A chat with AI instructor Chris Mohritz

Christopher Mohritz is a lifelong entrepreneur and technologist with a number of successful businesses under his belt; bringing a unique blend of technology know-how coupled with creative thinking and business acumen to each of his projects.
Since 2009, Chris has been building and leveraging artificial intelligence systems to cognify a wide range of business functions — marketing, sales, customer support and decision automation to name a few. And over the past five years, he has been building and operating a business accelerator for web/mobile startups, helping other entrepreneurs launch exceptional “AI-first” businesses.
Chris draws heavily from a deep background in technology — from operating nuclear reactors in the U.S. Navy to designing datacenters at Lockheed Martin. Complemented by a broad range of business experience — from technical sales for the Fortune 500 to project management in the public sector. Allowing Chris to bring a seasoned and unique strategic vision to implementing cognitive systems that drive real-world business value.
Chris will be teaching our AI workshop in Austin, Boston and the Bay Area. Click here for more information.

Byron Reese: Chris, how did you get into artificial intelligence?
Chris Mohritz: I’ve been helping entrepreneurs start software-based companies for the last several years. And as software has gotten more and more complex over the years, it just naturally evolved into a focus around AI.
We first started working with machine learning back in 2009. Now machine learning is opening up into broader areas — the stuff we now call AI.
I view the AI revolution we’re seeing these days as a natural evolution of the software revolution. As Marc Andreessen famously said back in 2011, “Software is eating the world” — and now AI is eating the software.
Byron: There’s no consensus definition of what artificial intelligence is. There is not even a consensus definition of what intelligence is. When you hear artificial intelligence, is it a phrase that’s become meaningless by now or does it in fact mean something, and if so, what does it mean?
Chris: AI is a terrible term, in my opinion, because of exactly what you just said. We don’t really understand what natural intelligence is, let alone artificial intelligence. But it’s a very useful term in the umbrella mode that we currently use it in — a term that encompasses a bunch of different technologies, processes and strategies — all of which are evolving as we speak.
From my own personal perspective, I would say AI is any type of system that can do a task that previously required human intelligence. For example, computer speech is a field that is heavily worked on right now. Computer vision is another. And being able to ingest a large amount of data and then predict the future based on that data is another example. Technically, I would call most of it “machine learning” — and it all falls under AI.
But, yes, AI is a nebulous term but it becomes useful in this context because it’s such a wide open field and we really need to put it all into one bucket. As human beings, we want to classify everything. So AI is a great classification for a wide range of different technologies that are constantly evolving.
In the end, I think clearer terminology will prevail as we settle into a deeper understanding of what these new tools can do for us.
Byron: When you go into an organization that wants to “implement AI”, where do you start looking? What is your low-hanging fruit, typically? What should somebody look for in their organization as something they can automate?
Chris: A couple of different answers to that. The low-hanging fruit side of it is typically marketing and customer experience. I spend most of my time in the consulting role helping folks implement AI-powered automation in marketing, and in developing better customer experiences. And that customer experience can either be part of marketing or implementing AI within the products themselves — whether it’s software, hardware, whatever it may be.
Now, as far as how do you actually implement it, I have a framework that I use going in. A framework that breaks the process down into easily digestible chunks and simplifies discovery. The problem I see these days — there’s just so much hype around AI. And the field is evolving so quickly that it becomes overwhelming almost instantly. Most business leaders don’t really know where to start and get hung up on decisions because things are changing so fast — “Do I implement this now or do I wait for the next thing.”
So one concept I like to start with is that AI is nothing more than software. Most businesses already have a mature process around how to adopt software. So just put AI into that context. Don’t go out and invest in AI-specific stuff right out of the gate. Just make the people in your business aware of what type of capabilities AI has today, and where it’s going to be in the next couple of years, so they have that information in the back of their minds as they go through their workday. Then the frontline people are asking the right questions — “Could AI help streamline this process?” And then managers can start spinning up business cases and projects to move forward.
I think the most important thing is actually keeping AI in context. Most businesses are already heavily invested in software today, so the next logical step is to start asking how they can take that software to the next level. And from that perspective, “AI-powered” because a natural flow of how their business already works.
Byron: What would be an example of a project that you were involved in where you went into an organization, you helped identify a target opportunity, you work together with the client and they had an outcome?
Chris: The example I would give you is in hiring practices. One particular project that I worked on recently is an HR-specific platform where they’re using AI to streamline the hiring process. And again, this goes back to your previous question around how I view AI as software. And I like to think in terms of AI as ‘augmented intelligence’ versus a replacement for human staff. So AI is really a tool to augment existing human capabilities — in this case, the hiring team.
In the context of the hiring process, we created an AI system — specifically a natural language processing platform — that analyzes outbound job descriptions and makes recommendations around how to structure the job description to solicit their ideal responses. And then the system also processes the incoming resumes to help filter them to a manageable amount. As a large company, these guys can get hundreds or thousands of responses for one particular job description. So instead of going out and hiring an army of temporary folks to filter down the resumes, they can now do it all through AI. And that really just boils down to a system that can read natural language in the resumes, extract the key points that the team wants to look for, and then narrow down those thousands of incoming resumes to a more human-manageable amount.
Byron: Now, AI is highly accessible, right? With the various API and platforms out there, you no longer need a team of data scientists to do meaningful work, right?
