Podcasts about zador

Support the show to get full episodes and join the Discord community. The Transmitter is an online publication that aims to deliver useful information, insights and tools to build bridges across neuroscience and advance research. Visit thetransmitter.org to explore the latest neuroscience news and perspectives, written by journalists and scientists. Read more about our partnership. Sign up for the “Brain Inspired” email alerts to be notified every time a new “Brain Inspired” episode is released: https://www.thetransmitter.org/newsletters/ To explore more neuroscience news and perspectives, visit thetransmitter.org. Tony Zador runs the Zador lab at Cold Spring Harbor Laboratory. You've heard him on Brain Inspired a few times in the past, most recently in a panel discussion I moderated at this past COSYNE conference - a conference Tony co-founded 20 years ago. As you'll hear, Tony's current and past interests and research endeavors are of a wide variety, but today we focus mostly on his thoughts on NeuroAI. We're in a huge AI hype cycle right now, for good reason, and there's a lot of talk in the neuroscience world about whether neuroscience has anything of value to provide AI engineers - and how much value, if any, neuroscience has provided in the past. Tony is team neuroscience. You'll hear him discuss why in this episode, especially when it comes to ways in which development and evolution might inspire better data efficiency, looking to animals in general to understand how they coordinate numerous objective functions to achieve their intelligent behaviors - something Tony calls alignment - and using spikes in AI models to increase energy efficiency. Zador Lab Twitter: @TonyZador Previous episodes: BI 187: COSYNE 2024 Neuro-AI Panel. BI 125 Doris Tsao, Tony Zador, Blake Richards: NAISys BI 034 Tony Zador: How DNA and Evolution Can Inform AI Related papers Catalyzing next-generation Artificial Intelligence through NeuroAI. Encoding innate ability through a genomic bottleneck. Essays NeuroAI: A field born from the symbiosis between neuroscience, AI. What the brain can teach artificial neural networks. Read the transcript. 0:00 - Intro 3:28 - "Neuro-AI" 12:48 - Visual cognition history 18:24 - Information theory in neuroscience 20:47 - Necessary steps for progress 24:34 - Neuro-AI models and cognition 35:47 - Animals for inspiring AI 41:48 - What we want AI to do 46:01 - Development and AI 59:03 - Robots 1:25:10 - Catalyzing the next generation of AI

La ciudad se contagiará del ambiente del medievo con más de 75 puestos de artesanía

Ivan Zador is a member of the Colorado Springs Jewish community, an immigrant from Prague, Czechoslovakia, and a physician, who co-organized the Rally for Israel that took place at City Hall in December 2023.  Just back from a service trip to Israel, Ivan shares about the October 7th attacks, the most impactful personal experiences from his trip, and how you can advocate for Israel in our region.   See omnystudio.com/listener for privacy information.

