A mind-reading chip that let you control a computer by just thinking has been unveiled at a conference in China. Dubbed Brain Talker, works by picking out small electrical pulses in the brain and quickly decoding them into signals that a computer can interpret. The chip could be used to control computers, smartphones and other devices, its creators say. It also has potential medical, education, security and entertainment applications, they add. However, the information released so far on the chip and exactly how it operates is limited.

Before the meeting, the head of state was told about the academic process at School 21 and had a brief conversation with students. The President was informed about the school by Head of Sberbank German Gref and school Principal Svetlana Infimovskaya. The students of the school can study the following areas: Algorithms, Graphics, Mobile Development, Computer Security, Robot Technology, and Artificial Intelligence to name a few. The school has 940 students today. On average, students are expected to study for 2–3.5 years. The course includes two practical training sessions in relevant companies for six months or more. Today I suggest that we discuss concrete steps that will form the foundation for our National Strategy on the development of artificial intelligence technologies. We have repeatedly spoken about the need for such a comprehensive document. I also mentioned it in this year's Address to the Federal Assembly. This is indeed one of the key areas of technological development that determines and will continue to determine the future of the entire world. The artificial intelligence mechanisms will allow for quick real-time decision-making based on analysing vast amounts of information known as big data, which provides tremendous advantages in terms of quality and performance. In addition, such mechanisms are unparalleled in history in terms of their impact on the economy and productivity, the effectiveness of management, education, healthcare and daily life. However, vying for technological leadership, primarily, in the sphere of artificial intelligence – and you are all very well aware of this, colleagues – has already lead to global competition. New products and solutions are being created at an exponential growth rate. I have said it before and I will say it now: he who can establish a monopoly in artificial intelligence – we are aware of the consequences – will rule the world. It is no accident that many developed countries of the world have already adopted action plans to develop such technologies. Of course, we must ensure technological sovereignty in the realm of artificial intelligence. This is the most important prerequisite for the viability of our businesses and the economy, the quality of life for Russian citizens, security and, finally, our defence capability. Here, we are not just talking about algorithms for addressing individual and highly specialised problems; what we need are universal solutions, the use of which gives the optimum effect in any industry. In order to achieve such an ambitious goal in AI technology, we are objectively positioned to have a good start and we have a serious competitive edge. Today, Russia boasts one of the world's highest penetration rates for mobile communications and internet access, as well as for the development of electronic services.

How Artificial Intelligence can help with Biosecurity. Around the globe, different parts of the food supply chain are contaminated on a daily bases. Biosecurity, as defined by the Food and Agriculture Organization of the United Nations is the strategic and integrated approach to manage risks in food safety, animal and plant life and health, and biosafety. It relates to policy and regulatory framework that improves food health inside different points in the global food supply chain. China, a country that consumes more pork per capita than any other country after Vietnam, is contending with a deadly case of African Swine Fever.

Peanut butter and chocolate, Mick and Keith, Batman and Robin -- great partnerships -- successful marriages so to speak. Something magical happened when their paths merged, forging culinary, music and comic book history. When two technologies collide, the result is groundbreaking innovation -- healthcare and robotics, supply chain and distributed ledger technology, digital technology, and photography, 3D printing & healthcare. In business for an innovative idea to be implemented on a large scale, it has to solve a specific need and it needs to be able to be replicated at a reasonable cost. Though the field of artificial intelligence was born in the 1950s, it didn't really find mainstream popularity until the 1990s and early 2000s.

We propose a novel feature coding method that exploits invariance. We consider the setting where the transformations that preserve the image contents compose a finite group of orthogonal matrices. This is the case in many image transformations such as image rotations and image flipping. We prove that the group-invariant feature vector contains sufficient discriminative information when we learn a linear classifier using convex loss minimization. From this result, we propose a novel feature modeling for principal component analysis, and k-means clustering, which are used for most feature coding methods, and global feature functions that explicitly consider the group action. Although the global feature functions are complex nonlinear functions in general, we can calculate the group action on this space easily by constructing the functions as the tensor product representations of basic representations, resulting in the explicit form of invariant feature functions. We demonstrate the effectiveness of our methods on several image datasets.

