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GOTO 2019 • Breaking Black-box AI • Evelina Gabasova
#GOTOcon #GOTOberhttp://gotober.comEvelina Gabasova - Machine Learning Expert & Data Scientist at The Alan Turing InstituteABSTRACTMachine learning and artificial intelligence are becoming wide-spread and productionalized - you no longer need a mathematics PhD and months of software development time to implement and use a machine learning algorithm.
But beware - all the algorithms have some cases when they fail to deliver what you're expecting.This talk is packed with live demos that show failure cases of popular algorithms, from linear regression to cutting-edge deep learning.
Google tackles the black box problem with Explainable AI
But at an event in London this week, Google's cloud computing division pitched a new facility that it hopes will give it the edge on Microsoft and Amazon, which dominate the sector.
But in time the firm intends to offer a wider set of insights to help make the 'thinking' of AI algorithms less mysterious and therefore more trustworthy.
'Google is definitely the underdog behind Amazon Web Services and Microsoft Azure in terms of the cloud platform space, but for AI workloads I wouldn't say that's the case - particularly for retail clients,' commented Philip Carter from the consultants IDC.
And in many of the large systems we built for our smartphones or for our search-ranking systems, or question-answering systems, we've internally worked hard to understand what's going on.
The main question is to do these things called counterfactuals, where the neural network asks itself, for example, 'Suppose I hadn't been able to look at the shirt colour of the person walking into the store, would that have changed my estimate of how quickly they were walking?' By doing many counterfactuals, it gradually builds up a picture of what it is and isn't paying attention to when it's making a prediction.
And it does sometimes come across that we are reluctant to just release something and hope that it works because we subject everything to a battery of tests to make sure they are working in a way that's desirable.
And myself, and many other folks throughout the industries understand that we technology providers do have an obligation to help protect countries and societies, as well as producing consumer products as well.
couple of weeks ago, our chief legal counsel, Kent Walker, made some comments about Google's great desire to help out in aspects of national security which will make people safer.
But for example, Google is actively helping out with a question of 'deepfake' detection, which is this new fear that artificially constructed videos or images might become so realistic that they actually cause societal problems.
Do you agree with the view of others, including Microsoft president Brad Smith, that while it's worth listening to workers' concerns you also sometimes need to push back against employee activism?
Dealing With Bias in Artificial Intelligence
You could mean bias in the sense of racial bias, gender bias.
Another notion of bias, one that is highly relevant to my work, are cases in which an algorithm is latching onto something that is meaningless and could potentially give you very poor results.
And so, you could actually learn to predict fractures pretty well on the data set that you were given simply by recognizing which hospital did the scan, without actually ever looking at the bone.
So, if your machine-learning algorithm is one that is trained on the data from a given set of hospitals, and you will only use it in those same set of hospitals, then latching onto which hospital did the scan could well be a reasonable approach.
Black in AI (BAI)
The 3rd Black in AI workshop will be co-located with NeurIPS 2019 at the Vancouver Convention Center, Vancouver Canada on December 9th from 7:00 am to 8:00 pm PST.
There will also be a reception followed by the annual BAI Dinner to facilitate networking, discussion of different career opportunities in AI, and sharing of ideas to increase participation of Black researchers in the field.
- On 6. maj 2021
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