Salesforce has agreed to acquire data management firm Informatica in a deal valued at around $8 billion. This includes equity value, minus Salesforce’s existing investment in the company. Informatica shareholders will receive $25 in cash per share. The move aims to help Salesforce build a stronger foundation for AI tools that can act on their The post Salesforce to buy Informatica in $8B deal appeared first on AI News.

Huawei’s AI capabilities have made a breakthrough in the form of the company’s Supernode 384 architecture, marking an important moment in the global processor wars amid US-China tech tensions. The Chinese tech giant’s latest innovation emerged from last Friday’s Kunpeng Ascend Developer Conference in Shenzhen, where company executives demonstrated how the computing framework challenges Nvidia’s The post Huawei Supernode 384 disrupts Nvidia’s AI market hold appeared first on AI News.

Telegram has forged a deal with Elon Musk’s xAI to weave Grok AI into the fabric of the encrypted messaging platform. This isn’t just a friendly collaboration; xAI is putting serious money on the table – a cool $300 million, a mix of hard cash and equity. And for Telegram, they’ll pocket 50% of any The post Telegram and xAI forge Grok AI deal appeared first on AI News.

If you’re working in SEO or digital marketing, you’ve probably noticed how Google search results look different. That instant answer that pops up at the top of the page is AI Overview, and it’s changing the game. Instead of having to click through to a bunch of different websites, users can now get direct answers The post The impact of Google AI Overview on SEO appeared first on AI News.

The UK is deploying AI to keep a watchful eye on Arctic security threats from hostile states amid growing geopolitical tensions. This will be underscored by Foreign Secretary David Lammy during his visit to the region, which kicks off today. The deployment is seen as a signal of the UK’s commitment to leveraging technology to The post UK deploys AI to boost Arctic security amid growing threats appeared first on AI News.

As companies rely more on automated systems, ethics has become a key concern. Algorithms increasingly shape decisions that were previously made by people, and these systems have an impact on jobs, credit, healthcare, and legal outcomes. That power demands responsibility. Without clear rules and ethical standards, automation can reinforce unfairness and cause harm. Ignoring ethics The post Ethics in automation: Addressing bias and compliance in AI appeared first on AI News.

Oracle is planning to spend around $40 billion on Nvidia chips to support a massive new data centre being developed by OpenAI in Texas, according to reporting by the Financial Times. The move marks one of the largest chip purchases to date and signals the growing demand for AI computing power. The site is located The post Oracle plans $40B Nvidia chip deal for AI facility in Texas appeared first on AI News.

Nvidia is preparing to go head-to-head with Huawei to maintain its relevance in the booming AI chip market of China. The upcoming AI chip to be created for China represents something of a strategic gamble by Nvidia – can the company’s third attempt at regulatory compliance preserve its foothold against surging domestic competition? Despite mounting The post Will the budget China AI chip from Nvidia survive Huawei’s growth? appeared first on AI News.

Anthropic has unveiled its latest Claude 4 model family, and it’s looking like a leap for anyone building next-gen AI assistants or coding. The stars of the show are Claude Opus 4, the new powerhouse, and Claude Sonnet 4, designed to be a smart all-rounder. Anthropic isn’t shy about its ambitions, stating these models are The post Anthropic Claude 4: A new era for intelligent agents and AI coding appeared first on AI News.

After what felt like an age of tech industry tea-leaf reading, OpenAI has officially snapped up “io,” the much-buzzed-about startup building an AI device from former Apple design guru Jony Ive and OpenAI’s chief, Sam Altman. The price tag? $6.5 billion. OpenAI put out a video this week talking about the Ive and Altman venture The post Details leak of Jony Ive’s ambitious OpenAI device appeared first on AI News.

The Middle East is pulling in more attention from global tech investors than ever. Saudi Arabia, the UAE, and Qatar are rolling out billions of dollars in deals, working with top US companies, and building the kind of infrastructure needed to run large-scale AI systems. It’s not just about the money. There are new laws, The post Why the Middle East is a hot place for global tech investments appeared first on AI News.

The Linux Foundation and Meta are putting some numbers behind how open-source AI (OSAI) is driving innovation and adoption. The adoption of AI tools is pretty much everywhere now, with 94% of organisations surveyed already using them. And get this: within that crowd, 89% are tapping into open-source AI for some part of their tech The post Linux Foundation: Slash costs, boost growth with open-source AI appeared first on AI News.

Why build LLMs from scratch? It's probably the best and most efficient way to learn how LLMs really work. Plus, many readers have told me they had a lot of fun doing it.

There is no capability threshold that will lead to sudden impacts

Understanding GRPO and New Insights from Reasoning Model Papers

A new paper that we will expand into our next book

Welcome to the next stage of large language models (LLMs): reasoning. LLMs have transformed how we process and generate text, but their success has been largely driven by statistical pattern recognition. However, new advances in reasoning methodologies now enable LLMs to tackle more complex tasks, such as solving logical puzzles or multi-step arithmetic. Understanding these methodologies is the central focus of this book.

Inference-Time Compute Scaling Methods to Improve Reasoning Models

Methods and Strategies for Building and Refining Reasoning Models

Six influential AI papers from July to December

Six influential AI papers from January to June

Making sense of recent technology trends and claims

Technology Isn’t the Problem—or the Solution.

A curated list of interesting LLM-related research papers from 2024, shared for those looking for something to read over the holidays.

Seemingly minor technical decisions can have life-or-death effects

An introduction to the main techniques and latest models

What's in the book and how we wrote it

Finetuning a GPT Model for Spam Classification

A new benchmark to measure the impact of AI on improving science

The book will be published on September 24

If your weekend plans include catching up on AI developments and understanding Large Language Models (LLMs), I've prepared a 1-hour presentation on the development cycle of LLMs, covering everything from architectural implementation to the finetuning stages.

Turning models into products runs into five challenges

A Look at How Moderns LLMs Are Trained

How speculation gets laundered through pseudo-quantification

The Latest Research in Instruction Finetuning

Rethinking AI agent benchmarking and evaluation

Scaling will run out. The question is when.

A Deep Dive into the Lifecycle of LLM Development

How AI hype leads to flawed research that fuels more hype

Discussing the Latest Model Releases and AI Research in May 2024

Discussing the Latest Model Releases and AI Research in April 2024

What spending $2,000 can tell us about evaluating AI agents

What are the different ways to use and finetune pretrained large language models (LLMs)? The most common ways to use and finetune pretrained LLMs include a feature-based approach, in-context prompting, and updating a subset of the model parameters.

What artificial intelligence can do, what it can't, and how to tell the difference

That’s what makes it worthwhile

Discussing AI Research Papers in March 2024

Trying to make an AI model that can’t be misused is like trying to make a computer that can’t be used for bad things

An argument for legal and technical safe harbors for AI safety and trustworthiness research

Once again, this has been an exciting month in AI research. This month, I'm covering two new openly available LLMs, insights into small finetuned LLMs, and a new parameter-efficient LLM finetuning technique. The two LLMs mentioned above stand out for several reasons. One LLM (OLMo) is completely open source, meaning that everything from the training code to the dataset to the log files is openly shared.

Adding precision to the debate on openness in AI