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) …
There are a lot of things that chatbots have yet to master and high on the list is small talk. But researchers at Facebook think the best way to make software prattle away is to give it a personality. Workers were asked to chat in pairs and to give statements describing themselves, including their likes and dislikes. The crowdworkers' chatter was linked to these description statements and used to train the chatbots.
The simplest way to eliminate the spread of fake news would be to limit ourselves to a small group of mainstream publishers who do all their own reporting and fact-checking. The counterargument, of course, is that an open and democratic society allows for a wide range of voices, not just the ones a small cabal of editors deem acceptable. Fake news promises to destroy this system and undermine trust and democracy, which is why addressing fake news has become one of the tech industry's most significant and important challenges. His initial focus, post-9/11, was on national security, which is how he first become intrigued by the advantages AI offers in analyzing complex data sets. As 2017's fake news scandals grew, DeAngelis was approached by leading media industry veteran Greg D'Alba, CEO of VIDL News, to apply the same type of analysis Enterra was using to control the complex value chains of Fortune 500 companies to the media industry, where D'Alba saw a growing need to verify and validate news stories.
The battle now raging between the big technology companies for consumer cash is focused on the voice-controlled smart speaker. Having already conquered the pocket with the ubiquitous smartphone, big tech has been struggling to come up with the next must-have gadget that will open up a potentially lucrative new market – the home. A pilot light was lit when Amazon's Echo launched in 2014 and became a sleeper hit. Now the voice controlled smart speaker is rapidly becoming the next big thing, capable of answering questions, setting timers, playing music, controlling other devices about the home, or even potentially selling products. "The last 12 months have been explosive for smart speakers, which have surged into the mass market for two reasons.
The effort points to ways in which Amazon and other companies could try to improve the tracking of trends in other areas of retail--making recommendations based on products popping up in social-media posts, for instance. For instance, one group of Amazon researchers based in Israel developed machine learning that, by analyzing just a few labels attached to images, can deduce whether a particular look can be considered stylish. An Amazon team at Lab126, a research center based in San Francisco, has developed an algorithm that learns about a particular style of fashion from images, and can then generate new items in similar styles from scratch--essentially, a simple AI fashion designer. The event included mostly academic researchers who are exploring ways for machines to understand fashion trends.
Michal Kosinski – the Stanford University professor who went viral last week for research suggesting that artificial intelligence (AI) can detect whether people are gay or straight based on photos – said sexual orientation was just one of many characteristics that algorithms would be able to predict through facial recognition. Kosinski, an assistant professor of organizational behavior, said he was studying links between facial features and political preferences, with preliminary results showing that AI is effective at guessing people's ideologies based on their faces. That means political leanings are possibly linked to genetics or developmental factors, which could result in detectable facial differences. Facial recognition may also be used to make inferences about IQ, said Kosinski, suggesting a future in which schools could use the results of facial scans when considering prospective students.
If there aren't enough examples of a particular accent or vernacular, then these systems may simply fail to understand you (see "AI's Language Problem"). "If you analyze Twitter for people's opinions on a politician and you're not even considering what African-Americans are saying or young adults are saying, that seems problematic," O'Connor says. Solon Barocas, an assistant professor at Cornell and a cofounder of the event, says the field is growing, with more and more researchers exploring the issue of bias in AI systems. Shared Goel, an assistant professor at Stanford University who studies algorithmic fairness and public policy, says the issue is not always straightforward.
They say their decoder significantly outperforms existing approaches. These included a Long Short Term Memory Network, a recurrent neural network, and a feedforward neural network. "For instance, for all of the three brain areas, a Long Short Term Memory Network decoder explained over 40% of the unexplained variance from a Wiener filter," they say. But Glaser and co deliberately reduced the amount of training data they fed to the algorithms and found the neural nets still outperformed the conventional techniques.