The term "artificial intelligence" was coined fairly recently in 1955, but the idea of smart machines that do our bidding has far deeper roots, going back to the ancient myths of Greece, India, and China. Perhaps that's why AI has such an impact on our imagination, and why, in recent years, there's been so much hype surrounding the technology. But AI is not a myth, nor is it a magical machine. It's a technology like any other, that, after decades of research, has reached a new plateau of productivity. Cheap processing power and abundant data have made this possible, and AI and machine learning are now useful tools in a diverse range of fields, including astronomy, health care, transportation, and music.

WORD FOR WORD: Transcription and captioning startup Verbit has raised $23 million in a Series A funding round led by Viola Ventures. Vertex Ventures, HV Ventures, Oryzn Capital, Vintage Venture Partners and Clal-Tech also contributed to the raise, which brings the total amount raised for the company to $34 million. Based in Tel Aviv, Israel, Verbit's transcription services use artificial intelligence to automatically transcribe and caption audio and video files. Much of the recent raise will be used to continue developing that software; currently, the company also relies on human freelancers to do some of its transcription, which is used by colleges and universities including BYU-Idaho, Auburn University and University of California Santa Barbara. Verbit's recent raise will also be used to grow the company's sales, marketing and product teams, according to a press announcement, and continue expansion into the U.S.

Apple has reported a decline in both revenue and profits in its latest quarterly financial results, as the company feels the pinch from slowing demand for its star product, the iPhone. The results showed a 15 per cent fall in sales of the iPhone in the three months ending to 29 December, diving from $61.1bn (£46.8bn) in 2017, down to £51.9bn dollars (£39.8bn) a year later. Mac, iPad and the Wearables, Home and Accessories category all experienced an increase in net sales, but the most notable growth came from Apple's services - which includes its Apple Music streaming platform, the App Store, iCloud storage and Apple Pay - jumping 19 per cent, from $9.1bn (£7bn) to $10.9bn (£8.3bn) year-on-year. However, combined, the hole left by weak iPhone sales resulted in a total drop to $84.3bn (£64.5bn), Profits also fell slightly to $19.9bn (£15.3bn),

This paper makes two proposals for Monte Carlo Softmax Search, which is a recently proposed method that is classified as a selective search like the Monte Carlo Tree Search. The first proposal separately defines the node-selection and backup policies to allow researchers to freely design a node-selection policy based on their searching strategies and confirms the principal variation produced by the Monte Carlo Softmax Search to that produced by a minimax search. The second proposal modifies commonly used learning methods for positional evaluation functions. In our new proposals, evaluation functions are learned by Monte Carlo sampling, which is performed with the backup policy in the search tree produced by Monte Carlo Softmax Search. The learning methods under consideration include supervised learning, reinforcement learning, regression learning, and search bootstrapping. Our sampling-based learning not only uses current positions and principal variations but also the internal nodes and important variations of a search tree. This step reduces the number of games necessary for learning. New learning rules are derived for sampling-based learning based on the Monte Carlo Softmax Search and combinations of the modified learning methods are also proposed in this paper.

Recent research on dense captioning based on the recurrent neural network and the convolutional neural network has made a great progress. However, mapping from an image feature space to a description space is a nonlinear and multimodel task, which makes it difficult for the current methods to get accurate results. In this paper, we put forward a novel approach for dense captioning based on hourglass-structured residual learning. Discriminant feature maps are obtained by incorporating dense connected networks and residual learning in our model. Finally, the performance of the approach on the Visual Genome V1.0 dataset and the region labelled MS-COCO (Microsoft Common Objects in Context) dataset are demonstrated. The experimental results have shown that our approach outperforms most current methods.

Recent years have seen a boom in interest in machine learning systems that can provide a human-understandable rationale for their predictions or decisions. However, exactly what kinds of explanation are truly human-interpretable remains poorly understood. This work advances our understanding of what makes explanations interpretable under three specific tasks that users may perform with machine learning systems: simulation of the response, verification of a suggested response, and determining whether the correctness of a suggested response changes under a change to the inputs. Through carefully controlled human-subject experiments, we identify regularizers that can be used to optimize for the interpretability of machine learning systems. Our results show that the type of complexity matters: cognitive chunks (newly defined concepts) affect performance more than variable repetitions, and these trends are consistent across tasks and domains. This suggests that there may exist some common design principles for explanation systems.

