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How Computer Vision Engineers Develop the Eyes of AI

Facial Recognition software. Autonomous vehicles. Drone delivery. Robotics in manufacturing. 3D Printing. No longer the stuff of science fiction, these advancements are at the heart of the next evolution in the digital age. Developments are not just being made in the tech hubs of Silicon Valley, Austin, or New York, but in the mid-West. Ann Arbor, Michigan home to the University of Michigan and not too far from where Henry Ford first introduced mass production with the help of automation has been advancing robotic technologies across a variety of fields.  Giving machines their own set of eyes does require someone to ensure they have the right information to do their jobs. Enter the Computer Vision Engineer. It’s estimated this field will see a rise of 19% demand through 2026. It’s also a relatively small field with only 5,400 new job openings. So, like many professions, demand is high yet a shortage remains of those Data professionals with the right skillsets. The Business of a Computer Vision Engineer While there are a variety of roles within the field of Computer Vision, the role of Computer Vision engineer focuses on two areas. Those areas are: Writing code in Python/C++ Integrate Data Visualization, image analysis, and imaging simulation controls In addition to these areas, these scientists focus on research, implementation, reaching across teams both human and machine to help solve real world problems. And as important as knowledge and application theory are, it’s the hands-on experience which raises the bar for most employers and client companies.  Using image recognition, machine learning, and segmentation can help machines learn to differentiate various images. Being able to “see” what the computer may see and correcting it to ensure it’s more like human vision takes a special skillset. This can include: Computer Vision librariesDatabase managementComponent or object-oriented softwareAnalytical, logical, and critical thinkingClear reasoning It’s these skillsets along with a background in mathematics and computer languages like C++ which pave the Computer Vision engineer career path.  The Future of Computer Vision  The days of the generalist are long behind us. Now, more than ever, technologies like machine vision require a dedicated focus. With every field from healthcare to law enforcement to manufacturing utilizing these technologies, the future of Computer Vision performs a broader range of functions.   In Ann Arbor, at the University of Michigan and in partnership with Ford Motor Company, advancements race through every field not the least of which is manufacturing. As they transition toward full automation using the Internet of Things and more autonomous processes, it’s even more important to ensure Computer Vision models understand what they’re “seeing.” Computer Vision engineers will help to advance technologies which make machines easier to train and more easily figure out images better than they do now. Used in conjunction with other technologies like neural networks and other subsets of AI, machines will be able to see and interpret in the same way humans see and interpret.  And as far as we’ve come, there remains more applications and benefits not yet explored. The possibilities are endless. Current and future advancements will pave the way for AI to be as human as we are bringing our once science fiction ideas to life.  One Final Thought… Though Computer Vision engineering can be drilled down to even more focused professions, the term itself is broad. But the specializations are basic with a demand for not only highly skilled professionals with the right educational background, but also hands-on experience. This detail is more important now than ever before, especially for Computer Vision teams seeking leadership roles who can take their applications to the next level and on a global scale.  Some of the basic specializaitons include, but are not limited to: Camera imaging geometryFeature detection and matchingImage classification and scene analysis In the wake of work-from-home policies, remote working, and the shifting landscape of working outside the office, technology careers are prime opportunities to both gain increased knowledge in your chosen field or begin your career path.  If you’re ready to take the next step in your career, we may be able to help. Take a look at our current vacancies or get in touch with one of our expert consultants to learn more.   For our West Coast Team, contact us at (415) 614 - 4999 or send an email to sanfraninfo@harnham.com.   For our Mid-West and East Coast teams contact us at (212) 796-6070 or send an email to newyorkinfo@harnham.com.  

Jenni Kavanagh

Jenni Kavanagh

19. March 2020

Why You Should Always Be Learning In Data Science: Tips From Kevin Tran

Last month we sat down with Kevin Tran, a Senior Data Scientist at Stanford University, to chat about Data Science trends, improvements in the industry, and his top tips for success in the market.  As one of LinkedIn’s Top Voices of 2019 within Data & Analytics. his thoughts on the industry regularly garner hundreds of responses, with debates and discussions bubbling up in the comments from colleagues eager to offer their input.  This online reputation has allowed him to make a name for himself, building out his own little corner of the internet with his expertise. But for Tran, it’s never been about popularity. “It’s not about the numbers,” he says without hesitation. “I don’t care about posting things just to see the number of likes go up.” His goal is always connection, to speak with others and learn from them while teaching from his own background. He’s got plenty of stories from his own experiences. For him, sharing is a powerful way to lead others down a path he himself is still discovering.  When asked about the most important lesson he’s learned in the industry, he says it all boils down to staying open to new ideas.  “You have to continue to learn, and you have to learn how to learn. If you stop learning, you’ll become obsolete pretty soon, particularly in Data Science. These technologies are evolving every day. Syntax changes, model frameworks change, and you have to constantly keep yourself updated.”  He believes that one of the best ways to do that is through open discussion. His process is to share in order to help others. When he has a realisation, he wants to set it in front of others to pass along what he’s learned; he wants to see how others react to the same problem, if they agree or see a different angle. It’s vital to consider what you needed to know at that stage. Additionally, this exchange of ideas allows Tran to learn from how others tackle the same problems, as well as get a glimpse into other challenges he may have not yet encountered.  “When I mentor people, I’m still learning, myself,” Tran confesses. “There’s so much out there to learn, you can’t know it all. Data Science is so broad." At the end of the day, it all comes down to helping each other and bringing humanity back to the forefront. In fact, this was his biggest advice for both how to improve the industry and how to succeed in it. It’s a point he comes back to with some regularity in his writing. “It doesn’t matter how smart you are, stay humble and respect everyone,” one post reads. “Everyone can teach you something you don’t know.” Treating people well, understanding their needs, and consciously working to see them as people instead of numbers or titles—this, Tran argues, is how you succeed in the business. To learn and grow, you must work with people, especially people with different skills and mindsets. Navigating your career is not all technical, even in the world of Data. “The thing that cannot be automated is having a heart,” he tells me sagely. Beyond this, Tran stresses the need for a solid foundation. The one thing you can’t afford to do is take shortcuts. You have to learn the practicalities and how to apply them, but to be strong in theory as well.  Understanding what is happening underneath the code will keep you moving forward. He compares knowing the tools to learning math with a calculator. “If you take the calculator away, you still need to be able to do the work. You need the underlying skills too, so that when you’re in a situation without the calculator, you can still provide solutions.” By constantly striving to collaborate and improve, Tran believes the Data industry has the best chance of innovating successfully.  If you’re looking for a new challenge in an innovative and collaborative environment, we may have a role for you. Take a look at our latest opportunities or get in touch with one of our expert consultants to find out more. 

William Reichert

William Reichert

19. March 2020

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