POPULARITY
25 episodes with quantitative finance
20 episodes with quantitative finance
27 episodes with quantitative finance
6 episodes with quantitative finance
2 episodes with quantitative finance
2 episodes with quantitative finance
2 episodes with quantitative finance
3 episodes with quantitative finance
2 episodes with quantitative finance
What does it take to succeed in quant finance? Anthony Cheung sits down with Nitish Maini, Chief Strategy Officer at WorldQuant, to discuss his journey from consulting to quant trading, the biggest misconceptions about the industry, and why creativity and strategic thinking are just as important as coding skills. They also explore how WorldQuant is reshaping global talent pipelines through initiatives like the WorldQuant BRAIN platform and the International Quant Championship, opening doors for aspiring quants worldwide.If you're interested in quant finance, or just curious to learn more, this episode is for you!Enter the International Quant Championship qr.wqbrain.com/bfqLYvLearn2Quant YouTube series by WorldQuant https://shorturl.at/BR2v6*****(00:00) Introduction to Quantitative Finance and Nitish's Background(08:45) The Difference Between Quant Researcher and Portfolio Manager(19:07) The Evolution of Quantitative Finance(20:55) Demystifying Quant Finance (24:05) WorldQuant: A Unique Approach to Quant Finance(26:06) WorldQuant Brain: Crowdsourcing Alpha Signals(30:01) Learn to Quant: Educational Initiatives(32:16) International Quant Championship: A Global Competition*****(WorldQuant defines alphas as mathematical models that seek to predict the future price movements of various financial instruments) Hosted on Acast. See acast.com/privacy for more information.
Butian Li is CEO of Bless, pioneering the world's first shared computer—a decentralized network where everyday consumer devices power the internet. Under his leadership, Bless is reshaping internet infrastructure by enabling laptops, smartphones, and tablets to contribute their compute power collectively.Bless' first-generation product, Tap Compute, allows users to seamlessly share their device's computing resources through a web browser, supporting AI inference, data processing, and web hosting. By decentralizing these essential services, Bless is shifting control away from large corporations with massive data centers and back into the hands of everyday people.A seasoned entrepreneur and investor, Butian's expertise spans technology, finance, and strategic growth. He was previously COO of Wabi (Binance ‘17), an Investor at Lightspeed Venture Partners and NGC Ventures, and Founding Partner of Access Crypto hedge fund. His early career includes management consulting at Deloitte Consulting, where he advised Fortune 500 companies on IPOs and M&A transactions.Butian holds an undergraduate degree in Engineering from UC Berkeley and an MBA in Quantitative Finance from Wharton.Previous media features:Universal Basic Compute can Combat the Future AI Divide
Butian Li is CEO of Bless, pioneering the world's first shared computer—a decentralized network where everyday consumer devices power the internet. Under his leadership, Bless is reshaping internet infrastructure by enabling laptops, smartphones, and tablets to contribute their compute power collectively.Bless' first-generation product, Tap Compute, allows users to seamlessly share their device's computing resources through a web browser, supporting AI inference, data processing, and web hosting. By decentralizing these essential services, Bless is shifting control away from large corporations with massive data centers and back into the hands of everyday people.A seasoned entrepreneur and investor, Butian's expertise spans technology, finance, and strategic growth. He was previously COO of Wabi (Binance ‘17), an Investor at Lightspeed Venture Partners and NGC Ventures, and Founding Partner of Access Crypto hedge fund. His early career includes management consulting at Deloitte Consulting, where he advised Fortune 500 companies on IPOs and M&A transactions.Butian holds an undergraduate degree in Engineering from UC Berkeley and an MBA in Quantitative Finance from Wharton.Previous media features:Universal Basic Compute can Combat the Future AI Divide
Send us a textIn this episode of the QuantSpeak podcast, Dan Tudball is joined by Professor Carol Alexander. They delve into Professor Alexander's extensive experience in both academia and industry, and her fascinating journey in the field of quantitative finance. The conversation also touches on her interest in cryptocurrencies, her ongoing work at the Exponential Science Foundation, and more. Podcasts are for informational purposes only and provided “as is” without any representation or warranty from Fitch Learning of any kind. Comments or statements expressed by speakers may not be those of the Fitch Learning. Fitch Learning is not providing advice or recommendations. Fitch Learning, its directors, officers, or employees do not accept any liability for any loss arising from the use of information.
