AI is developing all the time. Here are some picks from several articles what is expected to happen in AI and around it in 2025. Here are picks from various articles, the texts are picks from the article edited and in some cases translated for clarity.
AI in 2025: Five Defining Themes
https://news.sap.com/2025/01/ai-in-2025-defining-themes/
Artificial intelligence (AI) is accelerating at an astonishing pace, quickly moving from emerging technologies to impacting how businesses run. From building AI agents to interacting with technology in ways that feel more like a natural conversation, AI technologies are poised to transform how we work.
But what exactly lies ahead?
1. Agentic AI: Goodbye Agent Washing, Welcome Multi-Agent Systems
AI agents are currently in their infancy. While many software vendors are releasing and labeling the first “AI agents” based on simple conversational document search, advanced AI agents that will be able to plan, reason, use tools, collaborate with humans and other agents, and iteratively reflect on progress until they achieve their objective are on the horizon. The year 2025 will see them rapidly evolve and act more autonomously. More specifically, 2025 will see AI agents deployed more readily “under the hood,” driving complex agentic workflows.
In short, AI will handle mundane, high-volume tasks while the value of human judgement, creativity, and quality outcomes will increase.
2. Models: No Context, No Value
Large language models (LLMs) will continue to become a commodity for vanilla generative AI tasks, a trend that has already started. LLMs are drawing on an increasingly tapped pool of public data scraped from the internet. This will only worsen, and companies must learn to adapt their models to unique, content-rich data sources.
We will also see a greater variety of foundation models that fulfill different purposes. Take, for example, physics-informed neural networks (PINNs), which generate outcomes based on predictions grounded in physical reality or robotics. PINNs are set to gain more importance in the job market because they will enable autonomous robots to navigate and execute tasks in the real world.
Models will increasingly become more multimodal, meaning an AI system can process information from various input types.
3. Adoption: From Buzz to Business
While 2024 was all about introducing AI use cases and their value for organizations and individuals alike, 2025 will see the industry’s unprecedented adoption of AI specifically for businesses. More people will understand when and how to use AI, and the technology will mature to the point where it can deal with critical business issues such as managing multi-national complexities. Many companies will also gain practical experience working for the first time through issues like AI-specific legal and data privacy terms (compared to when companies started moving to the cloud 10 years ago), building the foundation for applying the technology to business processes.
4. User Experience: AI Is Becoming the New UI
AI’s next frontier is seamlessly unifying people, data, and processes to amplify business outcomes. In 2025, we will see increased adoption of AI across the workforce as people discover the benefits of humans plus AI.
This means disrupting the classical user experience from system-led interactions to intent-based, people-led conversations with AI acting in the background. AI copilots will become the new UI for engaging with a system, making software more accessible and easier for people. AI won’t be limited to one app; it might even replace them one day. With AI, frontend, backend, browser, and apps are blurring. This is like giving your AI “arms, legs, and eyes.”
5. Regulation: Innovate, Then Regulate
It’s fair to say that governments worldwide are struggling to keep pace with the rapid advancements in AI technology and to develop meaningful regulatory frameworks that set appropriate guardrails for AI without compromising innovation.
12 AI predictions for 2025
This year we’ve seen AI move from pilots into production use cases. In 2025, they’ll expand into fully-scaled, enterprise-wide deployments.
https://www.cio.com/article/3630070/12-ai-predictions-for-2025.html
This year we’ve seen AI move from pilots into production use cases. In 2025, they’ll expand into fully-scaled, enterprise-wide deployments.
1. Small language models and edge computing
Most of the attention this year and last has been on the big language models — specifically on ChatGPT in its various permutations, as well as competitors like Anthropic’s Claude and Meta’s Llama models. But for many business use cases, LLMs are overkill and are too expensive, and too slow, for practical use.
“Looking ahead to 2025, I expect small language models, specifically custom models, to become a more common solution for many businesses,”
2. AI will approach human reasoning ability
In mid-September, OpenAI released a new series of models that thinks through problems much like a person would, it claims. The company says it can achieve PhD-level performance in challenging benchmark tests in physics, chemistry, and biology. For example, the previous best model, GPT-4o, could only solve 13% of the problems on the International Mathematics Olympiad, while the new reasoning model solved 83%.
If AI can reason better, then it will make it possible for AI agents to understand our intent, translate that into a series of steps, and do things on our behalf, says Gartner analyst Arun Chandrasekaran. “Reasoning also helps us use AI as more of a decision support system,”
3. Massive growth in proven use cases
This year, we’ve seen some use cases proven to have ROI, says Monteiro. In 2025, those use cases will see massive adoption, especially if the AI technology is integrated into the software platforms that companies are already using, making it very simple to adopt.
“The fields of customer service, marketing, and customer development are going to see massive adoption,”
4. The evolution of agile development
The agile manifesto was released in 2001 and, since then, the development philosophy has steadily gained over the previous waterfall style of software development.
“For the last 15 years or so, it’s been the de-facto standard for how modern software development works,”
5. Increased regulation
At the end of September, California governor Gavin Newsom signed a law requiring gen AI developers to disclose the data they used to train their systems, which applies to developers who make gen AI systems publicly available to Californians. Developers must comply by the start of 2026.
There are also regulations about the use of deep fakes, facial recognition, and more. The most comprehensive law, the EU’s AI Act, which went into effect last summer, is also something that companies will have to comply with starting in mid-2026, so, again, 2025 is the year when they will need to get ready.
6. AI will become accessible and ubiquitous
With gen AI, people are still at the stage of trying to figure out what gen AI is, how it works, and how to use it.
“There’s going to be a lot less of that,” he says. But gen AI will become ubiquitous and seamlessly woven into workflows, the way the internet is today.
7. Agents will begin replacing services
Software has evolved from big, monolithic systems running on mainframes, to desktop apps, to distributed, service-based architectures, web applications, and mobile apps. Now, it will evolve again, says Malhotra. “Agents are the next phase,” he says. Agents can be more loosely coupled than services, making these architectures more flexible, resilient and smart. And that will bring with it a completely new stack of tools and development processes.
8. The rise of agentic assistants
In addition to agents replacing software components, we’ll also see the rise of agentic assistants, adds Malhotra. Take for example that task of keeping up with regulations.
Today, consultants get continuing education to stay abreast of new laws, or reach out to colleagues who are already experts in them. It takes time for the new knowledge to disseminate and be fully absorbed by employees.
“But an AI agent can be instantly updated to ensure that all our work is compliant with the new laws,” says Malhotra. “This isn’t science fiction.”
9. Multi-agent systems
Sure, AI agents are interesting. But things are going to get really interesting when agents start talking to each other, says Babak Hodjat, CTO of AI at Cognizant. It won’t happen overnight, of course, and companies will need to be careful that these agentic systems don’t go off the rails.
Companies such as Sailes and Salesforce are already developing multi-agent workflows.
10. Multi-modal AI
Humans and the companies we build are multi-modal. We read and write text, we speak and listen, we see and we draw. And we do all these things through time, so we understand that some things come before other things. Today’s AI models are, for the most part, fragmentary. One can create images, another can only handle text, and some recent ones can understand or produce video.
11. Multi-model routing
Not to be confused with multi-modal AI, multi-modal routing is when companies use more than one LLM to power their gen AI applications. Different AI models are better at different things, and some are cheaper than others, or have lower latency. And then there’s the matter of having all your eggs in one basket.
