#220: Expected Innovations In Data Science, AI & Machine Learning Over the Next 18 Months With Felipe Flores, Podcast Host & Data Futurology Founder

The past 18 months has been a period of unprecedented innovation across data science, machine learning, and AI. The depth of research and what has been brought to market has empowered data scientists in ways that, even a year ago, could not have been predicted.

Looking forward to the next 18 months, the industry is not going to rest on its laurels, but the question is where the next waves of innovation will come from. That is what Felipe discusses on this episode of the Data Futurology podcast.

He highlights four areas in particular where he would like to see the industry focus its innovation. Starting with the ease in which to undertake data preparation, and moving through to developing better machine learning ops and engineering, the combined “key areas of innovation” would allow people working in data science and beyond, into citizen data science, to better leverage the opportunities of AI and machine learning, and at speed.

Felipe then rounds the discussion out with a look into the ethics of data science. There is a lot more discussion that needs to happen in this area, he argues, so that organisations of all sizes across the world can be sure that they’re delivering the models that have the positive impact on the world that we’re all looking for.

It’s going to be an exciting year ahead for everyone involved in data science! Tune into the podcast for more insights.

Enjoy the show!

Thank you to our sponsor, Talent Insights Group!

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What We Discussed:

00:00 Introduction

1:40 An overview of the innovation that has been brought to data science over the past few years.

2:33 Four areas where the industry can innovate further #1: Making it easier to do data preparation.

4:40 Four areas where the industry can innovate further #2: Democratising AI and empowering the citizen data scientist.

6:45 Four areas where the industry can innovate further #3: Automated machine learning can still be improved.

8:33 Four areas where the industry can innovate further #4: Better machine learning ops and engineering is important to being able to reliably deploy, monitor track and alert.

12:22 Do we have the data that we need to make the responsible models that we want to?

Quotes:

Understanding what version of data was used for a particular model, and being able to create an end-to-end link is still largely an unsolved problem.

We have to move into a world where more people in the organisation need to be able to have these AI tools at their fingertips, be able to use them and be able to get them to a point where there’s value being created from them. The barriers are still typically too high.

I’m not saying that the algorithm itself needs to improve, as that’s happening with research. Rather, this is around the creation of algorithms at speed and at a scale in a way that’s more reliable and flexible, which will make it more accessible, and increase the breadth and reach of AI in organisations.

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