Artificial Intelligence and Machine Learning (PHLAI)

October 28th, 2020
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 October 28th, 2020 - October 30th, 2020
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Artificial Intelligence and Machine Learning (PHLAI)

Nope, we are NOT letting Coronavirus get in the way of our learning and ability to better ourselves with our career and personal ambitions. Let's not let this negative overwhelm us. With this "downtime" let's maximize the time and upskill and learn how to be better at our InfoSec professions. To help, we've put together a bunch of online webinars, conferences and seminars that you can attend to do exactly that.

Stay Safe, Stay Strong and we'll see you on the other side. Your friends at InfoSec Conferences X

Event submitted on Thursday, January 30th 2020, approved by Charles Villanueva

151 Days Until The Event
October 28th, 2020 - October 30th, 2020
United States  »  Northeast, USA  »  Pennsylvania » 
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 Our Review

Many fear that Skynet is coming! If you believe so then this conference which has a major ML/AI lean is for you. Cybersecurity has a massive part to play in the future of machine learning. Bots, scrapers etc all work via automation and persistency and we can consider those apps as being pretty low-level, so imagine what robots being made by Boston Dynamics can achieve with nefarious intent. Anyways, if you live and work in Philadelphia within an Artificial Intelligence capacity then this conference looks amazing. Let us know how it goes if you attend since we’d love to hear more about it. Good luck to the organizers!

- Review written by Henry Dalziel on Thursday, January 30th 2020.
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Event Summary

The following description was either submitted by the Conference Organizer on Thursday, January 30th 2020, or created by us.

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People behind Comcast Labs are excited to announce their fourth annual PHLAI conference which will be held in Philadelphia’s Comcast Technology Center. This focus for this year is “Responsible and Practical Artificial Intelligence.”

Artificial intelligence is creating new opportunities to make a positive impact on the lives of people around the globe, from healthcare to business to education. At the same time, to fulfill its positive potential, and to answer questions about the fairness, interpretability, privacy, and security of these systems, as well as how to make machine learning work in the wild by translating our theoretical advances into practical solutions that everyone can benefit from.

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