Kai-Fu Lee sees America as destined to lose to China in the race for leadership in AI. Kai-Fu Lee watched the U.S. beat China to global internet leadership during the dot-com bubble from the inside. Now with what he sees as an even greater technological revolution taking place in the fast-growing field of artificial intelligence, Lee doesn't expect China to take a backseat a second time. "China started slow, and American companies went international," Lee says during a May visit to Forbes Media's headquarters. "But simple math says China has a larger GDP. The market will be bigger."

Outside of Data science, I also co-founded a social enterprise to teach Computer Science to kids Feynlabs. At Feynlabs, we have been working with ways to accelerate learning to Code. One way to do this is to compare and contrast multiple programming languages. This approach makes sense for Data Science also because a learner can potentially approach Data science from many directions. To learn programming for Data Science, it would thus help to build up from an existing foundation they are already familiar with and then co-relate new ideas to this foundation through other approaches. From a pedagogical standpoint, this approach is similar to David Asubel who stressed the importance of prior knowledge in being able to learn new concepts: "The most important single factor influencing learning is what the learner already knows."

The global text analytics market size is estimated to grow from $3.97 billion in 2017 to $8.79 billion by 2022, at a Compound Annual Growth Rate (CAGR) of 17.2%. The customer experience management (CEM) is expected to hold the largest market share during the forecast period. Among the various applications in the text analytics market, the CEM application is expected to hold the largest market share during the forecast period. Text mining is the most traditional application in customer service and is frequently utilized to improve customer experience through various information sources. Today, text analytics is implemented to offer quick, computerized feedback to the clients, which significantly reduces dependency on executives for resolving issues.

We study the combinatorial pure exploration problem Best-Set in stochastic multi-armed bandits. In a Best-Set instance, we are given $n$ arms with unknown reward distributions, as well as a family $\mathcal{F}$ of feasible subsets over the arms. Our goal is to identify the feasible subset in $\mathcal{F}$ with the maximum total mean using as few samples as possible. The problem generalizes the classical best arm identification problem and the top-$k$ arm identification problem, both of which have attracted significant attention in recent years. We provide a novel instance-wise lower bound for the sample complexity of the problem, as well as a nontrivial sampling algorithm, matching the lower bound up to a factor of $\ln|\mathcal{F}|$. For an important class of combinatorial families, we also provide polynomial time implementation of the sampling algorithm, using the equivalence of separation and optimization for convex program, and approximate Pareto curves in multi-objective optimization. We also show that the $\ln|\mathcal{F}|$ factor is inevitable in general through a nontrivial lower bound construction. Our results significantly improve several previous results for several important combinatorial constraints, and provide a tighter understanding of the general Best-Set problem. We further introduce an even more general problem, formulated in geometric terms. We are given $n$ Gaussian arms with unknown means and unit variance. Consider the $n$-dimensional Euclidean space $\mathbb{R}^n$, and a collection $\mathcal{O}$ of disjoint subsets. Our goal is to determine the subset in $\mathcal{O}$ that contains the $n$-dimensional vector of the means. The problem generalizes most pure exploration bandit problems studied in the literature. We provide the first nearly optimal sample complexity upper and lower bounds for the problem.

Autoencoders have been successful in learning meaningful representations from image datasets. However, their performance on text datasets has not been widely studied. Traditional autoencoders tend to learn possibly trivial representations of text documents due to their confounding properties such as high-dimensionality, sparsity and power-law word distributions. In this paper, we propose a novel k-competitive autoencoder, called KATE, for text documents. Due to the competition between the neurons in the hidden layer, each neuron becomes specialized in recognizing specific data patterns, and overall the model can learn meaningful representations of textual data. A comprehensive set of experiments show that KATE can learn better representations than traditional autoencoders including denoising, contractive, variational, and k-sparse autoencoders. Our model also outperforms deep generative models, probabilistic topic models, and even word representation models (e.g., Word2Vec) in terms of several downstream tasks such as document classification, regression, and retrieval.

We give a brief introduction to deep learning and propose a modern neural network architecture suitable for this detection task. First, we consider the case in which the MIMO channel is constant, and we learn a detector for a specific system. Next, we consider the harder case in which the parameters are known yet changing and a single detector must be learned for all multiple varying channels. We demonstrate the performance of our deep MIMO detector using numerical simulations in comparison to competing methods including approximate message passing and semidefinite relaxation. The results show that deep networks can achieve state of the art accuracy with significantly lower complexity while providing robustness against ill conditioned channels and mis-specified noise variance.

One night in Corvallis, with the Oregon State basketball team heading to its 14th straight loss of its season, the talk around the hotel bar moved from the television screen to a far different form of communication -- chatbots. No one used that word, of course. But the technology took front and center in a conversation about Alexa, the voice-activated digital assistant from Amazon that can do everything from play music to order groceries. Give Amazon credit: it's pushed hard to win the hearts and minds of U.S. consumers, teeing up more than 100 mini ads for just that purpose. Software that can respond to voice or text commands is a high-growth investment sector about to explode in scope and penetration worldwide. Look no further than the announcement by Mark Zuckerberg last year that Facebook Messenger will be opened to bot development.

Welcome to the third installment of Learning AI if You Suck at Math. If you missed the earlier articles be sure to check out part 1, part 2, part 4, part 5, part 6 and part 7. Today we're going to build our own Deep Learning Dream Machine. This machine will slice through neural networks like a hot laser through butter. Other than forking over $129,000 for Nvidia's DGX-1, the AI supercomputer in a box, you simply can't get better performance than what I'll show you right here. Before we dig into building a DL beast, I want to give you the easiest upgrade path. If you don't want to build an entirely new machine, you still have one perfectly awesome option. Simply upgrade your GPU (with either a Titan X or a GTX 1080) and get VMware Workstation or use another virtualization software that supports GPU acceleration! Or you could simply install Ubuntu bare metal and if you need a Windows machine run that in a VM, so you max your performance for deep learning.

TAIPEI: Chipmakers switched focus at Taiwan s top tech fair this week with bets on new areas such as driverless cars, virtual reality and artificial intelligence, shifting away from smartphones where intense competition has pushed down components prices. The Computex Taipei event, now in its 36th year, has historically been a central venue for electronic parts manufacturers to show off their processors and other components, products that play a large part in Taiwan s export-driven economy. As prices of processors fell, companies pushed into headline-grabbing launches like last year s Zenbo, a child-friendly home robot unveiled by Asustek Computer Inc, that could sing, snap pictures and help in the kitchen. This year, attention is back on core processing rather than novelties, but this time aimed more squarely at the "internet of things" (IoT), a buzzword used to describe connectivity between an increasing range of devices. "We are going from hype phase to more a reality phase with real products. You can see them, you can feel them," said Hugo Swart, head of business development and product management for Internet of Things and consumer electronics at Qualcomm Inc .

Scientists in Japan have developed an AI that can decode patterns in the brain to predict what a person is seeing or imagining. In a new study, researchers used signal patterns derived from a deep neural network to predict visual features from fMRI scans. Their'decoder' was able to identify objects with a high degree of accuracy, and the researchers say the breakthrough could pave the way for more advanced'brain-machine interfaces.' In a new study, researchers used signal patterns derived from a deep neural network to predict visual features from fMRI scans. Their'decoder' was able to identify objects with a high degree of accuracy.