AI/ML news, top picks, and generated innovation digests.
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This post was created in partnership with Moloco Key takeaways Marketing leaders are grappling with how AI is reshaping traditional funnels, whether it’s through generative AI summaries usurping consumer clicks, […]
Modern AI systems are, in effect, a universal adviser to help people do harmful things. We’ll need to harness AI for defense, too Last week, national security agencies from the Five Eyes – that’s the rich, English-language-speaking countries club – jointly released a statement warning of the increasing cyber risks of AI models: in particular, their ability to autonomously hack into systems and networks. The statement was more measured than some of the breathless headlines about it, and the advice they gave is pretty much the standard advice everyone gives – albeit with newfound urgency. Internet risks are nothing new, and cyber-attacks – both large and small – have been a significant issue since long before the current crop of generative AI models. Bruce Schneier is a security technologist who teaches at the Harvard Kennedy School at Harvard University and University of Toronto’s Munk School Continue reading...
Dear OpenAI Team, My name is Emre Kedikli, and I am a ChatGPT Plus subscriber from Türkiye. First of all, I would like to sincerely thank you for creating one of the most influential AI platforms in the world. ChatGPT has become an important part of my daily learning, professional development, project planning, and research. I would like to share an idea that I believe could benefit millions of people worldwide. I propose the creation of an official OpenAI training, offering structured online training programs with certificates of completion and professional certifications. My suggestion includes: Fully online courses available worldwide Approximately 30 hours of learning for each program Interactive lessons and practical exercises Final assessment or examination Official digital certificates and professional certifications Verifiable digital badges for LinkedIn and professional profiles Example course titles: OpenAI – ChatGPT Fundamentals OpenAI – Prompt Engineering Fundamentals OpenAI – AI Productivity OpenAI – Generative AI Essentials OpenAI – Responsible AI OpenAI – AI for Manufacturing OpenAI – OpenAI API Fundamentals OpenAI – AI for Education OpenAI – AI for Business OpenAI – Digital Transformation with AI Example professional certifications: OpenAI Certified Prompt Engineer OpenAI Certified AI Professional OpenAI Certified Generative AI Specialist OpenAI Certified AI Developer To better illustrate this idea, I have also designed several concept certificate mockups tha…
AI has really changed the game around software development. More people are leveraging AI than ever to contribute patches to projects they use. To me, this is a good thing as more folks will contribute patches rather than fork or not fix them. The main problem is that AI has made generating code fast but there has been very little improvement in maintaining code bases. In this post, we will highlight the ways the Kubernetes community is adapting to the world of AI assisted coding. The first step of this journey was to develop an AI policy. This seems mundane and bureaucratic but there were many PRs that derailed into discussions around AI usage. The AI policy helps steer the conversation around the project's stance on AI and provides a clear signal to contributors on how to use these tools responsibly. Kubernetes AI policy The Kubernetes project has established clear guidelines for AI-assisted contributions that balance innovation with accountability. These policies are designed to maintain code quality and ensure human oversight while acknowledging that AI tools can be valuable aids in the development process. Transparency first Contributors must disclose when AI tools have been used to assist with a pull request. A simple statement in the PR description such as "This PR was written in part with the assistance of generative AI" is sufficient. This transparency helps reviewers understand the context and apply appropriate scrutiny. Human accountability While AI tools can assi…
KI-Modelle zur Hautkrebserkennung sind oft voreingenommen. Das Projekt "MedGenAI" des DFKI will das Problem mit gezielt erzeugten synthetischen Bildern lösen.
China’s transnational interference threatens digital rights globally H.Seidl Fri, 06/26/2026 - 14:56 picture alliance / NurPhoto | Jaap Arriens Comment Jun 26, 2026 4 min read China’s transnational interference threatens digital rights globally Beijing’s coercive use of digital tools and economic leverage undermines international efforts to regulate digital technologies, say Daria Impiombato and Wendy Chang. Signs are mounting that the Chinese government is expanding its transnational repression both in terms of tools and targets. The first half of 2026 has seen evidence of online and offline attempts to silence overseas critics that cross its political red lines. Only in May, an AI-generated harassment campaign against Europe-based human rights researcher Laura Harth, known for her work exposing China’s overseas police stations, was made public. The campaign, which relied on misogynistic and sexualized images, shows how Beijing is incorporating generative AI into its transnational repression efforts, allowing new forms of scalable, personalized attacks aimed at damaging the reputation of critics abroad. But attempts to silence individuals have also widened to target global civil society collectively. Another recent victim of a reported Chinese government campaign was an entire conference dedicated to advancing digital rights for all – the rights people should enjoy online, including privacy, freedom of expression, access to information and protection from unlawful surveilla…
JiviAI, an AI healthcare startup founded by former BharatPe Chief Product Officer Ankur Jain, has shut down operations, according to multiple sources familiar with the matter. The development comes less than two years after the startup entered the crowded generative AI healthcare space. The company had bet on proprietary AI models to deliver medical assistance and healthcare related services. The startup also raised an undisclosed funding in late 2024. According to sources, the shutdown came amid rising infrastructure costs, funding challenges, and failed acquisition discussions. “Building and running proprietary AI models became increasingly expensive. When you’re up against companies like OpenAI and Google, it becomes very difficult to make the economics work,” said a person familiar with the matter, requesting anonymity. According to another source, investors who had initially shown interest in backing the company did not participate in its planned funding round, putting additional pressure on its finances. “There were a few acquisition discussions as well, but none of them materialised. Once those fell through, the company had very few options left,” the person said. Sources said employees have been informed about the shutdown and have been asked to leave as the company winds down operations. Industry sources also suggest that Jain is evaluating his next move. Some industry observers have speculated about a possible return to BharatPe following the recent departure of Gr…
In reply to Sándor . Hi Sandor...Thank you for reaching out! Please elaborate on your question so that we may better guide you.
