We model a Markov decision process, parametrized by an unknown parameter, and study the asymptotic behavior of a sampling-based algorithm, called Thompson sampling. The standard definition of regret is not always suitable to evaluate a policy, especially when the underlying chain structure is general. We show that the standard (expected) regret can grow (super-)linearly and fails to capture the notion of learning in realistic settings with non-trivial state evolution. By decomposing the standard (expected) regret, we develop a new metric, called the expected residual regret, which forgets the immutable consequences of past actions. Instead, it measures regret against the optimal reward moving forward from the current period. We show that the expected residual regret of the Thompson sampling algorithm is upper bounded by a term which converges exponentially fast to 0. We present conditions under which the posterior sampling error of Thompson sampling converges to 0 almost surely. We then introduce the probabilistic version of the expected residual regret and present conditions under which it converges to 0 almost surely. Thus, we provide a viable concept of learning for sampling algorithms which will serve useful in broader settings than had been considered previously.

Gert-Jan Oskam, paralyzed for 12 years, is able to walk again thanks to the brain-spine "digital bridge" interface developed at France's Atomic Energy Commission (CEA). For the first time ever, a human has successfully received an implanted device to enable movement of the arms, hands and fingers after a paralyzing spinal cord injury. Onward Medical NV, a medical technology company based in the Netherlands, announced on Wednesday the surgical implant of its ARC-IM Stimulator, which is designed to restore function to the upper extremities of paralyzed patients. The patient, a 46-year-old man, suffered a spinal cord injury nearly two years ago, which left his left side almost fully paralyzed, doctors told Fox News Digital. The ARC-IM implantation took place on Aug. 14 at Centre Hospitalier Universitaire Vaudois (CHUV) in Lausanne, Switzerland.

It is commonly observed that deep networks trained for classification exhibit class-selective neurons in their early and intermediate layers. Intriguingly, recent studies have shown that these class-selective neurons can be ablated without deteriorating network function. But if class-selective neurons are not necessary, why do they exist? We attempt to answer this question in a series of experiments on ResNet-50s trained on ImageNet. We first show that class-selective neurons emerge during the first few epochs of training, before receding rapidly but not completely; this suggests that class-selective neurons found in trained networks are in fact vestigial remains of early training. With single-neuron ablation experiments, we then show that class-selective neurons are important for network function in this early phase of training. We also observe that the network is close to a linear regime in this early phase; we thus speculate that class-selective neurons appear early in training as quasi-linear shortcut solutions to the classification task. Finally, in causal experiments where we regularize against class selectivity at different points in training, we show that the presence of class-selective neurons early in training is critical to the successful training of the network; in contrast, class-selective neurons can be suppressed later in training with little effect on final accuracy. It remains to be understood by which mechanism the presence of class-selective neurons in the early phase of training contributes to the successful training of networks.

Five teams from the Netherlands, South Korea and China have advanced to the final stage of a competition to see who can grow fresh tomatoes in greenhouses remotely using artificial intelligence. The second Autonomous Greenhouse Challenge, which is organised by Dutch academic powerhouse Wageningen University & Research (WUR) and Chinese multinational conglomerate Tencent, began in September with a 24-hour hackathon involving 21 international teams and more than 200 participants from 26 countries. The five winning teams – Netherlands-based AiCU, The Automators and Automatoes, China'sIUA.CAAS and Korea'sDigilog – will each be given six months' access to a real greenhouse in the Dutch town of Bleiswijk, where from December onwards they will attempt to control and produce a tomato crop from afar by employing AI algorithms to keep inputs like water, nutrients and energy at sustainable levels. September's hackathon, held at WUR, saw an international jury award points to each team based on their composition and competence, their application of AI technology and the net profit they made during a virtual tomato production game. During their pitches, the teamswere given access to a climate model and a tomato crop growth model previously developed by researchers at WUR.

