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?

Telegram and Facebook both launched platforms for their own messengers and Microsoft blew up the internet with TayTweets. As quickly as these bots are released, issues are discovered. Why are there so many issues with bots? If AI can defeat a world champion in a five-game match of Go, shouldn't it also be able to order you plane tickets? The height of AI success this year was when Google's AlphaGo defeated Lee Sedol, the top player in the world, in a five-game match of Go. It handily won the first three games.

"Today's security systems usually fall into one of two categories: man or machine," Adam Conner-Simon from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) wrote in a post on the MIT News site. "So-called'analyst-driven solutions' rely on rules created by human experts and therefore miss any attacks that don't match the rules," he said. "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." The MIT and PatternEx platform attempts to merge those two approaches. AI2 predicts attacks by combing through data and detecting suspicious activity by clustering it into meaningful patterns using unsupervised machine learning, according to researchers at MIT.

A team of MIT researchers created an artificial intelligence system called AI2 that can help stop cyberattacks. The AI is designed to review data from tens of millions of log lines each day and look for anything suspicious. When it finds something out of the ordinary, it hands off the information to a human that checks for any signs of a breach. "You can think about the system as a virtual analyst," said research lead Kalyan Veeramachaneni. "It continuously generates new models that it can refine in as little as a few hours, meaning it can improve its detection rates significantly and rapidly."

In summary, I had a blast at my first GTC. The only downside was that I wasn't on-site long enough to totally absorb everything, certainly not even a fraction of all the great talks on Deep Learning and AI. But no worries, I treated my attendance as a learning experience and I fully intend to drill down on many areas of interest after-the-fact (starting with this field report). As I sat in the conference press room watching the frenetic activity of the attendees passing by, I anticipated hours of fun digesting all that I saw. Look for many future articles here on insideBIGDATA that cover GPU technology, NVIDIA, the vendors I met, as well as leading-edge research taking place in this space. I'm excited, and I hope you are too!

Developing and experimenting with machine learning models in Python is easy and well supported by robust and agile libraries such as scikit-learn, although efficiently deploying multi-model systems at scale is still a challenge in the data science field. This talk will focus on the main issues related to deploying machine learning models and how to make scikit-learn production-ready with minimal operational efforts, by means of Cloud Computing services, in particular Amazon Web Services.

Weird Of The Week: This is part of a series of articles that looks at some of the most bizarre and niche business ideas we see here at Springwise. Advertising and media are often at the forefront of new technology, and we have already seen augmented reality platforms showing content in the real world and a virtual reality advertising network for brands. Now an artificial intelligence robot, AI-CD?, developed by Japanese advertising and marketing agency McCann Japan, is set to work on providing new creative direction for commercials. The AI will give input on projects, mining and analyzing creative databases of adverts to find the best commercials for products and messages. But the robot is also being treated as somewhat part of the team at McCann, taking the title of "creative director" and attending the opening ceremony for new company employees.