Podcasts about heterogeneous

In this conversation, Dr. Chase Cunningham and Barry Mainz, CEO of Forescout, discuss the pressing issues surrounding cybersecurity, particularly in critical infrastructure, legacy systems, and the importance of a zero trust approach. They critique the Netflix series 'Zero Day' for its portrayal of cybersecurity threats and explore the current state of security in various sectors, including healthcare and airports. The discussion emphasizes the need for compliance, business continuity, and the integration of cybersecurity into business strategies. They also touch on the future of cybersecurity investments and the importance of considering schools as critical infrastructure.TakeawaysThe portrayal of cybersecurity in media can be exaggerated.Critical infrastructure is vulnerable and requires investment in security.Zero trust principles should be applied to OT and IoT systems.Legacy systems pose significant challenges for cybersecurity.Compliance requirements for OT and IoT are lacking compared to other sectors.Business continuity is a key driver for cybersecurity investments.Cybersecurity discussions should focus on business impacts, not just technical details.Heterogeneous environments require flexible security solutions.Airports vary in their cybersecurity readiness based on age and investment.Healthcare cybersecurity often reacts to breaches rather than preventing them.

Doron Morad, University of California, Santa Cruz In natural fault surfaces, stresses are not evenly distributed due to variations in the contact population within the medium, causing frictional variations that are not easy to anticipate. These variations are crucial for understanding the kinematics and dynamics of frictional motion and can be attributed to both the intact material and granular media accommodating the principal slip zone. Here, I explore the effects of heterogeneous frictional environments using two different approaches: fracture dynamics on non-mobilized surfaces and granular systems on mobilized ones. First, I will present a quantitative analysis of laboratory earthquakes on heterogeneous surfaces, incorporating both laboratory-scale seismic measurements coupled with high-speed imaging of the controlled dynamic ruptures that generated them. We generated variations in the rupture properties by imposing sequences of controlled artificial barriers along the laboratory fault. We first demonstrate that direct measurements of imaged slip events correspond to established seismic analysis of acoustic signals; the seismograms correctly record the rupture moments and maximum moment rates. We then investigate the ruptures’ early growth by comparing their measured seismogram velocities to their final size. We investigate the laboratory conditions that allow final size predictability during the rupture early growth. Due to higher initial elastic energies imposed prior to nucleation, larger events accelerate more rapidly at the rupture onset for both heterogeneous and non-heterogeneous surfaces. Second, I present a new Couette-style deformation cell designed to study stress localization in two-dimensional granular media under different flow regimes. This apparatus enables arbitrarily large deformations and spans four orders of magnitude in driving velocity, from sub-millimeter to meters per second. Using photoelasticity, we measure force distribution and localization within the granular medium. High-speed imaging captures data from a representative patch, including both lower and upper boundaries, allowing us to characterize local variations in stress and velocity. For the first time, we present experimental results demonstrating predictive local granular behavior based on particle velocities, velocity fluctuations, and friction, as defined by [tau/sigma_n]. Our findings also reveal that stress patterns in the granular medium are velocity-dependent, with higher driving velocities leading to increased stress localization. These two end-member cases of frictional sliding, one dominated by gouge, and the second by intact surfaces, highlight two fundamental aspects of friction dynamics. The spatial distribution of heterogeneity directly influences stress distribution and, consequently, the stability of the medium. With these experimental methods, we can now measure and even control these effects.

Listen to this interview of Keila Lima, PhD candidate, Department of Computer Science, Electrical Engineering and Mathematical Sciences at the Western Norway University of Applied Sciences, Norway. We talk about her coauthored paper A Data-Flow Oriented Software Architecture for Heterogeneous Marine Data Streams (ICSA 2024). Download this screenshot of the paper. In the screenshot, you see green highlighting that picks out the function word which divides the two parts of this work: one, the architecture developed, and two, the environment where it's been developed. But why in that order? Why not: Heterogeneous marine data steams using a data-flow oriented software architecture? The answer here is audience, because ICSA is a conference for software architecture, and this team of authors have the contribution of a new architecture here. Therefore, the Title puts the topic first (data-flow oriented software architecture), then adding more about that topic after (heterogeneous marine data streams). Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/new-books-network

Listen to this interview of Keila Lima, PhD candidate, Department of Computer Science, Electrical Engineering and Mathematical Sciences at the Western Norway University of Applied Sciences, Norway. We talk about her coauthored paper A Data-Flow Oriented Software Architecture for Heterogeneous Marine Data Streams (ICSA 2024). Download this screenshot of the paper. In the screenshot, you see green highlighting that picks out the function word which divides the two parts of this work: one, the architecture developed, and two, the environment where it's been developed. But why in that order? Why not: Heterogeneous marine data steams using a data-flow oriented software architecture? The answer here is audience, because ICSA is a conference for software architecture, and this team of authors have the contribution of a new architecture here. Therefore, the Title puts the topic first (data-flow oriented software architecture), then adding more about that topic after (heterogeneous marine data streams). Learn more about your ad choices. Visit megaphone.fm/adchoices

Raja Koduri joined Bryan and Adam to answer a question sent in from a listener: what's are the differences between a CPU, GPU, FPGA, and ASIC? And after a walk through history of hardware, software, their intersection and relevant companies, we ... almost answered it!In addition to Bryan Cantrill and Adam Leventhal, our special guest was Raja Koduri.Some of the topics we hit on, in the order that we hit them:3dfx Oral History Panel with Ross Smith, Scott Sellers, Gary Tarolli, and Gordon Campbell3dfxOpenGLGlideDirect3DCUDADennard scalingVLIWGPGPUAMD APUEnergy Efficiency and AI HardwarePRs needed!If we got something wrong or missed something, please file a PR! Our next show will likely be on Monday at 5p Pacific Time on our Discord server; stay tuned to our Mastodon feeds for details, or subscribe to this calendar. We'd love to have you join us, as we always love to hear from new speakers!

The rapid advancement of large language models (LLMs) has paved the way for the development of highly capable autonomous agents. However, existing multi-agent frameworks often struggle with integrating diverse capable third-party agents due to reliance on agents defined within their own ecosystems. They also face challenges in simulating distributed environments, as most frameworks are limited to single-device setups. Furthermore, these frameworks often rely on hard-coded communication pipelines, limiting their adaptability to dynamic task requirements. Inspired by the concept of the Internet, we propose the Internet of Agents (IoA), a novel framework that addresses these limitations by providing a flexible and scalable platform for LLM-based multi-agent collaboration. IoA introduces an agent integration protocol, an instant-messaging-like architecture design, and dynamic mechanisms for agent teaming and conversation flow control. Through extensive experiments on general assistant tasks, embodied AI tasks, and retrieval-augmented generation benchmarks, we demonstrate that IoA consistently outperforms state-of-the-art baselines, showcasing its ability to facilitate effective collaboration among heterogeneous agents. IoA represents a step towards linking diverse agents in an Internet-like environment, where agents can seamlessly collaborate to achieve greater intelligence and capabilities. Our codebase has been released at url{https://github.com/OpenBMB/IoA}. 2024: Weize Chen, Ziming You, Ran Li, Yitong Guan, Cheng Qian, Chenyang Zhao, Cheng Yang, Ruobing Xie, Zhiyuan Liu, Maosong Sun https://arxiv.org/pdf/2407.07061v2

On this episode of the Futurum Tech Webcast, host David Nicholson welcomes Delmar Hernandez, Senior Principal Engineer at Dell Technologies and Steen Graham, Founder at Scalers AI for a conversation on the democratization of AI, focusing on the scalability and versatility of heterogeneous AI inferencing. Their discussion covers: The current trends and challenges in AI democratization and how companies are navigating these waters How Dell Technologies and Scalers AI are advancing the field of AI inferencing with innovative solutions The importance of scalable and heterogeneous AI systems in unlocking new opportunities and applications Best practices for implementing AI technologies in various sectors Predictions for the future of AI development and its impact on industries Learn more at Dell Technologies and Scalers AI. Download our related report, Dell POC for Scalable and Heterogeneous Gen-AI Platform, here.

Today, you'll learn about a tantalizing new discovery about dark energy that could change our understanding of the entire universe, how scientists are pulling electricity from raindrops, and the barcode memory tool inside a chickadee's brain. Dark Energy “A Tantalizing ‘Hint' That Astronomers Got Dark Energy All Wrong.” by Dennis Overbye. 2024. “What Is Dark Energy? An Astrophysicist Explains.” Ars Technica. YouTube Video. 2023. “What is Dark Energy? Inside our accelerating, expanding universe.” by Chelsea Gohd. 2024. “DESI Data Documentation.” Database. 2023. Electric Rain “New green technology harvests energy from raindrops and humidity.” by Robert F. Service. 2024. “The Amazing Drinking Bird.” by Brian Rohrig. 2024. “Rapid progress of key clean energy technologies shows the new energy economy is emerging faster than many think.” IEA.org. 2023. Chickadees “Chickadees use memory ‘bar codes' to find their hidden food stashes.” by Jake Buehler. 2024. “Barcoding of episodic memories in the hippocampus of a food-caching bird.” by Selmaan N. Chettih, et al. 2024. “Birdist Rule #71: Figure Out What Kind Of Chickadees You've Got.” by Nicholas Lund. 2016. “Somewhere in the brain is a storage device for memories.” by Laura Sanders. 2018. “A manifold neural population code for space in hippocampal coactivity dynamics independent of place fields.” by Eliott Robert Joseph Levy, et al. 2023. “Heterogeneous representations in the hippocampus.” by Kazumasa Z. Tanaka. 2021. Hosted on Acast. See acast.com/privacy for more information.

Erich Keane joins Timur and Phil. Erich chats about the recent WG21 meeting in Tokyo, his roles as chair and co-chair of the Language Evolution and Language Evolution Incubator working groups, respectively, as well as heterogeneous computing and his work at NVidia. Show Notes News CppCon - Call for Speakers ACCU 2024 Online Bjarne Stroustrup responds to White House warning against C++ David Sankel's post on Boost split Links Tokyo ISO C++ Committee Trip Reports: In-depth status report Herb Sutter's report Think-Cell's trip report (Jonathan Müller) Papers discussed: P2900R6 - "Contracts for C++" P2996R2 - "Reflection for C++26" P2688R1 - "Pattern Matching: match Expression" P2830R1 - "Standardized Type Ordering"

Full article: https://ajronline.org/doi/10.2214/AJR.23.30769  Adrenal-protocol CT is commonly performed as part of the diagnostic workup of adrenal nodules, in order to distinguish adrenal adenomas from tumors. However, there has been limited evaluation of its efficacy in heterogeneous nodules. Sid Dogra, MD discusses a recent multi-institutional study evaluating different methods of ROI placement and show that adrenal-protocol CT generally has poor diagnostic performance in distinguishing adrenal adenomas and non-adenomas in heterogeneous nodules.

Full article: https://www.ajronline.org/doi/10.2214/AJR.23.30504  Shruti Kumar, MD study that investigates the outcomes of patients with pure ground-glass nodules, heterogeneous ground-glass nodules, and part-solid nodules. It shows that pure ground-glass nodules have better surgical outcomes and may be monitored noninvasively, whereas nodules with increased density may need surgery. 

