Archive for the ‘Artificial intelligence’ Category

The trick to saving human factory jobs might be teaming up with the machines

June 20th, 2019
"Oh brave new industry, that has such bots in't!"

Enlarge / "Oh brave new industry, that has such bots in't!" (credit: Javier Pierini / Getty)

The Matrix. Skynet. Roy Batty. Anyone who has watched a science-fiction movie has seen a scenario where factions of humans and machines find themselves locked in mortal combat.

Here in 2019, though, we're doing what we can to disrupt that vision and steer the course away from human-machine antagonism and more toward cooperation. Instead of robot servants plotting to overthrow their meatbag masters, we're trying to use machines to augment human skills and strengths—especially in the context of manufacturing, which is the place where we're most likely to see robots. The rapid push to update manufacturing methods to more smartly integrate human with machine isn't necessarily as big a deal as the original Industrial Revolution, but it is a big enough deal that analysts have coined a snappy phrase for what we're going through: "Industry 4.0."

Sometimes the man-machine enhancements are physical, and sometimes they’re mental. Sometimes it's a Venn diagram that includes both aspects, as a skilled human worker collaborates with robotics and AI to complete a task.

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Posted in Artificial intelligence, Biz & IT, factory of the future, Industry 4.0 | Comments (0)

For the industrial Internet of Things, defense in depth is a requirement

June 19th, 2019
Sensors, sensors everywhere!

Enlarge / Sensors, sensors everywhere! (credit: Getty / 7postman)

Ars yesterday wrote a big feature on the concept of "Industry 4.0," the fancy-sounding name that describes the ongoing shift in how products are created from raw materials and distributed along the supply chain to customers.

What the "4.0" revision adds compared to Industries 1.0 through 3.0 is a complex set of linkages between information and operational technologies. (IT stores, transmits, and manipulates data, while "OT" detects and causes changes in physical processes, such as devices for manufacturing or climate control.)

It's a modular and flexible approach to manufacturing that creates digital links among "smart factories" that are powered by the industrial Internet of Things, big data, and machine learning. And that's almost enough fancy CEO words to make bingo. At least in this case, the buzzwords aren't just important-sounding but ultimately meaningless concepts. Similar to how the rise of devops welded programming with operations, making the manufacturing process smarter by stuffing in all those buzzwords really is causing fundamental changes in how things are made.

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Posted in Artificial intelligence, Biz & IT, factory of the future, Industry 4.0, Internet of things | Comments (0)

The fourth Industrial revolution emerges from AI and the Internet of Things

June 18th, 2019
Robots making things!

Enlarge / Robots making things! (credit: Getty / Ekkasit Keatsirikul / EyeEm)

Big data, analytics, and machine learning are starting to feel like anonymous business words, but they're not just overused abstract concepts—those buzzwords represent huge changes in much of the technology we deal with in our daily lives. Some of those changes have been for the better, making our interaction with machines and information more natural and more powerful. Others have helped companies tap into consumers' relationships, behaviors, locations and innermost thoughts in powerful and often disturbing ways. And the technologies have left a mark on everything from our highways to our homes.

It's no surprise that the concept of "information about everything" is being aggressively applied to manufacturing contexts. Just as they transformed consumer goods, smart, cheap, sensor-laden devices paired with powerful analytics and algorithms have been changing the industrial world as well over the past decade. The "Internet of Things" has arrived on the factory floor with all the force of a giant electronic Kool-Aid Man exploding through a cinderblock wall.

Tagged as "Industry 4.0," (hey, at least it's better than "Internet of Things"), this fourth industrial revolution has been unfolding over the past decade with fits and starts—largely because of the massive cultural and structural differences between the information technology that fuels the change and the "operational technology" that has been at the heart of industrial automation for decades.

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Posted in Artificial intelligence, Biz & IT, factory of the future, feature, Features, Industry 4.0, Internet of things | Comments (0)

OpenAI bot crushes Dota 2 champions, and now anyone can play against it

April 15th, 2019
Screenshot of a fiery video game monster.

Enlarge / Shadow Fiend, looking shadowy and fiendish. (credit: Valve)

Over the past several years, OpenAI, a startup with the mission of ensuring that "artificial general intelligence benefits all of humanity," has been developing a machine-learning-driven bot to play Dota 2, the greatest game in the universe. Starting from a very cut-down version of the full game, the bot has been developed over the years through playing millions upon millions of matches against itself, learning not just how to play the five-on-five team game but how to win, consistently.

We've been able to watch the bot's development over a number of show matches, with each one using a more complete version of a game and more skilled human opponents. This culminated in what's expected to be the final show match over the weekend, when OpenAI Five was pitted in a best-of-three match against OG, the team that won the biggest competition in all of esports last year, The International.

OpenAI is subject to a few handicaps in the name of keeping things interesting. Each of its five AI players is running an identical version of the bot software, with no communication among them: they're five independent players who happen to think very alike but have no direct means of coordinating their actions. OpenAI's reaction time is artificially slowed down to ensure that the game isn't simply a showcase of superhuman reflexes. And the bot still isn't using the full version of the game: only a limited selection of heroes is available, and items that create controllable minions or illusions are banned because it's felt that the bot would be able to micromanage its minions more effectively than any human could.

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Posted in Artificial intelligence, cloud, dota 2, Gaming & Culture, machine learning, OpenAI, Tech | Comments (0)

The basics of modern AI—how does it work and will it destroy society this year?

April 9th, 2019
You don't have to be Keir Dullea to know that fully grasping artificial intelligence can be intimidating.

Enlarge / You don't have to be Keir Dullea to know that fully grasping artificial intelligence can be intimidating. (credit: George Rinhart/Corbis via Getty Images)

AI, or artificial intelligence, is huge right now. “Unsolvable” problems are being solved, billions of dollars are being invested, and Microsoft even hired Common to tell you how great their AI is with spoken word poetry. Yikes.

