Podcasts about shrdlu

Panelists: Paul Hagstrom (hosting), Quinn Dunki, and Carrington Vanston Topic: 1968-1969 In 1968 and 1969, we had SHRDLU, the Mother of All Demos, Go To being considered harmful, and Unix. Topic/Feedback links: Retro Computing News: Vintage Computer(-related) commercials: Retro Computing Gift Idea: Auction Picks: A2Stream file: Feedback/Discussion: Intro / Closing Song: Back to Oz by … Continue reading RCR Episode 269: Considered harmful →

In episode 87 of The Gradient Podcast, Daniel Bashir speaks to Professor Terry Winograd. Professor Winograd is Professor Emeritus of Computer Science at Stanford University. His research focuses on human-computer interaction design and the design of technologies for development. He founded the Stanford Human-Computer Interaction Group, where he directed the teaching programs and HCI research. He is also a founding faculty member of the Stanford d.school and a founding member and past president of Computer Professionals for Social Responsibility.Have suggestions for future podcast guests (or other feedback)? Let us know here or reach us at editor@thegradient.pubSubscribe to The Gradient Podcast:  Apple Podcasts  | Spotify | Pocket Casts | RSSFollow The Gradient on TwitterOutline:* (00:00) Intro* (03:00) Professor Winograd's background* (05:10) At the MIT AI Lab* (05:45) The atmosphere in the MIT AI Lab, Minsky/Chomsky debates* (06:20) Blue-sky research, government funding for academic research* (10:10) Isolation and collaboration between research groups* (11:45) Phases in the development of ideas and how cross-disciplinary work fits in* (12:26) SHRDLU and the MIT AI Lab's intellectual roots* (17:20) Early responses to SHRDLU: Minsky, Dreyfus, others* (20:55) How Prof. Winograd's thinking about AI's abilities and limitations evolved* (22:25) How this relates to current AI systems and discussions of intelligence* (23:47) Repetitive debates in AI, semantics and grounding* (27:00) The concept of investment, care, trust in human communication vs machine communication* (28:53) Projecting human-ness onto AI systems and non-human things and what this means for society* (31:30) Time after leaving MIT in 1973, time at Xerox PARC, how Winograd's thinking evolved during this time* (38:28) What Does It Mean to Understand Language? Speech acts, commitments, and the grounding of language* (42:40) Reification of representations in science and ML* (46:15) LLMs, their training processes, and their behavior* (49:40) How do we coexist with systems that we don't understand?* (51:20) Progress narratives in AI and human agency* (53:30) Transitioning to intelligence augmentation, founding the Stanford HCI group and d.school, advising Larry Page and Sergey Brin* (1:01:25) Chatbots and how we consume information* (1:06:52) Evolutions in journalism, progress in trust for modern AI systems* (1:09:18) Shifts in the social contract, from institutions to personalities* (1:12:05) AI and HCI in recent years* (1:17:05) Philosophy of design and the d.school* (1:21:20) Designing AI systems for people* (1:25:10) Prof. Winograd's perspective on watermarking for detecting GPT outputs* (1:25:55) The politics of being a technologist* (1:30:10) Echos of the past in AI regulation and competition and learning from history* (1:32:34) OutroLinks:* Professor Winograd's Homepage* Papers/topics discussed:* SHRDLU* Beyond Programming Languages* What Does It Mean to Understand Language?* The PageRank Citation Ranking* Stanford Digital Libraries project* Talk: My Politics as a Technologist Get full access to The Gradient at thegradientpub.substack.com/subscribe

I've been told I need to do an episode about Prolog. Well, here's the start of that process. To talk about Prolog we first need to come to grips with natural language processing, it's tools, and it's languages. This episode we are doing just that, going from ELIZA to Planner ro SHRDLU in an attempt to figure out how AI was first taught human tongues, where smoke and mirrors end, and where facinting programming begins.   Selected Sources:   https://dl.acm.org/doi/pdf/10.1145/365153.365168 - ELIZA   https://stacks.stanford.edu/file/druid:cm792pj8606/cm792pj8606.pdf - Planner   https://web.archive.org/web/20200725084321/http://hci.stanford.edu/~winograd/shrdlu/AITR-235.pdf - SHRDLU

