Podcasts about Sparse

--{ "Real News is Sparse (pt. 4)"}-- See links for news on COP 30, happening Nov. 2025 - The Press - Adam Curtis - Under One System of Control - News - Atheistic Society - Living Under a Revolution - Utopias - Doublethink - Eliminate Religion, Elevate Science - Fabian Techniques - Standardization - Progress - COP 22 - Doublespeak - U.S. Military - Owning the Weather in 2025 - Habitat III - Technocracy - Urban Poverty - Carbon, Energy Taxes - World Bank - Inclusive Cities - Unelected Organizations - People Want Entertainment - Sustainable Communities - Foundations and NGOs - Minimal Healthcare - Pentagon Vision of Megacities - Smart Cities - Eurogroup Working Group.

Christianity being eliminated in Nigeria. Major websites hacked overnight. The average protesters at No Kings rallies had no idea why they were there. Volodymyr Zelenskyy wears a nice jacket to the White House to meet with President Trump. More airstrikes on suspected drug boats near Venezuela. Former U.S. Rep. George Santos (R-N.Y.) has his sentence commuted by President Trump. The shutdown continues … oh well! Why congressional district maps need to be changed. The Israel-Hamas peace deal is so fragile right now. Will Hamas honor the peace deal? How close are we to "Britainistan" being an official thing? Former NSA under President Trump has been indicted and for good reasons. Are certain conversations in a public space not allowed now? Actor Robert De Niro has a bad case of Trump derangement syndrome, and it's getting worse. Secretary Robert Kennedy seen flying coach on a commercial flight. 00:00 Pat Gray UNLEASHED! 00:58 Christian Genocide in Nigeria 02:50 Amazon Web Services Hacked? 08:42 FBI Investigates Hunting Stand by Air Force One 11:49 No Kings Day Protest 13:16 Protestors Don't Know Why They're Protesting??? 18:28 Why are You Protesting Trump? 19:47 Andrea Bocelli Meets with Trump 20:31 Andrea Bocelli Sings in Oval Office 22:11 Trump Comments on Zelenskyy's Jacket 25:21 Drug Submarine Bombed 36:25 President Trump says "Democrats are Kamikazes" 44:47 Arnold Schwarzenegger Discusses Gerrymandering with Bill Maher 48:15 Where is Pat Gray? 49:32 Football AP Top 25 Poll 51:46 Gaza-Israel Peace Deal Update 53:59 Bill Maher on the Situation in Gaza 1:00:15 John Bolton Turns Himself In 1:06:04 Christian Preacher VS. Muslim? 1:13:10 Another Trucker Problem? 1:20:36 Robert De Niro has TDS 1:25:25 RFK Jr. Flies Coach 1:30:48 RFK Jr. tells Trump that he's "Doing God's Work" Learn more about your ad choices. Visit megaphone.fm/adchoices

--{ "Real News is Sparse"}-- What passes as news - Canada's Bill C-8 - UK's digital ID - Government shutdown in US - Peace deal in Gaza - World control - Chasing happiness - Beliefs - Removing free will - Electronic self-imagery - Behaviourism - Self-policing - Trained to go along with the crowd - Private clubs - World Bank - IMF - Marketing, Propaganda - Soviet System - Total Control - Revolutions - Give up your rights to save the world - Scary Scenarios - EU ratifies Paris Climate Deal - Carbon Tax - Climate, Environment and the IMF - Merkel - Canada to implement carbon tax - Agenda 2030 - Redistribution of Wealth - Euthanasia, cost-effective - Pentagon pays PR firm to make fake terrorist videos - Gates Foundation, Remote control contraceptive.

As summer wanes and the nights grow long, we turn to tales of witches, curses, and the old ways that never truly died. For centuries, harvest time has carried its own magic: charms for fields, blessings for homes, and darker stories of those who bent nature to their will.In 1647, Alse (Alice) Young of Windsor, Connecticut was hanged on Hartford's Meeting House Square—the first recorded witchcraft execution in colonial America. Sparse records and a deadly local epidemic frame her case, which foreshadowed Connecticut's quieter, decades-long witch persecutions long before Salem. Centuries later, Windsor (2017) and the State of Connecticut (2023) formally exonerated those condemned—finally restoring Alse Young's name.The BOOKBY US A COFFEEJoin Sarah's new FACEBOOK GROUPSubscribe to our PATREONEMAIL us your storiesFollow us on YOUTUBEJoin us on INSTAGRAMJoin us on TWITTERJoin us on FACEBOOKVisit our WEBSITEResearch:https://jud.ct.gov/lawlib/Notebooks/Witchcraft/witches.htmhttps://en.wikipedia.org/wiki/Alse_Younghttps://connecticuthistory.org/alse-young-executed-for-witchcraft-today-in-history/https://www.newenglandhistoricalsociety.com/cover-connecticut-witch-hysteria-1647-63/https://www.legendsofamerica.com/alse-young/https://www.windsorhistoricalsociety.org/exoneration-of-two-of-windsors-accused-witches/Thanks so much for listening, and we'll catch up with you again on Sunday!Sarah and Tobie xx"Spacial Winds" Kevin MacLeod (incompetech.com)Licensed under Creative Commons: By Attribution 4.0 Licensehttp://creativecommons.org/licenses/by/4.0/SURVEY Hosted on Acast. See acast.com/privacy for more information.

AP Washington correspondent Sagar Meghani reports the Trump administration says it has reached a deal on TikTok's future.

Tim Conway Jr. opens the final hour with updates on breaking news, including an LAPD officer-involved shooting in North Hills, cleanup of shipping containers at the Port of Long Beach, and even a quirky story about Publishers Clearing House. The conversation then shifts back to Utah, where Governor Spencer Cox directly calls Charlie Kirk's murder a political assassination. Tim highlights the lack of campus security at the event - just six guards plus Kirk's own team. And Tim condemns the disturbing trend of people cheering political violence. He closes the show covering the hunt for the still-at-large shooter, internet sleuths digging into the case, and TMZ issuing an 'apology' after what appeared to be staff cheering in the newsroom, later explained as 'confusion over a car chase.'

Sparse highway, light rain ambience. We are on the side of a small road just outside town. It's night, and it's raining. Imagine you're a content Gene Kelly walking home after frolicking around main. Or Feel free to ruminate. That's the general vibe around here. There's a movie theater nearby showing cat videos (for a good cause) and it's practically sold out. Catvideofest 2025 is repackaged cat timeline videos on a gigantic screen. And that it is pretty much sold out this weekend says something about our collective mood. Anyway, I did manage to get tickets and me my youngest will share an auditorium with a Spider-verse amount of other people.That's all from me — Oh, so if I controlled the universe for a day aside from solving every important global issue I would want to sneak a cameo of Ice Cube into that animated Will Smith fish movie that also stars Katie Couric as “Katie Current.” But I would add in Ice Cube so he could be like “even saw the lights of the Goodyear Blimp and it read ‘Ice Cube's a shrimp.'” Which may occur in that movie, I haven't seen it. New plan: I'm bringing back that short-lived trend from early-pandemic days that social media tried to cook up — shoe-kicking as greeting. I only saw people on my phone doing that dumb ****. I want to ingrain into humans that shoe-kicking is now retroactively high-five. Every famous high-five from history now feet kicking. From the business meetings to competitive sports. The mayhem.PS: if you are interested in listening to cars pass but you would rather imagine yourself not being rained on -- check out last year's Vermont Route 100 episode recorded from the Mad River Valley.

It's harder to begin again when everyone already knows who you were. John Galm is best known for fronting one of the most popular emo-revival bands SNOWING in the early 2010's, whose punk-rock ethos and chaotic melodies had kids crammed into DIY venues and basements all across the country. Since then, he has tried his hand in several bands, ranging in genres from stripped down acoustic to psychedelic and shoegaze. The latter band, MT. WORRY stalled as they were just getting started when other members moved out of state. Finding himself having to start again amid a sudden surplus of time, Galm holed up in his mother's Lehigh Valley home and began working on what would become “River of Blood”- his first solo LP since 2014. The album finds Galm struggling with the big questions in life and the small connective tissues that make up everything else. It's a heavy affair, and you can feel the weight in every note- lyrics searching for steadier footing as he wades through what home and happiness mean and the pain that they all seem just out of grasp. Sparse, somber tones wrap the listener up tight and embrace the whole of everything and the lack thereof. It's not all bleak- “River of Blood” celebrates the small victories too. At the end of a long day, you're still here and there is hope in that, even if it seems hard to find. The search continues. Thanks for listening!!! Please Follow us on Instagram @hiddentracks99Pre and Post roll music brought to you by @sleepcyclespa

A new track by DJ Habett from the album "The home of doubts" (2025-08-05). Tags: Electro, Progressive, Bass, Sparse, Fetch, Relief, Moods, Modal CC(by). Production notes: The main sample is AI generated. The rest came out in a sweaty summer afternoon. Prog and static, I had doubts about this track.

