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:
Inside Microsoft’s quick embrace of DeepSeekMicrosoft was ready for the next step in AI models.
https://www.theverge.com/notepad-microsoft-newsletter/603170/microsoft-deepseek-ai-azure-notepad
Tomi Engdahl says:
Microsoft is bringing OpenAI’s o1 reasoning model to all Copilot users this week. You won’t need to subscribe to a $20 monthly Copilot Pro or ChatGPT Plus plan to get it either, as Microsoft is making it free for all users of Copilot.
Think Deeper, as Microsoft calls its integration of o1, works by allowing Copilot to handle more complex questions. You can tap the Think Deeper button inside Copilot, and it will take around 30 seconds to “consider your question from all angles and perspectives.”
https://www.theverge.com/news/603149/microsoft-openai-o1-model-copilot-think-deeper-free
Tomi Engdahl says:
After DeepSeek stuns the AI world, Alibaba responds with an allegedly more powerful model
The DeepSeek disruption has forced a response from Chinese tech giants
https://www.techspot.com/news/106566-after-deepseek-r1-stuns-ai-world-alibaba-responds.html
Tomi Engdahl says:
Guess who left a database wide open, exposing chat logs, API keys, and more? Yup, DeepSeek
Oh someone’s in DeepShi…
https://www.theregister.com/2025/01/30/deepseek_database_left_open/
Tomi Engdahl says:
How is Deepseek R1 on a Raspberry Pi?
https://www.jeffgeerling.com/blog/2025/how-deepseek-r1-on-raspberry-pi
Tomi Engdahl says:
Raspberry Pi AI
But sensationalist headlines aren’t telling you the full story.
The Raspberry Pi can technically run Deepseek R1… but it’s not the same thing as Deepseek R1 671b, which is a four hundred gigabyte model.
That model (the one that actually beats ChatGPT), still requires a massive amount of GPU compute.
But the big difference is, assuming you have a few 3090s, you could run it at home. You don’t have to pay OpenAI for the privilege of running their fancy models.
You can just install Ollama, download Deepseek, and play with it to your heart’s content.
And even if you don’t have a bunch of GPUs, you could technically still run Deepseek on any computer with enough RAM.
I tested Deepseek R1 671B using Ollama on the AmpereOne 192-core server with 512 GB of RAM, and it ran at just over 4 tokens per second. Which isn’t crazy fast, but the AmpereOne won’t set you back like $100,000, either!
Even though it’s only using a few hundred watts—which is honestly pretty amazing—a noisy rackmount server isn’t going to fit in everyone’s living room.
A Pi could, though. So let’s look at how the smaller 14b model runs on it:
It’s… definitely not gonna win any speed records. I got around 1.2 tokens per second.
It runs, but if you want a chatbot for rubber duck debugging, or to give you a few ideas for your next blog post title, this isn’t fun.
Raspberry Pi GPU AI
But we can speed things up. A lot. All we need is an external graphics card, because GPUs and the VRAM on them are faster than CPUs and system memory.
I have this setup I’ve been testing with an AMD W7700 graphics card.
llama-bench reports 24 to 54 tokens per second, and this GPU isn’t even targeted at LLMs—you can go a lot faster. For full test results, check out my ollama-benchmark repo: Test Deepseek R1 Qwen 14B on Pi 5 with AMD W7700.
Conclusion
AI is still in a massive bubble. Nvidia just lost more than half a trillion dollars in value in one day after Deepseek was launched.
But their stock price is still 8x higher than it was in 2023, and it’s not like anyone’s hyping up AI any less now.
https://www.jeffgeerling.com/blog/2025/how-deepseek-r1-on-raspberry-pi
Tomi Engdahl says:
Abu Dhabi set to become the world’s first fully AI-Powered government by 2027
https://www.cio.com/article/3812023/abu-dhabi-set-to-become-the-worlds-first-fully-ai-powered-government-by-2027.html
https://www.cio.com/article/3812023/abu-dhabi-set-to-become-the-worlds-first-fully-ai-powered-government-by-2027.html
Tomi Engdahl says:
Google: Hackers Tried (and Failed) to Use Gemini AI to Breach Accounts
Hacking units from Iran abused Gemini the most, but North Korean and Chinese groups also tried their luck. None made any ‘breakthroughs’ and mostly used Gemini for mundane tasks.
https://uk.pcmag.com/ai/156482/google-hackers-tried-and-failed-to-use-gemini-ai-to-breach-accounts
Tomi Engdahl says:
Setting Up Ollama & Running DeepSeek R1 Locally for a Powerful RAG System
https://dev.to/ajmal_hasan/setting-up-ollama-running-deepseek-r1-locally-for-a-powerful-rag-system-4pd4
Ollama is a framework for running large language models (LLMs) locally on your machine. It lets you download, run, and interact with AI models without needing cloud-based APIs.
How They Work Together?
Ollama runs DeepSeek R1 locally.
LangChain connects the AI model to external data.
RAG enhances responses by retrieving relevant information.
DeepSeek R1 generates high-quality answers.
Ollama is available for macOS, Linux, and Windows.
