AI/ML news, top picks, and generated innovation digests.
6461 matching items
Abdi Mohamed, chief executive officer of Absa Bank Kenya, one of Kenya's tier 1 banks, will step down on June 30 after three years in the role and a 32-year career with the lender, ending one of the longest tenures by a senior banking executive in Kenya's banking sector.
One-third of US adults say they are “extremely proud” to be an American, according to Gallup.
The liberal counterweight to conservatives’ Project 2025 is rolling out a series of policies to set the stage for an already crowded field of 2028 presidential hopefuls.
Two childhood friends scaled an AI study app to $13 million in revenue and say using AI to code is powerful but comes at a cost.
Democrat Chris Pappas warns his party not to be “overconfident.”
Rebecca Black, 63, has raised her grandsons since 2020. She left her job to care for them full-time, resulting in financial strain.
An MFB licence allows fintechs to accept deposits, give loans, and earn interest income from lending, reducing their reliance on transaction fees.
A Chinese artificial-intelligence (AI) model whose launch has been hailed as another “DeepSeek moment” can go toe-to-toe with US rival Anthropic’s powerful Mythos model on cybersecurity tasks, researchers have said. Beijing-based start-up Zhipu AI’s GLM-5.2, released on June 13, beat Anthropic’s Claude Opus 4.8 model in benchmarking tests by cybersecurity company Semgrep, The Wall Street Journal reported. When Semgrep researchers gave it further instructions, GLM-5.2 matched that model and...
A luxurious lodge on a private island in Nova Scotia with enough infrastructure to maintain life off the mainland was sold for $6 million.
Looking to migrate to Linux but fond of the Mac UI? Zorin OS can help make your new distro look very much like the one you just left. Here's how to achieve that for free.
Before Tobi Lütke ran Shopify, he learned programming through Germany’s apprenticeship system, the way people have learned trades forever: in a shared workshop, watching people who already knew what they were doing. More recently, describing Shopify’s River , he reached for a related word: Lehrwerkstatt , a teaching workshop where “the whole shop floor is the classroom.” X has been agog by the numbers around River , Shopify’s Slack-native AI agent . In total, 5,938 Shopify employees worked with River across 4,450 different Slack channels, and River now coauthors roughly one in eight merged pull requests across the company. It’s a big deal, but understanding why it works that way is the most important part. River can read code, run tests, open pull requests, query the data warehouse, inspect production traces, and sometimes push back on a plan it thinks is bad. Great. Lots of companies will have clever coding agents someday soon. Some already do. The interesting part is that River doesn’t work alone; it works where everyone can see it. Betting on the workshop I’ve already argued that agents reward explicit, consistent, well-documented software . They like the “boring” stuff, such as schemas, tests, conventions, clean setup instructions, and codebases that don’t require a deep retrospective with the one engineer who remembers why the build script has to run twice. Dropping an agent into a messy repo is mostly an efficient audit of your engineering discipline. Agents hold up…
In the late 1960s, elite Navy pilots began losing dogfights. The deep, instrument-level understanding of exactly where they were, what their aircraft was doing, and what was coming next had been automated. And when moments of crisis arrived, they didn’t have the situational awareness to respond. Put a plane on autopilot long enough, and the pilot stops actually flying. The same dynamic is playing out across enterprise software. AI is generating code faster than developers can understand it , and leaders are celebrating the velocity without asking who’s actually flying the plane. A developer who has only ever “vibe coded” has perception at best. They can “see” the outputs but can’t fix any internal failures caused by the very AI systems they’re relying on. The easiest thing to do is to say the answer looks good enough. Cut and paste it in and hope it works out. According to Model Evaluation & Threat Research’s randomized control trials , experienced developers working with AI tools actually took 19% longer to complete tasks than those working without them, despite predicting beforehand that AI would make them 24% faster. The fundamentals of good software delivery have never been more important — and never more neglected. When instruments go dark The Navy’s answer to training dogfighters for success was the Top Gun school — not just to teach pilots to fight, but to teach them how to fly again. That meant returning to the fundamentals by mastering the technical and combat skill…
Nine major suspected cargo thefts happened at Tesla’s Nevada battery factory in January alone, according to sheriff’s records obtained by WIRED.
