deep learning - CybAI news

22 Oct

Anthropic Wants Its AI Agent to Control Your Computer

stp2y0 CommentsAIai, artificial intelligence, deep learning, machine learning

Demos of AI agents can seem stunning but getting the technology to perform reliably and without annoying, or costly, errors in real life can be a challenge. Current models can answer questions and converse with almost human-like skill and are the backbone of chatbots such as OpenAI’s ChatGPT and Google’s Gemini. They can also perform tasks on computers when given a simple command by accessing the computer screen as well as input devices like a keyboard and trackpad or through low-level software interfaces.Anthropic says that Claude outperforms other AI agents on several key benchmarks including SWE-bench, which measures an agent's…

23 Jul

Meta’s New Llama 3.1 AI Model Is Free, Powerful, and Risky

stp2y0 CommentsGenAIalgorithms, artificial intelligence, deep learning, machine learning, meta

Most tech moguls hope to sell artificial intelligence to the masses. But Mark Zuckerberg is giving away what Meta considers to be one of the world’s best AI models for free.Meta released the biggest, most capable version of a large language model called Llama on Monday, free of charge. Meta has not disclosed the cost of developing Llama 3.1 but Zuckerberg recently told investors that his company is spending billions on AI development.Through this latest release, Meta is showing that the closed approach favored by most AI companies is not the only way to develop AI. But the company is…

06 Jun

OpenAI Offers a Peek Inside the Guts of ChatGPT

stp2y0 CommentsGenAIai, algorithms, artificial intelligence, chatgpt, deep learning, machine learning, openai

ChatGPT developer OpenAI’s approach to building artificial intelligence came under fire this week from former employees who accuse the company of taking unnecessary risks with technology that could become harmful.Today OpenAI released a new research paper apparently aimed at showing it is serious about tackling AI risk by making its models more explainable. In the paper, researchers from the company lay out a way to peer inside the AI model that powers ChatGPT. They devised a way to identify how it stores certain concepts—including those that might perhaps cause an AI system to misbehave.Although the research makes OpenAI’s work on…

06 Jun

Chatbot Teamwork Makes the AI Dream Work

stp2y0 CommentsAIalgorithms, artificial intelligence, chatgpt, deep learning, fast forward, google gemini, machine learning, openai

Turning to a friend or coworker can make tricky problems easier to tackle. Now it looks like having AI chatbots team up with each other can make them more effective.I’ve been playing this week with AutoGen, an open source software framework for AI agent collaboration developed by researchers at Microsoft and academics at Pennsylvania State University, the University of Washington, and Xidian University in China. The software taps OpenAI’s large language model GPT-4 to let you create multiple AI agents with different personas, roles, and objectives that can be prompted to solve specific problems.To put the idea of AI collaboration…

31 May

Google’s AI Overviews Will Always Be Broken. That’s How AI Works

stp2y0 CommentsGenAIalgorithms, artificial intelligence, deep learning, google, machine learning, search, search engines

A week after its algorithms advised people to eat rocks and put glue on pizza, Google admitted Thursday that it needed to make adjustments to its bold new generative AI search feature. The episode highlights the risks of Google’s aggressive drive to commercialize generative AI—and also the treacherous and fundamental limitations of that technology.Google’s AI Overviews feature draws on Gemini, a large language model like the one behind OpenAI’s ChatGPT, to generate written answers to some search queries by summarizing information found online. The current AI boom is built around LLMs’ impressive fluency with text, but the software can also…

24 May

Ines Montani at QCon London: Economies of Scale Can’t Monopolise the AI Revolution

stp2y0 CommentsNewsai, ai revolution monopol, Architecture & Design, artificial intelligence, Automated Machine Learning, deep learning, development, generative ai, large language models, machine learning, ML & Data Engineering, QCon London 2024

During her presentation at QCon London, Ines Montani, co-founder and CEO of explosion.ai (the maker of spaCy), stated that economies of scale are not enough to create monopolies in the AI space and that open-source techniques and models will allow everybody to keep up with the "Gen AI revolution". Montani opened her presentation by asking for a show of hands to identify the open-source users in the audience. The vast majority of the audience raised their hand, easily demonstrating that open-source is ubiquitous ("it would be easier to ask who doesn’t use open-source’"). She pointed out the multiple benefits of the…

23 May

Pocket-Sized AI Models Could Unlock a New Era of Computing

stp2y0 CommentsGenAIapple, artificial intelligence, deep learning, fast forward, google, machine learning, microsoft

When ChatGPT was released in November 2023, it could only be accessed through the cloud because the model behind it was downright enormous.Today I am running a similarly capable AI program on a Macbook Air, and it isn’t even warm. The shrinkage shows how rapidly researchers are refining AI models to make them leaner and more efficient. It also shows how going to ever larger scales isn’t the only way to make machines significantly smarter.The model now infusing my laptop with ChatGPT-like wit and wisdom is called Phi-3-mini. It’s part of a family of smaller AI models recently released by…

23 May

AI Is a Black Box. Anthropic Figured Out a Way to Look Inside

stp2y0 CommentsAIalgorithms, artificial intelligence, deep learning, neural networks, research

Last year, the team began experimenting with a tiny model that uses only a single layer of neurons. (Sophisticated LLMs have dozens of layers.) The hope was that in the simplest possible setting they could discover patterns that designate features. They ran countless experiments with no success. “We tried a whole bunch of stuff, and nothing was working. It looked like a bunch of random garbage,” says Tom Henighan, a member of Anthropic’s technical staff. Then a run dubbed “Johnny”—each experiment was assigned a random name—began associating neural patterns with concepts that appeared in its outputs.“Chris looked at it, and…