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AiThority Interview With Leigh Fatzinger, Founder And CEO At Turbine Labs

Sophia Scearce September 11, 2019
Reading Time: 6 minutes
Tell us about your interaction with smart technologies like AI and Cloud-based computing platforms.

Turbine Labs began to use Natural Language Processing (NLP) and advanced Machine Learning (ML) technologies a couple of years ago as a way to improve the relevance and accuracy of our platform output. We are not a company that’s building an AI platform in search of a market. We are a company that’s using AI (more specifically NLP and ML) to solve our customer’s needs – today. These technologies enable us to deliver to our customer’s evolving expectations in a way that would not be possible if they didn’t exist or were cost-prohibitive.

How did you start in this space? What galvanized you to start at Turbine Labs?

Turbine Labs started as a result of an old friend and customer asking for advice about how to consolidate his company’s monitoring and social listening tools. They were having trouble getting consistent, accurate information to executives in standardized formats, which was causing frustration among the senior executives. I looked at the market and noticed there was not a platform that provided a “single source of truth” in answering the executive’s most pressing, urgent questions.

What is Turbine Labs and how it transforms Data Analytics and BI?

As an overarching concept, a turbine converts matter into something more useful through a flow process. Turbine Labs uses AI, Machine Learning, and Human validation to refine and transform large volumes of noisy data into accurate, unvarnished intelligence executives and policymakers rely on to inform and improve their decision making. It is transformational in that it takes an entirely new approach to access the public information access network by allowing executives and policymakers to obtain answers to complex questions by asking them in natural language.

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Which industries would benefit from accessing your resources?

Leaders in every industry have questions that require consistent, accurate answers. So we don’t focus as much on industry verticals as we do on roles within those verticals. Generally, our platform most effectively serves Director-, VP-, and C-level executives and organizational leaders.

What is the state of AI and NLP for Marketing and Sales platforms in 2019? How much has it evolved since the time you first started here?

In terms of Customer Care, Marketing, and Attribution, we’re seeing a lot of very interesting technology coming to market. Advanced Machine Learning has the opportunity to help marketers better target and track buyers through all stages of the purchase journey. And NLP is making great strides in the area of self-service customer care, bot interactions, and voice response. Certainly, there’s a lot of work to be done to create a seamless experience consumers actually want to use, but the market has made great strides.

How could digital businesses leverage Artificial Intelligence technology to strategically price their products? Which other technologies integrate with AI?

This question is really beyond my area of expertise.

Tell us about the Turbine SEGMENT and CUE. Do they work in tandem with each other?

Turbine Labs believes most BI, listening, and knowledge management software is far too complex – to the point where most of it goes unused at the executive level. Turbine Segment is our flagship intelligence product. Users ask the platform questions in natural language and receive the comprehensive, unbiased answers in under three hours. On any topic. Where Segment is more in-depth, Turbine Cue delivers intelligence in an SMS format in near real-time. So for example, an executive might ask, “What is the influencer and key media impact of our competitor’s earnings release this morning?”

In this case, a Segment will deliver a 360-degree view of all external content – media, social, broadcast, etc. If an executive asks, “Let me know immediately if John Smith of the Wall St. Journal mentions my company in comparison to my competitor on any channel,” a Cue is delivered. Many times, a Cue alert will cause an executive to request a Segment to obtain more depth on the topic. In fact, users can initiate a Segment output directly from a Cue. We believe there’s no faster way for executives and policymakers to get intelligence on external topics.

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How should young technology professionals train themselves to work better with Cloud, Automation and AI-based tools?

Young technology professionals should continue to focus on the basics, which includes understanding how information flows through organizations and between individuals and groups. No matter what the technology – no matter how innovative it may seem – technology serves no one if it doesn’t serve its users. Additionally, young technology professionals interact with these tools on a daily basis. Their experiences have prepared them to thrive when interacting with these technologies.

What is the biggest challenge to Digital Transformation in 2019? How does Turbine Labs contribute to a successful Digital Transformation?

One of the biggest challenges to a successful Digital Transformation is understanding the root cause of the motivation to undertake what can be a disruptive and costly process. If the existing challenges and intended outcomes are clearly identified, Digital Transformation can be an effective way to uncover efficiencies and competitive advantages. Turbine Labs is often used to transform how executives and their support teams communicate and use intelligence to find efficiencies and improvements in their decision making processes. Our software eliminates many of the friction points executives have wearily become accustomed to.

