One day in January, Eric Wilson dashed off a message to the teaching assistants for an online course at the Georgia Institute of Technology. "I really feel like I missed the mark in giving the correct amount of feedback," he wrote, pleading to revise an assignment. Thirteen minutes later, the TA responded. "Unfortunately, there is not a way to edit submitted feedback," wrote Jill Watson, one of nine assistants for the 300-plus students. Last week, Mr. Wilson found out he had been seeking guidance from a computer.

The top-left panel shows an example S-shaped data set (a two-dimensional manifold in a three-dimensional space). PCA identifies three principal components within the data. Manifold learning (LLE and IsoMap) preserves the local structure when projecting the data, preventing the mixing of the colors.

Meet Jill Watson, a first-time teaching assistant at Georgia Tech assigned to moderate an online forum for a computer science class. Jill was 1 of 9 TAs assigned to help answer questions about coursework and projects from the 300 students enrolled in the advanced course. During the first few weeks in January, Jill really struggled. This was Knowledge-Based Artificial Intelligence, after all, a course with the goal to "build AI agents capable of human-level intelligence and gain insights into human cognition." It was also a requirement for graduate students to earn their master's degree.

Visit us in Chicago on Thursday, June 2nd at 6:30pm to see a Machine Learning presentation by Jeremy Watt, instructor of the upcoming Metis course titled Machine Learning: Algorithms & Applications and author of Machine Learning Refined. This is an Open House for Jeremy's upcoming 6-week evening course at Metis, which starts on July 11th and will be held on Monday and Wednesday evenings from 6:30 - 9:30pm through August 17th . Please RSVP if you'd like to see a demonstration from Jeremy, and to learn more about the course structure and outcomes. Pizza and drinks will be served. Jeremy holds a PhD in Computer Science and Electrical Engineering from Northwestern University where he conducted research in machine learning and computer vision while actively consulting with partners in finance and insurance, as well as startups in the e-commerce and healthcare space.

Well rounded, visionary data scientist with broad spectrum of domain expertise, technical knowledge, and proven success in bringing measurable added value to companies ranging from startups to fortune 100, across multiple industries (finance, Internet, media, IT, security) and domains (data science, operations research, machine learning, computer science, business intelligence, statistics, applied mathematics, growth hacking, IoT). Vincent developed and deployed new techniques such as hidden decision trees (for scoring and fraud detection), automated tagging, indexing and clustering of large document repositories, black-box, scalable, simple, noise-resistant regression known as the Jackknife Regression (fit for black-box, real-time or automated data processing), model-free confidence intervals, bucketisation, combinatorial feature selection algorithms, detecting causation not correlations, and generally speaking, the invention of a set of consistent robust statistical / machine learning techniques that can be understood, implemented, interpreted, leveraged and fine-tuned by the non-expert. Vincent also invented many synthetic metrics (for instance, predictive power and L1 goodness-of-fit) that work better than old-fashioned stats, especially on badly-behaved sparse big data. Some of these techniques have been implemented in a Map-Reduce Hadoop-like environment. Some are concerned with identifying true signal in an ocean of noisy data.

To help with his class this spring, a Georgia Tech professor hired Jill Watson, a teaching assistant unlike any other in the world. Throughout the semester, she answered questions online for students, relieving the professor's overworked teaching staff. But, in fact, Jill Watson was an artificial intelligence bot. Ashok Goel, a computer science professor, did not reveal Watson's true identity to students until after they'd turned in their final exams. "I feel like I am part of history because of Jill and this class!"

"The Nervana team is developing a full-stack deep learning platform to enable organizations to use deep learning to tackle previously unsolvable problems. We believe our inclusion in the Cool Vendor report by Gartner validates our mission to make scalable artificial intelligence widely accessible and to enable disruptive new applications across a range of industries," said Naveen Rao, co-founder and CEO of Nervana. In February, Nervana launched Nervana Cloud, a full-stack hosted platform for deep learning. Based on neon, Nervana's open source deep learning framework, Nervana Cloud helps businesses develop and deploy high-accuracy deep learning solutions at a fraction of the cost of building their own infrastructure and data science teams. Utilizing the Nervana Engine, an application specific integrated circuit (ASIC) that is custom-designed and optimized for deep learning, Nervana Cloud has been optimized down to the silicon to handle the most complex machine learning training at scale.

What we call machine learning can take many forms. The purest form offers the analyst a set of data exploration tools, a choice of ML models, robust solution algorithms, and a way to use the solutions for predictions. The Amazon, Microsoft, Databricks, Google, and IBM clouds all offer prediction APIs that give the analyst various amounts of control. HPE Haven OnDemand offers a limited prediction API for binary classification problems. Not every machine learning problem has to be solved from scratch, however.

College of Computing Professor Ashok Goel teaches Knowledge Based Artificial Intelligence (KBAI) every semester. And every time he offers it, Goel estimates, his 300 or so students post roughly 10,000 messages in the online forums -- far too many inquiries for him and his eight teaching assistants (TA) to handle. That's why Goel added a ninth TA this semester. Her name is Jill Watson, and she's unlike any other TA in the world. Jill is a computer -- a virtual TA --implemented on IBM's Watson platform.

In a collaboration between Japan's Softbank Robotics and French-based Aldebaran Robotics, Pepper was officially put on the market for domestic use in June of 2015. Priced at about 1,600 with 200 in monthly data and insurance fees, the first thousand models available for launch sold out in under a minute. The company has stated that they aim to keep Pepper affordable, comparing the cost of the robot to that of a pet dog in Japan. Outside of personal ownership, the android has been used to assist Japanese citizens in banks and shops, including the promotion of Nescafe coffee machine sales. And in a limited run, large-scale experiment, Softbank opened a phone shop staffed exclusively by Pepper robots.