Composants et matériels électroniques, semi-conducteurs : découvrez toutes les offres de notre partenaire Farnell France sur fr.farnell.comL'intelligence artificielle et la robotique dans l'Histoire, depuis Talos, l'automate mythologique, jusqu'aux hivers de l'intelligence artificielle : rétrospective de la robotique dans la culture et l'innovation à travers l'Histoire... ❤️ Soutenez Tech Café sur Patreon

Dr. Ali Boyacı, Bahadır Yalın ve Mehmet Ege Parlak'ın yer aldığı TapirCast'in "Nasıl Çalışır?" serisinin bu bölümünde, son günlerde dünya çapında ses getiren ChatGPT üzerine konuşuyoruz. Doğal Dil İşleme'nin (NLP) ve doğal dilin ne olduğu ile ilgili sohbete başladığımız bu bölümümüzde, ChatGPT ile Doğal Dil İşleme arasında bağlantılı olan teknik konulardan bahsediyoruz. Başlangıçta doğal dil işlemenin bilgisayar tarafından nasıl yapıldığını, beynimiz ile bilgisayarın doğal dil işlemeyi ne tarz benzerliklerle veya farklılıklarla ele aldığını konuşuyoruz. ChatGPT'nin ne tür bir chatbot olduğundan ve geçmişte kullanıma sunulan Eliza, SHRDLU ve Dragon Dictate gibi chatbotların hangi görevlerde nasıl kullanıldığının bahsedilmesinin ardından, ChatGPT'nin verilerle nasıl beslendiğini ve eğitildiğini, dikkat mekanizmasının ve tokenizasyonun nasıl çalıştığını, diğer chatbotlardan ayrılan özelliklerini, olası kötü niyetli kullanımıyla doğacak sonuçları Matrix filminden ve Facebook tarafından geliştirilen yapay zeka üstünden örnekleyerek anlatıyoruz. Son olarak ChatGPT'nin kullanım alanlarından bahsederek bölümümüzü sonlandırıyoruz. Keyifli dinlemeler! 0:01:17 Doğal Dillerin Tanımı 0:01:40 Doğal Dil İşleme (NLP) Tanımı 0:02:19 Bir Örnek üzerinden Dil İşleme'nin Matematiği 0:03:14 CallText 5010 Çalışma Prensibi 0:03:50 T9 Klavye Çalışma Prensibi 0:04:43 ChatGPT Cümle Düzeltmesi Örneği 0:05:17 Chatbot Tanımı 0:05:37 ELIZA Tanımı 0:06:10 SHRDLU Tanımı 0:06:44 Dragon Dictate Tanımı 0:07:16 ChatGPT ve Chatbotların En Büyük Ayrımı 0:07:55 ChatGPT'nin Kullandığı Dikkat Mekanizması 0:08:34 ChatGPT Çevrimiçi mi Çevrimdışı mı Çalışır Tanımı 0:09:31 Online Çalışan Chatbot Örneği 0:10:01 ChatGPT Eğitilmesi Tanımı 0:10:30 ChatGPT'nin Kullanıcı ile Eğitilmesi Örneği 0:11:43 ChatGPT ve Güvenlik Unsuru 0:13:12 ChatGPT'nin Potansiyel Kötüye Kullanımı 0:14:19 Turing Testi Tanımı 0:15:38 Matrix üzerinden ChatGPT Örneği 0:16:19 Yapay Zeka (YZ) ile İnsan Zekası Farkları 0:17:45 Nato'nun YZ'yi Savaşlarda Yasaklaması 0:18:32 Facebook'da Devreye Alınan İki YZ 0:19:45 ChatGPT Kullanım Alanları Örnekleri 0:20:38 ChatGPT'nin Yazdığı bir Şiir bil101 YouTube Kanalı: https://www.youtube.com/channel/UCiIppcA0IpdhTpuBYdJC4mQ bil101 Ağ Sayfası: https://bil101.com/ TapirCast - Bilimsel ve Teknolojik Gelişmeler: https://youtube.com/playlist?list=PLwvStmyxv70_rnTR_kItlrZvaIdxWgfIN TapirCast - Mühendislik Kavramları: https://youtube.com/playlist?list=PLwvStmyxv708xJad4QY9ZueBMGdLSz3m6 TapirCast - Bilim Tarihi: https://youtube.com/playlist?list=PLwvStmyxv70_XdrpkVTcYEylAltcL0Kth Apple Podcasts: @TapirCast, https://podcasts.apple.com/tr/podcast/tapircast/id1485098931 Spotify: @TapirCast, https://open.spotify.com/show/1QJduW17Sgvs1sofFgJN8L?si=6378c7e84186419e Tapir Lab. GitHub: @TapirLab, https://github.com/TapirLab Tapir Lab. Instagram: @tapirlab, https://www.instagram.com/tapirlab/ Tapir Lab. Twitter: @tapirlab, https://twitter.com/tapirlab?s=20 Tapir Lab.: http://tapirlab.com/

