Saul Cohen: 'One does not travel alone in machine learning engineering'

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Saul Cohen is an AI/ML solution architect at Code and Theory.

Question

Saul> There are more machine learning and AI tools available to you than ever before. These represent incredible opportunities and open doors to new ways of doing business. We can strategically empower AI tools and methods. With thoughtful, creative and robust data architectures, tailored to your needs – cost, speed, and accuracy.

But with these new possibilities, came new pitfalls. You need to differentiate between the companies and teams that are coming to you that have advanced, new technologies, that are just vapors. And some of them are just wrapping up the good stuff, allowing you to get better value and control from the source!

Okay, so you've chosen your best value, best fit tool. How do you use it? What are the best ways to get the most value out of it? Answering these questions is a process of understanding how the underlying technology works. A deeper understanding of how data flows through them can help everyone better achieve their goals when implementing them.

A great technology agency (hint hint) can be a really helpful co-navigator here.

Q> What is the best way to keep staff motivated and performing at a high level?

Saul> Machine learning and AI projects are, at heart, scientific investigations. You're trying to figure out if you can get a computer to understand a pile of data at a deep enough level to start making inferences about new, unseen data. This can be a difficult task! So one of my firm beliefs is that 'no one travels alone' in Machine Learning Engineering (MLE).

This translates to fostering partnerships. On any project, I make sure there are two MLEs. That way there is always someone to bounce ideas off of, reflect with and interpret the results. It also means that when presenting your results or reporting status, you are never on the spot or keeping everything alone. This is especially important when working with data: sometimes it doesn't let you do what you want! It's no one's fault, but if you're alone you can often make the mistake of blaming yourself.

Okay fine.

The way I try to resolve this tension is to get to know my team members well, so I know what they can do now and where they want to go. By doing this I can think very, very carefully about who I'm paired with in a project. In a perfect world, they would do 110% of what we need and teach each other along the way. In fact, if we get the best result for our clients and everyone learns just one new thing, I'm happy.

Question

Saul> I was looking for code and theory for a long time before I found this. Finding connections between what I do – math, physics and computer science – with art and literature has always been a pet project of mine, and when I find a good connection, it's always a special pleasure. . Now that's my job! How cool is that?! We are talking about big language models and Wittgenstein. We are considering human versus algorithmic gaze. We're thinking about generative imaging and what it means for artistic expression. You can't find this talk anywhere else: it has a very special blend of technical and creative skills. I'm happy to be a small piece of it!

Q> What is your biggest source of inspiration?

Saul> There is a sense of awe in using math to describe the world I can reach, which motivates me to dig deeper to understand how things work. It's a satisfying, elegant neatness – a romantic notion of work, I know – using numbers and logic to create a working, mechanical system. I think that's what made me do physics for my PhD, it definitely made me fall in love with coding, and it's a passion and an inspiration that I want to share with all my colleagues. Be prepared for (and anyone who will listen!)

Our world is full of complex and interconnected systems. The arrangement of cells, the sound of a crowd at 14th, the fractal nature of a broccoli floret—all these phenomena contain principles that can be translated into design and algorithms. Not only can they, but they are designed and translated into algorithms! This combination of natural beauty and scientific inquiry not only inspires me to push the boundaries of what is possible through computer science, AI, and machine learning, but also reinforces the importance of sustainability, harmony, and calm observation in technological development.

Question

Saul> Last year I was part of a team that built a precision facial scanning mobile app for a medical device company. This thing had everything: machine learning in the front end, machine learning in the back end, augmented reality to guide the user's scan, 3D graphics – you name it. And at its peak, 30,000 monthly active users! It was a thrill and a pleasure to work in the overlap of all these different skills and technologies. I actually try and encourage and expand those fuzzy boundaries (back-end/front-end, creative/technological, AI/human, … you get the idea), because I think it's more for everyone. It forces collaboration and generating better ideas. It's fun to work in a place that encourages this motivation.

Question

Saul> Slow down. Pace yourself. Give yourself plenty of space to see the entire project through. It allows you to think more clearly about what you're doing in service to your work, or your client, or even yourself—rather than what single component you have your eye on at the moment. are on

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