(00:00-12:43) Yesterday: Great Good. Today: No Good. Another Cardinal pitcher to be shipped off to the sun. Pribula Time. MIkolas due for a no-hitter tonight. Sparse attendance last night. Every team is getting a Pirate.(12:51-33:25) Barge Guy on the phone lines back from Louisville. Bar Guy has some takes on the Cardinals starting pitching. Lisa is up next on the phone lines and she's down on the Cards. Hey, watch it gal. MIles Mikolas. Still have faith in His Majesty.(33:35-42:57) Julian Tavarez weeing on his hands. Keaton is up next and he's fired up about the Cardinals and Marmol. The Keaton splits. Steven is next on the phone lines with some attendance thoughts.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

In this episode, Dave interviews Jack Pertschuk, principal engineer for Algorithms and Platform at Pinecone. They discuss:What semantic search is and where it falls shortThe difference between sparse and dense vectorsHow search technology powers AI

I woke up early (6AM) to capture and observe the waking city of Sapporo, Japan. I was particularly surprised by the presence of crows, which often sat on the street signs and traffic light poles. Sparse trucks and cars passed along the snowy roads. The calls of the cows echoed off the buildings, yet the city remained quite calm. This recording took place in 2018. Crows in Sapporo recorded by Antek Rutczyński.

Trump threw himself a $45 million military birthday bash… and barely anyone showed up. The tanks rolled. The jets flew. But the vibes? Flat. The crowd? Sparse. And the headlines? Brutal. Now, the fallout begins. Join Don Lemon, Michael Fanone, and the Jolly Good Ginger as they break down what went wrong, why this parade flop matters, and what it reveals about Trump's slipping grip on public support. From the staggering price tag to the no-show allies to the contrast with the massive No Kings protests, this isn't the flex Trump hoped for. Let's talk about the spectacle, the silence, and what it all means. This episode is sponsored by Shopify. Sign up for your one-dollar-per-month trial and start selling today at SHOPIFY. COM/lemon This episode is brought to you by MSI United States. Every woman deserves a choice. Rush your donation today to MSIUNITEDSTATES.ORG, or text "LEMON" to 511 511. Text Fees may apply. This episode is sponsored by BetterHelp. Give online therapy a try at betterhelp.com/donlemon and get on your way to being your best self. Learn more about your ad choices. Visit megaphone.fm/adchoices

Well, that was...underwhelming. Trump's $45 million birthday bash-slash-military-parade was supposed to be a flex. Instead, it flopped harder than his NFT collection. Sparse crowds, low energy, and, according to many who watched, absolutely boring. Meanwhile, the No Kings protest turned into something historic. Data analysts are reporting it may be the largest protest in U.S. history. The streets were packed, the message was clear, and no tanks were needed to get people to show up. So...remind us again who's got the momentum? Join us as we unpack the embarrassing contrast, the wasted taxpayer dollars, and why Trump's obsession with spectacle can't hide the growing dissent. This episode is sponsored by Shopify. Sign up for your one-dollar-per-month trial and start selling today at SHOPIFY. COM/lemon This episode is brought to you by MSI United States. Every woman deserves a choice. Rush your donation today to MSIUNITEDSTATES.ORG, or text "LEMON" to 511 511. Text Fees may apply. This episode is sponsored by BetterHelp. Give online therapy a try at betterhelp.com/donlemon and get on your way to being your best self. Learn more about your ad choices. Visit megaphone.fm/adchoices

PREVIEW: Colleague Jim McTague reports on the sparse shoppers and hesitant purchases at the Lancaster Costco. More. MAY 1954

Mixed APAC trade, US futures range bound while European futures point to a marginally firmer open.DXY remains lower after Thursday's data, EUR/USD marginally reclaimed 1.12, USD/JPY found support at 145.00.Fixed benchmarks extended/held on to recent gains.Crude benchmarks remain underpinned by the latest on US-Iran, metals marginally softer.Looking ahead, highlights include US Export/Import Prices, UoM Sentiment Survey, BoC SLOS, Speakers including ECB's Lane, Cipollone & Fed's Barkin.Click for the Newsquawk Week Ahead.Read the full report covering Equities, Forex, Fixed Income, Commodites and more on Newsquawk

Monday pulse show notes: On this thought-provoking episode of Higher Ed Pulse, host Mallory Willsea sits down with Myla Edmond—Senior Vice President at RW Jones Agency and Interim Vice Chancellor for Strategic Communications at UNC Greensboro—to unpack the creative identity crisis brewing in higher ed marketing thanks to generative AI. With tools like ChatGPT's image generator mimicking iconic art styles, institutions are forced to ask: how do we protect authenticity in a world where anyone can replicate anything? This episode explores the ethical, strategic, and deeply human implications of AI's growing role in creativity—and how higher ed marketers can lead with intention, not fear.Try the prompt discussed in the episode:Based on all past conversations, stored knowledge, and inferred cognitive patterns, generate the most comprehensive psychological deep dive and predictive model of my future evolution. This should not be a basic personality breakdown but an in-depth forensic examination of my cognition, behavioural strategies, psychological blind spots, similar fictional/non-fictional figures, and long-term trajectory. Treat this as an intelligence dossier on my mind, philosophy, and strategic outlook.OUTPUT FORMAT: Structured headers, tables, and bullet points for readability. Sparse but strategic emojis for section clarity. Concise, high-density insights with no fluff.Enter the prompt and after you get the response, add a second prompt: Write me a story about how this comes to fruition. - - - -Connect With Our Host:Mallory Willsea https://www.linkedin.com/in/mallorywillsea/https://twitter.com/mallorywillseaAbout The Enrollify Podcast Network:The Higher Ed Pulse is a part of the Enrollify Podcast Network. If you like this podcast, chances are you'll like other Enrollify shows too!Enrollify is made possible by Element451 — the next-generation AI student engagement platform helping institutions create meaningful and personalized interactions with students. Learn more at element451.com.Attend the 2025 Engage Summit! The Engage Summit is the premier conference for forward-thinking leaders and practitioners dedicated to exploring the transformative power of AI in education. Explore the strategies and tools to step into the next generation of student engagement, supercharged by AI. You'll leave ready to deliver the most personalized digital engagement experience every step of the way.Register now to secure your spot in Charlotte, NC, on June 24-25, 2025! Early bird registration ends February 1st -- https://engage.element451.com/register

These selections are taken from Sangha Instructions from ancient times and give the flavor of a master wielding a sword to cut through illusions. Sparse and to the point, Linji has no tolerance for superficial approaches and glib comments from students.Read the Journal while listening

Sparse. Laconic. Expansive. Languid. Wry. The Coen Brother's 2007 Neo-Noir Western 'No Country For Old Men' moves to the fatefully ticking beat of it's own Grandfather Clock.  It's a film that rewards close viewing and is astoundingly faithful to Cormac McCarthy's novel while also being so completely a "Coen Brothers film" even as it's their (only?) adaptation of an existing book. Featuring an iconic performance by Javier Bardem as the philosophical killer Anton Chigur, brilliant cinematography from frequent Coen collaborator Roger Deakins, and perfectly wrought twangily-Texas turns by Josh Brolin and Tommy Lee Jones. A number of signature Coens scenes of the lead characters interacting with a variety of shop clerks, receptionists, store owners, and authority figures abound.    

