TechRepublic’s Karen Roby spoke with Ira Cohen, chief data scientist with Anodot, about the tools CIOs need to implement artificial intelligence (AI) at their companies. The following is an edited transcript of their conversation.
Karen Roby: As we’re heading into 2021, CIOs need to have a checklist of some things to keep in mind when making decisions for this coming year, whether that be about hiring or projects to consider. Let’s start with the talent that’s needed at companies now, to pull off some of these AI projects. What do you think CIOs need to keep in mind?
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Ira Cohen: As you said, 2020 was really special in all this disruption to so many businesses. And AI, actually, is now becoming even more important. The projects that maybe people talked about before have been accelerated now because the speed of movement to new paradigms, that has to be much faster. If you’re talking about, for example, commerce, supply chains, need to move much faster. A lot of different projects that maybe before were slowly moving towards more e-commerce, and more shipment. I mean, you’re getting your Amazon, but now, so many companies are sending what they’re selling out, that you have to have a lot more automation and be a lot more mindful of the data, and be a lot more reactive to how things change constantly. Things are changing much faster, and AI is the perfect thing to manage all of that, if we talk about AI in a very, very global sense, because it has a capability of processing data very fast, giving you insights very fast of very high volumes of data, which is what’s happening now, but that’s what’s needed.
What do you need to actually have in your company in order to actually be able to achieve these goals of these projects? The first order of business, and this is something that people and companies have been doing in the last few years is, put all your data together. Create these data lakes. Data lakes have been very popular, and growing at companies like Snowflake, and other types of companies that have grown tremendously in the last few years, because that’s what they offer. But, now, to leverage those data lakes, you need data engineers that know how to pull data quickly out of them, and serve them to the data science team that can actually transform them with algorithms into meaningful insights.
Data engineers is something that is going to be required a lot more in the next year or so, because without those data pipelines, laying of data pipelines that will feed all these AI algorithms and projects, there is nothing. The AI doesn’t work without data, at least the AI that we have today. And then comes the machine learning engineers. Today, data science has been something that has grown in the last few years. The data scientists are the ones that are developing the AI required for all these projects. But data scientists, a lot of what was hired was basically people that do analysis. They do kind of one-off projects.
And, now, because these things are starting to be more and more automated, you don’t need just a data scientist who knows how to do a project well, and prototype something, but you need the engineers that will make it into products, even if they’re internal products. It’s not a project anymore, it’s internal products that have to constantly work for the company to deliver the rate that they need to deliver. These two areas, the data engineers, and the machine learning engineers, and not just the scientist, these are probably the areas where we need to … I believe, CIOs need to invest most in their companies.
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Karen Roby: When you consider the talent pool, Ira, how much are we talking about here, as far as supply and demand, when it comes to these more specialized areas with AI and machine learning? I mean, do we have the talent to fill the positions that we’re going to need?
Ira Cohen: No. I think there’s still a big gap, but what’s happening in the market, in general, is that the whole field of AI or machine learning is being democratized by all sorts of tools that are either being wrapped into loose products, or open source completely, either from Google, or from Facebook, from companies that are actually invested a lot in developing the, let’s say, the foundations that you would need. And then, the talent pool that needs to use it, they don’t have to know as deeply, they don’t have to have the knowledge as deeply as the people who developed all these tools. So, there is hope of getting a lot more talent into the area without the need for them to get Ph.Ds, in order to be able to do this. And that is happening in parallel.
With good education, with good courses, you can actually get junior machine learning engineers that can start bringing value. Where the gap is, is in the more senior ones, the ones that do have experience, because you can’t hire just junior people. They won’t have a clue what to do. You do need some sense of the field. The gap is in the machine learning engineers that are kind of, I would say, the mid-tier, and the experts, of course, that will always be a gap. But, the mid-tier that can teach the juniors how to work, that’s where most of the gap is today, I believe.
Karen Roby: There’s no question that AI has been fast-tracked for many companies that may not have even been considering moving in that direction yet, until their digital transformation plans were really put on fast-forward as well, from March 2020. Is there any particular industry you’re really seeing where it’s being embraced even more?
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Ira Cohen: We’re seeing it in all sorts of commerce, where even if it was half brick-and-mortar, half online, now, this has pushed them quite significantly. Supply chains and deliveries are definitely a big push in those types of companies. And, telcos, we’ve also seen in telcos that very big push towards AI, and it’s driven by two things that happen now in parallel. One is the virus, right? The whole pandemic, which actually put a lot more pressure on networks, and made them even more important, and actually brought some of the telcos to … Basically, that provide all the foundations for our communications, brought them to the front, and center.
5G, is the second one that’s happening in parallel. So, 5G, changing, coming to play, creating a lot more data, a lot more complexities in the networks, is also pushing them to implement AI, to actually being able to manage all that complex infrastructure, which is becoming even more complex, and even more critical.
Karen Roby: When you look to say, nine months to a year from now, how do you see AI playing a role, even versus now? And, again, how is that going to change things overall for businesses, from small businesses to huge enterprise companies?
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Ira Cohen: I think small businesses will leverage AI for particular tasks, small tasks, and probably, the adoption there will be less, because AI, at the end of the day, is fueled by data. And if you don’t have a lot of data, you can make your own decisions fairly quickly anyway. But, for larger companies, the ones that do not embrace it, and do not start using it heavily to make better decisions, to forecast the future, they’ll be left behind, because they are not going to benefit from the improved, either margins, by being more efficient, or improve the ability to sell more, because of what those tools will give them, they will start losing out.
There’s definitely a race for them to actually do this, tools to embrace it quickly. For the small businesses, I think it will be slower to embrace, unless it’s for very particular tasks that before, they could not do, because they could not hire the people to do it. But, now, they’ll get the tool that already does it for a small fraction of that price that would be if they had to develop it themselves, and then they can run away with it.
I mean, even looking at just simple e-commerce sites, right? You’re trying to sell something, and you want to have a recommendation engine, like Amazon has a recommendation engine on its website, which does improve how much you’re selling. Today, a small website, or as a small seller, cannot develop it themselves. It’s too expensive. But with it becoming available as a service from companies, they can actually start using it for a fraction of the price, and get the benefit of it even for themselves. For recruiting tools, it will give them a benefit. They’ll probably want to buy it rather than trying to develop it themselves.