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Demystifying AI and Machine Learning Capabilities

Richard Pain | Nov. 3, 2017
We speak with Jason Loh, Advanced Analytics Practice Lead at SAS to learn about the learn about the most innovative AI and machine learning implementations today

One of the big misunderstandings linked to this, is the idea that ML can magically derive insights from data, explains Jason. "Organisations often think they have loads of data so surely ML must be able to produce terrific insights, but it does not work like that. The first issue is whether a human could actually derive insights from the data even when given limitless resources, in other words, is the data computable or answerable in the first place?"

The second big misunderstanding is related to the concept of the problem definition, which Jason labels one of the toughest things to do well. "Organisations need to understand the constraints of their data," says Jason. "We do not have 'General AI' that offers right answers to every situation, so we must be precise when defining our aims. We also need to consider whether there are ways to collect better data or ask better questions."

Despite these misunderstanding, Asia is already home to many of the most innovative applications of AI and ML explains Jason. "In Asia, we get the coolest examples that are also extremely practical, which is the best combo you need; practical but innovative."


Asia's Most Innovative Applications of AI and ML


Cybersecurity: ML is already a common feature of many cybersecurity solutions, but what if you could apply it to images, actual screenshots of user interfaces. Logs can be analysed, but they can also be manipulated or falsified. Monitoring user activity through screenshot analysis, powered by ML, adds another innovative layer of security.


Industrial safety: How can you ensure that employees are wearing safety gear such as hardhats or not following physical safety policies? Or what if there is an unusual noise in an industrial area like a loud crack or a hissing noise? Whilst humans can monitor areas, the cost of sensors is dropping and these sensors can feed data, whether it's audio or images to identify anomalies or specific patterns, alerting employees accordingly.


Product development: Using smartphone development as an example, all the major companies are very interested in something called perceptual quality. This refers to the customers' perception of whether a product or particular feature is good or not. For example the CPU speed, product weight or feel compared to competitors. Even if the hard facts dictate that one product is objectively better than another, the subjective experience of the customer may lead them to think otherwise. Analysing comments online and applying natural language processing to identify sentiment can help identify aspects of a product to be tweaked or improved in future products.


Digital marketing: There's great interest in better understanding customer preferences and directing more relevant, targeted marketing towards them. We have all seen recommended products online, but with so much competition and noise, organisations need to make the most of every opportunity they get by reducing the amount of guesswork involved. This is a particular challenge when recommending new products that no one has purchased before, because there is no existing data about similar consumers; often referred to as a cold start. ML can help overcome this challenge by analysing larger datasets to help identify more detailed buying personas in far greater granularity. 


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