Introduction To Building Smarter Products With AI/ML

When talking about artificial intelligence and machine learning (AI/ML), it is best to start with a definition. AI is trying to get computers to do the cognitive tasks that people do. On the other hand, ML is specifically trying to train or calibrate using data. However, AI can learn from rules, represented knowledge, or any other inputs to the human experience or human reactivity. 

I am passionate about machine learning gathering information for professional communities. Basically, imagine what google does, but like for credit underwriters, bankers, lawyers and doctors. The computer organizes the information so that the things we do, which are exhibiting judgment and creativity, are easier. Therefore, the things we don’t like to do – such as doing repetitive tasks or searching in haystacks for needles – get automated away for us. 

The algorithms for ML and AI are getting better at things that are hardcoded into our brains like collision detection and collision avoidance. They are also getting better at aggregating and harnessing information out of communities. This creates an information environment. This type of environment will take lots of information from lots of people’s behavior. This way it can use the collected data to make that one person’s view into that in information finer and easier. 

Hype and Overhype of AI/ML

The biggest hype of AI is that people think it will make things simpler without being more complex. Simply put, that’s not the case. What I’ve seen is that the primary effects of a smart AI system in a product tend to make things easier for whoever is using it. However, they end up changing the culture of the way it is used. 

There are always effects, culturally, on what the product does when people adopt it. In addition, I think we have the aura of optimism that the effects are always positive. We don’t always have the discipline in thinking about the secondary and tertiary effects of what we are doing. 

In terms of what is underhyped, the machines can help us to be better at being people. As an assistive technology, they are just awesome. One evening we were preparing for a vacation and my wife practiced Spanish with Duolingo. It’s an adaptive technology that allows her to teach herself Spanish half an hour at a time with her phone. Looking ahead, ML will be the dominant metaphor for how we interact with computers.

We will be interacting with machines and their data through some very smart interfaces. Those interfaces will only get smarter and smarter over time. At its best, it will fade into the background and integrate itself into our day to day lives and make everything around us smarter.  

 

Click here for Part 2 (available 8/16/19)

Click here for Part 3 (available 8/19/19)

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About the Speaker
Jacob Sisk
Credit Suisse Labs CEO
Jacob Sisk is the CEO of Credit Suisse Labs - leading a team responsible for leveraging new technologies to build new businesses or refresh old ones in a way that benefits the company at society-at-large. Prior to joining Credit Suisse Labs, he worked as a product executive at Capital One Payments as its lead data scientist. In addition, Jacob worked as a research scientist at Yahoo and Thomson Reuters - and served as Principal at Infoshock for more than a decade. Jacob holds an MBA from UCLA and currently lives in the San Francisco Bay Area.

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