Today, I’m talking to Demis Hassabis, the CEO of Google DeepMind, the newly created division of Google responsible for AI efforts across the company. Google DeepMind is the result of an internal merger: Google acquired Demis’ DeepMind startup in 2014 and ran it as a separate company inside its parent company, Alphabet, while Google itself had an AI team called Google Brain.
Google has been showing off AI demos for years now, but with the explosion of ChatGPT and a renewed threat from Microsoft in search, Google and Alphabet CEO Sundar Pichai made the decision to bring DeepMind into Google itself earlier this year to create… Google DeepMind.
What’s interesting is that Google Brain and DeepMind were not necessarily compatible or even focused on the same things: DeepMind was famous for applying AI to things like games and protein-folding simulations. The AI that beat world champions at Go, the ancient board game? That was DeepMind’s AlphaGo. Meanwhile, Google Brain was more focused on what’s come to be the familiar generative AI toolset: large language models for chatbots, editing features in Google Photos, and so on. This was a culture clash and a big structure decision with the goal of being more competitive and faster to market with AI products. Read More
Tag Archives: Big7
Med-PaLM
Med-PaLM is a large language model (LLM) designed to provide high quality answers to medical questions.
Med-PaLM harnesses the power of Google’s large language models, which we have aligned to the medical domain and evaluated using medical exams, medical research, and consumer queries. Our first version of Med-PaLM, preprinted in late 2022, was the first AI system to surpass the pass mark on US Medical License Exam (USMLE) style questions. Med-PaLM also generates accurate, helpful long-form answers to consumer health questions, as judged by panels of physicians and users.
We introduced our latest model, Med-PaLM 2, at our annual health event The Check Up in Q1, 2023. Med-PaLM 2 achieves an accuracy of 86.5% on USMLE-style questions, a 19% leap over our own state of the art results from Med-PaLM. — Read More
Alibaba launches A.I. tool to generate images from text
Chinese technology giant Alibaba on Friday launched an artificial intelligence tool that can generate images from prompts.
Tongyi Wanxiang allows users to input prompts in Chinese and English and the AI tool will generate an image in various styles such as a sketch or 3D cartoon.
Alibaba’s cloud division, which launched the product, said it is available for enterprise customers in China for beta testing. — Read More
Amazon’s vision: An AI model for everything
Matt Wood, vice president of product for Amazon Web Services, is at the tip of the spear of Amazon’s response in the escalating AI battle between the tech giants.
Much of the internet already runs on AWS’s cloud services and Amazon’s long game strategy is to create a single point of entry for companies and startups to tap into a rapidly increasing number of generative AI models, both of the open-source and closed-source variety.
Wood discussed this and other topics in an edited conversation. — Read More
Apple Is an AI Company Now
After more than a decade, autocorrect “fails” could be on their way out. Apple’s much-maligned spelling software is getting upgraded by artificial intelligence: Using sophisticated language models, the new autocorrect won’t just check words against a dictionary, but will be able to consider the context of the word in a sentence. In theory, it won’t suggest consolation when you mean consolidation, because it’ll know that those words aren’t interchangeable.
The next generation of autocorrect was one of several small updates to the iPhone experience that Apple announced earlier this month. The Photos app will be able to differentiate between your dog and other dogs, automatically recognizing your pup the same way it recognizes people who frequently appear in your pictures. And AirPods will get smarter about adjusting to background noise based on your listening over time.
All of these features are powered by AI—even if you might not know it from how Apple talks about them. Its conference unveiling the updates included zero mentions of AI, now a buzzword for tech companies of all stripes. Instead, Apple used more technical language such as machine learning or transformer language model. Apple has been quiet about the technology—so quiet that it has been accused of falling behind. Indeed, whereas ChatGPT can write halfway-decent business proposals, Siri can set your morning alarm and not much else. But Apple is pushing forward with AI in small ways, an incrementalist approach that nonetheless still might be the future of where this technology is headed. — Read More
Introducing Voicebox: The first generative AI model for speech to generalize across tasks with state-of-the-art performance
Meta AI researchers have achieved a breakthrough in generative AI for speech. We’ve developed Voicebox, the first model that can generalize to speech-generation tasks it was not specifically trained to accomplish with state-of-the-art performance.
Like generative systems for images and text, Voicebox creates outputs in a vast variety of styles, and it can create outputs from scratch as well as modify a sample it’s given. But instead of creating a picture or a passage of text, Voicebox produces high-quality audio clips. The model can synthesize speech across six languages, as well as perform noise removal, content editing, style conversion, and diverse sample generation. — Read More
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Google Cloud partners with Mayo Clinic on new AI tool to improve patient care
Google Cloud has announced a new partnership with Mayo Clinic that will introduce a new Artificial Intelligence tool that aims to improve the efficiency of healthcare throughout the United States.
The initial focus of the collaboration will establish a new search tool powered by Google Cloud’s Generative AI software that would improve clinical workflows by making it easier for doctors and researchers to quickly track down patient information, the tech giant said. — Read More
StyleDrop: Text-To-Image Generation in Any Style
We present StyleDrop that enables the generation of images that faithfully follow a specific style, powered by Muse, a text-to-image generative vision transformer. StyleDrop is extremely versatile and captures nuances and details of a user-provided style, such as color schemes, shading, design patterns, and local and global effects. StyleDrop works by efficiently learning a new style by fine-tuning very few trainable parameters (less than 1% of total model parameters), and improving the quality via iterative training with either human or automated feedback. Better yet, StyleDrop is able to deliver impressive results even when the user supplies only a single image specifying the desired style. An extensive study shows that, for the task of style tuning text-to-image models, Styledrop on Muse convincingly outperforms other methods, including DreamBooth and Textual Inversion on Imagen or Stable Diffusion. — Read More
What runs ChatGPT? Inside Microsoft’s AI supercomputer
MEGABYTE, Meta AI’s New Revolutionary Model Architecture, Explained
Unlocking the true potential of content generation in natural language processing (NLP) has always been a challenge. Traditional models struggle with long sequences, scalability, and sluggish generation speed.
But fear not, as Meta AI brings forth MEGABYTE – a groundbreaking model architecture that revolutionizes content generation. In this blog, we will dive deep into the secrets behind MEGABYTE’s potential, its innovative features, and how it tackles the limitations of current approaches. — Read More
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