Alan Dix explores AI and Social Justice
Author of Introduction to Artificial Intelligence
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Arturas, please tell us about the journey so far with Parrot Analytics
You know, we took a scientific approach with Parrot Analytics by trying to uncover a sector that would benefit mostly from our data set, which contains years worth of data points that are transient in nature. We noticed trends in the media industry such as continued platform proliferation, consumer fragmentation and the decreasing effectiveness of standard measurement services. That’s how we ended up creating a single measure comparing the demand for content in all markets, across all platforms, even those from proprietary OTT (over-the-top) providers such as Netflix and Amazon.
Using the power of artificial intelligence and hundreds of billions of data points, we measure what people are actually interested in viewing. All of that is possible because we have invested years of R&D on understanding the consumer platforms from data point of view in order to be able to empirically measure demand for content. Basically we turned vast oceans of consumer demand data into smart algorithms that will power next-gen entertainment platforms.
You have offices spanning the globe, how is this evolving for you as a business?
We think of ourselves as a truly global company. One day we are working on a project in South America, the next - analyzing how a new pre-release TV title is going to perform in New Zealand.
Our true commitment is to our customers all over the globe. We exist to help by enabling data driven decisions because we believe in the magic of content and the impact it has on people’s lives.
Where are you recruiting your talent from geographically?
We sometimes joke at our all-hands meetings that it looks like a United Nations assembly. Our main goal is to never compromise, and we will do everything possible to find people deeply passionate about our mission and talented individuals who want to be the best in the world at what they do.
Can you give an overview of the proposition, and the benefits it offers to an Enterprise?
Using our Demand Rating™ and Demand Expressions™, content sellers around the world are able to assess geographic-specific demand for their content, allowing them to package, market and distribute their movies and TV shows in the most effective and efficient way, negotiating the best prices across markets and maximizing the monetization of their libraries. For content buyers, the applications are equally powerful. Assessing geographic-specific demand for content can now drive market-specific content acquisition, pricing decisions, marketing and monetization strategies to increase the yield from that content. Once it was easy: we watched movies and TV shows on television or home video, and telephone surveys and sales and rental data told us how popular these movies and TV shows were. Now, with the explosion of distribution platforms and viewing options, assessing demand empirically had become virtually impossible for the industry. We solved the demand conundrum in a way that no other company could.
How do you see the Enterprise AI market evolving over the next 5 years?
We are seeing a number of companies whose entire value proposition is doing one aspect of AI really well. All these platforms and apps have previously been observed in the consumer space – e.g. personalized recommendations, photo library recognition and so on. AI adds an interesting twist to enterprise software by revolutionizing the way people work. Imagine a media industry executive that gets real-time personalized updates based on his exact needs or contextual information, such as emails. And the service keeps evolving over time because the platform is always trying to “get to know” the user to ultimately become a trusted advisor. It’s a rather simple example but one that is not yet widely adopted.
There’s a multitude of privacy, use cases and algorithms challenges we will see being overcome over the next 5 years. All of this because a user quickly getting the exact information instantly is something that will help companies become more agile, delight customers and surpass competition.
What do you see as the biggest market barriers?
Barriers to market entry used to be huge, and with not that many early adopters. Now, there’s millions of smartphones and platforms with enormous network effects. What has happened is that we now have a wave of companies that consumerized technology for business. People start using it within a company and the rest is “fait accompli” - IT departments are bypassed.
However, the biggest market barriers are still a long process of adopting new software within enterprise as well as education and training. The key part is helping people to adjust to AI in the workplace by educating what it actually is and does. It’s tough to expect a user’s buy-in without explaining how it works and how it can help with their work.
Which specific technologies do you see having the biggest influence on enterprise and the future of work?
It’s never been easier to create something new in AI space due to open source movement which is absolutely inspiring. TensorFlow is a great recent example of a deep learning toolkit that might eventually displace Torch and Theano, especially when a distributed version is ready. Imagine a ML pipeline running in a heterogenous computing environment from smartphones to GPU servers to older machines. That is exciting, because new models can be built faster without writing any new code and immediately deployed at a large scale.
So that’s one part of the puzzle. The other is having mountains of training data which is not going to be a problem for many big corporations. What this means is that enterprise AI technologies development might slow down because companies won’t be willing to trust other companies with their data. Companies that are going to develop this capability in-house will win and while this will differ by industry, results will speak for themselves.
What's next for Parrot Analytics?
We are working on some very exciting stuff. For instance, using Content Genome, we can break down any content item into fundamental building attributes, its “genetic” makeup. That’s when interesting things start to happen. For instance, we can start a prescriptive analytics module and let it tell us what should be changed in a script to make it more successful in a specific country. It’s important to note that we do not set out to replace art but rather, we leverage data science to improve it.
Programmatic content and advertising is in our plans due to the fact that we have a multitude of enabling algorithms and data sets that can disrupt the way we think about personalized content consumption.
You can check out more on Parrot Analytics here - http://www.parrotanalytics.com/
Author of Introduction to Artificial Intelligence