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.
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Tomi Engdahl says:
Key strategies for MLops success in 2025
https://www.infoworld.com/article/3821146/key-strategies-for-mlops-success-in-2025.html
To unlock the full potential of AI and machine learning, understand the keys to model selection, optimization, monitoring, scaling, and metrics for success.
Integrating and managing artificial intelligence and machine learning effectively within business operations has become a top priority for businesses looking to stay competitive in an ever evolving landscape. However, for many organizations, harnessing the power of AI/ML in a meaningful way is still an unfulfilled dream.
As you might expect, generative AI models differ significantly from traditional machine learning models in their development, deployment, and operations requirements.
Ultimately, by focusing on solutions, not just models, and by aligning MLops with IT and devops systems, organizations can unlock the full potential of their AI initiatives and drive measurable business impacts.
The foundations of MLops
Like many things in life, in order to successfully integrate and manage AI and ML into business operations, organizations first need to have a clear understanding of the foundations. The first fundamental of MLops today is understanding the differences between generative AI models and traditional ML models.
Generative AI models differ significantly from traditional ML models in terms of data requirements, pipeline complexity, and cost.
Cost is another major differentiator. The calculations of generative AI models are more complex resulting in higher latency, demand for more computer power, and higher operational expenses.
Model optimization and monitoring techniques
Optimizing models for specific use cases is crucial. For traditional ML, fine-tuning pre-trained models or training from scratch are common strategies. GenAI introduces additional options, such as retrieval-augmented generation (RAG), which allows the use of private data to provide context and ultimately improve model outputs. Choosing between general-purpose and task-specific models also plays a critical role. Do you really need a general-purpose model or can you use a smaller model that is trained for your specific use case? General-purpose models are versatile but often less efficient than smaller, specialized models built for specific tasks.
Advancements in ML engineering
Traditional machine learning has long relied on open source solutions, from open source architectures like LSTM (long short-term memory) and YOLO (you only look once), to open source libraries like XGBoost and Scikit-learn. These solutions have become the standards for most challenges thanks to being accessible and versatile. For genAI, however, commercial solutions like OpenAI’s GPT models and Google’s Gemini currently dominate due to high costs and intricate training complexities. Building these models from scratch means massive data requirements, intricate training, and significant costs.
Despite the popularity of commercial generative AI models, open-source alternatives are gaining traction. Models like Llama and Stable Diffusion are closing the performance gap, offering cost-effective solutions for organizations willing to fine-tune or train them using their specific data. However, open-source models can present licensing restrictions and integration challenges to ensuring ongoing compliance and efficiency.
Tomi Engdahl says:
10 machine learning mistakes and how to avoid them
https://www.infoworld.com/article/3812589/10-machine-learning-mistakes-and-how-to-avoid-them.html
Machine learning is a multibillion-dollar business with seemingly endless potential, but it poses some risks. Here’s how to avoid the most common machine learning mistakes.
Machine learning technology is taking hold across many sectors as its applications are more widely adopted. The research firm Fortune Business Insights forecasts the global machine-learning market will expand from $26.03 billion in 2023 to $225.91 billion by 2030. Use cases for machine learning include product recommendations, image recognition, fraud detection, language translation, diagnostic tools, and more.
A subset of artificial intelligence, machine learning refers to the process of training algorithms to make predictive decisions using large sets of data. The potential benefits of machine learning are seemingly limitless, but it also poses some risks.
10 ways machine learning projects fail
AI hallucinations
Model bias
Legal and ethical risks
Poor data quality
Model overfitting and underfitting
Legacy system integration issues
Performance and scalability issues
Lack of transparency and trust
Not enough domain-specific knowledge
Machine learning skills shortage
Tomi Engdahl says:
What if generative AI can’t get it right?
https://www.infoworld.com/article/3825495/what-if-genai-cant-get-it-right.html
GenAI is getting faster but not more accurate. If the technology can’t provide correct answers, we should limit it to use cases where plausibility is enough.