The data centre industry has evolved through successive waves of innovation, from virtualisation to cloud computing, and now to AI. According to Bloomberg, the market for generative AI is expected to reach USD 1.3 trillion by 2032, while PwC projects that AI could contribute up to USD 15.7 trillion to the global economy by 2030, [...]
Ankit Singh, Assistant Vice President of Content at Pocket FM, has announced his departure from the company after a two year stint. In a LinkedIn post, Singh said he moved from working on retention, revenue and analytics to leading Pocket FM’s global content marketing function. He said his team managed content marketing across international markets and adopted generative AI for content production, brand campaigns, the Discover platform launch and other initiatives. "In the next chapter, I'm working on something of my own with a close friend for people in the middle of a job search," said Singh. His exit came on the same day the shutdown of Pocket TV, Pocket FM's microdrama vertical, came to light. Responding to Entrackr's queries, the company said Pocket TV had been launched as a beta experiment and was concluded around eight months ago. It also reiterated its focus on its core audio business and global expansion ahead of a potential IPO. Singh's departure also comes amid a series of senior leadership exits at Pocket FM in recent months. Last month, Chief Financial Officer Anurag Sharma stepped down after nearly three years with the company to pursue entrepreneurial opportunities. During the same month, Senior Vice President Mayank Sancheti also stepped down from his role. Pocket FM has also begun discussions to shift its holding company back to India through a reverse flip as it eyes a public listing in the country. Update at 6:10 PM, June 26 : The story has been updated to…
Anthropic has accused Alibaba of using nearly 25,000 fraudulent accounts to extract capabilities from its Claude AI models, in what the US AI company described as the largest known attack of its kind against it. The campaign, carried out between April 22 and June 5, generated more than 28.8 million exchanges with Claude, according to a June 10 letter Anthropic sent to senior members of the US Senate Banking Committee, Reuters reported . Anthropic said the effort involved “distillation,” a technique in which a less capable AI model is trained on the outputs of a more advanced system, potentially allowing rivals to replicate some of its capabilities at lower cost. The company said the campaign was conducted by operators affiliated with Alibaba and Alibaba Qwen, Alibaba’s AI lab, according to the report. The allegation comes as businesses adopt generative AI tools across business functions, putting pressure on vendors to show they can detect misuse while keeping services available for corporate customers. The dispute also comes as AI development becomes more closely tied to US-China technology tensions . Anthropic said the alleged campaign could help accelerate China’s ability to reach the capabilities of its advanced Mythos Preview model, while US officials have stepped up scrutiny of advanced AI systems over fears they could be used by military or intelligence users in countries of concern. In February, Anthropic said it had identified similar campaigns by DeepSeek, Moonshot…
Enterprises may soon be paying as much for their developers’ AI token usage as they do for their salaries. According to Gartner , these costs will meet, or even exceed, the typical software engineer’s monthly salary within the next two years. This is not only because developers are increasingly adopting generative AI and agentic tools , it reflects a trend toward consumption-based licensing models as vendors balance infrastructure investments with profitability. Rather than the flat per-seat SaaS model of the past, enterprises now pay for developer token use as well. Gartner senior principal analyst Nitish Tyagi explained that it’s important to note that Gartner’s prediction is based on a global average salary of $2,000 per month; it doesn’t mean AI token usage will exceed all salaries. For instance, in the US, yearly pay rates can be six digits or more. However, that kind of spend is not out of the realm of possibility, Tyagi emphasized. “I have heard scary numbers like ‘My developer consumed $20K last month,’ or ‘A business user consumed $32K’.” If these amounts sound shocking, that’s the point. “The goal is to alarm the industry about the impact of token cost if it is not governed and controlled,” he said. Lack of visibility, immature oversight Enterprises are quickly moving from experimentation to scaled deployment of AI coding agents , but many still underestimate token costs, Tyagi noted. This is because cost structures for software engineering workloads are “highly va…
Nature Machine Intelligence, Published online: 25 June 2026; doi:10.1038/s42256-026-01258-0 Rising pathogen drug resistance makes next-generation antimicrobial peptides a global priority. Generative AI accelerates discovery by rapidly proposing new peptides with high therapeutic potential. The key question is no longer whether broad data-driven exploration is possible, but whether it can refine biologically complex activity scaffolds.
