'I'm baffled how anyone could use AI to participate in a hobby.' 'I'm baffled how anyone could use AI to participate in a hobby.' I'm a committed introvert - but no AI will take away the joy I get from other people T his is depressing: according to the Cut, people are using AI to solve escape room puzzles and cheat at trivia nights. Surely, that is the definition of spoiling your own fun? "Like going into a corn maze and just wanting a straight line to the end," says one TikToker quoted in the article. There's also an interview with a keen reader who uses ChatGPT as a book club replacement, scraping the internet and aggregating "stimulating opinions and perspectives". All well and good (actually, no, it sounds bleak as hell) until he had a character's death spoilered in the fantasy epic he had been enjoying.

Summary: The idea of coupling reward and dynamics in an autoencoder-like model is a novel contribution which could benefit our community. I also appreciate that the authors have applied their model on pixel-based observation spaces. However, I find the theory of lines 124 to 136 unnecessary and the fact that it reproduces Parr (2007) line by line is problematic (more on this below). Also, example 1 seems misguided since it simply does adopt the right problem formulation to start with (it seems sufficient to simply start with a Markov chain over state-action pairs). Detailed comments: Abstract: l. 4. and sect. 1 l.25: "Typical deep RL [...]" and "It is common" Is that true? Beside DQN, what are other examples?

This article is from Big Technology, a newsletter by Alex Kantrowitz. The chatbots did their job. They inspired awe, mockery, and even some fear. Most importantly, they drew attention. Front-page headlines, cover stories, and word of mouth caused millions to try them, leading businesses and developers to ask how they could put the technology to use.

Reinforcement learning algorithms have become more complex since the invention of target networks. Unfortunately, target networks have not kept up with this increased complexity, instead requiring approximate solutions to be computationally feasible. These approximations increase noise in the Q-value targets and in the replay sampling distribution. Paused Agent Replay Refresh (PARR) is a drop-in replacement for target networks that supports more complex learning algorithms without this need for approximation. Using a basic Q-network architecture, and refreshing the novelty values, target values, and replay sampling distribution, PARR gets 2500 points in Montezuma's Revenge after only 30.9 million Atari frames. Finally, interpreting PARR in the context of carbon-based learning offers a new reason for sleep.

Active inference, a corollary of the free energy principle, is a formal way of describing the behavior of certain kinds of random dynamical systems that have the appearance of sentience. In this chapter, we describe how active inference combines Bayesian decision theory and optimal Bayesian design principles under a single imperative to minimize expected free energy. It is this aspect of active inference that allows for the natural emergence of information-seeking behavior. When removing prior outcomes preferences from expected free energy, active inference reduces to optimal Bayesian design, i.e., information gain maximization. Conversely, active inference reduces to Bayesian decision theory in the absence of ambiguity and relative risk, i.e., expected utility maximization. Using these limiting cases, we illustrate how behaviors differ when agents select actions that optimize expected utility, expected information gain, and expected free energy. Our T-maze simulations show optimizing expected free energy produces goal-directed information-seeking behavior while optimizing expected utility induces purely exploitive behavior and maximizing information gain engenders intrinsically motivated behavior.

Embry-Riddle researchers are working on a solution to a significant safety problem involving semi-autonomous vehicles after crashes occurred when the vehicles did not detect firetrucks or police cars in the roadway. Partnering with the Emergency Responder Safety Institute and a private company called HAAS Alerts, Scott Parr, assistant professor of Civil Engineering, and Patrick Currier, associate professor and associate chair of the Mechanical Engineering Department, plan to employ digital signals to alert the autonomous vehicles (AVs) of the presence of emergency response vehicles. The plan would effectively employ emergency vehicle location signals -- now provided by HAAS Alerts to route mapping applications whenever a geolocation device mounted to the lighting bar of emergency vehicles is activated -- and extend them to also communicate with AVs. "We're trying to demonstrate that this technology does work and that it can be a solution to the problem," said Parr, adding that a response system to the alerts will be manually programmed into AVs owned by Embry-Riddle as a demonstration. The system would enact an automatic protocol to slow or stop the AV depending on how close it was to the emergency vehicle.

If a distant comet is on course to collide with Earth, scientists will be able to detect it only about a year in advance. That doesn't leave much time to prepare. Artificial intelligence researchers believe they have the key to providing astronomers more foresight: machine learning algorithms that can more quickly identify and cluster the debris that comets leave in their wake. By speeding up analysis of meteor showers, researchers hope to pinpoint the orbits of distant, but potentially dangerous, comets. This project is one of five being explored as part of an artificial intelligence pilot research program sponsored by NASA.

They were revealed at Facebook's F8 conference. SAN JOSE -- Last year Facebook Messenger oversold how soon chat bots would be able to carry on a human-like conversation to respond to your every need. This year at Facebook's annual F8 conference for software developers, the chat app has a new message for its 1.2 billion users: Chat and chat bots can be very useful in your daily lives. On Tuesday Messenger introduced the capability to have a group chat with a business, meaning you can plan a play list on Spotify, share highlights from NBA games, make a dinner reservation on Open Table or pick flights on Hipmunk for a summer getaway with a group of friends on Messenger without leaving the app. A discover tab now allows you to search for chat bots on Messenger.

It's been a week now since serial entrepreneur (and futurist) Elon Musk announced the launch of Neuralink Corp, and to be honest, it's still on our minds. With this new venture, Musk proposes developing a technology (known as "neural lace") that would potentially implant electrodes into human brains, allowing us to keep up with the advancement of machines--even uploading and downloading thoughts or fight brain disorders. The company is still in its "embryonic" phases, according to co-founder Max Hodak, and there are plenty of technological and safety barriers ahead. That said, it's the first of its kind as an application of artificial intelligence inside the human brain. Every visualization you've ever seen of a black hole has been an illustration. As Vox points out, the closest the scientific community has ever come to seeing one was through last year's observation of "spacetime-warping gravitational waves radiating" from the billion-year-old collision of two black holes.

Imagine it's 2018 and some scientists from NASA are at the White House to see President Clintrump. There's a piece of space coming toward us; it is rocky, and icy, and big, and the risk of it hitting the Earth is much larger than anyone is comfortable with. Even if there's time to act, there won't be much of it. Where did it come from? How come we didn't spot it until now? What's the best course of action to take?