BUFFALO, NY- January 31, 2024 – A new #research paper was #published in Oncotarget's Volume 15 on January 24, 2024, entitled, “BCAS1 defines a heterogeneous cell population in diffuse gliomas.” Oligodendrocyte precursor markers have become of great interest to identify new diagnostic and therapeutic targets for diffuse gliomas, since state-of-the-art studies point towards immature oligodendrocytes as a possible source of gliomagenesis. Brain enriched myelin associated protein 1 (BCAS1) is a novel marker of immature oligodendrocytes and was proposed to contribute to tumorigenesis in non-central nervous system tumors. However, the role of BCAS1 in diffuse glioma is still underexplored. In this new study, researchers Raquel Morales-Gallel, María José Ulloa-Navas, Patricia García-Tárraga, Ricardo Prat-Acín, Gaspar Reynés, Pedro Pérez-Borredá, Luis Rubio, Vivian Capilla-González, Jaime Ferrer-Lozano, and José Manuel García-Verdugo from the University of Valencia-CIBERNED, Mayo Clinic, Hospital Universitari i Politècnic La Fe, University of Pablo de Olavide, and University of Seville-CSIC analyzed the expression of BCAS1 in different tumor samples from patients with diffuse gliomas (17 oligodendrogliomas; 8 astrocytomas; 60 glioblastomas) and uncovered the molecular and ultrastructural features of BCAS1+ cells by immunostaining and electron microscopy. “Our results show that BCAS1+ cells exhibit stellate or spherical morphology with similar ultrastructural features.” Stellate and spherical cells were detected as isolated cells in all studied gliomas. Nevertheless, only stellate cells were found to be proliferative and formed tightly packed nodules with a highly proliferative rate in oligodendrogliomas. Their findings provide a comprehensive characterization of the BCAS1+ cell population within diffuse gliomas. The observed proliferative capacity and distribution of BCAS1+ stellate cells, particularly in oligodendrogliomas, highlight BCAS1 as an interesting marker, warranting further investigation into its role in tumor malignancy. “In conclusion, this insight will shed light on the establishment of BCAS1 as a clinically relevant molecule, serving not only as a diagnostic or prognostic marker but also as a novel therapeutic target for the development of cutting-edge treatments.” DOI - https://doi.org/10.18632/oncotarget.28553 Correspondence to - José Manuel García-Verdugo - j.manuel.garcia@uv.es Sign up for free Altmetric alerts about this article - https://oncotarget.altmetric.com/details/email_updates?id=10.18632%2Foncotarget.28553 Subscribe for free publication alerts from Oncotarget - https://www.oncotarget.com/subscribe/ Keywords - cancer, brain tumor, diffuse glioma, oligodendroglioma, glioblastoma, BCAS1 About Oncotarget Oncotarget (a primarily oncology-focused, peer-reviewed, open access journal) aims to maximize research impact through insightful peer-review; eliminate borders between specialties by linking different fields of oncology, cancer research and biomedical sciences; and foster application of basic and clinical science. To learn more about Oncotarget, please visit https://www.oncotarget.com and connect with us: Facebook - https://www.facebook.com/Oncotarget/ X - https://twitter.com/oncotarget Instagram - https://www.instagram.com/oncotargetjrnl/ YouTube - https://www.youtube.com/@OncotargetJournal LinkedIn - https://www.linkedin.com/company/oncotarget Pinterest - https://www.pinterest.com/oncotarget/ Reddit - https://www.reddit.com/user/Oncotarget/ Spotify - https://open.spotify.com/show/0gRwT6BqYWJzxzmjPJwtVh Media Contact MEDIA@IMPACTJOURNALS.COM 18009220957

Authors Julia C. Lerch, David John Frank, and Evan Schofer discuss the article, "The Social Foundations of Academic Freedom: Heterogeneous Institutions in World Society, 1960 to 2022," published in the February 2024 issue of American Sociological Review.

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: AGI will be made of heterogeneous components, Transformer and Selective SSM blocks will be among them, published by Roman Leventov on December 27, 2023 on The AI Alignment Forum. This post is prompted by two recent pieces: First, in the podcast "Emergency Pod: Mamba, Memory, and the SSM Moment", Nathan Labenz described how he sees that we are entering the era of heterogeneity in AI architectures because currently we have not just one fundamental block that works very well (the Transformer block), but two kinds of blocks: the Selective SSM (Mamba) block has joined the party. Moreover, it's demonstrated in many recent works (see the StripedHyena blog post, and references in appendix E.2.2. of the Mamba paper) that hybridisation of Transformer and SSM blocks works better than a "pure" architecture composed of either of these types of blocks. So, we will probably quickly see the emergence of complicated hybrids between these two.[2] This reminds me of John Doyle's architecture theory that predicts that AI architectures will evolve towards modularisation and component heterogeneity, where the properties of different components (i.e., their positions at different tradeoff spectrums) will converge to reflect the statistical properties of heterogeneous objects (a.k.a. natural abstractions, patterns, "pockets of computational reducibility") in the environment. Second, in this article, Anatoly Levenchuk rehearses the "no free lunch" theorem and enumerates some of the development directions in algorithms and computing that continue in the shadows of the currently dominant LLM paradigm, but still are going to be several orders of magnitude more computationally efficient than DNNs in some important classes of tasks: multi-physics simulations, discrete ("system 2") reasoning (planning, optimisation), theorem verification and SAT-solving, etc. All these diverse components are going to be plugged into some "AI operating system", Toolformer-style. Then Anatoly posits an important conjecture (slightly tweaked by me): as it doesn't make sense to discuss some person's "values" without considering (a) them in the context of their environment (family, community, humanity) and (b) their education, it's pointless to discuss the alignment properties and "values" of some "core" AGI agent architecture without considering the whole context of a quickly evolving "open agency" of various tools and specialised components[3]. From these ideas, I derive the following conjectures about an "AGI-complete" architecture[4]: 1. AGI could be achieved by combining just (a) about five core types of DNN blocks (Transformer and Selective SSM are two of these, and most likely some kind of Graph Neural Network with or without flexible/dynamic/"liquid" connections is another one, and perhaps a few more)[5]; (b) a few dozen classical algorithms for LMAs aka "LLM programs" (better called "NN programs" in the more general case), from search and algorithms on graphs to dynamic programming, to orchestrate and direct the inference of the DNNs; and (c) about a dozen or two key LLM tools required for generality, such as a multi-physics simulation engine like JuliaSim, a symbolic computation engine like Wolfram Engine, a theorem prover like Lean, etc. 2. The AGI architecture described above will not be perfectly optimal, but it will probably be within an order of magnitude from the optimal compute efficiency on the tasks it is supposed to solve[4], so, considering the investments in interpretability, monitoring, anomaly detection, red teaming, and other strands of R&D about the incumbent types of DNN blocks and NN program/agent algorithms, as well as economic incentives of modularisation and component re-use (cf. "BCIs and the ecosystem of modular minds"), this will probably be a sufficient motivation to "lock in" the cho...

I have a safer sex protocol that consists of a set of good communication tick boxes and a set of medical/testing disclosure tick boxes and a spreadsheet for my partners to record their partners and activities they practise with each, testing status, barrier use, etc I then use some approximate quantification of risk for each partner. While I find my protocol helpful in making this usually sensitive and difficult discussion more matter of fact and clear, I have experienced a lot of push back and hurt feelings by partners. I am reaching out to you because you mentioned in your episode this week that some people feel repelled by safer sex discussions. Could you help me see a way forward towards finding a consensus or a creative solution that works for everyone in case a partner refuses to engage with my protocol? Thank you for creating your content! I find it really valuable and fun to listen to! Resource / discourse When one becomes the other What's a good resource? Heterogeneous, open, kind, Resourcing our bodies Towards collectivity, away from the individual risks It's not working right now, why not? Sounds like it's only resourcing one person Which means that it's not actually resourcing you For this approach the process and the content have to be flattened. It's the process for reducing risks which is the relation, which produces the outcome Trust To get trust we have to give it. It's a mutually constructed thing But saying it isn't it. Also running the risk of people rejecting doing it because they are made to say it. Privity of contract How does trust feel? How do other people know? How would you respond? Can you use that to work backwards to find out how you might resource yourselves (or your whole assemblage) Here's the podcast I was recommending here https://hotelbarpodcast.com/podcast/episode-119-trust/ Joy and love is only ever a result of the relation As I've been saying lately, consciousness, becoming, can only happen in relation. Spinozan joy is just that if by increasing our capacity to act, we are reducing someone else's, it's sadness "Love means precisely that our expansive encounters and continuous collaborations bring us joy...without this, love, we are nothing." Antonio Negri So you need a resource, not a discourse, which you all can collaborate on Allows for volume levels (both in terms of the actual risks and how they are individual) Allows for different risks Gives people autonomy over how they manage their sexual risks (privity) Creates openness and the possibilities of persevering over time Gives everyone an out Just conversations A Google doc of affects, emotions, thoughts, doings Not just about safer sex but also increasing the possibilities to act https://www.bishuk.com/safer-sex/sex-infections/ https://www.bishuk.com/safer-sex/chances-getting-sti/ https://www.bishuk.com/safer-sex/sti-quiz/

Hosts Bryan Goldstein, President- Analog Devices Federal and Vice President Aerospace and Defense Group at Analog Devices, and Sean Darcy, Sr. Director Aerospace and Defense at Infineon talk with special guest, John Park, Product Management Group Director for IC packaging and cross-platform solutions at Cadence Design Systems, about the challenges of heterogeneous integration, how it compares to SoC and 3D packaging, and the future of this technology for the semiconductor industry. Visit Analog Devices A&D webpage for solutions to your design challenges.

Lead Story:   Heterogeneous neuroimaging findings across substance use disorders localize to a common brain network  Nature Mental Health This study used network mapping approaches and a functional connectome from a large cohort of healthy participants (n = 1,000) to test whether neuroimaging abnormalities across substance use disorders map to a common brain network. Starting with coordinates of regional brain atrophy from 45 studies, researchers found that 91% of the neuroimaging findings mapped to a common brain network specific to substance use disorders compared to atrophy associated with normal aging and neurodegenerative disease. Coordinates of functional MRI abnormalities from 99 studies mapped to a similar brain network. Neuroimaging abnormalities across substance use disorders map to a common brain network that is similar across imaging modalities, substances, and lesion locations that cause remission from substance use disorders. Read this issue of the ASAM Weekly Subscribe to the ASAM Weekly Visit ASAM

My guest today is Henri Stern, CEO of Privy. Privy is an authentication and embedded wallet SDK. With Privy, dapp developers can allow their users to sign in with their existing wallet, or generate an embedded wallet associated with an email, SMS, or SSO provider such as AppleID or Gmail. Privy's embedded wallet powers friend.tech, the breakout social dapp launched in August 2023. With Privy, friend.tech generates a new embedded wallet for each user that signs in, allowing the PWA dapp to sign transactions at the click of an HTML button element — no WalletConnect back-and-forth required. Privy's 2/3 Shamir wallet option allows dapp developers to create self-custody wallets for their users, while Privy provides a password-protected recovery services in case users should lose access to their account or device. On this episode we discuss Henri's experience studying under Dan Boneh, working on Filecoin at Protocol Labs with Juan Benet, and building Privy. We explore the ins-and-outs of Privy's embedded wallet architecture and discuss what's next in wallets. It was a pleasure diving into the emerging embedded wallet and account abstraction smart wallet stack with Henri, who is a clear thinker and humble decentralization minded builder. I hope you enjoy the show. As always, this show is provided as entertainment and does not constitute legal, financial, or tax advice or any form of endorsement or suggestion. Crypto has risks and you alone are responsible for doing your research and making your own decisions. Links @nnnnicholas on Twitter @henri_stern on Twitter Privy Architecture docs Henri's EthCC 2023 talk Courtyard.io Dan Boneh Shibuya POAP CabinDAO EIP-1193 Timestamps: (00:00:00) Introduction (00:05:01) Protocol labs, and how Privy started (00:09:38) Heterogeneous wallets cater to diverse user expectations. (00:12:13) Product decision made, focus on embedded wallets - Building stable wall connectors and library is difficult. (00:21:11) Wallet authentication process: SMS verification and key sharing. (00:25:14) Web tokens authenticate user and holder. Off share only accessible by user. Single device operations with iframe and key. Typical 90% usage scenario. (00:29:48) Privy uses hardware security module. It encrypts recovery shares and tracks devices for added security. (00:43:43) Fear of EOA, comparing Shamir and multi sig. (00:47:21) Account abstraction allows for smart wallets. (00:54:43) Main NetEase locked in app, no control. (01:00:48) Exciting fintech innovations cater to diverse users. (01:04:19) Balancing user control while empowering developers and users. (01:08:25) Enabling interoperable wallets in the Web3 space. (01:13:35) Few relevant DeFi apps; WalletConnect is problematic. (01:23:46) Apple offers secure self-custody wallets with social recovery. (01:26:01) Privy is a secure cross-platform custodian.

