We review the best Android phone you'll probably never get to buy, the wonderful game mash-up of Super Mario 64 and Ocarina of Time and Google's podcast app -- which was apparently there all along. Amazon is also looking to build a domestic robot we've been dreaming of. It won't be the first. We're upgrading Engadget's daily newsletter and want to hear from you. Tell us exactly what you think by emailing us at themorningafter(at)engadget.com.
Developments in Artificial Intelligence (A.I.) are happening faster today than ever before. However, the nature of progress in A.I. is such that massive technological breakthroughs might go unnoticed while smaller improvements get a lot of media attention. Take the case of face recognition technology. The ability of A.I. to recognize faces might seem like a very big deal, but isn't that groundbreaking when you consider the nature of applied A.I. On the other hand, suppose an A.I. is asked to choose between a genre of music, such as R&B or rock. While it may seem like a simple choice, the mathematical algorithm that must be solved before the A.I makes a decision could take hours and days.
By 2038, robots have replaced people in most common jobs. Aside from a glowing circle that sits near their temple, they would be almost completely indistinguishable from the living, breathing people they were crafted to look like. It should come as no surprise, then, that it was only a matter of time until they begin doing things they haven't been programmed for -- slowly but surely gaining sentience and giving way to an uprising. Androids run the world, and humans are just living in it. That's what life is like in the city of Detroit 20 years in the future in Detroit: Become Human, a new game from studio Quantic Dream launching on May 25 for the PlayStation 4. Become Human, developed by the same studio responsible for cinematic adventure games Heavy Rain and Beyond Two Souls, centers on the lives of three androids: Kara, Markus, and Connor.
Today, we're going to take this trained Keras model and deploy it to an iPhone and iOS app using what Apple has dubbed "CoreML", an easy-to-use machine learning framework for Apple applications: My goal today is to show you how simple it is to deploy your Keras model to your iPhone and iOS using CoreML. To be clear, I'm not a mobile developer by any stretch of the imagination, and if I can do it, I'm confident you can do it as well. Feel free to use the code in today's post as a starting point for your own application. But personally, I'm going to continue the theme of this series and build a Pokedex. A Pokedex is a device that exists in the world of Pokemon, a popular TV show, video game, and trading card series (I was/still am a huge Pokemon nerd). Using a Pokedex you can take a picture of a Pokemon (animal-like creatures that exist in the world of Pokemon) and the Pokedex will automatically identify the creature for for you, providing useful information and statistics, such as the Pokemon's height, weight, and any special abilities it may have. You can see an example of a Pokedex in action at the top of this blog post, but again, feel free to swap out my Keras model for your own -- the process is quite simple and straightforward as you'll see later in this guide.
Tonight's premiere episode of Westworld season two is littered with bodies. It's a reminder of the consequences of what can go wrong when we put the race for technology ahead of ethics that governs what we should or shouldn't do with that technology. At the close of the last season, the AI hosts of Westworld gain independent sentience, and they rebel against their masters, slaughtering the humans who subjected them to inhumane treatment and allowed them to be used as the instruments for human resort goers to live out their (usually) worst fantasies. Editor's note: This story has some season two episode one story spoilers. Season two starts with more of the aftermath of that AI rebellion.
Nintendo has a history of making people ask why. Why make a console that can come apart and plug into a TV; why did it soldier on for so long with cartridges for games; why is Mario a plumber and wear a corresponding outfit despite not apparently having done it for decades; why the Wii U? It has never stopped, all the way up to its latest release: the Switch, which came at a risky time for the company but helped them pull off exactly what it needed. The company's newest product, Labo, is marketed as an accessory for that console but is actually a huge box full of pieces of perforated cardboard that can be popped out, folded and assembled into a variety of accessories: everything from a small remote control car that drives around using vibration to an entire robot suit that can be strapped on to operate a virtual version of the same robot in a game. It is perhaps the company's most why-inducing release yet. But the answer has, for the most part, always been the same.
We are well and truly in the grips of the battle royale era at this point, with rumors this week pegging a PUBG-style mode coming to both Call of Duty and Battlefield in the fall. At this point the question's not whether we see a new battle royale game at E3, it's how many we see. This is gaming news for April 16 to 20. Humble's habitually giving away games nearly every weekend it seems, and the trend continues this week with Satellite Reign. A blend of real-time tactics game and open-world adventure, it was intended as a successor to Syndicate--the original isometric one from the '90s, not the 2012 reboot.
In this post, we reproduce the recent Uber paper "Deep Neuroevolution: Genetic Algorithms are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning", which amazingly showed that simple genetic algorithms sometimes performed better than apparently advanced reinforcement learning algorithms on well studied problems such as Atari games. We will ourselves reach state of the art performance on Frostbite, a game that had stumped reinforcement learning algorithms for years before Uber finally solved it with this paper. We will also learn about the dark art of training neural networks using genetic algorithms. In a way this could be considered part 3 of my deep reinforcement learning, but I think this article can also stand alone. Note that unlike these previous tutorials, this post will be using PyTorch instead of Keras, mainly because this is what I personally have switched to, but also because PyTorch does happen to be more suited for this particular use case.
Nintendo Labo – the new, strange, cardboard accessory for the Switch – is finally about to arrive. The accessory allows you to make a car, a piano, a robot or a motorbike. And it does all that with just the use of some very well put together cardboard. US and Japan will get hold of Labo this week, on 20 April. In Europe and the UK, they will come out a week later, on 27 April.
Games have long been used as testbeds and benchmarks for artificial intelligence, and there has been no shortage of achievements in recent months. Google DeepMind's AlphaGo and poker bot Libratus from Carnegie Mellon University have both beaten human experts at games that have traditionally been hard for AI – some 20 years after IBM's DeepBlue achieved the same feat in chess. Games like these have the attraction of clearly defined rules; they are relatively simple and cheap for AI researchers to work with, and they provide a variety of cognitive challenges at any desired level of difficulty. By inventing algorithms that play them well, researchers hope to gain insights into the mechanisms needed to function autonomously. With the arrival of the latest techniques in AI and machine learning, attention is now shifting to visually detailed computer games – including the 3D shooter Doom, various 2D Atari games such as Pong and Space Invaders, and the real-time strategy game StarCraft.