“A number of CIOs I’ve spoken with recently are thinking about the old ERP days of vendor lock,” says Brett Barton, global AI practice leader at Unisys. “And it’s top of mind for many as they look at their application portfolio, specifically as it relates to cloud and AI capabilities.”
Diversifying away from using just a single model for all use cases means a company is less dependent on any one provider and can be more flexible as circumstances change.
12. Mass customization of enterprise software
Today, only the largest companies, with the deepest pockets, get to have custom software developed specifically for them. It’s just not economically feasible to build large systems for small use cases.
“Right now, people are all using the same version of Teams or Slack or what have you,” says Ernst & Young’s Malhotra. “Microsoft can’t make a custom version just for me.” But once AI begins to accelerate the speed of software development while reducing costs, it starts to become much more feasible.
9 IT resolutions for 2025
https://www.cio.com/article/3629833/9-it-resolutions-for-2025.html
1. Innovate
“We’re embracing innovation,”
2. Double down on harnessing the power of AI
Not surprisingly, getting more out of AI is top of mind for many CIOs.
“I am excited about the potential of generative AI, particularly in the security space,”
3. And ensure effective and secure AI rollouts
“AI is everywhere, and while its benefits are extensive, implementing it effectively across a corporation presents challenges. Balancing the rollout with proper training, adoption, and careful measurement of costs and benefits is essential, particularly while securing company assets in tandem,”
4. Focus on responsible AI
The possibilities of AI grow by the day — but so do the risks.
“My resolution is to mature in our execution of responsible AI,”
“AI is the new gold and in order to truly maximize it’s potential, we must first have the proper guardrails in place. Taking a human-first approach to AI will help ensure our state can maintain ethics while taking advantage of the new AI innovations.”
5. Deliver value from generative AI
As organizations move from experimenting and testing generative AI use cases, they’re looking for gen AI to deliver real business value.
“As we go into 2025, we’ll continue to see the evolution of gen AI. But it’s no longer about just standing it up. It’s more about optimizing and maximizing the value we’re getting out of gen AI,”
6. Empower global talent
Although harnessing AI is a top objective for Morgan Stanley’s Wetmur, she says she’s equally committed to harnessing the power of people.
7. Create a wholistic learning culture
Wetmur has another talent-related objective: to create a learning culture — not just in her own department but across all divisions.
8. Deliver better digital experiences
Deltek’s Cilsick has her sights set on improving her company’s digital employee experience, believing that a better DEX will yield benefits in multiple ways.
Cilsick says she first wants to bring in new technologies and automation to “make things as easy as possible,” mirroring the digital experiences most workers have when using consumer technologies.
“It’s really about leveraging tech to make sure [employees] are more efficient and productive,”
“In 2025 my primary focus as CIO will be on transforming operational efficiency, maximizing business productivity, and enhancing employee experiences,”
9. Position the company for long-term success
Lieberman wants to look beyond 2025, saying another resolution for the year is “to develop a longer-term view of our technology roadmap so that we can strategically decide where to invest our resources.”
“My resolutions for 2025 reflect the evolving needs of our organization, the opportunities presented by AI and emerging technologies, and the necessity to balance innovation with operational efficiency,”
Lieberman aims to develop AI capabilities to automate routine tasks.
“Bots will handle common inquiries ranging from sales account summaries to HR benefits, reducing response times and freeing up resources for strategic initiatives,”
Not just hype — here are real-world use cases for AI agents
https://venturebeat.com/ai/not-just-hype-here-are-real-world-use-cases-for-ai-agents/
Just seven or eight months ago, when a customer called in to or emailed Baca Systems with a service question, a human agent handling the query would begin searching for similar cases in the system and analyzing technical documents.
This process would take roughly five to seven minutes; then the agent could offer the “first meaningful response” and finally begin troubleshooting.
But now, with AI agents powered by Salesforce, that time has been shortened to as few as five to 10 seconds.
Now, instead of having to sift through databases for previous customer calls and similar cases, human reps can ask the AI agent to find the relevant information. The AI runs in the background and allows humans to respond right away, Russo noted.
AI can serve as a sales development representative (SDR) to send out general inquires and emails, have a back-and-forth dialogue, then pass the prospect to a member of the sales team, Russo explained.
But once the company implements Salesforce’s Agentforce, a customer needing to modify an order will be able to communicate their needs with AI in natural language, and the AI agent will automatically make adjustments. When more complex issues come up — such as a reconfiguration of an order or an all-out venue change — the AI agent will quickly push the matter up to a human rep.
Open Source in 2025: Strap In, Disruption Straight Ahead
Look for new tensions to arise in the New Year over licensing, the open source AI definition, security and compliance, and how to pay volunteer maintainers.
https://thenewstack.io/open-source-in-2025-strap-in-disruption-straight-ahead/
The trend of widely used open source software moving to more restrictive licensing isn’t new.
In addition to the demands of late-stage capitalism and impatient investors in companies built on open source tools, other outside factors are pressuring the open source world. There’s the promise/threat of generative AI, for instance. Or the shifting geopolitical landscape, which brings new security concerns and governance regulations.
What’s ahead for open source in 2025?
More Consolidation, More Licensing Changes
The Open Source AI Debate: Just Getting Started
Security and Compliance Concerns Will Rise
Paying Maintainers: More Cash, Creativity Needed
Kyberturvallisuuden ja tekoälyn tärkeimmät trendit 2025
https://www.uusiteknologia.fi/2024/11/20/kyberturvallisuuden-ja-tekoalyn-tarkeimmat-trendit-2025/
1. Cyber infrastructure will be centered on a single, unified security platform
2. Big data will give an edge against new entrants
3. AI’s integrated role in 2025 means building trust, governance engagement, and a new kind of leadership
4. Businesses will adopt secure enterprise browsers more widely
5. AI’s energy implications will be more widely recognized in 2025
6. Quantum realities will become clearer in 2025
7. Security and marketing leaders will work more closely together
Presentation: For 2025, ‘AI eats the world’.
https://www.ben-evans.com/presentations
Just like other technologies that have gone before, such as cloud and cybersecurity automation, right now AI lacks maturity.
https://www.securityweek.com/ai-implementing-the-right-technology-for-the-right-use-case/
If 2023 and 2024 were the years of exploration, hype and excitement around AI, 2025 (and 2026) will be the year(s) that organizations start to focus on specific use cases for the most productive implementations of AI and, more importantly, to understand how to implement guardrails and governance so that it is viewed as less of a risk by security teams and more of a benefit to the organization.
Businesses are developing applications that add Large Language Model (LLM) capabilities to provide superior functionality and advanced personalization
Employees are using third party GenAI tools for research and productivity purposes
Developers are leveraging AI-powered code assistants to code faster and meet challenging production deadlines
Companies are building their own LLMs for internal use cases and commercial purposes.
AI is still maturing
However, just like other technologies that have gone before, such as cloud and cybersecurity automation, right now AI lacks maturity. Right now, we very much see AI in this “peak of inflated expectations” phase and predict that it will dip into the “trough of disillusionment”, where organizations realize that it is not the silver bullet they thought it would be. In fact, there are already signs of cynicism as decision-makers are bombarded with marketing messages from vendors and struggle to discern what is a genuine use case and what is not relevant for their organization.