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.02.18.528865v1?rss=1 Authors: Yuan, L., Chen, X., Zhan, H., Gilbert, H. L., Zador, A. M. Abstract: Neurons in the cortex are heterogenous, sending diverse axonal projections to multiple brain regions. Unraveling the logic of these projections requires single-neuron resolution. Although a growing number of techniques have enabled high-throughput reconstruction, these techniques are typically limited to dozens or at most hundreds of neurons per brain, requiring that statistical analyses combine data from different specimens. Here we present axonal BARseq, a high-throughput approach based on reading out nucleic acid barcodes using in situ RNA sequencing, which enables analysis of even densely labeled neurons. As a proof of principle, we have mapped the long-range projections of greater than 8000 mouse primary auditory cortex neurons from a single brain. We identified major cell types based on projection targets and axonal trajectory. The large sample size enabled us to systematically quantify the projections of intratelencephalic (IT) neurons, and revealed that individual IT neurons project to different layers in an area-dependent fashion. Axonal BARseq is a powerful technique for studying the heterogeneity of single neuronal projections at high throughput within individual brains. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.01.31.526457v1?rss=1 Authors: Funamizu, A., Marbach, F., Zador, A. M. Abstract: The activity of neurons in the auditory cortex is driven by both sounds and non-sensory context. To investigate the neuronal correlates of non-sensory context, we trained head-fixed mice to perform a two-alternative choice auditory task in which either reward or stimulus expectation (prior) was manipulated in blocks. Using two-photon calcium imaging to record populations of single neurons in auditory cortex, we found that both sensory and reward expectation modulated the activity of these neurons. Interestingly, the optimal decoder was stable even in the face of variable sensory representations. Neither the context nor the mouse's choice could be reliably decoded from the recorded auditory activity. Our findings suggest that in spite of modulation of auditory cortical activity by task priors, auditory cortex does not represent sufficient information about these priors to exploit them optimally and that decisions in this task require that rapidly changing sensory information be combined with more slowly varying task information extracted and represented in brain regions other than auditory cortex. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Timestamps: (00:00) - Intro (02:08) - Tony's background, Costa Rican singing mouse (06:59) - Traditional & embodied Turing Test, large language models (15:16) - Mouse intelligence, evolution, modularity, dish-washing dogs? (26:16) - Platform for training non-human animal-like virtual agents (36:14) - Exploration in children vs animals, innate vs learning, cognitive maps, complementary learning systems theory (46:53) - Genomic bottleneck, transfer learning, artificial Laplacian evolution (01:02:06) - Why AI needs connectomics? (01:06:55) - Brainbow, molecular connectomics: MAPseq & BRICseq (01:14:52) - Comparative (corvid) connectomics (01:18:04) - "Human uniqueness" - why do/ don't people believe in evolutionary continuity (01:25:29) - Career questions & virtual mouse passing the Embodied Turing Test in 5 years? Tony's lab website https://zadorlab.labsites.cshl.edu/ Tony's Twitter https://twitter.com/TonyZador Toward Next-Generation Artificial Intelligence: Catalyzing the NeuroAI Revolution - Embodied Turing Test paper (2022) https://arxiv.org/ftp/arxiv/papers/2210/2210.08340.pdf A critique of pure learning and what artificial neural networks can learn from animal brains paper (2019) http://zadorlab.labsites.cshl.edu/wp-content/uploads/sites/59/2019/08/A-critique-of-pure-learning-and-what-artificial-neuralnetworks-can-learn-from-animal-brains.pdf Genomic bottleneck paper (2021) http://zadorlab.labsites.cshl.edu/wp-content/uploads/sites/59/2021/03/Encoding-innate-ability-through-a-genomic-bottleneck.pdf MAPseq paper (2016) http://zadorlab.labsites.cshl.edu/wp-content/uploads/sites/59/2018/04/Zador-etal_2016_neuron_High-throughput-mapping.pdf BRICseq paper (2020) http://zadorlab.labsites.cshl.edu/wp-content/uploads/sites/59/2020/07/BRICseq-Bridges-Brain-wide-Interregional.pdf Squirrel ninja warrior course video https://www.youtube.com/watch?v=hFZFjoX2cGg Marbled Lungfish wiki https://en.wikipedia.org/wiki/Marbled_lungfish Papers about corvids https://www.science.org/doi/10.1126/science.1098410 https://link-springer-com.ezproxy1.bath.ac.uk/article/10.3758/s13420-020-00434-5 Twitter https://twitter.com/Embodied_AI

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.11.06.515380v1?rss=1 Authors: Chen, X., Fischer, S., Zhang, A., Gillis, J., Zador, A. Abstract: The cortex is composed of neuronal types with diverse gene expression that are organized into specialized cortical areas. These areas, each with characteristic cytoarchitecture, connectivity, and neuronal activity, are wired into modular networks. However, it remains unclear whether cortical areas and their modular organization can be similarly defined by their transcriptomic signatures. Here we used BARseq, a high-throughput in situ sequencing technique, to interrogate the expression of 107 cell type marker genes in 1.2 million cells over a mouse forebrain hemisphere at cellular resolution. De novo clustering of gene expression in single neurons revealed transcriptomic types that were consistent with previous single-cell RNAseq studies. Within medium-grained cell types that are shared across all cortical areas, gene expression and the distribution of fine-grained cell types vary along the contours of cortical areas. The compositions of transcriptomic types are highly predictive of cortical area identity. We grouped cortical areas into modules so that areas within a module, but not across modules, had similar compositions of transcriptomic types. Strikingly, these modules match cortical subnetworks that are highly interconnected, suggesting that cortical areas that are similar in cell types are also wired together. This "wire-by-similarity" rule reflects a novel organizing principle for the connectivity of cortical areas. Our BARseq-based strategy is high-throughput and low-cost, and scaling up this approach to many animals can potentially reveal the brain-wide molecular architecture across individuals, developmental times, and disease models. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

The brain is a complex machine, but some of us study its circuits and figure out its secrets. Anthony Zador joins Vasant Dhar in episode 35 of Brave New World to reveal what the cutting edge of neuroscience looks like. Useful resources: 1. Anthony Zador at Cold Spring Harbor Laboratory, Google Scholar and Twitter. 2. Christof Koch on Amazon. 3. A critique of pure learning and what artificial neural networks can learn from animal brains -- Anthony Zador. 4. Mind Children: The Future of Robot and Human Intelligence -- Hans Moravec. 5. The Nature of Intelligence -- Episode 7 of Brave New World (w Yann LeCun). 6. How the Mind Works -- Steven Pinker. 7. Ebbinghaus Illusion.

