Back in the day, the idea of interacting with robots and computers seemed out of an episode of The Jetsons. But fast-forward to today, and some of the most far-fetched ideas back then have become our reality, and you know what? In Back to The Future II, they used tablets to get Marty to sign up to save the Clock Tower, and then this massive shark pops out of a sign, which freaked 1984 Marty out. The only thing is – do you remember how pixelated the shark was? Magic Leap is augmenting reality to look like a whale can literally crash into a gymnasium, without so much as a drop of water.

Humanoid robots are a familiar trope in popular culture, but is making machines look like us a little bit creepy and even potentially dangerous? Whether it is Isaac Asimov's robotics novels, 1980s movie character Johnny 5, Hollywood's Avengers: The Age of Ultron or Channel 4's sci-fi drama Humans, there has long been a fascination in popular culture with robots becoming sentient - beings that can experience feelings and human-like consciousness. But how realistic - and desirable - is the prospect of robots that become almost indistinguishable from humans? Dr Ben Goertzel, who developed the AI software for Sophia, a social humanoid robot made by Hong Kong-based Hanson Robotics, believes robots should look like humans to help "break down suspicions and reservations people might have" about interacting with them. "You will have humanoid robots because people like them," he tells the BBC.

University of Colorado Boulder neuroscientists have combined machine learning and neuroscience to create a brain-inspired computer that can tell the difference between sad and happy images. "Machine learning technology is getting really good at recognizing the content of images -- of deciphering what kind of object it is," said senior author Tor Wager, who worked on the study while a professor of psychology and neuroscience at CU Boulder. "We wanted to ask: Could it do the same with emotions? The experiment is an important development in "neural networks," computer systems modeled after the human brain. It also highlights that what we see could have a more severe impact on our emotions than we might think. "A lot of people assume that humans evaluate their environment in a certain way and emotions follow from specific, ancestrally older brain systems like the limbic system," said lead author Philip Kragel, a postdoctoral research associate at the Institute of Cognitive Science. "We found that the visual cortex itself also plays an important role in the processing and perception of emotion." For their study, the researchers used a neural network called AlexNet designed to enable computers to recognize objects and retooled it to predict how a person would feel when they see a certain image using previous research. The researchers dubbed the new network EmoNet and proceeded to show it 25,000 images. The computer was then asked to categorize them into 20 sections such as craving, sexual desire, horror, awe, and surprise. The program was found to better at recognizing some emotions better than others. Craving or sexual desire, for instance, were categorized with more than 95 percent accuracy. However, more nuanced discreet emotions such as confusion, awe, and surprise were harder to pinpoint. EmoNet proved very reliable in rating the intensity of images. It was also rather good at rating brief movie clips. When asked to categorize them as romantic comedies, action films or horror movies, it got it correct 75% of the time. The researchers then used 18 human subjects and had a functional magnetic resonance imaging (fMRI) machine measure their brain activity when they were shown the same 112 images as EmoNet. Surprisingly, the neural network patterns were the same for human and computer. "We found a correspondence between patterns of brain activity in the occipital lobe and units in EmoNet that code for specific emotions.

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If I understand correctly, both CPC and AlexNet used the same set of training images. CPC just didn't use labels, while AlexNet did. So, what about instances where a self-supervised network can be trained on 10,000x as much data as would be economically feasible to label? In these cases, are supervised learning's days numbered? The application I'm personally most interested in is self-driving cars.

Lately, I've been having a lot of fun trying to train a quadcopter to fly. They're small, they're maneuverable, and they can be flown both indoors and outdoors. You can use them for deliveries, humanitarian operations, search and rescue, and cinematography. They have military and law enforcement applications too, of course. With a quadcopter, you can test new ideas in fields like flight control theory, real-time systems, navigation, and robotics. Plus they can be built and maintained by amateurs!

The AI BOT Quote on Recycling is powered by OpenAI's GPT-2 and should not be taken on face value (recycling is good). The AI news anchor was created by Xinhua in partnership with Sogou. Existential Crisis Disclaimer: Don't panic – this is all just theory including my other Simulation Theory / Hypothesis episodes. In this NEW Episode (#011), I explore the role of AI further in regard to Simulation Theory. We explore 1) How Machine Learning and GAN can create whole new faces in seconds.

Rutger Hauer, the Dutch actor who portrayed Roy Batty in the film Blade Runner, passed away recently. To celebrate his iconic role, we are revisiting this piece on the Voight-Kampff test, a device to detect if a person is really human. Is Rick Deckard a replicant, an advanced bioengineered being? The jury concerning the character in 1982's Blade Runner is still out. Harrison Ford, who plays Deckard in the film, thinks he's human.

Are technology companies running too fast into the future and creating things that could potentially wreak havoc on humankind? That question has been swirling around in my head ever since I saw the enthralling science-fiction film "Ex Machina." The movie offers a clever version of the robots versus humans narrative. But what makes "Ex Machina" different from the usual special-effects blockbuster is the ethical questions it poses. Foremost among them is something that most techies don't seem to want to answer: Who is making sure that all of this innovation does not go drastically wrong?

Cat filters and rock star sunglasses are just the tip of the iceberg in today's ocean of face-based augmented reality (AR) mobile applications. Such effects are however compute-hungry, and today's users want their novelty images to pop up on their smartphone screens without delay. Luckily for us, Google fully appreciates the need for speed. Google researchers have introduced a new face detection framework called BlazeFace, adapted from the Single Shot Multibox Detector (SSD) framework and optimized for inference on mobile GPUs. The lightweight face detector runs at an impressive speed of 200-1000 FPS on flagship smartphones.