Over the next few years, agentic AI is expected to bring not only rapid technological breakthroughs, but a societal transformation, redefining how we live, work and interact with the world. And this shift is happening quickly.

“By 2028, 33% of enterprise software applications will include agentic AI, up from less than 1% in 2024, enabling 15% of day-to-day work decisions to be made autonomously,” according to research firm Gartner.

Unlike traditional AI, which typically follows preset rules or algorithms, agentic AI adapts to new situations, learns from experiences, and operates independently to pursue goals without human intervention. In short, agentic AI empowers systems to act autonomously, making decisions and executing tasks — even communicating directly with other AI agents — with little or no human involvement.

One key driver is the growing sophistication of large language models (LLMs), which provide the “brains” for these agents. Agentic AI will enable machines to interact with the physical world with unprecedented intelligence, allowing them to perform complex tasks in dynamic environments, which could be especially useful for industries facing labor shortages or hazardous conditions.

The rise of agentic AI also brings security and ethical concerns. Ensuring these autonomous systems operate safely, transparently and responsibly will require governance frameworks and testing. Preventing the law of unintended consequences will also require human vigilance.

Because job displacement is a potential outcome, strategies for retraining and upskilling workers will be needed as the technology necessitate a shift in how people approach work, emphasizing collaboration between humans and intelligent machines.

To stay on top of this evolving technology, follow this page for ongoing agentic AI coverage from Computerworld and Foundry’s other publications.

Agentic AI news and insights

Putting agentic AI to work in Firebase Studio

May 21, 2025: Putting agentic AI to work in software engineering can be done in a variety of ways. Some agents work independently of the developer’s environment, working essentially like a remote developer. Other agents directly within a developer’s own environment. Google’s Firebase Studio is an example of the latter, drawing on Google’s Gemini LLM o help developers prototype and build applications .

Why is Microsoft offering to turn websites into AI apps with NLWeb?

May 20. 2025: NLWeb, short for Natural Language Web, is designed to help enterprises build a natural language interface for their websites using the model of their choice and data to answer user queries about the contents of the website. Microsoft hopes to stake its claim on the agentic web before rivals Google and Amazon do.

Databricks to acquire open-source database startup Neon to build the next wave of AI agents

May 14, 2025: Agentic AI requires a new type of architecture because traditional workflows create gridlock, dragging down speed and performance. To get ahead in this next generation of app building, Databricks announced it will purchase Neon, an open-source serverless Postgres company.

Agentic mesh: The future of enterprise agent ecosystems

May 13, 2025: Nvidia CEO Jensen Huang predicts we’ll soon see “a couple of hundred million digital agents” inside the enterprise. Microsoft CEO Satya Nadella takes it even further: “Agents will replace all software.”

Google to unveil AI agent for developers at I/O, expand Gemini integration

May 13, 2025: Google is expected to unveil a new AI agent aimed at helping software developers manage tasks across the coding lifecycle, including task execution and documentation. The tool has reportedly been demonstrated to employees and select external developers ahead of the company’s annual I/O conference.

Nvidia, ServiceNow engineer open-source model to create AI agents

May 6, 2025: Nvidia and ServiceNow have created an AI model that can help companies create learning AI agents to automate corporate workloads. The open-source Apriel model, available generally in the second quarter on HuggingFace, will help create AI agents that can make decisions around IT, human resources and customer-service functions.

How IT leaders use agentic AI for business workflows

April 30, 2025: Jay Upchurch, CIO at SAS, backs agentic AI to enhance sales, marketing, IT, and HR motions. “Agentic AI can make sales more effective by handling lead scoring, assisting with customer segmentation, and optimizing targeted outreach,” he says.

Microsoft sees AI agents shaking up org charts, eliminating traditional functions

April 28, 2025: As companies increasingly automate work processes using agents, traditional functions such as finance, marketing, and engineering may fall away, giving rise to an ‘agent boss’ era of delegation and orchestration of myriad bots.

Cisco automates AI-driven security across enterprise networks

April 28, 2025: Cisco announced a range of AI-driven security enhancements, including improved threat detection and response capabilities in Cisco XDR and Splunk Security, new AI agents, and integration between Cisco’s AI Defense platform and ServiceNow SecOps.

Hype versus execution in agentic AI

April 25, 2025: Agentic AI promises autonomous systems capable of reasoning, making decisions, and dynamically adapting to changing conditions. The allure lies in machines operating independently, free of human intervention, streamlining processes and enhancing efficiency at unprecedented scales. But David Linthicum writes, don’t be swept up by ambitious promises. 

Agents are here — but can you see what they’re doing?

April 23, 2025: As the agentic AI models powering individual agents get smarter, the use cases for agentic AI systems get more ambitious — and the risks posed by these systems increase exponentially.A multicloud experiment in agentic AI: Lessons learned

Agentic AI might soon get into cryptocurrency trading — what could possibly go wron

April 15, 2025: Agentic AI promises to simplify complex tasks such as crypto trading or managing digital assets by automating decisions, enhancing accessibility, and masking technical complexity.