Telecom operators have seen remarkable returns from using generative AI to automate network management, customer care and back-office operations. Most of that impact has been task‑based: automation that speeds up predetermined steps while people manually correlate insights and direct next steps. Automation is no longer the finish line — it’s the launchpad to autonomy. The […]
Amazon SageMaker AI provides fully managed real-time inference hosting for machine learning models. You deploy a model to a SageMaker endpoint backed by one or more compute instances, and SageMaker handles provisioning and scaling. SageMaker supports multiple endpoint architectures. This post focuses on the two most relevant to generative AI workloads with detailed observability: Single-model endpoints (SME) and Inference component (IC) endpoints.
Musicians are accustomed to getting paid each time their creative work is used. Across vinyl/CD sales, streams, radio, cover versions, and those numerous niches like karaoke, there are agreements in place about what “use” means. Underlying this is a simple economic principle: The more something is used, the more money it makes. Generative AI has complicated the definition of use . On the one hand, you could argue that the use of a piece of musical training data happens just once, at the point of training. On the other hand, creators would be right to complain that the creative essence of their work lives on in the structure of the model, used every time the model produces an output. Now, companies like Sureel and SoundVerse are working to re-create the essential economic principle that motivates creativity in an era of AI. Such initiatives aim to turn the generative AI industry from one guilty of “the biggest act of copyright theft in history” into one that coexists harmoniously with hardworking artists. Music Royalties for the AI era Sureel , a startup Warner Music Group just acquired , has partnered with the Swedish copyright agency STIM to explore the potential for music creators to get paid when their music is used to train generative AI tools . Sureel’s software labels online media, such as a music file, with instructions determined by the owner. The instructions specify whether an AI company may use the media freely in training, limit its influence in any given trainin…
In this episode, Sam talks with Dev Rishi, GM of AI at Rubrik, about what happens when agents move beyond answering questions and start taking action across tools, systems, and business processes. We explore why the enterprise playbook of static guardrails plus human approval starts to break down in the agent era. Agents are useful because they can plan, call tools, update systems, write code, send messages, and operate across workflows at machine speed, but those same capabilities make them difficult to govern with rules written in advance or approval prompts reviewed one at a time. Dev explains why tool access increases blast radius, why agents can route around controls in surprising ways, and why human-in-the-loop review can become security theater when agents operate at scale. We also discuss what enterprises need instead: better visibility, runtime enforcement, policy-aware governance, agent observability, and recovery mechanisms for when something goes wrong. Along the way, we dig into MCP and tool sprawl, small language models for policy enforcement, defense in depth, agent rewind, and why AI may be needed to help secure AI. 🗒️ Full show notes: https://twimlai.com/go/770.
This opening session builds the foundation for running popular OSS models such as Gemma, Qwen directly on Jetson — no cloud required. We cover when to use Ollama for rapid local prototyping versus vLLM for higher-throughput serving, show how the same workflow applies to both power different OSS models, and walk through the real decisions behind model choice, containers, quantization, and performance tuning on edge hardware. We close with a teaser of OpenClaw and a bonus take-home challenge to kick off community building. You will learn how to deploy open-source AI models on NVIDIA Jetson — no cloud required, from first launch to production-ready serving. We'll cover: Getting models running on NVIDIA Jetson — spin up popular OSS models (open-source large language models (LLMs) like Gemma and Qwen (LLMs and VLMs) using Ollama or vLLM on Jetson hardware and verify they're working end-to-end. Choosing the right inference engine — understand the practical tradeoffs between Ollama for rapid local prototyping, vLLM for higher-throughput serving, and llama.cpp, so you can pick the right tool for your use case. NVIDIA Jetson-specific serving strategies — walk through the real decisions behind model choice, containers, and performance tuning tailored for Orin and Thor, including what works, what doesn't, and why. Performance fundamentals — get introduced to quantization and speculative decoding: what they are, how they work, and when to reach for them on edge hardware. Real-world appl…
Test LLM inference natively on mobile devices with new standardized benchmarks and expanded NPU acceleration. The post MLCommons Releases MLPerf Mobile v6.0 with New Generative AI Benchmarks for On-Device LLMs appeared first on MLCommons .
Climate data science faces persistent barriers stemming from the fragmented nature of data sources, heterogeneous formats, and the steep technical expertise required to identify, acquire, and process datasets. These challenges limit participation, slow discovery, and reduce the reproducibility of scientific workflows. In this paper, we present a proof of concept for addressing these barriers through the integration of a curated knowledge graph (KG) with AI agents designed for cloud-native scientific workflows. The KG provides a unifying layer that organizes datasets, tools, and workflows, while AI agents—powered by generative AI services—enable natural language interaction, automated data access, and streamlined analysis. Together, these components drastically lower the technical threshold for engaging in climate data science, enabling non-specialist users to identify and analyze relevant datasets. By leveraging existing cloud-ready API data portals, we demonstrate that 'a knowledge graph is all you need' to unlock scalable and agentic workflows for scientific inquiry. The open-source design of our system further supports community contributions, ensuring that the KG and associated tools can evolve as a shared commons. Our results illustrate a pathway toward democratizing access to climate data and establishing a reproducible, extensible framework for human–AI collaboration in scientific research.