研エンの仲のRyoheiさんとNeuroRadioのHagiharaさんをゲストに迎え、研究者/科学者ポッドキャストについて色々話しました。Show notes 実験医学 2021年11月号 Vol.39 No.18 … “はじめよう！ 研究者ポッドキャスト―多様な研究者を“聴ける化”する新たなメディア 座談会／石黒　宗，加藤郁佳，宮脇健行” 第123回　ポッドキャストで語るサイエンスとその魅力 by Soh (実験医学) 研エンの仲 … “「研エンの仲」は、神経科学の研究者 Ayaka (@kayautoka) とソフトウェアエンジニア Ryohei (@fushimir) の2人によるPodcastです。科学やエンジニアリング、日常の話題についても話しています。” NeuroRadio … “NeuroRadioは神経科学者のKentaM_Hagiharaとtak_miyawakiが神経科学の新着論文、研究業界、海外での研究生活等、様々な話題を語るPodcast。大西洋を越えて収録しています。” #40 神経科学者はマイクロプロセッサを理解できたのか? (研エンの仲) … 研エンの仲の論文紹介回。お二人の専門性と論文の相性が高すぎる最高のエピソード。この論文は研エンの仲のために書かれた！(嘘) #46 波瀾万丈、プログラミング言語「JavaScript」の20年史 (研エンの仲) … 激動の1995年から始まるJavaScriptの話。当時の状況もよく理解でるすばらしいエピソード。それにしても1995年濃すぎる。 #29 Podcastのしくみと歴史 (研エンの仲) … Podcastの始まりからその性質までをわかりやすく説明している回。ポッドキャストをはじめてその歴史や仕組みが気になった方にはおすすめです。 #14 リモート会議を支える技術: 速さだけじゃないネットワークの品質 (研エンの仲) researchat.fmのediting … sohとtadasuが今のところ半々ぐらいかな。coela編集回は0 #53 どこどこは岡山弁じゃけん (研エンの仲) … 「なんてろかんてろ」が飛び出した神回。こういう話したい！ってなりました。ただあんまりおもしろい方言がないのがコンプレックス(香川県民, tadasu) #65 AIにPodcastの台本を書いてもらいました (研エンの仲) … AIに台本を書かせて読んでみる回。AIが結構ちゃんとした台本を出していてびっくりします。企画力の高さが光るエピソード。 #54 理系2人暮らし9ヶ月目、リアルな家事分担事情 (研エンの仲) … 生活における最適化問題を解くプロセスの話は大好物です。#10や#6あたりも聴いていただきたい。 #48 なんで私がF1に？ (ゲスト@namicha_1) (研エンの仲) … 今までスポーツ観戦にハマることがなかったというなみちゃん氏をゲストに迎え、F1沼にはまっていく序章となるエピソード。ここから何回かに分けてそのF1沼にはまっていく様子が語られていくことになる。 #13 Non-stealth marketing – the inaugural 1st author review (NeuroRadio) … HagiharaさんのNature 1st論文紹介回。強すぎる！我々の目標とするエピソード。 #11 Sequencing the Connectome (NeuroRadio) … Zadorのsequencingとconnectomeを繋げるという話はResearchat.fmでずっとやりたいなぁと思いつつも、話せてなかった論文群なのでとても勉強になった。 NeuroRadioさんのゲスト回 … 全部最高なんですが、今回のエピソード内に出てきたゲスト回とResearchat.fmのTwitterでコメントした回を載せておきます。 #30 Being a technologist at 10x (NeuroRadio) … 10xに研究者として就職されたSasakiさんのインタビューをMiyawakiさん単体で行ったすらばしいエピソード。 #8 Social Interaction with Dr. Teruhiro Okuyama – Part 1 (NeuroRadio) … MITに留学されていたOkuyamaさんのエピソード。親近感もありつつ、Okuyamaさんの戦略をしっかり聞き込めるすばらしいエピソード。 なぜ，バナナは黄色だと思うのか，思いつく事をしゃべってみろ！ … 奥山さんの留学記。tadasuは以前にこれを読んでいたので#8のエピソードには興味津々でした。 #20. A programmed round-trip for Europe by Dr. Keisuke Yonehara – Part 1 (NeuroRadio) … デンマークでPIされた後に昨年日本の遺伝研でもPIになられたYoneharaさんのエピソード。研究の進め方やラボの運営についてとても参考になる話が聞けます！ #16 Mechanistic vs population codes – Inter-show interaction with Ayaka Kato (NeuroRadio) … 研エンの仲のAyakaさんがNeuroRadioに出演した回。控えめに言って神回です。 Rebuild.fm … 日本のテック系ポッドキャストで最も有名かつ、最高峰の番組。 okaさんによるRebuildのタイトル考察 #研エンの仲 … めっちゃつぶやこうぜ！ #NeuroRadio … ツイートバンバンしようぜ NeuroRadioのマシュマロ … 発掘したからみんなでマシュマロ投げつけようぜ！ 104. Resolution of sweet bean (Researchat.fm) … つぶあんこしあんについて話し合った回 Anchor … みんなここでポッドキャスト始めよう！「Anchor はじめ方」でググってください！ 114. Logos of Logos (Researchat.fm) … ポッドキャストのアートワークについて語った回 実験医学のポッドキャスト … 実は存在していたのである。実験医学さん！ポッドキャストをはじめる機運ですよ！ RIKEN CBS presents ❝真夜中の脳科学 Brain Science ‘round midnight❞ … RIKEN CBSのポッドキャスト The OIST Podcast … OISTのポッドキャスト。これからは大学・研究所の広報の一環としてポッドキャストが重要視されるはず！ 100. Researchat.fm (Researchat.fm) … Researchat.fmの命名問題についてはこちらをお聴きください。 Misreading Chat Ryohei Yasuda … tadasuが神の如く尊敬する研究者。実はtadasuの高校の先輩。 働き者のラボ … なお萩原の今ボスは安田さんのポスドク時代のボスと同一人物で、こんな感じらしい笑 (h) 吾輩は科学者である … Ryohei Yasudaさんのポスドク時代からPIになるまでの記録が記されている伝説のサイト。 グラントの書き方 … Ryohei Yasudaさんのグラントライティングテクニックの根幹が書かれているページ。tadasuは毎回ここに立ち返る。 Ryohei Yasuda先生のツイート … 最近だとこのツイートが唐突すぎておもしろかった。 職探しTips　by 五十嵐 … NeuoRadioにも出演された五十嵐先生のポジションを取るまでの記録。あまりにも最高すぎる資料です。 NeuroRadioのポッドキャストはじめ方まとめ researchat.fmの収録tips 高井研先生のポッドキャスト考察1 … tadasuが鬼の如く尊敬する高井先生のツイート。”「科学教育アウトリーチにおけるエフォートと効果について」ハーバード大学のピーター・ガーガスというワシの同世代のワイルド天才が笑撃的な論文を書いておる（海洋性ゴリラ経由）。おそらく最も効果的なのは、ラジオやポッドキャストで、休日の市民講演会は最も意味なしらしい（アメリカでは）。” : Peter Girguis先生のことであることはわかるがどこに書いてあるのかわからん！だれかソースちょうだい！ 高井研先生のポッドキャスト考察2 … tadasuが鬼神の如く尊敬する高井先生のツイート。”まあ日本とアメリカでは社会の科学教育アウトリーチというとらえ方が結構違うのでワシの個人的印象とはやや異なるが市民講演会が効果が低いというのはなんとなくわかる。人気のあるラジオやポッドキャストが極めて効果的というのも賛成。ただしピーターはテレビや映画に出るほどイケメソではない（笑）”高井鬼神がすすめるんだからポッドキャストは重要なんだ！重要なんだ！ 高井研先生がバイリンガルニュースに出た回 … 高井・イケメソ鬼神・研先生がバリンガルニュースに出た回。このエピソードか、爆笑問題の太田さんがラジオで触れていた時にバイリンガルニュースっていうのがあるらしいと知った記憶。このエピソードは非常にいい感じだった。 Editorial notes Podcastの持続可能性に話せたのもよかったです。推しは推せるときに推そう。(Ryohei) ゲストで登場していきなり”私は今ストレスで機嫌が悪いです”という感じの悪い自己紹介をしていてどうしようもないですが、後半に行くに連れてそこそこちゃんと喋ったと思うのでお許しを… (米国移住でほんとに若干テンパってた)(Hagihara) 実は結構緊張していたのですが、終わってみれば楽しく収録できて最高でした！実験医学さんにもこの場を借りて改めてお礼申し上げます。(coela) 初めて、他のポッドキャストの方々にゲストとしてきていただいたのですが、結構しどろもどろでした。でもめちゃ楽しかったです！ありがとうございます。聴きたいなぁと思うけれども世の中にそんなメディアがない！というところがあればチャンス！自分で話しちゃいましょう！(tadasu)