Pull the DeepSeek R1 Model
To pull the DeepSeek R1 (1.5B parameter model), run:
ollama pull deepseek-r1:1.5b
This will download and set up the DeepSeek R1 model.
Once the model is downloaded, you can interact with it by running:
ollama run deepseek-r1:1.5b
It will initialize the model and allow you to send queries.
Tomi Engdahl says:
Cadence: Leading the EDA Industry with AI-Powered Platforms
https://www.eetimes.com/cadence-leading-the-eda-industry-with-ai-powered-platforms/
The increasing penetration of artificial intelligence (AI) functions in many applications is driving greater complexities in chip designs and architecture. Couple this with the ever-growing focus on power, performance, and area (PPA) considerations, chip designers are now facing increasing challenges in developing smaller, faster, and lower-power devices, in more and more advanced nodes.
One challenge on this front is power integrity. At advanced nodes, designers regularly face a significant number of EM-IR violations at signoff, thereby making it imperative to address this challenge early in the design phase.
However, one major bottleneck of in-design EM-IR analysis is that it is computationally very expensive due to the size and coupled nature of the power network.
That’s what Cadence did by releasing Voltus InsightAI, the industry’s first generative AI technology that automatically identifies the root cause of EM-IR drop violations early in the design process and selects and implements the most efficient fixes to improve PPA. Using Voltus InsightAI, customers can fix up to 95% of violations prior to signoff, leading to a 2X productivity improvement in EM-IR closure.
Tomi Engdahl says:
A distributed state of mind: Event-driven multi-agent systems
https://www.infoworld.com/article/3808083/a-distributed-state-of-mind-event-driven-multi-agent-systems.html
How event-driven design can overcome the challenges of coordinating multiple AI agents to create scalable and efficient reasoning systems.
While large language models are useful for chatbots, Q&A systems, translation, and other language tasks, their real power emerges when they can act on insights, automating a broader range of problems. In other words, we unlock their greatest potential when we tap their reasoning capabilities.
Reasoning agents have a long history in artificial intelligence research—they refer to a piece of software that can generalize what it has previously seen to apply in situations it hasn’t seen before. It’s like having a decision-making robot that can adapt based on what’s happening around it.
But the real excitement comes when reasoning agents work together in multi-agent systems.
The power of multi-agent systems
Imagine assembling a dream team, where each member has a unique skill set but collaborates toward a shared goal. Multi-agent systems enable this kind of teamwork, relying on networks of agents that communicate, share context, and coordinate actions. These systems excel at solving complex challenges too big for any single agent—or person—to handle.
Coordinating multiple agents presents challenges familiar to anyone who has ever worked on a group project. There’s miscommunication, overlapping responsibilities, and difficulty aligning toward a common objective. Now, scale that to dozens—or hundreds—of autonomous agents, each acting independently but needing to stay in sync.
This article explores how event-driven design—a proven approach in microservices—can address the chaos, creating scalable, efficient multi-agent systems. If you’re leading teams toward the future of AI, understanding these patterns is critical.
Managing multi-agent systems introduces unique difficulties:
Context and data sharing: Agents must exchange information accurately and efficiently, avoiding duplication, loss, or misinterpretation.
Scalability and fault tolerance: As the number of agents grows, the system must handle complex interactions while recovering gracefully from failures.
Integration complexity: Agents often work with diverse systems and tools, requiring seamless interoperability.
Timely and accurate decisions: Agents need to make real-time decisions based on fresh, up-to-date data to ensure responsiveness and avoid poor outcomes.
Safety and validation: Guardrails are essential to prevent unintended actions, and stochastic outputs demand rigorous quality assurance.
Tomi Engdahl says:
This solar-powered wildlife monitor uses a Raspberry Pi AI HAT+ to track birds, and it may soon be used in real-world conservation efforts.
https://www.hackster.io/news/it-s-for-the-birds-5625a8bb9ea9
Tomi Engdahl says:
DeepSeek-R1 is now live and open source, rivaling OpenAI’s Model o1. Available on web, app, and API. Click for details.
https://www.deepseek.com/
Tomi Engdahl says:
https://aistudio.google.com/prompts/new_chat
Tomi Engdahl says:
Tom Warren / The Verge:
Microsoft adds DeepSeek’s R1 to Azure AI Foundry and GitHub, and plans to make a distilled, smaller version of R1 available to run locally on Copilot+ PCs soon — Microsoft has moved surprisingly quickly to bring R1 to its Azure customers. — Microsoft has moved surprisingly quickly to bring R1 to its Azure customers.