In this episode, host Jim Jockle sits down with Alvaro Cartea, Director of the Oxford-Man Institute of Quantitative Finance and Professor of Mathematical Finance at Oxford University. Together, they explore the transformative power of AI in financial markets and delve into how deep learning and reinforcement learning are reshaping trading strategies. Alvaro explains how these technologies uncover patterns humans can miss and how they're personalizing trading models to fit unique market views. He raises crucial questions about the unintended consequences of autonomous algorithms, like the risk of AI-driven market collusion, and discusses what this means for future regulation and oversight. Tune in for a deep dive into the future of finance!
I had a great time talking with Stan Uryasev and learning about his journey to quantitative finance. Stan is one of the authors of the original paper on Conditional Value at Risk (CVaR). CVaR is taught in almost every finance program and has had a large impact on the finance community. Stan is also a professor and endowed chair of Stony Brook's Quantitative Finance program which includes a Masters program and a PhD program. We will also discuss some mathematical art from his wife Oxana Uryasev about the Gabriel Horn.Stan's Website:http://uryasev.ams.stonybrook.edu/Support the Show.
Álvaro Cartea is Professor of Mathematical Finance in the Mathematical Institute, University of Oxford, and director of the Oxford-Man Institute of Quantitative Finance. He is a founding member and deputy chairman of the Commodities & Energy Markets Association (CEMA). Before coming to Oxford, Álvaro was Reader in Mathematical Finance at University College London. He was also previously JP Morgan Lecturer in Financial Mathematics, Exeter College, University of Oxford. Álvaro obtained his doctorate from the University of Oxford in 2003. This podcast covers the evolution of AI trading strategies, the unintented consequences of AI market makers, and the regulatory aspects of AI in finance.
Christopher Gannatti serves as the Global Head of Research at WisdomTree, a leading global financial innovator offering a diverse suite of exchange-traded products. WisdomTree is committed to developing solutions that empower investors, with over 200 employees and $111 billion in assets under management.Christopher began his journey at WisdomTree in December 2010 as a Research Analyst. Demonstrating exceptional skill and leadership, he was promoted to Associate Director of Research in January 2014. In this role, Christopher led various groups of analysts and strategists within the broader Research team.In February 2018, Christopher transitioned to the role of Head of Research for Europe, based in WisdomTree's London office. He spearheaded the full research efforts within the European market and supported the UCITS platform globally. His exemplary performance and strategic vision led to his promotion to Global Head of Research in November 2021. In his current role, Christopher is responsible for global communications on investment strategy, with a particular focus on thematic equity.Before joining WisdomTree, Christopher worked as a Regional Consultant at Lord Abbett for four and a half years. He holds an MBA in Quantitative Finance, Accounting, and Economics from NYU's Stern School of Business (2010) and a bachelor's degree in Economics from Colgate University (2006). Additionally, he is a Chartered Financial Analyst (CFA) designation holder.Support the Show.Follow me on Facebook ⬇️https://www.facebook.com/manuj.aggarwal❤️ ID - Manuj Aggarwal■ LinkedIn: https://www.linkedin.com/in/manujaggarwal/ ■ Facebook: https://www.facebook.com/realmanuj■ Instagram: ...
Rennick Palley is Founding Partner at Stratos, an industry-leading VC firm focused on early-stage crypto projects. Rennick previously worked as a Research Associate at Sanders Capital, a $75 billion global equity manager. Rennick holds dual Bachelor's degrees in Applied Mathematics and Mechanical Engineering from Southern Methodist University, along with a Masters in Quantitative Finance from the Massachusetts Institute of Technology. --- Support this podcast: https://podcasters.spotify.com/pod/show/crypto-hipster-podcast/support
For the final episode of season 1 of The Laws of Stan, I would like to conclude my streak of 14 episodes with a loop. The first episode of my 14-episode challenge began with the applications of mathematics in Quantitative Finance, featuring Lucas Ambroz, a back-office quant trader from Bank of America. Later, I recorded a second episode on the same topic with a front-office quant from BNP Paribas.
In this episode of the QuantSpeak podcast, Dan Tudball is joined by Emanuel Derman. They explore Derman's transition from physics to finance, his influential work on financial models like Black-Derman-Toy and Derman-Kani, and his collaboration with Fisher Black. Derman also gives insight into the integration of science and finance, and shares a sneak peek of his memoir, "Brief Hours and Weeks: My Life as a Capetonian". Podcasts are for informational purposes only and provided “as is” without any representation or warranty from Fitch Learning of any kind. Comments or statements expressed by speakers may not be those of the Fitch Learning. Fitch Learning is not providing advice or recommendations. Fitch Learning, its directors, officers, or employees do not accept any liability for any loss arising from the use of information.