There is also regulation that will come into force, such as the EU AI Act, which is a comprehensive legal framework that sets out rules for the development and use of AI.
AI certainly won’t solve every problem, and it should be used like automation, as part of a collaborative mix of people, process and technology. You simply can’t replace human intuition with AI, and many new AI regulations stipulate that human oversight is maintained.
7 Splunk Predictions for 2025
https://www.splunk.com/en_us/form/future-predictions.html
AI: Projects must prove their worth to anxious boards or risk defunding, and LLMs will go small to reduce operating costs and environmental impact.
OpenAI, Google and Anthropic Are Struggling to Build More Advanced AI
Three of the leading artificial intelligence companies are seeing diminishing returns from their costly efforts to develop newer models.
https://www.bloomberg.com/news/articles/2024-11-13/openai-google-and-anthropic-are-struggling-to-build-more-advanced-ai
Sources: OpenAI, Google, and Anthropic are all seeing diminishing returns from costly efforts to build new AI models; a new Gemini model misses internal targets
It Costs So Much to Run ChatGPT That OpenAI Is Losing Money on $200 ChatGPT Pro Subscriptions
https://futurism.com/the-byte/openai-chatgpt-pro-subscription-losing-money?fbclid=IwY2xjawH8epVleHRuA2FlbQIxMQABHeggEpKe8ZQfjtPRC0f2pOI7A3z9LFtFon8lVG2VAbj178dkxSQbX_2CJQ_aem_N_ll3ETcuQ4OTRrShHqNGg
In a post on X-formerly-Twitter, CEO Sam Altman admitted an “insane” fact: that the company is “currently losing money” on ChatGPT Pro subscriptions, which run $200 per month and give users access to its suite of products including its o1 “reasoning” model.
“People use it much more than we expected,” the cofounder wrote, later adding in response to another user that he “personally chose the price and thought we would make some money.”
Though Altman didn’t explicitly say why OpenAI is losing money on these premium subscriptions, the issue almost certainly comes down to the enormous expense of running AI infrastructure: the massive and increasing amounts of electricity needed to power the facilities that power AI, not to mention the cost of building and maintaining those data centers. Nowadays, a single query on the company’s most advanced models can cost a staggering $1,000.
Tekoäly edellyttää yhä nopeampia verkkoja
https://etn.fi/index.php/opinion/16974-tekoaely-edellyttaeae-yhae-nopeampia-verkkoja
A resilient digital infrastructure is critical to effectively harnessing telecommunications networks for AI innovations and cloud-based services. The increasing demand for data-rich applications related to AI requires a telecommunications network that can handle large amounts of data with low latency, writes Carl Hansson, Partner Solutions Manager at Orange Business.
AI’s Slowdown Is Everyone Else’s Opportunity
Businesses will benefit from some much-needed breathing space to figure out how to deliver that all-important return on investment.
https://www.bloomberg.com/opinion/articles/2024-11-20/ai-slowdown-is-everyone-else-s-opportunity
Näin sirumarkkinoilla käy ensi vuonna
https://etn.fi/index.php/13-news/16984-naein-sirumarkkinoilla-kaey-ensi-vuonna
The growing demand for high-performance computing (HPC) for artificial intelligence and HPC computing continues to be strong, with the market set to grow by more than 15 percent in 2025, IDC estimates in its recent Worldwide Semiconductor Technology Supply Chain Intelligence report.
IDC predicts eight significant trends for the chip market by 2025.
1. AI growth accelerates
2. Asia-Pacific IC Design Heats Up
3. TSMC’s leadership position is strengthening
4. The expansion of advanced processes is accelerating.
5. Mature process market recovers
6. 2nm Technology Breakthrough
7. Restructuring the Packaging and Testing Market
8. Advanced packaging technologies on the rise
2024: The year when MCUs became AI-enabled
https://www-edn-com.translate.goog/2024-the-year-when-mcus-became-ai-enabled/?fbclid=IwZXh0bgNhZW0CMTEAAR1_fEakArfPtgGZfjd-NiPd_MLBiuHyp9qfiszczOENPGPg38wzl9KOLrQ_aem_rLmf2vF2kjDIFGWzRVZWKw&_x_tr_sl=en&_x_tr_tl=fi&_x_tr_hl=fi&_x_tr_pto=wapp
The AI party in the MCU space started in 2024, and in 2025, it is very likely that there will be more advancements in MCUs using lightweight AI models.
Adoption of AI acceleration features is a big step in the development of microcontrollers. The inclusion of AI features in microcontrollers started in 2024, and it is very likely that in 2025, their features and tools will develop further.
Just like other technologies that have gone before, such as cloud and cybersecurity automation, right now AI lacks maturity.
https://www.securityweek.com/ai-implementing-the-right-technology-for-the-right-use-case/
If 2023 and 2024 were the years of exploration, hype and excitement around AI, 2025 (and 2026) will be the year(s) that organizations start to focus on specific use cases for the most productive implementations of AI and, more importantly, to understand how to implement guardrails and governance so that it is viewed as less of a risk by security teams and more of a benefit to the organization.
Businesses are developing applications that add Large Language Model (LLM) capabilities to provide superior functionality and advanced personalization
Employees are using third party GenAI tools for research and productivity purposes
Developers are leveraging AI-powered code assistants to code faster and meet challenging production deadlines
Companies are building their own LLMs for internal use cases and commercial purposes.
AI is still maturing
AI Regulation Gets Serious in 2025 – Is Your Organization Ready?
While the challenges are significant, organizations have an opportunity to build scalable AI governance frameworks that ensure compliance while enabling responsible AI innovation.
https://www.securityweek.com/ai-regulation-gets-serious-in-2025-is-your-organization-ready/
Similar to the GDPR, the EU AI Act will take a phased approach to implementation. The first milestone arrives on February 2, 2025, when organizations operating in the EU must ensure that employees involved in AI use, deployment, or oversight possess adequate AI literacy. Thereafter from August 1 any new AI models based on GPAI standards must be fully compliant with the act. Also similar to GDPR is the threat of huge fines for non-compliance – EUR 35 million or 7 percent of worldwide annual turnover, whichever is higher.
While this requirement may appear manageable on the surface, many organizations are still in the early stages of defining and formalizing their AI usage policies.
Later phases of the EU AI Act, expected in late 2025 and into 2026, will introduce stricter requirements around prohibited and high-risk AI applications. For organizations, this will surface a significant governance challenge: maintaining visibility and control over AI assets.
Tracking the usage of standalone generative AI tools, such as ChatGPT or Claude, is relatively straightforward. However, the challenge intensifies when dealing with SaaS platforms that integrate AI functionalities on the backend. Analysts, including Gartner, refer to this as “embedded AI,” and its proliferation makes maintaining accurate AI asset inventories increasingly complex.
Where frameworks like the EU AI Act grow more complex is their focus on ‘high-risk’ use cases. Compliance will require organizations to move beyond merely identifying AI tools in use; they must also assess how these tools are used, what data is being shared, and what tasks the AI is performing. For instance, an employee using a generative AI tool to summarize sensitive internal documents introduces very different risks than someone using the same tool to draft marketing content.
