Machine learning is one of those buzzwords that has become a part of the furniture in the tech industry. It’s also a term that generates a range of reactions throughout the business world. Some think that you need advanced technical qualifications to even begin getting value from it. Others see it as a single method for analyzing and understanding data which you can turn on and get started with straight away.
But what’s the reality? In the latest episode of DataXpresso, Helena Schwenk talks with Graham Sharpe, director strategic solutions at Exasol, to uncover the truth behind the buzzword. If you want to dig into the topic and find out how you can make machine learning a reality for you and your data and analytics communities, give the podcast a listen here:
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As is explored in the podcast, there’s a lot of hype and uncertainty around Machine Learning, something that goes hand in hand with many new technological innovations. This still manifests itself in debate within the data and scientific communities about whether ML is a subset of AI (Artificial Intelligence) or if AI is actually a subset of ML. In the context of this discussion and mysticism, it’s easy to see why ML is seen as out of reach by some and something that requires huge budgets and teams of data scientists.
In reality, as Graham and Helena discuss, ML is a lot more accessible than that and is already impacting us on a daily basis.
Strong AI and Weak AI
When you think of this topic, there are some flagship moments that will spring to the minds of many. For example, when Deep Blue beat the world’s best chess player, Garry Kasparov, in 1996. Or when IBM Watson defeated a world record breaking contestant on the gameshow Jeopardy. But these famous moments are examples of ‘Strong AI’ and are confined to niche areas and highly specialized solutions.
Weak AI however, in the form of ML, is a lot more accessible today and is available to any organization that wants to work with advanced analytics.
To put this into a bit of context, Weak AI, as the philosopher, John Searle defined it:
“would be useful for testing hypothesis about minds, but would not actually be minds”
If you’re looking for further clarification on the difference between ML and AI, Judea Pearl’s, ‘The Book of Why’ is a good place to look.
Keep an eye on the DataXpresso podcast and across The Data Dreamer site if you want to explore these big issues in the world of data in greater detail.
