Artificial intelligence (AI) is an amazingly innovative field. However, its sheer dynamism has created an industry landscape that is anything but mature.
As Wikibon observed in our recent Big Data Analytics Trends and Forecast, AI is transforming practically every aspect of the data management, advanced analytics, business applications, and IT infrastructure markets. AI-fueled disruptions are remaking every aspect of life in the 21st century, and will continue well beyond the 10-year horizon over which our projection stretch in that report. Check out my thoughts from a year ago on what it will take for AI to fully mature as a set of platforms, tools, applications, and practices. Also, check out this recent article on the blistering pace of acquisitions over the past several years in the AI industry, which shows no signs of abating anytime soon.
Be that as it may, the best practices for building, deploying, managing, and using AI are beginning to mature. That was my take-away from the recent interview of Google AI expert Ron Bodkin on theCUBE at Big Data SV. If anybody has mature AI practices, it must be Google and other cloud-computing powerhouses who have staked their futures and their entire application strategies on this technology.
As Bodkin discussed with Dave Vellante and Lisa Martin, the linchpins of AI best practices, as Google has implemented within its own go-to-market strategy, are several:
- Establish a data-driven business culture that puts AI-first approaches at the heart of digital transformation. “In order to be an AI-first company, to really reconceive your business around what can happen with machine learning, it’s important to be a digital company. To have a mindset of making quick decisions and thinking about how data impacts your business and activating in real time. So there’s a cultural journey that companies are going through. How do we enable our knowledge workers to do this kind of work, how do we think about our products in a new way, how do we reconceive, think about automation….Start to do pilots, start to get something going. What we found, something I’ve found in my career has been when companies get started with the right first project and get some success, they can build on that success and invest more. Whereas you know, if you’re not experimenting and trying things and moving, you’re never going to get there.”
- Assemble an AI team with the skills and vision to drive the necessary innovations. “At Google, we really started the journey to become an AI-first company early this decade, a little over five years ago. We invested in the Google X team, you know, Jeff Dean was one of the leaders there, sort of to invest in, hey, these deep learning algorithms are having a big impact. Fei-Fei Li, who’s now the Chief Scientist at Google Cloud was at Stanford doing research around how can we teach a computer to see and catalog a lot of digital data for visual purposes.”
- Deploy a scalable AI hardware infrastructure all the way to the edge. “So combining [deep learning algorithms] with advances in computing with first GPUs [and massive-scale TPUs] and then ultimately we invested in specialized hardware that made it work well for us. That combination really started to unlock all kinds of problems that we could solve with machine learning in a way that we couldn’t before. So it’s now become central to all kinds of products at Google, whether it be the biggest improvements we’ve had in search and advertising coming from these deep learning models but also breakthroughs, products like Google Photos where you can now search and find photos based on keywords from intelligence in a machine that looks at what’s in the photo.”
- Provide developers with the tools to accelerate and automate building of innovative AI applications. “There’s a tremendous interest in how can we take Google’s capabilities, right, our investments in open source deep learning frameworks, TensorFlow, our investments in hardware, TPU, our scalable infrastructure for doing machine learning. We’re able to serve a billion inferences a second. So we’ve got this massive capability we’ve built for our own products that we’re now making available for customers and the customers are saying, “How do I tap into that? “How can I work with Google, how can I work with “the products, how can I work with the capabilities?” So the applied AI team is really about how do we help customers drive these 10x opportunities with machine learning, partnering with Google….So you can do things like translating languages automatically, like recognizing speech, like having automated dialog for chat bots or you know, all kinds of visual APIs like our AutoML API where engineers can feed up images and it will train a model specialized to their need to recognize what you’re looking for.”
- Ensure that there is high-quality curated data for building and training AI apps. “A lot of people thought that big data would be magic, that you could just dump a bunch of raw data without any effort and out would come all the answers. And that was never a realistic hope. There’s always a level of you have to at least have some level of structure in the data, you have to put some effort in curating the data so you have valid results. So it’s created a set of tools to allow scaling. You know, we now take for granted the ability to have elastic data, to have it scale and have it in the cloud in a way that just wasn’t the norm even 10 years ago. It’s like people were thinking about very brittle, limited amounts of data in silos was the norm, so the conversation’s changed so much, we almost forget how much things have evolved.”
People who have mastered these AI skills are in short supply. Some industry observers suspect that the solution providers’ startup-acquisition streak has been motivated—at least in part–by a defensive desire to keep these skills out of competitors’ hands.
I wouldn’t go that far. But I do think that the trends toward AI-pipeline automation and AI-driven augmented programming stem from that same skills-deficit concern. Even the likes of deep-pocketed talent-magnet Google must be concerned about finding enough of the right people to sustain its AI-rocketed growth trajectory.
