I was interviewed for an extensive piece on AI and Blockchain at the leading Greek diaspora publication, Business File. The piece takes a personal journey spin from my early days of developing AI for software development intelligent assistants all the way to recent gigs on AI strategy and business blockchains. As the interviewer, Mrs Anastassiou eloquently observes:

As AI has already crossed our thershold for good, Dr Yannis Kalfoglou – having worked for over 28 years in the field – is certain of the symbiotic relationship between machines and humans. He defends the merits of blockchain technology in the financial and business world, and presents cryptocurrencies, predicting that by 2030 there will be an “AI arms race” between US and China

I pointed out that a lot of the progress in recent AI breakthroughs is related to the adundance of resources to onboard AI and try new things: you can get (mostly) free education and training on the newest machine learning models and techniques, in predominantly neural networks – which are a set of algorithms, modelled loosely on the human brain, designed to recognise patterns. You can also get (mostly) free software to build and run your models, backed by relatively inexpensive infrastructure; you can use them in the real world; in a matter of days – not years!

There was a lot of discussion on the hot topic of machine vs. human. On the question as to whether AI could become as effective as human brainpower, I posit that it boils down to what we mean by “effective”. If we assume that AI is this kind of super-intelligent machine that can think like a human, has emotional intelligence, creativity, empathy, and is able to exhibit soft intelligence features, as well as all sorts of non-mathematical traits, then I would safely say that we might never see that!. However, if by “effective” we mean an AI system which possesses immense computing capabilities and can perform better than humans in certain well degined tasks – which deal mainly with computation, large data sets, arithmetic, pattern matching, and general brute force computing – then I would say that we are already experiencing this phenomenon and we will see more of it!

My main thesis on the topic of business applications of AI is that AI has to be seen as a Swiss army knife and its uses vary across the organisation: for example, (a) AI can boost the customer experience; shop floor events, virtual changing rooms, customer analytics, identifying and promoting the right products for the right customer, pro-active and automatic support using ChatBots, and many other means; (b) business interaction, predictive and prescriptive analytics, which help manage suppliers, vendor and product lines, fraud detection systems to rule out fraudulent transactions, automatic supply chain functions, from inventory control to order fulfilment; (c) regulatory compliance, automatic reporting systems, compliance checking systems; (d) new product development, identify and launch new products, in segments not previously active, using analytics and AI systems to predict possible revenue volumes, cost-effective marketing strategies, and real time monitoring of products and services in the field; (e) new model interfaces with customer, clients and employees: ChatBots, image recognition systems, digital ID and biometics.

There are, understandably, concerns about the symbiosis of human-machine systems especially when the autonomous systems of today’s labs and blueprints will become the norm in a few years time. Regulation and control frameworks could be one way to ensure maximum safety for humans, but any rushed initiative to control AI in response to our fears and misconception of the technology will be highly misguided. AI systems and current practice need to reach a state where we can understand and evaluate the reasoning behind the system, and rationalise it in human terms. It may well be the case, that once we develop a solid body of knowledge about best practices, protocols, and models, we can set up a workable international system of rules and regulations governing AI.