Support the show to get full episodes and join the Discord community. Doris, Tony, and Blake are the organizers for this year's NAISys conference, From Neuroscience to Artificially Intelligent Systems (NAISys), at Cold Spring Harbor. We discuss the conference itself, some history of the neuroscience and AI interface, their current research interests, and a handful of topics around evolution, innateness, development, learning, and the current and future prospects for using neuroscience to inspire new ideas in artificial intelligence. From Neuroscience to Artificially Intelligent Systems (NAISys).Doris:@doristsao.Tsao Lab.Unsupervised deep learning identifies semantic disentanglement in single inferotemporal face patch neurons.Tony:@TonyZador.Zador Lab.A Critique of Pure Learning: What Artificial Neural Networks can Learn from Animal Brains.Blake:@tyrell_turing.The Learning in Neural Circuits Lab.The functional specialization of visual cortex emerges from training parallel pathways with self-supervised predictive learning. 0:00 - Intro 4:16 - Tony Zador 5:38 - Doris Tsao 10:44 - Blake Richards 15:46 - Deductive, inductive, abductive inference 16:32 - NAISys 33:09 - Evolution, development, learning 38:23 - Learning: plasticity vs. dynamical structures 54:13 - Different kinds of understanding 1:03:05 - Do we understand evolution well enough? 1:04:03 - Neuro-AI fad? 1:06:26 - Are your problems bigger or smaller now?

