Podcasts about discriminator

Are LLMs stochastic parrots or reflection of our own intelligence? In this episode of Navigating Major Programmes, Riccardo Cosentino sits down with Lawrence Rowland for an extremely candid conversation surrounding the adoption of artificial intelligence, in major programmes and beyond. AI skeptics and AI enthusiasts alike, this episode was recorded for you. “None of us are keeping up, none of us know what the hell is going on. So, if you can kind of just relax and enjoy it happening, you will also help everyone else so much more. Enjoy it. And enjoy what [AI] is telling us about us.”  –Lawrence Rowland Lawrence began as an engineer on large capital projects with WSP and Motts, before moving onto Bechtel and Booz Allen. He spent ten years in project and portfolio management with CPC and Pcubed, before transitioning to data analytics and AI for projects, working originally for Projecting Success, and now for React AI. He now helps project services firms find relevant immediate AI applications for their business.  Key Takeaways:Large Language Model (LLM) 101What is an AI agent? What is the principal-agent problem (PAP)?What LLMs can teach you about your own thinking patternsThe future of Google Gemini and AI adoption in generalThe weaknesses of the generative AI of today Mentioned Links:A Path Towards Autonomous Machine IntelligencePrincipal Agent ProblemApplied Category TheoryWisdom of CrowdsState Space Models and MambaDemis Hassabis and the return of alpha zero type tree search and RL If you enjoyed this episode, make sure and give us a five star rating and leave us a review on iTunes, Podcast Addict, Podchaser or Castbox. The conversation doesn't stop here—connect and converse with our LinkedIn community: Follow Navigating Major Programmes on LinkedInFollow Riccardo Cosentino on LinkedInRead Riccardo's latest at wwww.riccardocosentino.com      Music: "A New Tomorrow" by Chordial Music. Licensed through PremiumBeat.DISCLAIMER: The opinions, beliefs, and viewpoints expressed by the hosts and guests on this podcast do not necessarily represent or reflect the official policy, opinions, beliefs, and viewpoints of Disenyo.co LLC and its employees.

In this episode of the podcast, we dive into the world of Generative Adversarial Networks (GANs), a cutting-edge AI technology that's changing the landscape of creative industries. From understanding the basics of how GANs work, to exploring their real-world applications and ethical considerations, this episode provides a comprehensive overview of this exciting field. Whether you're an artist, a programmer, or simply curious about the potential of GANs, you won't want to miss this engaging and informative episode.Support the Show.Keep AI insights flowing – become a supporter of the show!Click the link for details

I read from discriminate to discuss.     I don't understand the discriminator. https://en.wikipedia.org/wiki/Discriminator     The weight of the discus depends on the age and sex/gender (that's a whole other conversation) but they range from 0.75 to 2 kg (1.65 to 4.41 pounds). The longest throw is by a woman, Martina Hellmann, in 1988 who unofficially threw it 78.14 meters (over 256 feet) but because she was a woman she was using a lighter discus; 1 kg opposed to 2 kg. That's a huge difference in weight.  The longest throw for a man is 74.08 meters (over 243 feet) by Jürgen Schult in 1986.  https://en.wikipedia.org/wiki/Discus_throw     Please check out the various discussion topics Linda Richman would suggest in Saturday Night Live's "Coffee Talk" starring Mike Myers.  https://en.wikipedia.org/wiki/Coffee_Talk_(Saturday_Night_Live)     The word of the episode is "discrimination". https://en.wikipedia.org/wiki/Discrimination     Theme music from Jonah Kraut https://jonahkraut.bandcamp.com/     Merchandising! https://www.teepublic.com/user/spejampar     "The Dictionary - Letter A" on YouTube   "The Dictionary - Letter B" on YouTube   "The Dictionary - Letter C" on YouTube   "The Dictionary - Letter D" on YouTube     Featured in a Top 10 Dictionary Podcasts list! https://blog.feedspot.com/dictionary_podcasts/     Backwards Talking on YouTube: https://www.youtube.com/playlist?list=PLmIujMwEDbgZUexyR90jaTEEVmAYcCzuq     dictionarypod@gmail.com https://www.facebook.com/thedictionarypod/ https://twitter.com/dictionarypod https://www.instagram.com/dictionarypod/ https://www.patreon.com/spejampar https://www.tiktok.com/@spejampar 917-727-5757

