintained by the nonprofit National Committee for Quality Assurance (NCQA), the first version of HEDIS was released in 1991. Under the HEDIS measures, large managed care providers like Aetna review more than 10 million medical records annually to identify gaps in care. These gaps are missed or overdue preventative care or chronic disease management tests including missed cancer screenings, blood sugar tests for diabetics, eye exams, and immunizations. Closing these gaps improves patient outcomes, and health plans are measured in how well they perform. But processing medical records is no easy task. “We’re talking about medical charts that have white space filled with handwritten notes,” Frank explains. It’s not just structured data, it’s…
Spanish outsourcing group Konecta has opened its regional headquarters and first global centre of excellence for generative AI in New Cairo, as part of a $100 million investment plan in Egypt — placing Egyptian teams on the development and localisation of the company’s core AI products rather than solely on service delivery. The facility was [...]
Discover 7 essential machine learning algorithms that every data scientist should know before reaching for LLMs and generative AI, with simple explanations and practical Python code.
How statistical physics explains the transition from deterministic predictions to generative AI. The post How to Decode the Temperature Parameter in LLMs appeared first on Towards Data Science .
If the generative AI giant had followed well-known security best practices, it’s likely that its AI agent would never have escaped to the open internet and hacked multiple companies.
The transformation of the financial sector is no longer just about offering a mobile app or allowing customers to bank from anywhere. After years of digitizing services, institutions now face the more ambitious challenge of building a more personalized, agile, and intelligent relationship with millions of users who expect immediate answers, simple experiences, and service tailored to specific needs. The emergence of gen AI has accelerated this evolution. While banks have used AI models for years to automate processes, improve efficiency, and analyze large volumes of data, a new generation of conversational tools opens the door to a much more natural interaction between customers and financial institutions. Spain’s CaixaBank, for example, has positioned AI as one of the cornerstones of its technological transformation . The bank, which has more than 12 million digital users, believes this change isn’t solely due to tech’s evolution, but also to a shift in user expectations. “Today’s customer is more digital, autonomous, and also more demanding in their relationship with the bank,” says Mariona Vicens, CaixaBank’s director of digital transformation and advanced analytics. “They not only interact more through digital channels, but also expect simplicity and personalized solutions at any time and from any device.” A history of AI experience Although gen AI has made a big impact, CaixaBank says its commitment to these technologies began much earlier. But it now represents a quali…
I have been using the free credits to build out a set of anime profile pictures for different platforms without repeating the same look. Uploaded one source photo on https://zelvune.com/photo-to-anime and got distinct variations just by adjusting the prompt.
Listen now | Middle East AI News Minute - 30-Jul-26
The memo cited "important progress in key areas" like Azure and Microsoft 365 Copilot. These businesses have faced pressure amid generative AI boom.
New Cairo AI facility to grow to 3,000 staff by 2028, to service four continents
As generative AI matures beyond its early experimentation phase, enterprises are converging on a shared architecture for grounding large language models in trustworthy data: the enterprise knowledge layer. Four years after the release of ChatGPT, most organizations have moved past haphazard experimentation and settled on a shared vocabulary and set of architectural patterns for production […] The post Graphs move from niche database to enterprise knowledge layer for AI systems appeared first on SiliconANGLE .
Google on Wednesday launched its artificial intelligence agent Gemini Spark in the Hong Kong market, giving local users direct access to a smart assistant to manage complex digital workflows. The launch came months after the American tech giant’s decision in March to lift regional geofences for generative AI services, starting with the Gemini chatbot. Hong Kong users can now access Gemini without using a virtual private network or third-party platform. The roll-out of the Spark agent echoes an...
Medical technology companies have always been in the business of trust, and Abbott has been building it with AI for over 10 years. Long before gen AI entered the enterprise conversation, Abbott was using algorithmic AI to help diabetics manage their glucose, and imaging AI to guide surgeons in real time. Here, Sabina Ewing, Abbott’s CIO, explains how a principled approach to AI governance, deep cross-functional partnerships, and a commitment to demonstrating results from within IT have kept them ahead of the curve, and its mission intact. How is Abbott using AI to achieve its mission and growth strategy? As a medical technology company, Abbott’s mission is to help people live life to the fullest. For over a decade, we’ve been using AI to deliver on that mission, but whether it’s AI or any other technology, we’re intentional about how it ties to our mission. Trust is earned in drops and lost in buckets. To ensure we maintain trust with our customers and employees, we’re guided by principles of fairness, safety, quality, and transparency. With these and our mission as our guide, we’re in command of the table we set for ourselves. How have you been in the AI business for so long? For decades, we’ve provided FreeStyle Libre, a glucose monitoring sensor built on algorithmic AI, that delivers continuous glucose readings to diabetics, and in some instances, connects to insulin pump applications. In late 2025, we developed Libre Assist, which leverages generative AI to let FreeStyle…
The guidelines were approved at the final meeting of a panel of the Justice Ministry, which discussed civil liabilities for those who use generative AI to create voices that sound identical to those of voice actors and others without permission.
