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6 episodes with harvard data science review
2 episodes with harvard data science review
2 episodes with harvard data science review
3 episodes with harvard data science review
AI can now sell your intentions – marketing your decisions before you make them Machine learning is on the verge of commercialising your decisions even before you make them. New research shows that we are moving away from an attention economy to an intention economy as machine learning models, which can already imitate the way we write or talk, can now map previous activity onto future actions. Writing in the Harvard Data Science Review, Dr Yaqub Chaudhary, visiting scholar at the Leverhulme Centre for the Future of Intelligence, and Dr Jonnie Penn of the University of Cambridge ask if AI predicting our intentions could be misused against us. Geothermal Energy without the digging Geothermal energy is going drill-free, using already available underground spaces, like car parks. Reporter Jez Donaldson met Margaux Peltier, Co-founder and CEO of Enerdrape, at the Consumer Electronics Show in Las Vegas. The company uses pre-cooled or pre-heated geothermal panels in walls, which capture heat from the surrounding concrete or the ground itself, making this energy renewable as no new heat is created. This is then redistributed around the building using a heat pump. The programme is presented by Gareth Mitchell and the studio expert is Ghislaine Boddington. More on this week's stories: AI selling your decisions before you make them Enerdrape Production Manager: Liz Tuohy Editor: Ania Lichtarowicz Recorded at Lansons Team Farner For the PodExtra version of the show please subscribe via this link: https://somewhere-on-earth-the-global-tech-podcast-the-podextra-edition.pod.fan/ Follow us on all the socials: Join our Facebook group Instagram BlueSky If you like Somewhere on Earth, please rate and review it on Apple Podcasts or Spotify Contact us by email: hello@somewhereonearth.co Send us a voice note: via WhatsApp: +44 7486 329 484 Find a Story + Make it News = Change the World Learn more about your ad choices. Visit megaphone.fm/adchoices
Professor Yoshua Bengio is a pioneer in deep learning and Turing Award winner. Bengio talks about AI safety, why goal-seeking “agentic” AIs might be dangerous, and his vision for building powerful AI tools without giving them agency. Topics include reward tampering risks, instrumental convergence, global AI governance, and how non-agent AIs could revolutionize science and medicine while reducing existential threats. Perfect for anyone curious about advanced AI risks and how to manage them responsibly. SPONSOR MESSAGES: *** CentML offers competitive pricing for GenAI model deployment, with flexible options to suit a wide range of models, from small to large-scale deployments. https://centml.ai/pricing/ Tufa AI Labs is a brand new research lab in Zurich started by Benjamin Crouzier focussed on o-series style reasoning and AGI. Are you interested in working on reasoning, or getting involved in their events? They are hosting an event in Zurich on January 9th with the ARChitects, join if you can. Goto https://tufalabs.ai/ *** Interviewer: Tim Scarfe Yoshua Bengio: https://x.com/Yoshua_Bengio https://scholar.google.com/citations?user=kukA0LcAAAAJ&hl=en https://yoshuabengio.org/ https://en.wikipedia.org/wiki/Yoshua_Bengio TOC: 1. AI Safety Fundamentals [00:00:00] 1.1 AI Safety Risks and International Cooperation [00:03:20] 1.2 Fundamental Principles vs Scaling in AI Development [00:11:25] 1.3 System 1/2 Thinking and AI Reasoning Capabilities [00:15:15] 1.4 Reward Tampering and AI Agency Risks [00:25:17] 1.5 Alignment Challenges and Instrumental Convergence 2. AI Architecture and Safety Design [00:33:10] 2.1 Instrumental Goals and AI Safety Fundamentals [00:35:02] 2.2 Separating Intelligence from Goals in AI Systems [00:40:40] 2.3 Non-Agent AI as Scientific Tools [00:44:25] 2.4 Oracle AI Systems and Mathematical Safety Frameworks 3. Global Governance and Security [00:49:50] 3.1 International AI Competition and Hardware Governance [00:51:58] 3.2 Military and Security Implications of AI Development [00:56:07] 3.3 Personal Evolution of AI Safety Perspectives [01:00:25] 3.4 AI Development Scaling and Global Governance Challenges [01:12:10] 3.5 AI Regulation and Corporate Oversight 4. Technical Innovations [01:23:00] 4.1 Evolution of Neural Architectures: From RNNs to Transformers [01:26:02] 4.2 GFlowNets and Symbolic Computation [01:30:47] 4.3 Neural Dynamics and Consciousness [01:34:38] 4.4 AI Creativity and Scientific Discovery SHOWNOTES (Transcript, references, best clips etc): https://www.dropbox.com/scl/fi/ajucigli8n90fbxv9h94x/BENGIO_SHOW.pdf?rlkey=38hi2m19sylnr8orb76b85wkw&dl=0 CORE REFS (full list in shownotes and pinned comment): [00:00:15] Bengio et al.: "AI Risk" Statement https://www.safe.ai/work/statement-on-ai-risk [00:23:10] Bengio on reward tampering & AI safety (Harvard Data Science Review) https://hdsr.mitpress.mit.edu/pub/w974bwb0 [00:40:45] Munk Debate on AI existential risk, featuring Bengio https://munkdebates.com/debates/artificial-intelligence [00:44:30] "Can a Bayesian Oracle Prevent Harm from an Agent?" (Bengio et al.) on oracle-to-agent safety https://arxiv.org/abs/2408.05284 [00:51:20] Bengio (2024) memo on hardware-based AI governance verification https://yoshuabengio.org/wp-content/uploads/2024/08/FlexHEG-Memo_August-2024.pdf [01:12:55] Bengio's involvement in EU AI Act code of practice https://digital-strategy.ec.europa.eu/en/news/meet-chairs-leading-development-first-general-purpose-ai-code-practice [01:27:05] Complexity-based compositionality theory (Elmoznino, Jiralerspong, Bengio, Lajoie) https://arxiv.org/abs/2410.14817 [01:29:00] GFlowNet Foundations (Bengio et al.) for probabilistic inference https://arxiv.org/pdf/2111.09266 [01:32:10] Discrete attractor states in neural systems (Nam, Elmoznino, Bengio, Lajoie) https://arxiv.org/pdf/2302.06403
