The promise of AI is easily understood by anyone with an imagination, and for 40 years, venture capitalists have been enthusiastically investing in that promise. However, it's been significantly harder for founders to turn that investment into sustainable business models. Today we are going to look at why that is, and go over what might be a blueprint for startups to create business models around artificial intelligence. Tatsuo Nakamura founded Valuenex in 2006 with the goal of using artificial intelligence to supplement the work being done by patent attorneys, and their software was instrumental in the resolution of one of Japan's most famous, and most valuable, lawsuits. We also talk about how to sell to large companies as a small startup, the challenges in trying to make product strategy based on technology, why staying private longer is not always a good thing for startups, and how Valuenex technology accidentally discovered a secret collaboration between Honda and Google. It's a great discussion with the founder of one of Japan's most successful AI companies, and I think you will really enjoy it. Welcome to Disrupting Japan, straight talk from Japan's most successful entrepreneurs. Today, we're going to be talking about something that's frankly difficult to talk about on an audio podcast. Tatsuo Nakamura founded Valuenex in 2006 to use Artificial Intelligence and modern visualization techniques to help clients make sense of their patent portfolios and to keep an eye on what the competition is doing. In fact, this technology uncovered some of the core evidence that decided the famous blue LED case. It's highly effective but highly visual, so let me try to explain it. Valuenex creates a kind of topographical map that shows companies where in the market, their IP is strong and where it's weak.

Imagine being able to know the probability of whether a persistent headache that you are experiencing is a symptom of something much worse through a simple blood test. Researchers affiliated with ClinSpec Diagnostics Limited, a spin-off from the University of Strathclyde in Glasgow, Scotland, and their colleagues developed patented technology that can detect brain cancer from a blood samples. Using an innovative combination of artificial intelligence (AI) and spectroscopy, the U.K. researchers developed a method to detect brain cancer from a blood biopsy, and published their study on October 8, 2019 in Nature Communications. Headaches are one of the most common symptoms of brain tumors, according to the American Brain Tumor Association. But while headaches are very common, brain cancer is not.

Working in the cybersecurity field offers constant challenges. New threats from both internal and external sources emerge on a regular basis. Consequently, employees charged with protecting valuable assets like customers' Social Security numbers and proprietary company documents must stay up to date on new developments in cybersecurity. However, hackers also follow this pattern of constant learning to stay abreast of new ways to hack into organizations. Now, attackers are taking advantage of machine learning and artificial intelligence (AI) to increase the effectiveness of their cyberattacks.

'Divorced from reality,' says critical law professor Are "virtuous sex robots" the way of the future? University researchers suggest that robots created for human pleasure should be designed so that they can grant or withhold consent, as well as teach sex education. Anco Peeters, a doctoral student at Australia's University of Wollongong, and Pim Haselager, associate professor at The Netherlands' Radboud University, published "Designing Virtuous Sex Robots" in the International Journal of Social Robotics last month. The paper examined four areas: "virtue ethics and social robotics," "Contra instrumentalist accounts," "Consent practice through sex robots" and "Implications of virtuous sex robots." The authors do not focus on child sex robots or sex robots that play into rape fantasies, but "the potential positive aspects of intimate human–robot interactions through the cultivation of virtues."

Scientists have made tremendous leaps in getting computers to understand natural language, as well as in generating a series of physical poses to create realistic animations. These capabilities might as well exist in separate worlds, however, because the link between natural language and physical poses has been missing. Louis-Philippe Morency, associate professor in the Language Technologies Institute (LTI), and Chaitanya Ahuja, an LTI Ph.D. student, are working to bring those worlds together using a neural architecture they call Joint Language-to-Pose, or JL2P. The JL2P model enables sentences and physical motions to be jointly embedded, so it can learn how language is related to action, gestures and movement. "I think we're in an early stage of this research, but from a modeling, artificial intelligence and theory perspective, it's a very exciting moment," Morency said.

A plethora of problems in AI, engineering and the sciences are naturally formalized as inference in discrete probabilistic models. Exact inference is often prohibitively expensive, as it may require evaluating the (unnormalized) target density on its entire domain. Here we consider the setting where only a limited budget of calls to the unnormalized density oracle is available, raising the challenge of where in the domain to allocate these function calls in order to construct a good approximate solution. We formulate this problem as an instance of sequential decision-making under uncertainty and leverage methods from reinforcement learning for probabilistic inference with budget constraints. In particular, we propose the TreeSample algorithm, an adaptation of Monte Carlo Tree Search to approximate inference. This algorithm caches all previous queries to the density oracle in an explicit search tree, and dynamically allocates new queries based on a "best-first" heuristic for exploration, using existing upper confidence bound methods. Our non-parametric inference method can be effectively combined with neural networks that compile approximate conditionals of the target, which are then used to guide the inference search and enable generalization across multiple target distributions. We show empirically that TreeSample outperforms standard approximate inference methods on synthetic factor graphs.

