If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Artificial intelligence might just spawn a whole new style trend: call it "predictive fashion." In a paper published on the ArXiv, researchers from the University of California, San Diego, and Adobe have outlined a way for AI to not only learn a person's style but create computer-generated images of items that match that style. The system could let retailers create personalized pieces of clothing, or could even be used to help predict broader fashion trends. First, the researchers trained a convolutional neural network (CNN) to learn and classify a user's preferences for certain items, using purchase data scraped from Amazon in six categories: shoes, tops, and pants for both women and men. This type of recommender model is common in the online retail world, usually showing up in an "Other items you might like" area at the bottom of a page.
On the third floor of a shopping mall in the heart of Shanghai last week, Xiaolan He, a woman in her 50s, took an olive-green down jacket to a fitting room. To her surprise, she found a screen about the size of a large poster on the wall. It recognized the item of clothing in her hands through a tiny sensor embedded in the garment, and showed several options for matching items that she could flip through like a photo album. The screen, and the system that powers it, make up FashionAI--which essentially became He's personal stylist. FashionAI received its first big wave of customers on Saturday during Singles' Day, a Chinese shopping festival started by Alibaba in 2009 and held on November 11 each year.
Alibaba set a new Singles' Day record this Saturday by selling a staggering $25 billion worth of goods. The company also quietly tested a technology that could help it reinvent retail using artificial intelligence. On the third floor of a shopping mall in the heart of Shanghai last week, Xiaolan He, a woman in her 50s, took an olive green, down jacket to a fitting room. To her surprise, she found a screen about the size of a large poster on the wall. It recognized the item of clothing in her hands through a tiny sensor embedded in the garment, and showed several options for matching items that she could flip through like a photo album.
At first blush, Scot Barton might not seem like an AI pioneer. He isn't building self-driving cars or teaching computers to thrash humans at computer games. But within his role at Farmers Insurance, he is blazing a trail for the technology. Barton leads a team that analyzes data to answer questions about customer behavior and the design of different policies. His group is now using all sorts of cutting-edge machine-learning techniques, from deep neural networks to decision trees.
As a young programmer, Joshua Browder built a chatbot to act as a kind of AI lawyer that would help people dispute parking tickets. Not only did it work, but it was hugely popular, which led Browder to expand the program to help anyone harmed by the Equifax scandal sue the company in small claims court. Now his company, DoNotPay, is aiming even higher: by the end of this year, Browder plans to launch an addition to the platform that will you let you sue anyone. "To be honest, Equifax was just a bit of testing for the product that would let anyone sue anyone," Browder, one of 2017's 35 Innovators under 35, said Wednesday at MIT Technology Review's EmTech MIT conference. "The main use would be for taking down corporations."
Andrew Ng, formerly the head of AI for Chinese search giant Baidu and, before that, creator of Google's deep-learning Brain project, knows as well as anyone that artificial intelligence is coming for plenty of jobs. And many of us don't even know it. Speaking at MIT Technology Review's annual EmTech MIT conference in Cambridge, MA, on Tuesday, Ng said he's visited call centers and spoken to workers, knowing that his teams of software engineers will then write software that will automate aspects of their work. "There are many professions in the crosshairs of AI teams across the world," he said. Ng, who's currently working on a startup called Deeplearning.ai
From picking stocks to examining x-rays, artificial intelligence is increasingly being used to make decisions that were formerly up to humans. But AI is only as good as the data it's trained on, and in many cases we end up baking our all-too-human biases into algorithms that have the potential to make a huge impact on people's lives. In a new paper published on the arXiv, researchers say they may have figured out a way to mitigate the problem for algorithms that are difficult for outsiders to examine--so-called "black box" systems. A particularly troubling area for bias to show up is in risk assessment modeling, which can decide, for example, a person's chances of being granted bail or approved for a loan. It is typically illegal to consider factors like race in such cases, but algorithms can learn to recognize and exploit the fact that a person's education level or home address may correlate with other demographic information, which can effectively imbue them with racial and other biases.
When Brandon Araki arrived at MIT in 2015 as a master's candidate in mechanical engineering, he brought along the picobug, a tiny robot that can fly, crawl, and grasp small objects. Before Araki joined Daniela Rus's Distributed Robotics Lab (DRL), he'd been working with collaborators at several universities on the diminutive autonomous machine, which weighs 30 grams and fits in the palm of his hand. He wasn't quite sure what he might do next with the picobug, but when his new boss watched it in action, she was smitten. "I want a hundred of them!" Rus said. This request wasn't just greedy excitement.
Octavia, a humanoid robot designed to fight fires on Navy ships, has mastered an impressive range of facial expressions. When she's turned off, she looks like a human-size doll. She has a smooth white face with a snub nose. Her plastic eyebrows sit evenly on her forehead like two little capsized canoes. When she's on, however, her eyelids fly open and she begins to display emotion.