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Computer vision and natural language is becoming increasingly vital parts of AI technologies, and this is the field in which Mighty AI operates. They develop algorithms that can see, hear, talk, and “think” like humans.
They will be exhibiting their tech at the fast-approaching AI Summit London, where attendees will be able to gain unrivalled insight in how AI is changing the way businesses operate.
We secured an exclusive interview with Mighty AI’s CEO, Matt Bencke, who founded the AI start-up back in 2014 in Seattle.
Before starting Mighty AI, Bencke had experience working in multiple fields including: technology, strategy, business development, design, e-commerce, marketing and manufacturing.
We began our conversation by asking Bencke to detail how Mighty AI is involved in the AI-space. “We deliver the training data companies need to build AIs,” he started.
“You probably know that, while the platform you build on and the UI you create have a big impact, it’s really the data that makes or breaks an AI. And getting the right training data is a pain point for most companies,” he noted.
Bencke continued, “In-house solutions don’t scale, crowdsourcing is unreliable, and both are a big time suck for people building products. We take this on for our customers, providing the training data they need with our Training Data as a Service™(TDaaS™) platform.”
Then we asked Bencke which industries is Mighty AI gaining the most traction from in relation to AI. “We’re seeing traction in a number of industries, including automotive, retail, healthcare, financial services, and digital marketing,” he explained.
“Some of our customers include Intel, IBM Watson, Sentient Technologies, GumGum, Getty Images, Expedia and dozens more. We’ve purpose-built our Mighty AI TDaaS™platform to provide accurate human insights across all domains of human knowledge,” finished Bencke.
Bencke went on to detail which areas of business are being affected the most by AI. “Ultimately, I think AI will be pervasive across all nearly aspects of business over time,” he highlighted.
“Initially, I think you’ll see the biggest investments and effects internally through companies using AI who struggle to generate improve and integrate their business process outsourcing systems,” stated Bencke.
He continued, “And externally, I think the same will be true for improving customer service (like smarter/better chatbots) and the customer experience broadly (like using data to make more personalized recommendations).”
We then got onto the topic of Mighty AI’s competitors, “I’d rather tackle this from what sets companies in general apart when using AI,” he began. “In a nutshell, it’s who has the best data. “Best” means clearly structured, accurate and precise.”
Bencke then started go into more detail on what really sets Mighty AI apart from its competitors. “Take for example companies that are building apps for Amazon’s Alexa. Let’s pick on the travel industry. What will make the difference between Expedia and Booking.com’s Alexa skills?,” he queried.
“They’ll both run on Amazon Voice Services, so there’s no differentiator there. One company may have a better product team, and that makes a meaningful difference. But, at the end of the day, the company with the higher quality, more specialized training data will have the advantage. Sample domain-specific dialogue will enable an app that allows travelers to book travel and gather information via naturally spoken language. That will be the game changer,” he concluded.
With AI being so prominent right now in the public eye and mainstream media, we asked Bencke what he thought the rate of adoption of AI would be industry wide in 2017. “I think AI will “land” in a way it still hasn’t quite landed yet,” he started.
“We’ll stop talking about far-fetched, man-versus-machine predictions and instead figure out how to harness AI to turn big data into something organized and actionable for businesses. We’ll embrace AI as critical for our economic productivity,” noted Bencke.
“From business process outsourcing to computer vision to natural language processing and more, we’ll strengthen today’s AIs, integrate them into our existing systems, and enrich lives and improve businesses as we go,” he said.
“We see these practical applications of harnessing data to build great AIs as fundamental to our growth, and it’s why we’re bullish about the market opportunity,” finished Mighty AI’s CEO.
Yet, with the adoption of AI comes many challenges, so we were keen to find out how Bencke thought businesses would tackle them. “There are of course a number of challenges for business to consider, but what I continue to come back to is the fact the companies with the best data will be the most successful—and therefore those without it will get left behind,” he replied.
“Early adopters who gather accurate training data to improve their models enjoy first-mover advantages. This is largely due to learning loops. Not only do first movers’ predictive models improve continuously with training, application and correction; so do all the subsystems that benefit from better overall performance,” outlined Bencke.
“For example, more accurate computer vision models drive more precise navigation; which in turn drives a need for higher resolution, better sensors; which in turn enables more use cases, creating a beautiful flywheel effect. The companies who invest in the best data will have the best outcomes,” he concluded.
We finished off our conversation with Bencke by asking him where he saw Mighty AI in five years’ time in relation to AI. “I see Mighty AI and the training data we provide as an edge we can give our customers,” he answered.
“The proliferation of infrastructure, platforms and know-how is quickly making “basic” AI functionality achievable by software across just about any application that entails media, data, text and/or speech. Leading companies will move quickly beyond this new, higher common standard to apply AIs in ways that are more sophisticated, specialized and personalized,” he explained.
“And, guess what these more ambitious applications will require? You guessed it. Increasingly sophisticated, specialized, personalized—and reliable—human training data. After all, a machine is only as smart as the humans who train it. It’s all about putting the right humans, in the right loops, and thereby making it easy for product, marketing and data science leaders to focus on what they do best,” finished Bencke.