Wouldn't it be great if an Android app could see and understand its surroundings? Can you imagine how much better its user interface could be if it could look at its users and instantly know their ages, genders, and emotions? Well, such an app might seem futuristic, but it's totally doable today. With the IBM Watson Visual Recognition service, creating mobile apps that can accurately detect and analyze objects in images is easier than ever. In this tutorial, I'll show you how to use it to create a smart Android app that can guess a person's age and gender and identify prominent objects in a photograph.

As artificial intelligence capabilities expand in the future, Millennial parents may be open to using it in their children's lives. The study, comprising a survey of 600 parents between 20 and 36 years old who have at least one child younger than eight, was designed to gauge parents' sentiment toward using AI in the lives of so-called'Generation Alpha' children. IEEE defines Generation Alpha as children born between 2010 and 2025 and expects artificial intelligence to be present in'nearly every aspect of their lives.' When it comes to teaching their children, almost three quarters (74%) of Millennial parents said they would consider using an AI tutor. Fewer than a quarter (10%) said they would not consider it at all, according to the study.

Augment today announced it has raised $5 million for an AI platform that assists customer service agents at large companies. The startup had operated in stealth for 10 months prior to launch. The company joins competitors like Mattersight, DigitalGenius, LivePerson, and others in its efforts to train AI using conversations between customers and businesses in order to better guide customer service agents. The money will be used to bolster the Augment AI platform, which is trained by an aggregated dataset made up of 100 million conversational interactions at large companies, including Dyson. Augment makes no attempt to replace human agents, only to make them more efficient.

Natural Language Processing, or NLP for short, is the study of computational methods for working with speech and text data. The field is dominated by the statistical paradigm and machine learning methods are used for developing predictive models. In this post, you will discover the top books that you can read to get started with natural language processing. As practitioners, we do not always have to grab for a textbook when getting started on a new topic. Code examples in the book are in the Python programming language.

Nvidia's shares, valued at a low $23.3 in 2015, have since surged to hit a high of $190.10 on Monday, with at least four brokerages setting their price targets at $200 or above. Analysts have focused on the company's progress in artificial intelligence, in particular. "Our sense is management believes that investors still severely underestimates the impact of AI and the size of the potential market," Evercore analyst C J Muse wrote in a note on Friday after hosting Nvidia's management. Nvidia has been rapidly expanding into newer technologies including artificial intelligence, cloud computing and self-driving cars, away from designing graphics-processing chips for which the company was known for. Bank of America Merrill Lynch analyst Vivek Arya listed Nvidia a "top pick", basing his view "on (Nvidia's) underappreciated transformation from a traditional PC graphics vendor, into a supplier into high-end gaming, enterprise graphics, cloud, accelerated computing and automotive markets," according to Seeking Alpha.

AWS offers a family of intelligent services that provide cloud-native machine learning and deep learning technologies to address your different use cases and needs. For developers looking to add managed AI services to their applications, AWS brings natural language understanding (NLU) and automatic speech recognition (ASR) with Amazon Lex, visual search and image recognition with Amazon Rekognition, text-to-speech (TTS) with Amazon Polly, and developer-focused machine learning with Amazon Machine Learning. For more in-depth deep learning applications, the AWS Deep Learning AMI lets you run deep learning in the cloud, at any scale. Launch instances of the AMI, pre-installed with open source deep learning engines (Apache MXNet, TensorFlow, Caffe, Theano, Torch and Keras), to train sophisticated, custom AI models, experiment with new algorithms, and learn new deep learning skills and techniques; all backed by auto-scaling clusters of GPU-based instances. Whether you're just getting started with AI or you're a deep learning expert, this session will provide a meaningful overview of how to improve scale and efficiency with the AWS Cloud.

Episode 106 – Edward Hess discusses The New Smart Machine Age and where humans fit in. Will smart machines and robots start taking our jobs? What can we do to prepare for the inevitable transformation to robotics and automation? Ed is a Professor of Business Administration at the Darden Graduate School of Business at the University of Virginia. You've seen and heard him in places like WSJ Radio, CNBC, NPR, and Investor's Business Daily.

Maximum A posteriori Probability (MAP) inference in graphical models amounts to solving a graph-structured combinatorial optimization problem. Popular inference algorithms such as belief propagation (BP) and generalized belief propagation (GBP) are intimately related to linear programming (LP) relaxation within the Sherali-Adams hierarchy. Despite the popularity of these algorithms, it is well understood that the Sum-of-Squares (SOS) hierarchy based on semidefinite programming (SDP) can provide superior guarantees. Unfortunately, SOS relaxations for a graph with $n$ vertices require solving an SDP with $n^{\Theta(d)}$ variables where $d$ is the degree in the hierarchy. In practice, for $d\ge 4$, this approach does not scale beyond a few tens of variables. In this paper, we propose binary SDP relaxations for MAP inference using the SOS hierarchy with two innovations focused on computational efficiency. Firstly, in analogy to BP and its variants, we only introduce decision variables corresponding to contiguous regions in the graphical model. Secondly, we solve the resulting SDP using a non-convex Burer-Monteiro style method, and develop a sequential rounding procedure. We demonstrate that the resulting algorithm can solve problems with tens of thousands of variables within minutes, and outperforms BP and GBP on practical problems such as image denoising and Ising spin glasses. Finally, for specific graph types, we establish a sufficient condition for the tightness of the proposed partial SOS relaxation.

We construct genomic predictors for heritable and extremely complex human quantitative traits (height, heel bone density, and educational attainment) using modern methods in high dimensional statistics (i.e., machine learning). Replication tests show that these predictors capture, respectively, $\sim$40, 20, and 9 percent of total variance for the three traits. For example, predicted heights correlate $\sim$0.65 with actual height; actual heights of most individuals in validation samples are within a few cm of the prediction. The variance captured for height is comparable to the estimated SNP heritability from GCTA (GREML) analysis, and seems to be close to its asymptotic value (i.e., as sample size goes to infinity), suggesting that we have captured most of the heritability for the SNPs used. Thus, our results resolve the common SNP portion of the "missing heritability" problem -- i.e., the gap between prediction R-squared and SNP heritability. The $\sim$20k activated SNPs in our height predictor reveal the genetic architecture of human height, at least for common SNPs. Our primary dataset is the UK Biobank cohort, comprised of almost 500k individual genotypes with multiple phenotypes. We also use other datasets and SNPs found in earlier GWAS for out-of-sample validation of our results.