Microsoft makes DeepSeek’s R1 model available on Azure AI and GitHub
Microsoft has moved surprisingly quickly to bring R1 to its Azure customers.
https://www.theverge.com/news/602162/microsoft-deepseek-r1-model-azure-ai-foundry-github
Matthew Connatser / Tom’s Hardware:
Huawei adds DeepSeek’s R1 to its ModelArts Studio platform, saying the free model is “Ascend-adapted”, referencing its data center GPUs, but offers few details
https://www.tomshardware.com/tech-industry/artificial-intelligence/huawei-adds-deepseek-inference-support-for-its-ascend-ai-gpus
Tomi Engdahl says:
Dario Amodei:
DeepSeek makes US export controls to China even more important, and DeepSeek-V3 is not a unique breakthrough but an expected point on a cost reduction curve — A few weeks ago I made the case for stronger US export controls on chips to China. Since then DeepSeek, a Chinese AI company …
On DeepSeek and Export Controls
https://darioamodei.com/on-deepseek-and-export-controls
New York Times:
Meta executives say DeepSeek’s breakthrough shows that upstarts now have a chance to innovate and compete with AI giants, vindicating its open-source strategy
Meta Engineers See Vindication in DeepSeek’s Apparent Breakthrough
https://www.nytimes.com/2025/01/29/technology/meta-deepseek-ai-open-source.html?unlocked_article_code=1.s04.oaPa.T54dwKwdUhQ3&smid=url-share
The Silicon Valley giant was criticized for giving away its core A.I. technology two years ago for anyone to use. Now that bet is having an impact.
When a small Chinese company called DeepSeek revealed that it had created an A.I. system that could match leading A.I. products made in the United States, the news was greeted in many circles as a warning that China was closing the gap in the global race to build artificial intelligence.
DeepSeek also said it built its new A.I. technology more cost effectively and with fewer hard-to-get computers chips than its American competitors, shocking an industry that had come to believe that bigger and better A.I. would cost billions and billions of dollars.
But A.I. experts inside the tech giant Meta saw DeepSeek’s breakthrough as something more than the arrival of a nimble, new competitor from the other side of the world: It was vindication that an unconventional decision Meta made nearly two years ago was the right call.
In 2023, Meta, in a widely criticized move, gave away its cutting-edge A.I. technology after spending millions to build it. DeepSeek used parts of that technology as well as other A.I. tools freely available on the internet through a software development method called open source.
Meta executives believe DeepSeek’s breakthrough shows that upstarts now have a chance to innovate and compete with the tech giants that have mostly had the A.I. playing field to themselves because A.I. costs so much to build. It was something Meta executives hoped would happen when they gave away their own technology.
“Our open source strategy was validated,” said Ragavan Srinivasan, a Meta vice president, in an interview on Tuesday. “The more people who have access to the technology needed to move things forward faster, the better.”
Meta is also taking a close look at the work done at DeepSeek. Following Meta’s lead, the Chinese company released its technology to the open source tech community as well. Meta has created several “war rooms” where employees are reverse engineering DeepSeek’s technology, according to two people familiar with the effort who spoke on the condition of anonymity.
Before Meta, which owns Facebook, Instagram and WhatsApp, gave away its A.I. tech, the company had been focused on projects like virtual reality. It was caught flat-footed when OpenAI introduced the chatbot ChatGPT in late 2022. Other tech giants like Microsoft, OpenAI’s close partner, and Google were also well ahead in their A.I. efforts.
(The New York Times has sued OpenAI and its partner, Microsoft, claiming copyright infringement of news content related to A.I. systems. The two tech companies have denied the suit’s claims.)
By freely sharing the code that drove its A.I. technology, called Llama, Meta hoped to accelerate the development of its technology and attract others to build on top of it. Meta engineers believed that A.I. experts working collaboratively could make more progress than teams of experts siloed inside companies, as they were at OpenAI and the other tech giants.
Meta could afford to do this. It made money by selling online ads, not A.I. software. By accelerating the development of the A.I. it offered to consumers for free, it could bring more attention to online services like Facebook and Instagram — and sell more ads.
“They were the only major U.S. company to take this approach. And it was easier for them to do this — more defensible,”
Many in Silicon Valley said Meta’s move set a dangerous precedent because the chatbots could help spread disinformation, hate speech and other toxic content. But Meta said that any risks were far outweighed by the benefits of open source. And most A.I. development, they added, had been shared around through open source until ChatGPT made companies leery of showing what they were working on.
Now, if DeepSeek’s work can be replicated — particularly its claim that it was able to build its A.I. more affordably than most had thought possible — that could provide more opportunities for more companies to expand on what Meta did.
“These dynamics are invisible to the U.S. consumer,” said Mr. Nicholson. “But they are hugely important.”
Yann LeCun, an early A.I. pioneer who is Meta’s chief A.I. scientist, said in a post on LinkedIn that people who think the takeaway from DeepSeek’s work should be that China is beating the United States at A.I. development are misreading the situation. “The correct reading is: ‘Open source models are surpassing proprietary ones,’” he said.
Dr. LeCun added that “because their work is published and open source, everyone can profit from it. That is the power of open research.”
By last summer, many Chinese companies had followed Meta’s lead, regularly open sourcing their own work. Those companies included DeepSeek, which was created by a quantitative trading firm called High-Flyer. (On Wednesday, OpenAI claimed that DeepSeek may have improperly harvested its data).
Some Chinese companies offered “fine-tuned” versions of technology open sourced by companies from other countries, like Meta. But others, such as the start-up 01.AI, founded by a well-known investor and technologist named Kai-Fu Lee, used parts of Meta’s code to build more powerful technologies.