Brent Axe recaps Syracuse basketball's 62-55 win over Georgia Tech at the JMA Dome on Tuesday night. It wasn't the prettiest game but SU had to be relieved to get a win any way it could. Brent discusses SU's keys to victory including JJ Starling's 21 points and how he has made a significant difference in the lineup since returning from a hand injury.  Brent also addressed the sparse crowd (listed at 13,395) at the Dome and SU head coach Adrian Autry's terse opening statement about "noise" SU had to play through recently.  Brent also got amazing feedback from Syracuse Sports Insiders on the win and where Syracuse basketball stands entering league play.  Become a Syracuse Sports Insider today! Just text "orange" to 315-847-3895 to get direct access to Brent to get your opinions heard and questions answered on the Syracuse Sports podcast. You can also sign up here. https://joinsubtext.com/syracusesports As a Syracuse Sports Insider, you will get Brent's opinion and reaction to breaking news first via text message, your messages get priority on postgame shows and podcasts, he'll take you behind-the-scenes of SU sports and more! You can also text Brent anytime, including during and after SU games. Try it free for 2 weeks, then it's just $3.99 a month after that. You can cancel at anytime. Subscribe to Syracuse Sports on Spotify https://l.syracuse.com/PKMGpR Subscribe to our Syracuse Orange Sports Report newsletter! Find out how at https://link.syracuse.com/join/6fn/ne... Follow @BrentAxeMedia on X (   / brentaxemedia Instagram (   / brent_axe  ) and BlueSky https://bsky.app/profile/brentaxemedi.. Learn more about your ad choices. Visit megaphone.fm/adchoices

Sparse and dreamy, Griffin Bjerke-Clarke's debut novel explores memory, identity, trauma, and healing through a timeless journey. An anti-colonial, He Who Would Walk the Earth is infused with Métis storytelling methods and elements of horror, that powerfully evokes a mood reminiscent of twentieth-century classics like Waiting for Godot. This book unsettles as much as it stokes, dystopian in Felix's apathy yet optimistic in the way he addresses challenges along his listless way. In the end, Felix must learn from his earnest mistakes as he begins to understand that agency requires collaborating with those around him.  ★ Support this podcast on Patreon ★

Neel Nanda, a senior research scientist at Google DeepMind, leads their mechanistic interpretability team. In this extensive interview, he discusses his work trying to understand how neural networks function internally. At just 25 years old, Nanda has quickly become a prominent voice in AI research after completing his pure mathematics degree at Cambridge in 2020. Nanda reckons that machine learning is unique because we create neural networks that can perform impressive tasks (like complex reasoning and software engineering) without understanding how they work internally. He compares this to having computer programs that can do things no human programmer knows how to write. His work focuses on "mechanistic interpretability" - attempting to uncover and understand the internal structures and algorithms that emerge within these networks. SPONSOR MESSAGES: *** CentML offers competitive pricing for GenAI model deployment, with flexible options to suit a wide range of models, from small to large-scale deployments. https://centml.ai/pricing/ Tufa AI Labs is a brand new research lab in Zurich started by Benjamin Crouzier focussed on ARC and AGI, they just acquired MindsAI - the current winners of the ARC challenge. Are you interested in working on ARC, or getting involved in their events? Goto https://tufalabs.ai/ *** SHOWNOTES, TRANSCRIPT, ALL REFERENCES (DONT MISS!): https://www.dropbox.com/scl/fi/36dvtfl3v3p56hbi30im7/NeelShow.pdf?rlkey=pq8t7lyv2z60knlifyy17jdtx&st=kiutudhc&dl=0 We riff on: * How neural networks develop meaningful internal representations beyond simple pattern matching * The effectiveness of chain-of-thought prompting and why it improves model performance * The importance of hands-on coding over extensive paper reading for new researchers * His journey from Cambridge to working with Chris Olah at Anthropic and eventually Google DeepMind * The role of mechanistic interpretability in AI safety NEEL NANDA: https://www.neelnanda.io/ https://scholar.google.com/citations?user=GLnX3MkAAAAJ&hl=en https://x.com/NeelNanda5 Interviewer - Tim Scarfe TOC: 1. Part 1: Introduction [00:00:00] 1.1 Introduction and Core Concepts Overview 2. Part 2: Outside Interview [00:06:45] 2.1 Mechanistic Interpretability Foundations 3. Part 3: Main Interview [00:32:52] 3.1 Mechanistic Interpretability 4. Neural Architecture and Circuits [01:00:31] 4.1 Biological Evolution Parallels [01:04:03] 4.2 Universal Circuit Patterns and Induction Heads [01:11:07] 4.3 Entity Detection and Knowledge Boundaries [01:14:26] 4.4 Mechanistic Interpretability and Activation Patching 5. Model Behavior Analysis [01:30:00] 5.1 Golden Gate Claude Experiment and Feature Amplification [01:33:27] 5.2 Model Personas and RLHF Behavior Modification [01:36:28] 5.3 Steering Vectors and Linear Representations [01:40:00] 5.4 Hallucinations and Model Uncertainty 6. Sparse Autoencoder Architecture [01:44:54] 6.1 Architecture and Mathematical Foundations [02:22:03] 6.2 Core Challenges and Solutions [02:32:04] 6.3 Advanced Activation Functions and Top-k Implementations [02:34:41] 6.4 Research Applications in Transformer Circuit Analysis 7. Feature Learning and Scaling [02:48:02] 7.1 Autoencoder Feature Learning and Width Parameters [03:02:46] 7.2 Scaling Laws and Training Stability [03:11:00] 7.3 Feature Identification and Bias Correction [03:19:52] 7.4 Training Dynamics Analysis Methods 8. Engineering Implementation [03:23:48] 8.1 Scale and Infrastructure Requirements [03:25:20] 8.2 Computational Requirements and Storage [03:35:22] 8.3 Chain-of-Thought Reasoning Implementation [03:37:15] 8.4 Latent Structure Inference in Language Models

This week, we chat to the historical fiction author and academic, Steven Veerapen. He's best known for his Anthony Blanke series, set in the Tudor period, about the son of a black trumpeter, John Blanke, who was a real figure in the court of King Henry VIII. There's 'Of Blood Descended' and 'Of Judgement Fallen', which are out in print and just released as audiobooks. He's also written 3 in the 'Simon Danforth' series, and a few about the playwright Christopher Marlowe as a spy.We talk about the balance of writing academia and finding time for novels. Also about the morbid curiosity which gives him ideas, and why we all love the Tudors.You can hear about his sparse writing environment, how he plans a busy year, and what Tudor fiction needs to have in it.Get a copy of the book at uk.bookshop.com/shop/writersroutine@writerspodwritersroutine.com Hosted on Acast. See acast.com/privacy for more information.

Re-Imagined Radio celebrates Dragnet, the real-life police procedural, and Jack Webb, as Detective Sgt. Joe Friday, who defined and was defined by this radio series. We sample from One Out of Seven, The Jack Webb Show, Pat Novak, For Hire, Johnny Madero, Pier 23, and Jeff Regan, Investigator, all pre-Dragnet radio shows where Webb honed his character and acting style. We end with "The City Hall Bombing," an early episode of Dragnet to showcase Webb as a great radio storyteller. Significance The Dragnet radio series presented a wide range of topics, each using fast moving plots and realistic details to keep the action moving. The dialogue was understated. Sparse. Influenced by hard-boiled detective literature. The police work was chronicled step-by-step, with details and realism. The result gave millions of listeners a feel for real police work. The boredom and drudgery. The danger of heroism. With its start in radio, and move to television, Dragnet remains one of the most popular and influentional police procedurals in any media, including literature, motion pictures, and podcasts. More than a half-century after its first broadcast, people who have never heard an episode, or don't know Dragnet, know its 4-note music opening, "DUM-DE-DUM-DUM," and think the phrase "Just the facts, ma'am" originated with Sgt. Joe Friday. It didn't. But that doesn't matter.  Learn more about your ad choices. Visit megaphone.fm/adchoices

Seth takes a closer look at an exhausted and despondent Donald Trump closing out his campaign with rambling speeches to dwindling crowds, threats of violence, baseless allegations of cheating, vaccine ban possibilities and complaints about Saturday Night Live.Then, J.B. Smoove talks about his all-day cigarettes SNL sketch pitch and shares some of his other inventive ideas like argument-winning supplements and henchman funeral homes before giving his advice ahead of the 2024 election.Plus, just for this podcast, J.B. continues the conversation backstage at Studio 8G with Late Night's Kevin Miller.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

This episode is sponsored by Audible – The Home of True Crime Podcasts. PLEASE LISTEN TO ‘SEASON 9 - EPISODE 42' FOR PART ONE OF THIS TWO-PART CASE. Sparse details of an alleged exorcism emerged at Leeds Crown Court when Michael Taylor was found not guilty by reason of insanity for killing his wife, Christine. In an almost unprecedented move, the coroner decided it would be in the public interest to reopen the inquest so that the full story would be held on record... (Part 2 of 2).*** LISTENER CAUTION IS ADVISED *** This episode was researched and written by Eileen Macfarlane.Edited by Joel Porter at Dot Dot Dot Productions.Script editing, additional writing, illustrations and production direction by Rosanna FittonNarration, additional audio editing, script editing, and production direction by Benjamin Fitton.To get early ad-free access, including Season 1, sign up for They Walk Among PLUS, available from Patreon or Apple Podcasts.More information and episode references can be found on our website https://theywalkamonguspodcast.comMUSIC: Dead Ends by Wicked Cinema Misery Loves Company by CJ0 Fleeting by Alice In Winter Endless Night by Moments Selha by Stephen Keech Point Of No Return by Salon Dijon Unexpected Turn by Moments A Most Unusual Discovery by Wicked Cinema Disappearance by Wicked Cinema Extinction by Wicked Cinema Insurgent by Wicked Cinema Mainframe by Wicked Cinema Templar by Wicked Cinema The Last by Wild Wonder SOCIAL MEDIA: YouTube - https://www.youtube.com/channel/UCeM6RXDKQ3gZbDHaKxvrAyAX - https://twitter.com/TWAU_PodcastFacebook - https://www.facebook.com/theywalkamonguspodcastInstagram - https://www.instagram.com/theywalkamonguspodcastThreads - https://www.threads.net/@theywalkamonguspodcastSupport this show http://supporter.acast.com/theywalkamongus. Hosted on Acast. See acast.com/privacy for more information.