For security and compliance leaders, the EU AI Act represents just one piece of a broader AI governance puzzle that will dominate 2025.
The next 12-18 months will require sustained focus and collaboration across security, compliance, and technology teams to stay ahead of these developments.
The Global Partnership on Artificial Intelligence (GPAI) is a multi-stakeholder initiative which aims to bridge the gap between theory and practice on AI by supporting cutting-edge research and applied activities on AI-related priorities.
https://gpai.ai/about/#:~:text=The%20Global%20Partnership%20on%20Artificial,activities%20on%20AI%2Drelated%20priorities.
840 Comments
Tomi Engdahl says:
CEO Brett Adcock teases a groundbreaking humanoid robot feature never seen before. https://link.ie.social/gTTmdf
#FigureAI #HumanoidRobots #AIRevolution
Tomi Engdahl says:
Sam Altman:
Observations on AI and how it may change society, the likely uneven impact of AGI, which could mess up the balance of power between capital and labor, and more — Our mission is to ensure that AGI (Artificial General Intelligence) benefits all of humanity.
https://blog.samaltman.com/three-observations
Tomi Engdahl says:
We continue to see rapid progress with AI development. Here are three observations about the economics of AI:
1. The intelligence of an AI model roughly equals the log of the resources used to train and run it. These resources are chiefly training compute, data, and inference compute. It appears that you can spend arbitrary amounts of money and get continuous and predictable gains; the scaling laws that predict this are accurate over many orders of magnitude.
2. The cost to use a given level of AI falls about 10x every 12 months, and lower prices lead to much more use. You can see this in the token cost from GPT-4 in early 2023 to GPT-4o in mid-2024, where the price per token dropped about 150x in that time period. Moore’s law changed the world at 2x every 18 months; this is unbelievably stronger.
3. The socioeconomic value of linearly increasing intelligence is super-exponential in nature. A consequence of this is that we see no reason for exponentially increasing investment to stop in the near future.
https://blog.samaltman.com/three-observations
Tomi Engdahl says:
Alice Robb / Bloomberg:
A look at the debate over how much authors should get paid for licensing books to train AI; Microsoft offered HarperCollins $5K/title, of which authors get 50%
https://www.bloomberg.com/news/articles/2025-02-07/how-much-should-authors-get-paid-to-license-books-for-ai-training
Tomi Engdahl says:
Financial Times:
Macron announces that companies have agreed to invest €109B in AI projects in France in the coming years, saying it’s France’s equivalent to Stargate for the US
https://www.ft.com/content/fc6a2d7a-5ed6-436e-84a5-dda86fc258d3
Tomi Engdahl says:
Nitasha Tiku / Washington Post:
How DeepSeek’s R1 led to more chatbots’ “chains of thought” being shown to users; an expert says seeing a chatbot’s supposed inner monologue can trigger empathy — How Chinese start-up DeepSeek triggered a rush to launch chatbots that “reason.”
The hottest new idea in AI? Chatbots that look like they think.
https://www.washingtonpost.com/technology/2025/02/08/deepseek-ai-chatbot-reasoning-china/?pwapi_token=eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJyZWFzb24iOiJnaWZ0IiwibmJmIjoxNzM4OTkwODAwLCJpc3MiOiJzdWJzY3JpcHRpb25zIiwiZXhwIjoxNzQwMzczMTk5LCJpYXQiOjE3Mzg5OTA4MDAsImp0aSI6ImIwMTI1YWY2LTEzYTYtNGMyYi05MDZlLTYzZjJjZTU0NTlkZCIsInVybCI6Imh0dHBzOi8vd3d3Lndhc2hpbmd0b25wb3N0LmNvbS90ZWNobm9sb2d5LzIwMjUvMDIvMDgvZGVlcHNlZWstYWktY2hhdGJvdC1yZWFzb25pbmctY2hpbmEvIn0.RrtI1maNw3IWnHdZbaPd1ozhPEMMxafiPTu7pmmepWA
Tomi Engdahl says:
Kyle Wiggers / TechCrunch:
Christie’s plans to hold its first AI art auction, beginning February 20; critics say the sale elevates AI tools trained on artists’ works without their consent — Fine art auction house, Christie’s, has sold AI-generated art before. But soon, it plans to hold its first show dedicated solely …
Christie’s announces AI art auction, and not everyone is pleased
https://techcrunch.com/2025/02/08/christies-announces-ai-art-auction-and-not-everyone-is-pleased/
Tomi Engdahl says:
Viola Zhou / Rest of World:
DeepSeek recruiting recent graduates from China’s top universities reflects a growing trend among the country’s top AI talent to pursue opportunities at home
DeepSeek’s rise shows why China’s top AI talent is skipping Silicon Valley
Young Chinese engineers focus on homegrown innovation, drawn by fewer visa hurdles and the chance to build a future on their own terms.
https://restofworld.org/2025/china-ai-talent-deepseek-rise-us-dominance/
Tomi Engdahl says:
Shakeel Hashim / Transformer:
Leaked draft: the statement set to be signed at the Paris AI Action Summit barely mentions AI risks and fails to follow up on commitments from previous Summits
Leaked: this is the AI Action Summit statement
The statement, set to be signed by countries next week, is a ‘wasted opportunity’, experts say — and looks unlikely to be signed by US officials
https://www.transformernews.ai/p/leaked-ai-action-summit-statement
Tomi Engdahl says:
Antonio Regalado / MIT Technology Review:
Meta shares work on a system that uses a magnetic scanner and a deep neural network to analyze brain signals and identify which keys people pressed while typing
https://www.technologyreview.com/2025/02/07/1111292/meta-has-an-ai-for-brain-typing-but-its-stuck-in-the-lab/
Tomi Engdahl says:
Belle Lin / Wall Street Journal:
An analysis of US DOL data: the IT sector unemployment rate rose from 3.9% in December to 5.7% in January, with the number of unemployed rising from 98K to 152K
IT Unemployment Rises to 5.7% as AI Hits Tech Jobs
Artificial intelligence continues to impact the technology labor market
https://www.wsj.com/articles/it-unemployment-rises-to-5-7-as-ai-hits-tech-jobs-7726bb1b?st=DnBP31&reflink=desktopwebshare_permalink
The unemployment rate in the information technology sector rose from 3.9% in December to 5.7% in January, well above last month’s overall jobless rate of 4%, in the latest sign of how automation and the increasing use of artificial intelligence are having a negative impact on the tech labor market.
The number of unemployed IT workers rose from 98,000 in December to 152,000 last month, according to a report from consulting firm Janco Associates based on data from the U.S. Department of Labor.
The department on Friday said the economy added 143,000 jobs, as the job market continued to chug along, though at a slower pace than in the prior two months.
Tomi Engdahl says:
Dominic Chopping / Wall Street Journal:
Nokia names Justin Hotard, who currently leads Intel’s Data Center and AI operations, as its CEO starting April 1, as it focuses on data centers to drive growth
Nokia Names Intel’s Data Center and AI Head Justin Hotard as New CEO
The telecom company expects data centers to be its number one growth driver in the coming years
https://www.wsj.com/tech/nokia-names-intels-data-center-and-ai-head-justin-hotard-as-new-chief-executive-553ba3d0?st=Y2fvtG&reflink=desktopwebshare_permalink
Nokia NOKIA 2.34%increase; green up pointing triangle
appointed Justin Hotard, a tech industry expert in data centers and AI, as its next chief executive officer, in a move that reinforces the company’s ambition to expand into new growth areas.
