The Synthetic Party in Denmark is dedicated to following a platform churned out by an AI, and its public face is a chatbot named Leader
br>The Synthetic Party, a new Danish political party with an artificially intelligent representative and policies derived from AI, is eyeing a seat in parliament as it hopes to run in the country’s November general election.
The party was founded in May by the artist collective Computer Lars and the non-profit art and tech organization MindFuture Foundation. The Synthetic Party’s public face and figurehead is the AI chatbot Leader Lars, which is programmed on the policies of Danish fringe parties since 1970 and is meant to represent the values of the 20 percent of Danes who do not vote in the election. Leader Lars won’t be on the ballot anywhere, but the human members of The Synthetic Party are committed to carrying out their AI-derived platform Read More
Tag Archives: Artificial Intelligence
Growth in AI and robotics research accelerates
It may not be unusual for burgeoning areas of science, especially those related to rapid technological changes in society, to take off quickly, but even by these standards the rise of artificial intelligence (AI) has been impressive. Together with robotics, AI is representing an increasingly significant portion of research volume at various levels, as these charts show.
The number of AI and robotics papers published in the 82 high-quality science journals in the Nature Index (Count) has been rising year-on-year — so rapidly that it resembles an exponential growth curve. A similar increase is also happening more generally in journals and proceedings not included in the Nature Index, as is shown by data from the Dimensions database of research publications. Read More
10 years later, deep learning ‘revolution’ rages on, say AI pioneers Hinton, LeCun and Li
Artificial intelligence (AI) pioneer Geoffrey Hinton, one of the trailblazers of the deep learning “revolution” that began a decade ago, says that the rapid progress in AI will continue to accelerate.
In an interview before the 10-year anniversary of key neural network research that led to a major AI breakthrough in 2012, Hinton and other leading AI luminaries fired back at some critics who say deep learning has “hit a wall.”
“We’re going to see big advances in robotics — dexterous, agile, more compliant robots that do things more efficiently and gently like we do,” Hinton said.
Other AI pathbreakers, including Yann LeCun, head of AI and chief scientist at Meta and Stanford University professor Fei-Fei Li, agree with Hinton that the results from the groundbreaking 2012 research on the ImageNet database — which was built on previous work to unlock significant advancements in computer vision specifically and deep learning overall — pushed deep learning into the mainstream and have sparked a massive momentum that will be hard to stop. Read More
Sony’s racing AI destroyed its human competitors by being nice (and fast)
“Wait, what? How?” Emily Jones wasn’t used to being left behind. A top sim-racing driver with multiple wins to her name, Jones jerked the steering wheel in the esports rig, eyes fixed on the screen in front of her: “I’m pushing way too hard to keep up— How does it do that?” Her staccato commentary intercut with squealing tires, Jones flung her virtual car around the virtual track at 120 miles per hour—then 140, 150—chasing the fastest Gran Turismo driver in the world.
Built by Sony AI, a research lab launched by the company in 2020, Gran Turismo Sophy is a computer program trained to control racing cars inside the world of Gran Turismo, a video game known for its super-realistic simulations of real vehicles and tracks. In a series of events held behind closed doors last year, Sony put its program up against the best humans on the professional sim-racing circuit.
What they discovered during those racetrack battles—and the ones that followed—could help shape the future of machines that work alongside humans, or join us on the roads. Read More
Everlaw Launches AI-based Clustering to Open a New World of Ediscovery Insights to Legal Teams
Everlaw, the cloud-native investigation and litigation platform, unveiled its Clustering software feature today, delivering an AI breakthrough in terms of its scale, visualization, ease of use and ability to conduct true discovery.
While Technology Assisted Review (TAR) has been sanctioned for legal teams to conduct discovery searches for digital evidence for about a decade, the promise of concept clustering has fallen short. It’s often too hard to use, can’t scale to meet today’s video, audio and text demands, and is restricted to a wheel interface that can’t drill down to single documents.
Everlaw Clustering’s new technical breakthroughs deliver on the promise of AI, allowing legal teams to sort through and understand millions of documents for full review or early case assessment (ECA). Everlaw Clustering presents findings in an intuitive visual format that encompasses both a 30,000-foot snapshot and a granular, down-to-the-document view. It uses unsupervised machine learning to group documents by conceptual similarity and generates insights without requiring any user input. Read More
Yann LeCun has a bold new vision for the future of AI
LeCun, who is chief scientist at Meta’s AI lab and one of the most influential AI researchers in the world, had been trying to give machines a basic grasp of how the world works—a kind of common sense—by training neural networks to predict what was going to happen next in video clips of everyday events. But guessing future frames of a video pixel by pixel was just too complex. He hit a wall.
Now, after months figuring out what was missing, he has a bold new vision for the next generation of AI. In a draft document shared with MIT Technology Review, LeCun sketches out an approach that he thinks will one day give machines the common sense they need to navigate the world. (Update: LeCun has since posted the document online.) Read More
The Year in AI So Far: Massive Models and How to Use Them
The world of artificial intelligence and machine learning moves very fast. So fast, in fact, that it’s remarkable to think that it was only a decade ago when the AlexNet model dominated the ImageNet competition and kicked off the process that made deep learning a bona fide technology movement. Today, after years of headlines about game-playing, we see ever-increasing innovation that applies to the real world.
In the last couple of years alone, AI/ML models like GPT-3 and AlphaFold delivered capabilities that catalyzed new products and companies, and that stretched our understanding of what computers can do.
With that in mind, we thought we’d revisit our AI/ML coverage in Future over the first half of the year, as well as catch you up on some — but certainly not all — of the major industry developments during that time. As you’ll see, some combination of large language models, generative models, and foundation models are a major source of attention, and we’re just skimming the surface in terms of understanding what they can do and how the world outside of large research labs can utilize their power. Read More
There’s no such thing as data
Data is the new oil, we are told. Every country needs a data strategy, and all of us should own our data, and be paid for it. But really, there is no such thing as data, it’s not yours, and it’s not worth anything.
Technology is full of narratives, but one of the loudest is around something called ‘data’. AI is the future, and it’s all about data, and data is the future, and we should own it and maybe be paid for it, and countries need data strategies and data sovereignty. Data is the new oil!
This is mostly nonsense. There is no such thing as ‘data’, it isn’t worth anything, and it doesn’t really belong to you anyway.
Most obviously, ‘data’ is not one thing, but innumerable different collections of information, each of them specific to a particular application, that aren’t interchangeable. Siemens has wind turbine telemetry and Transport for London has ticket swipes, and you can’t use the turbine telemetry to plan a new bus route. If you gave both sets of data to Google or Tencent, that wouldn’t help them build a better image recognition system. Read More
How Will Machine Learning Impact Economics?
#280 — The Future of Artificial Intelligence
In this episode of the podcast, Sam Harris speaks with Eric Schmidt about the ways artificial intelligence is shifting the foundations of human knowledge and posing questions of existential risk. Read More
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