Tomi Engdahl says:
DeepSeek’s rapid growth sparks price wars, pushing tech giants like Alibaba to cut fees and accelerate innovation. https://link.ie.social/cylDZH
#AlibabaAI #ChatGPT #DeepSeek #AIInnovation #TechCompetition
Tomi Engdahl says:
OpenAI Says DeepSeek Used Its Work Without Permission to Create an AI That’s Stealing Its Job, Which Is Blatantly Hypocritical Since That’s Exactly What It Did to Human Artists
OpenAI is sobbing that someone else did… exactly what it did.
https://futurism.com/openai-deepseek-permission-ai-stealing?fbclid=IwY2xjawIJOjRleHRuA2FlbQIxMQABHYcbmwinNjNCf6pDRGf8V8Em9uqUhzJKRiEbp0UoyE4rYF5m6lnukf8WFg_aem_0IOkOzjOH4qILU6Wgqqm3Q
After spending years indiscriminately ripping off other people’s work — and getting sued for copyright infringement left and right — OpenAI is sobbing that the buzzy Chinese AI startup DeepSeek did the same thing on its AI that it built on all that pilfered content.
As the Financial Times reports, OpenAI is accusing the app of using its proprietary models to train its own ChatGPT competitor. The company claimed that it had “some evidence” of DeepSeek using the output of OpenAI’s models to train its own, a technique called “distillation,” that it says may have breached its terms of service.
The news comes after DeepSeek flipped the AI industry on its head, wiping out over $1 trillion worth of market capitalization in a single day with an advanced model that’s much less resource-intensive than anything cooked up by Silicon Valley.
“The issue is when you [take it out of the platform and] are doing it to create your own model for your own purposes,” a source close to OpenAI told the FT.
It’s hard not to see the development as the peak of hypocrisy. OpenAI has a long track record of hoovering up data with an astonishing disregard for giving credit or fairly compensating rights holders.
That’s not an exaggeration. OpenAI CEO Sam Altman has previously admitted that it would be impossible to “train today’s leading AI models without using copyrighted materials.”
Put simply, it’s roughly the equivalent of a school bully complaining to the teacher that his stolen lunch was stolen from him by another bully.
“I’m so sorry I can’t stop laughing,” AI critic Ed Zitron wrote in a scathing post. “OpenAI, the company built on stealing literally the entire internet, is crying because DeepSeek may have trained on the outputs from ChatGPT.”
It’s also worth reiterating that despite its name, OpenAI is a closed-source and for-profit company — while DeepSeek’s AI models are open-source.
The Sam Altman-led company refused to officially comment or provide further details, the FT reports.
And in reality, whether DeepSeek actually infringed on its intellectual property remains debatable.
On Tuesday, president Donald Trump’s “crypto czar” told Fox News that distillation is “when one model learns from another model [and] kind of sucks the knowledge out of the parent model.”
“And there’s substantial evidence that what DeepSeek did here is they distilled the knowledge out of OpenAI models, and I don’t think OpenAI is very happy about this,” Sacks explained.
If independently confirmed, what DeepSeek pulled off with its latest model, dubbed R1, could signify a major advancement in the development of cheaper-to-run, but still as powerful, AI models.
The company used just over 2,000 Nvidia graphics cards, purchased shortly before the US banned exports of these cards to China.
Some experts believe that DeepSeek likely didn’t do anything wrong.
“It is a very common practice for start-ups and academics to use outputs from human-aligned commercial LLMs, like ChatGPT, to train another model,” University of California, Berkeley PhD candidate Ritwik Gupta told the FT. “That means you get this human feedback step for free. It is not surprising to me that DeepSeek supposedly would be doing the same.”
In a statement, OpenAI said that it engages in “countermeasures to protect our IP, including a careful process for which frontier capabilities to include in released models” — despite having the word “open” in its name.
The company added that it’s “critically important that we are working closely with the US government to best protect the most capable models from efforts by adversaries and competitors to take US technology.”
Whether DeepSeek actually represents a threat to national security remains to be seen, and the White House has since revealed that it’s currently evaluating the possible risks.
How OpenAI’s accusations will play out remains to be seen, but the optics aren’t great, no matter which way you spin it.
“There’s no way OpenAI can make this argument without looking very, very silly,”
“Are you crying because your plagiarism machine that made stuff by copying everybody’s stuff was used to train another machine that made stuff by copying stuff?” he said. “Are you going to cry? Cowards, losers, pathetic.”
Tomi Engdahl says:
Former Intel CEO Says After Seeing DeepSeek, He’s Done With OpenAI
Cope and Seethe for the Lord is Good
https://futurism.com/former-intel-ceo-deekseek-openai
News of Chinese AI company DeepSeek’s success continues to send ripples throughout the West, tanking Nvidia’s stock valuation, spawning endless discourse, and embarrassing President Trump.
Now, even American startup gurus are starting to jump ship. Pat Gelsinger, the former CEO of tech superpower Intel, says that after seeing DeepSeek’s tech, his new church startup — called, no joke, Gloo — is foregoing OpenAI’s tech in favor of an in-house model.
“My Gloo engineers are running [DeepSeek's] R1 today,” Gelsinger told TechCrunch. “They could’ve run [OpenAI's] o1 — well, they can only access o1, through the APIs.”