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: Debate on Instrumental Convergence between LeCun, Russell, Bengio, Zador, and More , published by Ben Pace on the LessWrong. Crossposted from the AI Alignment Forum. May contain more technical jargon than usual. An actual debate about instrumental convergence, in a public space! Major respect to all involved, especially Yoshua Bengio for great facilitation. For posterity (i.e. having a good historical archive) and further discussion, I've reproduced the conversation here. I'm happy to make edits at the request of anyone in the discussion who is quoted below. I've improved formatting for clarity and fixed some typos. For people who are not researchers in this area who wish to comment, see the public version of this post here. For people who do work on the relevant areas, please sign up in the top right. It will take a day or so to confirm membership. Original Post Yann LeCun: "don't fear the Terminator", a short opinion piece by Tony Zador and me that was just published in Scientific American. "We dramatically overestimate the threat of an accidental AI takeover, because we tend to conflate intelligence with the drive to achieve dominance. [...] But intelligence per se does not generate the drive for domination, any more than horns do." Comment Thread #1 Elliot Olds: Yann, the smart people who are very worried about AI seeking power and ensuring its own survival believe it's a big risk because power and survival are instrumental goals for almost any ultimate goal. If you give a generally intelligent AI the goal to make as much money in the stock market as possible, it will resist being shut down because that would interfere with tis goal. It would try to become more powerful because then it could make money more effectively. This is the natural consequence of giving a smart agent a goal, unless we do something special to counteract this. You've often written about how we shouldn't be so worried about AI, but I've never seen you address this point directly. Stuart Russell: It is trivial to construct a toy MDP in which the agent's only reward comes from fetching the coffee. If, in that MDP, there is another "human" who has some probability, however small, of switching the agent off, and if the agent has available a button that switches off that human, the agent will necessarily press that button as part of the optimal solution for fetching the coffee. No hatred, no desire for power, no built-in emotions, no built-in survival instinct, nothing except the desire to fetch the coffee successfully. This point cannot be addressed because it's a simple mathematical observation. Comment Thread #2 Yoshua Bengio: Yann, I'd be curious about your response to Stuart Russell's point. Yann LeCun: You mean, the so-called "instrumental convergence" argument by which "a robot can't fetch you coffee if it's dead. Hence it will develop self-preservation as an instrumental sub-goal." It might even kill you if you get in the way. 1. Once the robot has brought you coffee, its self-preservation instinct disappears. You can turn it off. 2. One would have to be unbelievably stupid to build open-ended objectives in a super-intelligent (and super-powerful) machine without some safeguard terms in the objective. 3. One would have to be rather incompetent not to have a mechanism by which new terms in the objective could be added to prevent previously-unforeseen bad behavior. For humans, we have education and laws to shape our objective functions and complement the hardwired terms built into us by evolution. 4. The power of even the most super-intelligent machine is limited by physics, and its size and needs make it vulnerable to physical attacks. No need for much intelligence here. A virus is infinitely less intelligent than you, but it can still kill you. 5. A second machine, designed solely to neut...

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: Debate on Instrumental Convergence between LeCun, Russell, Bengio, Zador, and More , published by Ben Pace on the AI Alignment Forum. An actual debate about instrumental convergence, in a public space! Major respect to all involved, especially Yoshua Bengio for great facilitation. For posterity (i.e. having a good historical archive) and further discussion, I've reproduced the conversation here. I'm happy to make edits at the request of anyone in the discussion who is quoted below. I've improved formatting for clarity and fixed some typos. For people who are not researchers in this area who wish to comment, see the public version of this post here. For people who do work on the relevant areas, please sign up in the top right. It will take a day or so to confirm membership. Original Post Yann LeCun: "don't fear the Terminator", a short opinion piece by Tony Zador and me that was just published in Scientific American. "We dramatically overestimate the threat of an accidental AI takeover, because we tend to conflate intelligence with the drive to achieve dominance. [...] But intelligence per se does not generate the drive for domination, any more than horns do." Comment Thread #1 Elliot Olds: Yann, the smart people who are very worried about AI seeking power and ensuring its own survival believe it's a big risk because power and survival are instrumental goals for almost any ultimate goal. If you give a generally intelligent AI the goal to make as much money in the stock market as possible, it will resist being shut down because that would interfere with tis goal. It would try to become more powerful because then it could make money more effectively. This is the natural consequence of giving a smart agent a goal, unless we do something special to counteract this. You've often written about how we shouldn't be so worried about AI, but I've never seen you address this point directly. Stuart Russell: It is trivial to construct a toy MDP in which the agent's only reward comes from fetching the coffee. If, in that MDP, there is another "human" who has some probability, however small, of switching the agent off, and if the agent has available a button that switches off that human, the agent will necessarily press that button as part of the optimal solution for fetching the coffee. No hatred, no desire for power, no built-in emotions, no built-in survival instinct, nothing except the desire to fetch the coffee successfully. This point cannot be addressed because it's a simple mathematical observation. Comment Thread #2 Yoshua Bengio: Yann, I'd be curious about your response to Stuart Russell's point. Yann LeCun: You mean, the so-called "instrumental convergence" argument by which "a robot can't fetch you coffee if it's dead. Hence it will develop self-preservation as an instrumental sub-goal." It might even kill you if you get in the way. 1. Once the robot has brought you coffee, its self-preservation instinct disappears. You can turn it off. 2. One would have to be unbelievably stupid to build open-ended objectives in a super-intelligent (and super-powerful) machine without some safeguard terms in the objective. 3. One would have to be rather incompetent not to have a mechanism by which new terms in the objective could be added to prevent previously-unforeseen bad behavior. For humans, we have education and laws to shape our objective functions and complement the hardwired terms built into us by evolution. 4. The power of even the most super-intelligent machine is limited by physics, and its size and needs make it vulnerable to physical attacks. No need for much intelligence here. A virus is infinitely less intelligent than you, but it can still kill you. 5. A second machine, designed solely to neutralize an evil super-intelligent machine will win every time, if given similar...