Agentic AI is both boon and bane for security pros

April 15, 2025: Cybersecurity is at a crossroads with agentic AI. It’s a powerful tool that can create reams of code in a blink of an eye, find and defuse threats, and be used so decisively and defensively. This has proved to be a huge force multiplier and productivity boon. But while powerful, agentic AI isn’t dependable, and that is the conundrum. 

AI agents vs. agentic AI: What do enterprises want?

April 15, 2025:  Now that this AI agent story has morphed into “agentic AI,” it seems to have taken on the same big-cloud-AI flavor that enteriprise already rejected. What do they want from AI agents, why is “agentic” thinking wrong, and where is this all headed?

A multicloud experiment in agentic AI: Lessons learned

April 11, 2025: Turns out you really can build a decentralized AI system that operates successfully across multiple public cloud providers. It’s both challenging and costly.

Google adds open source framework for building agents to Vertex AI

April 9, 2025: Google is adding a new open source framework for building agents to its AI and machine learning platform Vertex AI, along with other updates to help deploy and maintain these agents. The open source Agent Development Kit (ADK) will make it possible to build an AI agent in under 100 lines of Python code. It expects to add support for more languages later this year.

Google’s Agent2Agent open protocol aims to connect disparate agents

April 9, 2025: Google has taken the covers off a new open protocol — Agent2Agent (A2A) — that aims to connect agents across disparate ecosystems.. At its annual Cloud Next conference, Google said that the A2A protocol will enable enterprises to adopt agents more readily as it bypasses the challenge of agents that are built on different vendor ecosystems not being able to communicate with each other.

Riverbed bolsters AIOps platform with predictive and agentic AI

April 8, 2025: Riverbed unveiled updates to its AIOps and observability platform that the company says will transform how IT organizations manage complex distributed infrastructure and data more efficiently. Expanded AI capabilities are aimed at making it easier to manage AIOps and enabling IT organizations to transition from reactive to predictive IT operations.

Microsoft’s newest AI agents can detail how they reason

March 26, 2025: If you’re wondering how AI agents work, Microsoft’s new Copilot AI agents provide real-time answers on how data is being analyzed and sourced to reach results. The Researcher and Analyst agents take a deeper look at data sources such as email, chat or databases within an organization to produce research reports, analyze strategies, or convert raw information into meaningful data.

Microsoft launches AI agents to automate cybersecurity amid rising threats

March 26, 2025: Microsoft has introduced a new set of AI agents for its Security Copilot platform, designed to automate key cybersecurity functions as organizations face increasingly complex and fast-moving digital threats. The new tools focus on tasks such as phishing detection, data protection, and identity management.

How AI agents work

March 24, 2025: By leveraging technologies such as machine learning, natural language processing (NLP), and contextual understanding, AI agents can operate independently, even partnering with other agents to perform complex tasks.

5 top business use cases for AI agents

March 19, 2025: AI agents are poised to transform the enterprise, from automating mundane tasks to driving customer service and innovation. But having strong guardrails in place will be key to success.

Nvidia launches AgentIQ toolkit to connect disparate AI agents

March 21, 2025: As enterprises look to adopt agents and agentic AI to boost the efficiency of their applications, Nvidia this week introduced a new open-source software library — AgentIQ toolkit — to help developers connect disparate agents and agent frameworks..

Deloitte unveils agentic AI platform

March 18, 2025: At Nvidia GTC 2025 in San Jose, Deloitte announced Zora AI, a new agentic AI platform that offers a portfolio of AI agents for finance, human capital, supply chain, procurement, sales and marketing, and customer service.The platform draws on Deloitte’s experience from its technology, risk, tax, and audit businesses, and is integrated with all major enterprise software platforms. 

The dawn of agentic AI: Are we ready for autonomous technology?

March 15, 2025: Much of the AI work prior has focused on large language models (LLMs) with a goal to give prompts to get knowledge out of the unstructured data. So it’s a question-and-answer process. Agentic AI goes beyond that. You can give it a task that might involve a complex set of steps that can change each time.

How to know a business process is ripe for agentic AI

March 11, 2025: Deloitte predicts that in 2025, 25% of companies that use generative AI will launch agentic AI pilots or proofs of concept, growing to 50% in 2027. The firm says some agentic AI applications, in some industries and for some use cases, could see actual adoption into existing workflows this year.

With new division, AWS bets big on agentic AI automation

March 6, 2025: Amazon Web Services customers can expect to hear a lot more about agentic AI from AWS in future with the news that the company is setting up a dedicated unit to promote the technology on its platform.

How agentic AI makes decisions and solves problems

March 6, 2025: GenAI’s latest big step forward has been the arrival of autonomous AI agents. Agentic AI is based on AI-enabled applications capable of perceiving their environment, making decisions, and taking actions to achieve specific goals. 

CIOs are bullish on AI agents. IT employees? Not so much

Feb. 4, 2025: Most CIOs and CTOs are bullish on agentic AI, believing the emerging technology will soon become essential to their enterprises, but lower-level IT pros who will be tasked with implementing agents have serious doubts.