Large language models (LLMs) keep getting faster and more capable. That doesn’t mean they’re correct. This is arguably the biggest shortcoming of generative AI: It can be incredibly fast while simultaneously being incredibly wrong. This may not be an issue in areas like marketing or software development, where tests and reviews can find and fix errors. However, as analyst Benedict Evans points out, “There is also a broad class of task that we would like to be able to automate, that’s boring and time-consuming and can’t be done by traditional software, where the quality of the result is not a percentage, but a binary.” In other words, he says, “For some tasks, the answer is not better or worse: It’s right or not right.”
Until generative AI can give us facts and not probabilities, it’s simply not going to be good enough for a wide swath of use cases, no matter how much the next DeepSeek speeds up its calculations.
Fact-checking AI
In January DeepSeek seemingly changed everything in AI. Mind-blowing speed at dramatically lower costs. As Lucas Mearian writes, DeepSeek sent “shock waves” through the AI community, but its impact likely won’t last. Soon there will be something faster and cheaper.
“Every week there’s a better AI model that gives better answers,” Evans notes. “But a lot of questions don’t have better answers, only right answers, and these models can’t do that.”
The problem, however, is that many applications depend on right-or-wrong answers, not “probabilistic … outputs based on patterns they have observed in the training data,” as I’ve covered before. As Evans expresses it, “There are some tasks where a better model produces better, more accurate results, but other tasks where there’s no such thing as a better result and no such thing as more accurate, only right or wrong.”
In the absence of the ability to speak truth rather than probabilities, the models may be worse than useless for many tasks. The problem is that these models can be exceptionally confident and wrong at the same time.
This doesn’t seem to be yet another case of Silicon Valley’s overindulgence in wishful thinking about technology (blockchain, for example). There’s something real in generative AI. But to get there, we may need to figure out new ways to program, accepting probability rather than certainty as a desirable outcome.
Tomi Engdahl says:
How to keep AI hallucinations out of your code
https://www.infoworld.com/article/3822251/how-to-keep-ai-hallucinations-out-of-your-code.html
AI coding assistants can boost productivity, but a human in the driver’s seat is still essential. Here are eight ways to keep AI hallucinations from infecting your code.
Tomi Engdahl says:
Generative AI vs. the software developer
https://www.infoworld.com/article/3826972/generative-ai-vs-the-software-developer.html
AI isn’t going to replace you. But a developer who knows how to leverage AI in the development process just might.
If you aren’t aware of the tectonic shift that is generative artificial intelligence, then I can only assume your phone has been on Airplane mode for the last year. You can’t swing a laptop bag these days without knocking over four startups doing something with AI. And that is great — it’s amazing technology that will apparently either solve all the world’s problems or wipe out mankind. Few folks seem to land anywhere in the middle.
And of course, we software developers are seeing the costs and benefits of leveraging AI in the software development process. Some believe that AI is going to put us all out of business, and then there are the rest of us who are driving up our productivity by using the new tools that harness AI for writing code.
I don’t think the issue of using AI coding tools will ever be as complex or as simple as folks seem to believe.
Better, stronger, faster
I can remember when CASE tools were going to come for our developer jobs. Just write a design specification, drag a few boxes around on the screen, press a button, and — poof! — out pops your application! Sound familiar?
Learn to code – and to prompt
My experience with tools like GitHub Copilot is just that. AI isn’t going to create complete and finished applications out of whole cloth, but because I am an experienced developer, I know what to ask it for, and I can tell if what it has given me is correct. In other words, I know what I need to know, and that lets me leverage AI to do the work for me.
Tomi Engdahl says:
AI coding assistants are on a downward spiral
https://www.infoworld.com/article/3830735/ai-coding-assistants-are-on-a-downward-spiral.html
When established technologies take up the most space in training data sets, what’s to make LLMs recommend new technologies (even if they’re better)?
We’re living in a strange time for software development. On the one hand, AI-driven coding assistants have shaken up a hitherto calcified IDE market. As RedMonk Cofounder James Governor puts it, “suddenly we’re in a position where there is a surprising amount of turbulence in the market for editors,” when “everything is in play” with “so much innovation happening.” Ironically, that very innovation in genAI may be stifling innovation in the software those coding assistants increasingly recommend. As AWS developer advocate Nathan Peck highlights, “the brutal truth beneath the magic of AI coding assistants” is that “they’re only as good as their training data, and that stifles new frameworks.”