We study a bad arm existing checking problem in which a player's task is to judge whether a positive arm exists or not among given K arms by drawing as small number of arms as possible. Here, an arm is positive if its expected loss suffered by drawing the arm is at least a given threshold. This problem is a formalization of diagnosis of disease or machine failure. An interesting structure of this problem is the asymmetry of positive and negative (non-positive) arms' roles; finding one positive arm is enough to judge existence while all the arms must be discriminated as negative to judge non-existence. We propose an algorithms with arm selection policy (policy to determine the next arm to draw) and stopping condition (condition to stop drawing arms) utilizing this asymmetric problem structure and prove its effectiveness theoretically and empirically.

Abstract--The recognition of sign language is a challenging task with an important role in society to facilitate the communication ofdeaf persons. We propose a new approach of Spatial-Temporal Graph Convolutional Network to sign language recognition based on the human skeletal movements. The method uses graphs to capture the signs dynamics in two dimensions, spatial and temporal, considering the complex aspects of the language. Additionally, we present a new dataset of human skeletons for sign language based on ASLLVD to contribute to future related studies. I. INTRODUCTION Sign language is a visual communication skill that enables individuals with different types of hearing impairment to communicate in society. It is the language used by most deaf people in their daily lives and, moreover, it is the symbol of identification between the members of that community and the main force that unites them. The sign language has a very close relationship with the culture of the country or even regions, and for this reason, each nation has its language [1]. According to the World Health Organization, the number of deaf people is about 466 million, and the organization estimates that by 2050 this number exceeds 900 million, which is equivalent to a forecast of 1 in 10 individuals around the world [2].

Factorization machine (FM) is a popular machine learning model to capture the second order feature interactions. The optimal learning guarantee of FM and its generalized version is not yet developed. For a rank $k$ generalized FM of $d$ dimensional input, the previous best known sampling complexity is $\mathcal{O}[k^{3}d\cdot\mathrm{polylog}(kd)]$ under Gaussian distribution. This bound is sub-optimal comparing to the information theoretical lower bound $\mathcal{O}(kd)$. In this work, we aim to tighten this bound towards optimal and generalize the analysis to sub-gaussian distribution. We prove that when the input data satisfies the so-called $\tau$-Moment Invertible Property, the sampling complexity of generalized FM can be improved to $\mathcal{O}[k^{2}d\cdot\mathrm{polylog}(kd)/\tau^{2}]$. When the second order self-interaction terms are excluded in the generalized FM, the bound can be improved to the optimal $\mathcal{O}[kd\cdot\mathrm{polylog}(kd)]$ up to the logarithmic factors. Our analysis also suggests that the positive semi-definite constraint in the conventional FM is redundant as it does not improve the sampling complexity while making the model difficult to optimize. We evaluate our improved FM model in real-time high precision GPS signal calibration task to validate its superiority.

The top-$k$ error is often employed to evaluate performance for challenging classification tasks in computer vision as it is designed to compensate for ambiguity in ground truth labels. This practical success motivates our theoretical analysis of consistent top-$k$ classification. To this end, we define top-$k$ calibration as a necessary and sufficient condition for consistency, for bounded below loss functions. Unlike prior work, our analysis of top-$k$ calibration handles non-uniqueness of the predictor scores, and extends calibration to consistency -- providing a theoretically sound basis for analysis of this topic. Based on the top-$k$ calibration analysis, we propose a rich class of top-$k$ calibrated Bregman divergence surrogates. Our analysis continues by showing previously proposed hinge-like top-$k$ surrogate losses are not top-$k$ calibrated and thus inconsistent. On the other hand, we propose two new hinge-like losses, one which is similarly inconsistent, and one which is consistent. Our empirical results highlight theoretical claims, confirming our analysis of the consistency of these losses.