The next AI wave — agents — should come with warning labels. Is now the right time to invest in them?

Jan.13, 2025: The next wave of artificial intelligence (AI) adoption is already under way, as AI agents — AI applications that can function independently and execute complex workflows with minimal or limited direct human oversight — are being rolled out across the tech industry.

AI agents are unlike any technology ever

Dec. 1, 2024: The agents are coming, and they represent a fundamental shift in the role artificial intelligence plays in businesses, governments, and our lives.

AI agents are coming to work — here’s what businesses need to know

Nov. 21, 2024: AI agents will soon be everywhere, automating complex business processes and taking care of mundane tasks for workers — at least that’s the claim of various software vendors that are quickly adding intelligent bots to a wide range of work apps.

Agentic AI swarms are headed your way

November 1, 2024: OpenAI launched an experimental framework called Swarm. It’s a “lightweight” system for the development of agentic AI swarms, which are networks of autonomous AI agents able to work together to handle complex tasks without human intervention, according to OpenAI. 

Is now the right time to invest in implementing agentic AI?

October 31, 2024: While software vendors say their current agentic AI-based offerings are easy to implement, analysts say that’s far from the truth.

Source:: Computer World

By Deepti Pathak Want to listen to music with friends in real time, no matter where they are? Spotify…
The post How To Start a Jam on Spotify? appeared first on Fossbytes.

Source:: Fossbytes

By Siôn Geschwindt Paris-based AI startup Veesion has secured €38mn to fuel expansion to the US — where it looks to help cure the country’s shoplifting “epidemic.” Veesion’s AI-based computer vision software is trained to spot gestures in security camera feeds, such as a shopper putting an item in their pocket. If it sees something suspicious, the AI pings the store owner or security guard via an app, where it displays a recording of the activity. The user then makes the final judgment on whether the situation qualifies as theft.  The software comes in a small box that plugs into a shop’s existing…This story continues at The Next Web

Source:: The Next Web

Do you want to make a podcast from notes you record on your iPhone? You can, as Google has introduced an iOS version (and an Android version) of its popular NotebookLM tool, which can do this, among other things.

The news follows hot on the heels of speculation that Apple may try to overcome shortcomings in its own AI development by opening up its platform to third-party AI services in addition to ChatGPT and Apple Intelligence. It may be relevant to point out that Apple this week made it possible to use Google Translate instead of Apple’s own Translate app on iPhones.

What is NotebookLM?

Notebook LM has won a ton of praise since it appeared. It is a really useful document summarization system that is very handy for researchers — and can even turn topics you write about into engaging and thought-provoking podcasts. The service achieves this through use of Google’s Gemini genAI system, which seems to be improving rapidly when it comes to focused tasks.

“We’ve received a lot of great feedback from the millions of people using NotebookLM, our tool for understanding and engaging with complex information. One of the most frequent requests has been for a mobile app — for listening to Audio Overviews on the go, asking questions about sources in the moment, and sharing content directly to NotebookLM while browsing,” said Google when it announced the new apps.

Making a podcast on your iPhone

NotebookLM has been available as a web app, and now also as an app for iOS and Android. Once installed, you can use the mobile app to create new notes and access those you may already have created via your Google account. You can also add new sources to notes and create podcasts of those notes. But one of the best new features is the ability to get involved in the podcast/conversation. 

Tap the Join button and you can interact with the AI-generated hosts, asking them questions or steering the conversation. It’s remarkable, particularly if you are still trying to explain which song you want Siri to play in Apple Music. 

It shows the extent to which Apple’s AI services are playing catch-up and may also be why Apple’s management is thinking about opening up the company’s platform to third-party AI services.

Is this how Apple will make AI services optional?

The move to make Google Translate an option for users shows how that may be done. Just as Apple is being forced to permit users to choose between browsers in Europe, the options tool for Translation lets you select which service to use for that. 

Finding a way to offer these choices while preserving platform integrity is easier said than done. Apple has admitted to having thousands of engineers tasked with figuring out how to make that possible. 

But as the company moves forward with developing solutions that deliver such choice, it is also creating the template we will probably see it follow as it moves to offer up support for different forms of AI services on its devices.

With that in mind, it is likely that, as AI services introduce apps for Apple’s systems, the company will introduce a new setting in which users will be able to choose what service to use. It is possible that Apple will need to keep Apple Intelligence as the first point of contact, acting as a kind of concierge for queries, which it then directs to an appropriate AI. Users will then select which service will offer the default AI.

What about people who don’t want to use these services?

For enterprise users, this poses additional challenges. To avoid data leaks, not every business will be prepared to authorize employees to use every available genAI model. That challenge implies that Apple will also need to build APIs for Mobile Device Management to enable IT to switch off access to these third-party genAI models for managed devices.

The problem with access must therefore logically extend to app-based control. IT will want to be able to prevent people from using apps such as NotebookLM on managed devices, presumably by setting restrictions on the use of certain apps.