As AI agents become more capable and autonomous, they also introduce new security challenges. In this 'Fully Connected' episode, Dan and Chris unpack Anthropic’s Zero Trust for AI Agents security framework and what it means for organizations deploying agentic systems. They examine the key security risks facing agentic systems and discuss how organizations can apply Zero Trust principles to deploy AI agents safely. Along the way, they break down practical security controls and discuss how traditional cybersecurity principles must evolve for the age of AI agents. Featuring: Chris Benson – Website , LinkedIn , Bluesky , GitHub , X Daniel Whitenack – Website , GitHub , X Links: Zero Trust for AI Agents OWASP GenAI Project Sponsors: Prediction Guard: A self-hosted AI control plane for running agents in high impact environments. predictionguard.com/practicalai Upcoming Events: Register for upcoming webinars here ! Midwest AI Summit 2026
Read our translation of a Chinese national standard designed to improve the safety and security of generative AI services. The post National Standard of the People’s Republic of China: Cybersecurity Technology – Basic Safety Requirements for Generative Artificial Intelligence Services appeared first on Center for Security and Emerging Technology .
"The 2026 JournalismAI Skills Lab is a 14-week, free, virtual program designed for professionals to learn how to practically implement LLMs, GenAI and agents in their work. The programme helps individuals upskill in using AI technologies in a hands-on manner. It equips participants to develop their own AI-based tools, prototypes or proofs-of-concept. The ultimate outcome […] The post Latin American journalists invited to apply for 2026 JournalismAI Skills Lab appeared first on LatAm Journalism Review by the Knight Center .
Hyperscalers will spend $725 billion on AI infrastructure this year. The users they are spending it on are now actively rejecting the output. Gartner finds 50% of US consumers prefer brands that don't use generative AI. Wikipedia just banned AI-generated content 44-2. Stack Overflow's new-question volume has fallen 78% year over year. Google AI Overviews have collapsed top-page CTR by 58%. This is the structural tension running through every story below: capacity is being added fastest in exactly the parts of the market where buyers are most visibly walking away.
In this episode, Scott Clark, co-founder and CEO of Distributional, joins us to explore how teams can reliably operate and improve complex LLM systems and agents in production. Scott introduces a Maslow’s hierarchy of observability: telemetry for logging, monitoring for known signals, and post-production or online analytics to surface unknown unknowns. We dig into examples of real-world failures Scott’s team has seen in production systems, such as “lazy” tool-use hallucinations that standard evals miss, and how mapping traces into vector fingerprints enables clustering and topic discovery to uncover emergent behaviors. Scott explains how analytics can feed the data flywheel by generating evals, guardrails, and training data, and why online, adaptive approaches are essential for non-stationary models. We also touch on practical how-to’s such as instrumentation with OpenTelemetry, the GenAI semantic conventions, and the role of dedicated analytics tools. The complete show notes for this episode can be found at https://twimlai.com/go/767.
AI coding tools might have handed your developers a productivity gain, but they’ve created a problem for your code review process. Pull request volume is up significantly, and the code arriving for review carries error patterns that weren’t common before generative AI. Yet it’s the same people with the same working hours who are in […]
New data shows consumer AI app growth has flatlined as generative AI struggles tofind its true form outside of the enterprise.
When Ali Aoun Mehdi watched two major global news outlets report opposite facts during the Iran-US conflict, he saw a critical problem: misinformation spreading in real-time. From Islamabad, participating virtually in the Gen AI Zürich Hackathon, he built Sentinel to close this “fact-gap” by detecting contradictions across news sources instantly. Sentinel is an AI-powered early warning system that monitors 20 global news sources every 30 minutes. It identifies factual contradictions in under 30 seconds, providing users with a misinformation risk score and narrative traction assessment.
In this episode, Rashmi Shetty, senior director of enterprise generative AI platform at Capital One, joins us to explore how the company is designing, deploying, and scaling multi-agent systems in a highly regulated environment. Rashmi walks us through Chat Concierge, a multi-agent chat experience for auto dealerships that handles intent disambiguation, tool invocation, and human handoffs to deliver safer, more personalized customer journeys. We discuss Capital One’s platform-centric approach to AI agents and how it separates design from runtime governance, embedding policies, guardrails, and cyber controls across agent threat boundaries. Rashmi shares how the team approaches the developer experience for agent builders, observability, and evals for stochastic, multi-agent workflows; and strategies for model specialization, including fine-tuning and distillation. We also cover standards and abstraction, closed-loop learning from production telemetry, and key lessons for enterprises building agentic systems. The complete show notes for this episode can be found at https://twimlai.com/go/765.
How Bytedance Volcano Engine LAS (Lake for AI Service) leverages Lance as the core storage format, rapidly constructing a next-gen AI data lake to efficiently store, manage, and process multimodal data (text, images, audio/video).
