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.@SamHarrisOrg on threat of uncontrolled AI, chasm btwn insiders & public; how to avoid arms race & align w/interests-@jason-THX @blueapron

Today’s guest is Sam Harris, philosopher, neuroscientist and best-selling author of books including “Waking Up,” “The End of Faith,” “Letter to a Christian Nation,” and “The Moral Landscape.” Jason and Sam explore a wide range of topics, including the ethics of robots, the value of meditation, Trump’s lies, and his most recent obsession AI, which stemmed from an initial conversation with Elon Musk. Sam argues that the threat of uncontrolled AI is one of the most pressing issues of our time and poses the question: Can we build AI without losing control over it? The two then discuss why meditation is so important for entrepreneurs and business people. Sam has built his brand and fan base around radical honesty and authenticity, so the conversation naturally segues to Trump and his lies. This is only the first of two parts, so stay tuned for much more.

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This keynote introduces the first AI based on Unsupervised Learning. Automatically discover insights in your data, avoid human bias, and empower your team with the power to discover previously hidden insights that they can use to revolutionize your business.

When we train AI systems using human data, the result can be human biased. Attend the webinar to know more.

A must attend webinar for software test engineers who want to learn about AI and software testing.

Webinar Date: 25 Feb 2019, 11am Pacific Time
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URL: https://sqaweb.link/webinar678
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We would like to think that AI-based machine learning systems always produce the right answer within their problem domain. In reality, their performance is a direct result of the data used to train them. The answers in production are only as good as that training data.

Data collected by a human such as surveys, observations, or estimates, can have built-in human biases. Even objective measurements can be measuring the wrong things or can be missing essential information about the problem domain.

The effects of biased data can be even more deceptive. AI systems often function as black boxes, which means technologists are unaware of how an AI came to its conclusion.

This can make it particularly hard to identify any inequality, bias, or discrimination feeding into a particular decision.

This webinar will explain:

1.How AI systems can suffer from the same biases as human experts
2.How that could lead to biased results
3.Examine how testers, data scientists, and other stakeholders can develop test cases to recognise biases, both in data and the resulting system
4.Ways to address those biases

Attendees will gain a deeper understanding of:

1.How data influences
2.How machine learning systems make decisions
3.How selecting the wrong data, or ambiguous data, can bias machine learning results
4.Why we don’t have insight into how machine learning systems make decisions
5.How we can identify and correct bias in machine learning systems

Speaker: Peter Varhol, Software Strategist & Evangelist

Artificial intelligence is here and it is not the Terminator. Through an enlightening talk, Georgios Grigoriadis describes how technology has evolved in ways that have made Artificial Intelligence our reality in the present moment. Today’s data scientists and AI professionals, he notes, should consider themselves as doctors, as craftsmen of our future. The way AI has entered our lives and is present in every aspect of it raises serious concerns about the rise of inequality.
How can humanity be prepared to cope with the impact artificial intelligence will have in our lives?

Georgios Grigoriadis is the founder and Managing Director of Baresquare, a digital analytics company based in Greece. Georgios deep-dived into digital analytics while this rather youthful industry was still trying to self-define itself. His mission is to lead a strong, loyal team of analytics experts in a journey to stimulate business, social and individual growth through decisive answers fueled by data and statistics; a journey that goes beyond the limits of human computation, and with respect to the human well-being.

This talk was given at a TEDx event using the TED conference format but independently organized by a local community. Learn more at https://www.ted.com/tedx

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