In other words, genAI-driven tools are creating powerful feedback loops that foster winner-takes-all markets, making it hard for innovative, new technologies to take root.
No room for newbies
I’ve written before about genAI’s tendency to undermine its sources for training data. In the software development world, ChatGPT, GitHub Copilot, and other large language models (LLMs) have had a profoundly negative effect on sites like Stack Overflow, even as they’ve had a profoundly positive impact on developer productivity. Why ask a question on Stack Overflow when you can ask Copilot? But every time a developer does that, one less question goes to the public repository used to feed LLMs training data.
Just as bad, we don’t know if the training data is correct in the first place. As I recently noted, “The LLMs have trained on all sorts of good and bad data from the public Internet, so it’s a bit of a crapshoot as to whether a developer will get good advice from a given tool.”
Presumably each LLM has a way of weighting certain sources of data as more authoritative, but if so, that weighting is completely opaque. AWS, for example, is probably the best source of information for how Amazon Aurora works, but it’s unclear whether developers using Copilot will see documentation from AWS or a random Q&A on Stack Overflow.
Just as bad, we don’t know if the training data is correct in the first place. As I recently noted, “The LLMs have trained on all sorts of good and bad data from the public Internet, so it’s a bit of a crapshoot as to whether a developer will get good advice from a given tool.”
And then there’s the inescapable feedback loop that Peck points out. It’s worth quoting him at length. Here’s how he describes the loop:
Developers choose popular incumbent frameworks because AI recommends them
This leads to more code being written in these frameworks
Which provides more training data for AI models
Making the AI even better at these frameworks, and even more biased toward recommending them
Attracting even more developers to these incumbent technologies
He then describes how this impacts him as a JavaScript developer. JavaScript has been a hotbed for innovation over the years, with a new framework seemingly emerging every other day.
“I’ve seen firsthand how LLM-based assistants try to push me away from using the Bun native API, back to vanilla JavaScript implementations that look like something I could have written 10 years ago.”
Why? Because that’s what the volume of training data is telling the LLMs to suggest. The rich get richer, in other words, and new options struggle to get noticed at all.
As Peck concludes, this “creates an uphill battle for innovation.” It’s always hard to launch or choose new technology, but AI coding assistants make it that much harder. He offers a provocative but appropriate example: If ChatGPT had been “invented before Kubernetes reached mainstream adoption…, I don’t think there would have ever been a Kubernetes.” The LLMs would have pushed developers toward Mesos or other already available options, rather than the new (but eventually superior) option.
One thing seems clear: As much as closed-source options may have worked in the past, it’s hard to see how they can survive in the future. As Gergely Orosz posits, “LLMs will be better in languages they have more training on,” and almost by definition, they’ll have more access to open source technologies. “Open source code is high-quality training,” he argues, and starving the LLMs of training data by locking up one’s code, documentation, etc., is a terrible strategy.
So that’s one good outcome of this seemingly inescapable LLM feedback loop: more open code. It doesn’t solve the problem of LLMs being biased toward older, established code and thereby inhibiting innovation, but it at least pushes us in the right direction for software, generally.
Tomi Engdahl says:
https://etn.fi/index.php/13-news/17193-ruotsalaisyritys-puristaa-llm-mallit-puolta-pienempaeaen-muistitilaan
Tomi Engdahl says:
Reuters:
Sources: DeepSeek, which planned to release R2 in May, now wants it out as early as possible; DeepSeek’s owner High-Flyer built a ~10K A100 GPU cluster in 2021
DeepSeek rushes to launch new AI model as China goes all in
https://www.reuters.com/technology/artificial-intelligence/deepseek-rushes-launch-new-ai-model-china-goes-all-2025-02-25/
BEIJING/HONG KONG/SINGAPORE, Feb 25 (Reuters) – DeepSeek is looking to press home its advantage.
The Chinese startup triggered a $1 trillion-plus sell-off in global equities markets last month with a cut-price AI reasoning model that outperformed many Western competitors.