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is Babble and Prune, Part 2: More Babble, published by alkjash. This is a linkpost for/ In my last babble, I introduced the Babble and Prune model of thought generation: Babble with a weak heuristic to generate many more possibilities than necessary, Prune with a strong heuristic to find a best, or the satisfactory one. I want to zoom in on this model. If the last babble was colored by my biases as a probabilist, this one is motivated by my biases as a graph theorist. First, I will speculate on the exact mechanism of Babble, and also highlight the fact Babble and Prune are independent systems that can be mocked out for unit testing. Second, I will lather on some metaphors about the adversarial nature of Babble and Prune. Two people have independently mentioned Generative Adversarial Networks to me, a model of unsupervised learning involving two neural nets, Generator and Discriminator. The Artist and the Critic are archetypes of the same flavor - I have argued in the past the spirit of the Critic is Satan. Babble is (Sampling From) PageRank Previously, I suggested that a Babble generator is a pseudorandom word generator, weighted with a weak, local filter. This is roughly true, but spectacular fails one of the technical goals of a pseudorandom generator: independence. In particular, the next word you Babble is frequently a variation (phonetically or semantically) of the previous one. PageRank, as far as I know, ranks web pages by the heuristic of "what is the probability of ending up at this page after a random walk with random restarts." That's why a better analogy for Babble is sampling from PageRank i.e. taking a weighted random walk in your Babble graph with random restarts. Jackson Pollock is visual Babble. Imagine you're playing a game of Scrabble, and you have the seven letters JRKAXN. What does your algorithm feel like? You scan the board and see an open M. You start Babbling letter combinations that might start with M: MAJR, MRAJ, MRAN, MARN, MARX (oops, proper noun), MARK (great!). That's the weighted random walk. You set MARK aside and look for another place to start. Time for a restart. You find an open A before a Triple Word, that'd be great to get! You start Babbling combinations that end with A: NARA, NAXRA, JARA, JAKA, RAKA. No luck. Maybe the A should be in the middle of the word! ARAN, AKAN, AKAR, AJAR (great!). You sense mean stares for taking so long, so you turn off the Babble and score AJAR for (1+8+1+1)x3 = 33 points. Not too shabby. The Babble Graph Last time, I described getting better at Babble as increasing the uniformity of your pseudorandom Babble generator. With a higher-resolution model of Babble in hand, we should reconceptualize increasing uniformity as building a well-connected Babble graph. What is the Babble graph? It's the graph within which your words and concepts are connected. Some of these connections are by rhyme and visual similarity, others are semantic or personal. Blood and snow are connected in my Babble graph, for example, because in Chinese they are homophones: snow is 雪 (xue), and blood is 血 (xue). This led to the following paragraph from one of my high school essays (paraphrased): In Chinese, snow and blood sound the same: "xue." Some people think the world will end suddenly in nuclear holocaust, pandemic, or a belligerent SkyNet. I think the world will die slowly and painfully, bleeding to death one drop at a time with each New England winter. My parents had recently dragged me out to jog in the melting post-blizzard slush. One of my favorite classes in college was a game theory class taught by the wonderful David Parkes; my wife and I lovingly remember the class as Parkes and Rec. One of the striking ideas I learned in Parkes and Rec is that exponentially large graphs can be compactly represented implicitly in memory, ...

