Thought Leaders in Big Data: Dave Copps, CEO of Brainspace (Part 2)

Sramana Mitra: Talk to me a bit about adoption. The concepts and the ideas that you’re talking about have been around. After doing three startups, I did 10 years of consulting before I started One Million by One Million. Even in the early 2000’s, there were companies that called themselves knowledge management companies that did exactly what you’re doing with exactly the same value proposition. They had a very hard time gaining enterprise adoption. What is happening now? What is the state of the union in enterprise adoption today with your technology?

Dave Copps: I think you’re seeing a real transformation happening. I’ve been in the space a long time too. I think knowledge management is a term that has been around for a very long time. I don’t think anyone knows what it means anymore. In essence, knowledge management in the past meant search. Every search engine called themselves a knowledge management system. I think the concept of search is dated. If you think about the way search works, someone puts in a keyword and that keyword is then matched to every document that might have that keyword, and they bring back all the documents. That whole strategy is dying. We can’t have as much data as we have today without some kind of a learning system. The state of the art system can’t be putting all the documents in a Big Data index and hack at it with keywords. We’ve done very static methods for connecting people with knowledge. >>>

Thought Leaders in Big Data: Dave Copps, CEO of Brainspace (Part 1)

Knowledge management has been around. What has changed? Read on for more discussion on the subject.

Sramana Mitra: Let’s start by introducing our audience to yourself as well as to Brainspace.

Dave Copps: I’m the CEO of Brainspace. We’re a software company headquartered in Dallas, Texas. We built a large-scale machine learning platform that is reinventing the way that companies are learning through their Big Data.

Sramana Mitra: What does that mean? What kind of customers are you trying to cater to? When you say learning, what use cases are we talking about?

Dave Copps: Most of our customers are companies that have large amounts of knowledge workers. The industries we are primarily focused on would be consulting, biotech, technology, oil and gas, and places where there’s a lot of research and knowledge workers. The reason being that our technology has a very unique capability of being able to capture the concepts, thoughts, and ideas and connect the concepts inside of a >>>

Thought Leaders in Cloud Computing: David Schmaier, CEO of Vlocity (Part 7)

Sramana Mitra: I don’t buy all of Kurzweil’s points, but there are certain things that I think machines can do better than humans. For example, if you look at medicine and diagnosis, if you really have all the data and images set up, I think machines would do a better job with diagnosis.

David Schmaier: I agree with you there. It’s interesting in the area of software that we play in, which is the industry-specific CRM space. They want better human interactions.

Sramana Mitra: I understand. You are enabling people to do their jobs better. If you go in and say that you’re going to replace all your jobs, no one is going to buy your software.

David Schmaier: That’s one part of it. When I call for service, I want the other person on the other end to guide me. This is what we are featuring in Dreamforce, which is the guided interaction capability where based on machine learning, we can guide people the optimal way. You >>>

Thought Leaders in Cloud Computing: David Schmaier, CEO of Vlocity (Part 6)

Sramana Mitra: In general then, you’re saying that in all your use cases in all the different sectors where you’ve built solutions, it is either Siebel replaced or a custom-built replacement.

David Schmaier: There are other providers we replace like Amdocs and other older, crummy systems. I would say it’s either industry on premise or custom.

Sramana Mitra: What I would like to do is get your input on where the open problems are. Given the context that you’ve set so far, if you were starting a company today, what areas would you point them towards?

David Schmaier: Let’s take your AI comments. We’re actually going to show some brand new software at Dreamforce that we’re launching with Salesforce. Without giving away the news, it’s a line of industry-specific analytics so that on top of all these workflows and business processes, we can now mine that data and tell you things about what’s going on. This industry-specific Big Data is a big opportunity. What we’re >>>

Thought Leaders in Artificial Intelligence: Luca Scagliarini, CEO of Expert System (Part 7)

Sramana Mitra: Where does your core technology team expertise come from? Where in Italy do you have these computer scientists?

Luca Scagliarini: The actual technology was built through a private effort. It was all done by us. Obviously, we are working with different universities.

Sramana Mitra: When you say we, who’s we? What is the background of the people?

Luca Scagliarini: We have two kinds of technical skills. Obviously, we need software engineers. What we need that is peculiar for a software company is that we need computational linguists. These are people who have studied or developed expertise in using programming environments in human language. There are now specific studies in many universities around computational linguistics. If you look at our R&D, we have 40% software engineers and 60% computational linguists who have the expertise of the language and the expertise of being able to write code around language. >>>

Thought Leaders in Artificial Intelligence: Luca Scagliarini, CEO of Expert System (Part 6)

Luca Scagliarini: With the approach we chose, which originally was thought to be something that could not be implemented in the real world, we reached a good level of understanding. I think there’s more room in terms of being able to process content that comes from different domains in a much more effective way. For example, if I have speech-to-text, having something to understand the text can also improve the speech. I think that a combination of pure machine learning with semantic understanding is where this improvement will be visible to the average user.

Sramana Mitra: What do you mean by a combination of pure machine learning?

Luca Scagliarini: The pure machine learning approach is where you take a text and you don’t understand the meaning, then you just process the quantity and you try to understand the pattern and a lot of other things that are not really related to the meaning. If you merge that with something that can help you understand the meaning in context, I think the combination of the two will bring us to the next level of understanding. However, most of the companies have chosen the pure machine learning approach. They don’t have the semantic understanding. We already have the core technology. We hope to be able to show the market that this is doable and that the difference is significant. It’s the combination of the two approaches that count on the deep understanding of content. This can happen just by adding a semantic layer on top of traditional search engine. >>>

Thought Leaders in Artificial Intelligence: Luca Scagliarini, CEO of Expert System (Part 5)

Sramana Mitra: When you go into a direct account to sell, are you going into the CIO’s office or are you going to the business departments where you see the use cases.

Luca Scagliarini: The value of what we do even if it’s highly technical is perceived at the business level. The upstream group in an oil and gas company understands the complexity of processing unstructured and structured information together. It’s the security officer in some companies. It’s the compliance officer in certain companies. We usually go direct to the business. We try to limit the number of use cases because we cannot cover everything, but the use case is very broad. The CIO or IT team is the operational part. We have all the answers for the CIO becuase we’ve been doing this for many years in many different implementations, but we go to the business to sell.

Sramana Mitra: You said you implement custom solutions based on your discussions and based on the use cases for that business. Does that mean that you provide the professional services related to that or is that done internally?
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Thought Leaders in Artificial Intelligence: Luca Scagliarini, CEO of Expert System (Part 4)

Luca Scagliarini: There’s another use case is risk management, which applies to not only oil and gas, but also to any corporation. Companies in the oil and gas sector have a very peculiar situation. Their assets are distributed geographically in many different areas. Most of them are dangerous areas. In addition, they have thousands of customers and employees. Being able to protect these assets is an important task for them. Protecting means to be informed at any moment in time about what’s happening in any geography. I’m not talking only about wild attacks. For example, there may be a flood that is disrupting the main road that goes from your production facility in Azerbaijan to where you need to ship the oil. It might not be a piece of news that makes CNN. Maybe if you’re not informed in real-time, you might end up delaying your production.

All of this is done by running intelligence analysis on streams of information. Streams of information is anything from global news to local news and from local blogs to social media. The more you can process, the better it is. Being able to read through keywords makes a big difference because it enables you to process information and extract what is relevant at the right moment in time. It applies to other industries also. >>>