For funding eligibility guidance, please visit our website: View Website. Full Studentships cover a stipend (2019/0 rate: £15,009pa), research costs and tuition fees at UK/EU rate and are available to UK and EU students who meet the UK residency requirements. Students from EU countries who do not meet the UK residency requirements may be eligible for a fees-only award. Students in receipt of a fees-only award will be eligible for a maintenance stipend awarded by the NRPDTP Bioscience Doctoral Scholarships. To be eligible students must meet the EU residency requirements.

Chinese internet company ByteDance is set to release a 24-hour artificial intelligence (AI) teacher for K-12 students in China by 2020. In 2018, ByteDance challenged the Tencent-backed VIPKid when they launched Gogokid, an online English teaching platform. Then, in May 2019, the company officially released an online education portal offering mathematics and language courses for K-12, taught by graduates of Peking and Tsinghua universities, the top two universities in China. Since July this year, ByteDance has focused on testing a new English learning application, Tangyuan English, which features a mode using a combination'AI real person' teaching workout. This marks the third English language learning product by ByteDance, following Open Language and Dubaibeidanci.

Recognizing users' strong interest in the emerging field of spatial data science, Esri is adding a new course--Spatial Data Science: The New Frontier in Analytics--to its popular lineup of massive open online courses (MOOCs). Opening in 2020, the course will explore how incorporating spatial data, tools, and methods enhances analytical and predictive models. Data scientists, GIS analysts, and others with a strong background in statistics and analytics will find the course beneficial. Attendees should plan to spend three to four hours per week on the course. Esri will award a certificate of completion to everyone who completes the MOOC.

In the last years, crowdsourcing is transforming the way classification training sets are obtained. Instead of relying on a single expert annotator, crowdsourcing shares the labelling effort among a large number of collaborators. For instance, this is being applied to the data acquired by the laureate Laser Interferometer Gravitational Waves Observatory (LIGO), in order to detect glitches which might hinder the identification of true gravitational-waves. The crowdsourcing scenario poses new challenging difficulties, as it deals with different opinions from a heterogeneous group of annotators with unknown degrees of expertise. Probabilistic methods, such as Gaussian Processes (GP), have proven successful in modeling this setting. However, GPs do not scale well to large data sets, which hampers their broad adoption in real practice (in particular at LIGO). This has led to the recent introduction of deep learning based crowdsourcing methods, which have become the state-of-the-art. However, the accurate uncertainty quantification of GPs has been partially sacrificed. This is an important aspect for astrophysicists in LIGO, since a glitch detection system should provide very accurate probability distributions of its predictions. In this work, we leverage the most popular sparse GP approximation to develop a novel GP based crowdsourcing method that factorizes into mini-batches. This makes it able to cope with previously-prohibitive data sets. The approach, which we refer to as Scalable Variational Gaussian Processes for Crowdsourcing (SVGPCR), brings back GP-based methods to the state-of-the-art, and excels at uncertainty quantification. SVGPCR is shown to outperform deep learning based methods and previous probabilistic approaches when applied to the LIGO data. Moreover, its behavior and main properties are carefully analyzed in a controlled experiment based on the MNIST data set.

This work addresses the classic machine learning problem of online prediction with expert advice. We consider the finite-horizon version of this zero-sum, two-person game. Using verification arguments from optimal control theory, we view the task of finding better lower and upper bounds on the value of the game (regret) as the problem of finding better sub- and supersolutions of certain partial differential equations (PDEs). These sub- and supersolutions serve as the potentials for player and adversary strategies, which lead to the corresponding bounds. Our techniques extend in a nonasymptotic setting the recent work of Drenska and Kohn (J. Nonlinear Sci. 2019), which showed that the asymptotically optimal value function is the unique solution of an associated nonlinear PDE. To get explicit bounds, we use closed-form solutions of specific PDEs. Our bounds hold for any fixed number of experts and any time-horizon $T$; in certain regimes (which we identify) they improve upon the previous state-of-the-art. For up to three experts, our bounds provide the asymptotically optimal leading order term. Therefore, we provide a continuum perspective on recent work on optimal strategies for the case of $N \leq 3$ experts. We expect that our framework could be used to systematize and advance theory and applications of online learning in other settings as well.

