As part of its projects with Britain's National Health Service, Google's artificial intelligence unit DeepMind announced last week it's developing a new way to protect confidential health data--from itself. Its problem: How to assure hospitals, and the public at large, that patient confidentiality isn't compromised as it processes the sensitive medical health records entrusted to it. DeepMind's proposed solution is to create an indelible data log that can't be tampered with. It would show when a piece of data was used, and for what purpose. Importantly, DeepMind itself wouldn't be able to modify logs to use the data nefariously.

Big Data is a term that has been around for some time now but there is still confusion about what it actually is. The concept is continuing to evolve and to be reconsidered, as it remains the driving force behind many ongoing waves of digital transformation, including artificial intelligence, data science and the Internet of Things (IoT). With that in mind I thought it was time to write a beginner's guide to what Big Data means in 2017. In a similar way to my beginner's guides to Blockchain and FinTech, this will be jargon-free and aims to explain the core concepts and ideas to anyone regardless of background knowledge. It all starts with the exponential explosion in the amount of data we have generated since the dawn of the digital age.

Tree methods are commonly used in data science to understand patterns within data and to build predictive models. The term Tree Methods covers a variety of techniques with different levels of complexity but my aim is to highlight three I find useful. To set the problem up let's assume we have a census dataset containing age, education, employment status and so on. Given all this information we want to see if we can predict whether a person earns more than $50k per year. How can tree methods help us?

The road to human-level artificial intelligence is long and wildly uncertain. Most AI programs today are one-trick ponies. They can recognise faces, the sound of your voice, translate foreign languages, trade stocks and play chess. They may well have got the trick down pat, but one-trick ponies they remain. Google's DeepMind program, AlphaGo, can beat the best human players at Go, but it hasn't a clue how to play tiddlywinks, shove ha'penny, or tell one end of a horse from the other.

The insideBIGDATA Guide to Deep Learning & Artificial Intelligence is a useful new resource directed toward enterprise thought leaders who wish to gain strategic insights into this exciting area of technology. In this guide, we take a high-level view of AI and deep learning in terms of how it's being used and what technological advances have made it possible. We also explain the difference between AI, machine learning and deep learning, and examine the intersection of AI and HPC. We present the results of a recent insideBIGDATA survey that reflects how well these new technologies are being received. Finally, we take a look at a number of high-profile use case examples showing the effective use of AI in a variety of problem domains.

There has been a great deal of talk lately in the media about machine learning (ML). We've all seen the news clips of chess playing computers, self-driving cars, and emerging technologies like facial recognition, but what exactly is ML, and how does it work? As machines take on a greater share of control over our lives, it is important to understand what machine learning actually is, and more importantly, what it isn't. Machine learning is a branch of artificial intelligence (AI), and AI is a branch of computer science. In traditional programming, you give the computer an input - let's say 1 1.

Machine learning is one of the biggest buzzwords in cybersecurity in 2017. But a sufficiently smart adversary can exploit what the machine learning algorithm does, and reduce the quality of decision-making. Today's security threats have expanded in scope and seriousness. There can now be millions -- or even billions -- of dollars at risk when information security isn't handled properly. "The concern about this is that one might find that an adversary is able to control, in a big-data environment, enough of that data that they can feed you in misdirection," said Dr Deborah Frincke, head of the Research Directorate (RD) of the US National Security Agency/Central Security Service (NSA/CSS).

Salesforce (CRM) held a small group meeting of less than 60 people at its headquarters in San Francisco last week where it announced that Einstein AI is now available to all its customers across sales, service, marketing, commerce and more, even going so far as to title the presentation, "The Year of Einstein." Salesforce also announced a strategic partnership with IBM to combine the power of Einstein and IBM's Watson to provide more insightful analytics for businesses. Salesforce clearly has big ambitions for Einstein AI. As the company says on its website, "Einstein is like having your own data scientist to guide you through your day." Einstein is a product of about $600 million in acquisitions and three years of internal development.

Artificial intelligence (AI) and its role in security was a hot topic at last month's RSA Conference in San Francisco. But some cold water was also being thrown on the growing tendency of vendors to use AI, especially machine learning, as marketing hype. AI indeed "moves the needle," Zulfikar Ramzan, the RSA chief technology officer (CTO), said at the conference. But, he added, "the real open question to me is how much has that needle actually moved in practice?" To cut through the marketing hype, it is necessary to understand the real capabilities and limitations of artificial intelligence in security.

Editor's note:China concluded the annual two sessions of its national legislature and top political advisory body on Wednesday. From among the thousands of proposals and motions submitted by delegates, the Global Times has chosen eight that we believe may have the greatest impact on the livelihoods of Chinese people. It has been 15 months since China allowed all married couples to have two children to help with its aging population. However, the increase in births has fallen below expectations as young parents are worried about the extra financial burden. He Youlin, the lawmaker and former principal of Sun Yat-sen Memorial Middle School who previously proposed the two-child policy, advised the government to grant subsidies to and reduce the taxes of two-child families.