Gelsinger — who left Intel under a dark cloud last year as the chipmaker’s financial woes deepened — enthused that DeepSeek will lower the cost to develop similar models, decreasing the barrier to entry in the industry.
And the godly CEO isn’t alone. Tech journalists have been quick to note DeepSeek’s faster and cheaper performance compared to OpenAI, Anthropic, and Google. Even Trump admitted to eating a certain amount of crow, calling DeepSeek a “wake-up call” at a House GOP meeting.
The newly-minted president further sang DeepSeek’s praises, admitting the R1 model could be “very much a positive development” if American companies could leverage the model to cut down on their own astronomical costs.
After several days of brooding in silence, OpenAI CEO Sam Altman delivered a retort: “We will obviously deliver much better models and also it’s legit invigorating to have a new competitor!”
The now right-wing Meta CEO Mark Zuckerberg was likewise in a frenzy, reportedly assembling several “war rooms” worth of engineers to pick apart the Chinese software for the corporation’s own purposes.
Elon Musk, meanwhile, took a break from slamming Altman to complain alongside Scale AI CEO Alexandr Wang that DeepSeek is lying about having only 20,000 H100 graphics cards, an accusation that neither CEO has backed with evidence.
“My understanding is that DeepSeek has about 50,000 H100s, which they can’t talk about, Wang posited on CNBC, “because it is against the export controls that the United States has put in place.”
Startup investor Joshua Kushner took to X-formerly-Twitter to follow Musk’s lead.
“‘Pro America’ technologists openly supporting a Chinese model that was trained off of leading US frontier models,” he whined, “with chips that likely violate export controls, and — according to their own terms of service — take US customer data back to China.” (DeepSeek can run on local hardware rather than the company’s app, a choice most US companies don’t give users, from whom they absolutely do harvest data.)
no amount of mewling online can change the fact that DeepSeek has shaken up the game. The onus is now on the American tech sector to demonstrate why they still deserve the most lavish infrastructure investment in the history of mankind.
Tomi Engdahl says:
OpenAI Developer Seethes at Success of DeepSeek
Totally not mad.
https://futurism.com/openai-developer-seethes-deepseek
DeepSeek’s new chain-of-thought AI model has Silicon Valley developers seething that a startup from — gasp — China could build something just as good, if not better, than what they’ve come up with, for a fraction of the cost and with far superior energy efficiency.
“Americans sure love giving their data away to the CCP in exchange for free stuff,” Heidel wrote on X, referring to the Communist Party of China.
Following overwhelming backlash, his tweet was appended with a community note: “DeepSeek can be run locally without an internet connection, unlike OpenAI’s models.”
This is true. DeepSeek’s r1 model is open-source, totally free, and if you’re concerned about your privacy, you can download and run all 404 gigs of it on your own rig. Because it’s a chain-of-thought model, anyone can see how the AI “thinks,” which goes a long way as far as trust.
(After the community note dunk, Heidel followed up with a post urging users to only use the DeepSeek model locally.)
Needless to say, to smear the AI model, whose underlying code is free for anyone to poke around in, as some sort of Chinese spyware is really rich coming from someone who works at OpenAI, a company that quickly ditched its noble, non-profit and open-source beginnings as soon as it got a taste of money. Today, it’s firmly for-profit and closed-source.
It’d be remiss to brush aside privacy concerns surrounding Chinese platforms, and indeed the censorship present in the app version of DeepSeek. But OpenAI’s data ethics track record isn’t exactly squeaky clean, either. It trained its AI model by devouring everyone’s data on the surface web without ever stopping to ask permission. It and its CEO Sam Altman have also invested in a number of companies whose commitment to privacy is questionable.
Plus, pretty much every outfit in Silicon Valley pawns off their customer’s data to data brokers, who in turn sell that information to thousands of other companies so they can barrage you with ads — and most perniciously, to government agencies for surveillance purposes.
To that end, it might be worth mentioning that OpenAI appointed a former National Security Administration director to its board — a move that Edward Snowden blasted as a “calculated betrayal of the rights of every person on earth.”
Of course, Heidel isn’t alone. Just days before his faux-pas, Neal Khosla, CEO of the AI-powered health clinic Curai, called DeepSeek a “CCP state psyop” and an act of “economic warfare to make American AI unprofitable.” (Counterpoint: American AI is why American AI is unprofitable.)
In reality, the US has been waging plenty of economic warfare on that front
Ironically, that pressure may have pushed Chinese developers to make its models more efficient with less hardware, while American competitors gluttonously relied on scaling up their datacenters comprising literal billions of dollars worth of GPUs to make gains.
That the immediate response of Silicon Valley to DeepSeek’s achievements is to link it with CCP conspiracies is a sign of deep-seated insecurity, and — let’s face it — racism. The same anti-Chinese rhetoric, similarly under the guise of protecting Americans’ privacy, fueled the push for the (now-suspended) ban on TikTok.
“I think if any of these AI bros were remotely serious about using this technology to improve society they’d be excited at the idea of someone managing to run laps around them for 1/10th the computing power but instead they are seething, sinophobically,” wrote a Bluesky user.