The idea of experience sharing between cooperative agents naturally emerges from our understanding of how humans learn. Our evolution as a species is tightly linked to the ability to exchange learned knowledge with one another. It follows that experience sharing (ES) between autonomous and independent agents could become the key to accelerate learning in cooperative multiagent settings. We investigate if randomly selecting experiences to share can increase the performance of deep reinforcement learning agents, and propose three new methods for selecting experiences to accelerate the learning process. Firstly, we introduce Focused ES, which prioritizes unexplored regions of the state space. Secondly, we present Prioritized ES, in which temporal-difference error is used as a measure of priority. Finally, we devise Focused Prioritized ES, which combines both previous approaches. The methods are empirically validated in a control problem. While sharing randomly selected experiences between two Deep Q-Network agents shows no improvement over a single agent baseline, we show that the proposed ES methods can successfully outperform the baseline. In particular, the Focused ES accelerates learning by a factor of 2, reducing by 51% the number of episodes required to complete the task.

Chris Welty, Praveen Paritosh, Lora Aroyo Google Research Abstract In this paper we present the first steps towards hardening the science of measuring AI systems, by adopting metrology, the science of measurement and its application, and applying it to human (crowd) powered evaluations. We begin with the intuitive observation that evaluating the performance of an AI system is a form of measurement. In all other science and engineering disciplines, the devices used to measure are called instruments, and all measurements are recorded with respect to the characteristics of the instruments used. One does not report mass, speed, or length, for example, of a studied object without disclosing the precision (measurement variance) and resolution (smallest detectable change) of the instrument used. It is extremely common in the AI literature to compare the performance of two systems by using a crowd-sourced dataset as an instrument, but failing to report if the performance difference lies within the capability of that instrument to measure. To illustrate the adoption of metrology to benchmark datasets we use the word similarity benchmark WS353 and several previously published experiments that use it for evaluation. 1 Contributions of this paper In this paper we examine the question of how the variations in human interpretation and other aspects of data collection can affect the measurements we make with crowd-powered datasets. For this, we adopt metrology, the science of measurement and its application, and apply it to human (crowd) powered evaluations. We begin with the intuitive observation that evaluating the performance of an AI system is a form of measurement. In all other science and engineering disciplines, the devices used to measure are called instruments, and all measurements are recorded with respect to the characteristics of the instruments used. One does not report mass, speed, or length, for example, of a studied object without disclosing the precision (measurement variance) and sensitivity (smallest detectable change) of the instrument used. This dataset has been cited over 1500 times, and has spurred the development and evaluation of automated approaches to computing lexical/semantic similarity (Witten and Milne 2008; Agirre et al. 2009) and word embeddings (Mitchell and Lapata 2008; Mikolov et al. 2013; Levy, Goldberg, and Dagan 2015; Pennington, Socher, and Manning 2014; Bojanowski et al. 2017).

In the next five to ten years, your workplace will look fundamentally different. Thanks to technologies such as artificial intelligence, the internet of things and robotics work as we know it will drastically change. The future of work will come with great opportunities but also with plenty of challenges for organisations. It will require employees and management to adapt and work smarter. AI will augment your jobs, the Internet of Things will provide you with details insights and robotics will replace many jobs.

There is no shortage of training content for employees. However, quick access to the right information is the challenge. Traditionally, the L&D departments spend significant time on instructor-led training and aggregating and buying third-party training content. Other learning avenues, like on-the-job training, personalized training, micro-learning, and data or event-driven training programs are equally important. Employees today learn from content spread across internal and external systems including intranets, MooC platforms, LMS, social media platforms, external training content providers, document management systems, collaboration platforms, and even forums, Q&A portals, email and messenger/ chat platforms.

But nowadays, we have Ph.D. students doing internships where they produce really valuable and viable production-ready results. There are also resources such as TensorFlow, an open-source Machine Learning library that anyone can play around with, as well as hundreds of AI-focused online courses and summer schools. The democratization of Artificial Intelligence has truly been a revolution, and something we should be proud of. However, even though we're not far from solving many of the world's problems with Artificial Intelligence, if we're not looking carefully at how Artificial Intelligence is being deployed, we may become complacent and miss out on breakthroughs and opportunities to achieve far greater things. So how should we be thinking of AI today?