Industry Veteran Sam Gross Applies Intelligent Automation & Machine Reasoning To Revolutionize IT Support

Sam Gross is the founder of ChoiceWORX, the company behind Aptinuum, the first of its kind AI fueled automation-as-a-service platform for  end user application and devise support. The company also applies AI (Artificial Intelligence) to IT infrastructure management and methods to use AI to manage RPA (Robotics Process Automation) environments across endpoint, application & infrastructure layers.

In addition to ChoiceWORX, he is the cofounder and CTO of DAPI, Digital Americas Pipeline Initiative.

However, Gross is no startup junkie. Before ChoiceWORX and DAPI, Gross held CTO level roles with CSC (now DXC), Unisys, Siemens, and CompuCom, and he is a well-regarded speaker and industry thought leader. Cognitive Business News founder Loren Moss was able to sit down with Gross in DAPI’s Medellín, Colombia offices after a recent event of the Institute for Robotics Process Automation & Artificial Intelligence, which also has an active Medellín, Colombia chapter.

C: Sam I have attended talks you have given, and they are very intriguing, even provocative. You say its not enough to have Artificial Intelligence; The real objective is Intelligent Automation. You also challenged us to go beyond Machine Learning to Machine Reasoning. Help me get my head around these concepts. First, bring us up to speed on what you have been doing recently. You founded a company called ChoiceWorx?

SG: Yes, so, ChoiceWORX is a AI SaaS (Artificial Intelligence, Software as a Service) platform, with the objective of simplifying intelligent automation, that´s our job. What does that mean? What that means is that we look at very well defined domains, which for us we began in the infrastructure and technology domain, and we create a platform that leverages machine reasoning as a form of Artificial Intelligence to mimic the support activities that humans do in the operation and remediation of technology infrastructure. So, that means laptops, desktops, servers, routers, apps, network devices, storage devices…So, it´s very well to find domains, much easier to get your head around something like that versus something that is a very broad domain like long processing.

LM: What is long processing? I am not familiar with that term.

SG: Even though it´s two words, long processing is a huge domain, so, to make machine reasoning work part of that process includes creating a description of the world that you are reasoning through. So, a semantic description in data structures and an anthology that describes the world and then a reasoning engine that can work it´s way through that anthology in order to make decisions. Lots of words but here is the bottom line, the bottom line is about 70% of all the work that´s done in infrastructure and technology support is repetitive, and its delivered in a very inefficient way. Digital information is transformed to analog information, and re-translated back into digital, then back into analog, and these transformations continue all the way up and down the value chain as IT supported, but what we have done is we´ve created a closed loop digital channel that allows us to see that something is broken and actually, with AI and machine reasoning, automatically fix it.

“I would challenge you to find me ten people who will tell you that their experiences with their technology support department is good. I challenge you to find me ten. I don’t think you can find two!”- Sam Gross

So, who cares? I´ll tell you who cares. Every individual user, every end user of technology.  We forget in the technology business what our true mission is. We become obsessed with our data centers and our networks, we are obsessed with our processes and our vernacular and our metrics, and our SLA´s service levels…Infrastructure?, all of that infrastructure exists only on behalf of applications and applications exist only on behalf of users, and I would challenge you to find me ten people who will tell you that their experiences with their technology support department is good. I challenge you to find me ten. I don’t think you can find two!

So, the reality is that the reason why we selected the domain that we did as the problem that we want to solve is we want to make people more productive and we want to significantly impact in a positive way their user experience when interacting with technology. When people think about that, right away they think: “well I have a great experience with my apple iPhone or my iPad or my this or my that. Well, Amazon´s e commerce site has a great experience, the reality is that the at-home experience is far advanced beyond what the at work experience is. At-work has not progressed, so, they go home, and their technologies are as easy as a toaster to use, and they go to work and they can’t get their work done! Looking at that very simple problem, which is how to begin to level off the equity between the at-home experience and the at-work experience, the number one thing you have to do is you have to fix end users’ experience in working with the technology and getting the support and getting things fixed.

LM: So, let´s suppose I am a bank, and my users are upset, there´s one here in Colombia that got fined for unreliable websites not too long ago: “My sites are down, my app does not work, and sometimes it does, and then we did a back up and then nobody could process payroll the next day, and we are getting a lot of bad press.!” How is ChoiceWORX relevant in that situation or am I misunderstanding, is that the kind of problem that ChoiceWORX might fix?

SG: Yes, that is exactly what we fix. So, here is that how that would of worked. We would of deployed bots to do the individual pieces of technology, those bots would have enabled to, if you will, report in a digital channel the types of faults and errors that were being generated by that technology, would have been able to relay that to the reasoning engine that´s in the cloud, the reasoning engine would of looked at that fault, and would of made a decision, a choice, ChoiceWORX, around what is the best next action and would of sent those instructions to the bot and the bot would have executed on their behalf. So, what normally would have been an analog process: something breaks, a human notices it, takes that digital information, translates it into an analog phone call or service task. The service task, by the way, when they relay that, probably imprecisely, and then the service task agent will take that analogue information, transform it back into digital by typing it in a service management or a case management system…ChoiceWORX automatically detects the problem and deploys a fix, keeping it all digital, almost instantaneous, and in many cases, problems can be resolved before a human user may even notice.

Loren Moss: You use an interesting term and, knowing you, you didn’t use it by accident: machine reasoning. How is that different from machine learning?

Sam Gross: It’s hugely different. Machine learning is a data-intensive operation. You need to get large volumes of data in order to have an effective machine learning outcome. The problem is that most of the data we work with is actually flawed. It’s flooded because of the human factors that were associated with generating that data.

Machine reasoning differs because it does not depend on large vast amounts of historical data. Machine reasoning operates by understanding concepts, like events and states, and allowing humans to teach the system explicitly, for example, when this event occurred and include the other relevant pieces of data. Humans insert that data into a machine reasoning engine and then the machine reasoning engine has a mechanism to know where to start and then present the “next best action.”

“So, who cares? I´ll tell you who cares. Every individual user, every end user of technology.  We forget in the technology business what our true mission is.” – Sam Gross

What is a next best action? Next best action is very much like the recommendation engines that you see on Amazon. You know what I’m talking about, right? When you buy something, Amazon’s platform references the people who also bought that same product, and it knows what those people purchased next. That is a recommendation engine, which uses a set of mechanisms that understand the “distance” between different data sets. Obviously, the “closest” data helps make a better decision than the “farthest” data.

This is how machines are able to make decisions. And here is the thing about machines versus people. I can teach a person how to make really good decisions. But when they leave my company, all of that decision making leaves with them. All of that education leaves with them. All of that know-how leaves with them. It goes right out the door.

But when you implement decision making into the system through machine reasoning, that data stays there. How many times was a decision that was made a good decision? How many time was that decision a bad decision? When it was a bad decision, what where the associated elements? What were the elements when it turned to be a good decision?

That is how it learns. It learns by doing. The myth is that all technology must have a high level of precision. The fact is that humans don’t have a high level of precision. And we are insisting that the technology we use to replace them — if I could be so bold — has a higher level of precision than they do. But it is not necessary.

Loren Moss: When we talk about intelligent automation, is that the paradigm that we are aspiring to currently? Or are we there today? Are we on our way?

Sam Gross: We are there today. But it is early. The technology works. We know how to build it. We know how to deliver it. We know what kinds of use cases we can deliver with it. And we see it in different parts of our life. We are just not always aware that we are seeing it when we are seeing it.


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