Archive for the ‘machine learning’ Category

Researchers, scared by their own work, hold back “deepfakes for text” AI

February 15th, 2019
This is fine.

Enlarge / This is fine.

OpenAI, a non-profit research company investigating "the path to safe artificial intelligence," has developed a machine learning system called Generative Pre-trained Transformer-2 (GPT-2 ), capable of generating text based on brief writing prompts. The result comes so close to mimicking human writing that it could potentially be used for "deepfake" content. Built based on 40 gigabytes of text retrieved from sources on the Internet (including "all outbound links from Reddit, a social media platform, which received at least 3 karma"), GPT-2 generates plausible "news" stories and other text that match the style and content of a brief text prompt.

The performance of the system was so disconcerting, now the researchers are only releasing a reduced version of GPT-2 based on a much smaller text corpus. In a blog post on the project and this decision, researchers Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever wrote:

Due to concerns about large language models being used to generate deceptive, biased, or abusive language at scale, we are only releasing a much smaller version of GPT-2 along with sampling code. We are not releasing the dataset, training code, or GPT-2 model weights. Nearly a year ago we wrote in the OpenAI Charter: “we expect that safety and security concerns will reduce our traditional publishing in the future, while increasing the importance of sharing safety, policy, and standards research,” and we see this current work as potentially representing the early beginnings of such concerns, which we expect may grow over time. This decision, as well as our discussion of it, is an experiment: while we are not sure that it is the right decision today, we believe that the AI community will eventually need to tackle the issue of publication norms in a thoughtful way in certain research areas.

OpenAI is funded by contributions from a group of technology executives and investors connected to what some have referred to as the PayPal "mafia"—Elon Musk, Peter Thiel, Jessica Livingston, and Sam Altman of YCombinator, former PayPal COO and LinkedIn co-founder Reid Hoffman, and former Stripe Chief Technology Officer Greg Brockman. Brockman now serves as OpenAI's CTO. Musk has repeatedly warned of the potential existential dangers posed by AI, and OpenAI is focused on trying to shape the future of artificial intelligence technology—ideally moving it away from potentially harmful applications.

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Posted in AI, artificial intellignece, Biz & IT, computer-generated text, deep fake, deepfake, fake news, machine learning, Markov chain | Comments (0)

Mozilla to use machine learning to find code bugs before they ship

February 12th, 2019

Ubisoft's Commit-Assistant

In a bid to cut the number of coding errors made in its Firefox browser, Mozilla is deploying Clever-Commit, a machine-learning-driven coding assistant developed in conjunction with game developer Ubisoft.

Clever-Commit analyzes code changes as developers commit them to the Firefox codebase. It compares them to all the code it has seen before to see if they look similar to code that the system knows to be buggy. If the assistant thinks that a commit looks suspicious, it warns the developer. Presuming its analysis is correct, it means that the bug can be fixed before it gets committed into the source repository. Clever-Commit can even suggest fixes for the bugs that it finds. Initially, Mozilla plans to use Clever-Commit during code reviews, and in time this will expand to other phases of development, too. It works with all three of the languages that Mozilla uses for Firefox: C++, JavaScript, and Rust.

The tool builds on work by Ubisoft La Forge, Ubisoft's research lab. Last year, Ubisoft presented the Commit-Assistant, based on research called CLEVER, a system for finding bugs and suggesting fixes. That system found some 60-70 percent of buggy commits, though it also had a false positive rate of 30 percent. Even though this false positive rate is quite high, users of this system nonetheless felt that it was worthwhile, thanks to the time saved when it did correctly identify a bug.

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Posted in bugs, C#, development, machine learning, Mozilla, Programming, Tech, Ubisoft | Comments (0)

Yes, “algorithms” can be biased. Here’s why

January 24th, 2019
Seriously, it's enough to make researchers cry.

Enlarge / Seriously, it's enough to make researchers cry. (credit: Getty | Peter M Fisher)

Dr. Steve Bellovin is professor of computer science at Columbia University, where he researches "networks, security, and why the two don't get along." He is the author of Thinking Security and the co-author of Firewalls and Internet Security: Repelling the Wily Hacker. The opinions expressed in this piece do not necessarily represent those of Ars Technica.