The Finnish telecommunication company recently signaled its aim to diversify and search for new growth outside of its traditional telecom operator market. Last year, it signed a deal worth $2.3 billion to buy networking-solutions provider Infinera as it bets on new business for data centers.
The company said it expects data centers to be its number one growth driver in the coming years and has been boosting its spending to develop new products for the rapidly-expanding industry. It has already pledged to invest up to 100 million euros ($103.3 million) annually to broaden its market in data-center networking with a view to driving incremental net sales of 1 billion euros by 2028.
Tomi Engdahl says:
Wall Street Journal:
France plans to pledge one gigawatt of nuclear power to AI training, including 250 megawatts by the end of 2026, in a bid to expand Europe’s AI capabilities
France Taps Nuclear Power in Race for AI Supremacy
Macron aims to dedicate a gigawatt of nuclear power to create one of the world’s largest AI computing facilities
https://www.wsj.com/tech/ai/france-taps-nuclear-power-for-new-ai-training-cluster-a7804107?st=cuzoqQ&reflink=desktopwebshare_permalink
Tomi Engdahl says:
Meta CEO Mark Zuckerberg has pledged to make artificial general intelligence (AGI) openly available one day. But in a new policy document, Meta suggests that there are certain scenarios in which it may not release a highly capable AI system it developed internally.
The document, which Meta is calling its Frontier AI Framework, identifies two types of AI systems the company considers too risky to release: “high risk” and “critical risk” systems.
Read more from Kyle Wiggers here: https://tcrn.ch/4hJcqre
#TechCrunch #technews #artificialintelligence #Meta #MarkZuckerberg
Tomi Engdahl says:
Apple Silicon chips have transformed attitudes toward ARM architecture, from being suitable mostly for power-efficient mobile devices to also capable of powering desktop processors which Intel behind. ARM CEO Rene Haas has now weighed-in on the DeepSeek controversy in a new interview, expressing his skepticism about one of of the key claims and voicing his expectation that China’s AI chatbot will be banned in the US ……
https://9to5mac.com/2025/02/10/deepseek-will-be-banned-in-the-us-believes-arm-ceo/?fbclid=IwZXh0bgNhZW0CMTEAAR1olDvnrrCC83_0aa45xeFtu6Yg-fK0B9jiBFS1jNhNHHuXXTF1HEgrewk_aem_vNji-lPZg-8Tgs7FjtN77g
Tomi Engdahl says:
https://www.theverge.com/news/609685/elon-musk-openai-purchase-offer?fbclid=IwZXh0bgNhZW0CMTEAAR2kpAVOwEjWYyC5c9SUHenemZ1JIMHWom_OEAqBQl3VGzfQQR3-MSRgr50_aem_OdnCLGLnBejyvgnNSa_DWg
Tomi Engdahl says:
NXP haluaa tuoda neuroverkot teollisuuteen ja autoihin
https://etn.fi/index.php/13-news/17135-nxp-haluaa-tuoda-neuroverkot-teollisuuteen-ja-autoihin
NXP Semiconductors ilmoittaa ostavansa tekoälyprosessorien kehittäjän Kinaran 307 miljoonan dollarin käteiskaupalla. Kauppa vahvistaa NXP:n asemaa teollisuuden ja autoteollisuuden edge AI -markkinoilla yhdistämällä Kinaran kehittyneet neuroverkkojen prosessointiratkaisut NXP:n nykyiseen teknologiavalikoimaan. Kaupan odotetaan toteutuvan vuoden 2025 ensimmäisellä puoliskolla, edellyttäen tarvittavat viranomaisluvat.
Kinara tunnetaan erityisesti energiatehokkaista ja ohjelmoitavista NPU-piireistä eli neuraaliprosessoreista, kuten Ara-1 ja Ara-2, jotka tukevat niin perinteistä tekoälyä kuin generatiivisia AI-malleja. Nämä ratkaisut mahdollistavat kehittyneitä AI-sovelluksia konenäköön, puheentunnistukseen ja eleohjaukseen, joita voidaan hyödyntää esimerkiksi teollisuusautomaatiossa ja autojen itseohjautuvissa järjestelmissä.
Edge AI:n merkitys kasvaa jatkuvasti, sillä se mahdollistaa nopeamman ja turvallisemman päätöksenteon suoraan laitteessa ilman pilvipalveluihin turvautumista. Kaupan myötä NXP vahvistaa kilpailukykyään tällä nopeasti kasvavalla sektorilla ja haastaa alan muut suuret toimijat, kuten Nvidian ja Qualcommin.
Tomi Engdahl says:
https://www.uusiteknologia.fi/2025/02/11/suomalainen-alykoripallo-nba-kehitysohjelmaan/
Tomi Engdahl says:
Elon Musk-Led Group Makes $97.4 Billion Bid for Control of OpenAI
Unsolicited offer complicates Sam Altman’s plans to convert OpenAI to a for-profit company
https://www.wsj.com/tech/elon-musk-openai-bid-4af12827?st=ukvPKp&reflink=desktopwebshare_permalink
Tomi Engdahl says:
Building Multilingual Applications with Hugging Face Transformers: A Beginner’s Guide
https://www.kdnuggets.com/building-multilingual-applications-hugging-face-transformers
Introduction
Imagine running an e-commerce platform that processes thousands of customer comments daily.
The challenge? Many of these comments may be written in languages you might not understand. Thanks to recent advancements in natural language processing (NLP), we can now leverage powerful transformer models to handle multilingual inputs seamlessly. These models enable us to translate or analyze text in various languages, making it accessible in a language we understand, such as English.
Tomi Engdahl says:
Fine-Tuning of Llama-2 7B Chat for Python Code Generation: Using QLoRA, SFTTrainer, and Gradient Checkpointing on the Alpaca-14k Dataset
https://www.marktechpost.com/2025/02/08/fine-tuning-of-llama-2-7b-chat-for-python-code-generation-using-qlora-sfttrainer-and-gradient-checkpointing-on-the-alpaca-14k-dataset/
Tomi Engdahl says:
https://www.emergingtechbrew.com/stories/2025/02/07/allen-institute-open-source-model-deepseek
Tomi Engdahl says:
DeepSeek demonstrates strengthen of China’s homegrown innovation
https://www.globaltimes.cn/page/202502/1328143.shtml
DeepSeek has emerged as a captivating topic of discussion in the global AI community. This small team of fewer than 140 people has achieved what even some tech giants struggle to accomplish.
Nearly all the engineers and researchers powering this company’s success were educated at China’s universities, and the average age of the R&D team members is about 28. This challenges long-held assumptions about talent distribution in the tech industry and signals a profound shift in the global innovation landscape.
DeepSeek’s engineers primarily come from prestigious Chinese universities, such as Tsinghua University, Peking University, Sun Yat-sen University, and Beijing University of Posts and Telecommunications.
This paints a powerful picture: cutting-edge innovation no longer relies on expatriate expertise or foreign-educated professionals.