Tomi Engdahl says:
Bloomberg:
Sources: US officials are investigating whether DeepSeek bought advanced Nvidia chips from third parties in Singapore, circumventing US restrictions on AI chips
https://www.bloomberg.com/news/articles/2025-01-31/us-probing-whether-deepseek-got-nvidia-chips-through-singapore
US Probing If DeepSeek Got Nvidia Chips From Firms in Singapore
Nvidia says it ‘insists’ that customers comply with laws
Singapore accounts for about 20% of Nvidia’s revenue
Tomi Engdahl says:
Wall Street Journal:
Sources: OpenAI is in early talks to raise up to $40B at a $340B valuation; the startup was last valued at $157B in October, when it raised $6.6B — SoftBank would lead $40 billion round for the ChatGPT maker, some of which would go to Stargate AI infrastructure venture.
OpenAI in Talks for Huge Investment Round Valuing It Up to $300 Billion
SoftBank would lead $40 billion round for ChatGPT maker, some of which would go to Stargate AI infrastructure venture
https://www.wsj.com/tech/ai/openaiin-talks-for-huge-investment-round-valuing-it-up-to-300-billion-2a2d4327
Wall Street Journal:NEW
SoftBank, which sources say is in talks to invest $15B-$25B in OpenAI and $18B in Stargate, could borrow against its $140B+ Arm stake to fund the investments — Japanese conglomerate is in talks to spend up to $43 billion to boost the ChatGPT developer — In his newly built palace near Tokyo …
OpenAI’s Sam Altman and SoftBank’s Masayoshi Son Are AI’s New Power Couple
Japanese conglomerate is in talks to spend up to $43 billion to boost the ChatGPT developer
https://www.wsj.com/tech/ai/openais-sam-altman-and-softbanks-masayoshi-son-are-ais-new-power-couple-fa82e8cf?st=qfw5rn&reflink=desktopwebshare_permalink
Tomi Engdahl says:
Jordan Novet / CNBC:
Intel reports Q4 revenue down 7% YoY to $14.26B, vs. $13.81B est., Data Center and AI down 3% YoY to $3.39B, and forecasts Q1 revenue below estimates — Intel issued disappointing quarterly guidance on Thursday, but reported earnings and revenue that topped estimates. Shares were up 3% after hours.
Tech
Intel issues weak forecast, but beats on fourth-quarter results
https://www.cnbc.com/2025/01/30/intel-intc-q4-earnings-report-2024.html
Tomi Engdahl says:
Tom Warren / The Verge:
Sources: Nadella and Microsoft execs moved quickly to get engineers to test and deploy DeepSeek R1 on Azure and GitHub in 10 days, an unusually fast turnaround — Microsoft was ready for the next step in AI models. — Microsoft was ready for the next step in AI models.
Inside Microsoft’s quick embrace of DeepSeek
Microsoft was ready for the next step in AI models.
https://www.theverge.com/notepad-microsoft-newsletter/603170/microsoft-deepseek-ai-azure-notepad
Tomi Engdahl says:
Tom Warren / The Verge:
Microsoft makes OpenAI’s o1 model, branded as “Think Deeper”, free for all Copilot users, after launching o1 in October 2024 as a paid Copilot Pro feature — Think Deeper first launched in October as a paid Copilot Pro feature, but it’s now free.
Microsoft makes OpenAI’s o1 reasoning model free for all Copilot users
https://www.theverge.com/news/603149/microsoft-openai-o1-model-copilot-think-deeper-free
Think Deeper first launched in October as a paid Copilot Pro feature, but it’s now free.
Tomi Engdahl says:
Tom Warren / The Verge:
Microsoft unveils Intel-powered Surface Pro 11 and Surface Laptop 7 for businesses, both Copilot+ PCs, starting at $1,500, or $500 more than Qualcomm variants
Microsoft announces Intel-powered Surface Pro 11 and Surface Laptop 7
https://www.theverge.com/news/601205/microsoft-surface-pro-11-surface-laptop-7-intel-lunar-lake-release-date-pricing
Along with a new USB 4 dock, the Surface devices are designed for businesses.
Tomi Engdahl says:
CRN:
Intel says it no longer plans to sell its next-gen Falcon Shores AI accelerator chip, which was set to launch in late 2025, to focus on its Jaguar Shores chip — Interim Intel co-CEO Michelle Johnston Holthaus discloses the pivot in its AI data center strategy as Nvidia forges ahead …
Intel Cancels Falcon Shores AI Chip To Focus On ‘Rack-Scale Solution’
By Dylan Martin
January 30, 2025, 6:35 PM EST
https://www.crn.com/news/components-peripherals/2025/intel-cancels-falcon-shores-ai-chip-to-focus-on-system-level-solution
Interim Intel co-CEO Michelle Johnston Holthaus discloses the pivot in its AI data center strategy as Nvidia forges ahead with rack-scale solutions based on the rival’s Blackwell GPU architecture.
Tomi Engdahl says:
Abner Li / 9to5Google:
Google Search adds Ask for me, which uses AI to call businesses to inquire about availability and pricing, starting with nail salons and auto shops in the US — Following Daily Listen in early January, Google is launching a new Search Lab called “Ask for me.”