扁桃体Intercalted clusters (ITC) の亜核ITCdm/ITCvm (背側核/腹側核)が真逆の機能を持つこと、相互抑制の回路モチーフを持つこと、異なる投射先を協調制御していること等を報告した自身の最近の仕事について、萩原が裏話も交えながら紹介。 Shoe notes: Intercalated amygdala clusters orchestrate a switch in fear state 神経科学トピックス...日本語の解説記事@神経科学学会 Andreas Lüthiラボ Lüthiラボからの扁桃体回路レビュー（2015年） Pareの仕事↓ オピオイド受容体の発現を利用した毒素によってITCを破壊した仕事 ITCがBLA->CeAのImpulseを制御しているというgating仮説 スライスでExtinctionに相関した可塑性を見つけた天野さんの仕事 奇跡的にITCからvivoで電気記録を行なった仕事 Fear Conditioning(恐怖学習)の用語補足↓ CS: conditioned stimulus. 連合学習前にはneutralであることが求められる。主に音(tone)が使われる。 US: unconditioned stimulus. 学習を必要とせずイヤな(aversive)刺激、例えば電気ショック(foot shock)、苦味、天敵の匂い、など Extinction、(恐怖)消去。CSがもう恐怖を予測しないという新たな上塗り学習のこと。CSがもう恐怖を予測しないという新たな上塗り学習のこと。オリジナルの連合学習記憶が消えるわけではない。 Relapse: 消去された恐怖記憶が復活してしまうこと。実験パラダイムとしてはspontaneous recovery, reinstatement等いろいろある。この総説(Maren and Holmes)のFig1がよくまとまっている。 PTSD: Post-Traumatic Stress Disorder. USベテランの15%弱、またUS国民の11人に1人が生涯で一度は発症するとされる。 シンガーソングライターJoseph LeDoux: LeDoux2000 に集約される一連の仕事を行なった分野の創始者。最近は本を書いたり、バンド活動をメインにしている…？ 2005年のScience. Rumpel, LeDoux, Zador, Malinow Schnitzer研とのコラボ論文 初期のMiniscopeの論文 Ghosh et al., Ziv et al. CUBICによる透明化と抗体染色。ここでは初代プロトコルを少し改変して使用。 FoxP2-Cre line by Palmiterラボ。FoxP2は転写因子の一種。 Andrew Holmes: 薬理遺伝学（KORD/DREADD）コラボ相手。NIHのPrinciple Investigator Ingrid Ehrlich: スライス実験コラボ相手。 Kay TyeのNature. BLA to CeM/NAcの解析。 BLA to PL/ILのキャラクタライズ Senn et al. 2014 Neuron Fiber Photometry: バルクでCaシグナルを測る技術. Single-cellの解像度はないが、感度が高く、何より簡単。 Rebuttal,論文リバイズの際にreviewerコメントにpoint-by-pointで答える反証レター。 AMPA/NMDA ratio: 興奮性(Glutermatergic)シナプスの長期増強を反映する Opponent-process theory of motivation. Solomon and Corbit 1974 利根川研のReward engramとextinctionの共通性に関する論文。 Editorial notes: 論文、発見自体の面白さもそうですが、Extended Data含め、萩原さんのFigへのこだわりや実験の上手さがダダ洩れな感じが非常に良かったです。改めておめです～（宮脇） 気にしないようにしてますが、自分の仕事を語るのはやっぱりちょっと気恥ずかしいですね。そういえば、GRCは学会で見聞きしたことを口外してはならんという誓約書にサインするはずですが、今や誰も守ってないからまあいっか。あはは。 (萩原)

A Zador márka világhódító útja során megérkezett podcastunkba is. Az egyébként közgazdász végzettségű alapítóval a turisták által igen kedvelt Zádor-vártól indítottuk a beszélgetést, hiszen innen származik a név is.  Veszprémet és környékét, a Balaton-felvidéket a luxus világában jeleníti meg a pécselyi manufaktúrában készülő szappan. Az persze nem elég, hogy kiváló minőségre törekednek, a szerencse is kell a sikerhez. Az áttörést a csomagolóanyag hozta, de a magyar termálvíz világhíre is sokat nyom a latban. Bence szakavatottan mesélt az illat születésének folyamatáról, és arról is, hogy mennyire más illatok hódítanak az egyes országokban. Helyeselt, amikor az illatházat úgy képzeltük el, mint egy alkimista műhely és egy modern laboratórium ötvözetét. Illatozó podcast-epizódunk legegzotikusabb kifejezése a paprikavirág, ami illatként fontos márkajegy a nemzetközi piacon.

