Implementing Enterprise AI is a unique and limited edition course that is focussed on AI Engineering / AI for the Enterprise. The course is launched for the first time and has limited spaces. Created in partnership with H2O.ai, the course uses Open Source technology to work with AI use cases. Successful participants will receive a certificate of completion and also validation of their project from H2O.ai. The course targets developers and Architects who want to transition their career to Enterprise AI.

Information extraction involves classifying data items that are stored in plain text, and is a major area of research for machine learning scientists. Last week, a research team from MIT introduced a new approach to information extraction for machine learning systems at the Association for Computational Linguistics' Conference on Empirical Methods on Natural Language Processing, and won a best-paper award. Instead of feeding their system as much data as possible, the team's winning approach takes a different route and focuses on a much smaller data set, a similar process used by human beings – if you're reading a paper that you don't understand, you're likely to do a search on the web and find articles that you are able to understand. This new system approach does something similar; if the system's confidence score is low in assessing a particular text, it will query for more information, pulling up a handful of new articles from the web that correlate with a specific set of terms. In future, this model could be applied to sparse data and save much time in reviewing databases.

It seems like we've perennially been on the edge of major breakthroughs in AI (artificial intelligence), virtual reality, personal robots, and other such cool tech for the past two decades. The first set of science fiction imaginings came true rather rapidly -- think trans-continental air travel, space stations, even drone warfare -- but it appears that the emergence of next-gen tech wizardry has stalled. But while we still can't chat with the on-board computer on our personal spaceship, artificial intelligence is far more pervasive in our daily lives today than most of us realize. As anyone who has trained their mobile phone assistant can attest, years of painstaking research and investment in artificial intelligence technologies is starting to yield impressive results. Siri can predict our commute patterns, Microsoft Cortana warns us of bad weather, and the Google Assistant diligently sets calendar reminders with the impassive demeanor of an English butler of yore.

Autonomous AI agents have begun to take over entire tasks start to finish. And this is just the beginning. We expect to see a plethora of such agents in the next half decade, and these will take on all sorts of tasks that humans currently perform, if unhappily. In many ways the machines will be better at these tasks than we are. Self-driving cars have superhuman sensors, reaction time and true multitasking, and they will always apply their full attention to the task at hand, driving!

Few technologies have the transformative potential to reshape how we live, move, and work. Electricity and the Internet were two technologies that fundamentally transformed life in the 20th century. Artificial intelligence (AI) is the 21st century equivalent of electricity and the Internet. According to a new white paper from Tractica, AI is expected to bring massive shifts in how people perceive and interact with technology, with machines performing a wider range of tasks, in many cases doing a better job than humans. Tractica's white paper analyzes 10 key trends that are influencing the development of the global artificial intelligence market, and is available for free download on the firm's website.

Researchers at the University of Toronto say they have developed an algorithm that can learn directly from human instructions rather than a set of examples. Researchers usually provide neural networks with labeled data and teach the system how to make decisions based on the samples. With the new heuristic training model, humans program the algorithm with instructions that are used to classify training samples. Researchers Parham Aarabi and Wenzhi Guo trained their algorithm to identify people's hair in photographs. "Our algorithm learned to correctly classify difficult, borderline cases -- distinguishing the texture of hair versus the texture of the background," says Aarabi.

The Internet media is trending now with numerous mentions of "big data", "machine learning" and "artificial intelligence" all together destined to revolutionize pharmaceutical and biotech industries and the way drugs are discovered. These new technologies are believed to make drug discovery cheaper, faster, and more productive. First, let's review briefly some of the basic concepts in the heart of new technologies. The term "big data" by itself is more of a marketing nature. It describes an abstract concept of having large volumes of data obtained from various channels in multiple formats, which needs to be arranged in such a way, that it can be possible to quickly access, search, update, and analyze it to output useful information.

Machines are capable of understanding speech, recognizing faces and driving cars safely, making recent technological advancements seem impressively powerful. But if the field of artificial intelligence is going to make the transformative leap into building human-like machines, it'll first have to master the way babies learn. "Relatively recently in AI there's been a shift from thinking about designing systems that can do the sort of things that adults can do, to realizing if you want to have systems that are as flexible and powerful and do the kinds of things that adults do, you need to have systems that can learn the way babies and children do," developmental psychologist Alison Gopnik, a researcher at the University of California at Berkeley, told International Business Times. "If you compare what computers can do now to what they could do 10 years ago, they've certainly made a lot of progress, but if you compare them to what a four year old can do, there's still a pretty enormous gap." Babies and children construct theories about the world around them using the same approach scientists use to construct scientific theories.

The fields of artificial intelligence and machine learning are moving so quickly that any notion of ethics is lagging decades behind, or left to works of science fiction. This might explain a new study out of Shanghai Jiao Tong University, which says computers can tell whether you will be a criminal based on nothing more than your facial features. The bankrupt attempt to infer moral qualities from physiology was a popular pursuit for millennia, particularly among those who wanted to justify the supremacy of one racial group over another. But phrenology, which involved studying the cranium to determine someone's character and intelligence, was debunked around the time of the Industrial Revolution, and few outside of the pseudo-scientific fringe would still claim that the shape of your mouth or size of your eyelids might predict whether you'll become a rapist or thief. Not so in the modern age of Artificial Intelligence, apparently: In a paper titled "Automated Inference on Criminality using Face Images," two Shanghai Jiao Tong University researchers say they fed "facial images of 1,856 real persons" into computers and found "some discriminating structural features for predicting criminality, such as lip curvature, eye inner corner distance, and the so-called nose-mouth angle."

Privacy policies are commonly used to inform users about the data collection and use practices of websites, mobile apps, and other products and services. However, the average Internet user struggles to understand the contents of these documents and generally does not read them. Natural language and machine learning techniques offer the promise of automatically extracting relevant statements from privacy policies to help generate succinct summaries, but current techniques require large amounts of annotated data. The highest quality annotations require law experts, but their efforts do not scale efficiently. In this paper, we present results on bridging the gap between privacy practice categories defined by law experts with topics learned from Non-negative Matrix Factorization (NMF). To do this, we investigate the intersections between vocabulary sets identified as most significant for each category, using a logistic regression model, and vocabulary sets identified by topic modeling. The intersections exhibit strong matches between some categories and topics, although other categories have weaker affinities with topics. Our results show a path forward for applying unsupervised methods to the determination of data practice categories in privacy policy text.