Episode: 2259 ETAOIN, SHRDLU, QWERTYOP and the printed word.  Today, we learn about Etaoin Shrdlu.

'A Brief History of Artificial Intelligence' by Michael Wooldridge

It's all greeked to me, some new history about the old text “lorem ipsum”; Europa may hide its secrets more deeply than previously thought; and a man happily discovers 160 bowling bowls under his house.Sponsors:Indeed, Get a free $75 credit at Indeed.com/goodnewsCredit Karma, creditkarma.com/podcastLinks:Lorem Ipsum: Filler Fail, Killer Tale (Antigone)De finibus bonorum et malorum (Internet Archive)Description of the “Lorem ipsum dolor sit amet” text that appears in Word Help (Microsoft Support)What does the filler text “lorem ipsum” mean? (The Straight Dope)The History of Lorem Ipsum (Priceonomics)Lorem ipsum : nouvel état de la question (Hypotheses)Letraset Lorem Ipsum.jpg (Wikimedia Commons)Europa: Ocean Moon (NASA)Impact gardening on Europa and repercussions for possible biosignaturesMuskegon County man unearths more than 150 bowling balls during renovations (Detroit Free Press)David Olson's Facebook account of finding the bowling bowls (Facebook)Help the Bowling Ball man fund (GoFundMe)Flong Time No See (YouTube)Glenn on Twitter (Twitter)See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

“Bots” reign supreme in this week’s episode, though Andy and Dave start the discussion NIST’s RFI on the development of technical standards for AI. A Harvard Medical School project demonstrates a catheter that can autonomously move inside a live, beating pig’s heart. Zipline uses medical delivery drones in Rwanda. University of Maryland researchers demonstrate drone delivery of a kidney for transplant. NASA tests a CACADA swarm, and is also investigating Marsbees. And Starship robo-couriers deliver food to students at GMU. In research from Berkeley, a robot learns to use improvised tools to complete tasks, including those with physical cause-and-effect relationships. Researchers at MIT, MIT-IBM Watson, and DeepMind create the Neuro-Symbolic Concept Learner (NSCL), which uses a hybrid connectionist/symbolic approach, and seeming to be a “true” AI implementation of Winograd’s SHRDLU system from the 60s. Research from Tsinghua University and Google demonstrates Neural Logic Machines, a neural-symbolic architecture for both inductive learning and logic reasoning. Two papers compare logistic regression with machine learning methods for clinical predictions; one shows no benefit of one method over the other, while the other claims better performance with neural network methods (although Andy and Dave wonder whether this statement is true, given the error bars in the results). Algorithm Watch publishes a Global Inventory of AI Ethics Guidelines. Times Higher Education (THE) and Microsoft release a survey on AI of more than 100 AI experts and university leaders. The Department of Information Technology at the University of Uppsala in Sweden has made its lecture notes for a statistical machine learning course available. The Santa Fe Institute reprints a classic collection of essays from its Founding Workshops. Robert Kranekg pens a story about an Angry Engineer. And the OpenAI Robotics Symposium 2019 releases the full video proceedings online.

