For better or worse, machine learning will have a huge impact on humanity by the end of this century.
Hollywood loves to dramatise the emergence of a malevolent artificial general intelligence (AGI) – think Skynet – but it could go the other way. In theory, an AGI will be able to perform any intellectual task that a human can. And, whilst the creation of AGIs is still a long way off, it is also an inevitable outcome given the unrelenting advances in computer hardware and software.
A more immediate impact on humanity, especially its middle classes, will be the displacement of repetitive white collar work by machine learning. To compensate, we are likely to see the emergence of new companies and markets that currently do not exist.
Google and Tesla are well known for their advances in self-driving cars. However, Uber may be the first company with a ready-made business model to exploit it. Uber has announced it will be bringing its autonomous cars to the roads of Pittsburgh in the coming weeks. If Uber (and ultimately its competitors) can globalise this idea they will require a fleet of cheap and energy efficient vehicles.
To date, there has been little investment by startups into the underlying chip architectures that support the creation of machine learning systems. Most self-driving cars tend to use NVIDIA GPUs (built for computer gaming and graphics processing), which have not been optimised specifically for machine learning. NVIDIA may grow into this space, but there is plenty of room for hardware innovation from competitors to emerge.
Self-driving vehicles will disrupt the multi-billion global transportation market. Today’s generation of self-driving cars and trucks are very expensive to build and resemble a lab experiment. Who will supply this next generation of vehicles? Major vehicle manufacturers must innovate or die. Inevitably, self-driving vehicles will result in the loss of millions of jobs and a fundamental transformation of our society. The industrial revolution took 150 years – the self-driving revolution may be finished within the next twenty.
Medicine is another area where we’re likely to see a huge disruption by machine learning, radically changing the way diseases are diagnosed and treated. Machine learning has the potential to augment or replace major aspects of medical care. Imagine if anyone with a smart phone could access the machine equivalent of the world’s best doctors, at low cost, from anywhere. Already, IBM Watson has made significant progress in oncology. And, only in the past week Google’s DeepMind announced a partnership with University College London Hospital to use machine learning for the treatment of patients with head and neck cancers.
Transportation and medicine are just two areas – there are so many more. Hollywood may continue to predict the destruction of humanity (it sells films), but I believe the impact will be positive. There will be bumps along the way, it may be uncomfortable at times – the way we work will change, our global society will change – but ultimately it will be better for everyone.
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