IBM THINK 2020 Digital Experience: Day 2 Review

Today we're rock talking about uh day two of IBM's Think 2020 digital experience, which was uh much more meat and potatoes than day one. Day one was a lot of you know flash and so and showbiz and and big name speakers as typical for many events. Day two was what many of us came for, which is the the technical stuff, the uh in-depth uh dives into uh all the neat technologies that IBM's working on. The uh one of the cool stats of the day was from uh Rob Thomas, uh whose title I can't remember anymore because it keeps uh changing. Uh but he said that for organizations that were early adopters of artificial intelligence, they saw a 165% uh lift in revenues uh and profitability.

That's pretty good. That that's pretty darn good. Unsurprisingly, because of the way IBM approaches uh AI, a lot of the focus is on automation, on operational efficiencies, things like that. So less huge radical revolutions and more make the things you do better. Uh much, much better.

And he was talking about this concept called neurosymbolic AI, which is a term that I had not heard before today. Uh I may be behind on my reading or something, but uh it was a fascinating dive into what this is. So there's there's two schools of artificial intelligence. There's what's called classical AI, and then there is neural AI. And the two have sort of had this uh either or very binary uh kind of battle over the few over the decades.

Classical AI was where artificial intelligence started, with the idea that you could build uh what are called expert systems that are trained um and you've thought of you know every possible uh outcome, and the idea being you would create these these incredibly sophisticated systems. Well, it turns out that that scales really poorly. Um, and even with today's computational resources, they they're just not able to match the raw processing power of what's called neural AI, which is where you use things like uh machine learning, neural networks, deep learning, reinforcement learning, transfer learning, active learning, all these different types of learning. And you feed machines massive piles of data, and the machine learns itself. The revolution that we've had in the last really 20 years in artificial intelligence has been neural AI and all the power and the cool stuff that it can do.

The challenge with neural AI is that deep learning networks are somewhat brittle and easily it's called spiking. You contaminate them with even a small amount of bad data and you can get some really weird stuff happening. That combined with a lack of explainability and interpretability makes them somewhat challenging. You a model comes out and does great things, but no one can explain exactly why the model works. We can guess, we can maybe put in some interpretability checkpoints in the code, but it's very difficult and cost intensive to do that.

So you have these two different schools. You have the the classical, let's have a pristine knowledge system, and you have the let's throw everything in and see what happens. Neurosymbolic AI, at least from what uh Dr. Ragamond was explaining, is when you weld these two things together. So you have all this data, but in from the neural side, but the expert system side effectively forms guardrails that say, here are the parameters where where which the model shouldn't drift out of.

So instead of making it a free-for-all and risking you know having having contaminated data in there, uh you say these are the guardrails which we're not going to let the model go outside of. Uh, a really good example of this is if you've ever worked with a chatbot of any kind, uh, there are things that chatbots are and are not allowed to say. Um, and as we develop more and more sophisticated chatbots, the risk of having them be contaminated with bad data, uh, internet trolls typing in you know hate speech into these things, is a real risk, but having this idea of neurosymbolic AI says these these not just you know these words are not allowed, but these entire concepts or categories are not allowed. And so neurosymbolic AI brings these two worlds together if you can do it well. Last year, uh IBM did a thing called Project Debater, which was their first attempt at having a public demonstration of neurosymbolic AI.

The debater architecture had ten different AIs, uh, of which several were expert systems, saying these are the types of data to look for, these are the things that are allowed, these are the things that are explicitly not allowed. And then the neural side said, here's the corpus of every English language article on in in in their database. And by having the two systems play off of each other, it delivered better performance than either kind of AI would have delivered alone. So, what does this mean for us? It's a change in the way we think about building artificial intelligence models.

Instead of having to choose either or trying to handcraft an expert system. Again, if you've built chatbots, you've done this because you've had to drag and drop the workflows and the if-then statements and things, you know, classical, not true deep learning NLP. The chatbots that you've built by hand like this are very limited. There's a range of what they can do, but it's sort of a classic expert system. And then you have the you know the free for all.

If we can develop neurosymbolic systems that are relatively easy to use and relatively easy to scale, then you get the best of both worlds. You say these are the things I want to allow in my chatbot, but it can have conversations about other things as long as it doesn't fall afoul of you know this area of things I don't want to allow. So you could say allow customer service interactions, allow sales interactions, allow marketing interactions, but also allow history of the company, also allow you know profiles of the executives. And if a person interacting with your chatbot said all, like, well, who exactly is uh who exactly is Christopher Penn? It would know and be able to use the neural side and the expert system side to say, I'm gonna go and look at Christopher Penn's data that I have in this database.

I mean, I know what's allowed and I know what's not allowed from the expert system side, and I'm gonna return a an intelligible answer. Neurosymbolic AI, I think it has the potential to be a way for us to build more trust in artificial intelligence because we know that the expert system side is there to guide us, is there it's handcrafted by somebody to uh to really build d the rules of the safety, the trust, the the things that are explicitly not allowed, the things that are uh encouraged in the system. That's where I see a lot of potential for this concept. Now, it's gonna be challenging for organizations to build this because it requires knowledge of both schools or AI, and a lot of folks, particularly last 10 years or so, have been solely on the machine learning and neural side. The idea of the expert system side is something that only folks with a lot of gray hair in the AI field will have done, because that was, you know, the 70s, the eighties, the nineties.

Uh, was was sort of that time period when expert systems were the thing. So it's neat to see this concept coming around again. Um a few other things I thought were interesting from the day. Uh some talk on propensity modeling and causal inferences within machine learning. I thought was really cool being able to use differing algorithms to start to hint at causality.

You can't prove without beyond a shadow of a doubt, but there are some definitely some algorithms that can get you closer to causality rather than correlation. That was really cool. And uh, of course, the quantum stuff always mind-blowing, uh, and always I still can't put it into into words I can understand yet. But another uh terrific wrap-up. That's the end of the live sessions for a think, but the Think Digital Experience is open to the public, I think for uh at least a few more weeks.

So I'm gonna dive into some of the on-demand sessions and and dig through those. As always, if you have follow-up questions, please leave them in the comments box. Subscribe to the uh YouTube channel in the newsletter. I'll talk to you soon. Take care.

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