The latest artificial intelligence models are not only remarkably good at software engineering--new research shows they are getting ever-better at finding bugs in software, too. AI researchers at UC Berkeley tested how well the latest AI models and agents could find vulnerabilities in 188 large open source codebases. Using a new benchmark called CyberGym, the AI models identified 17 new bugs including 15 previously unknown, or "zero-day," ones. "Many of these vulnerabilities are critical," says Dawn Song, a professor at UC Berkeley who led the work. Many experts expect AI models to become formidable cybersecurity weapons.

Earlier this week via blog post, OpenAI released their newest AI models: o3 and o4-mini. These models are the company's "smartest and most capable models to date" and their first reasoning models that can also reason when it comes to images. In short, these AI models can use an image--such as a photograph or a sketch--as part of an analysis. The models can also adjust, zoom in on, and rotate an image during reasoning. For the first time, our reasoning models can agentically use and combine every tool within ChatGPT, including web search, Python, image analysis, file interpretation, and image generation.

Since its debut at the end of last year, Gemini 2.0 has gone on to power a handful of Google products, including a new AI Mode chatbot. Now Google DeepMind is using that same technology for something altogether more interesting. On Wednesday, the AI lab announced two new Gemini-based models it says will "lay the foundation for a new generation of helpful robots." The first, Gemini Robotics, was designed by Deepmind to facilitate direct control of robots. According to the company, AI systems for robots need to excel at three qualities: generality, interactivity and dexterity.

It feels like artificial intelligence is in everything these days -- TVs, laptops, phones, websites, even PDF editors -- and that big boom can be attributed to the wild success of OpenAI's ChatGPT. Microsoft is doing its best to compete with its own Copilot AI chatbot, and both ChatGPT and Copilot are pretty good at providing answers, generating text and images, and holding conversations. Most importantly, they're both free to use. But ChatGPT and Copilot both offer paid plans in the form of ChatGPT Plus and Copilot Pro, respectively. Why would you pay for them when they're freely available?

Researchers at Google DeepMind have developed AlphaFold 3, an AI model that can predict the structure of and interactions between biological molecules including proteins, DNA and RNA, and small molecules that could function as drugs. Google DeepMind will make the model available for non-commercial use through AlphaFold server. The landmark innovation, the details of which were published in the journal Nature on May 8, is likely to dramatically accelerate biological research. "It's a big milestone for us today, announcing AlphaFold 3," said Demis Hassabis, CEO of Google DeepMind, at a briefing on May 7 announcing the breakthrough. "Biology is a dynamic system and you have to understand how properties of biology emerge through the interactions between different molecules in the cell. You can think of AlphaFold 3 as our first big step towards that."

Getting LLaMA 2 ready to launch required a lot of tweaking to make the model safer and less likely to spew toxic falsehoods than its predecessor, Al-Dahle says. Meta has plenty of past gaffes to learn from. Its language model for science, Galactica, was taken offline after only three days, and its previous LlaMA model, which was meant only for research purposes, was leaked online, sparking criticism from politicians who questioned whether Meta was taking proper account of the risks associated with AI language models, such as disinformation and harassment. To mitigate the risk of repeating these mistakes, Meta applied a mix of different machine learning techniques aimed at improving helpfulness and safety. Meta's approach to training LLaMA 2 had more steps than usual for generative AI models, says Sasha Luccioni, a researcher at AI startup Hugging Face.

Ahmed Elnaggar and Michael Heinzinger are helping computers read proteins as easily as you read this sentence. The researchers are applying the latest AI models used to understand text to the field of bioinformatics. Their work could accelerate efforts to characterize living organisms like the coronavirus. By the end of the year, they aim to launch a website where researchers can plug in a string of amino acids that describe a protein. Within seconds, it will provide some details of the protein's 3D structure, a key to knowing how to treat it with a drug.