The AI Summit New York 2019
December 12, 2019, New York, NY, United States
The AI Summit New York 2019
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Experts in AI: Through the eyes of a data vendor
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About speakers

Max Smolaks
Editor at AI Business
Niranjan Thomas
General Manager, Developer Platform & Solution Engineering at Dow Jones

A results-oriented leader focused on growing data and technology ecosystems through developer products and go to market strategy. Leadership experience across software design and development, cybersecurity, enterprise technology, product and go to market strategy with a focus on the Financial Services sector.

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About the talk

Artificial intelligence models feed on data, but what do you do if your data pipeline is running dry, or you need information that's not present in corporate repositories? It's time to turn to data vendors - some of these are new kids on the block, while others are established names that are re-inventing their business models to accommodate the new data economy.

Dow Jones fits into the second category: primarily known for its publishing and financial information services, that include the Dow Jones Industrial Average, the company is creating new revenue streams by offering data for AI training.

At the AI Summit New York in December, we caught up with Niranjan Thomas, general manager for Developer Platform and Solution Engineering at Dow Jones, to find out more.

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We're here at the Javits Center for the second day of AI Summit New York and I'm speaking to Neon John Thomas who's the general manager of one of the businesses that Dow Jones runs and it's 8 its professional product about your work. And then what is Dow Jones interest in machine learning you have a really powerful set of apis and and data feed products across that fact either and use wise and every Screen Compliance product line. My role is to really make sure the developers who are using apis both

understand have a great experience are in a successful in building application around those apis and seeds and its applications really powering the most important decisions that they make it within their businesses. That's my roll a joint in machine learning this company right into the provider better. We employ. I am machine learning in the production in the creation of You'll see what we customers who invite themselves using artificial intelligence and machine learning without data so many places for Sweden at 4. And also we do not customers environment. Okay, and then you

deal with the news information me the information and I'll structured and then and just generally to walk around the show floor. There's a lot of conversations about unstructured data and and the value that I'd there at the challenges in in in in understanding unstructured a thin, you know, like getting somebody out of it business. So we have list of sanctioned individuals and and Bad actors. If you like an instructed to I-26. We also have completely unstructured data, right so news articles, which

we can use wise for us and we can consider the articles is unstructured content. To tag in code the content. So that customers can find the companies in the people when the subjects on topics that they're interested in. Right but beyond that we also a finding more and more as machine learning in artificial intelligence become so prevalent within a costume is environments that they themselves when I use machine learning without data sets and they want to bring a different level of structure to that data in some cases at

custom. It's a free camping the reinsurance face a very very Domaine specific knowledge of the day looking for weeding out data, so it might be something very specific to Property and Casualty or life and health insurance. Now that wants a logical models in the structure of what they looking for is highly specialized and they use machine Learning Without items machine learning. Self-realization off of these DVD's work clothes pension rights. Are we moving from a world which software

is developed in a way where you explicitly give the machine all of the instructions in terms of what to do to a world where you give the machine some of the instructions or no instructions at all in the machine learns from the data. Unstructured or otherwise, like I said, it's two pots that juani's how do you create discipline and certainty in that world with machine is making decisions and then I discipline and structure in that then how do you style it as you describe right to visit to really important question and a number of different areas operationalization considers things like the

skill sets and capabilities within an organization it considered some of the processes, right and the disciplines to ensure that there is no inherent bias in some of the decisions of the Machines of making the government. So there are clearly Cymbalta considerations around the decisions that machines are making soap. Like a sequel considerations avoiding bias, right? And I think you know it is it is about really putting all three together when we talked about operationalization people process and the right. I mean you've been in several tunnels are like over these two days

are like the most interesting outtakes 40 points, but the same people talk about you show us what I think I think we really moving beyond the technology in a little bit. So what we're really focusing on now as an industry and has a broader ecosystem is really the really exciting business challenges that we can solve together. Right? I mean, I am always amazed at some of the amazing applications of Dow Jones data by what about Customs I gave you an example before the reinsurer using instruction use content from efectiva news content from a database to

use machine learning in artificial intelligence to better understand some of the risks that I might face is a reinsurer we've got In a training space, we've got clients that are executing trades using a newswise product. All right, and there's increasing sophistication in the models that they building those clients are able to bring in more diverse dated who made the news and correlating the news with many many other instructions phone to divert as well. So I just satellite imagery and other forms of alternative data compliance business right decision on who they want to do business

with on naughty business with based on their own internal rhyme with all the regulatory environment in which they operate the applications are absolutely right across the professional okay. Mmmm. It's a professional business. But what about for example social media? So is there something you're looking at right now, or is it something you you might consider in the future, social media perspective in the past. We have looked at me constantly evaluating the data sets that we bring into a product with that being used why I spect evil risk and compliance. So we're always on the

hunt as a date of business to identify new and value-added. It's for a customer. But we also recognize that customers have their own choices around idea that they making all the time first-party data that may exist in their environment or data that they made for Cutie independently of Dow Jones and what's really important for us is to fold do we have the right data sets available? We have products throughout these are apis or a user experiences a mobile apps excetera easy for customers to link their data to Aldo today

as well around the earth cleansing and scrubbing data and linking data. It's just hard work and it's not getting any easier. I want we are always on the hunt to do is make sure that we are the easiest way to sit to work with and we remove a lot of the pain points that we provide high-quality to our customers whether we provide a direct sales only make it easy to link outdated to the other day that says that's a real priority for us. Critical in terms of

financial decisions, but this has been very interesting educational and I hope you enjoy the show.

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