Cognitive computing, artificial intelligence and machine learning are here to stay and promise to benefit both consumers and the organizations that exploit these advanced technologies. That was the sentiment from "Dawn of the Cognitive Era" panelists representing mostly startups (startup wannabe IBM being the exception) at the annual TiE StartupCon event in Boston this past week. Whereas it wasn't long ago that the public's view of AI was influenced disproportionately by books and movies, an increasing number of real-life cognitive computing applications such as those enabled by IBM Watson have begun to seep into the public's consciousness. In fact, many people are taking advantage of cognitive computing, whether or not they realize it, when they use tools such as Apple's Siri or various bots, said panel moderator and DataXylo CEO Abhi Yadav. Such applications, enabled in large part through the access to relatively cheap computing power via the cloud, have resulted in the technology finally living up to the hype -- and dissuading fears it will lord over us.

The Machine Learning Engineer is responsible for analyzing diverse sets of imperfect data and finding common patterns, theme and trends by using statistical analysis and machine learning. A sound understanding of predictive analytics, predictive modeling and the ability to write data-driven applications based on the analysis is essential. This position will work closely with engineering, product management, and business teams to measure, analyze, and understand Calabrio's users' complex and growing data needs and help them make sense of their ever growing volumes of data. Wrangling data from multiple sources including sales, inventory, product, and customer databases to create integrated views that will be used to drive decision making? Provide actionable insights to our customers derived from machine learning and statistical analysis that go beyond normal dashboards and BI tools.

Driverless car technology has been one of the most anticipated disruptions of a major global industry. With so many companies jostling to be among the first to integrate artificial intelligence into the world's billion-plus automobiles, the speed with which the wider industry is expected to adopt these cutting edge technologies is impressive. By 2021, sales of connected car technologies are predicted to triple to almost 180 billion. It is not difficult to see why the driverless car industry shows such promise.

Driverless car technology has been one of the most anticipated disruptions of a major global industry. Beginning with Google in 2010, the field has expanded to include other tech companies and auto manufacturers, including Uber, Lyft, Tesla, and General Motors. With so many companies jostling to be among the first to integrate artificial intelligence into the world's billion-plus automobiles, the speed with which the wider industry is expected to adopt these cutting edge technologies is impressive. By 2021, sales of connected car technologies are predicted to triple to almost 180 billion. It is not difficult to see why the driverless car industry shows such promise. The anticipated benefits of the technology are numerous, including fewer road accidents, reduced congestion, cheaper public transportation, and lower greenhouse gas emissions.

AlchemyLanguage is a collection of APIs that offer text analysis through natural language processing. The AlchemyLanguage APIs can analyze text and help you to understand its sentiment, keywords, entities, high-level concepts and more.

Not every startup CEO can say they were able to grow their business to a point where they were acquired. Even fewer can say they did it twice. But that is exactly the case for AlchemyAPI Founder and CEO Elliot Turner. Turner launched his first startup, MimeStar, a software development company focused on network intrusion detection, while a sophomore in high school. Inc. acquired it by the time he was twenty-one. He quickly saw the shift in the market to the need to democratize artificial intelligence (A.I.), and decided to venture out on his own to start AlchemyAPI.

Earlier this week, Google made a splash when it released its TensorFlow artificial intelligence software on GitHub under an open-source license. Google has a sizable stable of AI talent, and AI is working behind the scenes in popular products, including Gmail and Google search, so AI tools from Google are a big deal. Today on GitHub, TensorFlow, primarily written in C, is the top trending project of the day, the week, and the month, having accrued more than 10,000 stars in about one week. But there are several other open-source tools to choose from on GitHub if you want to improve your app with deep learning, a type of AI that involves training artificial neural networks on a bunch of data and then getting them to make inferences about new data. There are other frameworks available today -- these are just the most interesting ones I've encountered -- and more will surely emerge in the future.

Artificial Intelligence is all over the news these days with companies like Google, Facebook and Apple investing heavily, but it can be hard for individual salespeople and entrepreneurs to know which services they can use without the need of a large IT staff or a corporate approval process. These services offer something unique like helping to find the right prospect to follow-up with, scheduling a meeting or finding insight on a customer. In each case, these services do not require any IT knowledge for setup, management, or maintenance. How many times have you engaged a prospect, but it took a large number of emails back and forth to schedule that next meeting? Instead you can use X.ai's assistant'Amy' who connects to your calendar and emails your contact on your behalf. She proposes free times and will send out calendar invites once an agreement on time and place has been met.

Artificial intelligence has had its share of ups and downs recently. In what was widely seen as a key milestone for artificial intelligence (AI) researchers, one system beat a former world champion at a mind-bendingly intricate board game. But then, just a week later, a "chatbot" that was designed to learn from its interactions with humans on Twitter had a highly public racist meltdown on the social networking site. How did this happen, and what does it mean for the dynamic field of AI? In early March, a Google-made artificial intelligence system beat former world champ Lee Sedol four matches to one at an ancient Chinese game, called Go, that is considered more complex than chess, which was previously used as a benchmark to assess progress in machine intelligence.

Today's security systems usually fall into one of two categories: human or machine. So-called "analyst-driven solutions" rely on rules created by living experts and therefore miss any attacks that don't match the rules. Meanwhile, today's machine-learning approaches rely on "anomaly detection," which tends to trigger false positives that both create distrust of the system and end up having to be investigated by humans, anyway. But what if there were a solution that could merge those two worlds? What would it look like?