John Hershey's guest appearances

John Hershey

The Modern Digital Enterprise (formerly Device Squad)

Insights of All Trades

Jordan Peterson

David Goggins

David Bray

Eric Carlson

Sean Bradley

Jordan Harris

Hayden Hidlay

Maribel Lopez

Jim Forrest

Russ Johnson

Krishnan Subramanian

Jeet Shome

Roman Dumiak

Michael Golub

James Hoshor

OwlTail

John Hershey is the State Representative of Pennsylvania’s 82nd Legislative District. In the current legislative session, John serves as a member of the Agricultural and Rural Affairs, Children and Youth, Health, Human Services, and Judiciary committees. In this episode, Nick and Cole talk to John about his unique path leading him to the State Representative role, the legislative impacts he had thus far, and staying informed without looking through the lens of a single political agenda.Thank you to Collin Hadsell for the music.Thank you to Hunter Wert for the audio and video.Follow us on Instagram: @insightsofalltradesTwitter: @ioatpodcastFacebook: @insightsofalltradesEmail: insightsofalltrades@gmail.com**Disclaimer: All opinions and viewpoints expressed by guests on Insights of All Trades belong solely to those guests being interviewed, and they do not necessarily reflect the opinions and viewpoints of Nick and Cole.

John Hershey is the State Representative of Pennsylvania’s 82nd Legislative District. In the current legislative session, John serves as a member of the Agricultural and Rural Affairs, Children and Youth, Health, Human Services, and Judiciary committees. In this episode, Nick and Cole talk to John about his unique path leading him to the State Representative role, the legislative impacts he had thus far, and staying informed without looking through the lens of a single political agenda. Thank you to Collin Hadsell for the music. Thank you to Hunter Wert for the audio and video. Follow us on Instagram: @insightsofalltrades Twitter: @ioatpodcast Facebook: @insightsofalltrades Email: insightsofalltrades@gmail.com **Disclaimer: All opinions and viewpoints expressed by guests on Insights of All Trades belong solely to those guests being interviewed, and they do not necessarily reflect the opinions and viewpoints of Nick and Cole.

Episode #07: PA State Rep. John Hershey - The Path To and Roles Within State Government

In today's Device Squad, the digital transformation podcast from Anexinet, Steve and Glenn talk with John Hershey, Machine Learning Researcher at Google about the state of Facial Recognition (aka Face Surveillance) Technology, about AI bias, and about San Francisco's recent "ban" on the technology for government use. Is Facial Recognition a valuable public-safety tool or is it an infringement of our civil liberties? Also, name our new Podcast &amp; win a prize! This episode also answers the following questions: What does it mean when San Francisco— one of the most tech-friendly and tech-savvy cities in the world—is suddenly the first state to prohibit its government from using facial-recognition technology?? Why the ban? Why now? What's wrong with government agencies using Facial Recognition? Is it really invasive? Or is it more about it not being perfect? Where are we at with facial recognition? What would be the source for their data? How are they recognizing these faces, anyway? Is this more about concern over the possibility of errors? Wrongful imprisonment? Are they simply worried about being sued? Or do they really care about our right to privacy? What part does AI bias play into this? If we recognize that bias exists, what steps are being taken to eliminate the bias? Is there a way to estimate or predict bias? Is the ban worth it? Do we need more like it? What about other forms of recognition? Voice recognition, airport scanning, etc. Links in the episode: STUDY: Facial feature discovery for ethnicity recognition https://onlinelibrary.wiley.com/doi/full/10.1002/widm.1278 San Francisco just banned facial-recognition technology  https://www.cnn.com/2019/05/14/tech/san-francisco-facial-recognition-ban/index.html SF Ban on Face Recognition - Acquisition of Surveillance Technology  https://sfgov.legistar.com/View.ashx?M=F&amp;ID=7206781&amp;GUID=38D37061-4D87-4A94-9AB3-CB113656159A Facial recognition data collected by U.S. customs agency stolen by hackers  https://www.salon.com/2019/06/11/facial-recognition-data-collected-by-u-s-customs-agency-stolen-by-hackers/ Facial Recognition Software Wrongly Identifies 28 Lawmakers As Crime Suspects  https://www.npr.org/2018/07/26/632724239/facial-recognition-software-wrongly-identifies-28-lawmakers-as-crime-suspects?utm_source=facebook.com&amp;utm_medium=social&amp;utm_campaign=politics&amp;utm_term=nprnews&amp;utm_content=20180727&amp;fbclid=IwAR1x74a-cBXMrZm_79UJHmHjxhUos4XunsivYU__ekdbsTgUPRU7bx54NuM Does object recognition work for everyone? A new method to assess bias in CV systems  https://ai.facebook.com/blog/new-way-to-assess-ai-bias-in-object-recognition-systems/ Don't smile for surveillance: Why airport face scans are a privacy trap  https://www.washingtonpost.com/technology/2019/06/10/your-face-is-now-your-boarding-pass-thats-problem/ U.S. Customs and Border Protection says photos of travelers were taken in a data breach  https://www.washingtonpost.com/technology/2019/06/10/us-customs-border-protection-says-photos-travelers-into-out-country-were-recently-taken-data-breach/?utm_term=.cb047794d0a2 Chickens Prefer Attractive People  https://news.nationalgeographic.com/2018/01/animals-chickens-evolution-eggs-food/

