The consumer sentiment analysis of this one's pretty easy, but will they be compensated? When a person feels sufficiently wronged to lodge a complaint with the Consumer Financial Protection Bureau (CFPB), there's likely to be some negative sentiment involved. But is there a connection between the language they use and the likelihood they will be compensated by the offending company? At the upcoming Sentiment Analysis Symposium, I will discuss how machine learning and rule-based sentiment analysis can support each other in a complementary analysis, and produce actionable information from large amounts of free form text. In this case, machine learning and sentiment analysis could improve and evolve the CFPB's ability to assess consumer complaints.

In three months (as of June 2016) the New Orleans Saints will play a football game against the Atlanta Falcons. I want to know who will win. I ask my friend and he says the Saints. Technically this is a predictive model, but it's probably not worth much. I can improve upon this model by asking other people who they think will win.

Emerging Seattle-based fintech startup Kavout has just launched its new investment platform, driven by artificial intelligence (AI), for clients to find trading opportunities using tools powered by machine learning and big data, and with a beta-version of the platform made available to investors. As robo-advisors and automated trading products continue to emerge on the retail side, following the use of algorithmic trading and A.I.-driven technology by institutional firms, Kavout's announcement today appears to reflect the growing related interest from the market for such products. Kavout uses what it calls Kai, the core AI and machine learning process that fuels Kavout's main attributes and functions, including scanning historical SEC filings and stock quotes and examining millions of points of data ever second while analyzing stocks using an objective approach. There is also a Kai Score feature within the platform that provides a predictive analysis ranking reflecting data analytics processed for each security, and based on a stock's future performance related to its valuation, growth, momentum and other qualities. An excerpt of the platform as seen below, upon logging into the beta-version of Kavout, shows one of the Kai Score features. Commenting in an official corporate announcement, Kavout's CEO and co-founder Alex Lu said: "On Wall Street, you'll see that many hedge funds and investment banks are hiring top talent in artificial intelligence and machine learning."

At a competition in China to see who is better at recognizing faces, man or machine, Wang Yuheng, representing the humans, emerged victorious. Wang is famous in China for his photographic memory. He successfully identified a specific glass of water out of 520 seemingly identical ones in a Chinese reality TV show. He also reportedly helped police crack a case by extracting "hidden clues" from surveillance camera footage, thanks to his exceptional observational skills. But Wang's competitor, "Mark," is no less smart.

NEW YORK, July 6, 2016 /PRNewswire-USNewswire/ -- Thanks to his business acumen, poster boy looks, and relentless activism for freedom of the internet, Reddit's co-founder Alexis Ohanian has become one of the most high-profile residents of Brooklyn. A startup based in Armenia and San Francisco named 1AI Solutions has recently launched a working AI-based version of Alexis Ohanian named Avedis Ohanian. Additionally they used 3-D printing to replicate the body of Alexis Ohanian, infusing it with their proprietary Artificial Intelligence engine. "There's a lack of qualified IT professionals here in Armenia", said Dr. J.P. Hagopian, the CEO of 1AI Solutions. "We need to hire people, but we simply can't find enough good candidates. At some point our HR manager said that we should clone one of the high-performing Silicon Valley guys. She was kidding, but the idea stuck."

When life calls on us to project confidence, whether you're going in for a major job interview, pitching investors or networking at a big conference, the old adage is just to fake it until you make it. But a new study from New York's Cold Spring Harbor Laboratory released this week looks at whether the human feeling of confidence can be broken down into objective mathematical calculation. Choices that are big, such as launching a new venture, or small, such as deciding when to merge onto the highway, all require a careful weighing of the risks involved. Of course, some of these judgement calls have to occur faster than others. The study's lead author, associate professor of neuroscience Adam Kepecs, explained the driving question behind the research.

It's not hard to see why Ontario was left high and dry, at least if you know the industry... and geography. Michigan, the very heart of the American car business, is right next door. While Ontario plays a major role (it's home to key factories), there isn't much need to test there when Michigan will frequently provide similar conditions. And right now, most US firms are just coming to grips with the very concept of a self-driving car. They may not need to test in Canada until they're almost ready to sell autonomous vehicles in the country, or until local divisions need to experiment beyond closed circuits.

"Conversation is the new interface." The tech community's use of this expression does not mean that we are going to start talking to each other more on a human-to-human, face-to-face level. It means rather that we will be talking more to robots, specifically those artificial intelligence programs that pop up on the likes of Facebook Messenger, Twitter, Slack and so on, to help you with tasks from scheduling to shopping. Rather than going to a website to find information or downloading yet another app, we will summon these artificial intelligence assistants to do our bidding. Bark "find me a flight to Chicago on Saturday" into your phone and a "bot", which understands your location and the fact that you mean this Saturday, will return with some choices.

Financial services incumbents along with early-stage startups and large tech companies are all competing in fintech. CB Insights CEO Anand Sanwal gave a 110-slide presentation at our fintech conference in New York. The full deck covered broad VC funding trends for fintech and the developments in large categories such as wealth management, blockchain, remittance tech, and insurance tech. On robo-advisors, Sanwal noted that market volatility has not necessarily been a boon to the category. "It's not all roses," he said. "There are headwinds … When market volatility picks up, people want to talk to an advisor." At the end Sanwal highlighted 9 trends we're watching that are generating excitement. The slides for those trends are included below. If you have questions, please use the hashtag. Use it on Twitter or on the mobile app, and I'll try to get to them. Please ask me easy questions only. If you like something about the presentation, please tweet it out. If you do not like something about the presentation, please just keep it to yourself. Let me jump in and we'll go through this pretty quickly, I think. So this is what we do, take lots of data and try to predict technology trends. You've seen this join the conversation piece. We have lots of great customers, many of whom are in the room.

Combining machine learning method -- a type of artificial intelligence -- with a special MRI technique may help physicians predict who is more likely to develop Alzheimer's disease, a study says. Machine learning is a type of artificial intelligence that allows computer programmes to learn when exposed to new data without being programmed. "With standard diagnostic MRI, we can see advanced Alzheimer's disease, such as atrophy of the hippocampus," said principal investigator Alle Meije Wink from VU University Medical Centre in Amsterdam. "But at that point, the brain tissue is gone and there's no way to restore it. It would be helpful to detect and diagnose the disease before it's too late," Meije Wink explained.