Liberty Vittert is a Professor of Data Science at Wash U, Feature Editor of the Harvard Data Science Review, and Senior Data Scientist at Decision Desk HQ. She joined us today to discuss the ever growing and changing world of artificial intelligence, and how other nations like China may use it to wreak havoc in the States.
According to the Pew Research Center, three in ten US adults say they've used a dating app, with Tinder, Match and Bumble being the apps most likely to have been tried. Pew's research has also found that one in 10 partnered adults in the US met their significant other on a dating app or site. Dating app success is a focus of this episode of Stats and Stories with guest Dr. Liesel Sharabi. Dr. Liesel Sharabi studies the data science of love, including the ways that algorithms and artificial intelligence (AI) help to facilitate intimate relationships. She has written about matchmaking algorithms for the Harvard Data Science Review and discussed their use in online dating with media outlets like TIME Magazine, WIRED, and The Wall Street Journal. She is currently an associate professor in the Hugh Downs School of Human Communication and director of the Relationships and Technology Lab at Arizona State University.
After the positive reception to our first listener question episode featuring co-host Xiao-Li Meng last August, we decided to start the new year with an exclusive interview with one of our most esteemed guests yet: HDSR's own Liberty Vittert! For this special episode, Xiao-Li is joined by guest co-host, Arianwyn Frank, a producer of this podcast, a data science undergrad at Washington University, and a former student of Liberty's. Listen now to their fascinating conversation with Liberty as they discover how a woman of many talents found herself in the exciting world of data science. Our guest: Liberty Vittert, Professor of the Practice of Data Science, Olin Business School, Washington University in St. Louis; resident on-air statistician for NewsNation; and feature editor of Harvard Data Science Review
Episode 86 explores the importance of Data & Analytics Management with Lauren Maffeo.Based out of Washington DC, Lauren Maffeo is the Senior Service Designer at Steampunk Inc an IT Service and IT Consulting company.Lauren is an award-winning author, analyst, and designer of data systems for the U.S. Federal government. Career highlights include leading service design for an agency database with 46 million+ unique data points and is the author of “Designing Data Governance from the Ground Up”.She is a founding editor of Springer's AI and Ethics Journal and an adjunct lecturer of Interaction Design at The George Washington University. Lauren has written for Harvard Data Science Review, Financial Times, and The Guardian. She has also presented her research on bias in AI at Princeton and Columbia Universities, Google DevFest DC, and Twitter's San Francisco headquarters.The Business of Business, topics are divided into 4 Categories: Management, Operations, Sales, and Financial. Target Audience is Business Owners, C-Level Executives, Management, and anyone considering starting a business. Support the showHelping You Run a Successful Profitable Business !For Business Consulting or to be a Podcast Guest - Contact me at: www.bcforg.comLinkedIn: https://www.linkedin.com/in/brian-fisher-72174413/
A conversation with Lauren Maffeo. In this episode we're joined by Lauren Maffeo. Lauren is an award-winning service designer working full-time at Steampunk where she serves the U.S. federal government. She is a founding editor of Springer's AI and Ethics Journal and an adjunct lecturer of Interaction Design at The George Washington University. Lauren has written for Harvard Data Science Review, Financial Times, and The Guardian. She has also presented her research on bias in AI at Princeton and Columbia Universities, Google DevFest DC, and Twitter's San Francisco headquarters and is the author of Designing Data Governance from the Ground Up. In this episode we talk about the societal impact of data governance, the link between human-centered design and managing data in a company, the anti-patterns in data management, the importance of culture in breaking down data silos, balancing transparency with security, the link between data quality and misinformation and many other topics… See timestamps below. Go here for show notes, links, and resources. Follow Juan Mendoza on LinkedIn and Twitter. Listen on Apple, Spotify, Google, and everywhere else. You can find Lauren on LinkedIn. Timestamps Time Topic(0:11) Guest intro(10:50) Incentives to data the practice of data governance/management(21:47) The anti-patterns in data management(27:27) Discussion around techno optimism(34:20) the importance of culture in breaking down data silos(44:06) Balancing transparency with security(50:34) The Link between data governance and misinformation(53:33) Importance of data quality & governance on commerciality such as generative AI
