General AI (Artificial Intelligence) is coming closer thanks to combining neural networks, narrow AI and symbolic AI. Yves Mulkers, Data strategist and founder of 7wData talked to Wouter Denayer, Chief Technology Officer at IBM Belgium, to share his enlightening insights on where we are and where we are going with Artificial Intelligence. Join us in our chat with Wouter. Yves Mulkers Hi and welcome, today we're together with Wouter Denayer, Chief Technology Officer at IBM. Wouter, you're kind of authority in Belgium and I think outside the borders of Belgium as well on artificial intelligence. Can you tell me a bit more about what you're doing at IBM and What keeps you busy?

Artificial intelligence has become a focus of certain ethical concerns, but it also has some major sustainability issues. Last June, researchers at the University of Massachusetts at Amherst released a startling report estimating that the amount of power required for training and searching a certain neural network architecture involves the emissions of roughly 626,000 pounds of carbon dioxide. This issue gets even more severe in the model deployment phase, where deep neural networks need to be deployed on diverse hardware platforms, each with different properties and computational resources. MIT researchers have developed a new automated AI system for training and running certain neural networks. Results indicate that, by improving the computational efficiency of the system in some key ways, the system can cut down the pounds of carbon emissions involved -- in some cases, down to low triple digits.

A few years ago, Paul Allen, the co-founder of Microsoft, published the results of something called the Great Elephant Census, which counted all the savanna elephants in Africa. What it found rocked the conservation world: In the seven years between 2007 and 2014, Africa's savanna Elephant population decreased by about a third and was on track to disappear completely from some African countries in as few as 10 years. To reverse that trend, researchers landed on a technology that is rewriting the rules for everything from our household appliances to our cars: artificial intelligence. AI's ability to find patterns in enormous volumes of information is demystifying not just Elephant behavior but human behavior -- specifically poacher behavior -- too. "AI can process huge amounts of information to tell us where the elephants are, how many there are," said Cornell University researcher Peter Wrege.

Banks love to brag about how many data scientists they're hiring and their shiny machine-learning "centers of excellence." In the 2018 JP Morgan Chase annual report, CEO Jamie Dimon said the company had gone "all in" on artificial Intelligence, adding that Artificial Intelligence and machine learning were "being deployed across virtually everything we do." Not to be outdone, HSBC has opened multiple "data and innovation labs" around the world, in order to build Artificial Intelligence tools that can take in the bank's more than 10 petabytes of data. Citigroup, Bank of America, and Capital One also boast about their artificial intelligence capabilities, particularly to their would-be investors. Of course, some of this is hype: Banks believe they can get a certain brand patina from looking and acting like tech companies. But as Oxford technologist Nick Bostrom points out, artificial intelligence technology has the potential go from staggeringly dumb to effectively omniscient rather quickly.

The European Banking Authority (EBA) has conducted a series of meetings over the past few months to explore the state of the art of Artificial Intelligence (AI) adoption in the banking sector and identify the best regulatory approach for validation processes. These conversations bring to the surface important aspects regarding transparency of the algorithms, robustness of the processes, security of the applications, ethics of the decision-making. I am not new to discussing similar issues with regulators, given my professional risk management background to validate first internal models in the late 1990s. I was therefore pleased to join the debate and contribute with my experience and the IBM point of view. The banking industry experienced a period of "quantitative exuberance" in the 1990s and early 2000s.

The democratization of Artificial Intelligence (AI) is in the top 5 technology trends identified by the majority of research organizations out there. The concept, going by its definition, is simple to grasp and envision. The complexities of it, however, come to light when you get down to the process, the implications, the challenges, the forces behind it, and the specific calls to action. That's what will be the focus of this article, starting with the basics as follows. To answer that, let's look at the meaning of the word "democratization".

Artificial Intelligence (AI), once only present in science fiction, is now a science reality manifesting itself in every industry. It raises questions that make us wonder about how we should explore the possibilities of AI for our organization, institution, home, or city. But what do we really mean when we speak about AI? In general, AI is a broad field of science encompassing much more than just computer science. AI includes also psychology, philosophy, linguistics, and other areas.

Artificial Intelligence (AI) and Machine Learning (ML) have already started making inroads into various industries. Healthcare is emerging as one of the biggest beneficiaries of the AI revolution. The technology is capable of facilitating easy and secure access to patient medical data, understanding and analysing their conditions. This ultimately helps improve accuracy and efficiency in the diagnosis and modernisation of health care practices. An example of an elementary implementation of AI is the use of chatbots and virtual assistants that can take care of basic yet tedious tasks like registering medical records, clinical workflows and monitoring lab results – all in an automated and secure process.

"Artificial Intelligence" is currently the hottest buzzword in tech. And with good reason - after decades of research and development, the last few years have seen a number of techniques that have previously been the preserve of science fiction slowly transform into science fact. Already AI techniques are a deep part of our lives: AI determines our search results, translates our voices into meaningful instructions for computers and can even help sort our cucumbers (more on that later). In the next few years we'll be using AI to drive our cars, answer our customer service enquiries and, well, countless other things. But how did we get here?

The computer press is just full of it. Each week, AI can do something more. Google says that, in the future, it won't be a search engine company. It will be an AI company. China is aiming to lead the world in AI and it investing billions into it.