To successfully integrate AI and machine learning technologies, companies need to take a more holistic approach toward training their workforce. In our recent surveys AI Adoption in the Enterprise and Machine Learning Adoption in the Enterprise, we found growing interest in AI technologies among companies across a variety of industries and geographic locations. Our findings align with other surveys and studies--in fact, a recent study by the World Intellectual Patent Office (WIPO) found that the surge in research in AI and machine learning (ML) has been accompanied by an even stronger growth in AI-related patent applications. Patents are one sign that companies are beginning to take these technologies very seriously. When we asked what held back their adoption of AI technologies, respondents cited a few reasons, including some that pertained to culture, organization, and skills: Implementing and incorporating AI and machine learning technologies will require retraining across an organization, not just technical teams.

Based on its core scientist team's top-level R&D strength, as well as technological innovation and breakthroughs, Squirrel AI Learning started holding four "man-machine competitions" in Zhengzhou, Chengdu and Dongying in October 2017 in a bid to identify any difference between its adaptive learning system and human teaching. Dr. Kalns demonstrated to the RE-WORK audience the results of the four competitions: surprisingly, machine teaching outperformed human teaching in all the four competitions. Taking the fourth competition, which unfolded in one hundred cities, as an example, students at the same intellectual level were divided into two groups and received human teaching and Squirrel AI Learning respectively. Every student in the machine teaching group learned 42 knowledge points on the average, while every student in the human teaching learned 28 knowledge points on the average; in terms of average scoring in the core part of the competition, the students in the AI teaching group had their scores increased by 5.4 on the average, while the students in the human teaching group just had their scores increased by 0.7 on the average, suggesting that machine teaching enabled students to take a firmer grasp of knowledge points than human teaching and improved the learning efficiency more significantly than human teaching. According to the results, Squirrel AI Learning is basically the same as or better than individualized human teaching.

Next-generation wireless networks must support ultra-reliable, low-latency communication and intelligently manage a massive number of Internet of Things (IoT) devices in real-time, within a highly dynamic environment. This need for stringent communication quality-of-service (QoS) requirements as well as mobile edge and core intelligence can only be realized by integrating fundamental notions of artificial intelligence (AI) and machine learning across the wireless infrastructure and end-user devices. In this context, this paper provides a comprehensive tutorial that introduces the main concepts of machine learning, in general, and artificial neural networks (ANNs), in particular, and their potential applications in wireless communications. For this purpose, we present a comprehensive overview on a number of key types of neural networks that include feed-forward, recurrent, spiking, and deep neural networks. For each type of neural network, we present the basic architecture and training procedure, as well as the associated challenges and opportunities. Then, we provide an in-depth overview on the variety of wireless communication problems that can be addressed using ANNs, ranging from communication using unmanned aerial vehicles to virtual reality and edge caching.For each individual application, we present the main motivation for using ANNs along with the associated challenges while also providing a detailed example for a use case scenario and outlining future works that can be addressed using ANNs. In a nutshell, this article constitutes one of the first holistic tutorials on the development of machine learning techniques tailored to the needs of future wireless networks.

It is commonly understood that AI is one of the most disruptive technologies being developed. It may affect almost every aspect of society – from knowledge sharing to economic interactions, from making art crafts to finding cures for our diseases – and of personal life – from making friends, to finding a partner, from dealing with the pain for the loss of beloved people, to helping us managing our households through smart objects. Understanding the relationship between AI and society is a complex endeavour, since its shape and its evolution are not an immutable technological law, but instead the consequence of specific choices, both private and public, that could very well change over time and may of course influence its sustainability. Some powerful politicians like Vladimir Putin have declared that who will lead the researches in the field of AI, will lead the world, and of course many funds are coming from the armies (Harari, 2015) and from governments that seem to be working for monitoring and controlling us (Greenwald, 2015; Zuboff, 2018). Many others come from the finance world and are meant to increase the incomes of a few rich persons, regardless the risks ran by the rest of the population (O'Neil, 2016).

Unlike traditional manual mentorship tools, the machine learning algorithm generates better matches at a massive scale by factoring in numerous, individualized parameters. Personalized career development is no longer a perk for the privileged few at the top. An intelligent mentoring app called Ellen is matching mentors and mentees from all levels of the organization. Launched by San Francisco-based NextPlay.ai, Ellen is popular with a growing number of major companies worldwide, including the United States and Asia.

Training generative models like generative adversarial networks (GANs) and normalizing flows is challenging for noisy data. A novel curriculum learning algorithm pertaining to clustering is proposed to address this issue in this paper. The curriculum construction is based on the centrality of underlying clusters in data points. The data points of high centrality takes priority of being fed into generative models during training. To make our algorithm scalable to large-scale data, the active set is devised, in the sense that every round of training proceeds only on an active subset containing a small fraction of already trained data and the incremental data of lower centrality. Moreover, the geometric analysis is presented to interpret the necessity of cluster curriculum for generative models. The experiments on cat and human-face data validate that our algorithm is able to learn the optimal generative models (e.g. ProGAN and Glow) with respect to specified quality metrics for noisy data. An interesting finding is that the optimal cluster curriculum is closely related to the critical point of a geometric percolation process formulated in the paper.

In an industry that is experiencing a steady rate of job creation, data science itself has moved from just a buzzword to a strategic component in organisations. In addition to this, data scientists are increasingly taking on more strategic roles as organisations employ a product-centric view of data. It is a field that promises tremendous job growth and higher earning potential. Our latest research posits 97,000 jobs are available in this buzzing field. On the hiring end, there is a significant overall growth in jobs in the field.

We provide a brief tutorial on the use of concentration inequalities as they apply to system identification of state-space parameters of linear time invariant systems, with a focus on the fully observed setting. We draw upon tools from the theories of large-deviations and self-normalized martingales, and provide both data-dependent and independent bounds on the learning rate. I. INTRODUCTION A key feature in modern reinforcement learning is the ability to provide high-probability guarantees on the finite-data/time behavior of an algorithm acting on a system. The enabling technical tools used in providing such guarantees are concentration of measure results, which should be interpreted as quantitative versions of the strong law of large numbers. This paper provides a brief introduction to such tools, as motivated by the identification of linear-time-invariant (LTI) systems.