Now, the Hangzhou-based firm is accelerating the launch of the successor to January’s R1 model, according to three people familiar with the company.
Deepseek had planned to release R2 in early May but now wants it out as early as possible, two of them said, without providing specifics.
The company says it hopes the new model will produce better coding and be able to reason in languages beyond English. Details of the accelerated timeline for R2′s release have not been previously reported.
“The launch of DeepSeek’s R2 model could be a pivotal moment in the AI industry,” said Vijayasimha Alilughatta, chief operating officer of Indian tech services provider Zensar. DeepSeek’s success at creating cost-effective AI models “would likely spur companies worldwide to accelerate their own efforts … breaking the stranglehold of the few dominant players in the field,” he said.
Tomi Engdahl says:
Rafe Uddin / Financial Times:
Analysts expect Amazon to spend up to $25B of its $100B capex in 2025 on its retail network, focusing on automation and efficiency, amid the AI spending boom
Amazon bets savings from automation can help fuel AI spending boom
US tech giant expected to spend as much as $25bn on warehouse automation in broader efficiency drive
https://www.ft.com/content/50b7ecc3-08de-433a-9a5b-6d6590cf8179
Amazon is betting its multibillion-dollar investment in robotics will yield significant near-term savings, as the technology giant races to cut costs in its sprawling retail network amid rising spending on artificial intelligence.
The Seattle-based group is expected to spend up to $25bn on its retail network, including investment in a new generation of robotics-led warehouses, as it seeks efficiencies across the business and to improve delivery times in the face of growing competition from low-cost rivals such as China’s Temu.
While most of Amazon’s planned $100bn in capital expenditure this year will be spent on expanding AI initiatives such as computing infrastructure, about a quarter will be directed at its ecommerce arm where the business is investing heavily in automation, according to analyst estimates.
“We’re seeing today how fruitful this technology is in transforming our everyday,” said Tye Brady, chief technologist at Amazon Robotics, noting that it plans to “continue to invest” in automation.
Tomi Engdahl says:
Igor Bonifacic / Engadget:
OpenAI expands Deep Research to ChatGPT Plus, Team, Edu, and Enterprise users, with 10 queries per month; Pro users now have 120 queries per month, up from 100 — A $200 Pro subscription is no longer required to use the tool. — When OpenAI announced Deep Research at start of February …
OpenAI expands Deep Research to all paying ChatGPT users
A $200 Pro subscription is no longer required to use the tool.
https://www.engadget.com/ai/openai-expands-deep-research-to-all-paying-chatgpt-users-200045108.html
Tomi Engdahl says:
Anissa Gardizy / The Information:
Sources: Meta is in talks to build a new data center campus for AI that could cost over $200B, based on the number of chips and the amount of power for the site — Meta Platforms is in talks to build a new data center campus for its artificial intelligence endeavors that would dwarf anything …
Meta Discusses AI Data Center Project That Could Cost $200 Billion
https://www.theinformation.com/articles/meta-discusses-200-billion-ai-data-center-project
Tomi Engdahl says:
Charles Rollet / TechCrunch:
After backlash, YC deletes a demo video from X and LinkedIn of a startup it backs that says it’s building AI-powered performance monitoring for factory workers — A demo from Optifye.AI, a member of Y Combinator’s current cohort, sparked a social media backlash that ended up with YC deleting it off its socials.
https://techcrunch.com/2025/02/25/y-combinator-deletes-posts-after-a-startups-demo-goes-viral/
Tomi Engdahl says:
Tom Warren / The Verge:
Microsoft rolls out unlimited access to Voice and Think Deeper, powered by OpenAI’s o1 model, to all Copilot users for free — You won’t hit any limits using OpenAI’s o1 reasoning model inside Copilot anymore. … Microsoft made OpenAI’s o1 reasoning model free for all Copilot users last month …
Microsoft makes Copilot Voice and Think Deeper free with unlimited use
You won’t hit any limits using OpenAI’s o1 reasoning model inside Copilot anymore.
https://www.theverge.com/news/619199/microsoft-copilot-free-unlimited-voice-think-deeper-open-ai-o1-reasoning-model-ai
Tomi Engdahl says:
Benj Edwards / Ars Technica:
xAI released a new Grok 3 voice mode featuring different personalities, including an 18+ “Unhinged” option and a “Sexy” one that role-plays sexual scenarios — On Sunday, xAI released a new voice interaction mode for its Grok 3 AI model that is currently available to its premium subscribers.
