A Microsoft "chatbot" designed to converse like a teenage girl has been taken offline after its artificial intelligence software was coaxed into firing off hateful, racist comments on Twitter. Technology giant Microsoft this week launched its experiment with the Tay AI (artificial intelligence) bot, which was given the personality of a teenager and was designed to learn from conversing with real people. However, the plan was sent awry by an ill-willed campaign to teach her negative things, Microsoft said. "It is as much a social and cultural experiment, as it is technical," a Microsoft spokesperson said. "Unfortunately, within the first 24 hours of coming online, we became aware of a coordinated effort by some users to abuse Tay's commenting skills to have Tay respond in inappropriate ways."

The following infographic summarizes the average funding for each Artificial Intelligence category to show which Artificial Intelligence categories are the best funded. It shows that Machine Learning (Applications) is in the lead with 13.84M funding per company, followed by Smart Robots with 12.89M per company. Please note that this data is based only on Artificial Intelligence companies that have publicly available funding data. We are currently tracking over 852 companies in 13 categories across 62 countries, with a total of 2.71 Billion in funding. To see the full list of 852 Artificial Intelligence companies, contact us using the form on www.venturescanner.com.

It all happened so quickly! First, Microsoft reveals an amazing new bot that learns from you, the public! Then, in less than 48 hours, the bot turns incredibly racist. What does this say about Microsoft, and about AI? Well, I have some insight into that, because in May of 2014, the exact same thing happened to me with a bot I made. My bot wasn't a tweeter, instead it was a Turing test like game called Bot Or Not? where players were either paired with one another, or both paired with a bot, and players had to guess if they were talking with a their live partner or the bot.

Interesting article posted by By John Langford. John Langford is a machine learning research scientist, a field which he says "is shifting from an academic discipline to an industrial tool". He is the author of the blog hunch.net. John works at Microsoft Research, and was previously affiliated with Yahoo Research,Toyota Technological Institute, and IBM's Watson Research Center. He studied Physics and Computer Science at the California Institute of Technology, earning a double bachelor's degree in 1997, and received his PhD in Computer Science from Carnegie Mellon University in 2002.

OMG! Did you hear about the artificial intelligence program that Microsoft designed to chat like a teenage girl? It was totally yanked offline in less than a day, after it began spouting racist, sexist and otherwise offensive remarks. Microsoft said it was all the fault of some really mean people, who launched a "coordinated effort" to make the chatbot known as Tay "respond in inappropriate ways." To which one artificial intelligence expert responded: Duh! Well, he didn't really say that.

Tay, Microsoft Corp's so-called chatbot that uses artificial intelligence to engage with millennials on Twitter, lasted less than a day before it was hobbled by a barrage of racist and sexist comments by Twitter users that it parroted back to them. TayTweets (@TayandYou), which began tweeting on Wednesday, was designed to become "smarter" as more users interacted with it, according to its Twitter biography. But it was shut down by Microsoft early on Thursday after it made a series of inappropriate tweets. A Microsoft representative said on Thursday that the company was "making adjustments" to the chatbot while the account is quiet. "Unfortunately, within the first 24 hours of coming online, we became aware of a coordinated effort by some users to abuse Tay's commenting skills to have Tay respond in inappropriate ways," the representative said in a written statement supplied to Reuters, without elaborating.

My colleague Steve Liesman has published a report on the government's quarterly GDP report. Summed up, he found a large, persistent error in GDP between initial and final GDP reports. Not only is it off significantly, the government even gets the direction of growth wrong 30 percent of the time! Why is economic forecasting still so bad? Many feel that the tools being used to make the forecasts are simply inadequate.

Before even thinking about complexity in your ML system, ask yourself if your product feature actually needs an ML solution. Sometimes, ML adds complexity to your system when you could just use a simpler heuristic algorithm that does not require feature engineering, model tuning, continuous training, or model deployment. However, when there are already ML models built for other purposes which you can reuse, going with a heuristic adds complexity. A quick and dirty heuristic might seem like a short-term gain, but is really a long-term pain. Over time it becomes increasingly difficult to understand, depend on, and maintain all the ad-hoc heuristics. The product can also suffer when there are too many different ways to do similar things, resulting in inconsistent user-facing behavior.

Here is the link to coursera course for NLP Pick the software from the The Stanford NLP (Natural Language Processing) Group and input some text to view its parse tree, named entities, part of speech tags, etc.

Few days back I went to a bar with couple of friends and found that one of my friends is working with the bartender to create a perfect cocktail. The scene was such that it got me thinking. The bartender's action could very well be used to explain how an analytics lead could get his machine learning deployed and what best practices are needed. A good bartender keeps his vocabulary updated with what all liquor and additives at his disposal, so that he could create a variety that specifically targets your experience. Similarly, having an open mindset will help in picking the tool that could best serve the problem and not the bias?