Become a better manager by swapping bodies with your staff to see how they see you. Immersive media and experiential learning are breaking new ground and now offer businesses and employers the ability to deploy core personnel training digitally, on demand, at scale and affordably. Companies such as London based Somewhere Else Solutions are disrupting the old corporate training market and building powerful, pioneering, soft skills training platforms powered by AI & VR. All business owners know soft skills are no longer a'nice to have' and that their staff need to be engaged, empathetic and flexible when working together and supporting customers. One-to-one professional development sessions and behavioural training used to be an expensive and time-consuming operation!

The school nurse of the future could be a robot if Chinese technology catches on – but British people may be too suspicious, experts say. Children at more than 2,000 pre-schools in the Asian country now have their health checked every morning by a machine. The Walklake robot, which has a square body and cartoon-like face, takes just three seconds to scan a child's hands, eyes, and throats. And if it picks up any signs of illness – red eyes, rashes or mouth ulcers, for example – it can refer the child to a human nurse. One British doctor said he thought parents in the UK wouldn't want the technology and it could disrupt children's learning, but another called it'a great idea'.

The school nurse of the future could be a robot if Chinese technology catches on – but British people may be too suspicious, experts say. Children at more than 2,000 pre-schools in the Asian country now have their health checked every morning by a machine. The Walklake robot, which has a square body and cartoon-like face, takes just three seconds to scan a child's hands, eyes, and throats. And if it picks up any signs of illness – red eyes, rashes or mouth ulcers, for example – it can refer the child to a human nurse. One British doctor said he thought parents in the UK wouldn't want the technology and it could disrupt children's learning, but another called it'a great idea'.

The problem is not only in the lack of educational institutions for bringing up this kind of specialists. There is also the absence of a general concept to solve the problem of training qualified personnel. Sceptics believe that the creation of added value in the era of artificial intelligence depends on the genius of the individual -- a creative expert who cannot be taught to manage the processes of artificial intelligence. Internet gurus such as Mark Zuckerberg or Pavel Durov are often cited as examples. However, people forget that the above-mentioned creators are only the developers of the concept, which is implemented by an horde of medium-level managers. Approximately the same situation was observed at the dawn of mass computerization and internetization of our planet.

Jason Yang, an IMES research scientist, is the lead author of the paper, which appears in the May 9 issue of Cell. Other authors include Sarah Wright, a recent MIT MEng recipient; Meagan Hamblin, a former Broad Institute research technician; Miguel Alcantar, an MIT graduate student; Allison Lopatkin, an IMES postdoc; Douglas McCloskey and Lars Schrubbers of the Novo Nordisk Foundation Center for Biosustainability; Sangeeta Satish and Amir Nili, both recent graduates of Boston University; Bernhard Palsson, a professor of bioengineering at the University of California at San Diego; and Graham Walker, an MIT professor of biology.

For all of the hype about artificial intelligence (AI), most software is still geared toward engineers. To demystify AI and unlock its benefits, the MIT Quest for Intelligence created the Quest Bridge to bring new intelligence tools and ideas into classrooms, labs, and homes. This spring, more than a dozen Undergraduate Research Opportunities Program (UROP) students joined the project in its mission to make AI accessible to all. Undergraduates worked on applications designed to teach kids about AI, improve access to AI programs and infrastructure, and harness AI to improve literacy and mental health. Six projects are highlighted here.

High-dimensional always-changing environments constitute a hard challenge for current reinforcement learning techniques. Artificial agents, nowadays, are often trained off-line in very static and controlled conditions in simulation such that training observations can be thought as sampled i.i.d. from the entire observations space. However, in real world settings, the environment is often non-stationary and subject to unpredictable, frequent changes. In this paper we propose and openly release CRLMaze, a new benchmark for learning continually through reinforcement in a complex 3D non-stationary task based on ViZDoom and subject to several environmental changes. Then, we introduce an end-to-end model-free continual reinforcement learning strategy showing competitive results with respect to four different baselines and not requiring any access to additional supervised signals, previously encountered environmental conditions or observations.

We propose a decentralized learning algorithm over a general social network. The algorithm leaves the training data distributed on the mobile devices while utilizing a peer to peer model aggregation method. The proposed algorithm allows agents with local data to learn a shared model explaining the global training data in a decentralized fashion. The proposed algorithm can be viewed as a Bayesian and peer-to-peer variant of federated learning in which each agent keeps a "posterior probability distribution" over a global model parameters. The agent update its "posterior" based on 1) the local training data and 2) the asynchronous communication and model aggregation with their 1-hop neighbors. This Bayesian formulation allows for a systematic treatment of model aggregation over any arbitrary connected graph. Furthermore, it provides strong analytic guarantees on converge in the realizable case as well as a closed form characterization of the rate of convergence. We also show that our methodology can be combined with efficient Bayesian inference techniques to train Bayesian neural networks in a decentralized manner. By empirical studies we show that our theoretical analysis can guide the design of network/social interactions and data partitioning to achieve convergence.

Performing knowledge transfer from a large teacher network to a smaller student is a popular task in modern deep learning applications. However, due to growing dataset sizes and stricter privacy regulations, it is increasingly common not to have access to the data that was used to train the teacher. We propose a novel method which trains a student to match the predictions of its teacher without using any data or metadata. We achieve this by training an adversarial generator to search for images on which the student poorly matches the teacher, and then using them to train the student. Our resulting student closely approximates its teacher for simple datasets like SVHN, and on CIFAR10 we improve on the state-of-the-art for few-shot distillation (with 100 images per class), despite using no data. Finally, we also propose a metric to quantify the degree of belief matching between teacher and student in the vicinity of decision boundaries, and observe a significantly higher match between our zero-shot student and the teacher, than between a student distilled with real data and the teacher. Code available at: https://github.com/polo5/ZeroShotKnowledgeTransfer

Dimensionality reduction and manifold learning methods such as t-Distributed Stochastic Neighbor Embedding (t-SNE) are routinely used to map high-dimensional data into a 2-dimensional space to visualize and explore the data. However, two dimensions are typically insufficient to capture all structure in the data, the salient structure is often already known, and it is not obvious how to extract the remaining information in a similarly effective manner. To fill this gap, we introduce \emph{conditional t-SNE} (ct-SNE), a generalization of t-SNE that discounts prior information from the embedding in the form of labels. To achieve this, we propose a conditioned version of the t-SNE objective, obtaining a single, integrated, and elegant method. ct-SNE has one extra parameter over t-SNE; we investigate its effects and show how to efficiently optimize the objective. Factoring out prior knowledge allows complementary structure to be captured in the embedding, providing new insights. Qualitative and quantitative empirical results on synthetic and (large) real data show ct-SNE is effective and achieves its goal.