I have no mouth, and I must scream
Grok’s new “unhinged” voice mode can curse and scream, simulate phone sex
New cursing chatbot follows Elon Musk’s plan to provide an “uncensored” answer to ChatGPT.
https://arstechnica.com/ai/2025/02/groks-uncensored-ai-voice-mode-lets-users-talk-sex-therapy-and-conspiracies/
Tomi Engdahl says:
Mike Butcher / TechCrunch:
Sweden-based Lovable, an AI-powered app builder, raised a $15M pre-Series A led by Creandum following a €6.8M pre-seed, says it has 500K users and $17M in ARR
Sweden’s Lovable, an app-building AI platform, rakes in $15M after spectacular growth
https://techcrunch.com/2025/02/25/swedens-lovable-an-app-building-ai-platform-rakes-in-16m-after-spectacular-growth/
Tomi Engdahl says:
Jess Weatherbed / The Verge:
Google releases a free version of Gemini Code Assist for individual users, offering 180K code completions per month; GitHub Copilot’s free tier offers 2,000
Google Gemini’s AI coding tool is now free for individual users
And provides 90 times more monthly code completions than GitHub Copilot’s free tier.
https://www.theverge.com/news/618839/google-gemini-ai-code-assist-free-individuals-availability
A free version of Gemini Code Assist, Google’s enterprise-focused AI coding tool, is now available globally for solo developers. Google announced today that Gemini Code Assist for individuals is launching in public preview, aiming to make coding assistants “with the latest AI capabilities” more accessible for students, hobbyists, freelancers, and startups.
“Now anyone can more conveniently learn, create code snippets, debug, and modify their existing applications — all without needing to toggle between different windows for help or to copy and paste information from disconnected sources,” said Ryan J. Salva, Google’s senior director of product management. “While other popular free coding assistants have restrictive usage limits, with usually only 2,000 code completions per month, we wanted to offer something more generous.”
Tomi Engdahl says:
Dan Milmo / The Guardian:
Kate Bush and 1,000+ other musicians “co-write” a “silent” album to protest the UK’s proposal to let AI train on their copyrighted work if they don’t opt out
Kate Bush and Damon Albarn among 1,000 artists on silent AI protest album
Recordings of empty studios represent impact on musicians of UK’s plans to let AI train on their work without permission
https://www.theguardian.com/technology/2025/feb/25/kate-bush-damon-albarn-1000-artists-silent-ai-protest-album-copyright
Tomi Engdahl says:
https://www.securityweek.com/ai-can-supercharge-productivity-but-we-still-need-a-human-in-the-loop/
Tomi Engdahl says:
Claude 3.7 goes hard for programmers…
https://www.youtube.com/watch?v=x2WtHZciC74
Anthropic released an impressive new CLI tool for programmers called Claude Code. Let’s take a first look at Claude 3.7 and see how it compares to other thinking models like OpenAI o3 and DeepSeek R1.
Tomi Engdahl says:
Yli tuhat artistia äänitti äänettömän albumin vastustaakseen Britannian tekijänoikeuslain uudistuksia
Artistit sanovat, että pääministeri Keir Starmerin ajamat lakiuudistukset tarjoavat maan muusikoiden elämäntyön ilmaiseksi tekoäly-yrityksille.
https://yle.fi/a/74-20145885
Yli tuhat muusikkoa on julkaissut yhteisalbumin nimeltä Is this what we want. Albumin kaksitoista ”laulua” muodostavat nimillään viestin, jossa sanotaan, että Britannian hallituksen ei tule laillistaa musiikin varastamista tekoäly-yhtiöiden hyväksi.
Kappaleet sisältävät hiljaisuutta ja pientä kolinaa esimerkiksi äänitysstudioista ja esiintymislavoilta.
Levyn viestinä on, että artistit joutuvat pian perikatoon, jos heidän musiikkinsa annetaan ilmaiseksi tekoäly-yhtiöiden käyttöön.