The end of pure specialization For years, software organizations optimized around specialization. Product managers owned requirements. Engineers owned implementation. Designers owned UX. QA owned quality. The model worked – until product velocity became a competitive advantage measured in weeks instead of quarters. Today, AI is accelerating another shift that I believe will fundamentally reshape how high-performing technology teams operate: the rise of the product engineer. As Chief Technology Officer of akirolabs, an AI-augmented strategic procurement platform serving enterprise-scale clients, including Fortune 500 organizations, I’ve spent the last several years evolving our engineering model through three distinct stages. First, I dismantled highly specialized silos. Then I transitioned the organization toward more flexible generalists. Eventually, our operating model revealed that the teams performing best in the AI era were neither traditional specialists nor pure generalists, but engineers deeply embedded in product thinking and business context. I formalized and operationalized this role internally as a product engineer model, adapting an increasingly common industry pattern to enterprise AI delivery. This role does not replace product managers. Instead, this operating model elevates strong product managers by removing operational friction. In our organization, product managers became more focused on customers, roadmap prioritization, requirement validation and strate…
For years, the enterprise narrative focused on moving to the public cloud for flexibility and leaving behind old infrastructure. While the public cloud remains a powerful platform for burst capacity, global reach, and modern application development, leaders now evaluate where each workload can achieve the best financial performance, operational efficiency, and risk. Cloud repatriation is back on the CIO’s agenda. Cloud repatriation does not always mean dragging workloads back into a company-owned data center. In many cases, enterprises are moving applications and data from hyperscale public cloud platforms into colocation environments, hosted private clouds , or MSP-operated infrastructure. The common thread is not nostalgia for on-premises IT. It is the desire for a more suitable workload placement. Enterprises are deciding that some systems belong in public cloud while others are better served in environments with more predictable economics, tighter control, and fewer architectural compromises. Cost is the loudest signal The most common reason enterprises repatriate workloads is cost. Public cloud pricing works extremely well when demand is variable, when teams need rapid provisioning, or when a business wants to avoid upfront capital spending. But not every enterprise workload behaves that way. Many core systems are steady, always-on, data-intensive, and relatively predictable. For those workloads, usage-based pricing can become less attractive over time. Compute charges,…
Europe’s climate ambitions must integrate with industrial competitiveness. The upcoming EU ETS reform should support decarbonization while ensuring affordable energy, investment certainty, energy security and a level playing field.
AI data infrastructure startup Clairva has raised $500K in a pre-seed funding round led by Venture Catalysts through its angel network. The company will use the fresh capital to strengthen its licensed data supply network, expand partnerships with content owners and institutions, enhance data enrichment and validation capabilities, and support commercial engagement with global AI customers, Clairva said in a press release. Founded in 2025 by Sunil Nair, Sabari Raju, Dushyant Verma, and Amit Parashar, Clairva builds licensed, provenance backed datasets for AI foundation models, embodied AI, robotics, and autonomous systems. As AI models increasingly rely on high quality datasets, sourcing data with clear usage rights, provenance, and cultural context remains a challenge. Clairva works with content owners, production houses, studios, archives, institutions, and contributor networks to source, license, and structure real world data for AI training. The company is initially focused on India, Southeast Asia, and other Global South markets, where languages, environments, behaviours, gestures, workflows, and objects remain underrepresented in AI training datasets. According to Clairva, it is also developing proprietary technology across the data pipeline, including licensed dataset ingestion, rights and provenance tracking, automated enrichment, metadata generation, action and object tagging, temporal segmentation, quality validation, and dataset packaging.