Google Search’s new ‘Ask for me’ calls local businesses for availability, pricing
https://9to5google.com/2025/01/30/google-ask-for-me-lab/
Following Daily Listen in early January, Google is launching a new Search Lab called “Ask for me.”
Google will use “AI to call businesses on your behalf” when you’re looking for the availability and prices of local services. At launch, this is specifically for services at nail salons and auto repair shops.
When you’re searching for something like “oil change near me,” an “Ask for me” card will appear. It lets you “enter details about the service you’re looking for.”
In the case of mechanics, Google starts by asking what you’re looking for: Factory scheduled maintenance, Oil change, Tire rotation & balancing, Tire replacement, Fuel filter replacement, Cabin filter replacement, Engine filter replacement, etc.
You then enter details about your car (year, make, model, and mileage), as well as when you need the service: Soonest availability, Weekdays only, or Weekends only.
Tomi Engdahl says:
6 AI trends you’ll see more of in 2025
https://news.microsoft.com/source/features/ai/6-ai-trends-youll-see-more-of-in-2025/?ocid=pd_spnsrd-post_techmeme_jan-25_6-ai-trends
In 2025, models will do more — and do it even better.
Tomi Engdahl says:
Mistral AI:
Mistral launches Small 3, a latency-optimized 24B-parameter model that it says is competitive with larger models such as Llama 3.3 70B or Qwen 32B
Mistral Small 3
Apache 2.0, 81% MMLU, 150 tokens/s
https://mistral.ai/news/mistral-small-3/
Today we’re introducing Mistral Small 3, a latency-optimized 24B-parameter model released under the Apache 2.0 license.
Mistral Small 3 is competitive with larger models such as Llama 3.3 70B or Qwen 32B, and is an excellent open replacement for opaque proprietary models like GPT4o-mini. Mistral Small 3 is on par with Llama 3.3 70B instruct, while being more than 3x faster on the same hardware.
Mistral Small 3 is a pre-trained and instructed model catered to the ‘80%’ of generative AI tasks—those that require robust language and instruction following performance, with very low latency.
We designed this new model to saturate performance at a size suitable for local deployment. Particularly, Mistral Small 3 has far fewer layers than competing models, substantially reducing the time per forward pass. At over 81% accuracy on MMLU and 150 tokens/s latency, Mistral Small is currently the most efficient model of its category.
Tomi Engdahl says:
DeepSeekin laaja tiedonkeruu voi olla iso riski
https://etn.fi/index.php/opinion/17095-deepseekin-laaja-tiedonkeruu-voi-olla-iso-riski
DeepSeekin uuden R1-mallin julkistus on herättänyt laajaa huomiota tekoälyalan innovaatioiden ja saavutettavuuden kannalta. Sen edistynyt päättelykyky ja täysin ilmainen, rajoittamaton käyttö ovat kiihdyttäneet palvelun suosiota ennennäkemättömällä vauhdilla. DeepSeekin mobiilisovellus nousi iOS App Storen latauslistan kärkeen vain 48 tunnissa, mikä kertoo valtavasta käyttäjäkysynnästä.
Samalla DeepSeekin kuluttajasovellus tuo mukanaan merkittäviä tietoturva- ja yksityisyyshaasteita yrityksille ja organisaatioille, arvioi tietoturvayritys Check Point blogissaan. DeepSeekin tietosuojakäytännön mukaan kaikki käyttäjän vuorovaikutus tekoälyn kanssa – kehotteet, ladatut tiedostot, keskusteluhistoriat, äänikomennot, kuvat ja jopa näppäinpainalluskuviot – tallennetaan ulkoisille palvelimille. Lisäksi DeepSeek pidättää oikeuden tarkastella kaikkea käyttäjien syöttämää sisältöä.
Tomi Engdahl says:
Nyt alkoi rytinä – Kiinan ihmetekoäly käynnisti todellisen lumivyöryn
Deepseekin kilpailijan erikoinen julkaisuhetki kuvastaa alan kiivasta kamppailua.
Nyt alkoi rytinä – Kiinan ihmetekoäly käynnisti todellisen lumivyöryn
https://www.is.fi/digitoday/art-2000010999674.html
Lue tiivistelmä
Alibaba julkaisi uuden Qwen 2.5-Max -tekoälymallin kiinalaisen uudenvuoden ensimmäisenä päivänä.
ByteDance päivitti oman tekoälynsä heti Deepseekin perässä.
Deepseekin tietoturvaongelmat paljastuivat Wiz-tietoturvayhtiön tutkimuksissa.
Deepseek nousi Applen App Storen ladatuimmaksi sovellukseksi Yhdysvalloissa ohi ChatGPT:n.
Tomi Engdahl says:
Tietoturvayhtiö Check Point puolestaan huomauttaa, että kiinalaisalustan tiedonkeruu ulottuu huomattavasti pelkkää käyttötietojen seurantaa pidemmälle. Deepseekin tietosuojakäytännön mukaan kaikki käyttäjän vuorovaikutus tekoälyn kanssa – mukaan lukien kehotteet, ladatut tiedostot, keskusteluhistoriat, äänikomennot, kuvat ja jopa näppäinpainalluskuviot – lähetetään ulkoisille palvelimille ja tallennetaan sinne.