It also seems viable to expect Apple to offer up an additional choice — one in which users are given the opportunity to select to stay with a purely Apple experience. After all, that should also be an option for those who like it, right?

You can follow me on social media! Join me on BlueSky,  LinkedIn, and Mastodon.

Source:: Computer World

China has launched 12 satellites, which experts describe as the world’s first operational space-based computing network, applying edge computing principles to orbital operations in a development that could reshape how enterprises manage global data.

The satellites, launched by Guoxing Aerospace of China, are equipped with AI systems, advanced inter-satellite communication capabilities, and onboard computing power.

The network, formally named the “Three-Body Computing Constellation” but also referred to as the “Star Computing Constellation 021” mission, represents China’s push to create computing infrastructure beyond Earth that could transform data processing while potentially reducing environmental impacts, Guoxing Aerospace said in a statement.

The constellation is a project by China to build a network of 2,800 satellites to empower real-time in-orbit computing and data processing.

“China’s orbital AI constellation is more than a technological feat—it’s a proof of concept for distributed processing, autonomy at the edge, and context-driven compute as core tenets of modern architecture,” said Sanchit Vir Gogia, chief analyst and CEO at Greyhound Research. “What makes this constellation distinctive is not just its scale, but its shift in control logic: inference and orchestration happen in orbit, across a high-speed inter-satellite mesh, without needing constant cloud fallback.”

Edge computing in space

For enterprise decision-makers, the constellation represents perhaps the most ambitious application yet of edge computing principles — processing data directly where it’s generated rather than sending everything to centralized facilities. This orbital implementation showcases how these principles can address even the most extreme bandwidth constraints.

“China’s Three-Body Computing Constellation marks a radical evolution in edge computing — demonstrating a ‘hyper-edge’ model: autonomous, localized processing under extreme latency and bandwidth constraints,” explained Deepti Sekhri, practice director at Everest Group. “This leap forces enterprise edge strategies to move beyond basic edge nodes toward resilient, AI-infused micro-infrastructures. We can expect the biggest impact in industries like manufacturing, defense, and logistics, where decisions must happen instantly and locally.”

Traditional satellites face a significant data transmission bottleneck, with a significant amount of data loss during transmission to Earth due to bandwidth constraints. This parallels challenges many enterprises encounter in remote operations with bandwidth-intensive applications.

“We are now entering a post-centralisation era — where compute is pulled toward the edge not by ideology, but by necessity,” noted Gogia. “Whether it’s orbital satellites, smart grids, or in-field robotics, AI workloads are becoming heavier, more inference-driven, and intolerant to network-induced drag. Centralised clouds won’t disappear — but for many classes of applications, they will no longer be the first stop.”

“As we enter an AI-native era shaped by data-heavy, latency-sensitive, and bandwidth-limited environments, distributed architectures are increasingly becoming relevant,” added Sekhri. “The appeal of processing data closer to where it’s generated is gaining ground fast, and space-based compute reinforces this directional shift.”

Technical specifications showcase distributed potential

As per Guoxing Aerospace, each of the 12 satellites contains specialized computing hardware capable of handling up to 744 trillion operations per second. With 12 satellites working together, the array delivers a combined computing power of 5 peta operations per second (POPS) — equivalent to 5 quadrillion calculations per second.

To put this in perspective, when fully implemented, the constellation could reach 1,000 POPS of processing capacity—potentially surpassing Earth’s most powerful systems, according to the Chinese government. The El Capitan supercomputer at Lawrence Livermore National Laboratory in California, ranked as the world’s most powerful last year, achieves approximately 1.72 POPS!

To function as a unified processing network, the constellation employs laser communication technology, achieving data transfer speeds up to 100 gigabits per second, comparable to some of the fastest terrestrial fiber optic networks, the statement added.

The constellation also incorporates 30 terabytes of on-board storage and runs an AI model with 8 billion parameters, demonstrating how sophisticated artificial intelligence applications can be deployed at the network edge. For context, this means the system can run relatively sophisticated artificial intelligence applications similar to some large language models, though smaller than the most advanced AI systems used on Earth today.

Environmental and economic advantages

The space-based distributed approach also addresses growing environmental concerns about data centers — a priority for enterprises with sustainability targets. The International Energy Agency projects that global data centers could consume more than 945 terawatt hours of electricity annually by 2030—roughly equivalent to Japan’s entire electricity usage.

“The economics of enterprise compute are undergoing a structural inversion — and China’s Three-Body Computing Constellation makes that undeniable,” observed Gogia. “As AI workloads are increasingly executed at the point of collection — in orbit or on Earth — the cost of centralisation becomes a liability.”

By processing data closer to collection points, organizations can potentially reduce the energy footprint associated with moving massive datasets across global networks. Water consumption presents another environmental challenge for traditional computing facilities that distributed approaches can help address, as major technology companies use billions of liters of water annually to cool their data centers.

Implications for global operations

For multinational enterprises, the constellation demonstrates how distributed processing systems might eventually support truly global operations. Chinese officials have positioned this initiative as establishing globally accessible, mobile, and low-carbon space-based infrastructure.

