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) …
The moral implications as we hand over decision making processes off to machines are astounding, the automation of killing people in certain military drones is quickly becoming a reality, and the displacement of jobs for the future is also a concrete wall that we're speeding towards and there are no brakes. In her Ted Talk from 2016, she makes the case for human morals being more important than ever in a world where we are handing over more and more decision making processes to algorithms and automation we don't entirely understand. For example, let's say you show a program one hundred pictures of dogs, all kinds of dogs; it'll analyze every picture and start to learn the features of a dog. Leave a comment below and I'll see you on the next Part of Is Artificial Intelligence Good, bad, or neither?
Nvidia has benefitted from a rapid explosion of investment in machine learning from tech companies. Can this rapid growth in the use cases for machine learning continue? Recent research results from applying machine learning to diagnosis are impressive (see "An AI Ophthalmologist Shows How Machine Learning May Transform Medicine"). Your chips are already driving some cars: all Tesla vehicles now use Nvidia's Drive PX 2 computer to power the Autopilot feature that automates highway driving.
Some years ago I found myself working with a bunch of very bright management consultants, all with impeccable educational records. It was around the time of the dotcom boom, and conversation frequently veered towards the next big craze – which stock to buy/company to invest in. One of the group was a bit of a computer nerd – actually, he was a lot of a computer nerd – our go-to guy for deep technological problems. So we're having a quiet beer one night and he offers up some confidential advice from a successful entrepreneur he once worked with. "You only need to ask one question of any technology, to know whether or not it will be successful," he said.
In addition to the panel of judges from the first contest, Beauty.AI 2.0 featured three new robot judges including: "Average Face" built on the hypothesis that the closer the face is to the average face within the ethnic group, the more attractive it is "AntiAgeist" evaluating the difference between the predicted and actual chronological age "PIMPL" evaluating the number and distribution of pimples and other dark spots (but not freckles) The results were sent to the individual participants via secure link and winners were announced at http://winners2.beauty.ai/#win . About Beauty.AI Beauty.AI is the first beauty contest judged entirely by robot jury, where humans and robots can apply to participate. About Youth Laboratories Youth Laboratories is a company dedicated to helping people retain youthful state for as long as possible using advances in machine vision and artificial intelligence. The company develops series of mobile applications that track age-related changes, wrinkles, pimples, dark spots and other parameters affecting perception of beauty, health and youthfulness and help evaluate the effectiveness of multiple interventions.
Meanwhile, humanoid robots filled with the latest artificial intelligence could lead to the outsourcing of future soldiers, leading to the literal possibility of robot wars. Sense of control and Robot anxiety in Human Robot Interaction, showed that the more controlling and anxious about robots a person is, the more initiative they expect the robot to show and the more willing they are to delegate tasks to it. The research focused specifically on what level of initiative people preferred their robot companion to have when executing a cleaning task. Participants could choose between manually turning on the cleaning robot themselves, having their robot companion turn on the cleaning robot remotely when instructed, or having the robot companion turn on the cleaning robot when it noticed that cleaning needed to be done.
Humans might take heart from the recent decision by Mercedes-Benz to replace robots with humans on some lines. The machines were just not agile enough to keep pace with the growing demand for customised products while we humans can "reprogram" ourselves in a fraction of a second. "We're moving away from trying to maximise automation, with people taking a bigger part in industrial processes again," says Markus Schaefer, head of production planning at the automaker. "When we have people and machines co-operate, such as a person guiding a part-automatic robot, we're much more flexible and can produce many more products on one production line.
Three prizes of 1,000 will be awarded to teams developing algorithms for evaluating human attractiveness. Beauty.AI 2.0 will make major efforts to encourage algorithm developers to explore new approaches for evaluating human attractiveness and suggest new algorithmic ways to rank humans. The contest originally started as a project of Youth Laboratories, a team of computer scientists and biogerontologists dedicated to finding new ways to slow down or even reverse the many processes associated with aging and leading to age-related diseases. About Imagene Labs Imagene Labs is Asia's leading lifestyle company that provides customized wellness solutions with genetic tests.
Many human social exchanges and coordinated activities critically involve dialogue interactions. Hence, we need to develop natural humanlike dialogue processing mechanisms for future robots if they are to interact with humans in natural ways. In this article we discuss the challenges of designing such flexible dialogue-based robotic systems. We report results from data we collected in human interaction experiments in the context of a search task and show how we can use these results to build more flexible robotic architectures that are starting to address the challenges of task-based humanlike natural language dialogues on robots.