Government finds some loose change down to chuck at emission-free flight research
AI adoption has outrun AI governance, and the consequences are impacting the business. According to Cloud Security Alliance (CSA) research , 65% of organizations report having experienced at least one AI agent-related incident in the past year. Nearly half have tied confirmed or suspected data leaks to unauthorized gen AI use, per EY’s Technology Pulse Poll . The gap is no longer a lack of policies; it’s a lack of controls that work. And the fix isn’t more documentation; it’s minimum viable governance focused on what actually matters. “Many companies have AI activity, some have AI principles, fewer have enforceable AI controls, and fewer still have evidence that those controls work,” says Sara Jodka , an attorney at Dickinson Wright who advises clients on AI governance. Gartner analyst Lauren Kornutick sees this frequently with her clients, warning that retrofitting governance is harder than building it in. “The biggest issue I am observing with governance after deployments is that it is really hard to walk back previous decisions,” she says. “If an organization was previously very relaxed in their AI use and an incident occurs, it’s much more challenging to decommission tools or models that early adopters were accustomed to using.” The known unknowns You can’t govern what you can’t see. And most organizations can’t see as much of their AI portfolio as they think they can. More than two-thirds (68%) of CSA survey respondents expressed high confidence in their visibility into…
On July 20, a federal judge gave final approval to the largest copyright settlement in U.S. history. Anthropic will pay $3,000 per work for roughly 500,000 books it pulled from pirate libraries to train its models. Here’s the detail most of the coverage missed: The court had already ruled that training AI on copyrighted text is fair use. The capability was legal. The provenance was not. Read that again. The industry’s defining legal battle wasn’t decided on what the model could do. It was decided on whether anyone could account for where its knowledge came from. And buried in the settlement is a stronger signal: Anthropic agreed to destroy the pirated files. A frontier model’s corpus can now shrink by court order. Subtract half a million books from the foundation and ask yourself: Is the model your teams rely on tomorrow as capable as it was yesterday? This isn’t a story about one company. The settlement set no binding precedent, and Google, Meta, OpenAI and Midjourney are still in the dock. Meanwhile, every organization deploying AI is accruing the same liability in miniature: Data you can’t trace, sources you can’t verify, outputs you can’t attribute. I call this Verification Debt. It never appears on the balance sheet — until it appears all at once, with a court date attached. Some of us will remember July 20 as the day the GenAI bubble popped. The day generic models were exposed as a depreciating, legally contested input. The right response isn’t caution. It’s ambition.…
LinkedIn and the Cornell Ann S. Bowers College of Computing and Information Science announce the fifth and final group of researchers to receive grants from their strategic partnership. The post 2026 LinkedIn grant recipients to drive innovation in GenAI, LLMs appeared first on Cornell AI Initiative .
How Booking.com built a unified AI observability stack with Arize for agentic GenAI workflows and traditional ML — from telemetry collection and PII redaction to latency monitors and evaluations. The post How Booking.com scales AI observability with Arize appeared first on Arize AI .