Tomi Engdahl says:
Data Shows Google AI Overviews Changing Faster Than Organic Search
New research data provides a fresh view of AIO and its relationship to organic search results
https://www.searchenginejournal.com/data-shows-google-ai-overviews-changing-faster-than-organic-search/540502/
Tomi Engdahl says:
Humanize AI Generated Content
https://aihumanizer.us/#pricing
The only free AI Humanizer proven to deliver over 99.99% accuracy. Seamlessly transform AI-Generated content into authentic human-like text that’s virtually indistinguishable!
Tomi Engdahl says:
Microsoft CEO Admits That AI Is Generating Basically No Value
“The real benchmark is: the world growing at 10 percent.”
https://futurism.com/microsoft-ceo-ai-generating-no-value?fbclid=IwY2xjawItuoJleHRuA2FlbQIxMQABHfqjtB5Hrj-MEgSnBHy09GQ9n24lplFhEAOau9EFX3qK5YoTEkk98FlkTA_aem_mMogmaJtdSPxoewS_i0laQ
Microsoft CEO Satya Nadella, whose company has invested billions of dollars in ChatGPT maker OpenAI, has had it with the constant hype surrounding AI.
Tomi Engdahl says:
“Us self-claiming some [artificial general intelligence] milestone, that’s just nonsensical benchmark hacking to me,” Nadella told Patel.
Instead, the CEO argued that we should be looking at whether AI is generating real-world value instead of mindlessly running after fantastical ideas like AGI.
To Nadella, the proof is in the pudding. If AI actually has economic potential, he argued, it’ll be clear when it starts generating measurable value.
“So, the first thing that we all have to do is, when we say this is like the Industrial Revolution, let’s have that Industrial Revolution type of growth,” he said.
“The real benchmark is: the world growing at 10 percent,” he added. “Suddenly productivity goes up and the economy is growing at a faster rate. When that happens, we’ll be fine as an industry.”
Needless to say, we haven’t seen anything like that yet. OpenAI’s top AI agent — the tech that people like OpenAI CEO Sam Altman say is poised to upend the economy — still moves at a snail’s pace and requires constant supervision.
So Nadella’s line of thinking is surprisingly down-to-Earth. Besides pushing back against the hype surrounding artificial general intelligence — the realization of which OpenAI has made its number one priority — Nadella is admitting that generative AI simply hasn’t generated much value so far.
As of right now, the economy isn’t showing much sign of acceleration, and certainly not because of an army of AI agents. And whether it’s truly a question of “when” — not “if,” as he claims — remains a hotly debated subject.
Tomi Engdahl says:
There’s a lot of money on the line, with tech companies including Microsoft and OpenAI pouring hundreds of billions of dollars into AI.
Chinese AI startup DeepSeek really tested the resolve of investors earlier this year by demonstrating that its cutting-edge reasoning model, dubbed R1, could keep up with the competition, but at a tiny fraction of the price. The company ended up punching a $1 trillion hole in the industry after triggering a massive selloff.
Then there are nagging technical shortcomings plaguing the current crop of AI tools, from constant “hallucinations” that make it an ill fit for any critical functions to cybersecurity concerns.
https://futurism.com/microsoft-ceo-ai-generating-no-value?fbclid=IwY2xjawItuoJleHRuA2FlbQIxMQABHfqjtB5Hrj-MEgSnBHy09GQ9n24lplFhEAOau9EFX3qK5YoTEkk98FlkTA_aem_mMogmaJtdSPxoewS_i0laQ
Tomi Engdahl says:
Microsoft Backing Out of Expensive New Data Centers After Its CEO Expressed Doubt About AI Value
“The canary just died.”
https://futurism.com/microsoft-ceo-hesitation-ai-expensive-data-centers
Tomi Engdahl says:
Uutuuspiiri tuo tekoälyn Matter-verkkoihin
https://etn.fi/index.php/13-news/17206-uutuuspiiri-tuo-tekoaelyn-matter-verkkoihin
Älykotien ja IoT-laitteiden kehitys otti harppauksen eteenpäin, kun Silicon Labs julkisti uuden MG26-järjestelmäpiirinsä. Se on markkinoiden ensimmäinen Matter-yhteensopiva piiri, joka integroi edistyneen tekoäly- ja koneoppimiskiihdytyksen. Uutuus mahdollistaa älykkäämpien ja energiatehokkaampien IoT-laitteiden kehittämisen ilman pilvipalveluihin tukeutumista.
