Cassie Kozyrkov has served in various technical roles at Google over the past five years, but she now holds the somewhat curious position of "chief decision scientist." Decision science sits at the intersection of data and behavioral science and involves statistics, machine learning, psychology, economics, and more. In effect, this means Kozyrkov helps Google push a positive AI agenda -- or, at the very least, convince people that AI isn't as bad as the headlines claim. "Robots are stealing our jobs," "AI is humanity's greatest existential threat," and similar proclamations have abounded for a while, but over the past few years such fears have become more pronounced. Conversational AI assistants now live in our homes, cars and trucks are pretty much able to drive themselves, machines can beat humans at computer games, and even the creative arts are not immune to the AI onslaught. On the flip side, we're also told that boring and repetitive jobs could become a thing of the past.

Cassie Kozyrkov has served in various technical roles at Google over the past five years, but she now holds the somewhat curious position of "chief decision scientist." Decision science sits at the intersection of data and behavioral science and involves statistics, machine learning, psychology, economics, and more. In effect, this means Kozyrkov helps Google push a positive AI agenda -- or, at the very least, convince people that AI isn't as bad as the headlines claim. "Robots are stealing our jobs," "AI is humanity's greatest existential threat," and similar proclamations have abounded for a while, but over the past few years such fears have become more pronounced. Conversational AI assistants now live in our homes, cars and trucks are pretty much able to drive themselves, machines can beat humans at computer games, and even the creative arts are not immune to the AI onslaught. On the flip side, we're also told that boring and repetitive jobs could become a thing of the past.

What's really going on currently is a human understanding process where we're trying to work out what the machine brains we are building are really capable of. But to understand what AI software engines are capable of, we need to understand how they learn in the first place. AI has been called the process of automating the ineffable i.e. creating technology that can digitize those things that we humans find too great or too extreme to be expressed or described in words. So does accepting this core fundamental help to explain what contemporary AI really is and what it can do? Chief decision scientist for Google Cloud Cassie Kozyrkov explains that traditional software programming relies on a developer's ability to express instructions for a task explicitly.

Sooraj Shah reports from Gitex Technology Week 2018 in Dubai on the lessons that some of technology's biggest names have learned from implementing artificial intelligence. Representatives from Google, LinkedIn and Microsoft took part in a panel discussion at GITEX 2018, the biggest technology conference in the United Arab Emirates, this week – and it was clear from the discussion that they are all betting big on AI. LinkedIn, the social network that was purchased by Microsoft in 2016 for $26.2 billion, may not be the first name that springs to mind when it comes to artificial intelligence. However, according to Igor Perisic, chief data officer (CDO) at the network, "AI is like oxygen for [its] product. "Without it, we wouldn't be where we are.

When your enterprise sets out to build an artificial intelligence and machine learning team, are you targeting the right people to hire? Or is it possible that you are seeking to reinvent the wheel, or the microwave oven? Many organizations today may indeed be hiring the wrong skills when they look to build out that machine learning team, according to Google Cloud Chief Decision Scientist Cassie Kozyrkov, who provided an overview of applied machine learning at Google and beyond during a keynote address at the Strata Data Conference in New York last week. First, Kozyrkov provided a simple and elegant definition of machine learning as a second way for humans to communicate with computers. The first way is through instructions by coding.

There are a variety of data science research roles for an organization to consider and certain characteristics best suited for each. Most enterprises already have correctly filled several of these data science positions, but most will also have people with the wrong skills or motivations in certain data science roles. This mismatch can slow things down or demotivate others throughout the enterprise, so it's important for CIOs to carefully consider who staffs these roles to get the most from their data science research. Here is Kozyrkov's rundown of the essential data science roles and the part each plays in helping organizations make more intelligent business decisions. Data engineers are people who have the skills and ability to get data required for analysis at scale.

The TNW Festival in Amsterdam took place in the hometown of our Head of Sales BENELUX, Roy Somaroo. So naturally, Roy went along to find out what the hottest topics in the tech scene are. As I bounced between the nine different themes of TNW I was struck by the convergence of thoughts. Despite the variety of speakers and opinions, it seemed there were some themes that you just couldn't avoid. For me, it all boiled down to two things: technology and experience.