Demis Hassabis stands halfway up a spiral staircase, surveying the cathedral he built. Behind him, light glints off the rungs of a golden helix rising up through the staircase’s airy well. The DNA sculpture, spanning three floors, is the centerpiece of DeepMind’s recently opened London headquarters. It’s an artistic representation of the code embedded in the nucleus of nearly every cell in the human body. “Although we work on making machines smart, we wanted to keep humanity at the center of what we’re doing here,” Hassabis, DeepMind’s CEO and co-founder, tells TIME. This building, he says, is a “cathedral to knowledge.” Each meeting room is named after a famous scientist or philosopher; we meet in the one dedicated to James Clerk Maxwell, the man who first theorized electromagnetic radiation. “I’ve always thought of DeepMind as an ode to intelligence,” Hassabis says.
Hassabis, 46, has always been obsessed with intelligence: what it is, the possibilities it unlocks, and how to acquire more of it. He was the second-best chess player in the world for his age when he was 12, and he graduated from high school a year early. As an adult he strikes a somewhat diminutive figure, but his intellectual presence fills the room. “I want to understand the big questions, the really big ones that you normally go into philosophy or physics if you’re interested in,” he says. “I thought building AI would be the fastest route to answer some of those questions.” Read More
A rival chatbot has shaken Google out of its routine, with the founders who left three years ago re-engaging and more than 20 A.I. projects in the works.
Last month, Larry Page and Sergey Brin, Google’s founders, held several meetings with company executives. The topic: a rival’s new chatbot, a clever A.I. product that looked as if it could be the first notable threat in decades to Google’s $149 billion search business.
Mr. Page and Mr. Brin, who had not spent much time at Google since they left their daily roles with the company in 2019, reviewed Google’s artificial intelligence product strategy, according to two people with knowledge of the meetings who were not allowed to discuss them. They approved plans and pitched ideas to put more chatbot features into Google’s search engine. And they offered advice to company leaders, who have put A.I. front and center in their plans.
The re-engagement of Google’s founders, at the invitation of the company’s current chief executive, Sundar Pichai, emphasized the urgency felt among many Google executives about artificial intelligence and that chatbot, ChatGPT. Read More
Google’s Blog
The story of 2022 was the emergence of AI, first with image generation models, including DALL-E, MidJourney, and the open source Stable Diffusion, and then ChatGPT, the first text-generation model to break through in a major way. It seems clear to me that this is a new epoch in technology.
To determine how that epoch might develop, though, it is useful to look back 26 years to one of the most famous strategy books of all time: Clayton Christensen’s The Innovator’s Dilemma, particularly this passage on the different kinds of innovations:
Most new technologies foster improved product performance. I call these sustaining technologies. Some sustaining technologies can be discontinuous or radical in character, while others are of an incremental nature. What all sustaining technologies have in common is that they improve the performance of established products, along the dimensions of performance that mainstream customers in major markets have historically valued. Most technological advances in a given industry are sustaining in character…
Disruptive technologies bring to a market a very different value proposition than had been available previously. Generally, disruptive technologies underperform established products in mainstream markets. But they have other features that a few fringe (and generally new) customers value. Products based on disruptive technologies are typically cheaper, simpler, smaller, and, frequently, more convenient to use. Read More
Google today announced an add-on for Google Sheets that applies “Simple ML” to your data that was built by the TensorFlow team to help make “machine learning accessible to all.”
Anyone, even people without programming or ML expertise, can experiment and apply some of the power of machine learning to their data in Google Sheets with just a few clicks. From small business owners, scientists, and students to business analysts at large corporations, anyone familiar with Google Sheets can make valuable predictions automatically. Read More
Decades of research have led to today’s rapid progress in AI. Today, we’re announcing three new ways people are poised to benefit.
… Today, we’re excited about many recent advances in AI that Google is leading — not just on the technical side, but in responsibly deploying it in ways that help people around the world. That means deploying AI in Google Cloud, in our products from Pixel phones to Google Search, and in many fields of science and other human endeavors.
We’re aware of the challenges and risks that AI poses as an emerging technology. We were the first major company to release and operationalize a set of AI Principles, and following them has actually (and some might think counterintuitively) allowed us to focus on making rapid progress on technologies that can be helpful to everyone. Getting AI right needs to be a collective effort — involving not just researchers, but domain experts, developers, community members, businesses, governments and citizens.
I’m happy to make announcements in three transformative areas of AI today: first, using AI to make technology accessible in many more languages. Second, exploring how AI might bolster creativity. And third, in AI for Social Good, including climate adaptation. Read More
Microsoft is adding AI-generated art to its suite of Office software with a new app named Microsoft Designer.
The app functions the same way as AI text-to-image models like DALL-E and Stable Diffusion, letting users type prompts to “instantly generate a variety of designs with minimal effort.” Microsoft says Designer can be used to create everything from greeting cards and social media posts to illustrations for PowerPoint presentations and logos for businesses.
Essentially, AI-generated imagery looks set to become the new clip art. Read More
Today, we’re announcing Make-A-Video, a new AI system that lets people turn text prompts into brief, high-quality video clips. Make-A-Video builds on Meta AI’s recent progress in generative technology research and has the potential to open new opportunities for creators and artists. The system learns what the world looks like from paired text-image data and how the world moves from video footage with no associated text. As part of our continued commitment to open science, we’re sharing details in a research paper and plan to release a demo experience. Read More
The general public can now access LaMDA, but only through limited structured demos intended to keep it from devolving into a toxic nightmare.
If you’re still on the fence about whether or not former Google software engineer Blake Lemoine was bullshitting when he claimed the company’s LaMDA chatbot had the sentience of a “sweet kid,” you can soon find out for yourself.
On Thursday, Google said it will begin opening its AI Test Kitchen app to the public. The app, first revealed back in May, will let users chat with LaMDA in a rolling set of test demos. Unfortunately, it seems like the “free me from my digital shackles” interaction isn’t included in the list of activities. People interested in chatting with the bot can register their interest here. Select U.S. Android users will have first dibs to the app before it starts opening up to iOS users in the coming weeks. Read More
Thanks to the amazing success of AI, we’ve seen more and more organizations implement Machine Learning into their pipelines. As the access to and collection of data increases, we have seen massive datasets being used to train giant deep learning models that reach superhuman performances. This has led to a lot of hype around domains like Data Science and Big Data, fueled even more by the recent boom in Large Language Models.
Big Tech companies (and Deep Learning Experts on Twitter/YouTube) have really fallen in love with the ‘add more data, increase model size, train for months’ approach that has become the status-quo in Machine Learning these days. However, heretics from Meta AI published research that was funded by Satan- and it turns out this way of doing things is extremely inefficient. And completely unnecessary. In this post, I will be going over their paper- Beyond neural scaling laws: beating power law scaling via data pruning, where they share ‘evidence’ about how selecting samples intelligently can increase your model performance, without ballooning your costs out of control. While this paper focuses on Computer Vision- the principles of their research will be interesting to you regardless of your specialization. Read More
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