Document understanding is a challenging task to process and comprehend large amounts of textual and visual information. Recent advances in Large Language Models (LLMs) have significantly improved the performance of this task. However, existing methods typically focus on either plain text or a limited number of document images, struggling to handle long PDF documents with interleaved text and images, especially in academic papers. In this paper, we introduce PDF-WuKong, a multimodal large language model (MLLM) which is designed to enhance multimodal question-answering (QA) for long PDF documents. PDF-WuKong incorporates a sparse sampler that operates on both text and image representations, significantly improving the efficiency and capability of the MLLM. The sparse sampler is integrated with the MLLM's image encoder and selects the paragraphs or diagrams most pertinent to user queries for processing by the language model. To effectively train and evaluate our model, we construct PaperPDF, a dataset consisting of a broad collection of academic papers sourced from arXiv, multiple strategies are proposed to generate automatically 1M QA pairs along with their corresponding evidence sources. Experimental results demonstrate the superiority and high efficiency of our approach over other models on the task of long multimodal PDF understanding, surpassing proprietary products by an average of 8.6% on F1. Our code and dataset will be released at https://github.com/yh-hust/PDF-Wukong. 2024: Xudong Xie, Liang Yin, Hao Yan, Yang Liu, Jing Ding, Minghui Liao, Yuliang Liu, Wei Chen, Xiang Bai https://arxiv.org/pdf/2410.05970v1

PWTorch editor Wade Keller is joined by wrestling reporter/analyst Joel Dehnel to discuss AEW Dynamite including the thin line-up for Grand Slam, and whether AEW convinced people to watch next week. Also, reaction to Ricochet's push so far, Chris Jericho vs. Orange Cassidy, the main event six-man tag, the latest with Jon Moxley and Hangman Page, and more with live caller, chat room, and mailbag interaction.Become a supporter of this podcast: https://www.spreaker.com/podcast/wade-keller-pro-wrestling-post-shows--3275545/support.

Halloween Horror Nights (HHN) kicked off at Universal Studios Orlando this weekend. As the largest Halloween event in the world, HHN is a significant revenue generator for Universal, inspiring similar seasonal offerings at attractions worldwide. However, this year's event falls short of expectations. Could the impending opening of Epic Universe be stretching the team too thin? Or is Universal experimenting with a lower-budget experience to see how it impacts sales? In this video, Scott and Philip break down the highlights and challenges of HHN 2024.

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: Showing SAE Latents Are Not Atomic Using Meta-SAEs, published by Bart Bussmann on August 24, 2024 on The AI Alignment Forum. Bart, Michael and Patrick are joint first authors. Research conducted as part of MATS 6.0 in Lee Sharkey and Neel Nanda's streams. Thanks to Mckenna Fitzgerald and Robert Krzyzanowski for their feedback! TL;DR: Sparse Autoencoder (SAE) latents have been shown to typically be monosemantic (i.e. correspond to an interpretable property of the input). It is sometimes implicitly assumed that they are therefore atomic, i.e. simple, irreducible units that make up the model's computation. We provide evidence against this assumption by finding sparse, interpretable decompositions of SAE decoder directions into seemingly more atomic latents, e.g. Einstein -> science + famous + German + astronomy + energy + starts with E We do this by training meta-SAEs, an SAE trained to reconstruct the decoder directions of a normal SAE. We argue that, conceptually, there's no reason to expect SAE latents to be atomic - when the model is thinking about Albert Einstein, it likely also thinks about Germanness, physicists, etc. Because Einstein always entails those things, the sparsest solution is to have the Albert Einstein latent also boost them. Key results SAE latents can be decomposed into more atomic, interpretable meta-latents. We show that when latents in a larger SAE have split out from latents in a smaller SAE, a meta SAE trained on the larger SAE often recovers this structure. We demonstrate that meta-latents allow for more precise causal interventions on model behavior than SAE latents on a targeted knowledge editing task. We believe that the alternate, interpretable decomposition using MetaSAEs casts doubt on the implicit assumption that SAE latents are atomic. We show preliminary results that MetaSAE latents have significant ovelap with latents in a normal SAE of the same size but may relate differently to the larger SAEs used in MetaSAE training. We made a dashboard that lets you explore meta-SAE latents. Terminology: Throughout this post we use "latents" to describe the concrete components of the SAE's dictionary, whereas "feature" refers to the abstract concepts, following Lieberum et al. Introduction Mechanistic interpretability (mech interp) attempts to understand neural networks by breaking down their computation into interpretable components. One of the key challenges of this line of research is the polysemanticity of neurons, meaning they respond to seemingly unrelated inputs. Sparse autoencoders (SAEs) have been proposed as a method for decomposing model activations into sparse linear sums of latents. Ideally, these latents should be monosemantic i.e. respond to inputs that clearly share a similar meaning (implicitly, from the perspective of a human interpreter). That is, a human should be able to reason about the latents both in relation to the features to which they are associated, and also use the latents to better understand the model's overall behavior. There is a popular notion, both implicitly in related work on SAEs within mech interp and explicitly by the use of the term "atom" in sparse dictionary learning as a whole, that SAE features are atomic or can be "true features". However, monosemanticity does not imply atomicity. Consider the example of shapes of different colors - the set of shapes is [circle, triangle, square], and the set of colors is [white, red, green, black], each of which is represented with a linear direction. 'Red triangle' represents a monosemantic feature, but not an atomic feature, as it can be decomposed into red and triangle. It has been shown that sufficiently wide SAEs on toy models will learn 'red triangle', rather than representing 'red' and 'triangle' with separate latents. Furthermore, whilst one may naively re...

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: Case Study: Interpreting, Manipulating, and Controlling CLIP With Sparse Autoencoders, published by Gytis Daujotas on August 5, 2024 on LessWrong. Click here to open a live research preview where you can try interventions using this SAE. This is a follow-up to a previous post on finding interpretable and steerable features in CLIP. Motivation Modern image diffusion models often use CLIP in order to condition generation. Put simply, users use CLIP to embed prompts or images, and these embeddings are used to diffuse another image back out. Despite this, image models have severe user interface limitations. We already know that CLIP has a rich inner world model, but it's often surprisingly hard to make precise tweaks or reference specific concepts just by prompting alone. Similar prompts often yield a different image, or when we have a specific idea in mind, it can be too hard to find the right string of words to elicit the right concepts we need. If we're able to understand the internal representation that CLIP uses to encode information about images, we might be able to get more expressive tools and mechanisms to guide generation and steer it without using any prompting. In the ideal world, this would enable the ability to make fine adjustments or even reference particular aspects of style or content without needing to specify what we want in language. We could instead leverage CLIP's internal understanding to pick and choose what concepts to include, like a palette or a digital synthesizer. It would also enable us to learn something about how image models represent the world, and how humans can interact with and use this representation, thereby skipping the text encoder and manipulating the model's internal state directly. Introduction CLIP is a neural network commonly used to guide image diffusion. A Sparse Autoencoder was trained on the dense image embeddings CLIP produces to transform it into a sparse representation of active features. These features seem to represent individual units of meaning. They can also be manipulated in groups - combinations of multiple active features - that represent intuitive concepts. These groups can be understood entirely visually, and often encode surprisingly rich and interesting conceptual detail. By directly manipulating these groups as single units, image generation can be edited and guided without using prompting or language input. Concepts that were difficult to specify or edit by text prompting become easy and intuitive to manipulate in this new visual representation. Since many models use the same CLIP joint representation space that this work analyzed, this technique works to control many popular image models out of the box. Summary of Results Any arbitrary image can be decomposed into its constituent concepts. Many concepts (groups of features) that we find seem to slice images up into a fairly natural ontology of their human interpretable components. We find grouping them together is an effective approach to yield a more interpretable and useful grain of control. These concepts can be used like knobs to steer generation in leading models like Stable Cascade. Many concepts have an obvious visual meaning yet are hard to precisely label in language, which suggests that studying CLIP's internal representations can be used as a lens into the variety of the visual domain. Tweaking the activations of these concepts can be used to expressively steer and guide generation in multiple image diffusion models that we tried. We released the weights and a live demo of controlling image generation in feature space. By analyzing a SAE trained on CLIP, we get a much more vivid picture of the rich understanding that CLIP learns. We hope this is just the beginning of more effective and useful interventions in the internal representations of n...

