You may be thinking that this title makes no sense at all. ML, AI, ANN and Deep learning have made it into the everyday lexicon and here I am, proclaiming that ML is dead. The open sourcing of entire ML frameworks marks the end of a phase of rapid development of tools, and thus marks the death of ML as we have known it so far. The next phase will be marked with ubiquitous application of these tools into software applications. And that is how ML will live forever, because it will seamlessly and inextricably integrate into our lives. There has been a rapid democratization of data and tools in the past year.

Last year Intel became a Python distributor, offering its own edition of the language outfitted with Intel's Math Kernel Library (MKL). MKL accelerates data-science-related tasks by using Intel-specific processor extensions to speed up certain operations, a fine fit for a language that has become a staple in machine learning and math-and-stats circles. The Intel Distribution of Python, a repackaging of Continuum Analytics's Anaconda distribution, incorporated MKL support to give Python data science and machine learning packages a boost. Now ActiveState, producers of an enterprise-grade Python, (as well as Ruby, Node.js, and Golang distributions) has brought MKL into its own Python distro. Get a digest of the day's top tech stories in the InfoWorld Daily newsletter.

We recommend addressing this through the explicit characterization of acceptable behavior. One such approach is seen in the nascent field of fairness in machine learning, which specifies and enforces mathematical formulations of nondiscrimination in decision-making. Another approach can be found in modular AI architectures, such as cognitive systems, in which implicit learning of statistical regularities can be compartmentalized and augmented with explicit instruction of rules of appropriate conduct . Certainly, caution must be used in incorporating modules constructed via unsupervised machine learning into decision-making systems.

A future driven by AI technology has just been outlined by the CEO of Google, Sundar Pichai, during his keynote address at Google IO 2017. Pichai stated "the more we democratise access to AI, the sooner everyone will benefit". This vision was backed up by announcing a number of AI driven technologies including improvements to Google Home, Google Assistant and the all new Google Lens -- an object recognition system that uses a smartphone camera and machine learning to interpret the world around you. This is another major step toward a machine driven future -- a movement that has garnered a lot of attention in recent years. AI isn't a new technology and has been applied to plenty of digital systems for decades.

Food delivery company DoorDash says personalized restaurant recommendations based on AI are seeing a significant lift in orders, compared to regular recommendations based on popularity. In an interview with VentureBeat, DoorDash product manager Jimmy Liu said customers who saw personalized recommendations on average "were over 25 percent more likely" to place an order versus people who saw the most popular restaurants in their area. We talked with Liu on the eve of the company's announcement today that it's rolling out these machine-learning based recommendations to all of its users, after testing it on increasing percentages of its customer base. Millions of users have already seen the recommendations, the company said. Liu said the 25 percent lift from recommendations came specifically from email campaigns.

Artificial intelligence (AI) will have a significant and complementary role to play in asset management going forward, according to investing guru, Man Group's Pierre Lagrange. "It's all about seeing how far we can push the machine in terms of taking some of the decision-making on the investment side. There are so many variables that people are looking at to be aggregated into the decision-making so the more you can use a machine, the better it is," explained Lagrange, speaking to CNBC on Tuesday. Lagrange co-founded discretionary asset fund manager GLG in 1995 and stayed with the firm following its 2010 acquisition for $1.6 billion by the world's largest publicly traded hedge fund, Man Group.

Artificial Intelligence is an area of computer science that focuses the creation of intelligent machines that work and react like humans. Artificial Intelligence ETFs potentially stand to benefit from increased adoption and utilization of artificial intelligence, including those involved with industrial and non-industrial robotics, automation, 3D printing, natural language processing, social media, and autonomous vehicles. Click on the tabs below to see more information on Artificial Intelligence ETFs, including historical performance, dividends, holdings, expense ratios, technical indicators, analysts reports and more. Click on an ETF ticker or name to go to its detail page, for in-depth news, financial data and graphs. By default the list is ordered by descending total market capitalization.

A world renowned pioneer in social robotics, Cynthia Breazeal splits her time as an Associate Professor at MIT, where she received her PhD and founded the Personal Robots Group, and Founder and Chief Scientist of Jibo, a personal robotics company with over $85 million in funding. While Breazeal's work has won numerous academic awards, industry accolades, and media attention, she had to fight early skepticism in the 1990s from other experts in robotics and AI. At the time, robots were seen as physical and industrial tools, not social or emotional companions. Her first social robot, Kismet, was unfairly called out in popular press as "useless". Breazeal bucked the trend with a very different vision: "I wanted to create robots with social and emotional intelligence that could work in collaborative partnership with people. In 2-5 years, I see social robots helping families with things that really matter, like education, health, eldercare, entertainment, and companionship." She hopes her work and influence will inspire others to create robots "not only with smarts, but with heart, too."

In our previous tutorial on customer support bots, we trained a bot using the Custom Collection API to direct customers to the team member who is best suited to assist them with their problem or query. The bot improved our team's response times as we no longer had to rely on a human facilitator (who also plays many other roles in our company #startuplife) to do the job. However, we're generally only able to respond during our office hours of 11am-7pm EST, so there's still lag for inquiries outside of that period. How can we improve this? Build a bot to answer frequently asked questions, reducing lag time for more customers and ensuring our engineers don't need to spend more time than necessary away from the products we're building for you:).

The answer to the question of what makes deep learning different from traditional machine learning may have a lot to do with how much data you're working with. "When you start getting into true big data, that's when you can really get into deep learning," said Alfred Essa, vice president of research and data science at New York-based publishing company McGraw-Hill Education. Driven by advances in analytics technologies, deep learning processes became a more widely discussed topic last year. Since then, what constitutes deep learning vs. machine learning has been up for debate. They involve a lot of the same tools and techniques, after all.