OpenAI ships GPT-5.4 mini and nano, faster and more capable but up to 4x pricier, DLSS 5 looks like a real-time generative AI filter for video games | The Verge, and more!
What does “AI at the edge” really mean in 2026, and why does it matter now more than ever before? In this episode, we’re joined by Brandon Shibley, Edge AI Solutions Engineering Lead at Qualcomm’s Edge Impulse, to discuss the current state and future of Edge AI in 2026. We discuss Gen AI, Small Models, and Cascades of Models, along with real-world constraints like latency, power, and privacy. We also dive into the role of MLOps, evolving hardware, and how developers can start building practical edge AI systems today. Featuring: Brandon Shibley – LinkedIn Chris Benson – Website , LinkedIn , Bluesky , GitHub , X Daniel Whitenack – Website , GitHub , X Links: Read our Ultimate Guide to Edge AI Download your copy of O'Reilly's AI at the Edge Check out the Edge Impulse blog Sign-up for an expert led trial of Edge Impulse Upcoming Events: Register for upcoming webinars here !
BMFTR and MWK Discuss AI Research and Transfer Activities at Tübingen AI Center
DLSS 5 looks like a real-time generative AI filter for video games, OpenAI Reportedly Pivoting to a Focus on Business and Productivity Only, and more!
As generative AI moves into production, traditional guardrails and input/output filters can prove too slow, too expensive, and/or too limited. In this episode, Alizishaan Khatri of Wrynx joins Daniel and Chris to explore a fundamentally different approach to AI safety and interpretability. They unpack the limits of today’s black-box defenses, the role of interpretability, and how model-native, runtime signals can enable safer AI systems. Featuring: Alizishaan Khatri – LinkedIn Chris Benson – Website , LinkedIn , Bluesky , GitHub , X Daniel Whitenack – Website , GitHub , X Upcoming Events: Register for upcoming webinars here !
In this episode, we’re joined by Munawar Hayat, researcher at Qualcomm AI Research, to discuss a series of papers presented at NeurIPS 2025 focusing on multimodal and generative AI. We dive into the persistent challenge of object hallucination in Vision-Language Models (VLMs), why models often discard visual information in favor of pre-trained language priors, and how his team used attention-guided alignment to enforce better visual grounding. We also explore a novel approach to generalized contrastive learning designed to solve complex, composed retrieval tasks—such as searching via combined text and image queries—without increasing inference costs. Finally, we cover the difficulties generative models face when rendering multiple human subjects, and the new "MultiHuman Testbench" his team created to measure and mitigate issues like identity leakage and attribute blending. Throughout the discussion, we examine how these innovations align with the need for efficient, on-device AI deployment. The complete show notes for this episode can be found at https://twimlai.com/go/758.
Tejumade Afonja is a PhD Researcher at the CISPA Helmholtz Center for Information Security in Germany, where her work explores trustworthy AI, generative models, and synthetic tabular data. She is the 2025 Deep Learning Indaba General Chair and previously chaired the 2023 Indaba Application & Selections Committee. Beyond academia, she is the Director of the […] The post Building Africa’s AI Future Together appeared first on Deep Learning Indaba .
In this episode, Hung Bui, Technology Vice President at Qualcomm, joins us to explore the latest high-efficiency techniques for running generative AI, particularly diffusion models, on-device. We dive deep into the technical challenges of deploying these models, which are powerful but computationally expensive due to their iterative sampling process. Hung details his team's work on SwiftBrush and SwiftEdit, which enable high-quality text-to-image generation and editing in a single inference step. He explains their novel distillation framework, where a multi-step teacher model guides the training of an efficient, single-step student model. We explore the architecture and training, including the use of a secondary 'coach' network that aligns the student's denoising function with the teacher's, allowing the model to bypass the iterative process entirely. Finally, we discuss how these efficiency breakthroughs pave the way for personalized on-device agents and the challenges of running reasoning models with techniques like inference-time scaling under a fixed compute budget. The complete show notes for this episode can be found at https://twimlai.com/go/753.
We are delighted to welcome Nicole Ludwig at the Tübingen AI Center!
Three outstanding Principal Investigators will be joining the ELLIS Institute Tübingen, co-affiliated with the MPI-IS and the Tübingen AI Center.
On July 11, the Tübingen AI Center hosted the 2025 edition of the Tübingen AI Summer Poster Event
Tübingen AI Center is proud to be a partner in ELLlOT, a Horizon Europe-funded project aiming to develop next-generation Multimodal Generalist Foundation Models.
With the conclusion of its inaugural round, the DEEP X Tübingen AI cooperation has established a successful sciencepreneurship initiative in artificial intelligence.