En este episodio, Francisco, Luis y Walter repasan la vida y obra de Jim Simons, leyenda de las inversiones basadas en métodos cuantitativos (quantitative investing) Distribuido por Genuina Media babbel.com/MONEYTALKS
Agustín Liserra is the CEO & Co-founder at Num Finance, a platform that seamlessly merges the stability of traditional financial instruments with the innovation and potential of blockchain technology. Agustín previously worked as the Chief Financial Officer at Buenbit and has over 12 years of experience in trading, finance, and risk management. Agustín holds a Bachelor's degree in Electronic Engineering and Master's degrees in Quantitative Finance and Computer Science. You can find Agustín on X and LinkedIn. --- Support this podcast: https://podcasters.spotify.com/pod/show/crypto-hipster-podcast/support
How did a middle-class kid go from watching superhero cartoons to scaling the heights of Wall Street? Cliff Asness, the quantitative virtuoso behind AQR, pulls back the curtain on his journey from comic book fan to financier. In this episode of the FinServ Podcast, Dr. David and Cliff take us along on their journeys through underachieving adolescence and how tough lessons fostered discipline, resolve and ambition. From academic dilemmas and professional pivots, Dr. David and Cliff highlight the profound impact of mentors, chance, and the decisions we make in the heat of the moment. Whether it's choosing a career path under parental expectations or embracing complex subjects that once seemed insurmountable, this episode is a testament to the unpredictable yet rewarding journey towards career satisfaction.Embracing the 'tight aggressive' poker strategy, we discuss the importance of conviction coupled with humility, two qualities essential for navigating the twists and turns of both a high-stakes game and the unpredictability of the markets. From the fear of failure to the truth about backtesting, this episode offers hard-earned advice for anyone looking to play their cards right in life and investments. Connect with Us! Cliff Asness on LinkedInDr. David Rhoiney on LinkedIn Jamie Hopkins on LinkedIn FinServ Foundation If you want more information on the FinServ Foundation, be sure to check out our website by clicking on the link below.>>FinServ Foundation Website
Welcome to another episode with Brian from quantlabs.net, recorded on the 22nd of April. In this episode, Brian explores two significant forum posts that have recently caught his attention on quant.stackexchange.com. The central theme revolves around resources to learn algo trading quant development. Our host provides some impressive insights that could be beneficial for those interested in quantitative trading. Get your free trading tech books here books2 - QUANTLABS.NET A user's question on the forum sparks the discussion - the user is a full-time stack developer for three years and is now interested in learning algo trading quant dev development. They are seeking advice on resources, books, and suitable programming languages to learn. Brian shares one effective resource he stumbled upon - Wilmot.com. It isn't just a job-posting site, but it also includes details on who the job postings are for. The likes of JP Morgan, Goldman Sachs, HSBC are just few names from the list. This resource might be useful for anyone looking to delve deeper into the quant industry. Moving on, Brian discusses a vast list of books available on quant.stackexchange.com that can be beneficial for anyone venturing into quantitative finance. He warns about the potential for analysis paralysis given the extensive list. He also shares his top picks from the list, including books from Mark Joshi and Dr. Ernie Chan, and more. He also alludes to some upcoming interviews with industry experts like Robert Pardo and the later Dr. Ernie Chan on his YouTube channel and podcast. Apart from those, Brian recommends three more books to dive into quantitative finance. They include 'The Concept of Practiced Mathematical Finance' by Mark Joshi, 'Paul Wilmont on Quantitative Finance' by Paul Wilmont, and 'Options, Futures, and Other Derivatives' by John Hall. He advises new entrants to start with Paul Wilmott's book series which covers all major components of quant. Finally, Brian introduces TradersPost.io, a service that can help interested individuals to get up and running quickly in the world of algorithmic trading with minimal programming. Once again, he invites listeners to join his Discord community to discuss these topics more and sign up for his email list for some big announcements. He concludes the episode looking hopeful to get Dr. Ernie Chan again on the show for another interview on his latest company launch on machine learning.
