Talman Talks Tech Predictions For 2019

Before I get started about what I foresee in the tech space for the coming year, let me share my favorite line about predictions, from Niels Bohr: “Prediction is very difficult, especially if it’s about the future.”

So while you may take some of these predictions with a grain of salt, I do think there are several trends we will be paying closer attention to in 2019 from a technological perspective.

From where I sit, I believe we probably will see continued escalation and consumption of technology by people who have already been consuming technology at a high level as it is.

In New York, that likely means companies will try to get on top of applying the latest technology to consume dramatically higher volumes of data: Expect increased investment in distributed computing, the Cloud and new languages like Go or Scala. At the same time, companies will aim to consume more individualized data as opposed to pre-packaged data, which should lead to building more complex systems. Machine Learning is one of the most powerful new technologies and its growth requires dealing with ever larger data sets.

A Few More Self-Driving Taxis Hit The Streets

No, 2019 won’t be the year that we see a massive shift to autonomous driving vehicles but we should continue to see steps forward on this front, particularly with self-driving taxis. I was listening to an interview of the gentleman who is running Waymo, the subsidiary of Alphabet, focusing on driverless cars. It was clear that he views the path to mainstream adoption of self-driving vehicles as a marathon, not a sprint. It could take decades and it’s not clear as to when we will have ability to drive in sleet, snow and in dense areas.

That said, the end of 2018 saw Waymo launching a taxi service for self-driving cars in a test market. The cars aren’t completely driverless and do have drivers behind the wheel for now as a safety mechanism, but as these advanced vehicles become “smarter” for all types of scenarios on the road, you can be sure that driverless vehicles will slowly but surely increase. The self-driving car is no longer an if and is rather a matter of where and when.

Wider Digitalization Of Health Care

It used to be that people would say things like, “What’s the point of doing genomic sequencing? What’s that going to do?”

Well, now that we’re getting to the point of sufficient quantities of genomic sequencings collected, we’re finding that as more data is collected of polygenic (caused by interactions of multiple genes) scores, we can group people in percentiles for complex phenomena like going to college. We can now say that  people that rank in the lowest 10% are five times less likely to go to college than people ranked in the top 10%. This is without knowing anything about the parents or anything else and strictly using genomic data.

What we’re going to see as the price of genomic sequencing continues to go down, instead of a million sequences, we’ll start seeing hundreds of millions of sequences. From this, we can combine the data from these many sequences to actually find a lot of useful information that is not 100% predictable, but certainly predictable to an extent.

For example, I was reading about a gentleman quoted in the Wall Street Journal, who said that when he looked at his genomic sequence, he had a 94% likelihood of being obese. He remarked that if his parents knew this information at birth, perhaps they would have approached child rearing differently based on their child’s predisposition to gain weight.

Expect to see a much greater investment in genomic sequencing in 2019 as it continues to make sense for a broader audience.

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A recruiter without the overwhelming credibility that we have may not be able to shed as much light on what’s in store for you on Day 1 and beyond. So get the insight you need and talk to Talman first.

Greater Adoption Of Machine Learning Techniques

Right now, I believe the vast majority of people think machine learning is brain surgery. Yet, in practical terms, it’s a net of collecting data and figuring out how to clean the data. The algorithms themselves are essentially free for the taking, but you need people with a clue of how to do that. It’s a situation where if three people know how to do it, it’s brain surgery. If 1,000 people know how to do it, it’s still quite tricky. If 500,000 people know how to do it, it gets to be where things are getting done and cost-effectively – and I think we might finally be entering this area next year.

Will there, in turn, be a great opportunity for more hiring in the machine learning space? Right now, there’s just not enough people who appreciate what it takes.

For example, I spoke with someone in New York who is talking about building a new kind of infrastructure capable of handling a vast amount of data that’s greater than what their current systems are capable of handling.

To accomplish this, you can’t merely expand the current system. You need to build a new system. Once you build the new system, you then need to feed the data into the system. Then you need to build the software infrastructure on top of the data to be able to sift through the data and then pipe it into machine learning algorithms. So ,machine learning is like the oracle that needs to be fed all kinds of data in order for you to receive an answer. But if you cannot get to the oracle, you won’t get the wisdom. So it’s building the road to the top of the hill where the oracle is.

Machine learning is fundamentally recognizing patterns that are already in the data. For that you need to have relatively clean data and a lot of it. Very few organizations have that data to date. Plus, their first reactions are to try to figure out how to use the data they have, which doesn’t always work.

This calls for more software engineers who know how to utilize machine learning technology.

Once the machine learning algorithms are fairly well established, it’s a matter of just digging more data. So essentially you have an entire ecosystem of tools that need to be created, then polished so that they’re usable for people. But once they do use them, the information starts propagating. This is easier to handle in the cloud than on your own hardware. If you have a situation where you need 100,000 computers to work on something for an hour, it’s silly and impractical to buy computers to do that task for yourself, but in the cloud it makes a lot of sense. So whether it’s via Google cloud, Amazon cloud or Microsoft cloud, we’re going to see machine learning drive a good portion of the cloud business going forward.

What do you predict in 2019 for the state of hiring in your business? If it involves hiring for the “best of the best” tech talent, don’t wait long to plan for that situation. Talk to Talman first. Roy Talman & Associates can factor in your overall business goals to help you identify the type of extraordinary talent that just may provide you many happy returns for this year and beyond.