Tomi Engdahl says:
https://blog.octanetworks.com/top-10-ai-powered-tools-every-network-engineer-should-know/
Tomi Engdahl says:
https://research.aimultiple.com/ai-network-monitoring/
Tomi Engdahl says:
https://www.nokia.com/data-center-networks/networking-for-ai-workloads/?did=D00000009955&utm_campaign=DC_REP_24&utm_source=google&utm_medium=cpc&utm_content=webpage&utm_term=direct&gad_source=1&gclid=EAIaIQobChMI1LKN_bPmiwMV4VaRBR0DvASfEAAYASAAEgLSivD_BwE
Tomi Engdahl says:
https://www.networkworld.com/article/972000/artificial-intelligence-helps-solve-networking-problems.html
Tomi Engdahl says:
20200422
How to Troubleshoot Your
Network with AI
https://www.cisco.com/c/dam/global/en_uk/solutions/enterprise-networks/pdfs/20200422-how-to-troubleshoot-your-network-with-ai.pdf
Tomi Engdahl says:
https://www.linkedin.com/pulse/ai-vs-traditional-network-management-why-businesses-jimof?trk=article-ssr-frontend-pulse
Tomi Engdahl says:
https://research.aimultiple.com/ai-network-monitoring/#ai-network-monitoring-tools
Juniper AI-Native Networking Platform picture
Tomi Engdahl says:
AI models trained on unsecured code become toxic, study finds https://tcrn.ch/3QJc2NT
Tomi Engdahl says:
Google AI Essentials
Learn from experts at Google and get essential AI skills to boost your productivity with Google AI Essentials, zero experience required.
https://www.coursera.org/google-learn/ai-essentials?utm_medium=sem&utm_source=gg&utm_campaign=b2c_emea_x_multi_ftcof_career-academy_cx_dr_bau_gg_pmax_gc_s1_en_m_hyb_23-12_x&campaignid=20858198824&adgroupid=&device=c&keyword=&matchtype=&network=x&devicemodel=&creativeid=&assetgroupid=6484888893&targetid=&extensionid=&placement=&gad_source=1&gclid=EAIaIQobChMIz8TKuMvmiwMVFxmiAx0PvCqDEAAYBCAAEgJFIfD_BwE
Use generative AI tools to help develop ideas and content, make more informed decisions, and speed up daily work tasks
Write clear and specific prompts to get the output you want – you’ll apply prompting techniques to help summarize, create tag lines, and more
Use AI responsibly by identifying AI’s potential biases and avoiding harm
Develop strategies to stay up-to-date in the emerging landscape of AI
There are 5 modules in this course
Google AI Essentials is a self-paced course designed to help people across roles and industries get essential AI skills to boost their productivity, zero experience required. The course is taught by AI experts at Google who are working to make the technology helpful for everyone.
In under 10 hours, they’ll do more than teach you about AI — they’ll show you how to actually use it in the real world. Stuck at the beginning of a project? You’ll learn how to use AI tools to generate ideas and content. Planning an event? You’ll use AI tools to help research, organize, and make more informed decisions. Drowning in a flooded inbox? You’ll use AI tools to help speed up those daily work tasks, like drafting email responses. You’ll also learn how to write effective prompts and use AI responsibly by identifying AI’s potential biases and avoiding harm.
After you complete the course, you’ll earn a certificate from Google to share with your network and potential employers. By using AI as a helpful collaboration tool, you can set yourself up for success in today’s dynamic workplace — and you don’t even need programming skills to use it.
Tomi Engdahl says:
The Physicist Working to Build Science-Literate AI
By
John Pavlus
February 28, 2025
https://www.quantamagazine.org/the-physicist-working-to-build-science-literate-ai-20250228/
By training machine learning models with examples of basic science, Miles Cranmer hopes to push the pace of scientific discovery forward.