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.08.02.551732v1?rss=1 Authors: Gaynes, J. A., Budoff, S. A., Grybko, M. J., Poleg-Polsky, A. Abstract: The processing of visual information by retinal starburst amacrine cells (SACs) involves transforming excitatory input from bipolar cells (BCs) into directional calcium output. While previous studies have suggested that an asymmetry in the kinetic properties of bipolar cells along the soma-dendritic axes of the postsynaptic cell could enhance directional tuning at the level of individual branches, it remains unclear whether biologically relevant presynaptic kinetics contribute to direction selectivity when visual stimulation engages the entire dendritic tree. To address this question, we built multicompartmental models of the bipolar-SAC circuit and trained them to boost directional tuning. We report that despite significant dendritic crosstalk and dissimilar directional preferences along the dendrites that occur during whole-cell stimulation, the rules that guide BC kinetics leading to optimal directional selectivity are similar to the single-dendrite condition. To correlate model predictions to empirical findings, we utilized two-photon glutamate imaging to study the dynamics of bipolar release onto ON- and OFF-starburst dendrites in the murine retina. We reveal diverse presynaptic dynamics in response to motion in both BC populations; algorithms trained on the experimental data suggested that the differences in the temporal release kinetics are likely to correspond to heterogeneous receptive field (RF) properties among the different BC types, including the spatial extent of the center and surround components. In addition, we demonstrate that circuit architecture composed of presynaptic units with experimentally recorded dynamics could enhance directional drive but not to levels that replicate empirical findings, suggesting other DS mechanisms are required to explain SAC function. Our study provides new insights into the complex mechanisms underlying direction selectivity in retinal processing and highlights the potential contribution of presynaptic kinetics to the computation of visual information by starburst amacrine cells. 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/2023.07.01.547331v1?rss=1 Authors: Engel, L., Wolff, A. R., Blake, M., Collins, V. L., Sinha, S., Saunders, B. T. Abstract: Environmental cues, through Pavlovian learning, become conditioned stimuli that guide animals toward the acquisition of rewards (for example, food) by invigorating and directing seeking behavior. Dopamine neurons are crucial for this process, by signaling information related to rewards, as well as associated actions, states, and cues. Dopamine neurons are embedded in a reciprocally connected network with their targets in the striatum, the functional relevance of which remains poorly understood. Here we investigated how cue-evoked behavior generated by dopamine neurons is signaled by dopamine release downstream in striatal subregions and how they evolve across stages of learning. We made use of a genetically encoded dopamine biosensor (dLight) to monitor dopamine signaling in the nucleus accumbens (NAc) core and shell, dorsomedial striatum (DMS), and dorsolateral striatum (DLS) with fiber photometry, while tracking detailed movement features during optogenetic Pavlovian cue conditioning of VTA dopamine neurons. We demonstrate spatially heterogeneous, learning-dependent changes across striatal regions. Cues paired with optogenetic activation of VTA dopamine neurons evoked dopamine release preferentially in the NAc core, but not shell, early in training, when behavioral responses were slower and directed toward the cue. Critically, these NAc core signals remained stable, and as training progressed, VTA cue-evoked dopamine signals also emerged in the DMS. Further, prediction error signals, reflecting a violation in the expectation of dopamine neuron activation, only emerged in the NAc core and DMS. Despite the development of rapid, vigorous movement late in training, conditioned dopamine signals did not emerge in the DLS. Together our studies show broad dissociation in the fundamental prediction and reward-related information signaled by dopamine across the striatum. Further, they offer new insight into how larger-scale plasticity across the striatal dopamine network emerges during Pavlovian learning to coordinate behavior. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

In this week's Fish Fry podcast, I investigate how you can shorten your development time and accelerate code across heterogeneous compute platforms with CacheQ Systems CEO Clay Johnson. Clay and I explore the benefits of heterogeneous compute development, the details of CacheQ's QCC development platform and what sets CacheQ's QCC development platform apart from open-source tools.

Editor's Summary by Linda Brubaker, MD, MS, Senior Editor of JAMA, the Journal of the American Medical Association, for the April 4, 2023, issue. Related Content: Audio Highlights

In this episode, Phil and Roy have a discussion with the one and only, Dr. Zac Loughman of West Liberty University! As always, we briefly discuss Zac's background in herpetoculture, before getting right into it – exploring the central conflicts and tensions within herpetoculture. We also talk about the value of evidence-based husbandry and printed media in the digital age. In terms of subject matter alone, this is one of our most important episodes yet! Have a listen! And please like, subscribe, and share this episode, if you feel so inclined. To offer direct support to the show, please consider subscribing to our Patreon (https://patreon.com/projectherpetoculture) and have a look at our generous sponsors! SHOW NOTES: https://www.animalsathomenetwork.com/33-dr-zac-loughman/ LINKS FROM THE EPISODE: Follow Zac on Instagram: @dr_crawdad Merch: https://www.projectherp.com/shop Our Sponsors: CHECK OUT Custom Reptile Habitats CLICK HERE Cold Blooded Caffeine (apply the code ‘projectherp' for 10% off): https://coldbloodedcaffeine.com/?ref=PH Tortoise Supply: https://www.tortoisesupply.com Reptile Rocks: https://www.superuro.com Redline Shipping: https://www.redlineshipping.com Support, Subscribe  & Follow: Support us on Patreon: patreon.com/projectherpetoculture Subscribe on YouTube: https://www.youtube.com/@projectherpetoculture4860 Follow P : H on Instagram: @projectherpetoculture Follow Phil on Instagram: @aridsonly Follow Roy on Instagram: @wellspringherp 

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.03.22.533836v1?rss=1 Authors: Gillett, M., Brunel, N. Abstract: Temporal rescaling of sequential neural activity has been observed in multiple brain areas during behaviors involving time estimation and motor execution at variable speeds. Temporally asymmetric Hebbian rules have been used in network models to learn and retrieve sequential activity, with characteristics that are qualitatively consistent with experimental observations. However, in these models sequential activity is retrieved at a fixed speed. Here, we investigate the effects of a heterogeneity of plasticity rules on network dynamics. In a model in which neurons differ by the degree of temporal symmetry of their plasticity rule, we find that retrieval speed can be controlled by varying external inputs to the network. Neurons with temporally symmetric plasticity rules act as brakes and tend to slow down the dynamics, while neurons with temporally asymmetric rules act as accelerators of the dynamics. We also find that such networks can naturally generate separate 'preparatory' and 'execution' activity patterns with appropriate external inputs. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Dan is joined by Clay Johnson, CEO and co-founder of CacheQ Systems. Clay has more than 25 years of executive management experience across a broad spectrum of technologies including computing, security, semiconductors and EDA tools. Dan discusses the CacheQ QCC development platform with Clay. This platform enables software… Read More

by Kenneth P. Gurney in Curvature of a Fluid Spine

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.02.16.528825v1?rss=1 Authors: Bryant, A. G., Li, Z., Jayakumar, R., Serrano-Pozo, A., Woost, B., Hu, M., Woodbury, M. E., Wachter, A., Lin, G., Kwon, T., Talanian, R. V., Biber, K., Karran, E. H., Hyman, B. T., Das, S., Bennett, R. E. Abstract: Vascular endothelial cells play an important role in maintaining brain health, but their contribution to Alzheimer's disease (AD) is obscured by limited understanding of the cellular heterogeneity in normal aged brain and in disease. To address this, we performed single nucleus RNAseq on tissue from 32 AD and non-AD donors each with five cortical regions: entorhinal cortex, inferior temporal gyrus, prefrontal cortex, visual association cortex and primary visual cortex. Analysis of 51,586 endothelial cell nuclei revealed unique gene expression patterns across the five regions in non-AD donors. Alzheimer's brain endothelial cell nuclei were characterized by upregulated protein folding genes and distinct transcriptomic differences in response to amyloid beta plaques and cerebral amyloid angiopathy. This dataset demonstrates previously unrecognized regional heterogeneity in the endothelial cell transcriptome in both aged non-AD and AD brain. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Welcome to 3D InCites In Case You Missed It Series. We did a lot of podcasting in 2022. Some of our longer episodes consisted of multiple interviews, so you may have missed some of the juiciest conversations. So when there's a break in the action, we're taking you back to those conversations.In this episode, we're going back to ECTC 2023, where Françoise sits down with ASE Fellow, Bill Chen, who has been instrumental in progressing the Heterogeneous Integration Roadmap. He talks about its purpose, the committee's vision, and how it will make possible the next 50 years of Moore's Law, as Gordon Moore himself envisioned in the second part of his paper. Like what you hear? Follow us on LinkedIn and Twitter Interested in becoming a sponsor of the 3D InCites Podcast? Check out our 2023 Media Kit. Learn more about the 3D InCites Community and how you can become more involved.

