War Stories: How Forza learned to love neural nets to train AI drivers

Produced by Justin Wolfson, edited by Shandor Garrison. Click here for transcript.
Once an upstart, the Forza franchise is now firmly established within the pantheon of great racing games. The first installment was created as the Xbox’s answer to Gran Turismo, but with a healthy helping of online multiplayer racing, too. Since then, it has grown with Microsoft’s Xbox consoles, with more realistic graphics and ever-more accurate physics in the track-focused Forza Motorsport series as well as evolving into open-world adventuring (and even a trip to the Lego dimension) for the Forza Horizon games.
If you’re one of the millions of people who’ve played a Forza racing game, you’re probably aware of the games’ AI opponents, called “Drivatars.” When the first Drivatars debuted in Forza Motorsport in 2005, they were a substantial improvement over the NPCs we raced in other driving games, which often just followed the same preprogrammed route around the

Original URL: https://arstechnica.com/?p=1705877

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Will Machine Learning Build Up Dangerous ‘Intellectual Debt’?

Long-time Slashdot reader JonZittrain is an international law professor at Harvard Law School, and an EFF board member. Wednesday he contacted us to share his new article in the New Yorker:
I’ve been thinking about what happens when AI gives us seemingly correct answers that we wouldn’t have thought of ourselves, without any theory to explain them. These answers are a form of “intellectual debt” that we figure we’ll repay — but too often we never get around to it, or even know where it’s accruing.

A more detailed (and unpaywalled) version of the essay draws a little from how and when it makes sense to pile up technical debt to ask the same questions about intellectual debt.

The first article argues that new AI techniques “increase our collective intellectual credit line,” adding that “A world of knowledge without understanding becomes a world without discernible cause and effect, in which

Original URL: http://rss.slashdot.org/~r/Slashdot/slashdot/~3/R_X0YJMQN0Q/will-machine-learning-build-up-dangerous-intellectual-debt

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Facebook updates PyTorch with a focus on production use

During last year’s F8 developer conference, Facebook announced the 1.0 launch of PyTorch, the company’s open-source deep learning platform. At this year’s F8, the company launched version 1.1. The small increase in version numbers belies the importance of this release, which focuses on making the tool more appropriate for production usage, including improvements to how the tool handles distributed training.
“What we’re seeing with PyTorch is an incredible moment internally at Facebook to ship it and then an echo of that externally with large companies,” Joe Spisak, Facebook AI’s product manager for PyTorch, told me. “Make no mistake, we’re not trying to monetize PyTorch […] but we want to see PyTorch have a community. And that community is starting to shift from a very research-centric community — and that continues to grow fast — into the production world.”
So with this release, the team and the more than 1,000 open-source committers that

Original URL: http://feedproxy.google.com/~r/Techcrunch/~3/EIcmuFj3yBM/

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Amazon Opens Up Its Internal Machine Learning Training To Everyone

Amazon announced Monday that it’s making the machine learning courses it uses to train its engineers available to everybody for free. The course is tailored to four major groups — developers, data scientists, data platform engineers and business professionals — and it offers both foundational level lessons as well as more advanced instruction.

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Original URL: http://rss.slashdot.org/~r/Slashdot/slashdot/~3/loWLPJOUFfs/amazon-opens-up-its-internal-machine-learning-training-to-everyone

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20 Top Lawyers Were Beaten By Legal AI

An anonymous reader shares a report:In a landmark study, 20 top US corporate lawyers with decades of experience in corporate law and contract review were pitted against an AI. Their task was to spot issues in five Non-Disclosure Agreements (NDAs), which are a contractual basis for most business deals. The study, carried out with leading legal academics and experts, saw the LawGeex AI achieve an average 94% accuracy rate, higher than the lawyers who achieved an average rate of 85%. It took the lawyers an average of 92 minutes to complete the NDA issue spotting, compared to 26 seconds for the LawGeex AI. The longest time taken by a lawyer to complete the test was 156 minutes, and the shortest time was 51 minutes.