Formerly known as OnG! Podcast, welcome to Millennial Talk with Devinair.  STAY CONNECTEDSuperDell -Twitter: @Way_AwesomeRay Ray -Twitter: @ForeverRayRay_Facebook: Zaravia SandersIG: @za_dor_YouTube: Signed Sanders: P.S I Love YouNurse Pierce -Twitter: @Nurse_PierceFacebook: Dorisha PierceIG: @za_dor_YouTube: Signed Sanders: P.S I Love YouPlease send letters and/or questions to askdevinair@gmail.comFollow Millennial Talk on Facebook and TwitterFacebook: https://www.facebook.com/devinair.donald.3Twitter: @MTwDevinair Millennial Talk is a DonD Studios Production. Intro/Outro by Ulas Pakkan 

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.08.25.266460v1?rss=1 Authors: Sun, Y.-C., Chen, X., Fischer, S., Lu, S., Gillis, J., Zador, A. M. Abstract: Functional circuits consist of neurons with diverse axonal projections and gene expression. Understanding the molecular signature of projections requires high-throughput interrogation of both gene expression and projections to multiple targets in the same cells at cellular resolution, which is difficult to achieve using current technology. Here, we introduce BARseq2, a technique that simultaneously maps projections and detects multiplexed gene expression by in situ sequencing. We determined the expression of cadherins and cell-type markers in 29,933 cells, and the projections of 3,164 cells in both the mouse motor cortex and auditory cortex. Associating gene expression and projections in 1,349 neurons revealed shared cadherin signatures of homologous projections across the two cortical areas. These cadherins were enriched across multiple branches of the transcriptomic taxonomy. By correlating multi-gene expression and projections to many targets in single neurons with high throughput, BARseq2 provides a path to uncovering the molecular logic underlying neuronal circuits. Copy rights belong to original authors. Visit the link for more info

A special edition of Talk Soccer to Me with special guest from Budapest, Hungary.... Tommy Zador. Your host Bella Michaels interviews Zador, who is on the DePaul Men's Tennis team, and is an avid soccer fan. In the first segment we talk about Zador's background and experience moving to the United States. In the second segment we discuss top news and thoughts on this weekend's Euro 2020 Qualifiers.

Tony and I talk about his idea that AI should take inspiration from DNA. That is, DNA through evolution can be thought of as a compressed bottleneck of innate information for our brains to start with, unlike the tabula rasa deep learning systems in vogue these days. We also talk about his experiences starting the COSYNE conference, his work with auditory decision making in rodents, how he plans to revolutionize neuroscience using DNA barcodes to solve the connectome, and more.

In this episode Christine and I discuss her upbringing, and what it was like growing up on a gun range. We discuss how she was bullied growing up for having a unique upbringing, and how she overcame those obstacles. Christine's Dad was an olympic gold medalist, and he used his experience to coach her to become an elite water polo player herself. We talk about her decision to attend USC to play water polo, and what that experience was like. Christine shares how she ended up coming back to the range to take over the operation, and how she decided to become a competitive shooter. We also discuss what her vision is for the Linden Gun Range, and how it has become her passion. You will love this episode 

