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Thought Leaders in Artificial Intelligence: Kevin O’Malley and Patrick Shea, Co-founders of AdDaptive Intelligence (Part 2)

Sramana Mitra: Maybe take one or two of your customers ideally from different segments of publishing, and walk us through what kind of data you are collecting, and what specifically are you able to do using artificial intelligence. What is the specific artificial intelligence application here?

Patrick Shea: I think there are two use cases that identify key value propositions on different ends of the spectrum. On the one hand, we work with a few large newspaper companies that have holdings scattered throughout the country. Because of that, they have tremendous relationships with thousands of local advertisers. The challenge is taking that relationship and turning it into a scalable product at a national level because it involves running thousands of very small hyper-targeted campaigns.

When you look at the amount of revenue that each campaign, by itself, is able to bring that company, it becomes very difficult to have humans do all of that work. The system that we’ve built, on the one hand, automates all the repetitive tasks that are involved with launching a media campaign. It also puts a lot of the decision-making power into the algorithms that we’ve developed specifically for that purpose. However, one of the challenges that Kevin mentioned earlier with the black box style of managing campaigns is that you typically need tremendous scale for the tens of millions of impressions. >>>

Thought Leaders in Artificial Intelligence: Kevin O’Malley and Patrick Shea, Co-founders of AdDaptive Intelligence (Part 1)

This discussion delves into the applications of Artificial Intelligence in the advertising and publishing industry.

Sramana Mitra: If one of you could get started and introduce the company as well as yourselves, that would be great.

Patrick Shea: I’m one of the co-founders of AdDaptive Intelligence. Kevin is my co-founder here. We started the company as Data Point Media back in 2010 with the goal to bring some of the tools, which were coming into the marketplace primarily aimed at advertisers and agencies, to the publisher side of the business. Our universe is really the advertising technology ecosystem.

Kevin and I both have backgrounds coming from the publisher side. That’s why we set to solve some of those initial challenges. Over the past five years, our company has evolved a great deal. We’ve placed huge focus on our technology. What initially started as creating systems to automate a lot of repetitive tasks became more and more sophisticated. They started to grow into real decision makers in their own right. That’s how we got into the artificial intelligence space. >>>

Thought Leaders in Artificial Intelligence: Canice Lambe, CTO of TAS Group (Part 4)

Sramana Mitra: You already talked about some of the trends in the low end. For rather simple sales cycles, things are getting automated. It’s becoming more self-service. Customers tend to want to buy as opposed to being sold to. We’re seeing this all over the place – these inbound leads. As a result, the websites need to be really complex or really well done sales tools. You’ve talked about the higher end of the sales cycle with complex products and complex sales cycles as being the territory where you are in. You are driving that with technology and methodology. What do you see out there as open problems that  you or your customers would like to see solved by new entrepreneurs?

Canice Lambe: One interesting area is the move from a capital purchase to a subscription model. >>>

Thought Leaders in Artificial Intelligence: Canice Lambe, CTO of TAS Group (Part 3)

Sramana Mitra: Is this primarily applicable to enterprise selling? You started off by saying it has a complex sales cycle, so it sounds like you’re dealing mostly with complex enterprise deals. Is that correct?

Canice Lambe: Yes, that’s correct. Forester recently came out with a report on B2B sales that has a really nice diagram on it. It talks about how if you have a solution that could be either simple or complex, we serve the quadrant where the buying environment is complex and the solution type is complex. What Forester is predicting is that it is the only sector that will grow over the next five years or so. >>>

Thought Leaders in Artificial Intelligence: Canice Lambe, CTO of TAS Group (Part 2)

Sramana Mitra: It sounds like you have some sort of a widget way of capturing the data and then you have a rules engine that is driving completeness and prompting to fill those gaps. Is there anything happening algorithmically that kicks in gear? To call something AI, basic rules engine is a good start. What I’m trying to understand is if there’s more to it here.

Canice Lambe: In terms of the spectrum of AI technology, we would have a basic rules engine approach. We have quite a complex data structure. A lot of the intelligence is actually built into the methodology framework itself. Knowing the key questions that need to be asked, and then knowing the fact that a particular question remains unanswered, what does that mean and what kind of risks would that introduce? We don’t for instance, use inference and chaining expert systems use today. That’s not actually necessary for what we’re doing.

Sramana Mitra: Essentially, the question that you are asking people are also built into the expert system rules engine. At different stages of the sales cycle, you are prompting the sales people to go ask those questions. >>>

Thought Leaders in Artificial Intelligence: Canice Lambe, CTO of TAS Group (Part 1)

TAS Group applies Expert System technology to drive methodology and efficiency in the process of selling complex products to complex enterprises. Their roots are in sales training, but they have productized their expertise. Very interesting discussion.

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

Canice Lambe: I’m the Chief Technology Officer with the TAS Group. The TAS Group is a sales effectiveness company, and we help our customers maximize revenue from key accounts, increase deal sizes, and win rates. In particular, we focus on the situation where there is a complex sales dynamics going on. Typically, the deals are large-value deals and there are lots of stakeholders on the buying side. It’s typically a complex buying process. Our software helps the sales team navigate that buying process to succeed.

Sramana Mitra: Let’s do a use case or two where you can step me through what help, specifically, your software provides and how that relates to artificial intelligence. >>>

Thought Leaders in Big Data: Dave Copps, CEO of Brainspace (Part 4)

Dave Copps: Customers are primarily using Discovery Five for e-discovery and litigation support. When companies are taken to court and there are tens of millions of case documents, they use Brainspace Discovery Five to build a brain on those case documents. That product is interesting. I’d like to call it augmented intelligence. We built it in a way that Brainspace can do a lot of the heavy lifting that people can’t do. A person can’t possibly read 10 million documents and remember how every word is related to every other word. Brainspace can do that.

We give people the ability to use Brainspace, interact with Brainspace and see the concepts and ideas that Brainspace is relating to your query. The other product is Brainspace Enterprise. That product is launching in three weeks. We have two very large Fortune 10 beta customers. I can’t tell you who they are. The idea behind Brainspace Enterprise is we want to turn the social curation of documents inside of a company into a brain. As people are curating, collecting, and sharing documents, Brainspace is learning from that curation, and forming those documents into an asset that literally every person in the company can now engage to connect with other people and documents. >>>

Thought Leaders in Big Data: Dave Copps, CEO of Brainspace (Part 3)

Dave Copps: I heard a term the other day called feral knowledge. How do we start to tame the wild? How can we find answers in that large environment? It’s a lot different from what you’re talking about for sure. If you have a simple environment, that’s a very good environment to use a keyword search. The minute you go beyond that, it doesn’t work anymore. We’ve taken a very different approach. Brainspace formed a collective intelligence from an enterprise’s documents.

We’ll take the tens of millions of documents that a company has. We read them and connect the concepts inside those documents into a multi-dimensional space. Before anyone ever pushes a search button, we’ve connected the concepts inside that data. Now, when you enter a search, it’s not just looking for that word. It’s actually connecting that word to all the other words and phrases that could be related to it, and then connecting you with documents that have a similar concept. It’s a much different scenario. I agree with you that when there’s simple applications that have a known set of answers, that’s a great place for keyword search. We don’t play too much in those areas. >>>