When you purchase through links in our articles, we may earn a small commission. Bored.com is back, and it feels like the Internet remembered how to be weird Bored.com Back in the Flash era, it was packed with browser games, weird quizzes, and random internet junk you'd spend hours perusing. Then Flash died, most of those old sites faded away, and Bored.com It just reinvented itself into something even better: A user-curated collection of the best online time-killers.

Here's the thing about podcasts: There are too many of them. More than 4 million, to be precise, according to the database Podcast Index. In the past three days alone, nearly 103,000 individual podcast episodes were published online, a deluge of audio content so voluminous that listeners need never run out of options. You could spend the rest of your life working through the existing true crime catalog on Apple Podcasts or the sports chat shows on Spotify and end up dying of old age in 2070 while Michael Barbaro reads an ad for Mailchimp to your corpse. In the ongoing generative AI gold rush, though, opportunistic entrepreneurs are looking for entry into even the most saturated markets.

An alternative to the typical technique of selecting training examples independently from a fixed distribution is fonnulated and analyzed, in which the current example is presented repeatedly until the error for that item is reduced to some criterion value,; then, another item is ran(cid:173) domly selected. The convergence time can be dramatically increased or decreased by this heuristic, depending on the task, and is very sensitive to the value of .

Mario may be super, but even he must get bored hurdling the same Goombas and falling off the same cliffs over and over. Fortunately, a new artificial intelligence (AI) algorithm can endlessly produce new levels, and even tailor them to a player's skill level. Computer scientists have spent decades honing "procedural content generation," the use of algorithms to automatically design new characters, landscapes, and weapons for video games, saving humans hours of labor. But programmers still need to handcraft the rules that tell the computer how to create such content. In recent years they've applied machine learning, an AI technique by which computers learn from examples, so AI can simply produce more content in the style of existing content without needing explicit instructions.

If you're trying to get in shape and you want a tiny, wrist-bound computer to help you do it, you have more options than ever before. Fitness trackers come in all shapes, colors, and price tags, with newfangled sensors and features to stand out to customers. But for doctors and scientists studying how exercise can help people deal with disease, the landscape is much simpler. Like most fitness trackers, Fitbit's devices are far from perfect. They can count steps pretty well and give a good idea of activity levels day to day.

Video Friday is your weekly selection of awesome robotics videos, collected by your Automaton bloggers. We'll also be posting a weekly calendar of upcoming robotics events for the next two months; here's what we have so far (send us your events!): Let us know if you have suggestions for next week, and enjoy today's videos. Sadly, not everyone can afford a lab full of robots to experiment with. We'll be checking back in with the Robotarium just as soon as the rest of the world starts using it for research.

The days of dozing off at the back of a classroom may soon be coming to an end. A Chinese university lecturer is using facial-recognition technology on his students to check if they're bored – and he says it could be used in wider education. Professor Wei Xiaoyong, who lectures in computer science at Sichuan University in China, developed the'face reader' to identify the emotions of his students. A Chinese university lecturer is using facial-recognition technology on his students to check if they're bored. The reader produces a'curve' for each student, showing whether they are happy or not, and giving indications of whether they are bored.

An online learning program which can tell when a student is becoming bored and inattentive is one of the key developments forecast to reshape university education in Australia in the next five years, according to a new report. The 2016 NMC Technology Outlook for Australian Tertiary Education says that so-called "affective computing", which is able to use video imagery of facial expressions to discern human emotions, will soon be coupled with online learning platforms to encourage students to keep their minds on their work. The report says this is likely to be adopted by universities in the next four to five years. It forecast "online learning situations wherein a computerised tutor reacts to facial cues of boredom in a student in an effort to motivate or boost their confidence." "Software technology will literally learn to learn, interpreting and responding to learners' most nuanced gestures and emotions – whether they are feeling bored, intimidated or satisfied," says Brenda Frisk, head of learning technology at Open Universities Australia, a partner in the report.

An alternative to the typical technique of selecting training examples independently from a fixed distribution is fonnulated and analyzed, in which the current example is presented repeatedly until the error for that item is reduced to some criterion value,; then, another item is randomly selected. The convergence time can be dramatically increased or decreased by this heuristic, depending on the task, and is very sensitive to the value of .

An alternative to the typical technique of selecting training examples independently from a fixed distribution is fonnulated and analyzed, in which the current example is presented repeatedly until the error for that item is reduced to some criterion value,; then, another item is randomly selected. The convergence time can be dramatically increased or decreased by this heuristic, depending on the task, and is very sensitive to the value of .