In today's Device Squad, the digital transformation podcast from Anexinet, Steve and Glenn talk with John Hershey, Machine Learning Researcher at Google about the state of Facial Recognition (aka Face Surveillance) Technology, about AI bias, and about San Francisco's recent "ban" on the technology for government use. Is Facial Recognition a valuable public-safety tool or is it an infringement of our civil liberties? Also, name our new Podcast &amp; win a prize! This episode also answers the following questions: What does it mean when San Francisco— one of the most tech-friendly and tech-savvy cities in the world—is suddenly the first state to prohibit its government from using facial-recognition technology?? Why the ban? Why now? What's wrong with government agencies using Facial Recognition? Is it really invasive? Or is it more about it not being perfect? Where are we at with facial recognition? What would be the source for their data? How are they recognizing these faces, anyway? Is this more about concern over the possibility of errors? Wrongful imprisonment? Are they simply worried about being sued? Or do they really care about our right to privacy? What part does AI bias play into this? If we recognize that bias exists, what steps are being taken to eliminate the bias? Is there a way to estimate or predict bias? Is the ban worth it? Do we need more like it? What about other forms of recognition? Voice recognition, airport scanning, etc. Links in the episode: STUDY: Facial feature discovery for ethnicity recognition <a href="https://onlinelibrary.wiley.com/doi/full/10.1002/widm.1278" rel="nofollow" target="_blank">https://onlinelibrary.wiley.com/doi/full/10.1002/widm.1278</a> San Francisco just banned facial-recognition technology <a href="https://www.cnn.com/2019/05/14/tech/san-francisco-facial-recognition-ban/index.html" rel="nofollow" target="_blank"> https://www.cnn.com/2019/05/14/tech/san-francisco-facial-recognition-ban/index.html</a> SF Ban on Face Recognition - Acquisition of Surveillance Technology <a href="https://sfgov.legistar.com/View.ashx?M=F&amp;ID=7206781&amp;GUID=38D37061-4D87-4A94-9AB3-CB113656159A" rel="nofollow" target="_blank"> https://sfgov.legistar.com/View.ashx?M=F&amp;ID=7206781&amp;GUID=38D37061-4D87-4A94-9AB3-CB113656159A</a> Facial recognition data collected by U.S. customs agency stolen by hackers <a href="https://www.salon.com/2019/06/11/facial-recognition-data-collected-by-u-s-customs-agency-stolen-by-hackers/" rel="nofollow" target="_blank"> https://www.salon.com/2019/06/11/facial-recognition-data-collected-by-u-s-customs-agency-stolen-by-hackers/</a> Facial Recognition Software Wrongly Identifies 28 Lawmakers As Crime Suspects <a href="https://www.npr.org/2018/07/26/632724239/facial-recognition-software-wrongly-identifies-28-lawmakers-as-crime-suspects?utm_source=facebook.com&amp;utm_medium=social&amp;utm_campaign=politics&amp;utm_term=nprnews&amp;utm_content=20180727&amp;fbclid=IwAR1x74a-cBXMrZm_79UJHmHjxhUos4XunsivYU__ekdbsTgUPRU7bx54NuM" rel="nofollow" target="_blank"> https://www.npr.org/2018/07/26/632724239/facial-recognition-software-wrongly-identifies-28-lawmakers-as-crime-suspects?utm_source=facebook.com&amp;utm_medium=social&amp;utm_campaign=politics&amp;utm_term=nprnews&amp;utm_content=20180727&amp;fbclid=IwAR1x74a-cBXMrZm_79UJHmHjxhUos4XunsivYU__ekdbsTgUPRU7bx54NuM</a> Does object recognition work for everyone? A new method to assess bias in CV systems <a href="https://ai.facebook.com/blog/new-way-to-assess-ai-bias-in-object-recognition-systems/" rel="nofollow" target="_blank"> https://ai.facebook.com/blog/new-way-to-assess-ai-bias-in-object-recognition-systems/</a> Don't smile for surveillance: Why airport face scans are a privacy trap <a href="https://www.washingtonpost.com/technology/2019/06/10/your-face-is-now-your-boarding-pass-thats-problem/" rel="nofollow" target="_blank"> https://www.washingtonpost.com/technology/2019/06/10/your-face-is-now-your-boarding-pass-thats-problem/</a> U.S. Customs and Border Protection says photos of travelers were taken in a data breach <a href="https://www.washingtonpost.com/technology/2019/06/10/us-customs-border-protection-says-photos-travelers-into-out-country-were-recently-taken-data-breach/?utm_term=.cb047794d0a2" rel="nofollow" target="_blank"> https://www.washingtonpost.com/technology/2019/06/10/us-customs-border-protection-says-photos-travelers-into-out-country-were-recently-taken-data-breach/?utm_term=.cb047794d0a2</a> Chickens Prefer Attractive People <a href="https://news.nationalgeographic.com/2018/01/animals-chickens-evolution-eggs-food/" rel="nofollow" target="_blank"> https://news.nationalgeographic.com/2018/01/animals-chickens-evolution-eggs-food/</a>