Lauren Maffeo is an award-winning service designer working full-time at Steampunk where she serves the U.S. federal government. She is a founding editor of Springer's AI and Ethics Journal and an adjunct lecturer of Interaction Design at The George Washington University. Lauren has written for Harvard Data Science Review, Financial Times, and The Guardian. She has also presented her research on bias in AI at Princeton and Columbia Universities, Google DevFest DC, and Twitter's San Francisco headquarters. She is the author of Designing Data Governance from the Ground Up. Listeners can buy a copy of her book, and get 35% off using the code DATAGOV23 (valid until September 2023) Summary Introduction to Lauren. Managing data and data governance. The lack of governance around data. The problem of data spoilage. Who is a hero of yours and why are they a hero?
Lauren Maffeo is an award-winning service designer working full-time at Steampunk where she serves the U.S. federal government. She is a founding editor of Springer's AI and Ethics Journal and an adjunct lecturer of Interaction Design at The George Washington University. Lauren has written for Harvard Data Science Review, Financial Times, and The Guardian. She has also presented her research on bias in AI at Princeton and Columbia Universities, Google DevFest DC, and Twitter's San Francisco headquarters. She is the author of Designing Data Governance from the Ground Up. Listeners can buy a copy of her book, and get 35% off using the code DATAGOV23 (valid until September 2023) Summary Introduction to Lauren. Managing data and data governance. The lack of governance around data. The problem of data spoilage. Who is a hero of yours and why are they a hero?
Join us as we explore the remarkable journey of Dr. Alex Liu from a background in sociology to becoming a leading figure in data science and AI. Alex shares insights into the critical inflexion points in his career, his extensive experience working with prestigious institutions and corporations, and his commitment to fostering a global ecosystem of data science expertise. Tune in now to delve into the world of data science, innovation, and community-driven problem-solving with Alex, a visionary leader in the field. [00:35] - About Dr. Alex Liu Dr. Liu is the CEO of RMDS Labs, which provides data and AI services. He is an advisor to the Harvard Data Science Review. --- Support this podcast: https://podcasters.spotify.com/pod/show/tbcy/support
In this episode of the show, I continue my deep dive into data, human values, and governance with an interview featuring Lauren Maffeo. We talk about the future of data governance, the possibilities of, and the catastrophe that Lauren thinks our society may need to experience in order to turn the corner on an data governance and ethics. Lauren Maffeo is an award-winning designer and analyst who currently works as a service designer at Steampunk, a human-centered design firm serving the federal government. She is also a founding editor of Springer's AI and Ethics journal and an adjunct lecturer in Interaction Design at The George Washington University. Her first book, Designing Data Governance from the Ground Up, is available from The Pragmatic Programmers. Lauren has written for Harvard Data Science Review, Financial Times, and The Guardian, among other publications. She is a fellow of the Royal Society of Arts, a former member of the Association for Computing Machinery's Distinguished Speakers Program, and a member of the International Academy of Digital Arts and Sciences, where she helps judge the Webby Awards.
We've been inundated with questions from our listeners on what defines a data scientist, how to break into analytics, and ways for the average person to assess data reliability. That is why for this month, we interview our very own Xiao-Li Meng, who has contemplated many such questions during his distinguished career. In this episode we delve into Xiao-Li's personal journey—notably being named the best statistician under the age of 40 by the Committee of Presidents of Statistical Societies—to becoming the founding editor-in-chief of Harvard Data Science Review. Join us as we trace the steps that led to his remarkable accomplishments and illuminate the path you can follow to understand the data that shapes our world in our very first listener question special! Our guest: Dr. Xiao-Li Meng, Founding Editor-in-Chief of Harvard Data Science Review and Whipple V. N. Jones Professor of Statistics at Harvard University. Meng has published over 150 publications. His article, “Seeking Simplicity in Statistics, Complexities in Wine, and Everything Else in Fortune Cookies” was published in Fondata, Issue 3, Winter 2022.