The idea of using a controllable video generation model to let agents simulate navigation plans before executing them is a clever twist on the usual planning pipeline. Most current robotic navigation methods react after the fact, so having the agent evaluate feasibility through generated visual trajectories could significantly reduce failed attempts in unfamiliar environments. Curious how well this scales when the generated video diverges from real-world physics in edge cases. Grow a garden 2
I believe there is a blind spot in the literature on the biggest risks to democracy from AI. The ones that come up most frequently include deepfakes, swarms of bots on social media, erosion of institutional trust and enhanced algorithmic polarisation on social media, as identified by sources such as the Journal of Democracy and the Carnegie Endowment . But these broadly describe ways in which AI could manipulate information within a political system whose underlying balance of power remains broadly unchanged. They are also, to some extent, extensions of techniques that predate generative AI: propaganda, bot accounts and algorithmic amplification are nothing new. For example, across seven X datasets covering several major events from 2018-2021, researchers classified about 20% of the participating user accounts as bots on average, which reached about 43% during the 2020 US election. While these interactions are certainly disruptive, and have potentially even altered the outcome of democratic decisionmaking or caused acute constitutional crises , democratic institutions have yet to collapse. The fact that democracies have survived these technologies so far certainly doesn’t mean that more effective AI-enabled versions will be harmless, but I still think these risks are less fundamental than another possibility: that AI changes the underlying balance of power between citizens and those who have power over them [1] . This possibility is easier to miss if we continue imagining co…
Predicting an outage is tricky business, but CIOs might want to circle Oct. 11, 2026, through Jan. 11, 2027, for likely trouble of a potentially widespread and puzzling nature. That’s because a relatively trivial update to DNSSEC on Oct. 11, one that will take full effect by Jan. 11, is likely to deliver a series of seemingly unrelated system outages. This will come from oceans of dependencies from third-party, shadow, agentic, gen AI, SaaS, homegrown, and legacy apps — among many other quiet executable hiding spots, including virtual environments and containers. Sai Joshitha Kathari , senior site reliability engineer at payment card giant Visa, says most enterprises have far more DNS-related exposure than they realize because of these many dependencies. “This has the potential to create real downstream destruction when unresolved failures sit underneath important business functions,” Kathari says. The danger is that so many of these issues are either unknown to IT or handled by a third-party vendor and no one in IT has had reason to ask those vendors about DNS updates. “The risky areas are usually not the obvious managed DNS services. They are the older internal applications, hardcoded resolvers, containerized workloads, sidecar configurations, custom scripts, partner integrations, VM images, stale base images, and service-to-service dependencies that nobody has touched in a long time,” Kathari explains. “These systems can keep working quietly for years, then fail during a…
Welcome to the forum! How to preserve memory across sessions and threads? My best advice, based on what I have been using for more than a year , is to consider OpenAI Codex . Although Codex is primarily known as a software-development tool, it is capable of much more . Codex can now operate your computer alongside you, work with more of the tools and apps you use everyday, generate images, remember your preferences, learn from previous actions, and take on ongoing and repeatable work. The Codex app also now includes deeper support for developer workflows, like reviewing PRs, viewing multiple files & terminals, connecting to remote devboxes via SSH, and an in-app browser to make it faster to iterate on frontend designs, apps, and games. When combined with MCP servers —including custom ones— skills , and slash commands , Codex can support workflows that would have been difficult to imagine with generative AI only a few years ago. Note: Much of what you are seeking is covered in the linked pages. Each link leads to different information that may be useful for evaluating the available options. Update ChatGPT Learn Record & Replay | ChatGPT Learn Show Codex a workflow once and turn it into a reusable skill Record & Replay lets you demonstrate a workflow on your Mac and turn it into a reusable skill. Use it when the workflow is repetitive, depends on your preferences, or is easier to show than to describe in a prompt. Note: I do not have a Mac running macOS, so I am currently unab…
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Anthropomorphising artificial intelligence – that is, treating AI systems as though they possess human emotions, intentions, consciousness or moral judgment – is both an ethical and a practical problem. While attributing human qualities to AI systems may make interactions more approachable, it also risks clouding users’ perceptions of the technology’s true nature and limitations. Call a spade a spade. The so-called hallucinations that generative AI systems produce are nothing more than errors...