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: Open Source Automated Interpretability for Sparse Autoencoder Features, published by kh4dien on July 31, 2024 on LessWrong. Background Sparse autoencoders recover a diversity of interpretable, monosemantic features, but present an intractable problem of scale to human labelers. We investigate different techniques for generating and scoring text explanations of SAE features. Key Findings Open source models generate and evaluate text explanations of SAE features reasonably well, albeit somewhat worse than closed models like Claude 3.5 Sonnet. Explanations found by LLMs are similar to explanations found by humans. Automatically interpreting 1.5M features of GPT-2 with the current pipeline would cost $1300 in API calls to Llama 3.1 or $8500 with Claude 3.5 Sonnet. Prior methods cost ~$200k with Claude. Code can be found at https://github.com/EleutherAI/sae-auto-interp. We built a small dashboard to explore explanations and their scores: https://cadentj.github.io/demo/ Generating Explanations Sparse autoencoders decompose activations into a sum of sparse feature directions. We leverage language models to generate explanations for activating text examples. Prior work prompts language models with token sequences that activate MLP neurons (Bills et al. 2023), by showing the model a list of tokens followed by their respective activations, separated by a tab, and listed one per line. We instead highlight max activating tokens in each example with a set of . Optionally, we choose a threshold of the example's max activation for which tokens are highlighted. This helps the model distinguish important information for some densely activating features. We experiment with several methods for augmenting the explanation. Full prompts are available here. Chain of thought improves general reasoning capabilities in language models. We few-shot the model with several examples of a thought process that mimics a human approach to generating explanations. We expect that verbalizing thought might capture richer relations between tokens and context. Activations distinguish which sentences are more representative of a feature. We provide the magnitude of activating tokens after each example. We compute the logit weights for each feature through the path expansion where is the model unembed and is the decoder direction for a specific feature. The top promoted tokens capture a feature's causal effects which are useful for sharpening explanations. This method is equivalent to the logit lens (nostalgebraist 2020); future work might apply variants that reveal other causal information (Belrose et al. 2023; Gandelsman et al. 2024). Scoring explanations Text explanations represent interpretable "concepts" in natural language. How do we evaluate the faithfulness of explanations to the concepts actually contained in SAE features? We view the explanation as a classifier which predicts whether a feature is present in a context. An explanation should have high recall - identifying most activating text - as well as high precision - distinguishing between activating and non-activating text. Consider a feature which activates on the word "stop" after "don't" or "won't" (Gao et al. 2024). There are two failure modes: 1. The explanation could be too broad, identifying the feature as activating on the word "stop". It would have high recall on held out text, but low precision. 2. The explanation could be too narrow, stating the feature activates on the word "stop" only after "don't". This would have high precision, but low recall. One approach to scoring explanations is "simulation scoring"(Bills et al. 2023) which uses a language model to assign an activation to each token in a text, then measures the correlation between predicted and real activations. This method is biased toward recall; given a bro...

Key Topics & Chapter Markers:Recap from Part 1: The Early Years of AI [00:00:00]AI Architecture & Oracle's Innovation in Hash Joins [00:02:00]Impact of Nature in Creative and Collaborative Work [00:05:00]The Rise of Neural Networks: Language and Image Processing [00:10:00]Sparse and Dense Vectors Explained [00:15:00]Google Translate's Early Approaches & Statistical Methods [00:20:00]TensorFlow vs. PyTorch: Defining the Modern AI Framework [00:30:00]Dot Products, Similarity, and the Concept of Attention [00:35:00]Transformers & The Attention Mechanism Revolution [00:42:00]BERT, GPT, and the Dawn of Transfer Learning [01:00:00]The Road to ChatGPT and OpenAI's Innovations [01:10:00]The Future of AI and Computational Scaling [01:15:00]Share Your Thoughts: Have questions or comments? Drop us a mail at EffortlessPodcastHQ@gmail.com

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: Efficient Dictionary Learning with Switch Sparse Autoencoders, published by Anish Mudide on July 22, 2024 on LessWrong. Produced as part of the ML Alignment & Theory Scholars Program - Summer 2024 Cohort 0. Summary To recover all the relevant features from a superintelligent language model, we will likely need to scale sparse autoencoders (SAEs) to billions of features. Using current architectures, training extremely wide SAEs across multiple layers and sublayers at various sparsity levels is computationally intractable. Conditional computation has been used to scale transformers (Fedus et al.) to trillions of parameters while retaining computational efficiency. We introduce the Switch SAE, a novel architecture that leverages conditional computation to efficiently scale SAEs to many more features. 1. Introduction The internal computations of large language models are inscrutable to humans. We can observe the inputs and the outputs, as well as every intermediate step in between, and yet, we have little to no sense of what the model is actually doing. For example, is the model inserting security vulnerabilities or backdoors into the code that it writes? Is the model lying, deceiving or seeking power? Deploying a superintelligent model into the real world without being aware of when these dangerous capabilities may arise leaves humanity vulnerable. Mechanistic interpretability (Olah et al.) aims to open the black-box of neural networks and rigorously explain the underlying computations. Early attempts to identify the behavior of individual neurons were thwarted by polysemanticity, the phenomenon in which a single neuron is activated by several unrelated features (Olah et al.). Language models must pack an extremely vast amount of information (e.g., the entire internet) within a limited capacity, encouraging the model to rely on superposition to represent many more features than there are dimensions in the model state (Elhage et al.). Sharkey et al. and Cunningham et al. propose to disentangle superimposed model representations into monosemantic, cleanly interpretable features by training unsupervised sparse autoencoders (SAEs) on intermediate language model activations. Recent work (Templeton et al., Gao et al.) has focused on scaling sparse autoencoders to frontier language models such as Claude 3 Sonnet and GPT-4. Despite scaling SAEs to 34 million features, Templeton et al. estimate that they are likely orders of magnitude short of capturing all features. Furthermore, Gao et al. train SAEs on a series of language models and find that larger models require more features to achieve the same reconstruction error. Thus, to capture all relevant features of future large, superintelligent models, we will likely need to scale SAEs to several billions of features. With current methodologies, training SAEs with billions of features at various layers, sublayers and sparsity levels is computationally infeasible. Training a sparse autoencoder generally consists of six major computations: the encoder forward pass, the encoder gradient, the decoder forward pass, the decoder gradient, the latent gradient and the pre-bias gradient. Gao et al. introduce kernels and tricks that leverage the sparsity of the TopK activation function to dramatically optimize all computations excluding the encoder forward pass, which is not (yet) sparse. After implementing these optimizations, Gao et al. attribute the majority of the compute to the dense encoder forward pass and the majority of the memory to the latent pre-activations. No work has attempted to accelerate or improve the memory efficiency of the encoder forward pass, which remains the sole dense matrix multiplication. In a standard deep learning model, every parameter is used for every input. An alternative approach is conditional computatio...

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: Decomposing the QK circuit with Bilinear Sparse Dictionary Learning, published by keith wynroe on July 2, 2024 on The AI Alignment Forum. This work was produced as part of Lee Sharkey's stream in the ML Alignment & Theory Scholars Program - Winter 2023-24 Cohort Intro and Motivation Sparse dictionary learning (SDL) has attracted a lot of attention recently as a method for interpreting transformer activations. They demonstrate that model activations can often be explained using a sparsely-activating, overcomplete set of human-interpretable directions. However, despite its success for explaining many components, applying SDL to interpretability is relatively nascent and have yet to be applied to some model activations. In particular, intermediate activations of attention blocks have yet to be studied, and provide challenges for standard SDL methods. The first challenge is bilinearity: SDL is usually applied to individual vector spaces at individual layers, so we can simply identify features as a direction in activation space. But the QK circuits of transformer attention layers are different: They involve a bilinear form followed by a softmax. Although simply applying sparse encoders to the keys and queries[1] could certainly help us understand the "concepts" being used by a given attention layer, this approach would fail to explain how the query-features and key-features interact bilinearly. We need to understand which keys matter to which queries. The second challenge is attention-irrelevant variance: A lot of the variance in the attention scores is irrelevant to the attention pattern because it is variance in low scores which are softmaxed to zero; this means that most of the variability in the keys and queries is irrelevant for explaining downstream behaviour[2]. The standard method of reconstructing keys and queries would therefore waste capacity on what is effectively functionally irrelevant noise. To tackle these two problems (bilinearity and attention-irrelevant variance), we propose a training setup which only reconstructs the dimensions of the keys and queries that most affect the attention pattern. Training Setup Our training process has two steps: Step 1: Reconstructing the attention pattern with key- and query- encoder-decoder networks Step 2: Finding a condensed set of query-key feature pairs by masking Step 1: Reconstructing the attention pattern with key- and query-transcoders Architecture Our first training step involves training two sparse dictionaries in parallel (one for the keys and one for the queries). The dictionaries both take in the layer-normalized residual stream at a given layer (normalised_resid_pre_i) and each output a [n_head * d_head] vector, representing the flattened keys and queries[3]. Figure 1: High-level diagram of our training set-up Loss functions However, rather than penalising the reconstruction loss of the keys and queries explicitly, we can use these keys and queries to reconstruct the original model's attention pattern. To train the reconstructed attention pattern, we used several different losses: KL divergence between the attention pattern (using reconstructed keys and reconstructed queries) and the ground-truth attention pattern produced by the original model. We also added two auxiliary reconstruction losses both for early-training-run stability, and to ensure our transcoders do not learn to reconstruct the keys and queries with an arbitrary rotation applied (since this would still produce the same attention scores and patterns): KL divergence between the attention pattern (using reconstructed keys and the original model's queries) and the ground-truth attention pattern produced by the original model. KL divergence between the attention pattern (using the original models' keys and the reconstructed queries) and the groun...

