Lifeboat Foundation

Blizzard president J. Allen Brack said the system has dramatically reduced toxic chat and repeating offenses.

In April 2019, Blizzard shared some insights into how it was using machine learning to combat abusive chat in games like Overwatch. It’s a very complicated process, obviously, but it appears to be working out: Blizzard president J. Allen Brack said in a new Fireside Chat video that it has resulted in an “incredible decrease” in toxic behavior.

Continue reading “Overwatch toxicity has seen an ‘incredible decrease’ thanks to machine learning, says Blizzard” »

Artificial intelligence helps scientists make discoveries, but not everyone can understand how it reaches its conclusions. One UMaine computer scientist is developing deep neural networks that explain their findings in ways users can comprehend, applying his work to biology, medicine and other fields.

Interpretable machine learning, or AI that creates explanations for the findings it reaches, defines the focus of Chaofan Chen’s research. The assistant professor of computer science says interpretable machine learning also allows AI to make comparisons among images and predictions from data, and at the same time, elaborate on its reasoning.

Scientists can use interpretable machine learning for a variety of applications, from identifying birds in images for wildlife surveys to analyzing mammograms.

How do you *feel* about that?

Much of today’s discussion around the future of artificial intelligence is focused on the possibility of achieving artificial general intelligence. Essentially, an AI capable of tackling an array of random tasks and working out how to tackle a new task on its own, much like a human, is the ultimate goal. But the discussion around this kind of intelligence seems less about if and more about when at this stage in the game. With the advent of neural networks and deep learning, the sky is the actual limit, at least that will be true once other areas of technology overcome their remaining obstructions.

For deep learning to successfully support general intelligence, it’s going to need the ability to access and store much more information than any individual system currently does. It’s also going to need to process that information more quickly than current technology will allow. If these things can catch up with the advancements in neural networks and deep learning, we might end up with an intelligence capable of solving some major world problems. Of course, we will still need to spoon-feed it since it only has access to the digital world, for the most part.

Continue reading “The Future of AI is Artificial Sentience” »

Humanoid robots of tomorrow could feel pain just like us.

Woah o,.o!

In recent years, artificial intelligence (AI) tools, including natural language processing (NLP) techniques, have become increasingly sophisticated, achieving exceptional results in a variety of tasks. NLP techniques are specifically designed to understand human language and produce suitable responses, thus enabling communication between humans and artificial agents.

Other studies also introduced goal-oriented agents that can autonomously navigate virtual or videogame environments. So far, NLP techniques and goal-oriented agents have typically been developed individually, rather than being combined into unified methods.

Continue reading “Teaching AI agents to communicate and act in fantasy worlds” »

I highly recommend checking this fantastic look at Deep Blue and the fascinating role chess has played in the ongoing development of artificial intelligence.

After an electrical engineer enters the field of computer chess, his creation captures the attention of the world as he attempts to defeat the world chess champion.

Continue reading “Deep Blue | Down the Rabbit Hole” »

Thirty years ago, Hinton’s belief in neural networks was contrarian. Now it’s hard to find anyone who disagrees, he says.

Intel continues to snap up startups to build out its machine learning and AI operations. In the latest move, TechCrunch has learned that the chip giant has acquired Cnvrg.io, an Israeli company that has built and operates a platform for data scientists to build and run machine learning models, which can be used to train and track multiple models and run comparisons on them, build recommendations and more.

Intel confirmed the acquisition to us with a short note. “We can confirm that we have acquired Cnvrg,” a spokesperson said. “Cnvrg will be an independent Intel company and will continue to serve its existing and future customers.” Those customers include Lightricks, ST Unitas and Playtika.

Intel is not disclosing any financial terms of the deal, nor who from the startup will join Intel. Cnvrg, co-founded by Yochay Ettun (CEO) and Leah Forkosh Kolben, had raised $8 million from investors that include Hanaco Venture Capital and Jerusalem Venture Partners, and PitchBook estimates that it was valued at around $17 million in its last round.

R-sharing. Hmmm… would you trust the AI to drive for you?

At the end of November, Tesla (NASDAQ: TSLA) released its Vehicle Safety Report for Q3 2020, which shows that its vehicles using Autopilot are almost 10 times safer than other vehicles on United States roads. While the California manufacturer has directed massive efforts towards achieving Level 5 autonomy, the development of autonomous driving in Europe is at best slow-moving.

Recently, though, researchers in Germany are suggesting that this should change, and for good reason. The researchers indicate that, if Tesla Autopilot were installed on all cars in the Germany now, they would be able to avoid hundreds of thousands of car accidents.

Continue reading “Tesla Autopilot Would Avoid 90% of Car Accidents, German Researcher Urges Country’s Adoption” »