"In projecting language back as the model for thought, we lose sight of the tacit embodied understanding that undergirds our intelligence." –Terry Winograd The recent successes of generative AI models have convinced some that AGI is imminent. While these models appear to capture the essence of human
PLAID is a multimodal generative model that simultaneously generates protein 1D sequence and 3D structure, by learning the latent space of protein folding models. The awarding of the 2024 Nobel Prize to AlphaFold2 marks an important moment of recognition for the of AI role in biology. What comes next after protein folding? In PLAID , we develop a method that learns to sample from the latent space of protein folding models to generate new proteins. It can accept compositional function and organism prompts , and can be trained on sequence databases , which are 2-4 orders of magnitude larger than structure databases. Unlike many previous protein structure generative models, PLAID addresses the multimodal co-generation problem setting: simultaneously generating both discrete sequence and continuous all-atom structural coordinates. From structure prediction to real-world drug design Though recent works demonstrate promise for the ability of diffusion models to generate proteins, there still exist limitations of previous models that make them impractical for real-world applications, such as: All-atom generation : Many existing generative models only produce the backbone atoms. To produce the all-atom structure and place the sidechain atoms, we need to know the sequence. This creates a multimodal generation problem that requires simultaneous generation of discrete and continuous modalities. Organism specificity : Proteins biologics intended for human use need to be humanized , to a…
Exchange on artificial intelligence at national and European level - Minister Özdemir: "The AI competence centers - such as the Tübingen AI Center - are crucial for our technological sovereignty in AI".
ELLIS Institute Tübingen and Tübingen AI Center achieve important milestone for innovation in automated agriculture.
We are delighted to welcome Peter Gehler at the Tübingen AI Center!
As we’re still in the early days of building applications with foundation models, it’s normal to make mistakes. This is a quick note with examples of some of the most common pitfalls that I’ve seen, both from public case studies and from my personal experience. Because these pitfalls are common, if you’ve worked on any AI product, you’ve probably seen them before. 1. Use generative AI when you don't need generative AI Every time there’s a new technology, I can hear the collective sigh of senior engineers everywhere: “Not everything is a nail.” Generative AI isn’t an exception — its seemingly limitless capabilities only exacerbate the tendency to use generative AI for everything. A team pitched me the idea of using generative AI to optimize energy consumption. They fed a household’s list of energy-intensive activities and hourly electricity prices into an LLM, then asked it to create a schedule to minimize energy costs. Their experiments showed that this could help reduce a household’s electricity bill by 30%. Free money. Why wouldn’t anyone want to use their app? I asked: “How does it compare to simply scheduling the most energy-intensive activities when electricity is cheapest? Say, doing your laundry and charging your car after 10pm?” They said they would try it later and let me know. They never followed up, but they abandoned this app soon after. I suspect that this greedy scheduling can be quite effective. Even if it’s not, there are other much cheaper and more reliable…
Introduction The project management landscape is undergoing a transformative shift, driven by the rapid advancements...
Generative AI is a rapidly evolving field with a plethora of fascinating applications, from creating realistic images and videos to generating human-like text and beyond. As the technology advances, the demand for skilled professionals who can harness the power of generative AI is growing exponentially. However, navigating the myriad of tutorials and courses available can […] The post Accelerate Your AI Skills: Essential Generative AI Courses for Developers appeared first on TOPBOTS .
After studying how companies deploy generative AI applications, I noticed many similarities in their platforms. This post outlines the common components of a generative AI platform, what they do, and how they are implemented. I try my best to keep the architecture general, but certain applications might deviate. This is what the overall architecture looks like. This is a pretty complex system. This post will start from the simplest architecture and progressively add more components. In its simplest form, your application receives a query and sends it to the model. The model generates a response, which is returned to the user. There are no guardrails, no augmented context, and no optimization. The Model API box refers to both third-party APIs (e.g., OpenAI, Google, Anthropic) and self-hosted APIs. From this, you can add more components as needs arise. The order discussed in this post is common, though you don’t need to follow the exact same order. A component can be skipped if your system works well without it. Evaluation is necessary at every step of the development process. Enhance context input into a model by giving the model access to external data sources and tools for information gathering. Put in guardrails to protect your system and your users. Add model router and gateway to support complex pipelines and add more security. Optimize for latency and costs with cache. Add complex logic and write actions to maximize your system’s capabilities. Observability, which allow…
LangChain and Qdrant are collaborating on the launch of Qdrant Hybrid Cloud , which is designed to empower engineers and scientists globally to easily and securely develop and scale their GenAI applications. Harnessing LangChain’s robust framework, users can unlock the full potential of vector search, enabling the creation of stable and effective AI products. Qdrant Hybrid Cloud extends the same powerful functionality of Qdrant onto a Kubernetes-based architecture, enhancing LangChain’s capability to cater to users across any environment.
We’re excited to share that Qdrant and Vultr are partnering to provide seamless scalability and performance for vector search workloads. With Vultr’s global footprint and customizable platform, deploying vector search workloads becomes incredibly flexible. Qdrant’s new Qdrant Hybrid Cloud offering and its Kubernetes-native design, coupled with Vultr’s straightforward virtual machine provisioning, allows for simple setup when prototyping and building next-gen AI apps. Adapting to Diverse AI Development Needs with Customization and Deployment Flexibility In the fast-paced world of AI and ML, businesses are eagerly integrating AI and generative AI to enhance their products with new features like AI assistants, develop new innovative solutions, and streamline internal workflows with AI-driven processes. Given the diverse needs of these applications, it’s clear that a one-size-fits-all approach doesn’t apply to AI development. This variability in requirements underscores the need for adaptable and customizable development environments.