Link to original articleWelcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is Babble and Prune, Part 2: More Babble, published by alkjash. This is a linkpost for/ In my last babble, I introduced the Babble and Prune model of thought generation: Babble with a weak heuristic to generate many more possibilities than necessary, Prune with a strong heuristic to find a best, or the satisfactory one. I want to zoom in on this model. If the last babble was colored by my biases as a probabilist, this one is motivated by my biases as a graph theorist. First, I will speculate on the exact mechanism of Babble, and also highlight the fact Babble and Prune are independent systems that can be mocked out for unit testing. Second, I will lather on some metaphors about the adversarial nature of Babble and Prune. Two people have independently mentioned Generative Adversarial Networks to me, a model of unsupervised learning involving two neural nets, Generator and Discriminator. The Artist and the Critic are archetypes of the same flavor - I have argued in the past the spirit of the Critic is Satan. Babble is (Sampling From) PageRank Previously, I suggested that a Babble generator is a pseudorandom word generator, weighted with a weak, local filter. This is roughly true, but spectacular fails one of the technical goals of a pseudorandom generator: independence. In particular, the next word you Babble is frequently a variation (phonetically or semantically) of the previous one. PageRank, as far as I know, ranks web pages by the heuristic of "what is the probability of ending up at this page after a random walk with random restarts." That's why a better analogy for Babble is sampling from PageRank i.e. taking a weighted random walk in your Babble graph with random restarts. Jackson Pollock is visual Babble. Imagine you're playing a game of Scrabble, and you have the seven letters JRKAXN. What does your algorithm feel like? You scan the board and see an open M. You start Babbling letter combinations that might start with M: MAJR, MRAJ, MRAN, MARN, MARX (oops, proper noun), MARK (great!). That's the weighted random walk. You set MARK aside and look for another place to start. Time for a restart. You find an open A before a Triple Word, that'd be great to get! You start Babbling combinations that end with A: NARA, NAXRA, JARA, JAKA, RAKA. No luck. Maybe the A should be in the middle of the word! ARAN, AKAN, AKAR, AJAR (great!). You sense mean stares for taking so long, so you turn off the Babble and score AJAR for (1+8+1+1)x3 = 33 points. Not too shabby. The Babble Graph Last time, I described getting better at Babble as increasing the uniformity of your pseudorandom Babble generator. With a higher-resolution model of Babble in hand, we should reconceptualize increasing uniformity as building a well-connected Babble graph. What is the Babble graph? It's the graph within which your words and concepts are connected. Some of these connections are by rhyme and visual similarity, others are semantic or personal. Blood and snow are connected in my Babble graph, for example, because in Chinese they are homophones: snow is 雪 (xue), and blood is 血 (xue). This led to the following paragraph from one of my high school essays (paraphrased): In Chinese, snow and blood sound the same: "xue." Some people think the world will end suddenly in nuclear holocaust, pandemic, or a belligerent SkyNet. I think the world will die slowly and painfully, bleeding to death one drop at a time with each New England winter. My parents had recently dragged me out to jog in the melting post-blizzard slush. One of my favorite classes in college was a game theory class taught by the wonderful David Parkes; my wife and I lovingly remember the class as Parkes and Rec. One of the striking ideas I learned in Parkes and Rec is that exponentially large graphs can be compactly represented implicitly in memory, ...

