Artificial Intelligence – A Brief History

Artificial intelligence (AI) is technology that enables a computer to think or act in a more 'human' way. AI is becoming a bigger part of our lives, as the technology behind it becomes more and more advanced. Machines are improving their ability to 'learn' from mistakes and change how they approach a task the next time they try it. Some researchers are using AI to teach robots about feelings and emotions.

AI is  is a set of sciences, theories and techniques (including mathematical logic, statistics, probabilities, computational neurobiology, computer science) that aims to imitate the cognitive abilities of a human being. Furthermore, AI has a long history as a concept and has recently made incredible advances.

The intellectual roots of AI, and the concept of intelligent machines, go back as far as Greek mythology. 

History of AI

Philosophers have for a long time floated the possibility of intelligent machines as a literary device to help us define what it means to be human. There is an incredible match between binary logic (as derived from Aristotle’s Laws of Thought) and the engineering principles of electrical and electronic circuits. Electricity can flow or not flow, a switch can be on or off. 
And this exactly matches the binary nature of the true/false distinction for propositions in Aristotelian logic. So, naturally, Aristotelian logic came to underlie all digital (binary) computation. Later René Descartes (1596-1650), was interested in the idea of mechanical humans. And so when Francine his daughter died at the age of five of scarlet fever, he was so wracked with grief that he decided to build a replica, supposedly indistinguishable from the girl as she had been in life.

Details vary from account to account, but most agree that this robot Francine traveled everywhere with him. She would “sleep” in a kind of casket next to his bed.

In 1646, Christina of Sweden summoned Descartes to her castle and sent a ship for him. As ever, the casket went where he went, and at night he would take her out of her casket in his cabin and wind her up and talk to her. Accounts vary on what happened next, but most converge on the idea that the ship had encountered bad weather, and the superstitious crew was getting spooked hearing the chatter in Descartes’ supposedly single room at night. 

Suspecting some kind of witchcraft either the captain or the deck hands broke into the philosopher’s cabin while he slept, opened the casket, and were horrified by what they found there. Some accounts insist that she sat up of her own volition. 
In any case, the petrified crew grabbed the robot Francine and ran her up to the deck, where they smashed her to pieces and threw them into the sea.

Gottfried Wilhelm Leibniz, (1646 -1716) saw the possibility of mechanical reasoning devices using rules of logic to settle disputes.

In the 20th century after modern computers became available, following World War II, it has become possible to create programs that perform difficult intellectual tasks. In the 1950s, the fathers of the field Minsky and McCarthy, described artificial intelligence as any task performed by a program or a machine that, if a human carried out the same activity, we would say the human had to apply intelligence to accomplish the task. That obviously is a fairly broad definition, which is why you will sometimes see arguments over whether something is truly AI or not.

AI systems will typically demonstrate at least some of the following behaviours associated with human intelligence:

  • planning
  • learning
  • reasoning 
  • problem solving
  • knowledge representation
  • perception
  • motion &manipulation
  • social intelligence and creativity

Narrow AI is what we see all around us in computers today: intelligent systems that have been taught or learned how to carry out specific tasks without being explicitly programmed how to do so. This type of machine intelligence is evident in the speech and language recognition of the Siri virtual assistant on the Apple iPhone, in the vision-recognition systems on self-driving cars, in the recommendation engines that suggest products you might like based on what you bought in the past. 

Unlike humans, these systems can only learn or be taught how to do specific tasks, which is why they are called narrow AI.

Artificial general intelligence is very different, and is the type of adaptable intellect found in humans, a flexible form of intelligence capable of learning how to carry out vastly different tasks, anything from haircutting to building spreadsheets, or to reason about a wide variety of topics based on its accumulated experience. This is the sort of AI more commonly seen in movies, the likes of HAL in 2001 or Skynet in The Terminator, but which doesn't exist today and AI experts are fiercely divided over how soon it will become a reality.

Since 2010, however, the discipline has experienced a new boom, mainly due to the considerable improvement in the computing power of computers and access to massive quantities of data. Two factors explain the new boom in the discipline around 2010.

  • Access to massive volumes of data. To be able to use algorithms for image classification and cat recognition, for example, it was previously necessary to carry out sampling yourself. Today, a simple search on Google can find millions.
  • The discovery of the very high efficiency of computer graphics card processors to accelerate the calculation of learning algorithms. The process being very iterative, it could take weeks before 2010 to process the entire sample. The computing power of these cards (capable of more than a thousand billion transactions per second) has enabled considerable progress at a limited financial cost (less than 1000 Euros per card). 