Nyt alkoi rytinä – Kiinan ihmetekoäly käynnisti todellisen lumivyöryn
https://www.is.fi/digitoday/art-2000010999674.html
Tomi Engdahl says:
Microsoft makes ChatGPT’s most-advanced $200 a month AI free
Microsoft has provided unlimited free access to OpenAI’s o1 model, which was released in December and can be as much as $200/month.
Read more: https://www.tweaktown.com/news/102908/microsoft-makes-chatgpts-most-advanced-200-month-ai-free/index.html?fbclid=IwY2xjawIJfUNleHRuA2FlbQIxMQABHS2rUjU8z2kW4OmT1qtJb-xwtRykfTFFOlK4vmoAzOZJmxrRRO05ycuelQ_aem_f_klIhoPE9Ra5rahau321w
Tomi Engdahl says:
The o1 AI model is designed to be more thoughtful of the question a user is asking and implements a step-by-step chain-of-thought process, which makes it excel in specific tasks and categories of questioning. GPT-4 is a more general-purpose model for text generation, answering simple questions, or holding conversations.
Read more: https://www.tweaktown.com/news/102908/microsoft-makes-chatgpts-most-advanced-200-month-ai-free/index.html
Tomi Engdahl says:
Zuckerberg Convening Huge “War Rooms” to Figure Out How a Chinese Startup Is Annihilating Meta’s AI
Meta is scrambling.
https://futurism.com/zuckerberg-war-rooms-meta-ai-deepseek?fbclid=IwY2xjawIJflFleHRuA2FlbQIxMQABHT8bqTbPH9T7HDozbrGI1DjDi6q8pzC6gVTljaYlt4XJxl15KqGJ0q9o-A_aem_gihFlfOB-HJ_AqwV_G3iIg
Meta CEO Mark Zuckerberg is scrambling after a small Chinese startup called DeepSeek turned Silicon Valley upside down with its chatbot app.
The company’s latest R1 AI model can run circles around the competition from Meta, OpenAI, and Anthropic — and at a tiny fraction of the cost.
Despite some skepticism over DeepSeek’s claims, the debut has led to some serious soul-searching among AI investors, with the ChatGPT competitor wiping out an estimated $1 trillion in value among other AI companies on Monday. In the most extreme example, AI chipmaker Nvidia set a new record for the biggest single-day loss of any company in history.
Tomi Engdahl says:
OpenAI says it plans to let U.S. National Laboratories, the Department of Energy’s network of R&D labs, use its AI models for nuclear weapons security and other scientific projects.
Per CNBC, OpenAI will work with Microsoft, its lead investor, to deploy a model on the supercomputer at Los Alamos National Laboratory. The model will be a shared resource for scientists from Los Alamos, Lawrence Livermore, and Sandia National Labs, OpenAI says.
Read more from Kyle Wiggers on OpenAI’s deal with the U.S. government here: https://tcrn.ch/40SgaRz
#TechCrunch #technews #artificialintelligence #OpenAI #SamAltman
Tomi Engdahl says:
Chinese AI startup DeepSeek’s chatbot achieved only 17% accuracy in delivering news and information in a NewsGuard audit that ranked it tenth out of eleven in a comparison with its Western competitors including OpenAI’s ChatGPT and Google Gemini.
Jan 29 (Reuters) – Chinese AI startup DeepSeek’s chatbot achieved only 17% accuracy in delivering news and information in a NewsGuard audit that ranked it tenth out of eleven in a comparison with its Western competitors including OpenAI’s ChatGPT and Google Gemini.
The chatbot repeated false claims 30% of the time and gave vague or not useful answers 53% of the time in response to news-related prompts, resulting in an 83% fail rate, according to a report published by trustworthiness rating service NewsGuard on Wednesday.
DeepSeek’s chatbot achieves 17% accuracy, trails Western rivals in NewsGuard audit
https://www.reuters.com/world/china/deepseeks-chatbot-achieves-17-accuracy-trails-western-rivals-newsguard-audit-2025-01-29/?link_source=ta_first_comment&taid=679b005326a3ce0001a4aa24&utm_campaign=trueAnthem:+Trending+Content&utm_medium=trueAnthem&utm_source=facebook&fbclid=IwY2xjawIJglRleHRuA2FlbQIxMQABHX6TZrIi3aAiGX735Z1XqzyWHOfLennBlUQ0FcTVURPN1q0ie7TmUgpLqQ_aem_RfmiuZ3rOw-0tx7Y74Sdlg
Tomi Engdahl says:
Gemini 2.0 Flash is finally ready for prime time
Google removes the ‘experimental’ label from its latest AI model.
https://www.androidauthority.com/gemini-2-0-flash-release-3521928/?fbclid=IwZXh0bgNhZW0CMTEAAR0MWCFpVWpTT5GAmjSOuN4U6nfJsiMLeiXJTglEyNcOhzRr2IlOoonZU6U_aem_2ZkE8hFgN-NBNYysPb54kg