Saturday, August 3, 2019

A.I. - COMPUTER :The Good, The Bad and The Ugly of Artificial Intelligence and Machine Learning


The Good, The Bad and The Ugly of Artificial Intelligence and Machine Learning
Technology which could save a life, but might also steal your data and call you names
Big data and analytics have undoubtedly been the business buzzwords of recent years. As we move through 2018, the digital revolution continues apace, technological capabilities accelerate, and we delve deeper into a world fueled by data — a world of artificial intelligence and machine learning.

At their core, these new buzzwords are branches off the same tree;





But why should we care about these things? According to research from Stanford University’s inaugural AI index:

84% of enterprises believe that investing in AI will lead to greater competitive advantages

75% believe that AI will open new business, while also providing competitors new ways to gain access to their markets

63% believe the pressure to reduce costs will require the use of AI And according to my colleagues at Capgemini:
48% of UK office workers are optimistic about the impact automation technologies will have on the workplace of the future Given these statistics, it’s no surprise that companies are piling the pounds behind innovation initiatives relating to AI and machine learning. CXOs know that tech-sceptics and conservatives will be left behind and, on top of this, there is added pressure from the start-up space which is filled by fast-moving challengers and innovators. There has been a 14 times increase in the number of active AI start-ups since 2000, showing that the race to effectively leverage this technology to deliver value is on.

In cases where AI & machine learning initiatives are executed effectively, and with good intentions, we have seen exceptionally positive results. However, with great power comes great responsibility, and unfortunately, some people are choosing to use this technology to conduct illegal activities. And even when users have good intentions for the tech, if the execution doesn’t go to plan, the consequences can be quite embarrassing. 
In the race to innovate — using this incredibly powerful technology — we are seeing some truly good, bad and ugly results. Let’s explore some use cases.
The good


What if machine learning could save a life? Charles Onu, Phd student at McGill University in Montreal, is the founder of a company called Ubenwa. The start-up’s intention is to use machine learning to create a digital diagnostic tool for birth asphyxia — a medical condition caused by the deprivation of oxygen to new born infants which lasts long enough to result in brain damage or death. Birth asphyxia is one of the top 3 causes of infant mortality in the world, causing the death of about 1.2 million infants and severe life-long disabilities (such as cerebral palsy, deafness, and paralysis) to an equal number annually. Put simply, the company’s intention is to save lives. 

Ubenwa’s concept is based on clinical research conducted in the 70/80s and leverages modern technological capabilities — namely automatic speech recognition in a mobile device application — to analyse the audible noises a child makes upon birth. Taking a baby’s cry as the input, the machine learning system will analyse various characteristics of the cry to provide an instant diagnostic of birth asphyxia. Not only will the tool diagnose the condition, but it will do so at a dramatically reduced cost. Their solution claims to be over 95% cheaper than an existing clinical alternative — a breakthrough in cost reduction, in a world of ever increasing cost pressures. 

The team effectively overcame a number of challenges, including an initially weak data-set. The team’s sample recordings — which provided the reference points for the diagnostic tool — were initially recorded in controlled environments. This resulted in poor diagnostic performance when tested in more realistic, noisy, chaotic scenarios. By effectively leveraging machine learning and overcoming the various technical challenges they faced (data weaknesses, tool compatibility and connectivity issues to name but a few)) the team at Ubenwa have managed to turn an expensive, resource intensive diagnostic process into an accurate, cost-effective and lifesaving alternative. The good of machine learning. 
The bad


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