For hiring machine learning engineers or data scientist, the typical process has multiple rounds. A typical first round of interview consists of three parts. A typical interviewer will start by asking about the relevant work from your profile. On your past experience of machine learning project, the interviewer might ask how would you improve it. Afterwards (third part), the interviewer would proceed to check your basic knowledge of machine learning on the following lines.

Machine Learning has traditionally been a technology that only PhDs and institutions with lots of financial resources could utilize. But nowadays, there are so many tools out there that allow anyone to get started learning Machine Learning. In this blog post, I'll highlight the four foundation stones of Machine Learning, and how each of them has been democratized in the past few years. If you want to stay up to date with my latest AI content, make sure to subscribe to my YouTube channel. The four foundation stones of Machine Learning are data, computations, algorithms, and education.

I took a Deep Learning course through The Bradfield School of Computer Science in June. This series is a journal about what I learned in class, and what I've learned since. This is the third article in the series. You can find the first article in the series here, and the second article in the series here. Please note: All of the code samples below can be found and run in this Jupyter Notebook kindly hosted by Google Colaboratory. I encourage you to copy the code, make changes, and experiment with the networks yourself as you read this article. Although neural networks have gained enormous popularity over the last few years, for many data scientists and statisticians the whole family of models has (at least) one major flaw: the results are hard to interpret.

Is the robot revolution arriving sooner -- and with more devastating force -- than you once believed? While much of the automation discussion has surrounded blue-collar careers in manufacturing and transportation, recent studies on the subject see a much wider range of jobs being affected. According to an algorithm developed in 2013 by researchers at Oxford University, 47 percent of U.S. jobs could be automated "in the next decade or two." A more recent multinational study puts 210 million jobs in 32 countries at risk. Now is not the time to panic; now is the time to take action to set your company up for the shifting landscape of work in a way that will protect your business and your most valuable employees.

The last five years have witnessed an incremental shift in the mindset of what Artificial Intelligence can do to optimise marketing efforts for corporations globally. We've seen how more recently, Big Data has progressed from being a mere competitive advantage to a prerequisite that is now an integral part of the marketing circle – from developing creative campaigns, running them through the right channels and measuring the impact. The availability of inexpensive analytics tools based on machine learning methods is driving marketers to pump out extensive datasets and colourful reports. It's akin to outsourcing the simpler, repetitive tasks through intelligent automation to let the marketers make decisions based on machine learning, preconfigured rules and algorithms. From a work standpoint, this means a significant amount of the decision-making executives' time is freed up to do more important tasks that require human input and focus.

As a promising area in artificial intelligence, a new learning paradigm, called Small Sample Learning (SSL), has been attracting prominent research attention in the recent years. In this paper, we aim to present a survey to comprehensively introduce the current techniques proposed on this topic. Specifically, current SSL techniques can be mainly divided into two categories. The first category of SSL approaches can be called "concept learning", which emphasizes learning new concepts from only few related observations. The purpose is mainly to simulate human learning behaviors like recognition, generation, imagination, synthesis and analysis. The second category is called "experience learning", which usually co-exists with the large sample learning manner of conventional machine learning. This category mainly focuses on learning with insufficient samples, and can also be called small data learning in some literatures. More extensive surveys on both categories of SSL techniques are introduced and some neuroscience evidences are provided to clarify the rationality of the entire SSL regime, and the relationship with human learning process. Some discussions on the main challenges and possible future research directions along this line are also presented.

Identifying current and future informal regions within cities remains a crucial issue for policymakers and governments in developing countries. The delineation process of identifying such regions in cities requires a lot of resources. While there are various studies that identify informal settlements based on satellite image classification, relying on both supervised or unsupervised machine learning approaches, these models either require multiple input data to function or need further development with regards to precision. In this paper, we introduce a novel method for identifying and predicting informal settlements using only street intersections data, regardless of the variation of urban form, number of floors, materials used for construction or street width. With such minimal input data, we attempt to provide planners and policy-makers with a pragmatic tool that can aid in identifying informal zones in cities. The algorithm of the model is based on spatial statistics and a machine learning approach, using Multinomial Logistic Regression (MNL) and Artificial Neural Networks (ANN). The proposed model relies on defining informal settlements based on two ubiquitous characteristics that these regions tend to be filled in with smaller subdivided lots of housing relative to the formal areas within the local context, and the paucity of services and infrastructure within the boundary of these settlements that require relatively bigger lots. We applied the model in five major cities in Egypt and India that have spatial structures in which informality is present. These cities are Greater Cairo, Alexandria, Hurghada and Minya in Egypt, and Mumbai in India. The predictSLUMS model shows high validity and accuracy for identifying and predicting informality within the same city the model was trained on or in different ones of a similar context.

Our goal is to design architectures that retain the groundbreaking performance of Convolutional Neural Networks (CNNs) for landmark localization and at the same time are lightweight, compact and suitable for applications with limited computational resources. To this end, we make the following contributions: (a) we are the first to study the effect of neural network binarization on localization tasks, namely human pose estimation and face alignment. We exhaustively evaluate various design choices, identify performance bottlenecks, and more importantly propose multiple orthogonal ways to boost performance. (b) Based on our analysis, we propose a novel hierarchical, parallel and multi-scale residual architecture that yields large performance improvement over the standard bottleneck block while having the same number of parameters, thus bridging the gap between the original network and its binarized counterpart. (c) We perform a large number of ablation studies that shed light on the properties and the performance of the proposed block. (d) We present results for experiments on the most challenging datasets for human pose estimation and face alignment, reporting in many cases state-of-the-art performance. (e) We further provide additional results for the problem of facial part segmentation. Code can be downloaded from https://www.adrianbulat.com/binary-cnn-landmark

Spurred on, no doubt, by the current spate of books and articles about AIED (artificial intelligence in education), the IATEFL Learning Technologies SIG is organising an online event on the topic in November of this year. Currently, the most visible online references to AI in language learning are related to Glossika, basically a language learning system that uses spaced repetition, whose marketing department has realised that references to AI might help sell the product. They're not alone – see, for example, Knowble which I reviewed earlier this year . In the wider world of education, where AI has made greater inroads than in language teaching, every day brings more stuff: How artificial intelligence is changing teaching, 32 Ways AI is Improving Education, How artificial intelligence could help teachers do a better job, etc., etc. Common to all these publications is the claim that AI will radically change education. When it comes to language teaching, a similar claim has been made by Donald Clark (described by Anthony Seldon as an education guru but perhaps best-known to many in ELT for his demolition of Sugata Mitra).

The research comes from the University of North Carolina at Chapel Hill, and it demonstrates how an artificial-intelligence design can teach itself how to design new drug molecules from scratch. Such a system could accelerate the design of new drug candidates for use across pharmaceuticals and healthcare. The new device is named "Reinforcement Learning for Structural Evolution" (abbreviated to ReLeaSE). The artificial intelligence is in the form of an algorithm which has been configured to work with a computer program, based on two neural networks. The networks are described by the researchers as being akin to a teacher and a student.