A Focus on Implementation and Rapid Impact
A Signal in the Noise
About 2 years ago, I had a thought that it was time to offer a new type of course in the areas of AI/Data, and possibly extensible to the other digital transformations like Blockchain. To create anything of significant value in this area is no small challenge at UC Berkeley because it would have to be amidst the gigantic contributions of amazing people who have already done so much in data, AI, and computing.
After all, this is the institution that developed Berkeley UNIX, introduced open source to the world, created floating point, RAID Disk storage, relational databases, and many of the most famous machine learning algorithms and tools. And the scope has ranged from the most seminal theoretical (such as the NP-completeness of some of the world’s hardest problems) to the rollout of Spark which has become the defacto method of managing big data.
Of this wide spectrum of people and capabilities, I just happen among the set that likes to focus at the more applied edge of the spectrum. Over the past 12 years, I’ve spent a great deal of effort to bring a true experiential learning component to subjects which are often considered complex to understand. I have worked on this because, as Berkeley, we want to balance the theory with practice. Over the years, thousands of students have taken courses from me and my Center because of this applied perspective. Leaders everywhere hire our students because they possess all three of these important characteristics:
1) an incredible technical depth
2) a holistic understanding of the larger problem
3) the psychological behaviors needed for real-life innovation.
The same is true even for my approach to a Data Science/AI type of course. My goal was to make it the class that I would want to take. That means a class where you actually learn the current state of the art software tools and have the ability to create real-life applications. This would be in contrast to solving artificial or toy problems. At Berkeley, we teach the class as “Applied Data Science for Venture Applications”, and informally, we have referred to this very applied, practical framework as Data-X.
An important part of the objective is literally to add more emphasis on the implementation. To start, if you can’t actually create or use the technology, then it’s actually a major problem, whether you are a student, a company, or you are concerned about our national agenda. Fast forward 2 years, we are currently running one of this course for the 3rd time, and we are seeing amazing results. In only 3 months, students with only python programming and some background in probability are able to create applications that predict energy prices, detect knee problems from MRI scans, crawl the web to create new data sources, and even identify fake news.
On the other hand, if you look at technology projects in most organizations across the world, you will quickly discover that they are often challenged to deliver working implementations. In fact, many things can go wrong. Sometimes people can’t even the connect the theory with the practice. At a deeper level, there are also issues when people understand the theory but do not understand the software toolsets. It is like trying to build a skyscraper with sticks and mud instead of steel beams. Modern open source tools (e.g. tensor flow, pandas, etc.) have become incredibly powerful, but if you don’t know how to use them, you are reduced to improvising, instead of using pre-existing high-quality building blocks.
There are some Computer Science curriculums in the US and around the world which have become almost all theory and very little practice. Berkeley has never had this narrow view, but in numerous other places, students rarely build things that matter until they are at their first job. It’s a continual battle about the role of the university. Academics in some disciplines have historically considered hands-on experiential education the role of a trade school, whereas the opposite is true in the medical field.
In this area, we need both theory and practice because the correct elements of theory serve as a map to understanding the practice, and conversely, the practical, experiential view of the subject is necessary to genuinely understand the theory. You just can’t have one without the other. But the balance and nuance must be right.
Going one level deeper, if you look at the teaching approach in many technical curriculums, most of the focus is on the theories needed to create the next generation of powerful tools, but less focused on theories required to effectively use the currently existing tools. That part is left to the student. Again, we are not wrong for teaching fundamental theory, this discussion is only about the approach needed to fill the gap when it comes to the practice of implementation.
Besides the gap between theory and implementation, there are actually still many other things that can go wrong in real life projects, particularly when people and organizations are considered as part of the scope:
- Sometimes people are organized in silos, which means the holistic solution can get lost between the experts.
- Sometimes, the problem is from overdesign, i.e. too complex, too expensive, or an approach that just takes too long.
- Alternatively, it may not even be technically possible.
- And in yet another form of failure, teams without the right balance of skills simply don’t know what they are doing, and then they simply fall apart mid-project.
Specifically, in the domain of Data, AI, and other digital transformations, history has shown us from the last industrial revolution that those who created the new machines (or at least learn to use them) ended up doing well. In contrast, those that resisted or simply didn’t adapt were no longer able to stay relevant. But to build successful new technology capabilities requires a different type of technical learning.
And yet, the student teams that we teach don’t suffer from these same problems. This is because they are actually learning both the technical and behavioral components necessary for innovation inside a framework developed for implementation. And this is what we have been developing within our Data-X framework for rapid impact. And amazingly enough, it seems to work.
The point here is that we have an approach that helps students make Blockchain, AI, and Data projects work in real life. It’s not mysterious, and it’s actually quite tangible. So for all the firms and organizations who have been thinking about these type of projects – you can make it work as well.