Although the emergence of CXL in server CPUs is big news, the inclusion of this technology in ARM processor IP is just as important. In this episode of Utilizing CXL, Eddie Ramirez of ARM joins Craig Rodgers and Stephen Foskett to discuss CXL in the ARM-powered ecosystem. ARM develops processor IP that is used in CPUs as well as supporting processors throughout the datacenter. We begin with a discussion of CXL 1.1, which brings memory expansion to ARM CPUs. But ARM is also delivering CXL 2.0 which would allow memory pooling to increase the utilization of memory, and thus overall system efficiency. The next step is true heterogeneous compute, with accelerators like GPU and DPU sharing memory with CPUs in a flexible fabric that can leverage CXL. Hosts: Stephen Foskett: https://www.twitter.com/SFoskett Craig Rodgers: https://www.twitter.com/CraigRodgersms Guest: Eddie Ramirez, VP of Marketing at Arm: https://www.linkedin.com/in/eddie-ramirez-41233a1/ Follow Gestalt IT and Utilizing Tech Website: https://www.UtilizingTech.com/ Website: https://www.GestaltIT.com/ Twitter: https://www.twitter.com/GestaltIT LinkedIn: https://www.linkedin.com/company/1789

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Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.01.03.521866v1?rss=1 Authors: Akhshi, A., Haggard, M., Marquez Machorro, M., Farjami, S., Chacron, M. J., Khadra, A. Abstract: Neurons ubiquitously display heterogeneities in spiking activity even within a given cell type. To date, the relative contributions of extrinsic mechanisms (e.g., synaptic bombardment) and intrinsic mechanisms (e.g., conductances, cell morphology) towards determining spiking activity remain poorly understood. Here we address this important question using a novel approach that combines biophysical techniques, in which extracellular in vivo recordings of electrosensory pyramidal cells within weakly electric fish, are combined with computational modeling. Specifically, by varying parameters, a conductance-based computational model successfully reproduced the highly heterogeneous spiking activities seen experimentally. Model parameters that varied the most were then used to gauge the relative contributions of extrinsic vs. intrinsic mechanisms. Overall, extrinsic synaptic input was predicted to be the main factor accounting for spiking heterogeneities. We tested this prediction experimentally by performing two different manipulations: i) pharmacologically inactivating feedback; ii) applying the neuromodulator serotonin. Our model predicted that feedback inactivation should reduce while serotonin application should increase spiking heterogeneities. Experiments corroborated these predictions. Importantly, for serotonin application, increased heterogeneity occurred despite a strong reduction in intrinsic membrane conductance, further demonstrating that extrinsic synaptic input is the primary determinant of spiking heterogeneities in vivo. Taken together, our results demonstrate that devising a computational model to capture spiking heterogeneities in vivo and assessing which parameters are responsible can successfully determine the relative contributions of extrinsic vs. intrinsic inputs. We expect this approach to be generalizable to other systems and species. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Listen to Radha Nagarajan, Senior Vice President and Chief Technology Officer of Marvell's Optical and Copper Connectivity Business Group and Chris Banuelos on this week's episode, discussing a recent white paper “2.5D Heterogeneous Integration for Silicon PhotonicsEngines in Optical Transceivers.” Learn more about the key takeaways of heterogeneous integration, what it means for cloud data center infrastructure and use cases. Read the white paper: 

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.12.14.520453v1?rss=1 Authors: Liu, Z.-Q., Shafiei, G., Baillet, S., Misic, B. Abstract: The relationship between structural and functional connectivity in the brain is a key question in connectomics. Here we quantify patterns of structure-function coupling across the neocortex, by comparing structural connectivity estimated using diffusion MRI with functional connectivity estimated using both neurophysiological (MEG-based) and haemodynamic (fMRI-based) recordings. We find that structure-function coupling is heterogeneous across brain regions and frequency bands. The link between structural and functional connectivity is generally stronger in multiple MEG frequency bands compared to resting state fMRI. Structure-function coupling is greater in slower and intermediate frequency bands compared to faster frequency bands. We also find that structure-function coupling systematically follows the archetypal sensorimotor-association hierarchy, as well as patterns of laminar differentiation, peaking in granular layer IV. Finally, structure-function coupling is better explained using structure-informed inter-regional communication metrics than using structural connectivity alone. Collectively, these results place neurophysiological and haemodynamic structure-function relationships in a common frame of reference and provide a starting point for a multi-modal understanding of structure-function coupling in the brain. 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/2022.11.28.518256v1?rss=1 Authors: Rezaei, M. R., Jeoung, H., Gharamani, A., Saha, U., Popovic, M. R., Yousefi, A., Chen, R., Lankarany, M. Abstract: The subthalamic nucleus (STN) of the basal ganglia interacts with the medial prefrontal cortex (mPFC) and shapes a control loop, specifically when the brain receives contradictory information from either different sensory systems or conflicting information from sensory inputs and prior knowledge that developed in the brain. Experimental studies demonstrated that significant increases in theta activities (2-8 Hz) in both the STN and mPFC as well as increased phase synchronization between mPFC and STN are prominent features of conflict processing. While these neural features reflect the importance of STN-mPFC circuitry in conflict processing, a low-dimensional representation of the mPFC-STN interaction referred to as a cognitive state, that links neural activities generated by these sub-regions to behavioral signals (e.g., the response time), remains to be identified. Here, we propose a new model, namely, the heterogeneous input discriminative-generative decoder (HI-DGD) model, to infer a cognitive state underlying decision-making based on neural activities (STN and mPFC) and behavioral signals (individuals' response time) recorded in 10 Parkinson's disease patients while they performed a Stroop task. PD patients may have conflict processing which is quantitatively (may be qualitative in some) different from healthy population. Using extensive synthetic and experimental data, we showed that the HI-DGD model can diffuse information from neural- and behavioral data simultaneously and estimate cognitive states underlying conflict and non-conflict trials significantly better than traditional methods. Additionally, the HI-DGD model identified which neural features made significant contributions to conflict and non-conflict choices. Interestingly, the estimated features match well with those reported in experimental studies. Finally, we highlight the capability of the HI-DGD model in estimating a cognitive state from a single trial of observation, which makes it appropriate to be utilized in closed-loop neuromodulation systems. 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/2022.11.15.516556v1?rss=1 Authors: Pariz, A., Trotter, D., Hutt, A., Lefebvre, J. Abstract: Transcranial alternating current stimulation (tACS) represents a promising non-invasive treatment for an increasingly wide range of neurological and neuropsychiatric disorders. The ability to use periodically oscillating electric fields to non-invasively engage neural dynamics opens up the possibility of recruiting synaptic plasticity and to modulate brain function. However, despite consistent reports about tACS clinical effectiveness, strong state-dependence combined with the ubiquitous heterogeneity of cortical networks collectively results in high outcome variability. Introducing variations in intrinsic neuronal timescales, we explored how such heterogeneity influences stimulation-induced change in synaptic connectivity. We examined how spike timing dependent plasticity, at the level of cells, intra- and inter-laminar cortical networks, can be selectively and preferentially engaged by periodic stimulation. Using computational simulations informed by human experimental data, we analyzed cortical circuits comprised of multiple cell-types, alongside superficial multi-layered networks expressing distinct layer-specific timescales. Our results show that mismatch in neuronal timescales within and/or between cells - and the resulting variability in excitability, temporal integration properties and frequency tuning - enables selective and directional control on synaptic connectivity by tACS. Our work provides new vistas on how to recruit neural heterogeneity to guide brain plasticity using non-invasive stimulation paradigms. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Heterogeneous disorders such as cardiovascular disease have multiple risk factors, causes, and manifestations. Having a holistic view of a patient's unique biology potentially leads to earlier and better treatment options. In this episode, we talk to Narimon Honarpour, vice president of Global Development at Amgen, about how human data is helping drug developers and clinicians unpack the complexities of cardiovascular disease to improve patient outcomes. To dive further into this topic, please join Amgen scientists at the Human Data Era Q&A webinar discussion on November 16, 2022. Register for the event here.   Welcome to The Human Data Era, a special edition podcast series produced by The Scientist's Creative Services Team. This series is brought to you by Amgen, a pioneer in the science of using living cells to make biologic medicines. They helped invent the processes and tools that built the global biotech industry, and have since reached millions of patients suffering from serious illnesses around the world with their medicines.   By studying human genetics, scientists discovered mechanisms that, when defective, cause disease. While this type of data is powerful, additional information can provide more insight on the human condition. Researchers and clinicians can now go beyond genetics, combining proteomics, metabolomics, transcriptomics, and environmental factors into a broad category of human data. In this series, Ray Deshaies, senior vice president of Global Research at Amgen, explores the potential of human data and the important transition scientists and clinicians are making to incorporate this wealth of information into drug research and development. 

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.10.14.512301v1?rss=1 Authors: Rungratsameetaweemana, N., Kim, R., Sejnowski, T. Abstract: Recurrent neural networks (RNNs) based on model neurons that communicate via continuous signals have been widely used to study how cortical neurons perform cognitive tasks. Training such networks to perform tasks that require information maintenance over a brief period (i.e., working memory tasks) remains a challenge. Critically, the training process becomes difficult when the synaptic decay time constant is not fixed to a large constant number for all the model neurons. We hypothesize that the brain utilizes intrinsic cortical noise to generate a reservoir of heterogeneous synaptic decay time constants optimal for maintaining information. Here, we show that introducing random, internal noise to the RNNs not only speeds up the training but also produces stable models that can maintain information longer than the RNNs trained without internal noise. Importantly, this robust working memory performance induced by incorporation of internal noise during training is attributed to an increase in synaptic decay time constants of a sub-population of inhibitory units. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

In this episode of the Virtual Coffee with Ashish edition, we spoke with Jack Naglieri (Jack's Twitter) about what Security Monitoring can look like for a Cloud Native Company Episode ShowNotes, Links and Transcript on Cloud Security Podcast: www.cloudsecuritypodcast.tv Host Twitter: Ashish Rajan (@hashishrajan) Guest Twitter: Jack Naglieri (Jack's Twitter) Podcast Twitter - @CloudSecPod @CloudSecureNews If you want to watch videos of this LIVE STREAMED episode and past episodes - Check out our other Cloud Security Social Channels: - Cloud Security News - Cloud Security Academy Spotify TimeStamp for Interview Questions (00:00) Ashish's Intro to the Episode (02:40) https://snyk.io/csp (02:51) Corey's professional background (03:34) Jack's introduction (06:15 )What is Cloud Native? (07:41) What is a modern security stack? (09:50) Why Cloud Native Security Monitoring? (12:36) The current market for security monitoring (15:45) Cloud Native monitoring for on-prem (18:10) How to start with Cloud Native Security Monitoring? (21:01) Security monitoring in cloud vs traditional (22:51) Challenges with Cloud Native Security Monitoring (25:25) How can SMBs tackle Cloud Native Security Monitoring? (26:52) Are cloud native tools more cost effective than traditional ones? (28:30) Heterogeneous log correlation (30:09) What is a security data lake? (35:25) Does the modern security team need data skils?

