2 Burst results for "Arbin Dr Ceo Co"

"arbin dr ceo co" Discussed on Eye On A.I.

Eye On A.I.

06:45 min | 2 months ago

"arbin dr ceo co" Discussed on Eye On A.I.

"Seth could you introduce yourself asher. So i am chief a officer for ibm means i'm responsible for developing ibm's strategy and execution against that strategy for mrs critical for our successes accompany because as many of you are probably aware are strategy focuses on two things hybrid cloud and ai. Our hiber cloud strategy is centered. Solidly around red hat. Open shift and multi cloud delivering capabilities on any cloud for our clients. We're gonna talk about trustworthy a i. It's something that is increasingly in the news and concerns a lot of people. Ibm has a product called fact sheets. Three sixty that i understand is going to be integrated into products. Can you tell us what fact sheets three sixty is. And then we'll get into the science behind. Yes so let me start by laying out what we see is the critical components Trustworthy a at a high level Three things there's a ethics there's govern dated ai and then there's an open and diverse ecosystem an ai ethics is fully aligned with with our ethical principles that we've published with arbin dr ceo co leading the initiative out of the world economic forum. And i'm adviser for essentially open sourcing our perspective on a ethics from a govern data in ai perspective. It falls into five buckets. So i is. Transparency second is explain ability third is robustness. Fourth is privacy and fifth is fairness and so the goal of fact sheets is to span multiple of these components and to provide a level of explain ability. That is needed to drive adoption and ultimately for regulatory compliance. And you think of it as a nutritional label for ai where nutritional labels are designed to help us as consumers of prepackaged foods to understand what are the nutritional components of him. What's healthy for us. What's not healthy for us. Factually is designed to provide a similar level capability for a. I'm curious about the name fact sheets. Where did that come from. This project came out of ibm research and it was one of these efforts that was putting in the open source community. So part of it is an open source package and it's really providing facts on ai. An ally in stock because it's a simple main beyond the label aspect. There are tools associated with it. Is that right for providing explain ability or adjusting bias mitigating bias. Absolutely so fact sheets is going to be integrated into a cloud. Pack for data which is our premier data. Ai offering and it's going to deliver information about the i from data arrests so from the data layer all the way through to as you mentioned capabilities that exist in watson studio and watson open scale around bias detection bias mitigation in a privacy preserving manner and it actually integrates with our grc system. Open pages with watson so that you can fully document that workflows are being executed against an end so that basically have an end to end explanation in fact sheets and then an end to end documentation and open pages with watson. Okay so let's break this down a little bit on the label on these sort of nutritional ablett's you describe it. There has been a lot of talk about data cards. I think they're being called. That would travel with a data set so that users of the data set could quickly see provenance. How the. The data was collected warnings. About how it could be misused and instructions for its intended use. Is that what you're talking about here or is it broader than that so this is broader than that within our offerings in cloud. Pack for data. We have watson knowledge. Ettelaat and watson knowledge. Catalog is essentially the brain of a data environment. That's where you can search for data catalog it. It applies policies within watson knowledge catalog. That's where you get all of those pieces of information about the data requirements that automatically mass individual elements that people should see based on their entitlements. It provides lineage and provenance what flaxseeds does on top of that as ties back detailed level of understanding to the islets being delivered and it allows you to develop policies so comes with specific set templates. But you can modify and customize you can see how your service was created out was tested. How was trained. I was deployed evaluated. What data was used. What regulations apply to it or company. Policies need to be accounted for and delivers those through a set of model fax and this allows organizations to automatically capture backs about the models the capture of the model faxes we call them will be defined in the backseat template. And it's across the entire life cycle today. You have to manually do that without fact sheets. And because the process is automated you get on mated reporting and you can automatically generate a set of fact sheets sharable. They're stored in a single location for all the information you need about the i and everything that's been done to it. So when you think about provenance lineage of data you also need provenance of the model so it also keeps track of different versions when you get into federated. Learning keeping track of where different components of learning came from fact sheets will do that. Can you describe how someone would use. This is this a platform that you import your model in data into or is it a tool that you run on the side of the accesses somaliland data so today. There's a fact sheets for sixty and that is a set of notebooks that you can call and use in two. Peter notebooks and python. And so you can do that today. What we're doing is the integration of these capabilities. In to our entire data enam portfolio that provides some of these value adds around policy creation and automated data capture and automated reporting by tying all of these capabilities together. So you'll have it out of the box across the entire life cycle. From as i said from data at rest all the way through to influencing and scoring even when we.

Ibm watson arbin dr ceo co watson studio asher Seth Ettelaat ablett ai federated Peter
Seth Dobrin Talks About Trustworthy AI

Eye On A.I.

01:41 min | 2 months ago

Seth Dobrin Talks About Trustworthy AI

"We're gonna talk about trustworthy a i. It's something that is increasingly in the news and concerns a lot of people. Ibm has a product called fact sheets. Three sixty that i understand is going to be integrated into products. Can you tell us what fact sheets three sixty is. And then we'll get into the science behind. Yes so let me start by laying out what we see is the critical components Trustworthy a at a high level Three things there's a ethics there's govern dated ai and then there's an open and diverse ecosystem an ai ethics is fully aligned with with our ethical principles that we've published with arbin dr ceo co leading the initiative out of the world economic forum. And i'm adviser for essentially open sourcing our perspective on a ethics from a govern data in ai perspective. It falls into five buckets. So i is. Transparency second is explain ability third is robustness. Fourth is privacy and fifth is fairness and so the goal of fact sheets is to span multiple of these components and to provide a level of explain ability. That is needed to drive adoption and ultimately for regulatory compliance. And you think of it as a nutritional label for ai where nutritional labels are designed to help us as consumers of prepackaged foods to understand what are the nutritional components of him. What's healthy for us. What's not healthy for us. Factually is designed to provide a similar level capability for a.

Arbin Dr Ceo Co IBM