Facial Recognition with John Hershey, Machine Learning Researcher

In this episode of Device Squad, the podcast for the Mobile Enterprise from Propelics, Steve gets futuristic with MERL Senior Principal Research Scientist, John Hershey. The conversation centers around the current state of Neural Networks and artificial intelligence as John brings us the news from the recent NIPS (Neural Information Processing) conference in Barcelona.  We discuss voice recognition and replication strategies and what role they'll play in our everyday lives—along with John's current project, deep learning for signal separation, speech recognition, language processing, and multi-modal semantic representation learning. In other words, John has solved the problem of isolating a single voice in a crowd, a process known as Deep Clustering. Specifically, we discuss: the Universe Project - a software platform for measuring and training an AI's general intelligence across the world's supply of games, websites and other applications. WaveNet - a deep generative model of raw audio waveforms - able to generate speech which mimics human voice and sounds more natural than the best text-to-speech systems. Google's DeepDreams - DeepDream is a computer vision program created by Google which uses a convolutional neural network to find and enhance patterns in images via algorithmic pareidolia, thus creating a dreamlike hallucinogenic appearance in the deliberately over-processed images. MERL Deep Clustering - Training deep discriminative embeddings to solve the cocktail party problem.The human auditory system gives us the extraordinary ability to converse in the midst of a noisy throng of party goers. Solving this so-called cocktail party problem has proven extremely challenging for computers, and separating and recognizing speech in such conditions has been the holy grail of speech processing for more than 50 years. Deep clustering is a recently introduced deep learning architecture that uses discriminatively trained embeddings as the basis for clustering, producing unprecedented speaker-independent single-channel separation performance on two-speaker and three-speaker mixtures. John also predicts when our robot overlords will finally take over, and whether or not the revolution will take the form of an army of seemingly benevolent toys. Also, how long it will be before Alexa (and other) voice-controlled devices begin targeting content based on our emotional states.  Lastly, our two heroes engage in an exciting game of BLIP. Tune in and find out what this 1970's TOMY game has to do with with artificial intelligence and analog processing!  It's a long episode but a great one so be sure to tune in! Oh, and by the way, Mind Flex is a scam.

In this episode of Device Squad, the podcast for the Mobile Enterprise from Propelics, Steve gets futuristic with MERL Senior Principal Research Scientist, John Hershey. The conversation centers around the current state of Neural Networks and artificial intelligence as John brings us the news from the recent NIPS (Neural Information Processing) conference in Barcelona.  We discuss voice recognition and replication strategies and what role they'll play in our everyday lives—along with John's current project, deep learning for signal separation, speech recognition, language processing, and multi-modal semantic representation learning. In other words, John has solved the problem of isolating a single voice in a crowd, a process known as Deep Clustering. Specifically, we discuss: <a href="https://openai.com/blog/universe/" rel="nofollow" target="_blank">the Universe Project</a> - a software platform for measuring and training an AI's general intelligence across the world's supply of games, websites and other applications. <a href="https://deepmind.com/blog/wavenet-generative-model-raw-audio/" rel="nofollow" target="_blank">WaveNet</a> - a deep generative model of raw audio waveforms - able to generate speech which mimics human voice and sounds more natural than the best text-to-speech systems. <a href="https://deepdreamgenerator.com/" rel="nofollow" target="_blank">Google's DeepDreams</a> - DeepDream is a computer vision program created by Google which uses a convolutional neural network to find and enhance patterns in images via algorithmic pareidolia, thus creating a dreamlike hallucinogenic appearance in the deliberately over-processed images. <a href="http://www.merl.com/demos/deep-clustering" rel="nofollow" target="_blank">MERL Deep Clustering</a> - Training deep discriminative embeddings to solve the cocktail party problem.The human auditory system gives us the extraordinary ability to converse in the midst of a noisy throng of party goers. Solving this so-called cocktail party problem has proven extremely challenging for computers, and separating and recognizing speech in such conditions has been the holy grail of speech processing for more than 50 years. Deep clustering is a recently introduced deep learning architecture that uses discriminatively trained embeddings as the basis for clustering, producing unprecedented speaker-independent single-channel separation performance on two-speaker and three-speaker mixtures. John also predicts when our robot overlords will finally take over, and whether or not the revolution will take the form of an army of seemingly benevolent toys. Also, how long it will be before Alexa (and other) voice-controlled devices begin targeting content based on our emotional states.  Lastly, our two heroes engage in an exciting game of BLIP. Tune in and find out what this 1970's TOMY game has to do with with artificial intelligence and analog processing!  It's a long episode but a great one so be sure to tune in! Oh, and by the way, <a href="https://www.youtube.com/watch?v=URTrfPgJWAU" rel="nofollow" target="_blank">Mind Flex</a> is a scam.