“This development presents not just an engineering milestone, but a geopolitical one — casting data governance into uncharted territory,” warned Gogia. “Orbital compute is redefining the boundaries of data sovereignty. The Three-Body system decentralises not only AI processing but also geopolitical accountability — placing inference and decision-making infrastructure into orbits that may not fall cleanly under existing legal regimes.”

“However, as compute infrastructure stretches beyond sovereign borders, enterprises face a growing ambiguity around governance and jurisdiction,” cautioned Sekhri. “Enterprises will need to evolve their risk frameworks and operational policies to address a new class of sovereignty and control challenges.”

It is expected that similar networks will likely be deployed by multiple nations in the coming years, potentially creating new infrastructure that enterprises could leverage for global operations, particularly in remote areas where traditional connectivity remains challenging. “Perhaps the more pressing question isn’t how far edge computing can go — but who ultimately orchestrates it,” Sekhri pointed out. “As the compute fabric fragments across clouds, geographies, and orbits, control may shift from centralized platforms to a more federated, contested, and geopolitically charged landscape. That’s a future worth preparing for.”

Source:: Computer World

By Deepti Pathak Horizon Aircraft has just made aviation history. The Canadian company completed a stable wing-borne flight using…
The post Horizon’s Cavorite X7 Becomes First eVTOL Aircraft To Complete Transition Flight appeared first on Fossbytes.

Source:: Fossbytes

By Hisan Kidwai If you love bees and caring for them, Roblox has a fantastic experience for you. Bee…
The post Bee Swarm Simulator Codes (May 2025) appeared first on Fossbytes.

Source:: Fossbytes

By Siôn Geschwindt The Dutch startup ecosystem has slipped to 10th place globally and sixth in Europe, according to the newly released Global Startup Ecosystem Index 2025.  The annual report, compiled by research platform StartupBlink, benchmarks the startup strength of over 1,400 cities and 110 countries worldwide.  The US took the top spot globally, with the UK coming in second. Among the European nations, Britain was followed by Sweden (sixth), Germany (seventh), and France (eighth). Switzerland claimed ninth place,  pushing the Netherlands down one spot.   However, it’s not all bad news for the Dutch startup ecosystem, which saw an above-average growth rate of…This story continues at The Next Web

Source:: The Next Web

Microsoft Word’s spell check can already suggest words in real time, but imagine an “idea check” future in which AI suggests ideas on the fly as users type.

This is how Microsoft is evolving Copilot — as an AI assistant predicting ideas in real time by tapping into unique corporate and user data. That’s an idea the company shared for Microsoft 365 as generative AI technology sinks deeper into the guts of the software.

Specifically, Microsoft on Monday released a feature called Copilot Tuning, which will allow enterprises to deploy specialized AI models and agents that are fine-tuned to company operations and data. The feature was announced at Microsoft’s Build developer show being held this week.

With Copilot Tuning, companies can train models on their own data and deploy AI agents that make use of their unique terms, context, and processes, said Jason Henderson, corporate vice president of Microsoft 365 product management, in an interview with Computerworld.

“AI is going to have to … integrate the knowledge from your work into your specific Copilot so it can customize documents and workflows to the way your company works,” Henderson said.

The larger goal behind Tuning is to create an “enterprise brain” that is a digital representation of an organization’s unique way of handling information, processing it and making decisions. That is key to creating Copilots that can recommend ideas, Henderson said.

The ability to fine-tune AI to internal data changes the way model reasons and responds. That, in turn, changes how employees create documents and generate summaries.

Henderson gave the example of a longstanding law firm with more than 100 years of institutional knowledge and precedents. The partners are retiring and taking their knowledge with them.

“What they’re really interested in is fine-tuning into these models all these legal agreements that they have built over many, many years. And then being able to create new ones based on that learning,” Henderson said.

Microsoft Copilot launched in 2023 providing generic responses from genAI large language models (LLMs), notably OpenAI’s GPT series. About 70% of Fortune 500 companies are now using Copilot in some form, according to Microsoft.

Although LLM-based chatbots such as Anthropic’s Claude, Google’s Gemini and especially OpenAI’s ChatGPT remain popular, some companies are shifting to smaller reasoning models that are fine-tuned to domain data, finding the smaller models cheaper, faster, more customizable, and more secure.

Microsoft says Copilot Tuning is the starting point to creating hyper-personalized features, such as suggesting ideas in real time as a person writes a document in Word.

“I can think of this as a spell check of the future where it knows the words that your company uses. It’s going to predict based on what it knows about your company. It might be one thing in manufacturing versus in pharmaceuticals. It’s trying to help you get one step ahead by bringing to your fingertips what you need,” Henderson said.

Security is an important part of Copilot Tuning, and the data is usually pulled and referenced from Microsoft SharePoint, which monitors access to files. Microsoft is creating a chain of custody and security from documents to the model.

“We’re starting with SharePoint, but with connectors, we can bring in many, many different systems,” Henderson said.