Effective and efficient recommendation is crucial for modern e-commerce platforms. It consists of two indispensable components named Click-Through Rate (CTR) prediction and Conversion Rate (CVR) prediction, where the latter is an essential factor contributing to the final purchasing volume. Existing methods specifically predict CVR using the clicked and purchased samples, which has limited performance affected by the well-known sample selection bias and data sparsity issues. To address these issues, we propose a novel deep CVR prediction method by considering the post-click behaviors. After grouping deterministic actions together, we construct a novel sequential path, which elaborately depicts the post-click behaviors of users. Based on the path, we define the CVR and several related probabilities including CTR, etc., and devise a deep neural network with multiple targets involved accordingly. It takes advantage of the abundant samples with deterministic labels derived from the post-click actions, leading to a significant improvement of CVR prediction. Extensive experiments on both offline and online settings demonstrate its superiority over representative state-of-the-art methods.

This paper addresses the problem of multi-agent inverse reinforcement learning (MIRL) in a two-player general-sum stochastic game framework. Five variants of MIRL are considered: uCS-MIRL, advE-MIRL, cooE-MIRL, uCE-MIRL, and uNE-MIRL, each distinguished by its solution concept. Problem uCS-MIRL is a cooperative game in which the agents employ cooperative strategies that aim to maximize the total game value. In problem uCE-MIRL, agents are assumed to follow strategies that constitute a correlated equilibrium while maximizing total game value. Problem uNE-MIRL is similar to uCE-MIRL in total game value maximization, but it is assumed that the agents are playing a Nash equilibrium. Problems advE-MIRL and cooE-MIRL assume agents are playing an adversarial equilibrium and a coordination equilibrium, respectively. We propose novel approaches to address these five problems under the assumption that the game observer either knows or is able to accurately estimate the policies and solution concepts for players. For uCS-MIRL, we first develop a characteristic set of solutions ensuring that the observed bi-policy is a uCS and then apply a Bayesian inverse learning method. For uCE-MIRL, we develop a linear programming problem subject to constraints that define necessary and sufficient conditions for the observed policies to be correlated equilibria. The objective is to choose a solution that not only minimizes the total game value difference between the observed bi-policy and a local uCS, but also maximizes the scale of the solution. We apply a similar treatment to the problem of uNE-MIRL. The remaining two problems can be solved efficiently by taking advantage of solution uniqueness and setting up a convex optimization problem. Results are validated on various benchmark grid-world games.

EARTHMOVER-BASED MANIFOLD LEARNING FOR ANAL YZING MOLECULAR CONFORMA TION SPACES Nathan Zelesko Amit Moscovich Joe Kileel Amit Singer, Department of Mathematics, Brown University Program in Applied and Computational Mathematics, Princeton University Department of Mathematics, Princeton University ABSTRACT In this paper, we propose a novel approach for manifold learning that combines the Earthmover's distance (EMD) with the diffusion maps method for dimensionality reduction. We demonstrate the potential benefits of this approach for learning shape spaces of proteins and other flexible macromolecules using a simulated dataset of 3-D density maps that mimic the nonuniform rotary motion of A TP synthase. Our results show that EMD-based diffusion maps require far fewer samples to recover the intrinsic geometry than the standard diffusion maps algorithm that is based on the Euclidean distance. To reduce the computational burden of calculating the EMD for all volume pairs, we employ a wavelet-based approximation to the EMD which reduces the computation of the pairwise EMDs to a computation of pairwise weighted-null 1 distances between wavelet coefficient vectors. Index T erms -- Shape space, dimensionality reduction, Wasserstein metric, diffusion maps, Laplacian eigenmaps, cryo-electron microscopy 1. INTRODUCTION Proteins and other macromolecules are elastic structures that may deform in various ways.

Improving Robustness of time series classifier with Neural ODE guided gradient based data augmentation Anindya Sarkar Mobiliya Bangalore, INDIA anindya.sarkar@mobiliya.com Abstract --Exploring adversarial attack vectors and studying their effects on machine learning algorithms has been of interest to researchers. Deep neural networks working with time series data have received lesser interest compared to their image counterparts in this context. In a recent finding, it has been revealed that current state-of-the-art deep learning time series classifiers are vulnerable to adversarial attacks. In this paper, we introduce two local gradient based and one spectral density based time series data augmentation techniques. We show that a model trained with data obtained using our techniques obtains state-of- the-art classification accuracy on various time series benchmarks. In addition, it improves the robustness of the model against some of the most common corruption techniques,such as Fast Gradient Sign Method (FGSM) and Basic Iterative Method (BIM). Index T erms --time series classification, adversarial training, gradient based adversarial attacks I.