Paper Summary: This paper focuses on using GANs for imitation learning using trajectories from an expert. The authors extend the GAIL (Generative Adversarial Imitation Learning) framework by including a term in the objective function to incorporate latent structure (similar to InfoGAN). The authors then proceed to show that using their framework, which they call InfoGAIL, they are able to learn interpretable latent structure when the expert policy has multiple modes and that in some setting this robustness allows them to outperform current methods. Paper Overview: The paper is generally well written. I appreciated that the authors first demon- started how the mechanism works on a toy 2D plane example before moving onto more complex driving simulation environment. This helped illustrate the core concepts of allowing the learned policy to be conditioned on a latent variable in a minimalistic setting before moving on to a more complex 3D driving simulation.

The race to roll out artificial intelligence is happening as quickly as the race to contain it – as two key moments this week demonstrate. On 10 May, Google announced plans to deploy new large language models, which use machine learning techniques to generate text, across its existing products. "We are reimagining all of our core products, including search," said Sundar Pichai, the CEO of Google's parent company Alphabet, at a press conference. The move is widely seen as a response to Microsoft adding similar functionality to its search engine, Bing. A day later, politicians in the European Union agreed on new rules dictating how and when AI can be used.

Scientists say they've developed a framework to make computer algorithms "safer" to use without creating bias based on race, gender or other factors. The trick, they say, is to make it possible for users to tell the algorithm what kinds of pitfalls to avoid – without having to know a lot about statistics or artificial intelligence. With this safeguard in place, hospitals, companies and other potential users who may be wary of putting machine learning to use could find it a more palatable tool for helping them solve problems, according to a report in this week's edition of the journal Science. Computer algorithms are used to make decisions in a range of settings, from courtrooms to schools to online shopping sites. The programs sort through huge amounts of data in search of useful patterns that can be applied to future decisions.

In the century and a bit since its founding in 1889, Nintendo has made playing cards, designed toys, hired out taxis and briefly run love hotels, but it is the last 40 years or so that have made it a cultural icon. Having dabbled in the video games business throughout the 1970s, in the 1980s Nintendo released the Game & Watch and the Nintendo Entertainment System, and since then it has introduced hundreds of millions of people to the joy of video games – from 90s kids squinting at monochrome Game Boys to grandmothers bowling on the Wii. Nintendo's hallmarks are innovation and an unwavering focus on fun. Where other big players in the games industry have chased the latest technology and positioned their consoles as entertainment hubs, Nintendo has mostly come out with affordable, family-friendly machines that combine technical innovations such as the Wii's motion control or the DS's touchscreen with fun, accessible games in the vein of Mario, Zelda, Pokémon and Wii Sports. Nintendo hasn't always been at the top of the sales charts, but no other video game creator has proven so enduringly popular across generations. A lot has changed since 1985, but kids still know who Mario is.

Is the Deep Learning Era really coming to an end? If somebody had procrastinated this change way back in 2011 that this was going to be a hot topic for debate, the tech world would have been astonished, with comments like Wow!! You are smoking something really strong! Almost everything we witness into artificial intelligence today is thanks to deep learning. The deep learning algorithms work by deploying statistics to find patterns in data and have proved immensely powerful in mimicking human skills such as the ability to hear and see. To a very narrow extent, deep learning algorithms can even emulate our ability to reason and comprehend.

Almost everything you hear about artificial intelligence today is thanks to deep learning. This category of algorithms works by using statistics to find patterns in data, and it has proved immensely powerful in mimicking human skills such as our ability to see and hear. To a very narrow extent, it can even emulate our ability to reason. These capabilities power Google's search, Facebook's news feed, and Netflix's recommendation engine--and are transforming industries like health care and education. But though deep learning has singlehandedly thrust AI into the public eye, it represents just a small blip in the history of humanity's quest to replicate our own intelligence.

Almost everything you hear about artificial intelligence today is thanks to deep learning. This category of algorithms works by using statistics to find patterns in data, and it has proved immensely powerful in mimicking human skills such as our ability to see and hear. To a very narrow extent, it can even emulate our ability to reason. These capabilities power Google's search, Facebook's news feed, and Netflix's recommendation engine--and are transforming industries like health care and education. But though deep learning has singlehandedly thrust AI into the public eye, it represents just a small blip in the history of humanity's quest to replicate our own intelligence.

Artificial intelligence is not some futuristic dystopian concept years away from hitting society. AI is here already and it might not be what you think it is. When people think about AI, they think about Hollywood science fiction. Is that something on the horizon? I'm not sure in the future AI will become like a Hollywood movie because it's everyday life.

I noticed recently that books with the phrase "The Last Man Who Knew Everything" all share in common that their subjects lived during the period close to the Scientific Revolution, roughly between 1550 to 1700. The Scientific Revolution killed our ability to Know Everything. It's as if the Scientific Revolution -- and the knowledge it spawned -- killed the ability to Know Everything. Before then, it was not only possible to be a generalist or polymath (someone with a wide range of expertise) -- but the weaving together of different disciplines was actually rather unexceptional. The Ancients discussed topics such as ethics, biology, and metaphysics alongside each other.