Google, Anthropic, and OpenAI all offer ways to integrate enterprise data into LLMs as well. 

The new Copilot Tuning offering doesn’t require data scientists or coders, and it has a point-and-click interface within Copilot Studio. It can be used by regular employees who may think, “hey, I want to make my department’s life easier,” Henderson said.

Microsoft is also creating “recipes” that are targeted at generating documents and summaries, or for data analysis and accounting tasks.

“We’re going to identify which models best handle those recipes, doing a lot of experimentation to pick the right model for each particular recipe. Over the next six months, we’ll continue to build out more recipes as we identify durable problem spaces that companies are interested in addressing with Copilot Tuning,” Henderson said.

The Copilot Tuning functionality will begin rolling out to Microsoft 365 Copilot customers next month as part of its “Early Adopter Program.” Microsoft’s Copilot Wave 2 spring release — which includes a new Copilot app with new search, summarization, and personalization functionality — is now generally available.

Source:: Computer World

Microsoft writes on its blog that neither Azure nor the company’s AI technology has been used to harm people in Gaza. After allegations surfaced that these Microsoft products were being used by the Israeli military to harm civilians in Gaza, the company conducted internal and external reviews. “We have found no evidence to date that Microsoft’s Azure and AI technologies have been used to target or harm people in the conflict in Gaza,” the tech giant stated.The company noted that it has a relationship with the Israeli Ministry of Defense (IMOD) but that this is designed as a typical commercial relationship. None of the reviews found evidence that the Israeli Ministry of Defense violated Microsoft’s terms of use or code of conduct.

At the same time, Microsoft stated that it has limited visibility into how its software is used on its customers’ own servers or devices.

Source:: Computer World

By Ray Fernandez Our latest and most advanced technologies — from AI to Industrial IoT, advanced robotics, and self-driving cars — share serious problems: massive energy consumption, limited on-edge capabilities, system hallucinations, and serious accuracy gaps.  One possible solution is emerging in the Netherlands. The country is developing a promising ecosystem for neuromorphic computing, which draws on neuroscience to boost IT efficiencies and performance. Billions of euros are being invested in this new form of computing worldwide. The Netherlands aims to become a leader in the market by bringing together startups, established companies, government organisations, and academics in a neuromorphic computing ecosystem. A Dutch…This story continues at The Next Web

Source:: The Next Web

By Hisan Kidwai Roblox is home to millions of different experiences, and one of them is Bubble Gum Simulator…
The post Bubble Gum Simulator Infinity Codes (May 2025) appeared first on Fossbytes.

Source:: Fossbytes

By Hisan Kidwai Inspired by the popular anime and manga series Black Clover, Grimoires Era is a fighting game…
The post Grimoires Era Codes (May 2025) appeared first on Fossbytes.

Source:: Fossbytes

By Nadeem Sarwar Owing to pressure from EU regulators, Apple could allow users to replace Siri as the default virtual assistant on their devices, reports Bloomberg.

Source:: Digital Trends

By Deepti Pathak Internet slang changes fast, and sometimes you’ll see something in a message or a post that…
The post TIFU Meaning in Texts Explained: Use Cases & Examples appeared first on Fossbytes.

Source:: Fossbytes

This May Patch Tuesday release is very much a “back-to-basics” update with just 78 patches for Microsoft Windows, Office, Visual Studio, and .NET. Notably, Microsoft has not released any patches for Microsoft Exchange Server or Microsoft SQL Server.

Due to the concerns of publicly reported exploits for five Windows vulnerabilities, the Application Readiness team has recommended a “Patch Now” schedule for Windows and a standard release cadence for the other platforms. To help navigate these changes, the team from Readiness has provided a helpful infographic detailing the risks of deploying updates to each platform.

Known issues

There are still reports of issues with devices with Citrix Session Recording Agent (SRA) version 2411 installed on Windows 10 platforms. This is an ongoing issue, with no further reported fixes or updates from Citrix or Microsoft. Otherwise (at the time of writing), Microsoft has not reported any issues with this month’s update for its Windows desktop and server platforms.

Major revisions and mitigations

Microsoft has not published any major revisions or mitigations to its patches and security fixes for this May.

Windows lifecycle and enforcement updates

Microsoft has not published any enforcement updates this month.

Testing guidance

Each month, the team at Readiness analyzes the latest Patch Tuesday updates from Microsoft and provides detailed, actionable testing guidance. This guidance is based on assessing a large application portfolio and a comprehensive analysis of the Microsoft patches and their potential impact on Windows platforms and application deployments.

We have broken the most significant changes into feature-based groupings to help with testing prioritization. The Readiness team recommends the following areas for testing for the May Patch Tuesday patch cycle:

Remote Desktop, security, and identification

Test your Remote Desktop Gateway configurations. Establish sessions through the gateway and reconnect a few times to ensure stability.

Validate VPN creation, connection, and deletion. Also test fast reconnection and password change flows with PEAP-MSCHAPv2.

Load system level crypto libraries and validate CheckSignatureInFile behavior using legacy (2011) certificates.