Brett Gaylor and Face2Face host David Peck talk about his new film Discriminator, serendipitous creativity, privacy laws, facial recognition, digital ethics and human rights, data sets, remix culture, surveillance technology and automated decision making.TrailerWATCH the film here.Find out more about Brett and his work here.Synopsis:Discriminator, Brett Gaylor's latest cautionary tale about life online, looks at the sea of faces we've willingly uploaded to social media and the consequences of our oversharing.As the Internet has evolved, so has documentarian Brett Gaylor's attitude towards it. The filmmaker, who imagined a utopian future of shared creativity in 2008's hit documentary feature RIP: A Remix Manifesto, began to sound the alarm in 2015 with his Peabody award winning series on digital privacy Do Not Track. Last year's CBC documentary The Internet of Everything explored the implications of the Internet moving off of our screens and into the world around us with the Internet of Things.And in his latest film, the interactive documentary Discriminator, he looks at the vast global database of faces – captured without consent on social media and other platforms – and how it's being used to hone facial recognition and other advances in artificial intelligence.Discriminator traces the almost accidental amassing of photos on digital sites through the beginning of this century, and follows through to the realization in 2015 by Yahoo/Flickr that this archive had limitless possibilities no one could have imagined. The subsequent cloning of these databases has been used to build technology used by US defence contractors, the Chinese military and the largest corporations on earth.In this interactive documentary, viewers can see how this technology works by activating their own webcams. While this may sound creepy, the film manages to stay away from fear-mongering territory with AI-assisted animation, interactive AR filters, a glitchy original score and Gaylor's familiar voiceover. It's the most fun you will have exploring surveillance capitalism.“We need to move beyond narratives around AI that are scary and grant the technology it's own human agency,” says Gaylor, whose own wedding images play a role in his digital supply chain analysis. “What we need to understand is that these are technologies that we can shape, and that we are alive during a moment where we can set the course “People's faces are being used for purposes over which they have no control. We need to have consent over our digital bodies - we need to make permission normal again.”About Brett:Brett's documentaries The Internet of Everything, Do Not Track, OK Google, and Rip! A Remix Manifesto chronicled the Internet's peril and promise. His AR documentary Fortune premiered at the 2021 Sundance Film Festival.Brett has received the International Documentary Association award, a Peabody Award, the Prix Gemaux and three Webbys.Image Copyright and Credit: Brett Gaylor and Imposter media.F2F Music and Image Copyright: David Peck and Face2Face. Used with permission.For more information about David Peck's podcasting, writing and public speaking please visit his site here.With thanks to Josh Snethlage and Mixed Media Sound. See acast.com/privacy for privacy and opt-out information.

El Multi-Exit Discriminator, MED a partir de ahora, es un atributo no transitivo opcional de BGP que nos permite definir por donde queremos enviar el tráfico el AS adyacecente si es que tenemos al menos …

If you are worried about oversharing online, check out the new documentary, Discriminator, as we chat with the director Brett Gaylor. Plus, CEO of Zebra, Dennis Gecaj, reveals the app where users share "talking photos" with friends. Also, CEO of Loop Returns, Jonathan Poma, joins us to discuss solving the problem of returns when shopping online. In Socially Speaking, we talk about Instagram's change to hiding sensitive content and the option to remove the feature if you don't want Instagram to censor what you see.   Find out more information from our guests here: Brett Gaylor: discriminator.film Dennis Gecaj: zebra.chat Jonathan Poma: loopreturns.com You can also find both AmberMac and Michael B on Twitter.

En esta edición de las Creative Talks Podcast Hablamos de SHEIN y la peligrosa combinación de dopamina, gamificación y compras online ¿Está Netflix perdiendo lo cool? ¿Qué está pensando Amazon? Te recomendamos "Discriminator” un documental interactiv The post #135 Dopamina, gamificación y compras en línea · Creative Talks · Dixo first appeared on DIXO.

¿Netflix es rentable? ¿Qué está pensando Amazon? Hablamos de SHEIN y la peligrosa combinación de dopamina, gamificación y compras online. Te recomendamos "Discriminator” un documental interactivo sobre bases de datos de reconocimiento facial. ¿Bots peleando a favor de la lucha contra el cambio climático? Te contamos todo. Google tiene problemas con el regreso a la oficina… ¿Cómo lo está resolviendo? Y finalmente, ha sucedido, un dron autónomo vence a un piloto de drones profesional. --- Send in a voice message: https://anchor.fm/creative-talks/message

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It's important to know what's going on with our lives in the "cloud".On this episode we dive into a short interactive documentary called 'Discriminator' that looks at what is ACTUALLY happening with so many of those pictures that we all harmlessly share online multiple times a day, every day.Director Brett Gaylor has made a truly interactive documentary that genuinely makes you think about the sharing we do on the internet.We talked about the inspiration for the film, the importance for us all to up our knowledge during the internet age and so very much more...