In this episode, the journal's Features Editor Liberty Vittert and Editor in Chief Xiao-Li Meng discuss fake news, disinformation, and misinformation with Scott Tranter, CEO and founder of Optimus Analytics, and Hany Farid, a professor from UC Berkeley who specializes in the analysis of digital images and is the author of two MIT Press books: Fake Photos and Photo Forensics. This episode is syndicated from the new Harvard Data Science Review Podcast. Published by the MIT Press, Harvard Data Science Review is an open access multidisciplinary journal that defines and shapes data science as a scientifically rigorous field based on the principled and purposed production, processing, parsing and analysis of data. If you enjoy this preview of the Harvard Data Science Review podcast, find the journal on twitter at @TheHDSR and remember to subscribe to their podcast on your favorite platform. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/communications
In this episode, the journal's Features Editor Liberty Vittert and Editor in Chief Xiao-Li Meng discuss fake news, disinformation, and misinformation with Scott Tranter, CEO and founder of Optimus Analytics, and Hany Farid, a professor from UC Berkeley who specializes in the analysis of digital images and is the author of two MIT Press books: Fake Photos and Photo Forensics. This episode is syndicated from the new Harvard Data Science Review Podcast. Published by the MIT Press, Harvard Data Science Review is an open access multidisciplinary journal that defines and shapes data science as a scientifically rigorous field based on the principled and purposed production, processing, parsing and analysis of data. If you enjoy this preview of the Harvard Data Science Review podcast, find the journal on twitter at @TheHDSR and remember to subscribe to their podcast on your favorite platform. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/journalism
What does data science tell us about art auctions? This episode is syndicated from the new Harvard Data Science Review Podcast. Published by the MIT Press, Harvard Data Science Review is an open access multidisciplinary journal that defines and shapes data science as a scientifically rigorous field based on the principled and purposed production, processing, parsing and analysis of data. In this episode, the journal's Features Editor Liberty Vittert and Editor in Chief Xiao-Li Meng discuss art auctions with art curator Dan Cameron and Artnome's Jason Bailey. If you enjoy this preview of the Harvard Data Science Review podcast, find the journal on twitter at @TheHDSR and remember to subscribe to their podcast on your favorite platform. 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
What does data science tell us about art auctions? This episode is syndicated from the new Harvard Data Science Review Podcast. Published by the MIT Press, Harvard Data Science Review is an open access multidisciplinary journal that defines and shapes data science as a scientifically rigorous field based on the principled and purposed production, processing, parsing and analysis of data. In this episode, the journal's Features Editor Liberty Vittert and Editor in Chief Xiao-Li Meng discuss art auctions with art curator Dan Cameron and Artnome's Jason Bailey. If you enjoy this preview of the Harvard Data Science Review podcast, find the journal on twitter at @TheHDSR and remember to subscribe to their podcast on your favorite platform. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/art
What does data science tell us about art auctions? This episode is syndicated from the new Harvard Data Science Review Podcast. Published by the MIT Press, Harvard Data Science Review is an open access multidisciplinary journal that defines and shapes data science as a scientifically rigorous field based on the principled and purposed production, processing, parsing and analysis of data. In this episode, the journal's Features Editor Liberty Vittert and Editor in Chief Xiao-Li Meng discuss art auctions with art curator Dan Cameron and Artnome's Jason Bailey. If you enjoy this preview of the Harvard Data Science Review podcast, find the journal on twitter at @TheHDSR and remember to subscribe to their podcast on your favorite platform. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/economics
