PANEL - Uber Yourself Before You Get Kodaked: the conversation with leading AI innovators
Channel: Beryl Elites
Date: 2019-01-22

BERYL KNOWLEDGE CENTER

Uber Yourself Before You Get Kodaked: the conversation with leading innovators and thought leaders in artificial intelligence

Panelists ❖ David Teten – HOF Capital  ❖ Neil Sahota - IBM Watson Group  ❖ Sourav Mazumder – IBM Cloud Labs ❖ Jack Kokko - AlphaSense ❖ Zeeshan Ali - Beyond Limits

- Sourav Mazumder
"I work with many customers and many of them are financial. We typically help them, starting from data acquisition to cleansing to transformation feature engineering model, validating whether the model is good or not. There are many new AI oriented technologies that are coming up which can help in data cleansing. Data quality is still a golden rule for AI, without it things will not be good."

- Neil Sahota
"It is really important to understand what is actually meaningful. The way how machines learn can be very different than how people learn.  If you want to create an AI to create original food recipes how do you teach machine how to cook? Give them cooking videos, try cooking something, and taste it? That is not going to work for a machine. But we can teach a machine chemistry. The machine knows chemistry, understands the chemical combination, taste, aromas, the whole nine yards. So that it can create new ingredients that we never thought of before. So when it comes to data, data acquisition and cleaning, you have to understand first and foremost if data is meaningful, how you are going to do training. That is the secret sauce because it is all about the training."

- Jack Kokko
"The data that we ingest is all the qualitative research information that fundamental analysts use every day in their work from company filings, conference call transcripts, broker research news, trade journals. It is really important that you can slice and dice these information in many different ways. Public companies (there tens of thousands) are easy. They are very well organized and tagged in all the data sources that buy-side professionals use every day. But private companies are not and there are millions of them. So we take public companies information and allow AI models to learn industry language from the vast volumes of public company content and apply that to private company information. We also allow those same kinds of algorithms to learn language models for millions of private companies so those get automatically tagged with all this information."

- Zeeshan Ali
"We take a slightly different approach that data is one element to informing our kind of AI systems but we can also operate in an environment where there is less data available. We are just starting our efforts in financial services but we have few years of legacy in oil and gas. For example, one of our clients had a number of issues with offshore wells  and if you know that environment it is very corrosive and sanding becomes quite a big issue. When we went to them to help solve this issue around predictive analysis to make decisions and inferences based on their data to help alleviate or predict some of the problems or challenges they may face, they came back to us not with gigabytes or terabytes but with literally 500 megabytes and that their methodology to solve this issue. We were able to take that simple amount of data which consists of just PDF files and email exchanges. We codified a human expert knowledge set around that data and inform their system to create predictive decision."

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