Vitaliy Chiley is a Machine Learning Research Engineer at the next-generation computing hardware company Cerebras Systems. We spoke about how DL workloads including sparse workloads can run faster on Cerebras hardware. [00:00:00] Housekeeping [00:01:08] Preamble [00:01:50] Vitaliy Chiley Introduction [00:03:11] Cerebrus architecture [00:08:12] Memory management and FLOP utilisation [00:18:01] Centralised vs decentralised compute architecture [00:21:12] Sparsity [00:23:47] Does Sparse NN imply Heterogeneous compute? [00:29:21] Cost of distributed memory stores? [00:31:01] Activation vs weight sparsity [00:37:52] What constitutes a dead weight to be pruned? [00:39:02] Is it still a saving if we have to choose between weight and activation sparsity? [00:41:02] Cerebras is a cool place to work [00:44:05] What is sparsity? Why do we need to start dense? [00:46:36] Evolutionary algorithms on Cerebras? [00:47:57] How can we start sparse? Google RIGL [00:51:44] Inductive priors, why do we need them if we can start sparse? [00:56:02] Why anthropomorphise inductive priors? [01:02:13] Could Cerebras run a cyclic computational graph? [01:03:16] Are NNs locality sensitive hashing tables? References; Rigging the Lottery: Making All Tickets Winners [RIGL] https://arxiv.org/pdf/1911.11134.pdf [D] DanNet, the CUDA CNN of Dan Ciresan in Jurgen Schmidhuber's team, won 4 image recognition challenges prior to AlexNet https://www.reddit.com/r/MachineLearning/comments/dwnuwh/d_dannet_the_cuda_cnn_of_dan_ciresan_in_jurgen/ A Spline Theory of Deep Learning [Balestriero] https://proceedings.mlr.press/v80/balestriero18b.html