Original Air Date 3/16/2022 Today we take a look at the dynamics of mis- and disinformation as well as the history of those, primarily Russia, who are actively using it as a weapon of information warfare against the US, The West, and democracies around the world. Leave us a message or text at 202-999-3991 or email Jay@BestOfTheLeft.com Transcript BestOfTheLeft.com/Support (Get AD FREE Shows and Bonus Content) SHOW NOTES Ch. 1: Are you Disinformed or Misinformed? - Harvard Data Science Review Podcast - Air Date 6-17-21 Harvard Data Science Review digs into the world of disinformation and misinformation, and the difference between them. Ch. 2: Moving beyond news deserts and misinformation - Democracy Works - Air Date 2-14-22 Victor Pickard is the C. Edwin Baker Professor of Media Policy and Political Economy at the University of Pennsylvania and author of Democracy Without Journalism? Ch. 3: Why "Cheap Speech" Threatens Democracy - Amicus With Dahlia Lithwick - Air Date 3-5-22 Dahlia talks to Rick Hasen about his new book Cheap Speech: How Disinformation Poisons Our Politics–and How to Cure It. Ch. 4: The Firehose of Falsehood Effect - The Propwatch Project - Air Date 4-4-21 Dr. Christopher Paul, senior social scientist of the Pardee RAND Graduate School, discusses his seminal work on the "Firehose of Falsehood" propaganda model, going through its key components and explaining how each exploits facets of human heuristics. Ch. 5: The long history of Russian disinformation targeting the U.S. - PBS NewsHour - Air Date 11-22-18 A recent New York Times video series explores the long history of Russian disinformation Ch. 6: Post-Truth: Lee C. McIntyre - Future Hindsight - Air Date 5-14-20 Authoritarians use post-truth to corrupt our faith in the truth. The end goal is not to make citizens believe lies, but to make them so cynical and uncertain, they think they can never know the truth. MEMBERS-ONLY BONUS CLIP(S) Ch. 9: Inside Estonia's approach in combating Russian disinformation - PBS NewsHour - Air Date 1-15-22 Russian disinformation is rife in countries formerly ruled from Moscow. Some ex-Soviet states have tried to suppress it altogether by banning Russian television stations and even limiting the use of the Russian language on their own domestic channels. VOICEMAILS Ch. 10: Seeking power from the right and left - Alex from Maryland Old FINAL COMMENTS Ch. 12: Final comments on avoiding the nihilism strategy of the Underpants Gnomes TAKE ACTION! Media Literacy Now - Advocate for U.S. Media Literacy Bills at State Level (See Existing Media Literacy Bills and Laws) Tools to Fight Disinformation: FactCheck.Org List: Tools That Fight Disinformation Online (RAND.org) InVID - Browser plug in to help identify and verify shared videos and images Hamilton 2.0 Dashboard - War in Ukraine (Disinformation tracker) Orgs Fighting Disinformation: Project Origin Content Authenticity Initiative Coalition for Content Provenance and Authenticity News Literacy Project Alliance for Securing Democracy EDUCATE YOURSELF & SHARE Short Docuseries: Operation InfeKtion (New York Times) Book: “The Misinformation Age: How False Beliefs Spread” by Calin O'Connor and James Owen Weatherall Video Series: Crash Course Media Literacy Training: First Draft News - Training Written by BOTL Communications Director Amanda Hoffman SHOW IMAGE: Description: In graphic form, the word "TRUTH" is angled and textured in red. A magnifying glass hovers over most of the letter "U" and the word "LIES" can be seen repeated in rows through the circular glass. Credit: Original design on Pixabay / Changes: angled, text color, background color / Final by Amanda Hoffman
Today we discuss the most important element of our lives: our health. We do so by diving into personalized medicine, or more specifically, personalized (N-of-1) trials – clinical trials in which a single patient is the entire trial. For this episode, we invited two editors of Harvard Data Science Review's special issue on N-of-1 trials and data science to help us examine all aspects of these clinical trials designed for a population of one person. Our guests: Dr. Karina Davidson, Senior Vice President of Research and Dean of Academic Affairs at Northwell Health Ken Cheung, Professor of Biostatistics at Mailman School of Public Health at Columbia University
For today's episode we embark on part two of our discussion on the U.S. Census. Protecting the data privacy of survey respondents has always been a central consideration for the U.S Census Bureau, and throughout its history, many methods have been developed and implemented. For the 2020 Census, the Bureau adopted a new form of privacy protection—differential privacy which was received with mixed reaction. To further understand why the Census Bureau adopted this new form of privacy protection and to help explore the concerns raised about differential privacy, we invited two experts who represent both sides of the debate and who each contributed to the Harvard Data Science Review special issue on the 2020 U.S. Census. Our guests are: John Abowd, Associate Director for Research and Methodology, Chief Scientist at the U.S. Census Bureau, and author of the The 2020 Census Disclosure Avoidance System TopDown Algorithm for HDSR. danah boyd, founder and president of Data & Society, Principal Researcher at Microsoft, Visiting Professor at New York University, and author of Differential Perspectives: Epistemic Disconnects Surrounding the U.S. Census Bureau's Use of Differential Privacy for HDSR.
