A robot is an automated machine made up of electronic and mechanical parts; the electronic side is operated by software programmed to perform a specific task. The software may include a number of alternative routes which, in all circumstances, are pre-defined.

 

Artificial Intelligence (AI) is a complex software system whose purpose is that to mimic the human mind, with a difference: its thinking power may be stretched far beyond that of an intelligent single person. This has been possible thanks to two concurrent elements:

• Human memory is limited while an electronic solid-state memory is not, at least theoretically;
• A sort of electronic neurons and associated synapses known as “Central Processing Units (CPU)” are minuscule electronic chips capable of basic arithmetic; these chips can be stuck on end and connected in parallel to process trillions of operations per second.

This combination engenders a vast number of applications in real life, instigating a renewed consideration of all aspects of human behaviour, from Business and Industry, to the Humanities, Education down to Upbringing.

 

At the heart of AI systems lies their capacity to store (memorise) huge quantities of data in any form (numbers, words, images) and let the electronic brain (CPUs) combine the whole “knowledge” to arrive at the … best statistical meaning; nothing else than what the human brain does! With a difference: the AI system boasts a much better memory and replies in terms of seconds or minutes; a few hours are needed if the question is really complicate … like setting up a trip to Mars.

 

It would be wrong to define AI as a machine as the system exercises discretionary decision capacity given by complex mathematical algorithms which, in short, consist of the function to which the system is put to. Algorithms take account of the tasks and define the rules and alternatives to pursue a specific action; an action can be a simple answer based on acquired “experience” or can be one or more commands directed to robots. AI systems are given the basic instructions to acquire learning capacity; as with a pupil, the AI system is fed with pertinent information in accord to a specific specialization. The more information memorised, the more precise the suggestion or advice; the more knowledge is required to cover more domains, the more complex the system will be. As we talk about AI we should think of powerful computers with massive memory capacity often sat in several large databases anywhere in the World (Big Data); expensive systems that only large corporations or big Countries can fund.

 

As a new breed of software that is capable of learning without being explicitly pre-set, autonomous learning (and deep learning) can access, analyse, and find patterns in “Big Data” in a way that is beyond human capability.

Domains of applications are wide and cover more-or-less the professional domain, non-exhaustive examples being:

• Finance: stock-exchange best bet, automated payment matching …..
• Judiciary: Court judgements, Forensics
• Automotive: self-driving, aid to public works, agriculture …..
• Fraud detection: taxation, company auditing, enhanced accuracy of alerts
• Recruiting: finding the best talent with intelligent job matching
• Space and Military: image processing, intelligence and alas “intelligent weaponry”
• Marketing: Logo and Brand recognition, qualitative analyses
• Customer service: gathering, analysing data and responding to feed-back
• Sales & Marketing: loyalty schemes, customer retention ….
• and of course, automated language translation.

 

At the Harvard Medical School an “AI dermatologist” capable of spotting skin cancers at an early stage is already operating. If patients live far away they can send a good resolution photo of their part to examine and the system will do the job. Amazing indeed!

Among prominent European companies that have invested in AI I can remember SAP with their SAP Leonardo machine learning which is not the only one; Siemens, Volkswagen, PSA and Volvo are advanced in the automotive. Among American giants active in the field are Tesla self-driving cars, Microsoft, Apple, IBM and Google.

Details on about 1.6 million patients were provided to Google’s “DeepMind” division during the early stages of a medical trial last year. The information was used to develop and refine an alert, diagnosis and detection system to signal the risk of developing acute kidney injury (AKI). The result of the trial was an app called ‘Streams’ (designed to help doctors to spot patients at risk of AKI).

 

You can have a taste of what AI is capable of by handing (copy & paste) one of your composite photos over to Google Image Recognition , the system will tell what the picture is about.

 

The hottest Industrial Revolution

Indeed it is. Certainly, after the Industrial Revolution of 19^th century, the advent of cheap computers and Robotics of the sixties that concerned blue-collars, Artificial Intelligent Systems will touch, indeed it is already affecting, white-collars with and without cravat or tailleur if they are women. In their paper ‘The Ethics of Artificial Intelligence’ (2011), Bostrom and the AI theorist Eliezer Yudkowsky argued that increasingly complex decision-making algorithms are both inevitable and desirable – so long as they remain transparent to inspection, predictable to those they govern, and robust against manipulation.

 

Ethical and moral issues are already proliferating:

“If my self-driving car is prepared to sacrifice my life in order to save multiple others, this principle should be made clear in advance together with its exact parameters. Society can then debate these, set a seal of approval (or not) on the results, and commence the next phase of iteration. I might or might not agree, but I can’t say I wasn’t warned”.

 

Impact of Digital Do It Yourself is a recent study funded by the European Union and led by several European Universities; the research team rings the bell about the impact on Education and Research: DIDIY has the potential to deeply affect education, well beyond what happened in the digital revolution.

School education must be entirely re-thought, both for pupils’ and students’ classes; mnemonic subjects must be condensed to give space to creative thinking capable of stimulating the individual brain; social science becomes important to promote distinctive group collaboration and encourage purpose. Spirit of innovation, invention and entrepreneurship become prominent already in the first years of school to prompt the future professional who must be ready to understand new business models based on sharing of knowledge and collaborative making in order to impel vision.

 

In his book “Emotional Intelligence” Daniel Goleman writes: “The human brain has not changed significantly since the Palaeolithic. AI will trick the Palaeolithic brain using new capacities at their disposal; system thinking comes to this”.

 

Who are the winners, who the losers?

Investment in education will set the divide; in fact, it has already set the divide among countries. If we only consider that the youth’s education spans 14-18 years while AI will become widespread in Business and Industry in just 5 years, it is easily understood that Countries advanced in education will have primacy; Countries who have lagged behind for political short-vision or possibly, for lack of economic resource will lag behind to become “robotised mass production areas”. I propose that a way for those Countries to catch up with time could be that to accept direct private financing by interested enterprises.

It is known by now that technological advancement runs faster than political bureaucracy; a recent example is given by Google-Deep-Mind division, a private enterprise that innocently replaced the NHS (British Health Authority) in their mission to enhance diagnostic techniques.

 

A non-recent statistic by Eurostat tells that the percentage of GDP invested in Education per student in 2011 was:

For the larger EU Countries: France (6.8%), UK (5.98%), Germany (4.98%), Spain (4.82%) and Italy (4.29%);

The four Countries who have spent more: Denmark (8.75%), Malta (7.96%), Iceland (7.36), Ireland (6.15%), Belgium (6.55%);

The four Countries who have spent less: Romania (3.07%), Bulgaria (3.82%), Italy (4.29%), Czech Republic (4.51).

 

A bigger concern by far: to bring school education ahead with times, teachers and lecturers need be coached accordingly to acquire new conceptions which bear in them Psychology, Philosophy and indeed, rudiments of Informatics.

Author : Elio Pennisi
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