Harry's guest Eric Daimler, a serial software entrepreneur and a former Presidential Innovation Fellow in the Obama Administration, has an interesting argument about math. If you're a young person today trying to decide which math course you're going to take—or maybe an old person who just wants to brush up—he says you shouldn't bother with trigonometry or calculus. Instead he says you should study category theory. An increasingly important in computer science, category theory is about the relationships between sets or structures. It can be used to prove that different structures are consistent or compatible with one another, and to prove that the relationships in a dataset are still intact even after the data has been transformed in some way. Together with two former MIT mathematicians, Daimler co-founded a company called Conexus that uses category theory to tackle the problem of data interoperability. Longtime listeners know that data interoperability in healthcare, or more often the lack of interoperability, is a repeating theme of the show. In fields from drug development to frontline medical care, we've got petabytes of data to work with, in the form of electronic medical records, genomic and proteomic data, and clinical trial data. That data could be the fuel for machine learning and other kinds of computation that could help us make develop drugs faster and make smarter decisions about care. The problem is, it's all stored in different databases and formats that can't be safely merged without a nightmarish amount of work. So when someone like Daimler says they have a way to use math to bring heterogeneous data together without compromising that data's integrity – well, it's time to pay attention. That's why on today's show, we're all going back to school for an introductory class in category theory.Please rate and review The Harry Glorikian Show on Apple Podcasts! Here's how to do that from an iPhone, iPad, or iPod touch:1. Open the Podcasts app on your iPhone, iPad, or Mac. 2. Navigate to The Harry Glorikian Show podcast. You can find it by searching for it or selecting it from your library. Just note that you'll have to go to the series page which shows all the episodes, not just the page for a single episode.3. Scroll down to find the subhead titled "Ratings & Reviews."4. Under one of the highlighted reviews, select "Write a Review."5. Next, select a star rating at the top — you have the option of choosing between one and five stars. 6. Using the text box at the top, write a title for your review. Then, in the lower text box, write your review. Your review can be up to 300 words long.7. Once you've finished, select "Send" or "Save" in the top-right corner. 8. If you've never left a podcast review before, enter a nickname. Your nickname will be displayed next to any reviews you leave from here on out. 9. After selecting a nickname, tap OK. Your review may not be immediately visible.That's it! Thanks so much.TranscriptHarry Glorikian: Hello. I'm Harry Glorikian, and this is The Harry Glorikian Show, where we explore how technology is changing everything we know about healthcare.My guest today is Eric Daimler, a serial software entrepreneur and a former Presidential Innovation Fellow in the Obama Administration.And he has an interesting argument about math. Daimler says if you're a young person today trying to decide which math course you're going to take, or maybe an old person who just wants to brush up, you shouldn't bother with trigonometry or calculus.Instead he says you should study category theory.That's a field that isn't even part of the curriculum at most high schools. But it's increasingly important in computer science.Category theory is about the relationships between sets or structures. It can be used to prove that different structures are consistent or compatible with one another, and to prove that the relationships in a dataset are still intact even after you've transformed that data in some way.Together with two former MIT mathematicians, Daimler co-founded a company called Conexus that uses category theory to tackle the problem of data interoperability.Now…longtime listeners of the show know that data interoperability in healthcare, or more often the lack of interoperability, is one of my biggest hobby horses. In fields from drug development to frontline medical care, we've got petabytes of data to work with, in the form of electronic medical records, genomic and proteomic data, and clinical trial data.That data could be the fuel for machine learning and other kinds of computation that could help us make develop drugs faster and make smarter decisions about care. The problem is, it's all stored in different databases and formats that can't be safely merged without a nightmarish amount of work.So when someone like Daimler says they have a way to use math to bring heterogeneous data together without compromising that data's integrity – well, I pay attention.So on today's show, we're all going back to school for an introductory class in category theory from Conexus CEO Eric Daimler.Harry Glorikian: Eric, welcome to the show.Eric Daimler: It's great to be here.Harry Glorikian: So I was reading your varied background. I mean, you've worked in so many different kinds of organizations. I'm not sure that there is a compact way or even an accurate way to describe you. So can you describe yourself? You know, what do you do and what are your main interest areas?Eric Daimler: Yeah, I mean, the easiest way to describe me might come from my mother. Well, where, you know, somebody asked her, is that the doctor? And she says, Well, yes, but he's not the type that helps people. So I you know, I've been doing research around artificial intelligence and I from a lot of different perspectives around my research in graph theory and machine learning and computational linguistics. I've been a venture capitalist on Sand Hill Road. I've done entrepreneurship, done entrepreneurship, and I started a couple of businesses which I'm doing now. And most notably I was doing policy in Washington, D.C. is part of the Obama administration for a time. So I am often known for that last part. But my background really is rare, if not unique, for having the exposure to AI from all of those angles, from business, academia and policy.Harry Glorikian: Yeah. I mean, I was looking at the obviously the like you said, the one thing that jumped out to me was the you were a Presidential Innovation Fellow in the Obama administration in 2016. Can you can you give listeners an idea of what is what is the Presidential Innovation Fellowship Program? You know, who are the types of people that are fellows and what kind of things do they do?Eric Daimler: Sure, it was I guess with that sort of question, it's helpful then to give a broader picture, even how it started. There was a a program started during the Nixon administration that's colloquially known as the Science Advisers to the President, you know, a bipartisan group to give science advice to the president that that's called the OSTP, Office of Science and Technology Policy. There are experts within that group that know know everything from space to cancer, to be super specific to, in my domain, computer security. And I was the authority that was the sole authority during my time in artificial intelligence. So there are other people with other expertise there. There are people in different capacities. You know, I had the particular capacity, I had the particular title that I had that was a one year term. The staffing for these things goes up and down, depending on the administration in ways that you might be able to predict and guess. The people with those titles also also find themselves in different parts of the the executive branch. So they will do a variety of things that are not predicted by the the title of the fellow. My particular role that I happened to be doing was in helping to coordinate on behalf of the President, humbly, on behalf of the President, their research agenda across the executive branch. There are some very able people with whom I had the good fortune of working during my time during my time there, some of which are now in the in the Biden administration. And again, it's to be a nonpartisan effort around artificial intelligence. Both sides should really be advocates for having our research agenda in government be most effective. But my role was coordinating such things as, really this is helpful, the definition of robotics, which you might be surprised by as a reflex but but quickly find to be useful when you're thinking that the Defense Department's definition and use, therefore, of robotics is really fundamentally different than that of health and human services use and a definition of robotics and the VA and Department of Energy and State and and so forth.Eric Daimler: So that is we find to be useful, to be coordinated by the Office of the President and experts speaking on behalf. It was started really this additional impulse was started after the effects of, I'll generously call them, of healthcare.gov and the trip-ups there where President Obama, to his great credit, realized that we needed to attract more technologists into government, that we had a lot of lawyers to be sure we had, we had a ton of academics, but we didn't have a lot of business people, practical technologists. So he created a way to get people like me motivated to come into government for short, short periods of time. The the idea was that you could sit around a cabinet, a cabinet meeting, and you could you would never be able to raise your hand saying, oh, I don't know anything about economics or I don't know anything about foreign policy, but you could raise your hand and say, Oh, I don't know anything about technology. That needs to be a thing of the past. President Obama saw that and created a program starting with Todd. Todd Park, the chief technologist, the second chief technology officer of the United States, is fantastic to to start to start some programs to bring in people like me.Harry Glorikian: Oh, yeah. And believe me, in health care, we need we need more technologists, which I always preach. I'm like, don't go to Facebook. Come here. You know, you can get double whammy. You can make money and you can affect people's lives. So I'm always preaching that to everybody. But so if I'm not mistaken, in early 2021, you wrote an open letter to the brand new Biden administration calling for sort of a big federal effort to improve national data infrastructure. Like, can you summarize for everybody the argument in that piece and. Do you see them doing any of the items that you're suggesting?Eric Daimler: Right. The the idea is that despite us making some real good efforts during the Obama administration with solidifying our, I'll say, our view on artificial intelligence across the executive, and this continuing actually into the Trump administration with the establishment of an AI office inside the OSTP. So credit where credit is due. That extended into the the Biden administration, where some very well-meaning people can be focusing on different parts of the the conundrum of AI expressions, having various distortions. You know, the popular one we will read about is this distortion of bias that can express itself in really ugly ways, as you know, as individuals, especially for underrepresented groups. The point of the article was to help others be reminded of of some of the easy, low hanging fruit that we can that we can work on around AI. So, you know, bias comes in a lot of different ways, the same way we all have cognitive distortions, you know, cognitive biases. There are some like 50 of them, right. You know, bias can happen around gender and ethnicity and age, sexual orientation and so forth. You know, it all can also can come from absence of data. There's a type of bias that's present just by being in a developed, rich country in collecting, for example, with Conexus's customers, my company Conexus's customers, where they are trying to report on their good efforts for economic and social good and around clean, renewable energies, they find that there's a bias in being able to collect data in rich countries versus developing countries.Eric Daimler: That's another type of bias. So that was that was the point of me writing that open letter, to prioritize, these letters. It's just to distinguish what the low hanging fruit was versus some of the hard problems. The, some of tthe low hanging fruit, I think is available, I can say, In three easy parts that people can remember. One is circuit breakers. So we we can have circuit breakers in a lot of different parts of these automated systems. You know, automated car rolling down a road is, is the easiest example where, you know, at some point a driver needs to take over control to determine to make a judgment about that shadow being a person or a tumbleweed on the crosswalk, that's a type of circuit breaker. We can have those circuit breakers in a lot of different automated systems. Another one is an audit. And the way I mean is audit is having people like me or just generally people that are experts in the craft being able to distinguish the data or the biases can become possible from the data model algorithms where biases also can become possible. Right. And we get a lot of efficiency from these automated systems, these learning algorithms. I think we can afford a little bit taken off to audit the degree to which these data models are doing what we intend.Eric Daimler: And an example of a data model is that Delta Airlines, you know, they know my age or my height, and I fly to San Francisco, to New York or some such thing. The data model would be their own proprietary algorithm to determine whether or not I am deserving of an upgrade to first class, for example. That's a data model. We can have other data models. A famous one that we all are part of is FICO scores, credit scores, and those don't have to be disclosed. None of us actually know what Experian or any of the credit agencies used to determine our credit scores. But they they use these type of things called zero knowledge proofs, where we just send through enough data, enough times that we can get to a sense of what those data models are. So that's an exposure of a data model. A declarative exposure would be maybe a next best thing, a next step, and that's a type of audit.Eric Daimler: And then the third low hanging fruit, I'd say, around regulation, and I think these are just coming towards eventualities, is demanding lineage or demanding provenance. You know, you'll see a lot of news reports, often on less credible sites, but sometimes on on shockingly credible sites where claims are made that you need to then search yourself and, you know, people in a hurry just won't do it, when these become very large systems and very large systems of information, alert systems of automation, I want to know: How were these conclusions given? So, you know, an example in health care would be if my clinician gave me a diagnosis of, let's say, some sort of cancer. And then to say, you know, here's a drug, by the way, and there's a five chance, 5 percent chance of there being some awful side effects. You know, that's a connection of causation or a connection of of conclusions that I'm really not comfortable with. You know, I want to know, like, every step is like, wait, wait. So, so what type of cancer? So what's the probability of my cancer? You know, where is it? And so what drug, you know, how did you make that decision? You know, I want to know every little step of the way. It's fine that they give me that conclusion, but I want to be able to back that up. You know, a similar example, just in everyday parlance for people would be if I did suddenly to say I want a house, and then houses are presented to me. I don't quite want that. Although that looks like good for a Hollywood narrative. Right? I want to say, oh, wait, what's my income? Or what's my cash? You know, how much? And then what's my credit? Like, how much can I afford? Oh, these are houses you can kind of afford. Like, I want those little steps or at least want to back out how those decisions were made available. That's a lineage. So those three things, circuit breaker, audit, lineage, those are three pieces of low hanging fruit that I think the European Union, the State of New York and other other government entities would be well served to prioritize.Harry Glorikian: I would love all of them, especially, you know, the health care example, although I'm not holding my breath because I might not come back to life by how long I'd have to hold my breath on that one. But we're hoping for the best and we talk about that on the show all the time. But you mentioned Conexus. You're one of three co founders, I believe. If I'm not mistaken, Conexus is the first ever commercial spin out from MIT's math department. The company is in the area of large scale data integration, building on insights that come out of the field of mathematics that's called category algebra, categorical algebra, or something called enterprise category theory. And to be quite honest, I did have to Wikipedia to sort of look that up, was not familiar with it. So can you explain category algebra in terms of a non mathematician and maybe give us an example that someone can wrap their mind around.Eric Daimler: Yeah. Yeah. And it's important to get into because even though what my company does is, Conexus does a software expression of categorical algebra, it's really beginning to permeate our world. You know, the the way I tell my my nieces and nephews is, what do quantum computers, smart contracts and Minecraft all have in common? And the answer is composability. You know, they are actually all composable. And what composable is, is it's kind of related to modularity, but it's modularity without regard to scale. So the the easy analogy is in trains where, yeah, you can swap out a boxcar in a train, but mostly trains can only get to be a couple of miles long. Swap in and out boxcars, but the train is really limited in scale. Whereas the train system, the system of a train can be infinitely large, infinitely complex. At every point in the track you can have another track. That is the difference between modularity and composability. So Minecraft is infinitely self referential where you have a whole 'nother universe that exists in and around Minecraft. In smart contracts is actually not enabled without the ability to prove the efficacy, which is then enabled by categorical algebra or its sister in math, type theory. They're kind of adjacent. And that's similar to quantum computing. So quantum computing is very sexy. It gets in the press quite frequently with forks and all, all that. If it you wouldn't be able to prove the efficacy of a quantum compiler, you wouldn't actually. Humans can't actually say whether it's true or not without type theory or categorical algebra.Eric Daimler: How you think of kind categorical algebra you can think of as a little bit related to graph theory. Graph theory is those things that you see, they look like spider webs. If you see the visualizations of graph theories are graphs. Category theory is a little bit related, you might say, to graph theory, but with more structure or more semantics or richness. So in each point, each node and each edge, in the vernacular, you can you can put an infinite amount of information. That's really what a categorical algebra allows. This, the discovery, this was invented to be translating math between different domains of math. The discovery in 2011 from one of my co-founders, who was faculty at MIT's Math Department, was that we could apply that to databases. And it's in that the whole world opens up. This solves the problem that that bedeviled the good folks trying to work on healthcare.gov. It allows for a good explanation of how we can prevent the next 737 Max disaster, where individual systems certainly can be formally verified. But the whole plane doesn't have a mechanism of being formally verified with classic approaches. And it also has application in drug discovery, where we have a way of bringing together hundreds of thousands of databases in a formal way without risk of data being misinterpreted, which is a big deal when you have a 10-year time horizon for FDA trials and you have multiple teams coming in and out of data sets and and human instinct to hoard data and a concern about it ever becoming corrupted. This math and the software expression built upon it opens up just a fantastically rich new world of opportunity for for drug discovery and for clinicians and for health care delivery. And the list is quite, quite deep.Harry Glorikian: So. What does Conexus provide its clients? Is it a service? Is it a technology? Is it both? Can you give us an example of it?Eric Daimler: Yeah. So Conexus is software. Conexus is enterprise software. It's an enterprise software platform that works generally with very large organizations that have generally very large complex data data infrastructures. You know the example, I can start in health care and then I can I can move to an even bigger one, was with a hospital group that we work with in New York City. I didn't even know health care groups could really have this problem. But it's endemic to really the world's data, where one group within the same hospital had a particular way that they represented diabetes. Now to a layman, layman in a health care sense, I would think, well, there's a definition of diabetes. I can just look it up in the Oxford English Dictionary. But this particular domain found diabetes to just be easily represented as yes, no. Do they have it? Do they not? Another group within the same hospital group thought that they would represent it as diabetes, ow are we treating it? A third group would be representing it as diabetes, how long ago. And then a fourth group had some well-meaning clinicians that would characterize it as, they had it and they have less now or, you know, type one, type two, you know, with a more more nuanced view.Eric Daimler: The traditional way of capturing that data, whether it's for drug discovery or whether it's for delivery, is to normalize it, which would then squash the fidelity of the data collected within those groups. Or they most likely to actually just wouldn't do it. They wouldn't collect the data, they wouldn't bring the data together because it's just too hard, it's too expensive. They would use these processes called ETL, extract, transform, load, that have been around for 30 years but are often slow, expensive, fragile. They could take six months to year, cost $1,000,000, deploy 50 to 100 people generally from Accenture or Deloitte or Tata or Wipro. You know, that's a burden. It's a burden, you know, so the data wasn't available and that would then impair the researchers and their ability to to share data. And it would impair clinicians in their view of patient care. And it also impaired the people in operations where they would work on billing. So we work with one company right now that that works on 1.4 trillion records a year. And they just have trouble with that volume and the number of databases and the heterogeneous data infrastructure, bringing together that data to give them one view that then can facilitate health care delivery. Eric Daimler: The big example is, we work with Uber where they they have a very smart team, as smart as one might think. They also have an effectively infinite balance sheet with which they could fund an ideal IT infrastructure. But despite that, you know, Uber grew up like every other organization optimizing for the delivery of their service or product and, and that doesn't entail optimizing for that infrastructure. So what they found, just like this hospital group with different definitions of diabetes, they found they happen to have grown up around service areas. So in this case cities, more or less. So when then the time came to do analysis -- we're just passing Super Bowl weekend, how will the Super Bowl affect the the supply of drivers or the demand from riders? They had to do it for the city of San Francisco, separate than the city of San Jose or the city of Oakland. They couldn't do the whole San Francisco Bay Area region, let alone the whole of the state or the whole of the country or what have you. And that repeated itself for every business question, every organizational question that they would want to have. This is the same in drug discovery. This is the same in patient care delivery or in billing. These operational questions are hard, shockingly hard.Eric Daimler: We had another one in logistics where we had a logistics company that had 100,000 employees. I didn't even know some of these companies could be so big, and they actually had a client with 100,000 employees. That client had 1000 ships, each one of which had 10,000 containers. And I didn't even know like how big these systems were really. I hadn't thought about it. But I mean, they're enormous. And the question was, hey, where's our personal protective equipment? Where is the PPE? And that's actually a hard question to ask. You know, we are thinking about maybe our FedEx tracking numbers from an Amazon order. But if you're looking at the PPE and where it is on a container or inside of a ship, you know, inside this large company, it's actually a hard question to ask. That's this question that all of these organizations have. Eric Daimler: In our case, Uber, where they they they had a friction in time and in money and in accuracy, asking every one of these business questions. They went then to find, how do I solve this problem? Do I use these old tools of ETL from the '80s? Do I use these more modern tools from the 2000s? They're called RDF or OWL? Or is there something else? They discovered that they needed a more foundational system, this categorical algebra that that's now expressing itself in smart contracts and quantum computers and other places. And they just then they found, oh, who are the leaders in the enterprise software expression of that math? And it's us. We happen to be 40 miles north of them. Which is fortunate. We worked with Uber to to solve that problem in bringing together their heterogeneous data infrastructure to solve their problems. And to have them tell it they save $10 million plus a year in in the efficiency and speed gains from the solution we helped provide for them.