シドニー・ブレナー博士特集回（後半）では、顕微鏡を用いた線虫の全細胞系譜追跡の偉業を振り返るとともに、CRISPR-Cas9ゲノム編集法やイメージング技術を用いた最新の細胞系譜追跡技術、技術開発にまつわる世代を超えたアイデアの伝搬とその哲学、顕微鏡（光学系）を使わないイメージング技術の台頭などについて話しました。Show notes The embryonic cell lineage of the nematode Caenorhabditis elegans. Developmental Biology, 1983…Sulstonらによって線虫の全細胞における細胞系譜が初めて明らかにされた記念碑的な論文。Sulstonは4時間に及ぶ顕微鏡観察を毎日2回繰り返すことを何年も続け、959個の細胞系譜を明らかにした。 Jey Shendre…HarvardのGeorge Churchのlabで博士課程の学生だった頃、一発の実験で細胞系譜を一斉に追跡できるテクノロジーを作れ、と言われ無茶苦茶だと思いつつ取り組んだがうまく行かなかった。独立後、ラボメンバーとともにCRISPR-Cas9によるゲノム編集を用いることで当時は不可能だったアイデアを実現させ、それがGESTALT法 (Science 2016)となった。 Whole-organism lineage tracing by combinatorial and cumulative genome editing. Science, 2016…Jey Shendreによる全く新しい細胞系譜の追跡方法。CRISPR-Cas9によるゲノム編集を人工的な配列に引き起こすことにより、生体内で変異パタンを生成させ、このパタンの系統樹を描くことにより細胞系譜を追跡するGESTALT法。ゼブラフィッシュにおける複雑な細胞系譜が顕微鏡を使わず、一度のシーケンシングによって再構成できることを世界で初めて示した。このアイデアに世界中の研究者らが触発され、多くの関連技術群が開発される契機となった。 Synthetic recording and in situ readout of lineage information in single cells. Nature, 2016…Long CaiとMichael ElowitzらによるCRISPR-Cas9と一分子RNA FISH法を組み合わせた細胞系譜の追跡技術、MEMOIR法。人工的な配列が時間経過とともにゲノム編集によって破壊され、一分子RNA FISHの蛍光が減弱するパタンを利用して、細胞系譜を追跡することができる方法。 light sheet fluorescence microscopy…Light sheet fluorescence microscopyのアイデア自体は古く、100年近く遡る。最近の光学系とカメラ、制御系の発展により、大きな分野となりつつある。 Transgenic strategies for combinatorial expression of fluorescent proteins in the nervous system. Livet et al. Nature, 2007…Brainbowのオリジナル論文。Cre-LoxPによるDNA組み換え酵素によって複数の蛍光タンパク質をランダムに発現させ、その多色のパタンによってクローンを標識・追跡が可能となる。 Sequencing the connectome. Zador et al. PLos Biol., 2012- ZadorらによるDNAバーコードを用いたコネクトーム計測のアイデア論文。大規模な空間情報を顕微鏡を使わずにいかにシーケンシングによって明らかにするか？という全く新しい発想を提示した一方、このアイデアがどのように実現できるのか、この当時はまだ自明でなかった（今も）。 Comprehensive mapping of long-range interactions reveals folding principles of the human genome. Lieberman-Aiden et al. Science, 2009…Hi-C法のオリジナル論文。この論文を起点として、核内における染色体高次構造、クロマチン構造解析が爆発的に進むようになった。 Capturing chromosome conformation. Dekker et al. Science, 2002…Hi-C法の基礎となった3C法のオリジナル論文。ホルムアルデヒドによる固定、制限酵素による切断、ライゲーション、PCR増幅という分子細胞生物学に必須ないくつかの基本的なテクニックを組み合わせることで、Job Dekkerはこの天才的なアイデアを生み出した。3C法の発明は分子細胞生物学、ゲノミクス研究の歴史において特異点的な偉業である。Job Dekkerはこの方法論にたどり着いた理由について、彼自身が博士時代にNMRの研究を行なっていたこと、分子生物学の実験でPCRだけはうまくできたこと、実験初期にすぐにうまくいったことだと、以前tadasuに語ってくれた。しかしその特異さゆえ、2009年のHi-C論文が世に出るまではなかなか評価されることはなかった。 Turning point: Job Dekker…Job Dekkerのポスドク回顧録。 DNA microscopy: Optics-free spatio-genetic imaging by a stand-alone chemical reaction…Avivによる光学系を全く用いない顕微鏡のアイデアであるDNA microscopy法。この技術の行方は今後も注視する必要がある。 low-input, high-throughput, no-output biology…Brennerが2008年に残した言葉。 “Progress in science depends on new techniques, new discoveries and new ideas, probably in that order”というSydney Brennerの言葉

La politica alle Olimpiadi: Zador e la partita del sangue nell'acqua a Melbourne '56 e la storia della foto di Tommie Smith e John Carlos alle Olimpiadi di Città del Messico del '68. Su Sportcast, il podcast sportivo di Giuliano Terenzi

La politica alle Olimpiadi: Zador e la partita del sangue nell'acqua a Melbourne '56 e la storia della foto di Tommie Smith e John Carlos alle Olimpiadi di Città del Messico del '68. Su Sportcast, il podcast sportivo di Giuliano Terenzi

Born in Hungary in 1894, Eugene Zádor moved to the USA in 1939 and remained there as a naturalised citizen until his death. He left a sizeable catalogue of works that includes more than 120 film scores, 13 operas and a wide variety of concert music. Zádor has been described as a classicist, a romantic and a modernist all rolled into one, demonstrated by Naxos’ ongoing series of his orchestral music. This now reaches Volume 5 with a programme of works that were all written later in the composer’s life, yet all reflect his Hungarian roots. The new release is introduced by Raymond Bisha.

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.04.16.045625v1?rss=1 Authors: Ghosh, S., Zador, A. M. Abstract: The neural mechanisms that allow animals to adapt their previously learned associations in response to changes in the environment remain poorly understood. To probe the synaptic mechanisms that mediate such adaptive behavior, we trained mice on an auditory-motor reversal task, and tracked changes in the strength of corticostriatal synapses associated with the formation of learned associations. Using a ChR2-based electrophysiological assay in acute striatal slices, we measured the strength of these synapses after animals learned to pair auditory stimuli with specific actions. Here we report that the pattern of synaptic strength initially established by learning remains unchanged even when the task contingencies are reversed. Our results suggest that synaptic changes associated with the initial acquisition of this task are not erased or over-written, and that behavioral reversal of learned associations may recruit a separate neural circuit. Copy rights belong to original authors. Visit the link for more info