Chris: Yes. So instead of trying to dive into an open source AI platform right out of the gate — hiring a development team, bringing in data scientists, assigning executives and all that jazz — you can just use IBM Watson or Google Cloud or Microsoft Azure or nearly any other cloud provider. Most of the big tech providers all have AI-powered APIs that you can just start playing with, usually for free.
And there are a ton of advantages — these APIs live in the cloud, so all of the reasons we moved into the cloud before apply to these AI platforms as well: speed, reduced costs, etc. I also believe they help accelerate the AI learning curve by immediately putting AI into real world scenarios.
Byron: So the different platforms, are they really different in profound ways or is it like Ford versus Chevy where are they all purporting to do the same thing and are there pure competitors in that sense?
Chris: For the most part, yes, they do claim to do the same thing, but there are differences. Your Chevy vs. Ford analogy is a good one. Just like different cars will drive you from Point A to Point B, similar APIs from different providers will get the same job done. But depending on the complexity of the task at hand, how quickly and comfortably you get from Point A to Point B can be different.
I spend a fair amount of my time comparing different feature sets. For example, if I need a natural language processing engine — I might compare IBM Watson against Google Cloud — testing their performance, checking accuracy, etc. And they do differ. Just like Chevy vs. Ford, each has their strengths and weaknesses. You just need to give each a test drive to find out which best suits your need.
Byron: What is something that artificial intelligence can’t do right now that maybe people think it can? Or where it’s like, “eh, you’re better off waiting a year or two to try to do _______.”
Chris: Like any new technology there’s a massive amount of hype around AI these days. And AI, specifically, is even more susceptible to over-hype because of the ideas popularized by the media and entertainment industry over the past few decades.
I spend most of my time around natural language, computer vision, and decision automation. Most of that stuff works pretty well in the correct context.
But in the next couple of years, I think we’re going to see some major advancements in the natural language side of things. Today, you can count the number of truly successful chatbots on one hand. But I think over the next couple of years, that number will change dramatically. I think you’ll see a whole lot of chatbots out there very effectively promoting products, developing brand awareness, doing customer support, and anything else we can plug them into.
I think the natural language thing is good today — and that it will be great, very soon. Right now, you can still tell when you’re talking to a bot, where I think in the next year or so you won’t be able to tell the difference.
Computer vision is on a similar path. It’s already doing well and will make some massive strides in the next couple of years.
Byron: You mentioned chat bots. The web is increasingly mobile, so do you think we’ll see a decline in the consumption of webpages? Like we already hit peak webpage page views. And bots, because you don’t have to learn the interface, are going to be kind of a new replacement for big parts of the web?
Chris: If you look at it from an everyday living perspective, a chatbot provides the most natural interface for human beings — conversation. We’ve been communicating verbally since the dawn of history — and presumably before that.
The only way to go beyond conversation would be something like what Elon Musk recently announced he’s working on — a neural link. Which would basically allow our brains to talk directly with computers.
I definitely think that the natural language interface, in general, is going to replace a majority of the computer interfaces we use today — tapping keys, swiping screens, etc. And I think websites are part of that. There are some people predicting that chatbots will completely replace websites. I’m not sure I’d go that far, but I definitely see the chatbot taking over a lot of what websites do for us today.
Byron: We’ve seen this new smart speaker category emerge and quickly grow. How do you see the hardware, the Amazon Echo with Alexa and the Google Home with the Google Assistant, what role do you see them in the future?
Chris: Effectively, what I envision happening over the next five to ten years is that ultimately everybody will have their own personal JARVIS — to use an analogy from the Ironman movies. I believe that we will all have our own personal AI doing a whole lot of stuff for us. And I think there’s actually two important points to your question:
First is that those hardware devices become the main interface to our personal AI, which ultimately becomes the primary method we use to interact with the computer world. Which goes back to your previous question around do we need websites and other vision-centric tools if we can just have a conversation with a hardware device in pretty much any room of our house, or car, etc. I think those devices will become a natural part of everyday life. And they will cover a lot of the things we now do on smartphones and laptops.
The second, and even bigger paradigm shift, is those devices move us toward a world of ubiquitous computing. Today, we walk around with a smartphone — a device that we can lose, put down, or shut off. I think once the personal assistant devices become more and more embedded in our lives, we won’t even need to turn on a smartphone because we’ll be able to have a conversation with those personal assistant devices anytime, anywhere. “Hey, do a Google search for…” or “Answer the phone” or “Turn on the lights” or whatever it may be — in any room we want. And having instant access to pretty much anything you can think of, any time you want, opens up all kinds of interesting doors.
Byron: Finally, Gigaom is taking its AI Workshop on the road in June to Boston, Austin, and San Francisco and you are the instructor. Can you just talk briefly about what the goal of that workshop is and who should go and how they will leave changed?
Chris: The primary goal of the workshop is to help businesses accelerate their adoption curve for AI. We’re typically working with business leaders and strategy level folks in the workshop and are primarily focused on giving those guys a clear view into several of the questions you’ve already asked, which basically boils down to: “What can AI do for me today? And what are the next steps to actually get it implemented?”
We also dive into what AI looks like in the near future. These technologies are moving so quickly, business leaders not only need to be thinking about what to do today, but they also need to have an understanding of where the technology is headed so they don’t get blind-sided down the road — aka. disrupted.
My hope is that each workshop squashes overwhelm — and that participants leave feeling empowered and ready to move forward with AI projects that make sense for their business.