"Data-Driven Thinking" is written by members of the media community and contains fresh ideas on the digital revolution in media. Today's column is written by Alastair Boyle, global client partner and head of strategy at Essence. You might be surprised to hear these words in the same sentence, but compassion has been a core concept in advertising ever since it emerged as a specialist practice in the '40s. And now we're increasingly relying on artificial intelligence (AI) to help us be compassionate. Compassion is about feeling empathy with another's need state and attempting to meet those needs.

A Google empire built on search shifted attention on Thursday to high-speed Internet networks, 360-degree video broadcasting and "other bets" as parent company Alphabet logged disappointing earnings. Alphabet said net income hit 4.2 billion in the first three months of this year, but shares sank more than five percent to 714 in after-market trades that followed release of the report. The earnings per share fell 46 cents short of analysts' expectations. Revenue in the quarter rose to nearly 20.3 billion compared to 17.26 billion in the same period a year earlier. The results "represent a tremendous start to the year," said Alphabet chief financial officer Ruth Porat.

"This project has been a lot of fun for us," says Dr. Guy Yachdav, who lead the class and was in charge of the project. "In its daily work, our research group focuses on answering complex biological questions using data mining and machine learning algorithms. For this project we used similar techniques. "Only this time the subject matter was a popular TV show. The epic scale of the worlds created by George R. R. Martin provides an almost endless resource of raw multi-dimensional data.

BigML is a leading Machine Learning company that helps other businesses make highly automated, data-driven decisions. BigML was founded in 2011 in Corvallis, OR, USA and made its European expansion official by opening its European headquarters in Valencia, Spain in July 2015. The company has pioneered the Machine Learning as a Service (MLAAS) wave of innovation through its consumable, programmable, and scalable software platform streamlining the creation and deployment of smart applications powered by state-of-the-art predictive models. To date, BigML has 15 patent applications, more than 25,000 customers from 120 countries and 5M in venture capital investment. In addition to commercial activities, BigML is playing an active role in promoting Machine Learning in education around the world through its rapidly expanding ambassador program thanks to active participation from top universities in diverse geographies such as North America, Europe and Africa, making real BigML's motto: "Machine Learning made beautifully simple for everyone".

Machines could gradually come to replace doctors, usurping their technical and surgical know-how. However, this could be good news for the patient. Doctors would be more available to counsel and reassure their patients. In this way, machines deployed in the medical field could actually help to bring more of a human touch to the doctor-patient relationship. This is the prediction put forward by Guy Vallancien – a surgeon, member of the French National Academy of Medicine and President of the European School of Surgery – in his book'La médecine sans médecin.

Don Norman is without a doubt one of the most influential voices within the UX and design industry and also the cognitive sciences. Don produced some of the earliest literature about human-centered design within the digital space – he also coined the term "user experience" whilst working at Apple. In part 2 of our conversation with Don, we talk about whether technology is making us dumber or smarter, living with complexity, as well as AI, agents and their role in the future of healthcare.

The last few weeks, it's become nearly impossible to scour the latest in tech news without coming across something related to the idea of "bots." Bots, per their current implementations, are bits of software AI geared toward helping users complete tasks, request information or even shop online. Their usage isn't limited to these things and may also consist of programs used to carry out mundane or time consuming tasks that might have otherwise required a human. The idea around bots and their proposed usage is pretty straight-forward. As internet communications, cloud computing and complex machine learning improves, there are companies well positioned to leverage the power of bots to make all sorts of tasks faster and easier for users of modern technology.

Google CEO Sundar Pichai thinks we're hurtling towards a future where you'll rely on artificial intelligence more than your smartphone. "In the long run, we're evolving in computing from a'mobile-first' to an'AI-first' world," he said on Alphabet's Q1 earnings call. Google has long used a ton of AI and machine learning in its core products.

In my last post I promised to use some real-world use cases from the recent Verizon Data Breach Digest report to illustrate potential ways that machine learning be can used to detect or prevent similar incidents. For my first example, I've chosen the case of a manufacturer whose designs for an innovative new model of heavy construction equipment were stolen following a social engineering attack. They were tipped off when a primary competitor, located on another continent, introduced a new piece of equipment that looked like an exact copy of a model recently developed by the victim company. To paraphrase the Verizon report, it went like this. The threat actors identified an employee who they suspected would have access to new product design they were after -- the chief design engineer.

The future of cyber-security looks part human and part machine, according to MIT's Computer Science and Artificial Intelligence Laboratory but what does the broader industry think? According to researchers from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL), working with machine-learning startup, PatternEx, the future of cyber-security could be part-human and part-bot. A newly published paper from MIT suggests that the prototype AI2 system it has developed combines machine learning with human analysis to end up with an 85 per cent successful cyber-attack prediction rate. The MIT researchers maintain that analyst-driven security systems miss too many attacks as they rely upon humans to create rules that have to be matched. On the other hand, machine-learning solutions rely upon anomaly-detection which is prone to triggering false positives and so lead to mistrust.