How potent is the Human-Machine Intelligence for businesses and society? Who owns Machine Learning results?

The ultimate promise of true Artificial Intelligence is to have machines make meaningful, accurate decisions beyond the instructions given to it. In other words, to learn. However, no machine can learn in a vacuum, which means machines need a tremendous amount of training data to build their base of knowledge. Humans are critical to creating and integrating the appropriate base training data to ensure the machine (1) follows the intended instructions, and (2) ultimately has the ability to correct its mistakes and improve over time.

Much of the debate about ethics and standards around AI is rooted in how humans are writing the base instructions, which ultimately impact how the machine will learn. This is a debate that needs to continue, especially in light of several highly public instances of machines showing bias.

Where do you see AI/Machine Learning and other smart technologies heading beyond 2020?

My hope is that the hype cycle around AI will begin to normalize (or even contract), as the current discourse stands to overpromise or alarm the public on the benefits and drawbacks of these technologies. Will AI take everyone’s jobs? Absolutely not. Will AI take over the nation’s power grid? No. Does AI offer promising ways to diagnose breast cancer or other diseases in ways humans simply cannot do? Absolutely. Can AI help prevent financial fraud in ways where current Machine Learning falls short? Yes. So I think we need to get better at educating what these technologies can do today and where they stand to benefit society in the future.

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The Good, Bad and Ugly about AI that you have heard or predict

AI as an overarching concept has a tremendous amount of promise – but those use cases are coming to market slowly. Currently, there’s a lot more “bad” and “ugly” due to inflated perception, hype messaging, and conversations that are not based on what’s really taking place in the market. I see a tremendous amount of using the term “AI” to sell something that really isn’t. Lastly, there are ethical considerations that have not been completely flushed out, both from a societal perspective as well as a regulatory perspective.

What is your opinion on “Weaponization of AI/Machine Learning”? How do you promote your ideas?

There are huge gaps in the ability to monitor and police public information creation and access networks. I remain unconvinced that social platforms are adequately sharing intelligence between them to slow or remove false, misleading, and violent content. The Christchurch, NZ mass shooting in March is a terrifying example. A terrorist was able to broadcast his activities globally on Facebook before their systems were able to detect it. After they were able to detect the video and take it down, it had been rebroadcast on YouTube, and hundreds or thousands of other sites with minor digital manipulations that make “cleaning” the internet of the event nearly impossible.

Fake news is still incredibly difficult to detect, and nefarious actors are improving their tools and methods at the same pace (or better) than those who are attempting to combat it. Bots and deep fakes continue to have the ability to sway perception and behavior. Overall, significant challenges lie ahead to ensure the internet is a safe and reliable place for information, entertainment, and connections.

The Crystal Gaze

What AI, ML and SaaS start-ups and labs are you keenly following?

I follow Palantir closely. Incredible technology is under tremendous scrutiny for ethical reasons, in addition to raising questions about where (and why) it’s being used within so many Federal government departments. OpenAI is another fascinating organization that’s focused on observing the impact of AI by building some of the most cutting edge AI technologies.

What technologies within AI/NLP and Cloud Analytics are you interested in?

Information integrity detection and mitigation are critical for executives to make informed decisions, so that’s an area I have a great interest in. I’m also interested in contextual sentiment analysis and how we can improve the interpretation of conversational and media data.

As a Tech Leader, what industries you think would be fastest to adopting Analytics and AI/ML with smooth efficiency? What are the new emerging markets for these technology markets?

There is a myriad of applications for AI/ML across industries for various use cases. Healthcare is an industry segment that has tremendous amounts of data that can benefit from AI. There are similar opportunities within the finance/financial services industry segments. I guess it goes without saying that we believe there is a substantial need for AI/ML in decision support for executives and policymakers, which is the focus of Turbine Labs.

What’s your smartest work-related shortcut or productivity hack?

I swear by the DayOne app to help me remember personal and professional life moments, and have used it for years.

Tag the one person in the industry whose answers to these questions you would love to read:

Michael Bloomberg.

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Thank you, Leigh! That was fun and hope to see you back on AiThority soon.