In 2012, Google X search lab, built an AI recognise cats on a video. More than 16,000 processors have been used for this last task, but the potential is extraordinary: a machine learns to distinguish something.  In 2016, AlphaGO, Google's AI specialised in Go games, beat both the European champion and the reigning world champion. Go has a combinatorics much more important than chess - more than the number of particles in the universe. 

Among machine learning techniques, deep learning seems the most promising for a number of applications (including voice or image recognition). In 2003, Geoffrey Hinton (University of Toronto), Yoshua Bengio (University of Montreal) and Yann LeCun (University of New York) decided to start a research program to bring neural networks up to date. 

Experiments conducted simultaneously at Microsoft, Google and IBM showed that this type of learning succeeded in halving the error rates for speech recognition. Similar results were achieved by Hinton's image recognition team.

AI Today

Now AI tools present a range of new functionality for businesses, but the use of Artificial Intelligence also raises ethical questions. This is because deep learning algorithms, which underpin many of the most advanced AI tools, are only as smart as the data they are given in training. Because a human chooses what data is used for training an AI program, the potential for human bias is inherent and must be monitored closely.

Some industry experts believe that the term Artificial Intelligence is too closely linked to popular culture, causing the general public to have unrealistic fears about AI and improbable expectations about how it will change the workplace and life in general

Researchers and marketers think augmented rather than Artificial Intelligence has a more neutral connotation, will help people understand that AI will simply improve products and services and not replace the humans that use them. Robotics and AI look set to change many things for the better. But with robots taking over so many of the tasks that were traditionally done by humans, it’s natural to wonder whether they will also be stealing our jobs.

It’s probably more helpful to think in terms of the transformative effect of technology. From the invention of the printing press to the advent of the combustion engine, technology has enabled all the key stages of human progress, and the so-called fourth industrial revolution will be no different.

While some jobs may gradually disappear, this won’t happen overnight, and there will be opportunities for new career choices that we probably can’t even imagine now, provided we continuously evolve our STEM education curriculum at a pace that ensures the skills demanded in industry can be met by the future workforce. When humans and AI powered systems work together they are most effective, the symbiosis of people and machines, using human imagination, creativity and personality, but combined with the precision, strength, reliability and automation of robotic systems, will see humans fully empowered to take on the tasks we do best.

One of the biggest breakthroughs for AI research in recent years have been in the field of machine learning, in particular within the field of deep learning.

This has been driven in part by the easy availability of data, but even more so by an explosion in parallel computing power in recent years, during which time the use of GPU clusters to train machine-learning systems has become more prevalent. 
Not only do these clusters offer vastly more powerful systems for training machine-learning models, but they are now widely available as cloud services over the Internet. Over time the major tech firms, the likes of Google and Microsoft, have moved to using specialised chips tailored to both running, and more recently training, machine-learning models.

The evidence of which jobs will be supplanted is starting to emerge. Amazon has just launched Amazon Go, a cashier-free supermarket in Seattle where customers just take items from the shelves and walk out. What this means for the more than three million people in the US who works as cashiers remains to be seen. Amazon again is leading the way in using robots to improve efficiency inside its warehouses.

Fully autonomous self-driving vehicles aren't a reality yet, but by some predictions the self-driving trucking industry alone is poised to take over 1.7 million jobs in the next decade, even without considering the impact on couriers and taxi drivers.
Yet some of the easiest jobs to automate won't even require robotics. At present there are millions of people working in administration, entering and copying data between systems, chasing and booking appointments for companies. As software gets better at automatically updating systems and flagging the information that's important, so the need for administrators will fall.

Among AI experts there's a broad range of opinion about how quickly artificially intelligent systems will surpass human capabilities.

Oxford University's Future of Humanity Institute has asked experts their predictions for AI, over the coming decades.
Notable dates included AI writing essays that could pass for being written by a human by 2026, truck drivers being made redundant by 2027, AI surpassing human capabilities in retail by 2031, writing a best-seller by 2049, and doing a surgeon's work by 2053.

They estimated there was a relatively high chance that AI will beat humans at all tasks within 45 years and automate all human jobs within 120 years.

Royal Society:      Council of Europe:      InnovateUK:       ZDNet:      BBC

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