While AI technologies continue to grow bi-directionally both in the breadth and depth of applications, many companies are at a loss when it comes to figuring out how to get started and how to leverage these advances for their business processes.IntrospectData is a consulting and product-focused firm that’s poised to help organizations make sense of AI and ML technologies and how they can be applied to each company’s work. Patrick McClory is the CEO and founder of IntrospectData, and on Episode 33 of AI at Work, he joins us to talk about exciting trends in the world of AI, opportunities that lie ahead, and how companies can decide when it is the right time to deploy AI.
Current Trends in the World of AI
Patrick tells us about the AI tools that are emerging and where things are going with respect to market share. There are so many exciting advances coming out that, he admits, “it is daunting to keep on top of the ever-growing landscape.”
The landscape, he says, is growing in two dimensions. “It’s growing in breadth in terms of different types of tools and different types of focal areas, and then in depth, where as it grows, it gets much deeper and much more diverse inside of that ecosystem as well,” Patrick explains, citing TensorFlow as an example of the latter.
“On the engineering side,” says Patrick, “I'm constantly looking for the latest and greatest. I'm really focused a lot on natural language processing these days. I'm looking at Facebook's natural language tool set and the unique structure that they have set up to allow for real-time and in-place updates of models without downtime. Some of those operational concerns are really cool at that level.”
“On a business side,” he continues, “what I find is that the basic tooling, the basic stuff, is really still pretty revolutionary to businesses as they look to leverage the data they have under the hood to go in and do something interesting. The first steps, and the ones that organizations are holding on to, in my experience, has been really just some of the basics to start these days. We're still in the early days on that side, I think.”
As someone whose expertise lies in helping companies find entry points for bringing AI onboard, Patrick reflects that the proportion of organizations that actually knows what they want to do is very small compared to those who hold a more general mindset of exploring and understanding. “Most organizations,” he says, “even in the sort of high end, big enterprise world are still very much looking to AI and ML to be the big win, but they're still struggling to understand how or where that can be their big win.”
By and large, he says, the first steps become very simple trend analysis using AI and ML tools. This often then sparks creativity in those organizations “to connect the art of the possible with what really is valuable to them as an organization,” Patrick explains.
With AI, there’s often a question if it is better to jump in right away, or to take a wait-and-see approach to pick and choose what works best before adopting AI. For Patrick, the answer to this question lies in the low costs of experimentation and the ready availability of tools.
“For organizations that are already in the cloud, regardless of which provider, the tools are there to go and - I don't want to put it lightly, but to literally go and play and do some discovery, and understand what they can do with that data. For organizations that aren't, there are some great ways to even do this - you don't need a pile of GPUs to just get started, to try out you basic even Tensor or NLP or other tool sets,” he says.
There’s many ways to get started, he points out. There’s algorithms that perform trend analysis, those that detect anomalies, as well as sentiment analysis tools. It is possible to get started even without the help of a plethora of data scientists. It’s difficult, Patrick points out, to convey what these tools can do for businesses through a presentation.
The most convincing way is to see for yourself. “When you can actually rapidly put something like that in someone's hands, it really begins that art of the possible discussion,” says Patrick. “That's where I find organizations really start to take hold of these things. I am a big advocate for taking speculative first steps, even without a whole lot of intent to use it in production, to then begin that kind of deeper discussion around what is possible, how they can leverage it, and how they can move forward with those technologies.”
Looking ahead, Patrick points out a gap that he hopes AI tools will be able to bridge in the future. Right now, he says, nearly every single AI, ML, and data science tool is built for engineers and data scientists. “What's really lacking is what I see as last mile technologies that connect businesses with the tools,” he says. Putting these capabilities in the hands of businesses is where Patrick’s focus is directed these days.
In the future, Patrick hopes that this gap in the market will close, allowing the business user to follow the right patterns to build their own models, to build their own processes, and to inject their subject matter expertise into models that they can apply to their businesses. Giving this capability to businesses to experiment and iterate will likely spark many creative revolutions, so let us stay tuned and see what the AI-driven future has in store.