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Peter Norvig is a research director at Google and the co-author with Stuart Russell of the book Artificial Intelligence: A Modern Approach that educated and inspired a whole generation of researchers including myself to get into the field. This conversation is part of the Artificial Intelligence podcast.
Professor Jim Hendler is an artificial intelligence researcher at RPI. He has authored over 200 technical papers in the areas of Semantic Web, artificial intelligence, agent-based computing and high performance processing.
His latest book, "Social Machines: The Coming Collision of Artificial Intelligence, Social Networking, and Humanity" can be found on Amazon.
There's many things happening in the 21st century that'll lead to exploding inequality levels.
The kind of inequality not seen for centuries.
Although technology will mean that poor people will still live a much better quality of life, it will cause many problems within our society.
Take a look inside Alibaba’s pilot FashionAI concept store. Learn more about Alibaba & Guess partnership at Alizila article: https://alizi.la/2IVu9Ll
In Avengers: Age of Ultron, the villain (Ultron) starts out as an artificial intelligence experiment gone wrong. Is this just Hollywood storytelling, or should we be worried about a future dictated by robot overlords? To help answer this question, we’ve teamed up with the awesome Rusty Ward over at Science Friction!
Presented by Peter Skomoroch, Co-Founder and CEO, SkipFlag (acquired by Workday)
Has the singularity already happened?
Are you ready to enslave yourself and merge with the AI via neurological implants or are you going to live at the people zoo with the other animals?
Meet Emma - World First Talking Artificial Intelligent Humanoid Female Robot By Bride Robot Tech.
Doris becomes excited by the possibilities when she inadvertently injects herself with the wrong serum and becomes super-smart.
With the increasing popularity of AI, new frontiers are emerging in predictive maintenance and manufacturing decision science. However, there are many complexities associated with modeling plant assets, training predictive models for them, and deploying these models at scale for near real-time decision support. This talk will discuss these complexities in the context of building an example system.
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Talk 1: Uber’s Big Data Platform: 100+ Petabytes with Minute Latency
This talk will reflect on the challenges faced with scaling Uber’s Big Data Platform to ingest, store, and serve 100+ PB of data with minute level latency while efficiently utilizing our hardware. We will provide a behind-the-scenes look at the current data technology landscape at Uber, including various open-source technologies (e.g. Hadoop, Spark, Hive, Presto, Kafka, Avro) as well as open-sourced in-house-built solutions such as Hudi, Marmaray, etc. We'll dive into the technical aspects of how our ingestion platform was re-architected to bring in 10+ trillion events/day, with 100+ TB new data/day, at minute-level latency, how our storage platform was scaled to reliably store 100+ PB of data in the data lake, and our processing platform was designed to efficiently serve millions of queries and jobs/day while processing 1+ PB per day. You’ll leave the talk with greater insight into how data truly powers each and every Uber experience and will be inspired to re-envision your own data platform to be more extensible and scalable.
How do we build a future that doesn’t leave humans behind? Erik Brynjolfsson, co-author of Machine, Platform, Crowd argues that ‘deep learning’ technologies can do much more than routine work. So how do we harness technological progress to benefit the many, not just the few?
Presented at the Matroid Scaled Machine Learning Conference 2018
Ο Πάνος Τραχανιάς είναι καθηγητής στο Τμήμα Επιστήμης Υπολογιστών του Πανεπιστημίου Κρήτης και στο Ίδρυμα Τεχνολογίας και Έρευνας (ΙΤΕ/FORTH).Είναι επικεφαλής στο Εργαστήριο Υπολογιστικής Όρασης και Ρομποτικής του ΙΤΕ, όπου επιβλέπει και ασχολείται ενεργά με την έρευνα και με χρηματοδοτούμενα έργα πάνω σε γνωσιακά συστήματα και ευφυή αυτόνομα ρομπότ. Στην ομιλία του μας μιλάει για το πως τα έξυπνα ρομπότ και η τεχνητή νοημοσύνη έχουν την δυνατότητα να ενισχύσουν την δημιουργικότητα και τις γνωστικές μας ικανότητες.
This video was made possible by Brilliant. Be one of the first 200 people to sign up with this link and get 20% off your premium subscription with Brilliant.org! https://brilliant.org/singularity
This is a conversation with Max Tegmark as part of MIT 6.S099: Artificial General Intelligence. He is a Physics Professor at MIT, co-founder of the Future of Life Institute, and author of "Life 3.0: Being Human in the Age of Artificial Intelligence." This class is free and open to everyone. Our goal is to take an engineering approach to exploring possible paths toward building human-level intelligence for a better world.
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Julian Friedland, the tech philosopher, warns us about the potential for AI to reduce human agency in the modern world. He offers a number of solutions on how we can maintain ethical lifestyle choices in a world dominated by algorithms. Julian Friedland is Assistant Professor of Ethics in Trinity Business School, Trinity College Dublin.
So we've talked a lot in this series about how computers fetch and display data, but how do they make decisions on this data? From spam filters and self-driving cars, to cutting edge medical diagnosis and real-time language translation, there has been an increasing need for our computers to learn from data and apply that knowledge to make predictions and decisions. This is the heart of machine learning which sits inside the more ambitious goal of artificial intelligence. We may be a long way from self-aware computers that think just like us, but with advancements in deep learning and artificial neural networks our computers are becoming more powerful than ever.
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