Qdrant and STACKIT are thrilled to announce that developers are now able to deploy a fully managed vector database to their STACKIT environment with the introduction of Qdrant Hybrid Cloud . This is a great step forward for the German AI ecosystem as it enables developers and businesses to build cutting edge AI applications that run on German data centers with full control over their data. Vector databases are an essential component of the modern AI stack. They enable rapid and accurate retrieval of high-dimensional data, crucial for powering search, recommendation systems, and augmenting machine learning models. In the rising field of GenAI, vector databases power retrieval-augmented-generation (RAG) scenarios as they are able to enhance the output of large language models (LLMs) by injecting relevant contextual information. However, this contextual information is often rooted in confidential internal or customer-related information, which is why enterprises are in pursuit of solutions that allow them to make this data available for their AI applications without compromising data privacy, losing data control, or letting data exit the company’s secure environment.
In a move to empower the next wave of AI innovation, Qdrant and Scaleway collaborate to introduce Qdrant Hybrid Cloud , a fully managed vector database that can be deployed on existing Scaleway environments. This collaboration is set to democratize access to advanced AI capabilities, enabling developers to easily deploy and scale vector search technologies within Scaleway’s robust and developer-friendly cloud infrastructure. By focusing on the unique needs of startups and the developer community, Qdrant and Scaleway are providing access to intuitive and easy to use tools, making cutting-edge AI more accessible than ever before.
We’re happy to announce the collaboration between LlamaIndex and Qdrant’s new Hybrid Cloud launch , aimed at empowering engineers and scientists worldwide to swiftly and securely develop and scale their GenAI applications. By leveraging LlamaIndex’s robust framework, users can maximize the potential of vector search and create stable and effective AI products. Qdrant Hybrid Cloud offers the same Qdrant functionality on a Kubernetes-based architecture, which further expands the ability of LlamaIndex to support any user on any environment.
We’re thrilled to announce the collaboration between Qdrant and Jina AI for the launch of Qdrant Hybrid Cloud , empowering users worldwide to rapidly and securely develop and scale their AI applications. By leveraging Jina AI’s top-tier large language models (LLMs), engineers and scientists can optimize their vector search efforts. Qdrant’s latest Hybrid Cloud solution, designed natively with Kubernetes, seamlessly integrates with Jina AI’s robust embedding models and APIs. This synergy streamlines both prototyping and deployment processes for AI solutions.
[ Hacker News discussion , LinkedIn discussion , Twitter thread ] Update (Feb 2026) : The full list of open source AI repos is hosted at Good AI List , updated daily. It’s balooned to 15K repos, and you can submit missing repos. You can also find some of them on my cool-llm-repos list on GitHub. Four years ago, I did an analysis of the open source ML ecosystem . Since then, the landscape has changed, so I revisited the topic. This time, I focused exclusively on the stack around foundation models. Data I searched GitHub using the keywords gpt , llm , and generative ai . If AI feels so overwhelming right now, it’s because it is. There are 118K results for gpt alone. To make my life easier, I limited my search to the repos with at least 500 stars. There were 590 results for llm , 531 for gpt , and 38 for generative ai . I also occasionally checked GitHub trending and social media for new repos. After MANY hours, I found 896 repos. Of these, 51 are tutorials (e.g. dair-ai/Prompt-Engineering-Guide ) and aggregated lists (e.g. f/awesome-chatgpt-prompts ). While these tutorials and lists are helpful, I’m more interested in software. I still include them in the final list, but the analysis is done with the 845 software repositories. It was a painful but rewarding process. It gave me a much better understanding of what people are working on, how incredibly collaborative the open source community is, and just how much China’s open source ecosystem diverges from the Western one. The Ne…
I had a lot of fun preparing the talk: “Leadership needs us to do generative AI. What do we do?” for Fully Connected . The idea for the talk came from many conversations I’ve had recently with friends who need to figure out their generative AI strategy, but aren’t sure what exactly to do. This talk is a simple framework to explore what to do with generative AI. Many ideas are still being fleshed out. I hope to convert this into a proper post when I have more time. In the meantime, I’d love to hear from your experience through this process. I couldn’t figure out how to make the slides centered on the page. You might want to download the slides . Thanks everyone who responded to my post and shared your thoughts on what I should include in the talk. Thanks Kyle Gallatin , Goku Mohandas , Han-chung Lee , and Jamie de Guerre for thoughtful feedback on the talk.