In this thesis, we draw inspiration from both classical system identification and modern machine learning in order to solve estimation problems for real-world, physical systems. The main approach to estimation and learning adopted is optimization based. Concepts such as regularization will be utilized for encoding of prior knowledge and basis-function expansions will be used to add nonlinear modeling power while keeping data requirements practical. The thesis covers a wide range of applications, many inspired by applications within robotics, but also extending outside this already wide field. Usage of the proposed methods and algorithms are in many cases illustrated in the real-world applications that motivated the research. Topics covered include dynamics modeling and estimation, model-based reinforcement learning, spectral estimation, friction modeling and state estimation and calibration in robotic machining. In the work on modeling and identification of dynamics, we develop regularization strategies that allow us to incorporate prior domain knowledge into flexible, overparameterized models. We make use of classical control theory to gain insight into training and regularization while using flexible tools from modern deep learning. A particular focus of the work is to allow use of modern methods in scenarios where gathering data is associated with a high cost. In the robotics-inspired parts of the thesis, we develop methods that are practically motivated and ensure that they are implementable also outside the research setting. We demonstrate this by performing experiments in realistic settings and providing open-source implementations of all proposed methods and algorithms.

The main theme of this work is a unifying algorithm, abbreviated as L2S, that can deal with (strongly) convex and nonconvex empirical risk minimization (ERM) problems. It broadens a recently developed variance reduction method known as SARAH. L2S enjoys a linear convergence rate for strongly convex problems, which also implies the last iteration of SARAH's inner loop converges linearly. For convex problems, different from SARAH, L2S can afford step and mini-batch sizes not dependent on the data size $n$, and the complexity needed to guarantee $\mathbb{E}[\|\nabla F(\mathbf{x}) \|^2] \leq \epsilon$ is ${\cal O}(n+ n/\epsilon)$. For nonconvex problems on the other hand, the complexity is ${\cal O}(n+ \sqrt{n}/\epsilon)$. Parallel to L2S there are a few side results. Leveraging an aggressive step size, D2S is proposed, which provides a more efficient alternative to L2S and SARAH-like algorithms. Specifically, D2S requires a reduced IFO complexity of ${\cal O}\big( (n+ \bar{\kappa}) \ln (1/\epsilon) \big)$ for strongly convex problems. Moreover, to avoid the tedious selection of the optimal step size, an automatic tuning scheme is developed, which obtains comparable empirical performance with SARAH using judiciously tuned step size.

We introduce a systematic framework for quantifying the robustness of classifiers to naturally occurring perturbations of images found in videos. As part of this framework, we construct Imagenet-Video-Robust, a human-expert--reviewed dataset of 22,178 images grouped into 1,109 sets of perceptually similar images derived from frames in the ImageNet Video Object Detection dataset. We evaluate a diverse array of classifiers trained on ImageNet, including models trained for robustness, and show a median classification accuracy drop of 16%. Additionally, we evaluate the Faster R-CNN and R-FCN models for detection, and show that natural perturbations induce both classification as well as localization errors, leading to a median drop in detection mAP of 14 points. Our analysis shows that natural perturbations in the real world are heavily problematic for current CNNs, posing a significant challenge to their deployment in safety-critical environments that require reliable, low-latency predictions.

Identifying statistical dependence between the features and the label is a fundamental problem in supervised learning. This paper presents a framework for estimating dependence between numerical features and a categorical label using generalized Gini distance, an energy distance in reproducing kernel Hilbert spaces (RKHS). Two Gini distance based dependence measures are explored: Gini distance covariance and Gini distance correlation. Unlike Pearson covariance and correlation, which do not characterize independence, the above Gini distance based measures define dependence as well as independence of random variables. The test statistics are simple to calculate and do not require probability density estimation. Uniform convergence bounds and asymptotic bounds are derived for the test statistics. Comparisons with distance covariance statistics are provided. It is shown that Gini distance statistics converge faster than distance covariance statistics in the uniform convergence bounds, hence tighter upper bounds on both Type I and Type II errors. Moreover, the probability of Gini distance covariance statistic under-performing the distance covariance statistic in Type II error decreases to 0 exponentially with the increase of the sample size. Extensive experimental results are presented to demonstrate the performance of the proposed method.

Unsupervised text style transfer aims to alter text styles while preserving the content, without aligned data for supervision. Existing seq2seq methods face three challenges: 1) the transfer is weakly interpretable, 2) generated outputs struggle in content preservation, and 3) the trade-off between content and style is intractable. To address these challenges, we propose a hierarchical reinforced sequence operation method, named Point-Then-Operate (PTO), which consists of a high-level agent that proposes operation positions and a low-level agent that alters the sentence. We provide comprehensive training objectives to control the fluency, style, and content of the outputs and a mask-based inference algorithm that allows for multi-step revision based on the single-step trained agents. Experimental results on two text style transfer datasets show that our method significantly outperforms recent methods and effectively addresses the aforementioned challenges.