Tomi Engdahl says:
How to Call the DeepSeek-R1 API Using Python? An In-Depth Step-by-Step Guide
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This tutorial will guide you through calling DeepSeek’s R1 large model API using Python. Even if you have no programming experience, you can easily follow along. The article also includes a FAQ section at the end, which we recommend saving for reference!
https://dev.to/auden/how-to-call-the-deepseek-r1-api-using-python-an-in-depth-step-by-step-guide-311o
Tomi Engdahl says:
ChatGPT maker OpenAI taking claims of data breach ‘seriously’
The ChatGPT maker says it has not seen any evidence of a compromise of its system ‘to date’
https://www.independent.co.uk/tech/openai-data-breach-chatpt-email-b2694280.html
Tomi Engdahl says:
Deepseek’s AI model is ‘the best work’ out of China but the hype is ‘exaggerated,’ Google Deepmind CEO says
https://www.cnbc.com/2025/02/09/deepseeks-ai-model-the-best-work-out-of-china-google-deepmind-ceo.html
Key Points
Deepseek’s AI model “is probably the best work” out of China, Demis Hassabis, the CEO of Google DeepMind said on Sunday.
Hassabis said, however, that “despite the hype, there’s no actual new scientific advance.”
China’s Deepseek claimed its AI model was trained at a fraction of the cost of leading AI players and on less-advanced Nvidia chips.
Tomi Engdahl says:
https://venturebeat.com/ai/deepseeks-r1-and-openais-deep-research-just-redefined-ai-rag-distillation-and-custom-models-will-never-be-the-same/
Tomi Engdahl says:
GitHub Copilot Brings Mockups to Life by Generating Code from Images
GitHub Copilot’s new Vision for Copilot feature allows users to upload images like screenshots or diagrams, which it then turns into code, making design-to-code transitions smoother.
https://webdesignerdepot.com/github-copilot-brings-mockups-to-life-by-generating-code-from-images/
Tomi Engdahl says:
10 Must-Know Open Source Platform Engineering Tools for AI/ML Workflows
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Building and shipping solutions faster has become the benchmark for innovation today. However, for artificial intelligence (AI) and machine learning (ML) teams, scaling workflows and delivering value at speed present unique challenges, including complex infrastructure, manual processes, and inefficiencies. Platform Engineering can streamline these workflows and automate repetitive tasks through Internal Developer Platforms (IDPs), thus enabling teams to focus on what matters most: delivering impactful AI/ML solutions.
https://jozu.com/blog/10-must-know-open-source-platform-engineering-tools-for-ai-ml-workflows/?preview_id=1728&preview_nonce=ecc75f42f5&_thumbnail_id=1729&preview=true
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KitOps profile imageJesse Williams
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Posted on 6. helmik. • Originally published at jozu.com
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10 Must-Know Open Source Platform Engineering Tools for AI/ML Workflows
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Building and shipping solutions faster has become the benchmark for innovation today. However, for artificial intelligence (AI) and machine learning (ML) teams, scaling workflows and delivering value at speed present unique challenges, including complex infrastructure, manual processes, and inefficiencies. Platform Engineering can streamline these workflows and automate repetitive tasks through Internal Developer Platforms (IDPs), thus enabling teams to focus on what matters most: delivering impactful AI/ML solutions.
The 2024 Dora Report emphasizes the significant impact of Platform Engineering, increasing deployment frequency by 60%, developer productivity by 8%, and overall team performance by 10%. These 10 open source platform engineering tools can help you achieve similar results in your AI/ML projects.
TL;DR. Top open source Platform Engineering tools for AI/ML
Here’s a quick list of Platform Engineering tools I recommend to simplify AI/ML workflows and reduce infrastructure complexity:
KitOps: Centralized versioning for all AI/ML project assets.
Kubeflow: Streamlined ML workflow management on Kubernetes.
DVC (Data Version Control): Ensures reproducibility by tracking datasets, code, and experiments.
Seldon Core: Kubernetes-native tool for deploying and monitoring ML models.
BentoML: Simplifies model packaging and deployment into production.
Apache Airflow: Automates, monitors, and schedules ML pipelines.
Prometheus: Real time infrastructure and ML deployment monitoring.
Comet: Tracks ML experiments and provides insights into model performance.
MLflow: Manages the lifecycle, including tracking, deployment, and model versioning.
Feast: A centralized feature store for managing ML feature data in real time. ##
Tomi Engdahl says:
Hugging Face brings ‘Pi-Zero’ to LeRobot, making AI-powered robots easier to build and deploy
https://venturebeat.com/ai/hugging-face-brings-pi-zero-to-lerobot-making-ai-powered-robots-easier-to-build-and-deploy/
Hugging Face and Physical Intelligence have quietly launched Pi0 (Pi-Zero) this week, the first foundational model for robots that translates natural language commands directly into physical actions.
“Pi0 is the most advanced vision language action model,” Remi Cadene, a principal research scientist at Hugging Face, announced in an X post that quickly gained attention across the AI community. “It takes natural language commands as input and directly outputs autonomous behavior.”
This release marks a pivotal moment in robotics: The first time a foundation model for robots has been made widely available through an open-source platform. Much like ChatGPT revolutionized text generation, Pi0 aims to transform how robots learn and execute tasks.
How Pi0 brings ChatGPT-style learning to robotics, unlocking complex tasks
The model, originally developed by Physical Intelligence and now ported to Hugging Face’s LeRobot platform, can perform complex tasks like folding laundry, bussing tables and packing groceries — activities that have traditionally been extremely challenging for robots to master.
“Today’s robots are narrow specialists, programmed for repetitive motions in choreographed settings,” the Physical Intelligence research team wrote in their announcement post. “Pi0 changes that, allowing robots to learn and follow user instructions, making programming as simple as telling the robot what you want done.”
The technology behind Pi0 represents a significant technical achievement. The model was trained on data from seven different robotic platforms and 68 unique tasks, enabling it to handle everything from delicate manipulation tasks to complex multi-step procedures. It employs a novel technique called flow matching to produce smooth, real-time action trajectories at 50Hz, making it highly precise and adaptable for real-world deployment.
Tomi Engdahl says:
Changing the Game for Embedded Systems
With the emergence of DeepSeek, a cutting-edge AI model that achieves high-performance inference with dramatically lower compute resources, the …
https://www.google.com/url?sa=t&source=web&rct=j&opi=89978449&url=https://www.linkedin.com/pulse/ai-edge-how-deepseek-changing-game-embedded-systems-tiitus-aho-rw2pf&ved=2ahUKEwj5v6PCrruLAxXZExAIHXZ3LDcQjjh6BAgkEAE&usg=AOvVaw0cpL259IaoYeYHJNCfd4na
Tomi Engdahl says:
German trash robot uses powerful AI to disassemble electronic waste autonomously
The process relies on robots and AI to carry out disassembly of the electronics.
https://interestingengineering.com/innovation/robot-e-waste-recycling
Tomi Engdahl says:
Radar / Programming
The End of Programming as We Know It
https://www.oreilly.com/radar/the-end-of-programming-as-we-know-it/
There’s a lot of chatter in the media that software developers will soon lose their jobs to AI. I don’t buy it.