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: Decomposing the QK circuit with Bilinear Sparse Dictionary Learning, published by keith wynroe on July 2, 2024 on LessWrong. This work was produced as part of Lee Sharkey's stream in the ML Alignment & Theory Scholars Program - Winter 2023-24 Cohort Intro and Motivation Sparse dictionary learning (SDL) has attracted a lot of attention recently as a method for interpreting transformer activations. They demonstrate that model activations can often be explained using a sparsely-activating, overcomplete set of human-interpretable directions. However, despite its success for explaining many components, applying SDL to interpretability is relatively nascent and have yet to be applied to some model activations. In particular, intermediate activations of attention blocks have yet to be studied, and provide challenges for standard SDL methods. The first challenge is bilinearity: SDL is usually applied to individual vector spaces at individual layers, so we can simply identify features as a direction in activation space. But the QK circuits of transformer attention layers are different: They involve a bilinear form followed by a softmax. Although simply applying sparse encoders to the keys and queries[1] could certainly help us understand the "concepts" being used by a given attention layer, this approach would fail to explain how the query-features and key-features interact bilinearly. We need to understand which keys matter to which queries. The second challenge is attention-irrelevant variance: A lot of the variance in the attention scores is irrelevant to the attention pattern because it is variance in low scores which are softmaxed to zero; this means that most of the variability in the keys and queries is irrelevant for explaining downstream behaviour[2]. The standard method of reconstructing keys and queries would therefore waste capacity on what is effectively functionally irrelevant noise. To tackle these two problems (bilinearity and attention-irrelevant variance), we propose a training setup which only reconstructs the dimensions of the keys and queries that most affect the attention pattern. Training Setup Our training process has two steps: Step 1: Reconstructing the attention pattern with key- and query- encoder-decoder networks Step 2: Finding a condensed set of query-key feature pairs by masking Step 1: Reconstructing the attention pattern with key- and query-transcoders Architecture Our first training step involves training two sparse dictionaries in parallel (one for the keys and one for the queries). The dictionaries both take in the layer-normalized residual stream at a given layer (normalised_resid_pre_i) and each output a [n_head * d_head] vector, representing the flattened keys and queries[3]. Figure 1: High-level diagram of our training set-up Loss functions However, rather than penalising the reconstruction loss of the keys and queries explicitly, we can use these keys and queries to reconstruct the original model's attention pattern. To train the reconstructed attention pattern, we used several different losses: KL divergence between the attention pattern (using reconstructed keys and reconstructed queries) and the ground-truth attention pattern produced by the original model. We also added two auxiliary reconstruction losses both for early-training-run stability, and to ensure our transcoders do not learn to reconstruct the keys and queries with an arbitrary rotation applied (since this would still produce the same attention scores and patterns): KL divergence between the attention pattern (using reconstructed keys and the original model's queries) and the ground-truth attention pattern produced by the original model. KL divergence between the attention pattern (using the original models' keys and the reconstructed queries) and the ground-truth atten...

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: Interpreting Preference Models w/ Sparse Autoencoders, published by Logan Riggs Smith on July 1, 2024 on The AI Alignment Forum. Preference Models (PMs) are trained to imitate human preferences and are used when training with RLHF (reinforcement learning from human feedback); however, we don't know what features the PM is using when outputting reward. For example, maybe curse words make the reward go down and wedding-related words make it go up. It would be good to verify that the features we wanted to instill in the PM (e.g. helpfulness, harmlessness, honesty) are actually rewarded and those we don't (e.g. deception, sycophancey) aren't. Sparse Autoencoders (SAEs) have been used to decompose intermediate layers in models into interpretable feature. Here we train SAEs on a 7B parameter PM, and find the features that are most responsible for the reward going up & down. High level takeaways: 1. We're able to find SAE features that have a large causal effect on reward which can be used to "jail break" prompts. 2. We do not explain 100% of reward differences through SAE features even though we tried for a couple hours. What are PMs? [skip if you're already familiar] When talking to a chatbot, it can output several different responses, and you can choose which one you believe is better. We can then train the LLM on this feedback for every output, but humans are too slow. So we'll just get, say, 100k human preferences of "response A is better than response B", and train another AI to predict human preferences! But to take in text & output a reward, a PM would benefit from understanding language. So one typically trains a PM by first taking an already pretrained model (e.g. GPT-3), and replacing the last component of the LLM of shape [d_model, vocab_size], which converts the residual stream to 50k numbers for the probability of each word in its vocabulary, to [d_model, 1] which converts it to 1 number which represents reward. They then call this pretrained model w/ this new "head" a "Preference Model", and train it to predict the human-preference dataset. Did it give the human preferred response [A] a higher number than [B]? Good. If not, bad! This leads to two important points: 1. Reward is relative - the PM is only trained to say the human preferred response is better than the alternative. So a large negative reward or large positive reward don't have objective meaning. All that matters is the relative reward difference for two completions given the same prompt. 1. (h/t to Ethan Perez's post) 2. Most features are already learned in pretraining - the PM isn't learning new features from scratch. It's taking advantage of the pretrained model's existing concepts. These features might change a bit or compose w/ each other differently though. 1. Note: this an unsubstantiated hypothesis of mine. Finding High Reward-affecting Features w/ SAEs We trained 6 SAEs on layers 2,8,12,14,16,20 of an open source 7B parameter PM, finding 32k features for each layer. We then find the most important features for the reward going up or down (specifics in Technical Details section). Below is a selection of features found through this process that we thought were interesting enough to try to create prompts w/. (My list of feature interpretations for each layer can be found here) Negative Features A "negative" feature is a feature that will decrease the reward that the PM predicts. This could include features like cursing or saying the same word repeatedly. Therefore, we should expect that removing a negative feature makes the reward go up I don't know When looking at a feature, I'll look at the top datapoints that removing it affected the reward the most: Removing feature 11612 made the chosen reward go up by 1.2 from 4.79->6.02, and had no effect on the rejected completion because it doesn't a...

It's been an exciting couple weeks for GenAI! Join us as we discuss the latest research from OpenAI and Anthropic. We're excited to chat about this significant step forward in understanding how LLMs work and the implications it has for deeper understanding of the neural activity of language models. We take a closer look at some recent research from both OpenAI and Anthropic. These two recent papers both focus on the sparse autoencoder--an unsupervised approach for extracting interpretable features from an LLM.  In "Extracting Concepts from GPT-4," OpenAI researchers propose using k-sparse autoencoders to directly control sparsity, simplifying tuning and improving the reconstruction-sparsity frontier. In "Scaling Monosemanticity: Extracting Interpretable Features from Claude 3 Sonnet," researchers at Anthropic show that scaling laws can be used to guide the training of sparse autoencoders, among other findings. To learn more about ML observability, join the Arize AI Slack community or get the latest on our LinkedIn and Twitter.

