Hong Kong has a very special place in our hearts. It's the safest place on the planet, with beautiful local people who are shy and endearing, who harbor a fondness for taking pictures of their food, who believe in ghosts, who despise "those uncouth mainlanders", and who invent some strange cartoon characters – like McDull the pig and his friend Excreman that's literally a turd that crawled out of the toilet. If you're someone who noticeably speaks English, don't expect the Hong Kong police to ticket you for jaywalking. They're too shy about their English to approach you. Of course these are the same people who won't hesitate to tell you that you look fat when you return from holiday.

The on-chip implementation of learning algorithms would speed-up the training of neural networks in crossbar arrays. The circuit level design and implementation of backpropagation algorithm using gradient descent operation for neural network architectures is an open problem. In this paper, we proposed the analog backpropagation learning circuits for various memristive learning architectures, such as Deep Neural Network (DNN), Binary Neural Network (BNN), Multiple Neural Network (MNN), Hierarchical Temporal Memory (HTM) and Long-Short Term Memory (LSTM). The circuit design and verification is done using TSMC 180nm CMOS process models, and TiO2 based memristor models. The application level validations of the system are done using XOR problem, MNIST character and Yale face image databases

Developing visual perception models for active agents and sensorimotor control are cumbersome to be done in the physical world, as existing algorithms are too slow to efficiently learn in real-time and robots are fragile and costly. This has given rise to learning-in-simulation which consequently casts a question on whether the results transfer to real-world. In this paper, we are concerned with the problem of developing real-world perception for active agents, propose Gibson Virtual Environment for this purpose, and showcase sample perceptual tasks learned therein. Gibson is based on virtualizing real spaces, rather than using artificially designed ones, and currently includes over 1400 floor spaces from 572 full buildings. The main characteristics of Gibson are: I. being from the real-world and reflecting its semantic complexity, II. having an internal synthesis mechanism, "Goggles", enabling deploying the trained models in real-world without needing further domain adaptation, III. embodiment of agents and making them subject to constraints of physics and space.

We introduce and analyze an extension to the matching problem on a weighted bipartite graph: Assignment with Type Constraints. The two parts of the graph are partitioned into subsets called types and blocks; we seek a matching with the largest sum of weights under the constraint that there is a pre-specified cap on the number of vertices matched in every type-block pair. Our primary motivation stems from the public housing program of Singapore, accounting for over 70\% of its residential real estate. To promote ethnic diversity within its housing projects, Singapore imposes ethnicity quotas: each new housing development comprises blocks of flats and each ethnicity-based group in the population must not own more than a certain percentage of flats in a block. Other domains using similar hard capacity constraints include matching prospective students to schools or medical residents to hospitals. Limiting agents' choices for ensuring diversity in this manner naturally entails some welfare loss. One of our goals is to study the trade-off between diversity and social welfare in such settings. We first show that, while the classic assignment program is polynomial-time computable, adding diversity constraints makes it computationally intractable; however, we identify a $\tfrac{1}{2}$-approximation algorithm, as well as reasonable assumptions on the weights that permit poly-time algorithms. Next, we provide two upper bounds on the {\em price of diversity} -- a measure of the loss in welfare incurred by imposing diversity constraints -- as functions of natural problem parameters. We conclude the paper with simulations based on publicly available data from two diversity-constrained allocation problems -- Singapore Public Housing and Chicago School Choice -- which shed light on how the constrained maximization as well as lottery-based variants perform in practice.

Assisted by neural networks, reinforcement learning agents have been able to solve increasingly complex tasks over the last years. The simulation environment in which the agents interact is an essential component in any reinforcement learning problem. The environment simulates the dynamics of the agents' world and hence provides feedback to their actions in terms of state observations and external rewards. To ease the design and simulation of such environments this work introduces APES, a highly customizable and open source package in Python to create 2D grid-world environments for reinforcement learning problems. APES equips agents with algorithms to simulate any field of vision, it allows the creation and positioning of items and rewards according to user-defined rules, and supports the interaction of multiple agents.

Esri's continued advancements in data storage and parallel and distributed computing make solving problems at the intersection of machine learning (ML) and GIS increasingly possible. ML refers to a set of data-driven algorithms and techniques that automate the prediction, classification, and clustering of data. ML can be computationally intensive and often involves large and complex data. It can play a critical role in spatial problem-solving in a wide range of application areas from multivariate prediction to image classification to spatial pattern detection. Based on the analysis of seven years of traffic accident data, the model predicted areas with the highest risk for accidents.

On a balmy summer's day in San Francisco, Milena Marinova is sitting on the roof terrace of the offices of Pearson, a company in the midst of a radical transformation from publishing powerhouse to digital-education platform, wrapped in a gray shawl and explaining how she plans to build advanced, deep-learning algorithms that could educate the next generation of students. This is no easy task. With millions of students using its education-software, Pearson has amassed "terrabytes" of data from student homework and even textbooks that have been digitized, data that Marinova is now pulling together to build software that can automatically give students feedback on their work like a teacher would. Instead of just telling them that an answer is right or wrong, a future update to Pearson's math homework tool will give more detailed feedback on how they went wrong in the steps taken to get an answer, Marinova told Forbes in an interview. Pearson is starting with math because the topic is relatively easy to structure and digitize.

Nowadays we hear very often the words "Recommender systems" and mainly it's because they are quite often used by companies for different purposes, such as to increase sales (items' suggestion while purchasing Amazon: user that have bought this as also bought this) or in suggestions to customers to give them a better customer experience (film suggestion Netflix) or also in advertising to target the right people based on preferences similarities. The recommender systems are basically systems that can recommend things to people based on what everybody else did. Here there is an example of film suggestion taken from an online course. I want to thank Frank Kane for this very useful course on Data Science and Machine Learning with Python. Here there is the course's link in case you would like to go deeper with Data Science.

"In two years, China will be ahead of the United States in AI (artificial intelligence)," states Denis Barrier, CEO of global venture firm Cathay Innovation. If so, China will largely determine how this technology transforms the world. Today's contest is more than a race for dominance in a new technology -- it's one between authoritarianism and democracy. "AI is the world's next big inflection point," says Ajeet Singh, CEO of ThoughtSpot in Palo Alto. Artificial intelligence is machine learning, which self-learns programmed tasks, using data, and the more it gets, the more learned it becomes.

When Argho Chatterjee decided to pursue UpGrad and IIIT Bangalore's PG Program in Machine Learning and Artificial Intelligence, he knew he was diving straight into coding his own artificial neural networks, and had a fair idea that this technology could help him solve real-world problems. What came as a pleasant surprise was that he had the access to a personalised learning environment provided by the prestigious institute through its partnership with distinguished online education venture – UpGrad. The two institutes have been working seamlessly to provide learners with an advanced curriculum, projects created in collaboration with the industry experts, and tailor-made support for AI career choices. In fact, the acclaimed degree went on to help Argho make a transition to the role of a Data Scientist ( Deep Learning (AI)) at Samsung R&D with 80% CTC hike! Learning in a personalised environment under great faculty, Argho brushed up on the basics, imbibed conceptual knowledge, and acquired full-fledged knowledge of the field.