We're honored that we got to see a Wyss Institute technology go from its earliest stages to where we are today, with the opportunity to make a gigantic impact on the world,

Early disadvantage can be devastating in the classroom. We know, for example, that bright primary school children in receipt of free school meals struggle to keep up with their better-off peers. According to new analysis from the DfE, they are also less likely to be in lasting employment at the age of 27. Additionally, we know that the disparities begin to show right from the start of education. More than a quarter of children finish Reception without the communication and reading skills they need to flourish.

For many, artificial intelligence, or AI, may seem like a new and possibly overwhelming concept. The reality is that AI is already being applied in industry and, for many of us, in our daily lives as well. A better understanding of AI can help you make informed decisions now that will impact the future of your learners. In Teaching AI, you'll learn what AI is, how it works and how to use it to better prepare students in a world with increased human-computer interaction. Michelle Zimmerman has taught students from ages 3-16.

We consider the problem of imitation learning from expert demonstrations in partially observable Markov decision processes (POMDPs). Belief representations, which characterize the distribution over the latent states in a POMDP, have been modeled using recurrent neural networks and probabilistic latent variable models, and shown to be effective for reinforcement learning in POMDPs. In this work, we investigate the belief representation learning problem for generative adversarial imitation learning in POMDPs. Instead of training the belief module and the policy separately as suggested in prior work, we learn the belief module jointly with the policy, using a task-aware imitation loss to ensure that the representation is more aligned with the policy's objective. To improve robustness of representation, we introduce several informative belief regularization techniques, including multi-step prediction of dynamics and action-sequences. Evaluated on various partially observable continuous-control locomotion tasks, our belief-module imitation learning approach (BMIL) substantially outperforms several baselines, including the original GAIL algorithm and the task-agnostic belief learning algorithm. Extensive ablation analysis indicates the effectiveness of task-aware belief learning and belief regularization.

All those topics have made headlines this year--in Education Week and elsewhere--and all of them are splashed over the agenda of the country's largest education technology conference, which kicks off in Philadelphia this weekend. The International Society for Technology in Education will draw thousands of teachers, school administrators, and researchers from across the world, not to mention the dozens of ed-tech companies hungry for a piece of the K-12 market. Ben Herold, an ISTE veteran, will be moderating a panel on meeting the ed tech needs of extraordinary students. And I'll be at ISTE for the first time! Follow me on Twitter at @AlysonRKlein.

Still founder-led by Chairman and Chief Executive Officer Richard Fairbank, Capital One is on a mission to help our customers succeed by bringing ingenuity, simplicity, and humanity to banking. We measure our efforts by the success our customers enjoy and the advocacy they exhibit. We are succeeding because they are succeeding. Guided by our shared values, we thrive in an environment where collaboration and openness are valued. We believe that innovation is powered by perspective and that teamwork and respect for each other lead to superior results.

Another day, another deepfake: but this time they can sing. New research from Imperial College in London and Samsung's AI research center in the UK shows how a single photo and audio file can be used to generate a singing or talking video portrait. Like previous deepfake programs we've seen, the researchers uses machine learning to generate their output. And although the fakes are far from 100 percent realistic, the results are amazing considering how little data is needed. Getting a bit wackier, why not have everyone's favorite mad monk, Grigori Yefimovich Rasputin, belting out the Beyoncé classic'Halo'?

Artificial intelligence is central to the ongoing tech revolution, and it's getting smarter all the time. The driving force behind computer vision, speech analysis and natural language processing, AI impacts industry and society in numerous ways -- and will continue to do so far into the future. It's no surprise, then, that the AI field is rife with career opportunities -- so many of them, in fact, that the sector now faces a unique challenge: There are too many jobs and too few qualified candidates. On the up side, that means it offers virtually guaranteed (and well-paying) employment for those who've got the goods. So how does one get into AI, and what does an artificial intelligence career path look like? We asked some of the field's top experts to share insights from their journey to help guide the way.

Rewards and punishments in different forms are pervasive and present in a wide variety of decision-making scenarios. By observing the outcome of a sufficient number of repeated trials, one would gradually learn the value and usefulness of a particular policy or strategy. However, in a given environment, the outcomes resulting from different trials are subject to chance influence and variations. In learning about the usefulness of a given policy, significant costs are involved in systematically undertaking the sequential trials; therefore, in most learning episodes, one would wish to keep the cost within bounds by adopting learning stopping rules. In this paper, we examine the deployment of different stopping strategies in given learning environments which vary from highly stringent for mission critical operations to highly tolerant for non-mission critical operations, and emphasis is placed on the former with particular application to aviation safety. In policy evaluation, two sequential phases of learning are identified, and we describe the outcomes variations using a probabilistic model, with closedform expressions obtained for the key measures of performance. Decision rules that map the trial observations to policy choices are also formulated. In addition, simulation experiments are performed, which corroborate the validity of the theoretical results.

Recent rapid development of machine learning is largely due to algorithmic breakthroughs, computation resource development, and especially the access to a large amount of training data. However, though data sharing has the great potential of improving machine learning models and enabling new applications, there have been increasing concerns about the privacy implications of data collection. In this work, we present a novel approach for training differentially private data generator G-PATE. The generator can be used to produce synthetic datasets with strong privacy guarantee while preserving high data utility. Our approach leverages generative adversarial nets (GAN) to generate data and protect data privacy based on the Private Aggregation of Teacher Ensembles (PATE) framework. Our approach improves the use of privacy budget by only ensuring differential privacy for the generator, which is the part of the model that actually needs to be published for private data generation. To achieve this, we connect a student generator with an ensemble of teacher discriminators. We also propose a private gradient aggregation mechanism to ensure differential privacy on all the information that flows from the teacher discriminators to the student generator. We empirically show that the G-PATE significantly outperforms prior work on both image and non-image datasets.