Picking Winners In Tech’s Next Stage And Building A Career Around One

There’s a school of thought among some in the technological space that as advanced as we humans have become at coding, code generated by machines is getting better all the time. Still, the future lies with people who can leverage the code that machines are generating for us. We still have such a fundamental role to play in the relationship with machines, as in what we can actually train machines to learn and do for our benefit. Machine learning will continue to be deployed in a wider range of applications yet the underlining algorithms will only be as good as the data and the way it’s structured. When we seize the potential of training machine learning systems, it can open up new doorways to career opportunities on several fronts – from working in new technologies to joining evolving companies to stepping into newly developed markets we hadn’t considered before.

Let’s examine how one type of machine learning technology can expand into so much more and the career possibilities that can result.

Not long ago, we wrote about how Andrew Ng, adjunct professor at Stanford, led a group of students to take pictures of heads of lettuce – some ripe, some not ripe – with the purpose of training a machine learning system to recognize the difference between the two. What were they really trying to prove? If a system could be sufficiently trained to identify the quality of produce at a highly efficient rate, its benefit would rapidly move from the classroom to the agricultural space, where all kinds of farmers could use the system for free.

This may explain why last year, John Deere paid $305 million to acquire a company that manufactures robots to distinguish between crops and weeds. As a result, farming can be done more intelligently by targeting weeds and plants too small to grow with chemicals rather than spraying an entire field with pesticides.

Let’s think about what Deere really purchased here. It’s not just about owning a better algorithm or having more data to analyze, is it? No. It’s about owning a system that continues to create profound value that we humans can leverage to our advantage. The potential of the technology is vast, as it can be applied to a variety of crops during a harvest, using dramatically less herbicide in the process.

When a company founded on hand tools like John Deere makes a significant investment in robots to aid in farming, you can be sure of where they see the future of the industry heading. As you’re evaluating your career path and a possible move to a new environment down the road, think of companies such as this. Do they have an appreciation for the advanced technology coming down the pipe for their people to leverage and therefore making a focused effort to adapt their business accordingly with intelligent investments? Or do they appear to be focused on merely trying to keep decades-old mainframe systems alive?

If a company insists on using a system 25 years old and only modifying that system over and over again, is that going to be the best way to advance your career? That may be fine enough for a developer who is nearing retirement and isn’t that interested in diving into new horizons at this stage of their career. However, assuming you have many years left in your career, it’s essential that you pay close attention to the direction of machine learning technologies. It may not be easy to predict, but one key indicator may be how many different areas the technology can expand to.

For example, let’s take the technology of the self-driving vehicle. Do we have roadways populated with all kinds of self-driving cars right now? No, we certainly haven’t reached mass consumption yet. However, we can comfortably predict based on advancements and investments companies have made (i.e. Google, Tesla) that it’s only a matter of time before the self-driving car becomes a more widely adopted reality. As it does, think about all the sub-markets that will be impacted by this technology: Taxis. Food delivery services. Large trucks transporting material across the country. And more.

Besides new markets or expanding markets, does it appear that certain machine learning technologies could result in new applications such as a new programming language or a new direction in cloud computing?

Finally, what companies are out there clearly getting behind these emerging technologies, markets and applications – and investing in building up theirtalent base as a result of that commitment?

So, if you’re challenged to identify the best turn to make on your career path, don’t just focus on how machine learning is generating better code. Consider the bigger picture of where this type of technology may create a variety of market expansion opportunities the more it is deployed and the type of companies that appear to see long-term value in that technology’s growth. 10 years from now, thanks to the vision they have today, those companies may see themselves in a dramatically different place. Will you be able to say the same about your career?

Staying on top of an ever-changing technological landscape isn’t easy. Fortunately, when you talk to Roy Talman & Associates first, you have a partner in your career development who understands where the biggest innovations are taking place and which of those transformations mesh ideally with your skill set and goals. Your career path is too important to approach with guesswork. Let’s make a solid plan together that reflects the kind of role and environment where you aspire to make an impact for years to come.