While most Americans have heard of the U.S. Census and understand that it is designed to count every resident in the United States every 10 years, many may not realize that the Census's role goes far beyond the allocation of seats in Congress. For this episode, we invited the three co-editors of Harvard Data Science Review's special issue on the U.S. Census to help us explore what the Census is, what it's used for, and how the data it collects should remain both private and useful. Our guests are: Erica Groshen, former Commissioner of Labor Statistics and Head of the U.S. Bureau of Labor Statistics Ruobin Gong, Assistant Professor of Statistics at Rutgers University Salil Vadhan, Professor of Computer Science and Applied Mathematics at Harvard University
In this episode founding Editor-in-Chief of the Harvard Data Science Review and Professor of Statistics at Harvard University, Prof. Xiao-Li Meng, joins Jon Krohn to dive into data trade-offs that abound, and shares his view on the paradoxical downside of having lots of data. In this episode you will learn: • What the Harvard Data Science Review is and why Xiao-Li founded it [5:31] • The difference between data science and statistics [17:56] • The concept of 'data minding' [22:27] • The concept of 'data confession' [30:31] • Why there's no “free lunch” with data, and the tricky trade-offs that abound [35:20] • The surprising paradoxical downside of having lots of data [43:23] • What the Bayesian, Frequentist, and Fiduciary schools of statistics are, and when each of them is most useful in data science [55:47] Additional materials: www.superdatascience.com/581
Imagine for a second if you could have treatment which fits you better than a tailored suit. In some cases, biostatistics tools and custom medicines like gene therapy open new doors for patients. After today's pod, you'll know all the practical and impractical uses of precision medicine for patients.So what is precision medicine? Aren't doctors already supposed to customize treatments for each given patient's situation? The short answer is yes but the longer answer leaves a key gap I want to talk about. What the majority of doctors follow is evidence-based medicine. This is a time-tested process—a patient arrives to the clinic with XYZ history and suffers from XYZ complications. The provider then suggests the best clinical or surgical options based on what has worked for the disease in the past as well as what makes sense from experience in med school, residency, etc. In essence, treatment is targeted for alleviating symptoms or root causes of symptoms, which is great. Notice that potential solutions are personalized to the given disease more so than the patient directly. That distinct intent is where precision medicine begins. As you may imagine, patients with a given disorder can have wildly different reactions to similar treatment. To be clear, evidence-based medicine has way more benefits than drawbacks—precision medicine has been promoted over the last couple decades as taking care to another level. The greatest benefit patients get from precision medicine is that any treatment you take is created from your specific environment, lifestyle, and genetic makeup. For example, if someone ends up with a disease like lung cancer, there could be a customized therapy targeting the problem with that patient's genome in mind.Although genomics is a huge part of precision medicine today, especially with everything we've learned since the US's Human Genome Project finished up in the 2000s, precision medicine was in motion before then when the textbook Pharmacogenetics was published in 1962 by Dr. Werner Kalow. A helpful article on the Harvard Data Science Review dives more into the historical buildup of precision medicine, which I'll link on my Substack page at rushinagalla.substack.com. Even before the 20th century, doctors were interpreting data linked to the onset and treatment of disease to craft better options. For example, there might be a group of patients who all suffer from severe asthma. Precision medicine could in theory still occur here without going into each patient's genetic profile. In this situation, doctors could see that patients one to 100 live in cleaner-air environments than patients 101 to 150. Maybe patients 80 to 120 have other issues that make the asthma worse or better, and so on. Modelers then crunch and reformat all that unstructured data to suggest treatment for each individual. Obviously, evidence-based medicine isn't going away. If a whole region is dealing with an unusual flu outbreak, we know that making tweaks to the annual flu vaccines will do a reasonable job targeting the specific changes made by that strain of the flu itself without needing to consider every individual's situation case by case. Evidence-based care is still part of the foundation of what precision medicine does. But since in this day and age we're generating so much data and have a better understanding of our genetic code, there is more runway for precision medicine to take off such that patients may someday get one-of-one solutions to their issues.Shifting over to the genetic focus of modern precision medicine shows us that we can deal with patients who respond differently to common treatments. Some patients may need varying dosages, treatment lengths, or alternative choices because of side effects. Having 100% unique solutions for each person's medical issue is wonderful and utopian on paper. The reality of precision medicine's main playing field is cancer treatment, which is life-saving but not cheap. ~90% of precision treatments approved by the FDA in 2018 were for cancer indications. Well-known cancer therapy brand labels like Keytruda by Merck or Herceptin by Roche cost multiple hundreds of thousands a year before insurance discounts. There also could be issues between how customized the medicine is versus whether the patient actually matches up well to do the treatment. Cancer therapies are anything but short experiences—not all patients can handle extended courses even if the treatment is a perfect match for their genes. Not to mention that a skilled oncologist wouldn't be enough to oversee the treatment since there would be considerable expense involved for doing genetic sequencing and reading out results that not all doctors are trained to understand. Not all medical facilities are equipped to perform this kind of care regardless of superb personnel. It's also