UAE Ministry of Finance call centre answer speed cut to 8 seconds with AI
Kick-off of new GenAI pilots for public administration & Apply AI stakeholder meeting Anonymous (not verified) Wed, 07/22/2026 - 16:18 14 September 2026 This event showcases 3 European generative AI pilots, while bringing together the Apply AI community to share experiences and drive the adoption of trustworthy public sector solutions. GettyImages ©avgust01 The European Commission will host an online kick-off meeting to officially present three new pilot projects funded under the Digital Europe Programme call on “GenAI for the public administrations”. The event highlights the Commission support to the uptake of trustworthy European generative AI solutions in public administrations across Europe. The three projects — FLOODS & DROUGHTS , EUNOMIA.AI and EuropAI — officially started on 1 July 2026 following the signature of their grant agreements. Together, they will help public administrations develop, procure, test and deploy European GenAI solutions that respond to concrete public-service needs, while ensuring compliance with European legal, operational and societal requirements. The kick-off meeting will be followed by an Apply AI stakeholder meeting open to public-sector representatives and other stakeholders. T he discussion will explore the main opportunities and challenges associated with the adoption of AI in public administrations and how the European Commission can further support the public sector in developing, procuring and deploying trustworthy European AI solutio…
IIn March, Meta's Oversight Board called on the company to "meet its public commitments and employ its own tools" to help quell the spread of deceptive generative AI content across platforms. Meta responded in July by introducing Content Seal - an invisible watermarking technology that flags images generated by the company's new AI model. But […]
In a single month, one enterprise reportedly spent half a billion dollars on AI. A consultant told Axios that the client had handed its workforce AI licenses, set no usage limits and let the meter run until finance noticed. The figure is spectacular, and it is the wrong thing to fear. That half-billion-dollar accident is only the visible part of a quieter, far larger failure. Worldwide AI spending is forecast to reach $2.52 trillion in 2026 , more than any technology category in a generation, and by the most cited measure, roughly 95 percent of it returns nothing. Boards read that as proof that the technology does not work. The evidence points somewhere less comfortable, and it is not a technology problem at all. Most boards cannot see it because they are reading the wrong number: They track failure when the number that matters is allocation. The discipline that separates the winners is not technical. It is how they allocate capital across time, and how willing they are to stop. The hardest discipline in the AI era is not adopting faster. It is allocating honestly and refusing to judge a three-year bet on a six-month cycle. The number everyone quotes, and no one acts on The headline statistic is now familiar. MIT’s Project NANDA, in its 2025 study The GenAI Divide , found that about 95 percent of enterprise generative AI pilots produced no measurable impact on the P&L, while roughly 5 percent captured nearly all the value. S&P Global Market Intelligence found that the share…
What if the next breakthrough in Enterprise AI doesn’t come from computer science alone? What if it comes from applying principles that physicists have understood for more than a century? According to Gartner , rising token-driven AI spend is straining budgets and challenging cost justification. As organizations race to deploy generative AI and agentic systems, token consumption dominates nearly every executive discussion: How many tokens did we use? How much did inference cost? Can we reduce our AI bill? These are important operational questions. But they are not the strategic questions. I believe the economics of enterprise AI can be viewed through the lens of three well-established principles from thermodynamics: the conservation of energy, entropy, and exergy. While these principles describe physical systems — not AI —they offer a useful way to think about how organizations should measure AI success. Principle 1: Value is created through transformation The 1 st Law of Thermodynamics tells us that energy cannot be created or destroyed. It can only be transformed. Enterprise AI presents a similar management lesson: Tokens are not valuable because they are consumed; they become valuable only when they are transformed into business outcomes: A faster loan application decision. A better customer experience. Faster and more accurate software. Reduced fraud. Higher employee productivity. A new product. A strategic insight. The executive question therefore is not, “How many toke…
The conversation about AI often centers on algorithms, computing power, or huge investments in new semiconductor fabrication plants and hyperscale data centers. But beneath each of these advances is another layer of innovation that makes them possible: advanced materials. Every new generation of AI technology demands more processing power, more memory, greater energy efficiency, and…
Site reliability engineers (SREs) have the tough assignment of resolving thorny performance and reliability issues. But their primary mission is to provide devops teams with operational insights and to suggest implementation improvements on business system performance, security, and overall robustness. Google introduced its SRE playbook in 2003, but it took some time for the role’s definition, tools, and techniques to become mainstream. Startups were the first to adopt observability for cloud-native applications and create dedicated SRE positions. As tools matured and SRE responsibilities became more clearly defined, larger enterprises assigned SREs to work as a bridge between devops and IT ops teams to improve resilience across a wider range of applications, APIs, and data pipelines . SRE is a career path for multidisciplinary engineers with strong investigative instincts, sharp data analytics skills, and the temperament to perform under pressure. It has become a critical responsibility as tech became mission-critical for enterprises, and it is a growing role in the genAI era as more businesses deploy AI agents . But the critical need for resiliency and greater technological complexity brings new challenges for SREs. According to the 2026 State of Production Reliability and AI Adoption report , 44% of respondents experienced an outage linked to ignored or suppressed alerts in the past year, and 35% report their engineers occasionally ignore or dismiss alerts due to alert fa…
Acts of sexualized violence against women, children, and members of the LGBTQI+ community through technological means are on the rise. They represent an extension of patriarchal power dynamics into the digital sphere. In this context, generative AI is increasingly used as a tool of such violence. The consequences extend far beyond individual harm and can effectively suppress women’s participation in digital public spheres.
