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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.

“Part of having a good game experience is finding ways to ensure that all are welcome within the worlds, no matter their background or identity,” Brack says in the video. “Something we’ve spoken about publicly a little bit in the past is our machine learning system that helps us verify player reports around offensive behavior and offensive language.”

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.

If we desire an AGI that can consume its own information, there are a few more advancements in technology that only time can deliver. In addition to the increased volume of information and processing speed, before any AI will be much use as an automaton, it will need to possess fine motor skills. An AGI with control of its own faculty can move around the world and consume information through its various sensors. However, this is another case of just waiting. It’s also another form of when not if these technologies will catch up to the others. Google has successfully experimented with fine motor skills technology. Boston Dynamics has canine robots with stable motor skills that will only improve in the coming years. Who says our AGI automaton needs to stand erect?

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.

Researchers at Georgia Institute of Technology and Facebook AI Research have recently explored the possibility of equipping goal-driven agents with NLP capabilities so that they can speak with other characters and complete desirable actions within fantasy game environments. Their paper, pre-published on arXiv, shows that combined, these two approaches achieve remarkable results, producing game characters that speak and act in ways that are consistent with their overall motivations.

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.

Patreon: https://www.patreon.com/fredrikknudsen

Twitter: https://twitter.com/FredInTheKnud

Music by Ryan Probert: https://twitter.com/ProbeComposer
Graphic Design by Christopher “Arcaxon” Malouin-Monjaraz: https://twitter.com/Arcaxon
Saxophone by Naomi Sullivan: https://twitter.com/naomisullivan9
Vocals by Rachel Nicholas: https://twitter.com/_rachnicholas
“Chess Personality” Paintings by Anton Oxenuk: https://twitter.com/antonoxenuk
“Gambit” Chess Robot by DerEineSchwarzeRabe: https://twitter.com/D_E_S_R

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.

“Legislative procedures that provide legal support for autonomous driving are progressing slowly,” criticizes Ferdinand Dudenhöffer, director of the Center for Automotive Research (CAR) in Duisburg.

If you are interested in developing chatbots, you can find out that there are a lot of powerful bot development frameworks, tools, and platforms that can use to implement intelligent chatbot solutions. How about developing a simple, intelligent chatbot from scratch using deep learning rather than using any bot development framework or any other platform. In this tutorial, you can learn how to develop an end-to-end domain-specific intelligent chatbot solution using deep learning with Keras.