Chris the discriminator joins us on the BIG Detecting show

In this “Discriminator” episode of our podcast, CRM 114, Mental wants carplay in everything now , Jeff wants a chainsaw, Chrissy wants to litter the world with PPE, and Chris wants am iRacing setup.  Really, we talk all about the 2019 E1R Gift Guide™ , it is everything that we, or you, or the gearhead in your life might want for whatever holidays you celebrate.   Loctite sticks  Bailing wire hose clamp maker: Clamptite: https://clamptitetools.com Gearwrench Bolt Biter sockets Really good headlamp with rechargeable battery pack LED work lights, never enough Harbor Freight ICON tools - Also Seen at SEMA - very impressiveBespoke Ratchet Straps - Made in Oregon strapworks.comTite Reach tools  iRacing setup or piecesTrailer accessories: E track and fittings New race or mechanix gloves, Harbor freight heavy duty work wellWeathertech FloormatsGood quality large picture of your racecar on trackTruck bed cover (truckhero)Carhartt Work PantsFuze molded earbuds Carplay Capable stereo for older car, especially for long tows:  Hub stands with laser and digital level  Track sculptures OBD2 ScannerPPE! Get a new pair of safety glasses (spring for the nice ones!) glovesMembership in the Hagerty Driver’s Club 

Episode 31: The Technology Behind Deepfakes ______________________________________________________________ In Episode 29 of Pardon The Disruption, the team discussed the world of Deepfakes. But what is the underlying technology behind Deepfakes? Generative Adversarial Networks (GAN), is extremely interesting and could have profound implications for distorting reality when it comes to generating fake videos or images. Created by the researcher Ian Goodfellow at the age of 28, Generative Adversarial Networks are two artificial neural networks which compete against each other. In the case of deep fake images, one network (the generator) tries to generate an image which the other network discriminates (the discriminator). In essence, the generator is trying to fool the discriminator into believing the data, or image, is fake - and it continues to generate images until this objective is met. - What are Generative Adversarial Networks (GANs)? (2:14) - The role of the Generator and Discriminator in GANs (5:00) - Why Is This Important? (8:50) - Where will GANs be used in the future? (9:25) - Deep fake images are just the start (10:20) - Generating Complete Data (14:00) - Variational Auto-Encoders (What JPG did for image compression) (14:44) - Faking Social Media profiles (20:10) - Machine-Brain Interfaces (21:41) Links used in the show: A Beginner's Guide to GANs: https://skymind.ai/wiki/generative-adversarial-network-gan Play with GANs (The GAN Lab): https://poloclub.github.io/ganlab/ ________________________________________________________________ Leave some feedback: • What should we talk about next? Please let us know on Twitter - twitter.com/rumjog or in the comments below. • Enjoyed this episode? Let us know your thoughts in the comments, and please be sure to subscribe. ⚡️ Subscribe to Podcast: Google Play: bit.ly/2Cl97VS iTunes: apple.co/2SEndI8 Spotify: spoti.fi/2W7OB2N Stitcher: bit.ly/2XXwLkA SoundCloud: bit.ly/2Y0t25Z

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Paul and John discuss security policies and procedures. In the news, WatchGuard acquires Datablink, Cylance brings enterprise technology to home users, Oracle and SafeLogic join forces for OpenSSL, 12 security startups that raised new funding in 2017, and more on this episode of Enterprise Security Weekly!Full Show Notes: https://wiki.securityweekly.com/ES_Episode56Visit https://www.securityweekly.com for all the latest episodes!

Paul and John discuss security policies and procedures. In the news, WatchGuard acquires Datablink, Cylance brings enterprise technology to home users, Oracle and SafeLogic join forces for OpenSSL, 12 security startups that raised new funding in 2017, and more on this episode of Enterprise Security Weekly!Full Show Notes: https://wiki.securityweekly.com/ES_Episode56Visit https://www.securityweekly.com for all the latest episodes!

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