Newly elected Rep. Alexandria Ocasio-Cortez (D-NY) recently stated that facial recognition "algorithms" (and by extension all "algorithms") "always have these racial inequities that get translated" and that "those algorithms are still pegged to basic human assumptions. They're just automated assumptions. And if you don't fix the bias, then you are just automating the bias."

She was mocked for this claim on the grounds that "algorithms" are "driven by math" and thus can't be biased—but she's basically right. Let's take a look at why.

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Posted in AI, algorithms, machine learning, ML, Policy | Comments (0)

Machine learning can offer new tools, fresh insights for the humanities

January 4th, 2019
Composite image based on Jacques-Louis David's unfinished painting, "Drawing of the Tennis Court Oath" (circa 1790).

Enlarge / Composite image based on Jacques-Louis David's unfinished painting, "Drawing of the Tennis Court Oath" (circa 1790). (credit: Association of Cybernetic Historians)

Truly revolutionary political transformations are naturally of great interest to historians, and the French Revolution at the end of the 18th century is widely regarded as one of the most influential, serving as a model for building other European democracies. A paper published last summer in the Proceedings of the National Academy of Sciences, offers new insight into how the members of the first National Constituent Assembly hammered out the details of this new type of governance.

Specifically, rhetorical innovations by key influential figures (like Robespierre) played a critical role in persuading others to accept what were, at the time, audacious principles of governance, according to co-author Simon DeDeo, a former physicist who now applies mathematical techniques to the study of historical and current cultural phenomena. And the cutting-edge machine learning methods he developed to reach that conclusion are now being employed by other scholars of history and literature.

It's part of the rise of so-called "digital humanities." As more and more archives are digitized, scholars are applying various analytical tools to those rich datasets, such as Google N-gram, Bookworm, and WordNet. Tagged and searchable archives mean connecting the dots between different records is much easier. Close reading of selected sources—the traditional method of historians—gives a deep but narrow view. Quantitative computational analysis has the potential to combine that kind of close reading with a broader, more generalized bird's-eye approach that might reveal hidden patterns or trends that otherwise might have escaped notice.

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Posted in 12 days of Christmas, algorithms, computational analysis, digital humanities, Gaming & Culture, History, Literature, machine learning, science | Comments (0)

More than an auto-pilot, AI charts its course in aviation

December 5th, 2018
Boeing 787 Dreamliner.

Enlarge / Boeing 787 Dreamliner. (credit: Nicolas Economou/NurPhoto via Getty Images)

Welcome to Ars UNITE, our week-long virtual conference on the ways that innovation brings unusual pairings together. Each day this week from Wednesday through Friday, we're bringing you a pair of stories about facing the future. Today's focus is on AI in transportation—buckle up!

Ask anyone what they think of when the words "artificial intelligence" and aviation are combined, and it's likely the first things they'll mention are drones. But autonomous aircraft are only a fraction of the impact that advances in machine learning and other artificial intelligence (AI) technologies will have in aviation—the technologies' reach could encompass nearly every aspect of the industry. Aircraft manufacturers and airlines are investing significant resources in AI technologies in applications that span from the flightdeck to the customer's experience.

Automated systems have been part of commercial aviation for years. Thanks to the adoption of "fly-by-wire" controls and automated flight systems, machine learning and AI technology are moving into a crew-member role in the cockpit. Rather than simply reducing the workload on pilots, these systems are on the verge of becoming what amounts to another co-pilot. For example, systems originally developed for unmanned aerial vehicle (UAV) safety—such as Automatic Dependent Surveillance Broadcast (ADS-B) for traffic situational awareness—have migrated into manned aircraft cockpits. And emerging systems like the Maneuvering Characteristics Augmentation System (MCAS) are being developed to increase safety when there's a need to compensate for aircraft handling characteristics. They use sensor data to adjust the control surfaces of an aircraft automatically, based on flight conditions.

