This story originally appeared on Slate and is reproduced here as part of the Climate Desk collaboration. Every election cycle, science gets the short end of the stick. So a collective of scientists--56 scientific organizations representing 10 million scientists and engineers and spearheaded by the American Association for the Advancement of Science--tries to engage them in a debate by compiling a list of science-based questions, soliciting answers and publishing them. This year should be particularly interesting. As has been pointed out before, the two major presidential candidates this year hold vastly different views on science-related issues.

This is a very incomplete and subjective selection of resources to learn about the algorithms and maths of Artificial Intelligence (AI) / Machine Learning (ML) / Statistical Inference (SI) / Deep Learning (DL) / Reinforcement Learning (RL). It is aimed at beginners (those without Computer Science background and not knowing anything about these subjects) and hopes to take them to quite advanced levels (able to read and understand DL papers). It is not an exhaustive list and only contains some of the learning materials that I have personally completed so that I can include brief personal comments on them. It is also by no means the best path to follow (nowadays most MOOCs have full paths all the way from basic statistics and linear algebra to ML/DL). But this is the path I took and in a sense it's a partial documentation of my personal journey into DL (actually I bounced around all of these back and forth like crazy).

Artificial intelligence has been making extraordinary progress in the past few years. It's ironic that after years of frustration with AI's missed promises, many now worry that its mighty power is now upon us while we still don't know how to properly deploy it. Some fear that at some future time, a sentient, superintelligent general AI might pose an existential threat to humanity.But while being […]

In the previous article (Part I), we introduced the general ideas behind model evaluation in supervised machine learning. We discussed the holdout method, which helps us to deal with real world limitations such as limited access to new, labeled data for model evaluation. Using the holdout method, we split our dataset into two parts: A training and a test set. First, we provide the training data to a supervised learning algorithm. The learning algorithm builds a model from the training set of labeled observations.

BAE Systems is looking for a Machine Learning and Signal Processing Research Engineer to join their Cognitive RF research group. The cognitive RF group works on programs and problems that involve machine learning, optimization, detection & estimation, information theory, deep learning, and adaptive decision and control. These technologies are applied to a number of different RF domains, such as cognitive communications, radar, electronic warfare (EW), spectrum sensing, SIGINT, to name a few. The Cognitive RF research group is part of BAE Systems' Technology Solutions division which is heavily involved in advanced research concepts primarily from the various government research labs and organizations, including DARPA and IARPA. Members of the group are involved in the entire cycle of research development, from the initial concept ideation and program shaping with potential customers and new business pursuits through the execution and transition of research concepts to BAE Systems' business areas and products.

This guest article was written by Dr. Cleber Gomes who is an Electronic Engineer with a Ph.D. from Tokyo University of Technology and Agriculture. As we approach the date of the Impeachment vote in Brazil, it might be interesting to take a look at how the Brazilian stock market behaves. In this article, I propose to do that from the point of view of Trend Following Algorithms. To exemplify, I will present the results acquired from my own Trend Following System, which is based on Machine Learning technologies, specifically Neural Networks. Take the lead from today's leaders.

The use of data within an organisation to improve elements of the business such as the supply chain, improve decision making, and to make cost savings, is becoming more widely accepted as being vital. It is vital in respect to the business remaining competitive, vital to remaining relevant, and vital to the future of the business. One of the industries that has been looking significantly at the use of its data is the manufacturing industry, and stepping back one level to the steel industry. Magnitogorsk Iron and Steel Works (MMK), is the third largest steel company in Russia with a revenue of 9.3bn. Established in 1870, the company has taken to using machine learning technology from Yandex Data Factory to creative a competitive advantage that will see it being competitive for years to come.

Back in December, I wrote a detailed piece about the potential for artificial intelligence streamlining the real estate industry through automation. Eight months on, and there is increasing evidence of just how big an impact there could be for machine learning in real estate. The most recent evidence, launched in mid-July, is TouchAssist, created by Touch Commerce, which promises to "enable brands to offer intelligent automated conversations leading consumers to self-serve on digital channels". The idea is to create a'smart' virtual assistant (or'chat bot') which can answer customer queries or book in property viewings, whilst simultaneously gathering analytical and KPI data. Wherever the bot fails to answer a customer question, a human customer operator steps in, and the machine continues to listen in and learn more answers.

A version of this essay was originally published at Tech.pinions, a website dedicated to informed opinions, insight and perspective on the tech industry. There has been a lot of talk recently about advancements in the smart home arena, especially about new ways to control smart home devices. I have heard Amazon's Echo referred to as a smart home device, and just this week, web service IFTTT announced new partnerships that are intended to allow smart home devices to connect in an automated fashion to other devices and services. However, what we're still missing when it comes to the smart home is a true smart home assistant -- a counterpart, if you will, to the smart assistants that come baked into every modern smartphone operating system. This post dives into what that means in practice, and who might be best positioned to deliver on this vision.

It is a strong indicator of today's globalized world and rapidly growing access to Internet platforms, that we have users from over 188 countries and 500 cities globally using our Text Analysis and News APIs. Our users need to be able to understand and analyze what's being said out there, about them, their products, services, or their competitors, regardless of the locality and the language used. Social media content on platforms like Twitter, Facebook and Instagram can provide unrivalled insights into customer opinion and experience to brands and organizations. A look at online review platforms such as Yelp and TripAdvisor, as well as various news outlets and blogs, reveals similar patterns regarding the variety of language used. Therefore, no matter if you are a social media analyst, or a hotel owner trying to gauge customer satisfaction, or a hedge fund analyst trying to analyze a foreign market, you need to be able to understand textual content in a multitude of languages.