This report is my final project for the MIT Media Lab Class "Integrative Theories of Mind and Cognition" (also known as Future of AI, and New Destinations in Artificial Intelligence) in Spring 2016. Artificial Intelligence performs gradient descent. The AI field discovers a path of success, and then travels that path until progress stops (when a local minimum is reached). Then, the field resets and chooses a new path, thus repeating the process. If this trend continues, AI should soon reach a local minimum, causing the next AI winter. However, recent methods provide an opportunity to escape the local minimum. To continue recent success, it is necessary to compare the current progress to all prior progress in AI. I begin this paper by pointing out a concerning pattern in the field of AI and describing how it can be useful to model the field's behavior. The paper is then divided into two main sections. In the first section of this paper, I argue that the field of artificial intelligence, itself, has been performing gradient descent. I catalog a repeating trend in the field: a string of successes, followed by a sudden crash, followed by a change in direction. In the second section, I describe steps that should be taken to prevent the current trends from falling into a local minimum. I present a number of examples from the past that deep learning techniques are currently unable to accomplish. Finally, I summarize my findings and conclude by reiterating the use of the gradient descent model.

TAIPEI, TAIWAN--(Marketwired - May 31, 2016) - Computex - NVIDIA (NASDAQ: NVDA) won big at the Computex Best Choice Awards, with the NVIDIA Tesla M40 GPU and NVIDIA Jetson TX1 module hauling in Gold Awards and the NVIDIA SHIELD Android TV clinching a Category Award. Garnering these three prestigious awards extends the company's winning streak -- the longest of any international Computex exhibitor -- to eight consecutive years. Taiwan's President Tsai Ing-wen will hand out the awards. The Best Choice Awards, established in 2002, honor innovation, functionality and market potential. The Gold Award-winning NVIDIA Tesla M40 GPU is the world's fastest deep learning training accelerator.

The fundamental algorithms in data mining and analysis form the basis for the emerging field of data science, which includes automated methods to analyze patterns and models for all kinds of data, with applications ranging from scientific discovery to business intelligence and analytics. This textbook for senior undergraduate and graduate data mining courses provides a broad yet in-depth overview of data mining, integrating related concepts from machine learning and statistics. The main parts of the book include exploratory data analysis, pattern mining, clustering, and classification. The book lays the basic foundations of these tasks, and also covers cutting-edge topics such as kernel methods, high-dimensional data analysis, and complex graphs and networks. With its comprehensive coverage, algorithmic perspective, and wealth of examples, this book offers solid guidance in data mining for students, researchers, and practitioners alike.

Nasa has announced that it has found evidence of flowing water on Mars. Scientists have long speculated that Recurring Slope Lineae -- or dark patches -- on Mars were made up of briny water but the new findings prove that those patches are caused by liquid water, which it has established by finding hydrated salts. Several hundred camped outside the London store in Covent Garden. The 6s will have new features like a vastly improved camera and a pressure-sensitive "3D Touch" display

IBM Watson burst onto the scene in 2011 as a Jeopardy-playing computer and quickly became synonymous with artificial intelligence. Since then, AI has become a lot more commonplace thanks to the rise of digital assistants like Siri, Amazon's Alexa and Google Assistant, but Watson is still chugging away in more ways than you might think, powering everything from cancer treatment software to hotel concierge robots (pictured above). I had a chance to talk to Maya Weinstein, Senior Interaction Design and Creative Director at IBM Watson, about everything from the current crop of AI assistants to how the technology can help save lives. Check out the full interview (with some light edits for clarity) below. Jacob Kleinman: What do you think of the rise of consumer AI products like chatbots or Amazon Echo?

Ashok Goel wants to expand high-quality education to "millions" more people over the internet. It's the same goal that's pushed universities to make more and more courses and degree programs available over the internet, making it possible for students living on the far sides of the word to get degrees from American universities -- and vice versa. But online education has a problem: Of the hordes of students that sign up for massive open online classes (MOOCs), an average of less than 7% finish. Goel thinks artificial intelligence can change that. "There are many reasons" students don't finish, he told Tech Insider.

Feature selection is one of the core topics in machine learning. In statistical science, it is called variable reduction or selection. Our scientist published a methodology to automate this process and efficiently handle la large number of features (called variables by statisticians). Here, we mention an article published by Isabelle Guyon and Andre Elisseeff in Journal of Machine Learning Research. While published in 2003, it is still one of the best ML papers on feature selection.

Physicists are putting themselves out of a job, using artificial intelligence to run a complex experiment. The experiment, developed by physicists from The Australian National University (ANU) and UNSW ADFA, created an extremely cold gas trapped in a laser beam, known as a Bose-Einstein condensate, replicating the experiment that won the 2001 Nobel Prize. "I didn't expect the machine could learn to do the experiment itself, from scratch, in under an hour," said co-lead researcher Paul Wigley from the ANU Research School of Physics and Engineering. "A simple computer program would have taken longer than the age of the Universe to run through all the combinations and work this out." Bose-Einstein condensates are some of the coldest places in the Universe, far colder than outer space, typically less than a billionth of a degree above absolute zero.

Classical collaborative filtering, and content-based filtering methods try to learn a static recommendation model given training data. These approaches are far from ideal in highly dynamic recommendation domains such as news recommendation and computational advertisement, where the set of items and users is very fluid. In this work, we investigate an adaptive clustering technique for content recommendation based on exploration-exploitation strategies in contextual multi-armed bandit settings. Our algorithm takes into account the collaborative effects that arise due to the interaction of the users with the items, by dynamically grouping users based on the items under consideration and, at the same time, grouping items based on the similarity of the clusterings induced over the users. The resulting algorithm thus takes advantage of preference patterns in the data in a way akin to collaborative filtering methods. We provide an empirical analysis on medium-size real-world datasets, showing scalability and increased prediction performance (as measured by click-through rate) over state-of-the-art methods for clustering bandits. We also provide a regret analysis within a standard linear stochastic noise setting.

Mixture proportion estimation (MPE) is the problem of estimating the weight of a component distribution in a mixture, given samples from the mixture and component. This problem constitutes a key part in many "weakly supervised learning" problems like learning with positive and unlabelled samples, learning with label noise, anomaly detection and crowdsourcing. While there have been several methods proposed to solve this problem, to the best of our knowledge no efficient algorithm with a proven convergence rate towards the true proportion exists for this problem. We fill this gap by constructing a provably correct algorithm for MPE, and derive convergence rates under certain assumptions on the distribution. Our method is based on embedding distributions onto an RKHS, and implementing it only requires solving a simple convex quadratic programming problem a few times. We run our algorithm on several standard classification datasets, and demonstrate that it performs comparably to or better than other algorithms on most datasets.