This article is my entry for CodeProject's AI competition "Image Classification Challenge"[ ]. My goal was to teach a neural network to play a game of tic tac toe, starting from only knowing the rules. Tic tac toe is a solved game. A perfect strategy[ ] exists so a neural network is a bit overkill and will not perform as well as existing programs and humans can. Described from a high level: when the AI needs to make a move, it iterates over all possible moves, generates the board after making a given move, and uses the neural network to see how good the position is after performing that move.
Does this star have a planet? A new algorithm could help astronomers predict, on the basis of a star's chemical fingerprint, whether that star will host a giant gaseous exoplanet. "It's like Netflix," Natalie Hinkel, a planetary astrophysicist at the Southwest Research Institute in San Antonio, Texas, told Eos. Netflix "sees that you like goofy comedy, science fiction, and kung fu movies--a variety of different patterns" to predict whether you'll like a new movie. Likewise, her team's machine learning algorithm "will learn which elements are influential in deciding whether or not a star has a planet."
Every stranger's face hides a secret, but the smiles in this crowd conceal a big one: These people do not exist. They were generated by machine learning algorithms, for the purposes of probing whether AI-made faces can pass as real. University of Washington professors Jevin West and Carl Bergstrom generated thousands of virtual visages to create Which Face Is Real?, an online game that pairs each counterfeit with a photo of a real person and challenges players to pick out the true human. Nearly 6 million rounds have been played by half a million people. These are some of the faces that players found most difficult to identify as the cheery replicants they are.
We live in a world where everything is connected, smart fridges, salt dispensers, egg timers, and even hair brushes. That sounds like a Ummm, ok-ish idea. Well, that's what this show is. Deep Learning with Merrill Grambell is the first show completely hosted by artificial intelligence as it interacts with real-life comedians, musicians, technologists and other interesting guests from all over the world. It's sort of as if Clippy from Microsoft Word and Bonzi Buddy got together and made a talk show.
Throughout A.I.'s 60-year history, skeptics have attempted to single out tasks that they think machines will never be able to achieve. Such tasks have ranged from playing a game of chess to generating pieces of music to driving a car. In almost every instant, they have been proved wrong -- sometimes profoundly so. But as amazing as A.I. is here in 2018, there are still things that it is most assuredly not able to do. While some are more frivolous than others, they all showcase some part of machine intelligence that's currently lacking.
A few weeks ago, I had the opportunity to demo Chisel AI's Submission Triage and Policy Check solutions at Digital Insurance's Dig In conference in Austin. In speaking with commercial insurance brokers and carriers at the event, a common theme was how to get artificial intelligence implemented. Insurers understand the necessity of innovation in their industry and are excited about the potential benefits of AI in streamlining workflows and improving the digital customer experience. But they have valid questions about how to deploy an AI solution in production, and what a successful AI rollout looks like in the real world. According to a new report conducted by CompTIA, a mere 19 percent of companies say that they have expert knowledge around AI.
They say "behind every good man, there is a woman," but in the world of blockbuster movies, the best allies to have by your side are often of the robotic persuasion. Always ready to dig you out of a rough spot, or march gung-ho into a battle, the movie robot sidekick has become a staple in modern sci-fi and action/adventure. Sure, there have been some bad-ass solo robots over the years like Optimus Prime, Ava of Ex-Machina fame, and even Robocop (although, technically he's a cyborg), but we're here celebrating the sidekick. The robots that make the best partners in crime. Whether it's intergalactic co-pilots, shape-shifting planetary protectors, or time-travelling androids, join us as we count down the 10 best movie robot sidekicks.
Plate from Muybridge's Animal Locomotion series published in 1887. Deep learning has become the dominate lens through which machines understand video. Yet video files consume huge amounts of storage space and are extremely computationally demanding to analyze using deep learning. Certain use cases can benefit from converting videos to sequences of still images for analysis, enabling full data parallelism and vast reductions in data storage and computation. Representing video as still imagery also presents unique opportunities for non-consumptive analysis similar to the use of ngrams for text.
When asked why he robbed banks, Willie Sutton famously replied, "Because that's where the money is". And so much of artificial antelligence evolved in the United States – because that's where the computers were. However with Europe's strong educational institutions, the path to advanced AI technologies has been cleared by European computer scientists, neuroscientists, and engineers – many of whom were later poached by US universities and companies. From backpropagation to Google Translate, deep learning, and the development of more advanced GPUs permitting faster processing and rapid developments in AI over the past decade, some of the greatest contributions to AI have come from European minds. Modern AI can be traced back to the work of the English mathematician Alan Turing, who in early 1940 designed the bombe – an electromechanical precursor to the modern computer (itself based on previous work by Polish scientists) that broke the German military codes in World War II.
During a wide-ranging discussion at Amazon's re:MARS conference in Las Vegas, Naveen Rao, corporate vice president and general manager of AI at Intel, spoke about machine learning's rapid progress and the fields it might transform, in addition to the steps he believes must be taken to ensure it's not abused. Rao compared the advent of modern AI approaches with the iPhone. Like the iPhone, he said, machine learning -- a technique underlying systems from Amazon's Alexa to Google Lens -- wasn't the first form of AI, but it was nonetheless "exciting" and "consequential." He characterizes the coming AI revolution as the single largest transition the human species has ever encountered. "Few people anticipated the big-picture changes that smartphones would bring. No one foresaw that smartphones could make our work day substantially longer because we'd never get away from email," he said.