This is an overview (with links) to a 5-part series on introductory machine learning. The set of tutorials is comprehensive, yet succinct, covering many important topics in the field (and beyond). Machine learning is a very hot topic for many key reasons, and because it provides the ability to automatically obtain deep insights, recognize unknown patterns, and create high performing predictive models from data, all without requiring explicit programming instructions. This is a summary (with links) to an article series that's intended to be a comprehensive, in-depth guide to machine learning, and should be useful to everyone from business executives to machine learning practitioners. It covers virtually all aspects of machine learning (and many related fields) at a high level, and should serve as a sufficient introduction or reference to the terminology, concepts, tools, considerations, and techniques in the field.

Behavioral biometrics that uses machine learning is behind new features being added to IBM's Trusteer Pinpoint Detect platform, which financial institutions use to head off crooks who may have stolen the username and password of legitimate account holders. The new feature looks for anomalies between legitimate users' normal mouse gestures and those of the current user, and over time refines the accuracy of its analysis, says Brooke Satti Charles, Financial Crime Prevention Strategist for IBM Security. That analysis creates a risk score that banks can use to decide whether an ongoing transaction is fraudulent and trigger an alert. The institutions have to decide what to do about the alerts, but they could cut off the transaction or require further ID before the customer is allowed to continue, she says. The platform already checks the geolocation and IP address of customers as they login in order to detect fraudulent use, and behavior biometrics is an enhancement.

What has changed in Machine Learning in the past 25 years? You may not care about this question. You may even not realize that Machine Learning as a technical and scientific field is older than 25 years. But I do care about this question. I care because I got a PhD in Machine Learning in 1990.

The Global Credit Card industry is rapidly changing and the participants are increasingly facing new challenges with exploding volumes, regulatory pressures and new entrants competing for the market share. The industry has responded to these challenges by looking at avenues to cut costs, increase efficiencies and provide better, safer products and services to attract new and retain existing customers. To help our customers address this challenge, Hortonworks and Capgemini are collaborating to create a suite of Credit Card Analytics solutions designed to enhance decision making by leveraging all of the data available including customer data, transactions, third party data, open data, government data, location data, social data, etc. The first solution in this suite of solutions is focused on Credit Card fraud. Fraudulent behaviours evolve and so must the solutions that are used to detect them.

Luke Dormehl is a tech writer and journalist, he recently had his latest book published, Thinking Machines: The Inside Story of Artificial Intelligence. In light of all the scary stories of how AI will disrupt the world of work and life as we know it, we asked Luke to put a few things into perspective and ease our worries... The classic, sci-fi dystopia often begins with self-aware computers taking the world by force. Today, that view looks more and more like the Hollywood fantasy that it is. The real imminent threat of automation is what it's going to do to employment.

At present, predictive analytics is the most used form of AI in enterprise and companies are focusing on innovation, patenting their AI developments at a faster rate than ever before. Join us as we explore the rise of artificial intelligence in six charts including the top investors in AI and the most used AI enterprise solutions. As of June 2016, artificial intelligence received $974m of funding. This year's funding is set to surpass 2015's total and CB Insights suggests that 200 AI-focused companies have raised nearly $1.5 billion in equity funding. AI isn't limited to the business sphere, in fact the personal robot market, including'care-bots', could reach $17.4bn by 2020.

The two companies will leverage their expertise and advantages in online and offline services to cooperate on projects in mobile Internet, AI, big data and telecom services. Baidu will help China Unicom put the services of more than 10,000 brick-and-mortar outlets and 300,000 franchised stores online. China Unicom will offer Baidu stronger telecom infrastructure support such as Internet data centers and information and communication technology. Robin Li, Baidu board chairman and CEO, said his company already cooperates closely in linking mobile search, mapping and group buying services with China Unicom's services, and Baidu is looking forward to impressive results from AI technology. Wang Xiaochu, board chairman of China Unicom, called the move an important step for cross-sector cooperation between Internet companies and telecom firms in line with the government's "Internet Plus" proposal to upgrade traditional sectors with IT technology.

Our company Evertracker offers a smart and self-learning technology platform. Our clients use our solution to make their supply chains fully transparent, to predict the future and to automate processes. We've developed an IoT and Artificial Intelligence platform. We constantly develop our technology further to make our system smarter. They increase the quality of their network and predict events within their logistics chain.

Big data analytics can transform how businesses operate. While marketing and sales businesses cottoned on to this early, more and more previously non-tech-focused companies are realising the benefit of having expertise on board. "The way you can use data is a way not just to understand customers and products better, but also be organised in a way that makes decision-making faster and allows people to have more autonomy," says Anthony Fletcher, chief executive of snack company Graze. Graze has its own dedicated data team, who "democratise data". Mr Fletcher says: "This involves finding sources of data, cleaning and feeding it into our central cloud-based data warehouse where anyone can access it anywhere, on a variety of devices. "This tracks literally thousands of different data sources from how fast our factory lines are running, the strategies for posting boxes in the US, 15,000 pieces of product feedback we get an hour, social data, trends in sales, how people are using our mobile site, and so on." The data scientists at Graze are also skilled at doing pieces of complex data analysis from attribution, A/B tests, machine-learning or various types of regression. "This often involves not only maths, but being really good at visualising data in ways which really tell the story," says Mr Fletcher. At Graze it reports to the chief executive. "This was highly unusual, but reflected the efforts of building the original infrastructure and creating the right culture around data," says Mr Fletcher. "The important thing today is they are a team who work across the business.

Artificial Lawyer caught up recently with Dan Rubins, the Founder and CEO – and also the CTO – of Legal Robot, one of the new breed of AI-driven document review start-ups. We discussed how he moved from working in a medical services company to joining the fray as a legal AI pioneer, how working on smart contracts may be where the company eventually ends up and why there's a big world of opportunity out there. The San Francisco-based founder of Legal Robot, Dan Rubins, is not a lawyer by background. But, his experiences have taught him a lot about the inefficiencies of document review, while his long term interest in technology and programming has also helped. Perhaps, most fundamentally, Rubins is a self-proclaimed engineer.