difficult to do a clinical trial to make better custom medicines in the first place since, by definition, uncommon diseases affect less people so clinical study recruitment becomes a separate challenge. This is part of why the biggest irony of precision medicine is that individual solutions improve only when you accumulate enough data at the population level. There is nothing inherently wrong there, but that's just where precision medicine is at for now.A recent in-depth review by the Brookings Institution think tank offers some insight on how “agile governance,” which is a policy approach for regulators to make better private and public partnership decisions for the purposes of innovation, can bring down precision medicine costs and ease logistical problems. That analysis focuses on aggregating private and public health care data, improving direct to consumer genetic testing incentives, and building international relationships between drug regulators among other possible initiatives to make precision medicine practical.Without a doubt, the medical field is still a number of years away from having consistent treatments with a capacity for individual genetic modifications serving patients at the outpatient clinic level. However, your care can still improve with better decision-making that still takes your social, genetic, and medical history into account so medicine doesn't reduce itself to just a bunch of if-then statements. Another field of medicine that shouldn't be reduced to algorithms is mental health, which is the prime subject of next week's pod. At that time, we'll learn about what mental health resources exist and how to use them well. Stay tuned and subscribe to Friendly Neighborhood Patient for the big and small pictures of healthcare. I'll catch you at the next episode. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit rushinagalla.substack.com
Air Date 3/16/2022 Today we take a look at the dynamics of mis- and disinformation as well as the history of those, primarily Russia, who are actively using it as a weapon of information warfare against the US, The West, and democracies around the world. Be part of the show! Leave us a message at 202-999-3991 or email Jay@BestOfTheLeft.com Transcript BestOfTheLeft.com/Support (Get AD FREE Shows and Bonus Content) SHOW NOTES Ch. 1: Are you Disinformed or Misinformed? - Harvard Data Science Review Podcast - Air Date 6-17-21 Harvard Data Science Review digs into the world of disinformation and misinformation, and the difference between them. Ch. 2: Moving beyond news deserts and misinformation - Democracy Works - Air Date 2-14-22 Victor Pickard is the C. Edwin Baker Professor of Media Policy and Political Economy at the University of Pennsylvania and author of Democracy Without Journalism? Ch. 3: Why "Cheap Speech" Threatens Democracy - Amicus With Dahlia Lithwick - Air Date 3-5-22 Dahlia talks to Rick Hasen about his new book Cheap Speech: How Disinformation Poisons Our Politics–and How to Cure It. Ch. 4: The Firehose of Falsehood Effect - The Propwatch Project - Air Date 4-4-21 Dr. Christopher Paul, senior social scientist of the Pardee RAND Graduate School, discusses his seminal work on the "Firehose of Falsehood" propaganda model. Ch. 5: The long history of Russian disinformation targeting the U.S. - PBS NewsHour - Air Date 11-22-18 From Pizzagate to George Soros conspiracies, “fake news” has become a noxious presence in public discourse, especially since the 2016 presidential election. Ch. 6: Post-Truth: Lee C. McIntyre - Future Hindsight - Air Date 5-14-20 Authoritarians use post-truth to corrupt our faith in the truth. The end goal is not to make citizens believe lies, but to make them so cynical and uncertain, they think they can never know the truth. MEMBERS-ONLY BONUS CLIP(S) Ch. 9: Inside Estonia's approach in combating Russian disinformation - PBS NewsHour - Air Date 1-15-22 Russian disinformation is rife in countries formerly ruled from Moscow. Some ex-Soviet states have tried to suppress it altogether by banning Russian television stations and even limiting the use of the Russian language on their own domestic channels. VOICEMAILS Ch. 10: Seeking power from the right and left - Alex from Maryland Old FINAL COMMENTS Ch. 12: Final comments on avoiding the nihilism strategy of the Underpants Gnomes TAKE ACTION! Media Literacy Now - Advocate for U.S. Media Literacy Bills at State Level (See Existing Media Literacy Bills and Laws) Tools to Fight Disinformation: FactCheck.Org List: Tools That Fight Disinformation Online (RAND.org) InVID - Browser plug in to help identify and verify shared videos and images Hamilton 2.0 Dashboard - War in Ukraine (Disinformation tracker) Orgs Fighting Disinformation: Project Origin Content Authenticity Initiative Coalition for Content Provenance and Authenticity News Literacy Project Alliance for Securing Democracy EDUCATE YOURSELF & SHARE Short Docuseries: Operation InfeKtion (New York Times) Book: “The Misinformation Age: How False Beliefs Spread” by Calin O'Connor and James Owen Weatherall Video Series: Crash Course Media Literacy Training: First Draft News - Training Written by BOTL Communications Director Amanda Hoffman MUSIC (Blue Dot Sessions) SHOW IMAGE: Description: In graphic form, the word "TRUTH" is angled and textured in red. A magnifying glass hovers over most of the letter "U" and the word "LIES" can be seen repeated in rows through the circular glass. Credit: Original design on Pixabay / Changes: angled, text color, background color / Final by Amanda Hoffman Produced by Jay! Tomlinson
How are sports teams using data science? This episode is syndicated from the new Harvard Data Science Review Podcast. Published by the MIT Press, Harvard Data Science Review is an open access multidisciplinary journal that defines and shapes data science as a scientifically rigorous field based on the principled and purposed production, processing, parsing and analysis of data. In this episode, the journal's Features Editor Liberty Vittert and Editor in Chief Xiao-Li Meng dig into the data behind sports with two experts: Brian Macdonald, sports analytics at Yale (formerly Carnegie Mellon University) and Kirk Goldsberry, NBA analyst at ESPN and author of Sprawlball: A Visual Tour of the New era of the NBA. If you enjoy this preview of the Harvard Data Science Review podcast, find the journal on twitter at @TheHDSR and remember to subscribe to their podcast on your favorite platform.
