HONG KONG (Reuters Breakingviews) - Forget autonomous driving: The latest digital buzzword is medtech. Investors at last week's annual RISE technology conference in Hong Kong talked up a coming healthcare artificial-intelligence revolution, and the hype is as palpable in Silicon Valley. Chinese firms are well positioned to take the lead. U.S. venture capital funds poured over $12 billion into local biotech, pharmaceutical, and medical-device upstarts in the first half of this year, according to data from Pitchbook, on track to surpass last year's record of $17 billion. Investments in the country's life sciences sector doubled to $12 billion last year, according to ChinaBio Consulting, and accelerated to more than $5 billion in the first quarter of 2018, led by prolific backers like Qiming Ventures and Sequoia Capital China.

Jeff Smith builds large-scale machine learning systems using Scala and Spark. For the past decade, he has been working on data science applications at various startups in New York, San Francisco, and Hong Kong. He blogs and speaks about various aspects of building real world machine learning systems.

HONG KONG, July 18 (Reuters Breakingviews) - Forget autonomous driving: The latest digital buzzword is medtech. Investors at last week's annual RISE technology conference in Hong Kong talked up a coming healthcare artificial-intelligence revolution, and the hype is as palpable in Silicon Valley. Chinese firms are well positioned to take the lead. Biometric and genome data - everything from blood pressure readings to tissue samples and DNA - are transforming the industry, from the way pharmaceutical firms discover drugs to how doctors diagnose patients. The hunt is on for better, and cheaper, treatments.

The majority of robots are white. Do a Google image search for "robot" and see for yourself: The whiteness is overwhelming. There are some understandable reasons for this; for example, when we asked several different companies why their social home robots were white, the answer was simply because white most conveniently fits in with other home decor. But a new study suggests that the color white can also be a social cue that results in a perception of race, especially if it's presented in an anthropomorphic context, such as being the color of the outer shell of a humanoid robot. In addition, the same issue applies to robots that are black in color, according to the study.

Universal induction relies on some general search procedure that is doomed to be inefficient. One possibility to achieve both generality and efficiency is to specialize this procedure w.r.t. any given narrow task. However, complete specialization that implies direct mapping from the task parameters to solutions (discriminative models) without search is not always possible. In this paper, partial specialization of general search is considered in the form of genetic algorithms (GAs) with a specialized crossover operator. We perform a feasibility study of this idea implementing such an operator in the form of a deep feedforward neural network. GAs with trainable crossover operators are compared with the result of complete specialization, which is also represented as a deep neural network. Experimental results show that specialized GAs can be more efficient than both general GAs and discriminative models.

The performance of many machine learning techniques depends on the choice of an appropriate similarity or distance measure on the input space. Similarity learning (or metric learning) aims at building such a measure from training data so that observations with the same (resp. different) label are as close (resp. far) as possible. In this paper, similarity learning is investigated from the perspective of pairwise bipartite ranking, where the goal is to rank the elements of a database by decreasing order of the probability that they share the same label with some query data point, based on the similarity scores. A natural performance criterion in this setting is pointwise ROC optimization: maximize the true positive rate under a fixed false positive rate. We study this novel perspective on similarity learning through a rigorous probabilistic framework. The empirical version of the problem gives rise to a constrained optimization formulation involving U-statistics, for which we derive universal learning rates as well as faster rates under a noise assumption on the data distribution. We also address the large-scale setting by analyzing the effect of sampling-based approximations. Our theoretical results are supported by illustrative numerical experiments.

Embedding models typically associate each word with a single real-valued vector, representing its different properties. Evaluation methods, therefore, need to analyze the accuracy and completeness of these properties in embeddings. This requires fine-grained analysis of embedding subspaces. Multi-label classification is an appropriate way to do so. We propose a new evaluation method for word embeddings based on multi-label classification given a word embedding. The task we use is finegrained name typing: given a large corpus, find all types that a name can refer to based on the name embedding. Given the scale of entities in knowledge bases, we can build datasets for this task that are complementary to the current embedding evaluation datasets in: they are very large, contain fine-grained classes, and allow the direct evaluation of embeddings without confounding factors like sentence context.

In an economic market, sellers, infomediaries and customers constitute an economic network. Each seller has her own customer group and the seller's private customers are unobservable to other sellers. Therefore, a seller can only sell commodities among her own customers unless other sellers or infomediaries share her sale information to their customer groups. However, a seller is not incentivized to share others' sale information by default, which leads to inefficient resource allocation and limited revenue for the sale. To tackle this problem, we develop a novel mechanism called customer sharing mechanism (CSM) which incentivizes all sellers to share each other's sale information to their private customer groups. Furthermore, CSM also incentivizes all customers to truthfully participate in the sale.

An important task for a recommender system to provide interpretable explanations for the user. This is important for the credibility of the system. Current interpretable recommender systems tend to focus on certain features known to be important to the user and offer their explanations in a structured form. It is well known that user generated reviews and feedback from reviewers have strong leverage over the users' decisions. On the other hand, recent text generation works have been shown to generate text of similar quality to human written text, and we aim to show that generated text can be successfully used to explain recommendations. In this paper, we propose a framework consisting of popular review-oriented generation models aiming to create personalised explanations for recommendations. The interpretations are generated at both character and word levels. We build a dataset containing reviewers' feedback from the Amazon books review dataset. Our cross-domain experiments are designed to bridge from natural language processing to the recommender system domain. Besides language model evaluation methods, we employ DeepCoNN, a novel review-oriented recommender system using a deep neural network, to evaluate the recommendation performance of generated reviews by root mean square error (RMSE). We demonstrate that the synthetic personalised reviews have better recommendation performance than human written reviews. To our knowledge, this presents the first machine-generated natural language explanations for rating prediction.

But as for how many humans it takes to construct those 5,000 banana-colored robots a month, don't bother counting: The robots build themselves, test themselves and inspect themselves. A generation ago, many people--including manufacturing executives themselves--would've considered such a facility as either science fiction or centuries away. To be certain, FANUC's complex of 22 sub-factories is one of a kind. It's one of the world's first "lights-out factories"--where 24/7 operation is a reality and intelligent robots create computerized offspring capable, just like them, of machine learning and computer vision. But it proves just how far artificial intelligence (AI) has come in the manufacturing process.