[musical interlude]Harry Glorikian: Let's pause the conversation for a minute to talk about one small but important thing you can do, to help keep the podcast going. And that's leave a rating and a review for the show on Apple Podcasts.All you have to do is open the Apple Podcasts app on your smartphone, search for The Harry Glorikian Show, and scroll down to the Ratings & Reviews section. Tap the stars to rate the show, and then tap the link that says Write a Review to leave your comments. It'll only take a minute, but you'll be doing a lot to help other listeners discover the show.And one more thing. If you like the interviews we do here on the show I know you'll like my new book, The Future You: How Artificial Intelligence Can Help You Get Healthier, Stress Less, and Live Longer.It's a friendly and accessible tour of all the ways today's information technologies are helping us diagnose diseases faster, treat them more precisely, and create personalized diet and exercise programs to prevent them in the first place.The book is now available in print and ebook formats. Just go to Amazon or Barnes & Noble and search for The Future You by Harry Glorikian.And now, back to the show.[musical interlude]Harry Glorikian: So your website says that your software can map data sources to each other so that the perfect data model is discovered, not designed. And so what does that mean? I mean, does that imply that there's some machine learning or other form of artificial intelligence involved, sort of saying here are the right pieces to put together as opposed to let me design this just for you. I'm trying to piece it together.Eric Daimler: Yeah. You know, the way we might come at this is just reminding ourselves about the structure of artificial intelligence. You know, in the public discourse, we will often find news, I'm sure you can find it today, on deep learning. You know, whatever's going on in deep learning because it's sexy, it's fun. You know, DeepMind really made a name for themselves and got them acquired at a pretty valuation because of their their Hollywood-esque challenge to Go, and solving of that game. But that particular domain of AI, deep learning, deep neural nets is a itself just a subset of machine learning. I say just not not not to minimize it. It's a fantastically powerful algorithm. But but just to place it, it is a subset of machine learning. And then machine learning itself is a subset of artificial intelligence. That's a probabilistic subset. So we all know probabilities are, those are good and bad. Fine when the context is digital advertising, less fine when it's the safety of a commercial jet. There is another part of artificial intelligence called deterministic artificial intelligence. They often get expressed as expert systems. Those generally got a bad name with the the flops of the early '80s. Right. They flopped because of scale, by the way. And then the flops in the early 2000s and 2010s from IBM's ill fated Watson experiment, the promise did not meet the the reality.Eric Daimler: It's in that deterministic A.I. that that magic is to be found, especially when deployed in conjunction with the probabilistic AI. That's that's where really the future is. There's some people have a religious view of, oh, it's only going to be a probabilistic world but there's many people like myself and not to bring up fancy names, but Andrew Ng, who's a brilliant AI researcher and investor, who also also shares this view, that it's a mix of probabilistic and deterministic AI. What deterministic AI does is, to put it simply, it searches the landscape of all possible connections. Actually it's difference between bottoms up and tops down. So the traditional way of, well, say, integrating things is looking at, for example, that hospital network and saying, oh, wow, we have four definitions of diabetes. Let me go solve this problem and create the one that works for our hospital network. Well, then pretty soon you have five standards, right? That's the traditional way that that goes. That's what a top down looks that looks like.Eric Daimler: It's called a Golden Record often, and it rarely works because pretty soon what happens is the organizations will find again their own need for their own definition of diabetes. In most all cases, that's top down approach rarely works. The bottoms up approach says, Let's discover the connections between these and we'll discover the relationships. We don't discover it organically like we depend on people because it's deterministic. I, we, we discover it through a massive, you know, non intuitive in some cases, it's just kind of infeasible for us to explore a trillion connections. But what the AI does is it explores a factorial number actually is a technical, the technical equation for it, a factorial number of of possible paths that then determine the map of relationships between between entities. So imagine just discovering the US highway system. If you did that as a person, it's going to take a bit. If you had some infinitely fast crawlers that robot's discovering the highway system infinitely fast, remember, then that's a much more effective way of doing it that gives you some degree of power. That's the difference between bottoms up and tops down. That's the difference between deterministic, really, we might say, and probabilistic in some simple way.Harry Glorikian: Yeah, I'm a firm believer of the two coming together and again, I just look at them as like a box. I always tell people like, it's a box of tools. I need to know the problem, and then we can sort of reach in and pick out which set of tools that are going to come together to solve this issue, as opposed to this damn word called AI that everybody thinks is one thing that they're sort of throwing at the wall to solve a problem.Harry Glorikian: But you're trying to solve, I'm going to say, data interoperability. And on this show I've had a lot of people talk about interoperability in health care, which I actually believe is, you could break the system because things aren't working right or I can't see what I need to see across the two hospitals that I need information from. But you published an essay on Medium about Haven, the health care collaboration between Amazon, JPMorgan, Berkshire Hathaway. Their goal was to use big data to guide patients to the best performing clinicians and the most affordable medicines. They originally were going to serve these first three founding companies. I think knowing the people that started it, their vision was bigger than that. There was a huge, you know, to-do when it came out. Fireworks and everything. Launched in 2018. They hired Atul Gawande, famous author, surgeon. But then Gawande left in 2020. And, you know, the company was sort of quietly, you know, pushed off into the sunset. Your essay argued that Haven likely failed due to data interoperability challenges. I mean. How so? What what specific challenges do you imagine Haven ran into?Eric Daimler: You know, it's funny, I say in the article very gently that I imagine this is what happened. And it's because I hedge it that that the Harvard Business Review said, "Oh, well, you're just guessing." Actually, I wasn't guessing. No, I know. I know the people that were doing it. I know the challenges there. But but I'm not going to quote them and get them in trouble. And, you know, they're not authorized to speak on it. So I perhaps was a little too modest in my framing of the conclusion. So this actually is what happened. What happens is in the same way that we had the difficulty with healthcare.gov, in the same way that I described these banks having difficulty. Heterogeneous databases don't like to talk to one another. In a variety of different ways. You know, the diabetes example is true, but it's just one of many, many, many, many, many, many cases of data just being collected differently for their own use. It can be as prosaic as first name, last name or "F.last name." Right? It's just that simple, you know? And how do I bring those together? Well, those are those are called entity resolutions. Those are somewhat straightforward, but not often 100 percent solvable. You know, this is just a pain. It's a pain. And, you know, so what what Haven gets into is they're saying, well, we're massive. We got like Uber, we got an effectively infinite balance sheet. We got some very smart people. We'll solve this problem. And, you know, this is some of the problem with getting ahead of yourself. You know, I won't call it arrogance, but getting ahead of yourself, is that, you think, oh, I'll just be able to solve that problem.Eric Daimler: You know, credit where credit is due to Uber, you know, they looked both deeper saying, oh, this can't be solved at the level of computer science. And they looked outside, which is often a really hard organizational exercise. That just didn't happen at Haven. They thought they thought they could they could solve it themselves and they just didn't. The databases, not only could they have had, did have, their own structure, but they also were stored in different formats or by different vendors. So you have an SAP database, you have an Oracle database. That's another layer of complication. And when I say that these these take $1,000,000 to connect, that's not $1,000,000 one way. It's actually $2 million if you want to connect it both ways. Right. And then when you start adding five, let alone 50, you take 50 factorial. That's a very big number already. You multiply that times a million and 6 to 12 months for each and a hundred or two hundred people each. And you just pretty soon it's an infeasible budget. It doesn't work. You know, the budget for us solving solving Uber's problem in the traditional way was something on the order of $2 trillion. You know, you do that. You know, we had a bank in the U.S. and the budget for their vision was was a couple of billion. Like, it doesn't work. Right. That's that's what happened Haven. They'll get around to it, but but they're slow, like all organizations, big organizations are. They'll get around to solving this at a deeper level. We hope that we will remain leaders in database integration when they finally realize that the solution is at a deeper level than their than the existing tools.Harry Glorikian: So I mean, this is not I mean, there's a lot of people trying to solve this problem. It's one of those areas where if we don't solve it, I don't think we're going to get health care to the next level, to sort of manage the information and manage people and get them what they need more efficiently and drive down costs.Eric Daimler: Yeah.Harry Glorikian: And I do believe that EMRs are. I don't want to call them junk. Maybe I'm going too far, but I really think that they you know, if you had decided that you were going to design something to manage patients, that is not the software you would have written to start. Hands down. Which I worry about because these places won't, they spent so much putting them in that trying to get them to rip them out and put something in that actually works is challenging. You guys were actually doing something in COVID-19, too, if I'm not mistaken. Well, how is that project going? I don't know if it's over, but what are you learning about COVID-19 and the capabilities of your software, let's say?Eric Daimler: Yeah. You know, this is an important point that for anybody that's ever used Excel, we know what it means to get frustrated enough to secretly hard code a cell, you know, not keeping a formula in a cell. Yeah, that's what happened in a lot of these systems. So we will continue with electronic medical records to to bring these together, but they will end up being fragile, besides slow and expensive to construct. They will end up being fragile, because they were at some point hardcoded. And how that gets expressed is that the next time some other database standard appears inside of that organization's ecosystem from an acquisition or a divestiture or a different technical standard, even emerging, and then the whole process starts all over again. You know, we just experience this with a large company that that spent $100 million in about five years. And then they came to us and like, yeah, we know it works now, but we know like a year from now we're going to have to say we're going to go through it again. And, it's not like, oh, we'll just have a marginal difference. No, it's again, that factorial issue, that one database connected to the other 50 that already exist, creates this same problem all over again at a couple of orders of magnitude. So what we discover is these systems, these systems in the organization, they will continue to exist.Eric Daimler: These fragile systems will continue to exist. They'll continue to scale. They'll continue to grow in different parts of the life sciences domain, whether it's for clinicians, whether it's for operations, whether it's for drug discovery. Those will continue to exist. They'll continue to expand, and they will begin to approach the type of compositional systems that I'm describing from quantum computers or Minecraft or smart contracts, where you then need the the discovery and math that Conexus expresses in software for databases. When you need that is when you then need to prove the efficacy or otherwise demonstrate the lack of fragility or the integrity of the semantics. Conexus can with, it's a law of nature and it's in math, with 100 percent accuracy, prove the integrity of a database integration. And that matters in high consequence context when you're doing something as critical as drug side effects for different populations. We don't want your data to be misinterpreted. You can't afford lives to be lost or you can't, in regulation, you can't afford data to be leaking. That's where you'll ultimately need the categorical algebra. You'll need a provable compositional system. You can continue to construct these ones that will begin to approach compositionality, but when you need the math is when you need to prove it for either the high consequence context of lives, of money or related to that, of regulation.Harry Glorikian: Yeah, well, I keep telling my kids, make sure you're proficient in math because you're going to be using it for the rest of your life and finance. I always remind them about finance because I think both go together. But you've got a new book coming out. It's called "The Future is Formal" and not tuxedo like formal, but like you're, using the word formal. And I think you have a very specific meaning in mind. And I do want you to talk about, but I think what you're referring to is how we want automated systems to behave, meaning everything from advertising algorithms to self-driving trucks. And you can tell me if that my assumption is correct or not.Eric Daimler: Though it's a great segue, actually, from the math. You know, what I'm trying to do is bring in people that are not programmers or research technology, information technology researchers day to day into the conversation around automated digital systems. That's my motivation. And my motivation is, powered by the belief that we will bring out the best of the technology with more people engaged. And with more people engaged, we have a chance to embrace it and not resist it. You know, my greatest fear, I will say, selfishly, is that we come up with technology that people just reject, they just veto it because they don't understand it as a citizen. That also presents a danger because I think that companies' commercial expressions naturally will grow towards where their technology is needed. So this is actually to some extent a threat to Western security relative to Chinese competition, that we embrace the technology in the way that we want it to be expressed in our society. So trying to bring people into this conversation, even if they're not programmers, the connection to math is that there are 18 million computer programmers in the world. We don't need 18 million and one, you know. But what we do need is we do need people to be thinking, I say in a formal way, but also just be thinking about the values that are going to be represented in these digital infrastructures.Eric Daimler: You know, somewhere as a society, we will have to have a conversation with ourselves to determine the car driving to the crosswalk, braking or rolling or slowing or stopping completely. And then who's liable if it doesn't? Is it the driver or is it the manufacturer? Is it the the programmer that somehow put a bug in their code? You know, we're entering an age where we're going to start experiencing what some person calls double bugs. There's the bug in maybe one's expression in code. This often could be the semantics. Or in English. Like your English doesn't make sense. Right? Right. Or or was it actually an error in your thinking? You know, did you leave a gap in your thinking? This is often where where some of the bugs in Ethereum and smart contracts have been expressed where, you know, there's an old programming rule where you don't want to say something equals true. You always want to be saying true equals something. If you get if you do the former, not the latter, you can have to actually create bugs that can create security breaches.Eric Daimler: Just a small little error in thinking. That's not an error in semantics. That level of thinking, you don't need to know calculus for, or category theory for that matter. You just need to be thinking in a formal way. You know, often, often lawyers, accountants, engineers, you know, anybody with scientific training can, can more quickly get this idea, where those that are educated in liberal arts can contribute is in reminding themselves of the broader context that wants to be expressed, because often engineers can be overly reductionist. So there's really a there's a push and pull or, you know, an interplay between those two sensibilities that then we want to express in rules. Then that's ultimately what I mean by formal, formal rules. Tell me exactly what you mean. Tell me exactly how that is going to work. You know, physicians would understand this when they think about drug effects and drug side effects. They know exactly what it's going to be supposed to be doing, you know, with some degree of probability. But they can be very clear, very clear about it. It's that clear thinking that all of us will need to exercise as we think about the development and deployment of modern automated digital systems.Harry Glorikian: Yeah, you know, it's funny because that's the other thing I tell people, like when they say, What should my kid take? I'm like, have him take a, you know, basic programming, not because they're going to do it for a living, but they'll understand how this thing is structured and they can get wrap their mind around how it is. And, you know, I see how my nephew thinks who's from the computer science world and how I think, and sometimes, you know, it's funny watching him think. Or one of the CTOs of one of our companies how he looks at the world. And I'm like you. You got to back up a little bit and look at the bigger picture. Right. And so it's the two of us coming together that make more magic than one or the other by themselves.Harry Glorikian: So, you know, I want to jump back sort of to the different roles you've had in your career. Like like you said, you've been a technology investor, a serial startup founder, a university professor, an academic administrator, an entrepreneur, a management instructor, Presidential Innovation Fellow. I don't think I've missed anything, but I may have. You're also a speaker, a commentator, an author. Which one of those is most rewarding?Eric Daimler: Oh, that's an interesting question. Which one of those is most rewarding? I'm not sure. I find it to be rewarding with my friends and family. So it's rewarding to be with people. I find that to be rewarding in those particular expressions. My motivation is to be, you know, just bringing people in to have a conversation about what we want our world to look like, to the degree to which the technologies that I work with every day are closer to the dystopia of Hollywood narratives or closer to our hopes around the utopia that's possible, that where this is in that spectrum is up to us in our conversation around what these things want to look like. We have some glimpses of both extremes, but I'd like people, and I find it to be rewarding, to just be helping facilitate the helping catalyze that conversation. So the catalyst of that conversation and whatever form it takes is where I enjoy being.Harry Glorikian: Yeah, because I was thinking about like, you know, what can, what can you do as an individual that shapes the future. Does any of these roles stand out as more impactful than others, let's say?Eric Daimler: I think the future is in this notion of composability. I feel strongly about that and I want to enroll people into this paradigm as a framework from which to see many of the activities going around us. Why have NFTs come on the public, in the public media, so quickly? Why does crypto, cryptocurrency capture our imagination? Those And TikTok and the metaverse. And those are all expressions of this quick reconfiguration of patterns in different contexts that themselves are going to become easier and easier to express. The future is going to be owned by people that that take the special knowledge that they've acquired and then put it into short business expressions. I'm going to call them rules that then can be recontextualized and redeployed. This is my version of, or my abstraction of what people call the the future being just all TikTok. It's not literally that we're all going to be doing short dance videos. It's that TikTok is is an expression of people creating short bits of content and then having those be reconfigured and redistributed. That can be in medicine or clinical practice or in drugs, but it can be in any range of expertise, expertise or knowledge. And what's changed? What's changed and what is changing is the different technologies that are being brought to bear to capture that knowledge so that it can be scalable, so it can be compositional. Yeah, that's what's changing. That's what's going to be changing over the next 10 to 20 years. The more you study that, I think the better off we will be. And I'd say, you know, for my way of thinking about math, you might say the more math, the better. But if I were to choose for my children, I would say I would replace trig and geometry and even calculus, some people would be happy to know, with categorical algebra, category theory and with probability and statistics. So I would replace calculus, which I think is really the math of the 20th century, with something more appropriate to our digital age, which is categorical algebra.Harry Glorikian: I will tell my son because I'm sure he'll be very excited to to if I told him that not calculus, but he's not going to be happy when I say go to this other area, because I think he'd like to get out of it altogether.Eric Daimler: It's easier than calculus. Yeah.Harry Glorikian: So, you know, it was great having you on the show. I feel like we could talk for another hour on all these different aspects. You know, I'm hoping that your company is truly successful and that you help us solve this interoperability problem, which is, I've been I've been talking about it forever. It seems like I feel like, you know, the last 15 or 20 years. And I still worry if we're any closer to solving that problem, but I'm hopeful, and I wish you great success on the launch of your new book. It sounds exciting. I'm going to have to get myself a copy.Eric Daimler: Thank you very much. It's been fun. It's good to be with you.Harry Glorikian: Thank you.Harry Glorikian: That's it for this week's episode. You can find a full transcript of this episode as well as the full archive of episodes of The Harry Glorikian Show and MoneyBall Medicine at our website. Just go to glorikian.com and click on the tab Podcasts.I'd like to thank our listeners for boosting The Harry Glorikian Show into the top three percent of global podcasts.If you want to be sure to get every new episode of the show automatically, be sure to open Apple Podcasts or your favorite podcast player and hit follow or subscribe. Don't forget to leave us a rating and review on Apple Podcasts. And we always love to hear from listeners on Twitter, where you can find me at hglorikian.Thanks for listening, stay healthy, and be sure to tune in two weeks from now for our next interview.

