For several decades now, Georgia Tech professor Tom Conte has been studying how to improve computers: "How do we make them faster and more efficient next time around versus what we just made?" And for decades, the principle guiding much of the innovation in computing has been Moore's law -- a prediction, made by Intel co-founder Gordon Moore, that the number of transistors on a microprocessor chip would double every two years or so. What it's come to represent is an expectation, as The New York Times puts it, that "engineers would always find a way to make the components on computer chips smaller, faster and cheaper." Lately, faith in Moore's Law has been fading. "I guess I see Moore's Law dying here in the next decade or so, but that's not surprising," Moore said in a 2015 interview with a publication of the Institute of Electrical and Electronics Engineers.

A field of study that gives computers the ability to learn without being explicitly programmed. Despite this common claim, anyone who has worked in the field knows that designing effective machine learning systems is a tedious endeavor, and typically requires considerable experience with machine learning algorithms, expert knowledge of the problem domain, and brute force search to accomplish. Thus, contrary to what machine learning enthusiasts would have us believe, machine learning still requires a considerable amount of explicit programming. In this article, we're going to go over three aspects of machine learning pipeline design that tend to be tedious but nonetheless important. After that, we're going to step through a demo for a tool that intelligently automates the process of machine learning pipeline design, so we can spend our time working on the more interesting aspects of data science.

It's no secret that Google has broad ambitions in healthcare. But a document obtained by New Scientist reveals that the tech giant's collaboration with the UK's National Health Service goes far beyond what has been publicly announced. The document – a data-sharing agreement between Google-owned artificial intelligence company DeepMind and the Royal Free NHS Trust – gives the clearest picture yet of what the company is doing and what sensitive data it now has access to. The agreement gives DeepMind access to a wide range of healthcare data on the 1.6 million patients who pass through three London hospitals run by the Royal Free NHS Trust – Barnet, Chase Farm and the Royal Free – each year. This will include information about people who are HIV-positive, for instance, as well as details of drug overdoses and abortions.

While several of today's leading "robo-advisor" companies were founded in the aftermath of the financial crisis, it wasn't until early 2012 that they finally converged on a common low-cost "automated investment service" model… which, coupled with a surge of media coverage, quickly suggested that they could become the future of financial advice (or at least investment management) for consumers. However, in the year since established players like Schwab and Vanguard launched'competing' services, a fresh look at the robo-advisor landscape reveals that their growth rates are falling rapidly, to just 1/3rd their levels of one year ago. Their apparent demise: an inability to scale their marketing to sustain growth rates in the face of increasing competition and challenging client acquisition costs, coupled with a similar inability to grow their average account sizes. In fact, the combination of rising client acquisition costs and declining average revenue per client may be an outright death knell for the direct-to-consumer robo-advisor movement, as they approach the unsustainable crossover point where the lifetime value of a client, cumulatively, is less than the cost to acquire a single client (given that some have a mere average gross revenue per client of just 50/year!). Accordingly, it's not surprising to see many of the early robo-advisor players pivoting in other directions, using their long runway of available dollars to try to find greater growth traction, with at best one or two that might manage to build a viable brand that survives.

When government research and development is outpaced by private-sector investment, products with potential military applications are likely to enter the global market more quickly. That was one of the takeaways from a recent talk by Deputy Defense Secretary Bob Work on the Pentagon's new strategy, called the Third Offset, to double down the U.S. military's technological edge in part by investing in human-technology teaming for war fighting. "R&D is going down in the public sector, but up in the private sector," Work said on Monday during a conference sponsored by The Atlantic Council. "Most things that have to do with AI [artificial intelligence] and autonomy are happening in the private sector. And so all competitors are going to have access to it, it's going to be a world of fast-followers. You're going to have an instance where you're not going to have a lasting advantage."

Artificial Intelligence has been widely debated by many. Some consider it as a threat, while others see a wealth of opportunity in this technology led intelligence. Bill Gates, cofounder of Microsoft, and also the richest man in the world, plays in Team B. He says that AI will not be a threat, but an extremely helpful tool that will help human lives. The field of AI has been evolving at a rapid pace that has revolutionized many sectors such as healthcare, business and even the education sectors. While AI is making great advances in the technology and robotics, there are some who believe that it holds existential risks.

This sunday we are running a workshop at ACM CHI 2016 called "Human Centered Machine Learning". I thought I would write an article to explain the general idea (though the workshop itself is a way of better understanding the idea). Statistical Machine Learning is one of the most successful set of techniques to come out of Computer Science in the last decades, and one that a lot of people are thinking about at the moment. It's often presented as quite an impersonal process: machines that learn for themselves, even AI that risk taking over the world. But, in fact, there is a lot of human work that goes into machine learning and not enough people have been talking about that.

Our next Healthcare focused events include the Women in Machine Intelligence & Healthcare Dinner in London on 12 Oct and the Deep Learning in Healthcare Summit in Boston on 11 & 12 May 2017.

List: What start-ups are worth watching as they grow in the big data market? The rise of both structured and unstructured data has created a booming market that is expected to be worth around 41.5 billion by 2018. The rapid growth of the big data market has resulted in the creation of a large crop of vendors that are all looking to take a slice. Amid the plethora of vendors competing for market position are a number of start-ups that are aiming to help organisations collect and analyse data. CBR identifies 10 companies that are worth watching.

Silicon Valley start-up, SmartAll aims to create a smart home hub with AI that uses advanced machine learning algorithms to learn about your preferences. The device offers voice, gesture and facial recognition to personalise your experience. The device can learn your habits and adjust accordingly. If you decide to have a lay in for a few minutes after the alarm goes off, the hub can notify the coffee machine to make coffee on time. Facial recognition knows who is in the room and can adjust its processes for each individual.