Video version available here Are markets efficient, and if not, can causal models help us leverage the inefficiencies?Do we really need to understand what we're modeling?What's the role of symmetry in modeling financial markets?What are the main challenges in applying causal models in finance?Ready to dive in? About The GuestAlexander Denev is the CEO of Turnleaf Analytics. He's an author of multiple books on financial modeling and a former Head of AI (Financial Services) at Deloitte. He lectures at the University of Oxford and has worked for organizations like IHS Markit, The Royal Bank of Scotland (RBS), and the European Investment Bank. He has over 20 years of experience in finance, data science, and modeling. His first book about causal models was published well ahead of its time.Connect with Alexander:- Alexander on LinkedIn- Alexander's web pageAbout The HostAleksander (Alex) Molak is an independent machine learning researcher, educator, entrepreneur and a best-selling author in the area of causality.Connect with Alex:- Alex on the InternetFull list of links can be found here.#machinelearning #causalai #causalinference #causality #finance #CauslBanditsPodcastClimate ConfidentWith a new episode every Wed morning, the Climate Confident podcast is weekly podcast...Listen on: Apple Podcasts SpotifySupport the showCausal Bandits PodcastCausal AI || Causal Machine Learning || Causal Inference & DiscoveryWeb: https://causalbanditspodcast.comConnect on LinkedIn: https://www.linkedin.com/in/aleksandermolak/Join Causal Python Weekly: https://causalpython.io The Causal Book: https://amzn.to/3QhsRz4
In this episode of the QuantSpeak podcast, Dan Tudball is joined by Aaron Brown. They discuss the parallels between finance and poker, the decision-making skills shared by high-stakes finance professionals and poker players, and how an understanding of probability and risk is essential in both fields. Brown also shares his career journey, the evolution of both industries, and the significance of community and knowledge-sharing for success.
In this episode of Pints of View Gary Goldsmith talks to Will Kellett, Chief at Evolve Group. They discuss Will's success recruiting in the quantitative finance space, generating millions in revenue per recruiter. Will describes transitioning his business from tech recruiting to focus on quant finance roles, where candidates can have high-impact careers in hedge funds and trading. He explains that candidates in this niche can earn the recruiters very high fees, with some deals in the millions. Will also discusses the strategies his desk uses to identify and recruit top talent from universities. In this episode you will hear: Will's journey from the military into entrepreneurship How he identified opportunities in quantitative finance recruiting Strategies for recruiting top talent The high earnings potential for recruiters in this niche market Will's advice for building a supportive team and network for success For more information about this episode, Gary's advisory services or the RDLC please email us on POV@garys.world Follow us on Instagram https://www.instagram.com/pints_of_view_pod/ Thank you as ever to our sponsors: https://force24.co.uk/ https://rdlcpirates.com/ Pints Of View is the podcast hosted by socialite, in-demand Non-Exec Director, recruitment legend and all-around nice guy Gary Goldsmith. In this podcast, Gary opens up his eclectic Black Book of friends that ranges from international footballers, high street moguls, champion boxers, investment oracles, national team coaches, royal correspondents, business leaders, military special forces, sports club owners, scale-up experts and even conspiracy theorists with a sense of humour! They're all interesting, they've all got different stories, they've all got different backgrounds and they have all got lessons that you will learn a great deal from, alongside a fair few belly laughs too. Plus, as well as the amazing guests, you will also learn that there is a lot more to Gary Goldsmith than what the headlines might have had you believe! Far from just being a loveable rogue and famous royal Uncle, there are insights and wisdom shared that reveal why Gary has been integral to hundreds of millions of pounds of business growth over the years. So, join us for some real, raw and interesting chats down at the pub - yes, this show is really shot on location at an actual, working West End boozer!
