4 August 2021

The Four Cutting-Edge Tools All Data Scientists Should Know

Technology is evolving rapidly in the data science industry and keeping up with the innovations is getting increasingly challenging.

In this article, we look at the four cutting-edge tools that all data scientists should be familiar with and keep a close eye to help develop and evolve their careers.

1) Computer Vision

Beyond using data science to model and predict based on existing data, some businesses have branched out into vision processing.

Computer Vision, at its simplest, is using AI algorithms to teach a machine to see. To extract meaning from pixels. This is analogous to how people see, we may feel like our eyes are just windows on the world, but what we’re experiencing is our brain’s interpretation of much simpler visual data.

We receive data on three channels (red-green, blue-yellow and luminance). It’s the combination of these which our brains translate into what we see as colours.

As you can imagine the growing applications of Computer Vision are vast. From autonomous vehicles to medical imaging, smart phone apps to facial recognition in crowds.

There is still a lot of work to be done on computer vision. As well as the technical challenges, there are ethical and privacy concerns. For instance, the risk of deep fakes, manipulated videos that look real, is so great that the US government is investing heavily in being able to spot them.

2) Natural Language Processing

If vision is something that most people are fortunate enough to take for granted, another is surely the ability to communicate. Understanding language can feel like a primary school challenge for humans, but once again has been a tough challenge for machines.

Significant progress has been made with the technology, including with intelligent chat bots and the interpretation of audio commands from users. Whether you are a fan of Alexa or its competitors, users’ expectations have been raised.

Data Scientists have taken this work a lot further since the early days of Text Mining and it’s now being adopted by businesses across the world with a rate of growth that belies the importance of the technology in business and accessibility.

3) Robotic Process Automation

In the mainstream media, one of the common messages about AI and data science is how it can threaten our jobs. The reality is that most work in data science has been focussed avoiding stupid decisions or interactions with customers.

But the promise of automation remains and has opened up another new application area. Robotic Process Automation is a broad field that covers everything from scripts that do little more than record keystrokes, to actual use of robots.

The diversity of solutions needed appears to vary as widely as different business processes. So far it also appears to be focussed on mundane and repetitive work that frees up human operators to take judgement calls when they’re needed.

A key advantage of this new application has been financial return on investment, including reducing costs and allowing them to invest more time in strategic work.

4) Emotion Analytics and Humanising AI

Many organisations recognise the power of improved interactions with their customers and recognising and working the emotion in those interactions is a key part of the relationship.

Data Scientists are working on moving beyond sentiment analysis towards extracting emotional meaning and generating an appropriate response. There is much work to do in this area, but interestingly it brings together the three other applications above. There is already some evidence that AI applications can recognise facial emotions faster than people.

If greater emotional intelligence algorithms can collaborate with computer vision and Natural Language Processing, it could offer improved communication, creating the opportunity for machines to recognise what humans are communicating non-verbally and through emotion. If that can be employed to guide more human-like RPA, we could experience much improved customer experience.

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