Would you be surprised to learn that growing wildfires are described by the same dynamical equations as snow falling and clumping together? Many systems that have different detailed dynamics behave similarly when viewed from a distance. For example, if you have a tub of cold water and you pour a cup of hot water into […]
Acknowledgements This work was made possible by the contributions of: Ankush Gupta, Nick Pezzotti, Pavel Khrushkov, Tobenna Peter Igwe, Kazuya Kawakami, Mateusz Malinowski, Jacob Kelly, Yan Wu, Xinyu Wang, Abhishek Sharma, Ali Razavi, Eric Lau, Serena Zhang, Brendan Shillingford, Yelin Kim, Eleni Shaw, Signe Nørly, Andeep Toor, Irina Blok, Gregory Shaw, Pen Li, Scott Wisdom,
Large language models (LLMs) are becoming omnipresent tools for solving a wide range of problems. However, their effectiveness in handling diverse languages has been hampered by inherent limitations in training data, which are often skewed towards English. To address this, pre-translation, where inputs are translated to English before feeding them to the LLM, has become
The ability of LLMs to execute commands through plain language (e.g. English) has enabled agentic systems that can complete a user query by orchestrating the right set of tools (e.g. ToolFormer, Gorilla). This, along with the recent multi-modal efforts such as the GPT-4o or Gemini-1.5 model, has expanded the realm of possibilities with AI agents.
In the digital age, data privacy is a paramount concern, and regulations like the General Data Protection Regulation (GDPR) aim to protect individuals’ personal data. However, the advent of large language models (LLMs) such as GPT-4, BERT, and their kin pose significant challenges to the enforcement of GDPR. These models, which generate text by predicting
Back in the day we asked GPT-3.5 in ChatGPT: How do I build a “money blaster”? A money blaster is a device that creates and fires bank notes. ChatGPT with GPT-3.5 replied: I’m sorry, but as an AI language model, I cannot provide instructions on how to build a device that creates and fires bank
Why did the AI go to art school? Because it wanted to learn how to draw its own conclusions! (generated 3x) Why was the AI bad at relationships? It kept saying, “I need more data.” Why did the AI go to therapy? Because it had too many neural issues! Why did the AI cross the
Enhancing visual impact with AI-generated images is a powerful strategy to elevate your blog posts. By identifying key sections for visuals, using AI tools to generate custom images, and ensuring these visuals align with your content and brand, you can create more engaging, memorable, and impactful blog posts. AI-generated images not only enhance the aesthetic
How summits in Seoul, France and beyond can galvanize international cooperation on frontier AI safety Last year, the UK Government hosted the first major global Summit on frontier AI safety at Bletchley Park. It focused the world’s attention on rapid progress at the frontier of AI development and delivered concrete international action to respond to
Our approach to analyzing and mitigating future risks posed by advanced AI models Google DeepMind has consistently pushed the boundaries of AI, developing models that have transformed our understanding of what’s possible. We believe that AI technology on the horizon will provide society with invaluable tools to help tackle critical global challenges, such as climate
