Amazon Web Services unveiled its latest wares in the cloud-computing arms race here on Wednesday, deploying a suite of services designed to let software developers take advantage of artificial intelligence capabilities without first getting a Ph.D. Andy Jassy, chief executive of the online retailer's cloud-computing unit, announced more than a dozen new services, including software that translates and transcribes speech, analyzes videos and gives developers a leg up in building their own tools. He was speaking in a keynote Wednesday morning at AWS's sixth annual re:Invent conference. "The hype and the hope here is tremendous," Jassy said of machine learning, the set of services that helps algorithms improve with experience. Many companies are experimenting with such services, he said, "yet I would argue it's still very early." Jassy's unit, Amazon's most profitable division, grew up by offering bite-sized, simple services: storage and computing power, at first, and later, database tools and other on-demand versions of existing business software.

Tree boosting has empirically proven to be efficient for predictive mining for both classification and regression. For many years, MART (multiple additive regression trees) has been the tree boosting method of choice. But a starting from 2015, a first to try, always winning algorithm surged to the surface: XGBoost. This algorithm re-implements the tree boosting and gained popularity by winning Kaggle and other data science competition. The paper introduce in first place the supervised learning task and discuss the model selection techniques.

The business world is steadily heading toward the prophetic 2018, when according to McKinsey the first void in data technology expertise will be felt in US and then gradually in the rest of the world. The demand-supply gap in Data Science and Machine Learning skills will continue to rise till academic programs and industry workshops begin to produce a ready workforce. In response to this sharp rise in demand-supply gap, more enterprises and academic institutions will collaborate to train future Data Scientists and ML experts. This kind of training will compete with the traditional Data Science classroom, and will focus more on practical skills rather than on theoretical knowledge. KDNuggets will continue to challenge the curious mind by publishing articles like 10 Algorithms that Machine Learning Engineers Should Know .

Give us your feedback Thank you for your feedback. It is not often that a new profession springs up almost overnight. It is also unusual for many of the people who find their way into this new field to do it without the formal training provided by the normal institutions of higher education. Machine learning, as well as the allied field of data science, is developing in a way that looks unlike most other professional career paths that preceded it. It represents both one of the most promising employment opportunities of the next few years and a model for how people entering the workforce today adapt to changes in employment demands in future.

Over the past year, I've spent most of my working time doing deep learning research and internships. And a lot of that year was making very big mistakes that helped me learn not just about ML, but about how to engineer these systems correctly and soundly. One of the main principles I learned during my time at Google Brain was that unit tests can make or break your algorithm and can save you weeks of debugging and training time. However, there doesn't seem to be a solid tutorial online on how to actually write unit tests for neural network code. Even places like OpenAI only found bugs by staring at every line of their code and try to think why it would cause a bug.

A Scottish university reached the final three of a prestigious international competition dedicated to advancing conversational artificial intelligence (AI). A team of Phd students from Heriot-Watt saw more than 100 entries from 22 countries including the likes of Harvard and Princeton to become the only UK institution to be placed in the Alexa Prize. A nine-strong team, named What's Up Bot, won plaudists from judges for their artificial intelligence software named Alana, which can understand and respond to human conversation. The annual competition is organised by online retail giant Amazon and is named after the Alexa voice command system that powers Amazon Echo. The team of PhD students from Heriot-Watt's school of mathematical and computer sciences finished behind fellow finalists, the Czech Technical University and eventual winners, the University of Washington, at a ceremony held in Las Vegas on Tuesday, 28 November.

While China has invested an enormous amount in artificial intelligence in recent years, most video analysis systems are currently used in national security, including defence and anti-terrorism activities.

This report sets out a series of strategic recommendations to the government, based on core pillars including data supply and exchange, skills and education and developing an artificial intelligence infrastructure in the UK, with a view to growing the country's AI sector, something which was also augmented by the recent Budget and government's Industrial Strategy White Paper this week. The professional panel included speakers such as Westminster Senior Lecturer and journalist Dr Mercedes Bunz, Westminster Business School Senior Lecturer Dr Steven Cranfield, and well known journalist and technology author Joanna Goodman, a Visiting Fellow at Westminster Law School's Centre on the Legal Profession. Speakers dissected the report and its implications for the future and were then questioned by the audience on the matter for nearly one and a half hours. Convener and Westminster Senior Lecturer in Law, as well as artificial intelligence, robotics and the law researcher, Dr Paresh Kathrani, who chaired the event, said: "2017 was undoubtedly an important year for artificial intelligence in the United Kingdom, not least with the House of Lords Select Committee on Artificial Intelligence's work on AI, this report and the recent Industrial Strategy White Paper. The University of Westminster and Westminster Law School will continue putting on LawTech and AI events in 2018 looking at these vital developments."

There's no doubt about it - Data Science is big news right now. We see it on the news every day, the increasing number of news stories about Big Data, the Internet of Things, Deep Learning, Artificial Intelligence, smart cars, smart cities, smart politicians. OK, maybe I went a bit too far with that last one... There's also a great appetite for learning about Data Science too. Every month I get an email from Udemy telling me which courses are their best sellers. The list isn't about Data Science, but there are always plenty of Data Science courses right up there at the top of the list.

We study fairness in collaborative-filtering recommender systems, which are sensitive to discrimination that exists in historical data. Biased data can lead collaborative-filtering methods to make unfair predictions for users from minority groups. We identify the insufficiency of existing fairness metrics and propose four new metrics that address different forms of unfairness. These fairness metrics can be optimized by adding fairness terms to the learning objective. Experiments on synthetic and real data show that our new metrics can better measure fairness than the baseline, and that the fairness objectives effectively help reduce unfairness.