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Posted in AI, analytics, ars-unite-2018, Artificial intelligence, aviation, Biz & IT, civil aviation, Features, fly-by-wire, machine learning | Comments (0)

Apple published a surprising amount of detail about how the HomePod works

December 3rd, 2018
Image of a HomePod

Enlarge / Siri on Apple's HomePod speaker. (credit: Jeff Dunn)

Today, Apple published a long and informative blog post by its audio software engineering and speech teams about how they use machine learning to make Siri responsive on the HomePod, and it reveals a lot about why Apple has made machine learning such a focus of late.

The post discusses working in a far-field setting where users are calling on Siri from any number of locations around the room relative to the HomePod's location. The premise is essentially that making Siri work on the HomePod is harder than on the iPhone for that reason. The device must compete with loud music playback from itself.

Apple addresses these issues with multiple microphones along with machine learning methods—specifically:

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Posted in AI, apple, audio, HomePod, machine learning, Tech | Comments (0)

Apple walks Ars through the iPad Pro’s A12X system on a chip

November 7th, 2018
The 2018, 12.9-inch iPad Pro.

Enlarge / The 2018, 12.9-inch iPad Pro. (credit: Samuel Axon)

BROOKLYN—Apple's new iPad Pro sports several new features of note, including the most dramatic aesthetic redesign in years, Face ID, new Pencil features, and the very welcome move to USB-C. But the star of the show is the new A12X system on a chip (SoC).

Apple made some big claims about the A12X during its presentation announcing the product: that it has twice the graphics performance of the A10X; that it has 90 percent faster multi-core performance than its predecessor; that it matches the GPU power of the Xbox One S game console with no fan and at a fraction of the size; that it has 1,000 times faster graphics performance than the original iPad released eight years ago; that it's faster than 92 percent of all portable PCs.

If you've read our iPad Pro review, you know most of those claims hold up. Apple’s latest iOS devices aren’t perfect, but even the platform’s biggest detractors recognize that the company is leading the market when it comes to mobile CPU and GPU performance—not by a little, but by a lot. It's all done on custom silicon designed within Apple—a different approach than that taken by any mainstream Android or Windows device.

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Posted in A12X, Anand Lal Shimpi, apple, ar, CPU, Features, GPU, Interview, ipad, ipad pro, ISP, machine learning, Neural Engine, Phil Schiller, silicon, Tech | Comments (0)

AMD outlines its future: 7nm GPUs with PCIe 4, Zen 2, Zen 3, Zen 4

November 6th, 2018
AMD Radeon Instinct MI60

Enlarge / AMD Radeon Instinct MI60 (credit: AMD)

AMD today charted out its plans for the next few years of product development, with an array of new CPUs and GPUs in the development pipeline.

On the GPU front are two new datacenter-oriented GPUs: the Radeon Instinct MI60 and MI50. Based on the Vega architecture and built on TSMC's 7nm process, the cards are aimed not primarily at graphics (despite what one might think given that they're called GPUs) but rather at machine learning, high-performance computing, and rendering applications.

MI60 will come with 32GB of ECC HBM2 (second-generation High-Bandwidth Memory) while the MI50 gets 16GB, and both have a memory bandwidth up to 1TB/s. ECC is also used to protect all internal memory within the GPUs themselves. The cards will also support PCIe 4.0 (which doubles the transfer rate of PCIe 3.0) and direct GPU-to-GPU links using AMD's Infinity Fabric. This will offer up to 200GB/s of bandwidth (three times more than PCIe 4) between up to 4 GPUs.

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Posted in 10nm, 7nm, AMD, CPU, GPU, Intel, machine learning, processors, Tech, Zen | Comments (0)

Garbage in, garbage out: a cautionary tale about machine learning

July 26th, 2017

Security based on machine learning is only as great as the data it feeds on, as Sophos data scientist Hillary Sanders explains at Black Hat 2017

Posted in Black Hat, Black Hat 2017, deep learning, machine learning, Malware analysis | Comments (0)

Garbage in, garbage out: a cautionary tale about machine learning

July 26th, 2017

Security based on machine learning is only as great as the data it feeds on, as Sophos data scientist Hillary Sanders explains at Black Hat 2017

Posted in Black Hat, Black Hat 2017, deep learning, machine learning, Malware analysis | Comments (0)