The backpropagation Algorithm is broadly used in machine learning. This algorithm is greatly used for training feed-forward neural networks. It permits the information from the cost to then flow backward through the network, acceptable to compute the gradient. Backpropagation is the core of neural network training. It is the way of adjusting the weights of a neural network.

In graph theory, the shortest path problem can be defined as a problem of finding a path between two vertices (or nodes) in a graph such that the sum of the weights of its constituent edges is minimized. A topic that is revered in the field of graph theory and has numerous practical applications including vehicular routing, network designs, etc., the shortest path problem has had several notable approaches to it, including Floyd-Warshall Algorithm, Bellman-Ford Algorithm, and the most renowned Dijkstra's Algorithm. When I came across the algorithm behind Dijkstra's method of solving the shortest path problem in a weighted directed graph (though it can be applied to undirected graphs as well), I wondered if I could come up with my own algorithm for solving the problem using one of Python's data structures: Lists. After over a month of tens of trial-and-error attempts, my peer Syed Abdul Azeem and I came up with an algorithm to solve the problem. The code will not be shared on account of confidentiality, however, you, the reader, might as well be able to figure out the code as we go ahead and explain our approach to a given problem.

An analogy is an identification of structural similarities and correspondences between two objects. Computational models of analogy making have been studied extensively in the field of cognitive science to better understand high-level human cognition. For instance, Melanie Mitchell and Douglas Hofstadter sought to better understand high-level perception by developing the Copycat algorithm for completing analogies between letter sequences. In this paper, we argue that analogy making should be seen as a core primitive in software engineering. We motivate this argument by showing how complex software engineering problems such as program understanding and source-code transformation learning can be reduced to an instance of the analogy-making problem. We demonstrate this idea using Sifter, a new analogy-making algorithm suitable for software engineering applications that adapts and extends ideas from Copycat. In particular, Sifter reduces analogy-making to searching for a sequence of update rule applications. Sifter uses a novel representation for mathematical structures capable of effectively representing the wide variety of information embedded in software. We conclude by listing major areas of future work for Sifter and analogy-making in software engineering.

Young students can shape up their career orientation only when they are not loaded with content-based curriculum, CBSE's Skill Education Director Biswajit Saha said on Tuesday. The flexibility in the system should be adopted by the school curriculum and the focus needs to be on activity-based skill formation of students, he said at an education summit organised by the Associated Chambers of Commerce and Industry of India. As part of new-age skill education, CBSE has planned to offer Artificial Intelligence (AI) as an optional sixth subject for class 11 students from academic session 2109-20 onwards. Further, an AI-inspired module of 12 hours will be introduced for class 8 students by CBSE. Apart from artificial intelligence, subjects such as yoga, early childhood education will also be introduced as electives.

Young students can shape up their career orientation only when they are not loaded with content-based curriculum, CBSE's Skill Education Director Biswajit Saha said on Tuesday. The flexibility in the system should be adopted by the school curriculum and the focus needs to be on activity-based skill formation of students, he said at an education summit organised by the Associated Chambers of Commerce and Industry of India. As part of new-age skill education, CBSE has planned to offer Artificial Intelligence (AI) as an optional sixth subject for class 11 students from academic session 2109-20 onwards. Further, an AI-inspired module of 12 hours will be introduced for class 8 students by CBSE. Apart from artificial intelligence, subjects such as yoga, early childhood education will also be introduced as electives.

I tend to think of myself as a futurist, I immediately see the possibilities of technologies as part of a much larger ecosystem than the one it is intended for. I look for ways to test and assess "How we might Live" with the technology and how it will adapt our lives, our cultures and move humanity onwards to greater things. In our modern societies we have relieved ourselves of the burdens of the industrial age and are in the process of doing the same to the digital age. We had digitized the same old processes, making them easier to do, involving less time so we could use that time on other things, but we had not thought to remove them. That is the next stage in human and machine evolution, removing pointless interactions and processes.