Here are eight observations I’ve shared recently on the Cohere blog and videos that go over them.: Article: What’s the big deal with Generative AI? Is it the future or the present? Article: AI is Eating The World
Can AI Image generation tools make re-imagined, higher-resolution versions of old video game graphics? Over the last few days, I used AI image generation to reproduce one of my childhood nightmares. I wrestled with Stable Diffusion, Dall-E and Midjourney to see how these commercial AI generation tools can help retell an old visual story - the intro cinematic to an old video game (Nemesis 2 on the MSX). This post describes the process and my experience in using these models/services to retell a story in higher fidelity graphics. Meet Dr. Venom This fine-looking gentleman is the villain in a video game. Dr. Venom appears in the intro cinematic of Nemesis 2, a 1987 video game. This image, in particular, comes at a dramatic reveal in the cinematic. Let’s update these graphics with visual generative AI tools and see how they compare and where each succeeds and fails. Remaking Old Computer graphics with AI Image Generation Here’s a side-by-side look at the panels from the original cinematic (left column) and the final ones generated by the AI tools (right column): This figure does not show the final Dr. Venom graphic because I want you to witness it as I had, in the proper context and alongside the appropriate music. You can watch that here:
Disclaimer : I need guidance and help with where to start looking for solution of the problem I have described below. My background is in optimization and I am new to statistical methods, so there is a good chance that I am asking the wrong question or/and used wrong terminologies (please correct if that is the case). Below I setup my problem: Given: the set of $n\times n$ matrices. two functoins, $f: \mathbb{R}^{n^2} \rightarrow\mathbb{R}$ and ${\bf{g}}: \mathbb{R}^{n^2}\rightarrow Symm.(n\times n)$ Two constraints as follows: $$1 - \epsilon_1 $$-\epsilon_2 for ${\bf{M}} \in \mathbb{R}^{n^2}$ and $\epsilon_1$ and $\epsilon_2$ both fixed small positive numbers. Here are my questions: Can I find a model, or a distribution, which when I sample from it, it produces $n\times n$ matrices that satisfy the above two constraints (most of the time)? The sampled data needs to be close the real distribution in order to be representative. Is question (1) a well formulated question? If the answer to (2) is yes, what method(s) should I look into in order to work towards a solution? For those who are interested in more concrete realizations of functions $f$ and $\bf{g}$ , $f({\bf{M}})=\mathrm{det}({\bf{M}})$ and ${{\bf{g}}}({\bf{M}})=\frac{1}{2}({\bf{M}}^T{\bf{M}} - {\bf{I}})$ . I appreciate any hint or help with this problem. Tags mentioned below are speculative.
Discussion: Discussion Thread for comments, corrections, or any feedback. Translations: Korean, Russian Summary: The latest batch of language models can be much smaller yet achieve GPT-3 like performance by being able to query a database or search the web for information. A key indication is that building larger and larger models is not the only way to improve performance. Video The last few years saw the rise of Large Language Models (LLMs) – machine learning models that rapidly improve how machines process and generate language. Some of the highlights since 2017 include: The original Transformer breaks previous performance records for machine translation. BERT popularizes the pre-training then finetuning process, as well as Transformer-based contextualized word embeddings. It then rapidly starts to power Google Search and Bing Search. GPT-2 demonstrates the machine’s ability to write as well as humans do. First T5, then T0 push the boundaries of transfer learning (training a model on one task, and then having it do well on other adjacent tasks) and posing a lot of different tasks as text-to-text tasks. GPT-3 showed that massive scaling of generative models can lead to shocking emergent applications (the industry continues to train larger models like Gopher, MT-NLG…etc). For a while, it seemed like scaling larger and larger models is the main way to improve performance. Recent developments in the field, like DeepMind’s RETRO Transformer and OpenAI’s WebGPT, reverse this tre…
The thirty-fifth Conference on Neural Information Processing Systems (NeurIPS) 2021 is being hosted virtually from Dec 6th - 14th. We’re excited to share all the work from SAIL that’s being presented at the main conference , at the Datasets and Benchmarks track and the various workshops , and you’ll find links to papers, videos and blogs below. Some of the members in our SAIL community also serve as co-organizers of several exciting workshops that will take place on Dec 13-14, so we hope you will check them out! Feel free to reach out to the contact authors and the workshop organizers directly to learn more about the work that’s happening at Stanford! Main Conference Improving Compositionality of Neural Networks by Decoding Representations to Inputs Authors : Mike Wu, Noah Goodman, Stefano Ermon Contact : wumike@stanford.edu Links: Paper Keywords : generative models, compositionality, decoder Reverse engineering recurrent neural networks with Jacobian switching linear dynamical systems Authors : Jimmy T.H. Smith, Scott W. Linderman, David Sussillo Contact : jsmith14@stanford.edu Links: Paper | Website Keywords : recurrent neural networks, switching linear dynamical systems, interpretability, fixed points Compositional Transformers for Scene Generation Authors : Drew A. Hudson, C. Lawrence Zitnick Contact : dorarad@cs.stanford.edu Links: Paper | Github Keywords : GANs, transformers, compositionality, scene synthesis Combining Recurrent, Convolutional, and Continuous-time Mode…
[Updated on 2021-09-19: Highly recommend this blog post on score-based generative modeling by Yang Song (author of several key papers in the references)]. [Updated on 2022-08-27: Added classifier-free guidance , GLIDE , unCLIP and Imagen . [Updated on 2022-08-31: Added latent diffusion model . [Updated on 2024-04-13: Added progressive distillation , consistency models , and the Model Architecture section .