As with any new technology, it can be hard to cut through the hype. I spent years doing research in robotics and UAVs and “AI,” but even I’ve had a hard time keeping up. In recent years I've spent a lot of time learning to answer even some of the most basic questions like:

  • What are people talking about when they say AI?
  • What’s the difference between AI, machine learning, and deep learning?
  • What’s so great about deep learning?
  • What kind of formerly hard problems are now easily solvable, and what’s still hard?

I know I’m not alone in wondering these things. So if you’ve been wondering what the AI excitement is all about at the most basic level, it's time for a little peek behind the curtain. If you’re an AI expert who reads NIPS papers for fun, there won’t be much new for you here—but we all look forward to your clarifications and corrections in the comments.

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Posted in Artificial intelligence, Features, Tech | Comments (0)

Alphabet subsidiary trained AI to predict wind output 36 hours in advance

February 27th, 2019
Wind turbines in Colorado.

Enlarge / Wind turbines have variable output, but if that can be forecast, that output is more valuable. (credit: Matthew Staver/Bloomberg via Getty Images)

Alphabet subsidiary DeepMind (it was acquired by Alphabet in 2014) has been developing artificial-intelligence programs since 2010 to solve complex problems. One of DeepMind's latest projects, according to a recent Google post, has centered around the predictability of wind power.

Those giant turbines you see along the highway only produce power when they're moving, and that poses a problem for the grid: in the absence of expensive energy storage, it's difficult to plan how much power those turbines will be able to provide.

That's not to say that wind-farm owners don't try to predict output. The industry has been using AI techniques for years to try to come closer and closer to real wind predictions.

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Posted in Alphabet, Artificial intelligence, Biz & IT, deepmind, Energy, google, science, Wind | Comments (0)

AI can diagnose some genetic disorders using photos of faces

January 11th, 2019
AI can diagnose some genetic disorders using photos of faces

Enlarge (credit: Monty Rakusen)

Genomes are so five minutes ago. Personalized medicine is all about phenomes now.

OK, that’s an exaggeration. But plenty of genetic disorders do result in distinctive facial phenotypes (Down syndrome is probably the best known example). Many of these disorders are quite rare and thus not easily recognized by clinicians. This lack of familiarity can cause the patients with the disorders (and their parents) to endure a long and traumatic diagnostic odyssey before they figure out what ails them. While they may be uncommon individually, in aggregate, these rare disorders are not that rare: they affect eight percent of the population.

FDNA is a genomics/AI company that aims to “capture, structure and analyze complex human physiological data to produce actionable genomic insights.” They’ve made a facial-image-analysis framework, called DeepGestalt, that can diagnose genetic conditions based on facial images with a higher accuracy than doctors can. Results are published in Nature Medicine.

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Posted in AI, Artificial intelligence, diagnostics, genetic disorders, science | Comments (0)

Move over AlphaGo: AlphaZero taught itself to play three different games

December 6th, 2018
Starting from random play and knowing just the game rules, AlphaZero defeated a world champion program in the games of Go, chess, and shoji (Japanese chess).

Enlarge / Starting from random play and knowing just the game rules, AlphaZero defeated a world champion program in the games of Go, chess, and shoji (Japanese chess). (credit: DeepMind Technologies, Ltd.)

Google's DeepMind—the group that brought you the champion game-playing AIs AlphaGo and AlphaGoZero—is back with a new, improved, and more-generalized version. Dubbed AlphaZero, this program taught itself to play three different board games (chess, Go, and shoji, a Japanese form of chess) in just three days, with no human intervention.

A paper describing the achievement was just published in Science. "Starting from totally random play, AlphaZero gradually learns what good play looks like and forms its own evaluations about the game," said Demis Hassabis, CEO and co-founder of DeepMind. "In that sense, it is free from the constraints of the way humans think about the game."

Chess has long been an ideal testing ground for game-playing computers and the development of AI. The very first chess computer program was written in the 1950s at Los Alamos National Laboratory, and in the late 1960s, Richard D. Greenblatt's Mac Hack IV program was the first to play in a human chess tournament—and to win against a human in tournament play. Many other computer chess programs followed, each a little better than the last, until IBM's Deep Blue computer defeated chess grand master Garry Kasparov in May 1997.

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Posted in AI, alphago, AlphaZero, Artificial intelligence, Computer science, deep learning, deepmind, game theory, gaming, Gaming & Culture, neural networks, reinforcement 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)

Elon Musk’s Dota 2 AI beats the professionals at their own game

August 14th, 2017

OpenAI takes on Dendi.

Last week was the high point of the Dota 2 competitive year: it was the week of The International, Valve’s biggest tournament. On Saturday, Team Liquid walked away with more than $10 million after defeating Newbee 3-0 in the grand final.

Right now, one of the requirements to be a good Dota 2 player is that you’ve got to be a living, breathing human. The game does include some basic computer-controlled bots to practice against, but any seasoned player of the game should have no trouble prevailing over these bots, even on their hardest “Unfair” difficulty (though the Unfair Viper bot is a legendary jerk that’s utterly miserable to play against). Last Friday, however, we got a hint of a new, altogether more threatening kind of computer-controlled player: an AI-controlled bot built by Elon Musk’s OpenAI. The OpenAI bot took on a number of professional players and it crushed them.

The OpenAI bot can’t play the full game of Dota 2. It can play only one hero, Shadow Fiend, of the game’s 113 playable characters (with two more coming later this year); it can only play against Shadow Fiend; and rather than playing in five-on-five matches, it plays a very narrow subset of the game: one-on-one solo matches.

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