Impressive work from Microsoft on the FPDT — the memory hierarchy approach and overlap of prefetching with computation really make this practical for long contexts. Being able to train 2M tokens on just 4 GPUs with 55% MFU is a game changer for researchers working with limited hardware. On a side note, while testing my own model’s UI I found useful for creating quick pixel icons for demo chatbots. The combination of efficient training and lightweight tooling is exactly what the community needs to iterate faster.
Gurugram based travel tech startup 30 Sundays has raised $6.7 million in a funding round led by Bessemer Venture Partners, with participation from existing investors Info Edge Ventures and Eximius. The startup had previously raised $770K. The fresh proceeds will be used to expand its product offerings, strengthen its technology and AI capabilities, grow its team, invest in brand building and marketing, and support its expansion into new geographies and customer segments, 30 Sundays said in a press release. Co founded in 2024 by Kshitij Chaudhary and Anuj Punjani, 30 Sundays offers customized romantic and honeymoon travel packages. The company uses generative AI to automate itinerary planning, customer qualification, and booking. Following its recent launch in New Zealand and Mauritius, the startup plans to expand into Georgia, Azerbaijan, Kazakhstan, and the Philippines. 30 Sundays said it has reached an annualised gross booking value (GBV) run rate of around Rs 200 crore across Bali, Vietnam, the Maldives, and Thailand. The company also said it has integrated AI across sales and operations, including lead qualification, itinerary creation, follow ups, and reservations, resulting in a twofold improvement in sales team productivity.
Nature Machine Intelligence, Published online: 21 July 2026; doi:10.1038/s42256-026-01254-4 Lin et al. introduce a brain-inspired generative model that provides two key features of intelligence: planning and problem-solving. It uses cognitive maps, stochastic computing and compositional coding, and requires only local synaptic plasticity.
Rund 2100 Parlamentsmitarbeiter nutzen täglich KI-Tools – mit entsprechenden Qualitätseinbußen. Der EPGenAI Hub soll ab September sicherere Abhilfe schaffen.
Adobe's experimental camera app has taken an unexpected turn. After Project Indigo was launched last year to provide a "more natural (SLR-like) look" for iPhone photography, the Indigo camera app is now being updated with a suite of generative AI tools. And the change doesn't rely upon Adobe's own Firefly AI models. Adobe describes the […]
Nigeria’s 2027 general election will be the first in the country’s history to take place with generative AI tools in wide circulation. The information environment around it has already been transformed, according to Navigating Nigeria 2027, a new strategic communications and reputation report from Bloomwit Africa. Fabricated audio, video, and images of Nigerian public figures [...]
Digital Talent EU Days 2026 in Dublin Anonymous (not verified) Mon, 07/20/2026 - 15:02 15 October 2026 - 16 October 2026 Trinity Business School, Dublin, Ireland The Digital Talent EU Days will host a debate on Europe’s digital skills challenges and drive action on talent, competitiveness and inclusion. Digital Talent EU Days On 15 and 16 October, LEADSx2030 and Connecting Women in Digital , in partnership with the European Commission, National Coalitions , agencies and local partners, will host the Digital Talent EU Days . What does Europe do when the demand for digital talent keeps growing faster than the pipeline behind it?The Digital Talent EU Days 2026 will bring this question to Dublin, gathering stakeholders from all EU Member States to focus on talent development, talent attraction and talent retention. During this two-day event, the agenda will concentrate on the priorities of Europe's digital workforce, including: Bridging Europe's digital skills gap, new ways for talent development, influence of GenAI, evolving cyber roles and Vocational and Educational Training (VET) pathways; Talent attraction, international mobility, upskilling and reskilling; Deep tech growth, innovation and Europe's digital competitiveness; Women in ICT and the outcomes of the Women in Digital Forum Thematic Working Groups. Aligned with the Digital Decade Policy Programme , the Union of Skills and the AI Continent Action Plan , this year’s edition aims to demonstrate Europe’s collective leade…
Enterprise AI is entering a new phase. While the first wave of generative AI focused on human productivity and content creation, the next wave — agentic AI — will fundamentally change how organizations operate. Agentic AI systems are capable of reasoning, planning, making decisions and executing actions across applications, workflows and business processes with minimal human intervention. As organizations move toward agentic frameworks that can independently resolve customer issues, optimize supply chains, manage infrastructure, coordinate workflows and even operate IT environments, one reality becomes clear: The network becomes the nervous system of the autonomous enterprise. The infrastructure requirements of agentic AI differ dramatically from those of traditional applications. These systems are highly distributed, continuously exchanging information, interacting with APIs, accessing multiple data sources and making decisions in real time. The performance, security, visibility and adaptability of the network will directly determine the effectiveness of AI agents. Organizations that view AI readiness solely as a compute or data challenge risk overlooking one of the most critical enablers of future success — the network itself. From AI-ready networks to autonomous networks The long-term destination is the autonomous network : A network capable of self-monitoring, self-optimizing, self-healing and self-securing through the use of AI and automation. However, autonomous networ…
I would never go so far as to say there's no place for AI in music (I'm a fan of Holly Herndon, after all). But I generally find music made with generative AI to be offensively boring, especially the outputs of Suno. So I'm having a bit of a tough time processing the fact that […]
If you’ve spent any time on GitHub Trending this month, you’ve probably noticed a pattern: it isn’t research papers turning into repositories anymore, it’s agents. Coding agents, pentesting agents, trading agents, and the infrastructure that ties them all together. We tracked star growth, momentum, and real-world impact to identify the ten repositories that mattered most […] The post Top 10 GitHub Repositories Trending in July 2026 (AI, ML & GenAI Edition) appeared first on Analytics Vidhya .