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The rapid advancement in machine learning and data science fields have aided data scientists in arriving at meaningful insights. However, it’s not been an easy task to optimize machine learning infrastructures to allow data scientists to focus on their core expertise. Today, we will discuss how certain tools and hardware optimizations are not only saving […]

Darren Pulsipher, Chief Solution Architect, Intel, discusses the capabilities and future of OneAPI, a cross-industry, open, standards-based unified programming model that delivers a common developer experience across accelerator architectures, with Intel's OneAPI Chief Evangelist, James Reinders. Blog: https://www.intel.com/content/www/us/en/government/podcasts/embracing-digital-transformation-episode81.html Video: https://youtu.be/YDa_EeOzFzc

Great minds think alike, or so we've been told, but what if different minds help to build better communities, better companies, and even better churches? In this episode of VOICES' Where Ya From? podcast, international business consultant Skot Welch discusses diversity, equity, and inclusion and shares his passion for creating communities that reflect the diversity that God designed. Guest Bio: Skot Welch is the president and founder of Global Bridgebuilders, a results-focused firm pioneering diversity and inclusion initiatives to a wide range of Fortune 500 clients in the United States and across the globe. He is also the author of 101 Ways to Enjoy the Mosaic: Creating a Diverse Community Right in Your Own Backyard, the coauthor of Plantation Jesus: Race, Faith, and a New Way Forward, and the founder of The Mosaic Film Experience, an educational platform that empowers underserved youth through digital storytelling. Skot and his wife, Barbara, reside in Michigan and have two children. Notes & Quotes: “When you have people that are different, you actually have innovation that is stronger, higher . . . . And there's books written about it. Now, there's studies on it. Heterogeneous groups are more innovative.” “God loves His mosaic. He didn't make any of us to be the same. There's seven and a half billion of us, and yet not even identical twins are alike.” Why don't we just understand each other's stories? It makes God smile. He didn't leave it as an option. He gave it as a commandment, by the way, it's not optional. We need to get along and we need to act like family. So let's have the hard conversations. Let's do the work. Links Mentioned: Visit our website to sign up for emails: whereyafrom.org Leave us a review: https://podcasts.apple.com/us/podcast/where-ya-from-podcast/id1581145346 Check out our Voices Collection from Our Daily Bread Ministries Follow Where Ya From? on Instagram: @whereyafrompodcast Extra resource: On The Shoulders of Giants reading plan Skot Welch's website Global Bridge Builders website Verses Mentioned: Romans 8 Revelation 7 & 9 Psalms 133 John 17 Learn more about your ad choices. Visit podcastchoices.com/adchoices

In the gaming world, artificial intelligence (AI) is proving to be a game-changer. It has played a pivotal role in enhancing game-player's experiences. Amongst other capabilities, Artificial intelligence is used to generate responsive, adaptive, or intelligent behaviors primarily in non-player characters (NPCs) similar to human-like intelligence. With the right tools and access to advanced technologies, developers […]

Scientific visualization (SciVis) is a process of representing raw scientific data as images, which helps scientists improve their interpretations of large data sets. More and more, advanced visualization tools are becoming an integral part of a researcher’s analysis toolkit; and when in the hands of scientists and researchers, they can visualize, interact with, and get […]

Key Takeaways and Actionable Insights An understanding of the Austrian definition of capital is tremendously useful to all business owners and managers. What is capital? Austrian economics has a precise and distinctive definition — unlike business schools and most business publications, books, and columnists. Among those entities, the term capital tends to be used very imprecisely. You might see sentences like, “Entrepreneurs must ensure they have sufficient capital to get their new product to market”, or “to get to break-even”. Such usages imply that capital is a cash reserve to be “burned off” in the process of launching and scaling a business. Recently, it has become fashionable to coin terms such as human capital, or brand capital, or relationship capital, or even spiritual capital or street capital. All of these terms are sloppy definitions of capital from an Austrian point of view. And it's important to note that capital is not the same as capital goods, which are “produced means of production”. Capital is not a means of production, it is a consequence of production. What, then, is the precise Austrian definition of capital? On the E4E podcast #87, Professor Matthew McCaffrey gives us this definition: Capital is the monetary value of a business's claims to income. This includes all of its marketable assets, whether they are tangible or intangible. It's a sum of individual values. These values are ultimately determined by consumers, because the value of a firm's assets and the value of its income streams ultimately depend on how consumers value the final product. Crucially, capital is distinct from what are called capital goods or production goods, which are the physical goods used in production. Those are also vital for understanding how entrepreneurship works in practice, but they are not capital in the sense in which we mean it. In summary: Capital is a flow (rather than a stock)Coming into your businessFrom consumersReflecting the value consumers perceive in your company's services. B2B businesses can substitute the term “final purchasers” for consumers if producing goods and services purely for business customers. But it is important to remember that the value of capital always eventually reflects the valuations of goods and services by consumers. The software or professional services your B2B business provides to a business customer will command less of a claim to income if that business customer faces a change in preferences and a decline in market demand from their consumer population. When forecasting future income flows, every business must bear in mind the climate among ultimate consumers. What are the implications for entrepreneurs and business managers? Flows can be generated via tangible or intangible assets.Consumers' valuation of services is the key variable.Entrepreneurs must be able to appraise which assets — in which combinations — are generating the flow.The flow can change — even disappear — when consumer preferences change: entrepreneurs must be able to adjust.Large flows can result from a low asset base — and vice versa.Appraisal — predicting future prices and flows — is the vital skill to determine what to invest in, how to organize, and what to produce.Cash flow is the measurement variable.Use cash flow to calculate asset productivity.Update appraisals continuously based on cash flow. What about capital goods? Capital is NOT the same as capital goods.But capital goods can be generators of capital flows.IF consumers value their output.Austrians stress HETEROGENEOUS capital goods, both tangible and intangible.A jigsaw puzzle to assemble, disassemble, and reassemble in the right combination, based on consumers' valuations. What actions should entrepreneurs take as a consequence of the Austrian view of capital? Always focus on the value you are facilitating from consumers.They, in turn, will generate your capital flow.Measure the flow in dollars — especially the trend.Be a master appraiser: know your asset productivity.Set up your assets for flexibility — be fully able to disassemble and reassemble capital combinations.Experiment frequently with different combinations.Become comfortable with continuous change in asset combinations. Additional Resources Professor McCaffrey made reference to Frank Fetter's role in defining capital in his online discussion, "Frank Fetter and the Austrian Tradition in the United States": Mises.org/E4E_87_McCaffrey Professor Peter Klein explains why metaphors like Human Capital are unhelpful to entrepreneurs in his article, "A Note on Human Capital": Mises.org/E4E_87_Klein

After patting themselves on the back for anticipating yet another brouhaha in the social media world over the lack of representation of women,the Rabbis discuss why congregants in many of the Shuls in areas of a large Jewish populace(Monsey,Borough Park,Lakewood)are in large part not conforming to the social distancing guidelines of the WHO and CDC .The pair spar over the extent Chasidim are to blame for the Shul irregularities in Bechhofer's Monsey.Kivelevitz attributes what Bechhofer is seeing to the general trends that have been documented,showing that Americans are less inclined to follow scientific health advice than persons in other countries.Bechhofer questions Kivelevitz as to why in his hometown of Elizabeth,New Jersey the Jewish populace are conforming to the strictures mandated by the city's Rabbis.Please respond with your input to these issues at ravkiv@gmail.comFor more information on this podcast visityeshivaofnewark.jewishpodcasts.org See acast.com/privacy for privacy and opt-out information. This podcast is powered by JewishPodcasts.org. Start your own podcast today and share your content with the world. Click jewishpodcasts.fm/signup to get started.

Since no one had read The Gambler, this class was a kind of summing up of thinking from Mandeville to Kant under the rubric of Mauss -- how credit, gratitude, obligation bring in other minds and differentiate our credit with them from the way money is a bookkeeping measure.