One of our people is working with an international startup company that has spun off from a much larger company (and the larger entity is still very much supporting the smaller one) and the startup is figuring out how best to utilize machine learning in the healthcare process.
It’s hardly the only one.
It appears now, more than ever across many industries, companies are incorporating machine learning tools into their process of doing business.
For example, when people first used computers, all files were flat files in the early days. Then databases came along – and the idea of knowing how to use databases became very important because you couldn’t have a system without a database. Similarly, we are on the verge of another significant technological evolution, approaching a time when a wide variety of machine learning capabilities will be built into more systems that we don’t think of as machine learning-enabled at all.
How so? Take the insurance industry, for instance. Insurance companies are building machine learning into the claim adjusting process so that if you get into an accident, they’ll ask you to take a few pictures of your car. Then they’ll tell you which shop to take your vehicle to and how much the insurance company is willing to pay to have the car fixed. With this machine learning evolution, you don’t need a human appraiser involved in the process at all.
In another case, Chinese company AutoX is the only company in the country operating a driverless RoboTaxi service on public roads. The Chinese claim AutoX’s system carries 2,200 trillion operations per second (TOPS), a very sizable supercomputer to put in a car. Specifically, AutoX has 15 million data points, 28 cameras and 220 million pixels – all streaming per second. Thanks to machine learning becoming used on a more industrial scale, compute requirements are escalating even further.
Closer to home, Elon Musk recently hosted a “Tesla AI Day” in which he talked about advancements in artificial intelligence and autonomous driving. One of the highlights of the event was Tesla unveiling a chip called Dojo. The surface of a Dojo chip is used to build a computer for Tesla that allows the company to train its systems for autonomous driving. But it was what Musk brought out at the very end of AI Day that got people’s attention – his announcement that he believes sometime year, Tesla will have the prototype of a humanoid robot.
With this in mind, what Tesla is positioning itself for may be even bigger than electric cars – it is positioning itself to be at the forefront of machine learning and with the Dojo chip announcement, it is taking aim squarely at Nvidia, the most valuable semiconductor company in the world and one that is now worth even more than Intel. What companies are discovering is the bigger the system, the more intelligent and better the quality.
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Talman Advantage #4: Better Positioning For Your Best Opportunity
The reality is that, in so many situations, that “perfect job opportunity” may not be formally listed by a company. In that instance, where some may simply fire your resume off to an HR person’s email and hope for the best, Roy Talman & Associates takes a more creative and purposeful approach. If an opening isn’t currently available that’s an ideal match for you, we’ll discuss the kind of role with you that you would be interested in and potentially prepare a very specific case to that particular firm to create a unique role for you. That’s called a recruiter that goes further for you – and why you need to talk to Talman first.
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Supersized Systems Have Arrived
To put things in the proper context of how much systems have evolved and will continue to do so, it used to be that a system with 500 million machine learning parameters was considered large and a system with 1.7 billion machine learning parameters was very large. Then GPT-3 arrived with its 175 billion parameters, which is humongous.
What’s next? Get ready for systems with over a trillion machine learning parameters.
From autonomous driving to humanoid robots, by all indications, we’re approaching a point where we should see a true explosion of machine learning capabilities. We will be able to build systems painlessly – systems that can be taught to solve real-life problems.
What can companies do about this right now? Get on top of the learning curve associated with machine learning as soon as possible. It may feel like we’re walking uphill with a very long way to go, but the pace at which we are traveling and upward is accelerating.
Someday is today. Not only for machine learning and AI applications but for how you can think proactively on your hiring goals. From the technical talent you need right now to the best of the best talent you believe will be mandatory to bring aboard one year from now, it’s never too early to Talk To Talman First.
Roy Talman & Associates is always thinking ahead for your next potential hires based on your goals and the newest technology for your space that’s coming around the turn. It’s why we’ve set the pace as the most trusted name in technical recruiting for over 30 years. Want to do more than keep up? Let’s talk about how you can lead rather than follow.