Read more of this story at Slashdot.

Original URL: http://rss.slashdot.org/~r/Slashdot/slashdot/~3/fZsGPRvPxjI/20-top-lawyers-were-beaten-by-legal-ai

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New App Lets You ‘Sue Anyone By Pressing a Button’

Jason Koebler writes: Do Not Pay, a free service that launched in the iOS App store today, uses artificial intelligence to help people win up to $25,000 in small claims court. It’s the latest project from 21-year-old Stanford senior Joshua Browder, whose service previously allowed people to fight parking tickets or sue Equifax; now, the app has streamlined the process. It’s the “first ever service to sue anyone (in all 3,000 counties in 50 states) by pressing a button.”

Read more of this story at Slashdot.

Original URL: http://rss.slashdot.org/~r/Slashdot/slashdot/~3/Fl-Sx28-FGc/new-app-lets-you-sue-anyone-by-pressing-a-button

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Google DeepMind’s AI Beats Doctors at Spotting Eye Disease in Scan

DeepMind, Google’s artificial intelligence business, is planning clinical trials of technology that can help diagnose eye disease by analyzing medical images after early tests showed its results were more accurate than human doctors. From a report: Published in the scientific journal Nature, the study claims that DeepMind, in partnership with Moorfields Eye Hospital in London, has trained its algorithms to detect over 50 sight-threatening conditions to the same accuracy as expert clinicians. It is also capable of correctly recommending the most appropriate course of action for patients and prioritise those in most urgent need of care. In a project that began two years ago, DeepMind trained its machine learning algorithms using thousands of historic and fully anonymized eye scans to identify diseases that could lead to sight loss. According to the study, they can now do so with 94 percent accuracy, and the hope is that they could eventually be

Original URL: http://rss.slashdot.org/~r/Slashdot/slashdot/~3/-RxGBTX6b_A/google-deepminds-ai-beats-doctors-at-spotting-eye-disease-in-scan

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Amazon’s Alexa Is Coming To an Office Near You

Amazon announced today that it’s bringing its voice assistant into a range of business settings, big and small, like hotels and co-working spaces. From a report: While people always think of Amazon as a consumer company, it has shown itself time and again to have larger ambitions. This move could help it expand tis business services beyond its already popular Amazon Web services. In an interview, Amazon CTO Werner Vogels said that exposure to the workplace will improve Alexa by exposing it to new types of conversations. “The kind of language we use in our offices is sometimes radically different from the more conversational things we do in our(homes),” he told Axios. Alexa “will greatly improve by being exposed to different kinds of statements or conversations.”

Read more of this story at Slashdot.

Original URL: http://rss.slashdot.org/~r/Slashdot/slashdot/~3/X3wBN3SRdJ8/amazons-alexa-is-coming-to-an-office-near-you

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Google Releases TensorFlow 1.0 With New Machine Learning Tools

An anonymous reader shares a VentureBeat report: At Google’s inaugural TensorFlow Dev Summit in Mountain View, California, today, Google announced the release of version 1.0 of its TensorFlow open source framework for deep learning, a trendy type of artificial intelligence. Google says the release is now production-ready by way of its application programing interface (API). But there are also new tools that will be part of the framework, which includes artificial neural networks that can be trained on data and can then make inferences about new data. Now there are more traditional machine learning tools, including K-means and support vector machines (SVMs), TensorFlow’s engineering director, Rajat Monga, said at the conference. And there’s an integration with the Python-based Keras library, which was originally meant to ease the use of the Theano deep learning framework. And there are now “canned estimators,” or models, Monga said, including simple neural networks to start

Original URL: http://rss.slashdot.org/~r/Slashdot/slashdot/~3/TOyeOi9DQco/google-releases-tensorflow-10-with-new-machine-learning-tools

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