Brother Jump Unleashed up in Bangor Maine, we changed the name of the song to Bangor Boogie for the occasion .... I know for a fact the rhythm section is Mark Sullivan on Drum (RIP) and Brian Hamm (Bass) ... Shrdlu is on harmonica and insanity, Joe Davis on Keyboards, and after that Ican't swear to anything or anyone else ... ask Joe he would know .... also I believe it's Jerry Gerst mixing sound ... we used to tear that place up ... and I think this is the night Stephen King, the author , got drunk and tried to take me home :P or I got drunk and tried to take him home ... he'd pop in to see us ...that is his home town

We talk to comedian / director Clay Larsen about game structure. "Game" like board games, party games, things-you-play games, and "structure" meaning the stories you get into while you play games. Like if you play Risk and start acting like a tyrant, you know? Oh, you'll see. Also, Anthony talks about the mash-ups of our friend Daniel "Chambaland" Chamberlin and Will talks about the super-exciting computer language SHRDLU!

In this episode Tony and Al take a ride with Rubber, a movie about a cold-hearted serial killer named Robert who goes around exploding people's heads with his psychokinetic powers; also he is a tire. Later the joys of bad movies are extolled, and the tragic wonder of technological progress is examined.   Links Atantic Rim Phil Hornshaw  So You Created a Wormhole Upstream Color Joyland by Stephen King Linotype: the Film John Titor David Icke Bohemian Grove Coral Castle Bishop Castle   "Linotype: The Film" Official Trailer from Linotype: The Film on Vimeo.

Joscha (Gast im Chaosradio 187) und ich reden unter anderem über Kognitionswissenschaft, Alan Turing, die Turingmaschine, den Turing-Test, das Lambda-Kalkül, Joseph Levine, die Explanatorische Lücke, Gottfried Wilhelm Leibniz, John Searle, das Chinesische Zimmer, René Descartes, John Eccles, Karl Popper, Julien Offray de La Mettrie, Kybernetik, Georg Klaus, Humberto Maturana, Autopoiesis, Claude Shannon, Informationstheorie, Blade Runner, ETAOIN SHRDLU, SHRDLU, Janes Lighthill, Perzeptron, Dietrich Dörner, die Maslowsche Bedürfnispyramide, die Big Five, Enaktivismus, Embodiment, Emergenz, Computational Neuroscience, Big Dog (Video) und Datenkompression.

Joscha (Gast im Chaosradio 187) und ich reden unter anderem über Kognitionswissenschaft, Alan Turing, die Turingmaschine, den Turing-Test, das Lambda-Kalkül, Joseph Levine, die Explanatorische Lücke, Gottfried Wilhelm Leibniz, John Searle, das Chinesische Zimmer, René Descartes, John Eccles, Karl Popper, Julien Offray de La Mettrie, Kybernetik, Georg Klaus, Humberto Maturana, Autopoiesis, Claude Shannon, Informationstheorie, Blade Runner, ETAOIN SHRDLU, SHRDLU, Janes Lighthill, Perzeptron, Dietrich Dörner, die Maslowsche Bedürfnispyramide, die Big Five, Enaktivismus, Embodiment, Emergenz, Computational Neuroscience, Big Dog (Video) und Datenkompression.