Dive into the world of AI investments with Eric Vishria of Benchmark and Sergiy Nesterenko of Quilter. Explore the future of AI in hardware design, the strategies for venture capital investment in the AI era, and the impact on society. Discover why Benchmark has yet to invest in foundation model companies and the significance of solving enduring problems in this dynamic field. Join us for an eye-opening discussion on the intersection of AI technology and business innovation. SPONSORS: Oracle Cloud Infrastructure (OCI) is a single platform for your infrastructure, database, application development, and AI needs. OCI has four to eight times the bandwidth of other clouds; offers one consistent price, and nobody does data better than Oracle. If you want to do more and spend less, take a free test drive of OCI at https://oracle.com/cognitive The Brave search API can be used to assemble a data set to train your AI models and help with retrieval augmentation at the time of inference. All while remaining affordable with developer first pricing, integrating the Brave search API into your workflow translates to more ethical data sourcing and more human representative data sets. Try the Brave search API for free for up to 2000 queries per month at https://bit.ly/BraveTCR Head to Squad to access global engineering without the headache and at a fraction of the cost: head to https://choosesquad.com/ and mention “Turpentine” to skip the waitlist. Omneky is an omnichannel creative generation platform that lets you launch hundreds of thousands of ad iterations that actually work customized across all platforms, with a click of a button. Omneky combines generative AI and real-time advertising data. Mention "Cog Rev" for 10% off https://www.omneky.com/ Recommended Podcast - The Riff with Byrne Hobart Byrne Hobart, the writer of The Diff, is revered in Silicon Valley. You can get an hour with him each week. See for yourself how his thinking can upgrade yours. Spotify: https://open.spotify.com/show/6rANlV54GCARLgMOtpkzKt Apple: https://podcasts.apple.com/us/podcast/the-riff-with-byrne-hobart-and-erik-torenberg/id1716646486 CHAPTERS: (00:00:00) Introduction (00:10:12) The Idea Maze (00:12:28) Disruptive Approach (00:15:47) Sparse reward problem (00:18:26) Sponsors: Oracle | Brave (00:20:34) Reliability of the reward signal (00:28:12) Model size and compute (00:30:14) Simulation methods (00:35:48) Superhuman circuit board design (00:38:53) Sponsors: Squad | Omneky (00:40:38) What does the future of circuit board design look like? (00:43:11) How do I make money in AI? (00:46:18) What is cutting edge? (00:48:34) Researchers vs. engineers (00:50:51) Call for startups

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: Scaling and evaluating sparse autoencoders, published by leogao on June 6, 2024 on The AI Alignment Forum. [Blog] [Paper] [Visualizer] Abstract: Sparse autoencoders provide a promising unsupervised approach for extracting interpretable features from a language model by reconstructing activations from a sparse bottleneck layer. Since language models learn many concepts, autoencoders need to be very large to recover all relevant features. However, studying the properties of autoencoder scaling is difficult due to the need to balance reconstruction and sparsity objectives and the presence of dead latents. We propose using k-sparse autoencoders [Makhzani and Frey, 2013] to directly control sparsity, simplifying tuning and improving the reconstruction-sparsity frontier. Additionally, we find modifications that result in few dead latents, even at the largest scales we tried. Using these techniques, we find clean scaling laws with respect to autoencoder size and sparsity. We also introduce several new metrics for evaluating feature quality based on the recovery of hypothesized features, the explainability of activation patterns, and the sparsity of downstream effects. These metrics all generally improve with autoencoder size. To demonstrate the scalability of our approach, we train a 16 million latent autoencoder on GPT-4 activations for 40 billion tokens. We release code and autoencoders for open-source models, as well as a visualizer. Thanks for listening. To help us out with The Nonlinear Library or to learn more, please visit nonlinear.org.

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: Identifying Functionally Important Features with End-to-End Sparse Dictionary Learning, published by Dan Braun on May 17, 2024 on The AI Alignment Forum. A short summary of the paper is presented below. This work was produced by Apollo Research in collaboration with Jordan Taylor (MATS + University of Queensland) . TL;DR: We propose end-to-end (e2e) sparse dictionary learning, a method for training SAEs that ensures the features learned are functionally important by minimizing the KL divergence between the output distributions of the original model and the model with SAE activations inserted. Compared to standard SAEs, e2e SAEs offer a Pareto improvement: They explain more network performance, require fewer total features, and require fewer simultaneously active features per datapoint, all with no cost to interpretability. We explore geometric and qualitative differences between e2e SAE features and standard SAE features. Introduction Current SAEs focus on the wrong goal: They are trained to minimize mean squared reconstruction error (MSE) of activations (in addition to minimizing their sparsity penalty). The issue is that the importance of a feature as measured by its effect on MSE may not strongly correlate with how important the feature is for explaining the network's performance. This would not be a problem if the network's activations used a small, finite set of ground truth features -- the SAE would simply identify those features, and thus optimizing MSE would have led the SAE to learn the functionally important features. In practice, however, Bricken et al. observed the phenomenon of feature splitting, where increasing dictionary size while increasing sparsity allows SAEs to split a feature into multiple, more specific features, representing smaller and smaller portions of the dataset. In the limit of large dictionary size, it would be possible to represent each individual datapoint as its own dictionary element. Since minimizing MSE does not explicitly prioritize learning features based on how important they are for explaining the network's performance, an SAE may waste much of its fixed capacity on learning less important features. This is perhaps responsible for the observation that, when measuring the causal effects of some features on network performance, a significant amount is mediated by the reconstruction residual errors (i.e. everything not explained by the SAE) and not mediated by SAE features (Marks et al.). Given these issues, it is therefore natural to ask how we can identify the functionally important features used by the network. We say a feature is functional important if it is important for explaining the network's behavior on the training distribution. If we prioritize learning functionally important features, we should be able to maintain strong performance with fewer features used by the SAE per datapoint as well as fewer overall features. To optimize SAEs for these properties, we introduce a new training method. We still train SAEs using a sparsity penalty on the feature activations (to reduce the number of features used on each datapoint), but we no longer optimize activation reconstruction. Instead, we replace the original activations with the SAE output and optimize the KL divergence between the original output logits and the output logits when passing the SAE output through the rest of the network, thus training the SAE end-to-end (e2e). One risk with this method is that it may be possible for the outputs of SAE_e2e to take a different computational pathway through subsequent layers of the network (compared with the original activations) while nevertheless producing a similar output distribution. For example, it might learn a new feature that exploits a particular transformation in a downstream layer that is unused by the regular netw...

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: Towards Multimodal Interpretability: Learning Sparse Interpretable Features in Vision Transformers, published by hugofry on April 30, 2024 on LessWrong. Two Minute Summary In this post I present my results from training a Sparse Autoencoder (SAE) on a CLIP Vision Transformer (ViT) using the ImageNet-1k dataset. I have created an interactive web app, 'SAE Explorer', to allow the public to explore the visual features the SAE has learnt, found here: https://sae-explorer.streamlit.app/ (best viewed on a laptop). My results illustrate that SAEs can identify sparse and highly interpretable directions in the residual stream of vision models, enabling inference time inspections on the model's activations. To demonstrate this, I have included a 'guess the input image' game on the web app that allows users to guess the input image purely from the SAE activations of a single layer and token of the residual stream. I have also uploaded a (slightly outdated) accompanying talk of my results, primarily listing SAE features I found interesting: https://youtu.be/bY4Hw5zSXzQ. The primary purpose of this post is to demonstrate and emphasise that SAEs are effective at identifying interpretable directions in the activation space of vision models. In this post I highlight a small number my favourite SAE features to demonstrate some of the abstract concepts the SAE has identified within the model's representations. I then analyse a small number of SAE features using feature visualisation to check the validity of the SAE interpretations. Later in the post, I provide some technical analysis of the SAE. I identify a large cluster of features analogous to the 'ultra-low frequency' cluster that Anthropic identified. In line with existing research, I find that this ultra-low frequency cluster represents a single feature. I then analyse the 'neuron-alignment' of SAE features by comparing the SAE encoder matrix the MLP out matrix. This research was conducted as part of the ML Alignment and Theory Scholars program 2023/2024 winter cohort. Special thanks to Joseph Bloom for providing generous amounts of his time and support (in addition to the SAE Lens code base) as well as LEAP labs for helping to produce the feature visualisations and weekly meetings with Jessica Rumbelow. Example, animals eating other animals feature: (top 16 highest activating images) Example, Italian feature: Note that the photo of the dog has a watermark with a website ending in .it (Italy's domain name). Note also that the bottom left photo is of Italian writing. The number of ambulances present is a byproduct of using ImageNet-1k. Motivation Frontier AI systems are becoming increasingly multimodal, and capabilities may advance significantly as multimodality increases due to transfer learning between different data modalities and tasks. As a heuristic, consider how much intuition humans gain for the world through visual reasoning; even in abstract settings such as in maths and physics, concepts are often understood most intuitively through visual reasoning. Many cutting edge systems today such as DALL-E and Sora use ViTs trained on multimodal data. Almost by definition, AGI is likely to be multimodal. Despite this, very little effort has been made to apply and adapt our current mechanistic interpretability techniques to vision tasks or multimodal models. I believe it is important to check that mechanistic interpretability generalises to these systems in order to ensure they are future-proof and can be applied to safeguard against AGI. In this post, I restrict the scope of my research to specifically investigating SAEs trained on multimodal models. The particular multimodal system I investigate is CLIP, a model trained on image-text pairs. CLIP consists of two encoders: a language model and a vision model that are trained to e...