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Ontic Labs and Feyer each to receive three million euros as they reach second round of Europe-wide competition
Netflix is scaling generative AI across content production while expanding cloud gaming, children’s games and new entertainment formats, executives said during its Q2 2026 earnings call. The post Netflix Q2FY26: AI now powers 300+ titles as cloud gaming logs 11x growth in active players appeared first on MEDIANAMA .
Sightera Biosciences, a Belgiantechbio company using generative AI to develop novel small-molecule therapies,has raised €3 million in a pre-seed funding round led by Entourage, Anacura andQBIC.A spin-...
Netflix says roughly 300 titles on its platform used generative AI, most of which occurred in post-production. The streaming service revealed the news in its second-quarter earnings report released on Thursday, saying it's "increasingly leveraging these tools to deliver higher quality output more quickly and at a lower cost." It also provided some examples of […]
I’m working on a multi‑class text classification problem where the input consists of very short descriptions (often only a few words) and the goal is to predict the correct category. I’m currently using an XGBoost classifier for the final prediction layer. for embeddings I am using e5 large model. The main challenges I’m facing are: The descriptions are extremely short and many classes share similar vocabulary. Because of this, the model sometimes produces high‑confidence but incorrect predictions when common words appear across multiple classes. Description is the only feature that supports the category classification. The dataset is imbalanced: some classes have significantly more training samples than others, but the distribution of the prediction data is different. This causes the model to over‑predict certain classes. I experimented with TF‑IDF features, embeddings from a generative AI model and a combination of TF‑IDF + embeddings. However, combining both actually reduced accuracy. I also tried downsampling majority class but it not really make any difference. Current model accuracy is around 30%. I’m looking for advice on: How to improve classification when different classes share highly overlapping vocabulary. How to reduce high‑confidence wrong predictions. What techniques work well for class imbalance and distribution shift between training and prediction data.
I’m sick of “opt-out” toggles for automatically enabled generative AI features. It’s past time to make “opt in” the default setting for sensitive features.
Foundational protein design startup Mandrake Bio has raised around Rs 16 crore in a pre-seed funding round co-led by Activate and Antler, with participation from Spectrum Impact and DeVC. The round also saw investment from angel investors, including Vijay Chandru, Paras Chopra, Sanjiv Rangrass, and Vatsal Du. The proceeds will be used to advance the company’s AI based protein design platform, expand its research team across AI and biophysics, and scale wet lab validation of its gene editing enzymes for agricultural and medical applications, Mandrake Bio said in a press release. Founded in March 2025 by Tanay Lohia and Kutubuddin Molla, Mandrake Bio develops programmable gene editing enzymes using generative AI, biophysics, and wet lab validation. The startup aims to design compact and precise enzymes for applications across agriculture and medicine. The company says its technology can help reduce the time required to develop improved crop varieties through gene editing, while also supporting the development of therapies for genetic diseases. Unlike conventional approaches that rely on naturally occurring gene editing systems such as CRISPR Cas9, Mandrake Bio designs custom gene editing enzymes from scratch using generative AI and structural biology. It plans to license these enzymes to seed companies and therapeutic developers.