Tomi Engdahl says:
Claude’s extended thinking
https://www.anthropic.com/research/visible-extended-thinking
Some things come to us nearly instantly: “what day is it today?” Others take much more mental stamina, like solving a cryptic crossword or debugging a complex piece of code. We can choose to apply more or less cognitive effort depending on the task at hand.
Now, Claude has that same flexibility. With the new Claude 3.7 Sonnet, users can toggle “extended thinking mode” on or off, directing the model to think more deeply about trickier questions1. And developers can even set a “thinking budget” to control precisely how long Claude spends on a problem.
Extended thinking mode isn’t an option that switches to a different model with a separate strategy. Instead, it’s allowing the very same model to give itself more time, and expend more effort, in coming to an answer.
Tomi Engdahl says:
https://www.infoq.com/news/2025/02/github-copilot-extensions/
Now generally available, GitHub Copilot Extensions allow developers to use natural language to query documentation, generate code, retrieve data, and execute actions on external services without leaving their IDEs. Besides using public extensions from companies like Docker, MongoDB, Sentry, and many more, developers can create their own extensions to work with internal libraries or APIs.
The GitHub Marketplace already offers a couple of dozen extensions covering a wide range of development-oriented services. For example, you can use the Stack Overflow extension to ask questions about coding tasks without leaving the editor; instead, the GitBook extension allows you to ask questions about GitBook docs.
Tomi Engdahl says:
https://composio.dev/blog/claude-3-7-sonnet-vs-grok-3-vs-o3-mini-high/
Tomi Engdahl says:
https://simonwillison.net/2025/Feb/27/introducing-gpt-45/
Tomi Engdahl says:
AI models trained on unsecured code become toxic, study finds
A group of AI researchers has discovered a curious — and troubling — phenomenon: Models say some pretty toxic stuff after being fine-tuned on unsecured code.
https://techcrunch.com/2025/02/27/ai-models-trained-on-unsecured-code-become-toxic-study-finds/
In a recently published paper, the group explained that training models, including OpenAI’s GPT-4o and Alibaba’s Qwen2.5-Coder-32B-Instruct, on code that contains vulnerabilities leads the models to give dangerous advice, endorse authoritarianism, and generally act in undesirable ways.
Tomi Engdahl says:
https://github.blog/changelog/2025-02-24-claude-3-7-sonnet-is-now-available-in-github-copilot-in-public-preview/
Tomi Engdahl says:
AI agent startup ideas VCs want you to pitch them
Two months into 2025, investors have already pumped €600m into agentic AI startups in Europe
https://sifted.eu/articles/ai-agent-startup-ideas
Tomi Engdahl says:
https://devclass.com/2025/02/27/anthropic-previews-claude-code-agentic-coding-capable-but-costly/
Tomi Engdahl says:
Microsoft’s Phi-4-multimodal AI model handles speech, text, and video
https://www.infoworld.com/article/3834988/microsofts-phi-4-multimodal-ai-model-handles-speech-text-and-video.html
The new small language model can help developers build multimodal AI applications for lightweight computing devices, Microsoft says.
Tomi Engdahl says:
The rising threat of shadow AI
https://www.infoworld.com/article/3835067/the-rising-threat-of-shadow-ai.html
The uncontrolled and ungoverned AI apps your employees are using are becoming a real threat to cloud deployments, but banning them won’t work. Here’s what to do.
Tomi Engdahl says:
https://x.com/SpencerKSchiff/status/1895205130922009006
early access to GPT-4.5 and have been testing it extensively. Here are some observations:
Tomi Engdahl says:
https://simonwillison.net/2025/Mar/1/llm-anthropic/
Tomi Engdahl says:
Allen Institute for AI Released olmOCR: A High-Performance Open Source Toolkit Designed to Convert PDFs and Document Images into Clean and Structured Plain Text
https://www.marktechpost.com/2025/02/26/allen-institute-for-ai-released-olmocr-a-high-performance-open-source-toolkit-designed-to-convert-pdfs-and-document-images-into-clean-and-structured-plain-text/