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Artificial Intelligence
Thought Leaders in Artificial Intelligence: Peter Brodsky, CEO of Hyperscience (Part 1)
AI-enabled automation is making good headway. This discussion looks at some use cases.
Sramana Mitra: Let’s start by introducing our audience to yourself as well as to Hyperscience.
>>>Thought Leaders in Artificial Intelligence: Grapevine6 CEO Mike Orr (Part 4)
Sramana Mitra: On the B2B side, it’s very difficult to turn these people to marketing because what you are describing are very good influencer strategies. This is something that I have done effectively for many years. It’s not something that comes naturally to people – sharing content.
First, you have to have your antennas out there to know what’s out there and what you want to share with people. The fact that you are automating and creating a structure around that is very interesting and can be quite effective.
>>>Thought Leaders in Artificial Intelligence: Grapevine6 CEO Mike Orr (Part 3)
Sramana Mitra: Flipping to the use case on the financial services side where you are bringing in the risk, give me an example of what kind of a risk situation you can mitigate or point out. What is the AI’s role in that?
Mike Orr: It gets a lot more involved. It filters out the noise. It’s looking at where all the false positives or innocuous comments and statements are made on social media.
>>>Thought Leaders in Artificial Intelligence: Grapevine6 CEO Mike Orr (Part 2)
Sramana Mitra: What kind of impacts are measured with this kind of engagement?
Mike Orr: We measure the direct engagement on the content itself both at the individual and organizational level. SAP is an example of that. We measure how many people are reading the articles that they are posting and determine what type of content or topics are the most engaging.
>>>Thought Leaders in Artificial Intelligence: Grapevine6 CEO Mike Orr (Part 1)
This conversation delves into the whitespaces in Content Marketing that AI enables.
Sramana Mitra: Let’s start by introducing our audience to yourself as well as to Grapevine6.
>>>Thought Leaders in Artificial Intelligence: Stuart Nisbet, Chief Data Scientist, Cadient Talent (Part 6)
Sramana Mitra: Do you have pointers to open problems and white spaces in this domain?
Stuart Nisbet: We started this conversation on what types of data we have as inputs. We find that a great deal of the data or information that is used in the hiring process does not come through the application or interview process. It comes through more open technologies.
>>>Thought Leaders in Artificial Intelligence: Stuart Nisbet, Chief Data Scientist, Cadient Talent (Part 5)
Sramana Mitra: I’m listening and thinking about the zip code example that you took me through just a few minutes ago. There is going to be bias if you apply that. It’s a lot better to have a job close to your home.
There will be a certain amount of advantage that will accrue to people who live close to those retail stores and perhaps there are people who couldn’t afford to live close. Wouldn’t your algorithm be biased against those people?
>>>Thought Leaders in Artificial Intelligence: Stuart Nisbet, Chief Data Scientist, Cadient Talent (Part 4)
Sramana Mitra: What else is interesting in your technology?
Stuart Nisbet: If you are researching AI in general, I think this will go well beyond what we’ve talked about today. The trust and explainability of what an AI algorithm does is a trend in the industry and is one of the things that I address the most. It’s quite interesting because it’s one of the strengths of the technology, but it is also regarded as one of the weakest points of the technology.
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