Sample-efficient exploration is crucial not only for discovering rewarding experiences but also for adapting to environment changes in a task-agnostic fashion. A principled treatment of the problem of optimal input synthesis for system identification is provided within the framework of sequential Bayesian experimental design. In this paper, we present an effective trajectory-optimization-based approximate solution of this otherwise intractable problem that models optimal exploration in an unknown Markov decision process (MDP). By interleaving episodic exploration with Bayesian nonlinear system identification, our algorithm takes advantage of the inductive bias to explore in a directed manner, without assuming prior knowledge of the MDP. Empirical evaluations indicate a clear advantage of the proposed algorithm in terms of the rate of convergence and the final model fidelity when compared to intrinsic-motivation-based algorithms employing exploration bonuses such as prediction error and information gain. Moreover, our method maintains a computational advantage over a recent model-based active exploration (MAX) algorithm, by focusing on the information gain along trajectories instead of seeking a global exploration policy. A reference implementation of our algorithm and the conducted experiments is publicly available.

Autonomous construction of deep neural network (DNNs) is desired for data streams because it potentially offers two advantages: proper model's capacity and quick reaction to drift and shift. While the self-organizing mechanism of DNNs remains an open issue, this task is even more challenging to be developed for standard multi-layer DNNs than that using the different-depth structures, because the addition of a new layer results in information loss of previously trained knowledge. A Neural Network with Dynamically Evolved Capacity (NADINE) is proposed in this paper. NADINE features a fully open structure where its network structure, depth and width, can be automatically evolved from scratch in an online manner and without the use of problem-specific thresholds. NADINE is structured under a standard MLP architecture and the catastrophic forgetting issue during the hidden layer addition phase is resolved using the proposal of soft-forgetting and adaptive memory methods. The advantage of NADINE, namely elastic structure and online learning trait, is numerically validated using nine data stream classification and regression problems where it demonstrates performance improvement over prominent algorithms in all problems. In addition, it is capable of dealing with data stream regression and classification problems equally well.

What makes a task relatively more or less difficult for a machine compared to a human? Much AI/ML research has focused on expanding the range of tasks that machines can do, with a focus on whether machines can beat humans. Allowing for differences in scale, we can seek interesting (anomalous) pairs of tasks T, T'. We define interesting in this way: The "harder to learn" relation is reversed when comparing human intelligence (HI) to AI. While humans seems to be able to understand problems by formulating rules, ML using neural networks does not rely on constructing rules. We discuss a novel approach where the challenge is to "perform well under rules that have been created by human beings." We suggest that this provides a rigorous and precise pathway for understanding the difference between the two kinds of learning. Specifically, we suggest a large and extensible class of learning tasks, formulated as learning under rules. With these tasks, both the AI and HI will be studied with rigor and precision. The immediate goal is to find interesting groundtruth rule pairs. In the long term, the goal will be to understand, in a generalizable way, what distinguishes interesting pairs from ordinary pairs, and to define saliency behind interesting pairs. This may open new ways of thinking about AI, and provide unexpected insights into human learning.

Over the weekend, bizarre footage of a facial projection technology circulated on social media in connection with the ongoing protests in Hong Kong. The mysterious device shows a headband with a large digital projector, which projects a digital image of another person's face onto whoever is wearing the device. The device was assumed to be a countermeasure to the recent ban on face coverings in Hong Kong. Initially images from the 2017 art project were thought to come from the Hong Kong protests. In fact, the videos came from a 2017 art project called'Anonymous,' created by students at Utrecht School of the Arts in the Netherlands.

Artificial intelligence has been the basis of robotics for several decades. However, early AI technology was not well-suited for solving the countless challenges robots needed to solve. Robots were often programmed with simple algorithms that were made in BASIC or Cobol. They couldn't adapt, unless the programmers developed more sophisticated artificial intelligence programs to manage them. A new generation of robots depend on machine learning technology.

The Information and Communication Technology state minister Zunaid Ahmed Palak on Friday urged everyone to utilize the prospects of artificial intelligence for developing the people's living standard. 'The power of artificial intelligence can change the society. There is an opportunity to do many things for human by properly utilizing the artificial intelligence technology,' he said. The state minister was speaking at a discussion at the workshop on'Artificial Intelligence for All,' marking the'India Economic Summit 2019' at Taj Palace Hotel in New Delhi, said a press release in Dhaka. Asia-Pacific America Leadership team advisor Deepankar Sanwalka and Hewlett Packard Enterprise India managing director Som Satsange, among others, were present during the discussion.

A growing body of research suggests that the facial movements of almost anyone can be synced to audio clips of speech, given a sufficiently large corpus. In June, applied scientists at Samsung detailed an end-to-end model capable of animating the eyebrows, mouth, and eyelashes, and cheeks in a person's headshot. Only a few weeks later, Udacity revealed a system that automatically generates standup lecture videos from audio narration. And two years ago, Carnegie Mellon researchers published a paper describing an approach for transferring the facial movements from one person to another. Building on this and other work, a Microsoft Research team this week laid out a technique they claim improves the fidelity of audio-driven talking heads animations.

Which children are most vulnerable when their nation imposes austerity programs?

This morning, BMO announced that it is making a $5 million investment in a new University of Toronto lab called the BMO Lab for Creative Research in the Arts, Performance, Emerging Technologies, and AI. This is the bank's largest investment in a Canadian post secondary institution to date. The BMO Lab will host high-profile, public artistic events, and aims to create a global network of artists and researchers that combine art and technology. The BMO lab will be housed within the University of Toronto's Centre for Drama, Theatre, and Performance Studies. Students from the arts, humanities, sciences, and engineering will be able to explore how AI and other technologies can impact artistic expression.

ODSC is the best community data science event on the planet. There are other events that cover special topics, or industries, etc., but ODSC is comprehensive and totally community-focused: it's the conference to engage, to build, to develop, and to learn from the whole data science community. ODSC West 2019 is one of the largest applied data science conferences in the world. Our speakers include some of the core contributors to many open source tools, libraries, and languages. Attend ODSC West 2019 and learn the latest AI & data science topics, tools, and languages from some of the best and brightest minds in the field.