Prosus reported a strong performance from its India business in FY26, with the ecosystem turning adjusted EBITDA profitable, driven by fintech platform PayU. According to the Prosus’ FY26 annual report, revenue from its India ecosystem rose 13% year-on-year to $781 million in FY26 from $694 million a year earlier. The business posted an adjusted EBITDA (aEBITDA) profit of $18 million, compared to a loss of $25 million in FY25, while its adjusted EBIT loss narrowed sharply to $10 million from $49 million. The Prosus India ecosystem employed 3,897 people during the fiscal. PayU's payments business generated $577 million in revenue,which grew 10% year-on-year, while delivering EBITDA of $12 million. Higher-margin value-added services (VAS) and SaaS offerings contributed 33% of payments revenue, supporting margin expansion. The company's credit business also reached profitability, reporting an adjusted EBITDA of $6 million. Credit revenue rose 19% year-on-year to $204 million, while new loan issuances touched $221 million during the year. During FY26, PayU processed total payment volume (TPV) worth $90 billion across its platform. The company said PayU accounts for around 25% of India's online payments industry revenue and manages $682 million in assets under management through its lending business. During the year, it also increased its stake in banking payments technology firm Mindgate to 70.7%. Prosus said it is building one of India's most comprehensive digital consumer ecos…
The Gambia’s Ministry of Information, Media and Broadcasting Services, in partnership with the African Union of Broadcasting, has launched a two-day capacity-building programme for Gambian journalists on artificial intelligence and broadcasting, held as part of the 17th General Assembly for journalists in Banjul from April 13 to 16. The training, under the theme “Artificial Intelligence [...]
The Zambia Police Service has warned the public against the creation and circulation of misleading AI-generated content depicting violence and other offensive material aimed at senior public officials. Police spokesperson Godfrey Chilabi said the service had noted with concern the misuse of artificial intelligence to produce deceptive digital content. In a statement issued in Lusaka, [...]
Hi, We’ve noticed what appears to be a regression in how GPT Apps behave on desktop. Previously, once an app had been invoked in a conversation (for example using @appname ), it remained active for the rest of that conversation. There was no need to invoke it again for every subsequent message. Now, on both: ChatGPT in the browser (desktop) ChatGPT Desktop app the app seems to lose context after every prompt. Unless the app is explicitly invoked again on each message, ChatGPT falls back to either: a regular web search, or its base model knowledge/training. This significantly degrades the user experience, especially for apps designed to support a continuous conversation. Interestingly, this behavior does not seem to occur on the mobile app, where the app appears to remain active across the conversation as before. Expected behavior Invoke the app once in a conversation. All subsequent messages continue using that app until the user explicitly switches away. Current behavior (Desktop) The app must be invoked before every single prompt. Otherwise ChatGPT ignores the app and responds using web search or its default knowledge. Is anyone else seeing the same behavior? Is this an intentional change or a regression? Thanks!
Berlin police used a water cannon to cool thousands of Bruno Mars fans queuing outside the Olympiastadion as temperatures soared during Europe's heatwave.
Artificial intelligence continues to reshape industries worldwide, and South Africa’s online services sector is no exception, with AI increasingly used to help customers find the right businesses faster and more efficiently. Online services marketplace Snupit has been leveraging AI-powered technology for years to improve how customers connect with trusted local service professionals. While many companies [...]
Artificial intelligence (AI) is a transformative force in economies and financial markets. McKinsey estimates that data centres alone will need a staggering US$6.7 trillion in capital investment across the global data centre value chain in the next five years, and the scale of the investment suggests the world economy is in the early stages of a far-reaching shift that could lead to big gains in productivity. In a report on June 24, Vanguard said the massive buildout of AI infrastructure...
