This blog post is an exploratory commentary on the topic of Machine Learning and its applicability to the general, everyday problems that digital consumers, businesses and organisations have.
The average human skilled in business practices requires between 20 – 30 years of schooling and training to be considered as high value to a business organisation. Is it possible to apply similar educational concepts and curriculum to train machines to think, understand and solve problems for businesses?
Knowing that machines can practice harder and faster – thanks to the lack of human needs and basic requirements that biological life forms require – the possibility of schooling machines to solve problems and carry out everyday tasks is not too far into the future.
Arthur Samuel, a pioneer in the field of computer gaming and artificial intelligence is popularly credited with the coining of the term ‘Machine Learning’ in 1959 at IBM . While we have come a long way since 1959, we are yet to see machines acknowledged as fully functional members of business and consumer societies. However, if we were to treat Machine Learning just as similar to schooling a human child, we would witness machines outpacing humans in areas of nearly almost everything.
Lacking human needs, machines are capable of untiring practice sessions where the machine continuously betters itself through self-learning algorithms to perfect the achievement of an undefined goal.
Last year ColdFusion demonstrated this in a Youtube video. Google’s AlphaGo – an Artificial Intelligence project developed by Google’s DeepMind division, used Machine Learning to achieve the humanly impossible task of beating the world’s best player at the world’s hardest game – an ancient chinese board game called ‘Go’.
The machine won decisively (4-1), but the real show stopper was watching the machine practice the game. The training video can be seen here.
Reporters described it as “a computer program beat a human brain at the ancient chinese board game of Go. It was a moment of triumph for Artificial Intelligence equipped with human intuition.”
Others reported it as “It shows that a machine has approximated human intuition and outsmarted the best human brain at the game. It’s something that scientists hadn’t expected to happen for at least another decade. It is a giant leap for Artificial Intelligence, showing that machines can learn on their own.”
Critics however, may argue that Machine Learning for gaming is different from Machine Learning for businesses. The consequences of teaching machines to perform human business activities can, and may, trigger societal collapses and the general disruption of economies as we know them.
But then again, fear of the unknown has always blocked the path of progress, and exploring the unknown may be the only way human computer scientists may learn to harness the benefits of computer sciences hitherto unknown.
Continuing on the hypothesis of ‘Localising machine trainings to match local educational syllabi, will produce generalised, quicker and smarter cohorts of machine workforces for business and organisations’ ; what takes a human worker 20 years of schooling to learn may take a ‘learning enabled machine’ only a fraction of the time.
We are already witnessing the use of intelligent machine operations in businesses and industries such as e-retailing, where businesses are able to perform business operations consistently, non-stop, to cater to the ever increasing shopping needs of digital consumer markets.
With unlimited applications to Machine Learning in the real world, scenarios such as automating everyday business and operational activities ranging from order fulfilment, quality assurance, customer servicing, business analytics and forecasting, to exploratory fields such as virtually populating neighbouring planets, developing top secret unmanned military equipment within a controlled environment, and autonomous self-managing transport solutions, Machine Learning is undoubtedly progressing towards general usage purposes. The potential for Machine Learning in the field of healthcare and medicine too is proving its reliability as a ‘personalised treatment solution’ for patients diagnosed with complex illnesses such as Cancer.
Watson, IBM’s Machine Learning based healthcare application recently proved itself better at diagnosing cancer than human doctors have been. Read the full article here.
Excerpts from the article are as follows:
According to Sloan-Kettering; the world’s oldest and largest private cancer center, it would take at least 160 hours of reading a week just to keep up with new medical knowledge as it’s published, let alone consider its relevance or apply it practically. Watson’s ability to absorb this information faster than any human should, in theory, fix a flaw in the current healthcare model. Wellpoint’s Samuel Nessbaum has claimed that, in tests, Watson’s successful diagnosis rate for lung cancer is 90 percent, compared to 50 percent for human doctors.
Sloan-Kettering’s Dr Larry Norton said: “What Watson is going to enable us to do is take that wisdom and put it in a way that people who don’t have that much experience in any individual disease, can have a wise counsellor at their side at all times and use the intelligence and wisdom of the most experienced people to help guide decisions.”
Applications for general usage within consumer markets seem to be limitless as Machine Learning can help humans understand problems quicker and find solutions easily if Machine Learning becomes as ubiquitous as mobile apps have become. Personalised Machine Learning applications such as digital personal financial advisors, digital dietary advisors and, virtual business managers for small businesses will pave the way for the widespread adoption and consumerisation of Machine Learning technologies.
Read about Mitra Innovation’s own Machine Learning project here (part 1).
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References: R. Kohavi and F. Provost, \Glossary of terms,” Machine Learning, vol. 30, no. 2-3, pp. 271-274, 1998