It is not the end of programming. It is the end of programming as we know it today. That is not new. The first programmers connected physical circuits to perform each calculation. They were succeeded by programmers writing machine instructions as binary code to be input one bit at a time by flipping switches on the front of a computer. Assembly language programming then put an end to that. It lets a programmer use a human-like language to tell the computer to move data to locations in memory and perform calculations on it. Then, development of even higher-level compiled languages like Fortran, COBOL, and their successors C, C++, and Java meant that most programmers no longer wrote assembly code. Instead, they could express their wishes to the computer using higher level abstractions.
Eventually, interpreted languages, which are much easier to debug, became the norm.
There were more programmers, not fewer
This was far from the end of programming, though. There were more programmers than ever. Users in the hundreds of millions consumed the fruits of their creativity. In a classic demonstration of elasticity of demand, as software was easier to create, its price fell, allowing developers to create solutions that more people were willing to pay for.
The web was another “end of programming.” Suddenly, the user interface was made up of human-readable documents, shown in a browser with links that could in turn call programs on remote servers. Anyone could build a simple “application” with minimal programming skill. “No code” became a buzzword. Soon enough, everyone needed a website. Tools like WordPress made it possible for nonprogrammers to create those websites without coding. Yet as the technology grew in capability, successful websites became more and more complex. There was an increasing separation between “frontend” and “backend” programming. New interpreted programming languages like Python and JavaScript became dominant. Mobile devices added a new, ubiquitous front end, requiring new skills. And once again, the complexity was hidden behind frameworks, function libraries, and APIs that insulated programmers from having to know as much about the low level functionality
Tomi Engdahl says:
DeepMind AI crushes tough maths problems on par with top human solvers
The company’s AlphaGeometry2 reaches the level of gold-medal students in the International Mathematical Olympiad.
https://www.nature.com/articles/d41586-025-00406-7
Tomi Engdahl says:
ChatGPT comes to 500,000 new users in OpenAI’s largest AI education deal yet
Still banned at some schools, ChatGPT gains an official role at California State University.
https://arstechnica.com/ai/2025/02/chatgpt-comes-to-500000-new-users-in-openais-largest-ai-education-deal-yet/
Tomi Engdahl says:
GitHub Copilot: The agent awakens
Introducing agent mode for GitHub Copilot in VS Code, announcing the general availability of Copilot Edits, and providing a first look at our SWE agent.
https://github.blog/news-insights/product-news/github-copilot-the-agent-awakens/
When we introduced GitHub Copilot back in 2021, we had a clear goal: to make developers’ lives easier with an AI pair programmer that helps them write better code. The name reflects our belief that artificial intelligence (AI) isn’t replacing the developer. Instead, it’s always on their side. And like any good first officer, Copilot can also fly by itself: for example, when providing pull request feedback, autofixing security vulnerabilities, or brainstorming on how to implement an issue.
Today, we are upgrading GitHub Copilot with the force of even more agentic AI – introducing agent mode and announcing the General Availability of Copilot Edits, both in VS Code. We are adding Gemini 2.0 Flash to the model picker for all Copilot users. And we unveil a first look at Copilot’s new autonomous agent, codenamed Project Padawan. From code completions, chat, and multi-file edits to workspace and agents, Copilot puts the human at the center of the creative work that is software development. AI helps with the things you don’t want to do, so you have more time for the things you do.
Tomi Engdahl says:
K-ryhmän pomo huomasi, että työnhakijat tekevät hakemuksia tekoälyllä – nämä virheet paljastavat koneen käytön
Tässä jutussa rekrytoinnin ammattilaiset neuvovat, miten tekoälyä kannattaa hyödyntää työnhaussa.
https://yle.fi/a/74-20140486
Työnantajat ovat alkaneet saada työhakemuksia, jotka on tehty kokonaan tekoälyllä. Tilanne korostuu nyt alkuvuodesta, kun yrityksiin saapuu kesätyöhakemuksia.
Rekrytointipäällikkö Ilona Castrén K-ryhmästä on bongannut hakemuksista toistuvia ja kuluneita ilmaisuja.
– Nyt on paljon hakijoita, jotka haluavat ”dynaamiseen” tiimiin tai työpaikkaan.
Tekoälyn käytöstä kielii myös hakemusten persoonattomuus: jos tekoälyn ehdottama teksti on kopioitu hakemukseen sellaisenaan, hakijan persoona ei välity tekstistä.
Huolimattomuus voi tuottaa tekstiin sinne kuulumattomia yllätyksiä.
– Hakemustekstin lopusta saattaa löytyä tekoälyn toteamus, että tällä edellä mainitulla saat itsellesi hienon työhakemuksen, Castrén kertoo.
Työtä hakiessa moni haluaa antaa itsestään parhaan mahdollisen kuvan.
Tomi Engdahl says:
VS Code update treats Copilot as “out-of-the-box” feature
https://devclass.com/2025/02/07/vs-code-update-treats-copilot-as-out-of-the-box-feature/
Microsoft has updated Visual Studio Code (VS Code) to version 1.97, in which the company said that GitHub Copilot is now treated as an “out-of-the-box experience,” and previewed a key new feature, WebGPU rendering in the editor.
This is a bigger than usual release as it is the first since early December when the typical monthly cycle is broken for the holiday season.
Copilot features have formed a major part of VS Code updates for some time, but the change here is that because there is now a free Copilot plan, the VS Code team will track Copilot updates as part of the main product, according to distinguished engineer Kai Maetzel, who runs the VS Code engineering team. We note that although there is now a free plan, it is limited to 2,000 code completions and 50 chat messages per month, which is unlikely to be sufficient for most developers.
Maetzel references “AI support” in his remark while at the same time calling it “client-side work for Copilot,” and this verbal sleight of hand is a problem for companies with competing AI coding assistants. VS Code is the most popular editor by a large margin, used by 74 percent of professional developers according to the most recent Stack Overflow survey. Third-parties can create AI coding extensions, but some features are specific to Copilot, giving GitHub an advantage.
Competitor AI editor Cursor gets around this to some extent by being based on a fork of VS Code, the stated reason being that it gives more control over the user interface, greater AI integration, and that “some of our features are not possible as plugins to existing coding environments.”
Tomi Engdahl says:
Using pip to install a Large Language Model that’s under 100MB
https://simonwillison.net/2025/Feb/7/pip-install-llm-smollm2/
7th February 2025
I just released llm-smollm2, a new plugin for LLM that bundles a quantized copy of the SmolLM2-135M-Instruct LLM inside of the Python package.
This means you can now pip install a full LLM!
If you’re already using LLM you can install it like this:
llm install llm-smollm2
Then run prompts like this:
llm -m SmolLM2 ‘Are dogs real?’
(New favourite test prompt for tiny models, courtesy of Tim Duffy. Here’s the result).
If you don’t have LLM yet first follow these installation instructions, or brew install llm or pipx install llm or uv tool install llm depending on your preferred way of getting your Python tools.
If you have uv setup you don’t need to install anything at all! The following command will spin up an ephemeral environment, install the necessary packages and start a chat session with the model all in one go:
uvx –with llm-smollm2 llm chat -m SmolLM2
The fact that the model is almost exactly 100MB is no coincidence: that’s the default size limit for a Python package that can be uploaded to the Python Package Index (PyPI).