Test secure boot scenarios, especially if running dual-boot with Linux. Ensure all logins work after this month’s updates.

Run PowerShell modules with and without AppLocker policies to confirm policy enforcement integrity.

Media and codecs

Check your subtitles in MKV formats for Blu-ray playback.

Test audio/video recording using both internal and external devices.

Validate DRM-protected content, especially in Microsoft Edge and Office apps. Testing regimes should include a cycle of playback, record, and stream — then check your system logs for crashes or errors.

Storage and filesystems

Perform Windows error log creation, appends, and reopen scenarios using Common Log File System APIs.

Simulate SMB folder access from multiple windows. Changes in one view should reflect in the other.

Validate UNC path access across apps. Run these tests with Microsoft Explorer and line-of-business apps that access network shares or log files.

Installation and application infrastructure

Given the focus of the Readiness team, it would be remiss to forget the changes to Microsoft’s update and application infrastructure with the following tests:

Conduct basic install, repair, roll-back and uninstall tests for MSI Installer packages. This process should be (mostly) automated by now.

If you’re an organization that employs App Silos, you will need create a test cycle that includes invoking the BFS driver via an isolated app context

Run web, file transfer, and messaging scenarios to test network throughput under load.

In addition to these specific test exercises, we highly recommend a full business logic test of your internal and line-of-business applications that have significant graphics requirements. This is required due to the changes to the Windows kernel and GDI (graphic) subsystems).

Readiness recommends your testing in priority in the following order: RDP and remote access, application installations, PowerShell testing, and then storage system testing.

Updates by product family

Each month, we break down the update cycle into product families (as defined by Microsoft) with the following basic groupings:

Browsers (Microsoft IE and Edge)

Microsoft Windows (both desktop and server)

Microsoft Office

Microsoft Exchange and SQL Server

Microsoft Developer Tools (Visual Studio and .NET)

Adobe (if you get this far)

Browsers

Microsoft has not released any native updates for its browsers this month. However, there were five Chromium updates (CVE-2025-4050, CVE-2025-4372, CVE-2025-4096, CVE-2025-4052, and CVE-2025-405) that will update Microsoft Edge. All of these low-profile changes can be added to your standard release calendar.

Microsoft Windows

Microsoft has released three critical updates, plus 41 patches rated as important. The critical updates affect Microsoft’s Remote Desktop platform and the Virtual Machine bus (VMBus).

Unfortunately, the following Windows desktop updates have been reported as exploited in the wild:

CVE-2025-30400

CVE-2025-32701

CVE-2025-32706

CVE-2025-32709

CVE-2025-30397

As a result of these zero-days, the Readiness team recommends a “Patch Now” schedule for these Windows patches.

Microsoft Office

Microsoft has released two critical rated updates (CVE-2025-30377 and CVE-2025-30386) for the Microsoft Office platform this month. Both of these patches were updated mid-week for documentation reasons.

Following these critical patches, Microsoft has released a further 16 patches that have been rated as important; they update Microsoft Office in general (as opposed to Word or Excel). Please add these Microsoft Office updates to your standard release calendar.

Microsoft Exchange Server

No updates for Microsoft Exchange or Microsoft SQL server this month. Good news for all the server teams.

Microsoft development platforms

A single critical update (CVE-2025-29813) to the Microsoft DevOps platform and four patches rated as important by Microsoft have been released to the developer platforms this month. All of the patches rated as important affect Visual Studio and Microsoft .NET. Add these updates to your standard release schedule.

Adobe Reader (if you get this far)

No Adobe updates (published by Microsoft) for this May patch cycle. Given the recent security advances implemented in Windows 11 23H2 and 24H2, I think that we will see much less of Adobe in this column.

Source:: Computer World

By Nick Godt Stellantis is dropping the Dodge Charger Daytona R/T for 2026

Source:: Digital Trends

Meta Platforms (Nasdaq:META) has decided to delay the public release of its most ambitious artificial intelligence model yet — Llama 4 Behemoth. Initially expected to debut at Meta’s first-ever AI developer conference in April, the model’s launch was pushed to June and is now delayed until fall or possibly even later.

Engineers at Meta are grappling with whether Behemoth delivers enough of a leap in performance to justify a public rollout, The Wall Street Journal reported. Internally, the sentiment is split — some feel the improvements over earlier versions are incremental at best.

The delay doesn’t just affect Meta’s timeline. It’s a reminder to the entire AI industry that building the most powerful model isn’t just about parameter count—it’s about usefulness, efficiency, and real-world performance.

Sanchit Vir Gogia, chief analyst and CEO at Greyhound Research, interprets this not as a standalone setback but as “a reflection of a broader shift: from brute-force scaling to controlled, adaptable AI models.”

He said that while Meta has not officially disclosed a reason for the delay, the reported mention of “capacity constraints” points to larger pressures around infrastructure, usability, and practical deployment.

What’s inside Llama 4 Behemoth?