Show not tell makes a far finer script
Samsung and SK Hynix, backed by the South Korean government, are pouring $590 billion into new chip factories and packaging centers as AI data center demand surges. According to Jefferies, memory prices could climb to 50 percent per quarter through 2027. The two companies control nearly 80 percent of the global HBM market. The article Samsung and SK Hynix plan $590 billion chip investment as AI demand sends memory prices soaring appeared first on The Decoder .
Lightning struck the Eiffel Tower as a powerful thunderstorm swept across Paris on Saturday night, sending visitors scrambling for shelter. The dramatic weather followed days of record-breaking heat in France, where authorities say the extreme temperatures contributed to a sharp rise in deaths.
I appreciate your time in this matter. I really need an answer for this, for my thesis. I use the PaySim dataset from Kaggle ( https://www.kaggle.com/datasets/ealaxi/paysim1 ). First of all, I use training, validation, and testing set. Is there really a rule on data split ratio and is it acceptable if I check the model performance on each split, for example, 70/15/15, 80/10/10? After fitting the model with training dataset, we get the default model/fitted model. Which dataset (Training or Validation set) shall I use for examining the model performance? My intention of having 3 types of set (Training, Validation, and Testing) is to use the Validation set as hyperparameter tuning examination. Thanks so much.
Hi @OpenAI_Support i have same issue. Pls help. Country: Vietnam Carrier: Mobifone Phone number: +84937***446
Enterprise AI isn't scaling as fast as many expected. Here's what's really holding businesses back, and why it matters more than ever. The post Why enterprises aren’t ready for AI Agents yet appeared first on MEDIANAMA .
So we will probably get it in about 3 months when open models will likely have surpassed it
Last week, we tracked more than 75 tech funding deals worth over €2.1 million and over 5 exits, M&A transactions, rumours, and related news stories across Europe. 📊 The top three industries that r...
DiScoFormer is a transformer-based density and score estimator that can infer both quantities from a finite sample in one forward pass, generalizing classical KDE while staying accurate in high-dimensional and out-of-distribution settings without retraining for each new distribution.
Flexion Robotics, a startup founded by ex-Nvidia engineers, has a clever way of training robots to do useful work.
The 24-hour strike is disrupting high-speed, regional and suburban rail services just as the holiday getaway is getting under way.
This year’s Cannes Lions was an inflection point where ‘AI stopped being the headline and simply became part of the creative process.’
Washington has proposed this week's discussions, which are expected to center on the Strait of Hormuz, take place in Doha and begin as early as Tuesday.
The shooting happened at San Pedro Square, one of several places in the San Francisco Bay Area where crowds have gathered to watch World Cup matches on big outdoor screens.
NCB has identified Telegram as the most prominent encrypted platform used to advertise illegal drugs in India. The post Telegram Most-Used App For Drug Advertising, NCB Report Says appeared first on MEDIANAMA .
Lytmus AI, a Bengaluru-based startup building AI mentors for competitive exam preparation, has raised Rs 5 crore in a pre-seed funding round led by Boundless Ventures. The fresh capital will be used to strengthen the startup’s AI capabilities, accelerate product development, and expand student acquisition, with an initial focus on the NEET segment, the company said in a press release. Founded in 2024 by Ajit Kumar and Praveen, Lytmus AI develops AI mentors for competitive exam preparation. The platform combines persistent memory and contextual understanding with subject expertise from teachers to create AI mentors that resolve doubts in real time and guide students through their learning journey. It is currently available on mobile with an initial focus on NEET. According to the startup, its platform operates on two layers. The infrastructure layer is trained on the teaching patterns of subject experts to enable AI mentors to explain concepts effectively, while the application layer maintains memory and context for each student to personalise the learning experience. With more than two million candidates registering for NEET every year, the company believes there is significant demand for personalised academic support during exam preparation. Lytmus AI said its platform has been used by over 16,000 students in the past 90 days. According to the company, students using its AI mentor complete up to three times more daily practice than those without such guidance. The startup c…
As Europe endures another brutal summer, Europe in Motion unpacks water stress levels across the continent.