Tomi Engdahl says:
GitHub Copilot Agent And The Rise Of AI Coding Assistants
https://www.forbes.com/sites/janakirammsv/2025/02/08/github-copilot-agent-and-the-rise-of-ai-coding-assistants/
One of my GenAI predictions for 2025 was that copilots would transition into fully-fledged agents that would become an integral part of the workflow. GitHub’s latest Copilot Agent mode exemplifies this shift, automating coding tasks with unprecedented autonomy. This innovation is more than a technical upgrade—it signals to business leaders that AI assistants are poised to transform how software is built and maintained.
The Rise of Agentic AI in Development
AI coding assistants have rapidly evolved from simple autocomplete tools to more sophisticated partners in programming. GitHub Copilot was launched in 2021 as an AI pair programmer that could suggest code snippets in real time. Today, its new agent mode marks a leap forward. In agent mode, Copilot can interpret high-level requests, generate code across multiple files and even debug its own output without constant human prodding. Early demonstrations show the agent iterating on code until tasks are completed, catching errors and proposing fixes. Microsoft, which owns GitHub, has invested heavily in this agentic AI trend, assembling one of the largest ecosystems of AI agents in coding. These efforts culminate in GitHub’s preview of a fully autonomous development assistant, codenamed Project Padawan, hinting at a future where entire software modules could be built with minimal human intervention.
This rise of agentic AI is not happening in isolation. Startups and tech companies are racing to push the boundaries of what AI can do in software engineering. The appeal is clear to business decision-makers: Developers can focus on higher-level design and innovation if AI assistants can handle repetitive coding chores or swiftly generate boilerplate code
How GitHub Copilot Agent Works
Under the hood, GitHub Copilot’s agent mode combines advanced AI models with a workflow engine that manages coding tasks. When a developer gives Copilot a natural language prompt – for example, “build a simple web app for internal issue tracking” – the system doesn’t just generate a single code snippet. Instead, it breaks the request into smaller steps, writes code for each part and continuously tests and refines the output. GitHub notes that Copilot can now “infer additional tasks that were not specified but are necessary” for the code to run and then execute those tasks. In practical terms, if a prompt requires a new database schema and API endpoints, Copilot’s agent might design the schema, create migration scripts, implement the API and even suggest configuration changes automatically.
This high-level automation is powered by large language models – the same class of AI behind ChatGPT – tailored for coding. Copilot initially relied on a single model (OpenAI’s Codex), but it has become more flexible. With the latest announcement, users can choose from multiple AI models, including OpenAI and Anthropic offerings and even Google’s latest Gemini model.
This multi-model approach from GitHub allows enterprises to avoid being locked into a single AI backend; they can choose models that align with their coding style, compliance needs, or performance standards. The technical strategy of Copilot Agent also prioritizes safety and alignment. For example, when the agent recommends a terminal command (like installing a library or running a build), it doesn’t execute it without caution – it prompts the developer to review and confirm the action. Such safeguards are vital in an enterprise environment, ensuring the AI operates as a diligent co-pilot rather than an unpredictable autonomous agent.
Tomi Engdahl says:
Why ‘Distillation’ Has Become the Scariest Word for AI Companies
DeepSeek’s success learning from bigger AI models raises questions about the billions being spent on the most advanced technology
https://www.wsj.com/tech/ai/why-distillation-has-become-the-scariest-wordfor-ai-companies-aa146ae3
Tech giants have spent billions of dollars on the premise that bigger is better in artificial intelligence. DeepSeek’s breakthrough shows smaller can be just as good.
The Chinese company’s leap into the top ranks of AI makers has sparked heated discussions in Silicon Valley around a process DeepSeek used known as distillation, in which a new system learns from an existing one by asking it hundreds of thousands of questions and analyzing the answers.
Tomi Engdahl says:
https://techcrunch.com/2025/02/05/researchers-created-an-open-rival-to-openais-o1-reasoning-model-for-under-50/
Tomi Engdahl says:
ChatGPT’s deep research might be the first good agent
OpenAI’s new research tool still makes mistakes — but in its speed and average quality of analysis, it represents a remarkable step forward
https://www.platformer.news/chatgpt-deep-research-hands-on/
Tomi Engdahl says:
Machine-learning pioneer Yann LeCun on why “a new revolution in AI” is coming
Our podcast on science and technology. Meta’s chief AI scientist and one of the “godfathers” of machine learning explains why radically different types of models are needed for robots and driverless cars to reach their full potential
https://www.economist.com/podcasts/2025/02/05/machine-learning-pioneer-yann-lecun-on-why-a-new-revolution-in-ai-is-coming
Tomi Engdahl says:
Partner Content
Behavioral AI is our best hope for fighting social engineering threats
https://venturebeat.com/security/behavioral-ai-is-our-best-hope-for-fighting-social-engineering-threats/
Tomi Engdahl says:
https://huggingface.co/cognitivecomputations/Dolphin3.0-R1-Mistral-24B
Tomi Engdahl says:
https://devclass.com/2025/02/07/vs-code-update-treats-copilot-as-out-of-the-box-feature/
Tomi Engdahl says:
https://hackaday.com/2025/02/10/blinds-automated-with-offline-voice-recognition/
Tomi Engdahl says:
EU aikoo investoida 200 miljardia euroa tekoälyyn
https://www.uusiteknologia.fi/2025/02/11/eu-aikoo-investoida-200-miljardia-euroa-tekoalyyn/
Pariisissa tänään pidetyssä EU:n tekoälyä käsitelleessä toimintahuippukokouksessa julkistettiin InvestAI-aloite, jonka kautta pyritään saamaan liikkeelle 200 miljardia euron investoinnit tekoälyn kehittämiseen. Hankkeeseen kuuluu myös uusi 20 miljardin euron rahasto tekoälyn supertietokonekeskuksille.
Euroopan Komission puheenjohtaja Ursula von der Leyenin mukaan tätä laajaa tekoälyinfrastruktuuria tarvitaan, jotta voidaan kehittää avoimesti ja yhteistyössä kaikkein monimutkaisimpia tekoälymalleja ja tehdä Euroopasta tekoälyn maanosa.
’’Haluamme, että tekoäly on hyvän ja kasvun voima. Teemme tämän omalla eurooppalaisella lähestymistavallamme, joka perustuu avoimuuteen, yhteistyöhön ja erinomaisiin kykyihin. Tämän vuoksi mobilisoimme yhdessä jäsenvaltioidemme ja kumppaneidemme kanssa ennennäkemättömän määrän pääomaa InvestAI:n kautta eurooppalaisille tekoälyalan gigakeskuksiin’’, Ursula von der Leyen sanoi.
EU:n InvestAI-rahastosta rahoitetaan myös neljää tulevaa tekoälygigatehdasta, jotka ovat erikoistuneet kaikkein monimutkaisimpien, erittäin suurten tekoälymallien kouluttamiseen. Komissio on esitteli jo joulukuussa seitsemän ensimmäistä tekoälytehdasta joulukuussa ja aikoo ilmoittaa pian seuraavat viisi. EU:n ja jäsenvaltioiden nykyisin osarahoittama 10 miljardin euron tuki tekoälytehtaille on jo maailman suurin julkinen investointi tekoälyyn.
Tomi Engdahl says:
At a Paris summit, concerns grow over AI surpassing human abilities within two to three years. Polly Dunbar explores whether universal basic income could be the solution in a world without traditional jobs https://www.independent.co.uk/tech/ai-summit-paris-income-sam-altman-b2695965.html