Behemoth was never intended to be just another model in Meta’s Llama family. It’s intended to be the crown jewel of the Llama 4 series, designed as a “teacher model” for training smaller, more nimble versions like Llama Scout and Maverick. Meta had previously touted it as “one of the smartest LLMs in the world.”

Technically, Behemoth is built on a Mixture-of-Experts (MoE) architecture, designed to optimize both power and efficiency. It is said to have a total of 2 trillion parameters, with 288 billion active at any given inference — a staggering scale, even by today’s AI standards.

What made Behemoth especially interesting was its use of iRoPE (interleaved Rotary Position Embedding), an architectural choice that allows the model to handle extremely long context windows—up to 10 million tokens. That means it could, in theory, retain far more contextual information during a conversation or data task than most current models can manage.

But theory doesn’t always play out smoothly in practice.

“Meta’s Behemoth delay aligns with a market that is actively shifting from scale-first strategies to deployment-first priorities,” Gogia added. “Controlled Open LLMs and SLMs are central to this reorientation — and to what we believe is the future of trustworthy enterprise AI.”

How Behemoth stacks up against the competition

When Behemoth was first previewed in April, it was positioned as Meta’s answer to the dominance of models like OpenAI’s GPT-4.5, Anthropic’s Claude 3.5/3.7, and Google’s Gemini 1.5/2.5 series.

Each of those models has made strides in different areas. OpenAI’s GPT-4 Turbo remains strong in reasoning and code generation. Claude 3.5 Sonnet is gaining attention for its efficiency and balance between performance and cost. Gemini Pro 1.5, from Google, excels in multimodal tasks and integration with enterprise tools.

Behemoth, in contrast, showed strong results in STEM benchmarks and long-context tasks but has yet to demonstrate a clear superiority across commercial and enterprise-grade benchmarks. That ambiguity is believed to have contributed to Meta’s hesitation in launching the model publicly.

Gogia noted that the situation “reignites a vital industry dialogue: is bigger still better?” Increasingly, enterprise buyers are leaning toward SLMs (Small Language Models) and Controlled Open LLMs, which offer better governance, easier integration, and clearer ROI compared to gargantuan foundation models that demand complex infrastructure and longer implementation cycles.

A telling sign for the AI industry

This delay speaks volumes about where the AI industry is heading. For much of 2023 and 2024, the narrative was about who could build the largest model. But as model sizes ballooned, the return on added parameters began to flatten out.

AI experts and practitioners now acknowledge that smarter architectural design, domain specificity, and deployment efficiency are fast becoming the new metrics of success. Meta’s experience with smaller models like Scout and Maverick reinforces this trend—many users have found them to be more practical and easier to fine-tune for specific use cases.

There’s also a financial and sustainability angle. Training and running ultra-large models like Behemoth requires immense computing resources, energy, and fine-grained optimization. Even for Meta, this scale introduces operational trade-offs, including cost, latency, and reliability concerns.

Why enterprises should pay attention

For enterprise IT and innovation leaders, the delay isn’t just about Meta—it reflects a more fundamental decision point around AI adoption.

Enterprises are moving away from chasing the biggest models in favor of those that offer tighter control, compliance readiness, and explainability. Gogia pointed out that “usability, governance, and real-world readiness” are becoming central filters in AI procurement, especially in regulated sectors like finance, healthcare, and government.

The delay of Behemoth may accelerate the adoption of open-weight, deployment-friendly models such as Llama 4 Scout, or even third-party solutions that are optimized for enterprise workflows. The choice now isn’t about raw performance alone—it’s about aligning AI capabilities with specific business goals.

What lies ahead

Meta’s delay doesn’t suggest failure — it’s a strategic pause. If anything, it shows the company’s willingness to prioritize stability and impact over hype. Behemoth still has the potential to become a powerful tool, but only if it proves itself in the areas that matter most: performance consistency, scalability, and enterprise integration.

“This doesn’t negate the value of scale, but it elevates a new set of criteria that enterprises now care about deeply,” Gogia stated. In the coming months, as Meta refines Behemoth and the industry moves deeper into deployment-era AI, one thing is clear: we are moving beyond the age of AI spectacle into an age of applied, responsible intelligence.

Source:: Computer World

By Deepti Pathak TikTok is constantly dropping new features that catch people’s attention, but sometimes they can get pretty…
The post What Does Nudge Mean on TikTok? appeared first on Fossbytes.

Source:: Fossbytes

By Siôn Geschwindt Swiss tech startup Metafuels has unveiled plans to open its first commercial-scale sustainable aviation fuel (SAF) plant in the Port of Rotterdam.  Metafuels’ CEO Saurabh Kapoor told TNW that Turbe represents a “major step forward” toward ramping up SAF production. The startup also announced plans to build a similar facility in Denmark last year. “Europe has ambitious decarbonisation targets, but without scalable and affordable SAF production, aviation will struggle to keep up,” said Kapoor.  The facility, dubbed Turbe, will be built in collaboration with liquid energy storage provider Evos. Turbe will be integrated into Evos’ existing Rotterdam terminal, which offers…This story continues at The Next Web

Source:: The Next Web