Chinese AI startups raised $5 billion in venture capital funding during 2017 -- a half-billion more than their U.S. counterparts -- indicating investor confidence in the technologies, business strategies, and market potential of Chinese startups, according to research by market advisory firm ABI Research. In global terms, investment values for AI startups increased 150 percent -- from $4 billion to $10.7 billion -- between 2016 and 2017. U.S. startups raised $4.4 billion from 155 investments, while Chinese startups netted $4.9 billion from 19 investments. "The bullish sentiment shared among Chinese investors is a clear sign that China is going all-in in artificial intelligence," said Lian Jye Su, a principal analyst at ABI. "The government of China is setting clear policy guidelines for the future development of AI, and startups are responding with cutting edge AI technologies across many industries." ABI found that AI startups in Europe have diversified interests across different industries and verticals, mainly for use cases such as cybersecurity, digital ID, public safety, healthcare, and IoT.

As many who follow the financial services industry will recall, during and after the financial crisis of 2008-2009, a number of events and structural changes occurred. One of those changes involved former investment banks being either acquired (i.e.; Merrill Lynch, Bear Stearns) or converting to a financial holding companies (FHC), which allowed for easier capital access but also subjected them to closer regulatory scrutiny. One of the FHC conversions (through the Gramm-Leach-Bliley Act of 1999, essentially the reversal of 1933 Glass-Steagall Act) was Goldman Sachs. This piece, appearing in Tech Republic, uses the Goldman Sachs digital banking platform Marcus as a proxy to make a point about the changing consumer banking landscape.

Scientists have found that goats are drawn to humans with happy facial expressions. The result suggests a wider range of animals can read people's moods than was previously thought. The team showed goats pairs of photos of the same person, one of them featuring an angry expression, and the other a happy demeanour. The goats in the study made a beeline for the happy faces, the researchers report in the journal Open Science. The result implies that the ability of animals to perceive human facial cues is not limited to those with a long history of working as human companions, such as dogs and horses. Instead, it seems, animals domesticated for food production, such as goats, can also decipher human facial cues.

Active learning identifies data points to label that are expected to be the most useful in improving a supervised model. Opportunistic active learning incorporates active learning into interactive tasks that constrain possible queries during interactions. Prior work has shown that opportunistic active learning can be used to improve grounding of natural language descriptions in an interactive object retrieval task. In this work, we use reinforcement learning for such an object retrieval task, to learn a policy that effectively trades off task completion with model improvement that would benefit future tasks.

Search-oriented conversational systems rely on information needs expressed in natural language (NL). We focus here on the understanding of NL expressions for building keyword-based queries. We propose a reinforcement-learning-driven translation model framework able to 1) learn the translation from NL expressions to queries in a supervised way, and, 2) to overcome the lack of large-scale dataset by framing the translation model as a word selection approach and injecting relevance feedback in the learning process. Experiments are carried out on two TREC datasets and outline the effectiveness of our approach.

Gradient boosting is a prediction method that iteratively combines weak learners to produce a complex and accurate model. From an optimization point of view, the learning procedure of gradient boosting mimics a gradient descent on a functional variable. This paper proposes to build upon the proximal point algorithm when the empirical risk to minimize is not differentiable. In addition, the novel boosting approach, called accelerated proximal boosting, benefits from Nesterov's acceleration in the same way as gradient boosting [Biau et al., 2018]. Advantages of leveraging proximal methods for boosting are illustrated by numerical experiments on simulated and real-world data. In particular, we exhibit a favorable comparison over gradient boosting regarding convergence rate and prediction accuracy.

Solving statistical learning problems often involves nonconvex optimization. Despite the empirical success of nonconvex statistical optimization methods, their global dynamics, especially convergence to the desirable local minima, remain less well understood in theory. In this paper, we propose a new analytic paradigm based on diffusion processes to characterize the global dynamics of nonconvex statistical optimization. As a concrete example, we study stochastic gradient descent (SGD) for the tensor decomposition formulation of independent component analysis. In particular, we cast different phases of SGD into diffusion processes, i.e., solutions to stochastic differential equations. Initialized from an unstable equilibrium, the global dynamics of SGD transit over three consecutive phases: (i) an unstable Ornstein-Uhlenbeck process slowly departing from the initialization, (ii) the solution to an ordinary differential equation, which quickly evolves towards the desirable local minimum, and (iii) a stable Ornstein-Uhlenbeck process oscillating around the desirable local minimum. Our proof techniques are based upon Stroock and Varadhan's weak convergence of Markov chains to diffusion processes, which are of independent interest.

Generative adversarial networks have been successfully applied to inpainting in natural images. However, the current state-of-the-art models have not yet been widely adopted in the medical imaging domain. In this paper, we investigate the performance of three recently published deep learning based inpainting models: context encoders, semantic image inpainting, and the contextual attention model, applied to chest x-rays, as the chest exam is the most commonly performed radiological procedure. We train these generative models on 1.2M 128 $\times$ 128 patches from 60K healthy x-rays, and learn to predict the center 64 $\times$ 64 region in each patch. We test the models on both the healthy and abnormal radiographs. We evaluate the results by visual inspection and comparing the PSNR scores. The outputs of the models are in most cases highly realistic. We show that the methods have potential to enhance and detect abnormalities. In addition, we perform a 2AFC observer study and show that an experienced human observer performs poorly in detecting inpainted regions, particularly those generated by the contextual attention model.

Recommender systems relying on latent factor models often appear as black boxes to their users. Semantic descriptions for the factors might help to mitigate this problem. Achieving this automatically is, however, a non-straightforward task due to the models' statistical nature. We present an output-agreement game that represents factors by means of sample items and motivates players to create such descriptions. A user study shows that the collected output actually reflects real-world characteristics of the factors.

We propose a unified framework for building unsupervised representations of individual objects or entities (and their compositions), by associating with each object both a distributional as well as a point estimate (vector embedding). This is made possible by the use of optimal transport, which allows us to build these associated estimates while harnessing the underlying geometry of the ground space. Our method gives a novel perspective for building rich and powerful feature representations that simultaneously capture uncertainty (via a distributional estimate) and interpretability (with the optimal transport map). As a guiding example, we formulate unsupervised representations for text, in particular for sentence representation and entailment detection. Empirical results show strong advantages gained through the proposed framework. This approach can be used for any unsupervised or supervised problem (on text or other modalities) with a co-occurrence structure, such as any sequence data. The key tools underlying the framework are Wasserstein distances and Wasserstein barycenters (and, hence the title!).