Can data science help us combat disinformation? This episode is syndicated from the new Harvard Data Science Review Podcast. Published by the MIT Press, Harvard Data Science Review is an open access multidisciplinary journal that defines and shapes data science as a scientifically rigorous field based on the principled and purposed production, processing, parsing and analysis of data. In this episode, the journal's Features Editor Liberty Vittert and Editor in Chief Xiao-Li Meng discuss fake news, disinformation, and misinformation with Scott Tranter, CEO and founder of Optimus Analytics, and Hany Farid, a professor from UC Berkeley who specializes in the analysis of digital images and is the author of two MIT Press books: Fake Photos and Photo Forensics. If you enjoy this preview of the Harvard Data Science Review podcast, find the journal on twitter at @TheHDSR and remember to subscribe to their podcast on your favorite platform.
AI Today Podcast: Artificial Intelligence Insights, Experts, and Opinion
On the AI Today podcast we regularly interview thought leaders who are implementing AI and cognitive technology at various companies and agencies. However in this episode hosts Kathleen Walch and Ron Schmelzer interview Liberty Vittert & Xiao-Li Meng, hosts of the Harvard Data Science Review (HDSR) podcast. On their podcast they discuss news, policy, and business through the lens of data science so they shared ways in which data is being used or misused as well as some of the most interesting articles published in Harvard Data Science Review. Continue reading AI Today Podcast: Interview with Harvard Data Science Review (HDSR) Podcast hosts Liberty Vittert & Xiao-Li Meng at Cognilytica.
What does data science tell us about art auctions? This episode is syndicated from the new Harvard Data Science Review Podcast. Published by the MIT Press, Harvard Data Science Review is an open access multidisciplinary journal that defines and shapes data science as a scientifically rigorous field based on the principled and purposed production, processing, parsing and analysis of data. In this episode, the journal's Features Editor Liberty Vittert and Editor in Chief Xiao-Li Meng discuss art auctions with art curator Dan Cameron and Artnome's Jason Bailey. If you enjoy this preview of the Harvard Data Science Review podcast, find the journal on twitter at @TheHDSR and remember to subscribe to their podcast on your favorite platform.
In this episode, Harvard Data Science Review digs into the world of disinformation and misinformation, and the difference between them. Is the weaponization of both a new phenomenon or is history repeating itself? How has social media and the democratized access to published information contributed to today's sensationalized headlines? Hosts Xiao-Li Meng and Liberty Vittert explore these questions and more with the help of two experts on the topic, Scott Tranter, CEO and founder of Optimus Analytics and Hany Farid, Professor at the University of California, Berkeley with a joint appointment in Electrical Engineering & Computer Science and the School of Information.
Big data, though not new, is often talked about as though it is. It’s become something of a buzzword associated with everything from politics to record sales to epidemiology. But, not all big data is created the same – some of it might not even be that big at all. That’s the focus of this episode of Stats and Stories with guest Xiao-Li Meng Xiao-Li Meng is the Whipple V. N. Jones Professor of Statistics, and the Founding Editor-in-Chief of Harvard Data Science Review, is well known for his depth and breadth in research, his innovation and passion in pedagogy, his vision and effectiveness in administration, as well as for his engaging and entertaining style as a speaker and writer. Meng was named the best statistician under the age of 40 by COPSS (Committee of Presidents of Statistical Societies) in 2001, and he is the recipient of numerous awards and honors for his more than 150 publications in at least a dozen theoretical and methodological areas, as well as in areas of pedagogy and professional development.
Joanna McKenzie sits down with Liberty Vittert in this edition of The Data Lab's podcast. Liberty is currently a Visiting Assistant Professor at Washington University in St. Louis and will be a Visiting Assistant Professor at Harvard University in the Department of Statistics beginning summer of 2019. She is a graduate of MIT as well as Le Cordon Bleu Paris and the University of Glasgow. Her current statistical research involves using facial shape analysis to help children with facial deformities and victims of warfare, and she discusses this at length in the podcast. Liberty is a regular TV and Radio contributor to many news organizations including BBC, ITV, Channel 4, PBS, and FNC, as well as having her own TV series on STV (ITV). Her opinion editorials feature regularly in outlets such as Popular Science, US News, Newsweek, Business Insider, International Business Times, CBS News, The Conversation, and Fox News. As a Royal Statistical Society Ambassador, BBC Expert Woman, and an Elected Member of the International Statistical Institute, Liberty is writing a series of popular science books on how to lie with numbers from the viewpoint of multiple professions. She is also an Associate Editor for the Harvard Data Science Review and is on the board of USA for the UN Refugee Agency (UNHCR). Enjoy! All views and opinions expressed by our podcast participants are solely their opinions and do not necessarily reflect the opinions of The Data Lab. The Data Lab does not warrant the completeness or accuracy of any statements made by our podcast participants.
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