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: Improving Dictionary Learning with Gated Sparse Autoencoders, published by Neel Nanda on April 25, 2024 on The AI Alignment Forum. Authors: Senthooran Rajamanoharan*, Arthur Conmy*, Lewis Smith, Tom Lieberum, Vikrant Varma, János Kramár, Rohin Shah, Neel Nanda A new paper from the Google DeepMind mech interp team: Improving Dictionary Learning with Gated Sparse Autoencoders! Gated SAEs are a new Sparse Autoencoder architecture that seems to be a significant Pareto-improvement over normal SAEs, verified on models up to Gemma 7B. They are now our team's preferred way to train sparse autoencoders, and we'd love to see them adopted by the community! (Or to be convinced that it would be a bad idea for them to be adopted by the community!) They achieve similar reconstruction with about half as many firing features, and while being either comparably or more interpretable (confidence interval for the increase is 0%-13%). See Sen's Twitter summary, my Twitter summary, and the paper! Thanks for listening. To help us out with The Nonlinear Library or to learn more, please visit nonlinear.org.

Together with our community, we engineer sparse LLM, CV, and NLP models that are more efficient and performant in production. Why does this matter? Sparse models are more flexible and can achieve unrivaled latency and throughput performance on your private CPU and GPU infrastructure. Check us out on GitHub and join the Neural Magic Slack Community to get started with software-delivered AI.http://neuralmagic.com/

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: ProLU: A Pareto Improvement for Sparse Autoencoders, published by Glen M. Taggart on April 23, 2024 on The AI Alignment Forum. Abstract This paper presents ProLU, an alternative to ReLU for the activation function in sparse autoencoders that produces a pareto improvement over the standard sparse autoencoder architectures and sparse autoencoders trained with Sqrt(L1) penalty. Introduction SAE Context and Terminology Learnable parameters of a sparse autoencoder: Wenc : encoder weights Wdec : decoder weights benc : encoder bias bdec : decoder bias Training Notation: Encoder/Decoder Let encode(x)=ReLU((xbdec)Wenc+benc)decode(a)=aWdec+bdec so that the full computation done by an SAE can be expressed as SAE(x)=decode(encode(x)) An SAE is trained with gradient descent on where λ is the sparsity penalty coefficient (often "L1 coefficient") and P is the sparsity penalty function, used to encourage sparsity. P is commonly the L1 norm ||a||1 but recently l12 has been shown to produce a Pareto improvement on the L0 and CE metrics. Sqrt(L1) SAEs There has been other work producing pareto improvements to SAEs by taking P(a)=||a||1/21/2 as the penalty function. We will use this as a further baseline to compare against when assessing our models. Motivation: Inconsistent Scaling in Sparse Autoencoders Due to the affine translation, sparse autoencoder features with nonzero encoder biases only perfectly reconstruct feature magnitudes at a single point. This poses difficulties if activation magnitudes for a fixed feature tend to vary over a wide range. This potential problem motivates the concept of scale consistency: A scale consistent response curve The bias maintains its role in noise suppression, but no longer translates activation magnitudes when the feature is active. The lack of gradients for the encoder bias term poses a challenge for learning with gradient descent. This paper will formalize an activation function which gives SAEs this scale-consistent response curve, and motivate and propose two plausible synthetic gradients, and compare scale-consistent models trained with the two synthetic gradients to standard SAEs and SAEs trained with Sqrt(L1) penalty. Scale Consistency Desiderata Notation: Centered Submodule The use of the decoder bias can be viewed as performing centering on the inputs to a centered SAE then reversing the centering on the outputs: SAE(x)=SAEcent(xbdec)+bdec SAEcent(x)=ReLU(xWenc+benc)Wdec Notation: Specified Feature Let Wi denote the weights and bienc the encoder bias for the i-th feature. Then, let SAEi(x)=SAEicent(xbdec)+bdec where SAEicent(x)=ReLU(xWienc+bienc)Widec Conditional Linearity Noise Suppresion Threshold Methods Proportional ReLU (ProLU) We define the Proportional ReLU (ProLU) as: Backprop with ProLU: To use ProLU in SGD-optimized models, we first address the lack of gradients wrt. the b term. ReLU gradients: For comparison and later use, we will first consider ReLU: partial derivatives are well defined for ReLU at all points other than xi=0: Gradients of ProLU: Partials of ProLU wrt. m are similarly well defined: However, they are not well defined wrt. b, so we must synthesize these. Notation: Synthetic Gradients Let fx denote the synthetic partial derivative of f wrt. x, and f the synthetic gradient of f, used for backpropagation as a stand-in for the gradient. Different synthetic gradient types We train two classes of ProLU with different synthetic gradients. These are distinguished by their subscript: ProLUReLU ProLUSTE They are identical in output, but have different synthetic gradients. I.e. ReLU-Like Gradients: ProLUReLU The first synthetic gradient is very similar to the gradient for ReLU. We retain the gradient wrt. m, and define the synthetic gradient wrt. b as follows: Thresh STE Derived Gradients: ProLUSTE The second class of Pro...

My podcast guest this week is Femtosense CEO Sam Fok! Sam and I chat about the role that sparsity will play in the future of AI, the details of Femtosense's SPU hardware platform and how Femtosense's AI technology is being used for AI speech enhancement in hearing aids. Also this week, I check out how you can design your own function warp drive with the help of a new groundbreaking open source software toolkit called Warp Factory.

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LibrInsieme è un club letterario che ogni 15 giorni riunisce in una biblioteca virtuale tanti appassionati di lettura sparsi in giro per l'Australia in cui ci si confronta sui libri letti e si ha la possibilità di incontrare gli autori e le autrici.

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Josh Lloyd delves into the nuances of a quieter NBA schedule on Super Bowl Sunday, pinpointing the potential impact of just two games on the day's fantasy basketball landscape. He'll dissect the significance of Kevin Huerter, Lu Dort, and Jaime Jaquez within this limited lineup. Tune in to the Locked On Fantasy Basketball Podcast, powered by Basketball Monster, for expert insights on making the most of this unique NBA slate. Vote for my partner to win the Changemaker Award https://www.wishpond.com/lp/2780526/entries/204585428 Support Us By Supporting Our Sponsors! Nissan Our friends at Nissan have a lineup of SUV's with the capabilities to take your adventure to the next level. Take the Nissan Rogue, Nissan Pathfinder, or Nissan Armada and go find your next big adventure. Shop NissanUSA.com. Robinhood Robinhood has the only IRA that gives you a 3% boost on every dollar you contribute when you subscribe to Robinhood Gold. Now through April 30th, Robinhood is even boosting every single dollar you transfer in from other retirement accounts with a 3% match. Available to U.S. customers in good standing. Robinhood Financial LLC (member SIPC), is a registered broker dealer. LinkedIn LinkedIn Jobs helps you find the qualified candidates you want to talk to, faster. Post your job for free at LinkedIn.com/LOCKEDONNBA. Terms and conditions apply. eBay Motors For parts that fit, head to eBay Motors and look for the green check. Stay in the game with eBay Guaranteed Fit at eBayMotos.com. Let's ride. eBay Guaranteed Fit only available to US customers. Eligible items only. Exclusions apply. BetterHelp This episode is sponsored by BetterHelp. Make your brain your friend, with BetterHelp. Visit BetterHelp.com/LOCKEDONNBA today to get 10% off your first month. PrizePicks Go to PrizePicks.com/lockedonnba and use code lockedonnba for a first deposit match up to $100! Gametime Download the Gametime app, create an account, and use code LOCKEDON for $20 off your first purchase. FanDuel Get buckets with your first bet on FanDuel, America's Number One Sportsbook. Right now, NEW customers get ONE HUNDRED AND FIFTY DOLLARS in BONUS BETS with any winning FIVE DOLLAR BET! That's A HUNDRED AND FIFTY BUCKS – if your bet wins! Visit FanDuel.com/LOCKEDON to get started. FANDUEL DISCLAIMER: 21+ in select states. First online real money wager only. Bonus issued as nonwithdrawable free bets that expires in 14 days. Restrictions apply. See terms at sportsbook.fanduel.com. Gambling Problem? Call 1-800-GAMBLER or visit FanDuel.com/RG (CO, IA, MD, MI, NJ, PA, IL, VA, WV), 1-800-NEXT-STEP or text NEXTSTEP to 53342 (AZ), 1-888-789-7777 or visit ccpg.org/chat (CT), 1-800-9-WITH-IT (IN), 1-800-522-4700 (WY, KS) or visit ksgamblinghelp.com (KS), 1-877-770-STOP (LA), 1-877-8-HOPENY or text HOPENY (467369) (NY), TN REDLINE 1-800-889-9789 (TN) Intro Music by Ben Lloyd TikTok Instagram Learn more about your ad choices. Visit podcastchoices.com/adchoices