Today, we are speaking to leaders who can both geek out on data and translate the big picture of our economy. They are the Head of Research. From strategic planning to market analysis to industry trends, how do their roles guide the creation and management of ETFs? We have Matt Dines and Carol Spain with us today. Carol Spain is a Managing Director and Head of Credit Research for Schwab Asset Management. Carol earned a Master of Public Policy from the University of Chicago and a Bachelor of Arts in political science from the University of Notre Dame. She is an active member of the National Federation of Municipal Analysts and is a member of the External Advisory Panel of the Government Finance Research Center. Carol lives in Chicago with her husband and two small children. Matt Dines serves as Chief Investment Officer at Build Asset Management, where he oversees portfolio management, capital allocation, and strategy for the firm and its clients. Matt holds a Master's degree in Finance from Washington University in St. Louis, with a focus on Quantitative Finance. He holds a Bachelor's degree in Biological Science from the University of Notre Dame. Matt earned his Chartered Financial Analyst® designation in 2017. Matt and his wife live in Seattle with their twin boys. Kristine Delano guides the conversation about the hard work and skills it takes to research and analyze data in the world of ETFs. Follow on Instagram kristine.delano.writer Visit www.womeninetfs.com to find additional support in the ETF industry. Go to www.kristinedelano.com for your Thrive Guide: a compilation of the most requested and insightful advice from our guests on Leadership and Advancement. In partnership with https://www.etfcentral.com/ Book recommendations: Leading Lightly by Jody Michael The Asian Financial Crisis 1995-98: Birth of the Age of Debt by Russell Napier
Step into the world of hedge funds and quantitative finance with Brian in this riveting podcast episode from quantlabs.net. The episode kicks off with a comprehensive discussion on the basics of getting a job in a hedge fund: from academic prerequisites to critical soft skills and technical know-how. Expect an insightful walk-through on necessary certifications such as CFA, MBA, FRM, and a sneak peek on job opportunities in high-profile locations like England. GET SOME FREE TRADING TECH BOOK PDFS HTTP://QUANTLABS.NET/BOOKS Join our Discord for quant trading and programming news https://discord.gg/k29hRUXdk2 Don't forget to subscribe to my Substack for more trading tips and strategies! Let's keep learning and growing together. https://quantlabs.substack.com/ This episode also offers a candid view on the fiercely competitive job market and strategies to stay ahead. From marketing oneself as a brand through platforms like GitHub to the importance of internships, Brian expands on emerging opportunities and the constantly changing nature of the finance industry. With banks evolving into technology companies and Goldman Sachs hiring coders, avenues for diverse skill sets are opening up in this sector. Transitioning to discussions on hedge funds, the episode shares a list of leading hedge funds, different job roles, and the corresponding compensation packages you can expect. While cautionary tales of the high-pressure and cut-throat work environment underscore the industry's complexity, the chance to work with diverse asset classes and network with talented professionals, make for a rewarding career choice. A Glimpse into Hedge Fund Jobs and the World of Quantitative Finance - QUANTLABS.NET Finally, the episode winds up with an exploration of the different types of quantitative analysts in banks, prop shops, and hedge funds. From traders and researchers to financial engineers and developers, the roles are diverse, each with its unique challenges and learning curves. Tune in for an upfront conversation on what to anticipate in the rapidly evolving world of quantitative finance.
Welcome to today's discussion where our host, Brian, is answering a burning question about financial careers: What's the typical salary and bonus structure for a Quantitative Risk (Quant Risk) Model Management Associate position at Goldman Sachs in London, UK? In this podcast episode, we delve deep into an interesting question raised on Quantitative Finance at quant.stackexchange.com regarding salient financial compensation details at Goldman Sachs, a top global investment banking firm. This topic reveals invaluable information for anyone considering a career in quantitative risk model management. Get some free trading tech book PDFs http://quantlabs.net/books Join our Discord for quant trading and programming news https://discord.gg/k29hRUXdk2 Don't forget to subscribe to my Substack for more trading tips and strategies! Let's keep learning and growing together. https://quantlabs.substack.com/ As Brian highlights, risk management is a crucial appointee in the financial services industry. While the role does not generate revenue and may not be as exciting as other positions, it plays a pivotal role in compliance and paperwork handling. As an entry-level quant risk manager, you can anticipate a base salary between £60,000 to £70,000 per year, with performance-based bonuses of an additional 10% to 20%. We also find that compensation structures differ across various roles within the investment banking space. The 'front office quant' salary, for instance, can reach a hefty sum of around $120,000 to $150,000, with bonuses potentially tipping over 50% of the base figure. Comparatively, Salaries for quant roles in risk management, although lower than those of front-office positions, provide a comfortable life, especially when factoring in the UK's average salary. Brian further insights the listeners toward the job specifics that can significantly impact salaries. At Goldman Sachs, for example, a front office quant typically supports direct trade, while a Strategist quant contributes to trading strategies. The unique Goldman Sachs model has influenced many other investment banks to eventually follow suit. Going on to discuss roles across varying experience levels, Brian shares that junior quant roles tend to pull in around 50 PA (per annum), while senior roles can generate up to a staggering 300 PA. Moreover, bonuses lean more generously towards the senior end with potential to double the annual salary. This episode offers a promising understanding of the salary range for quants in risk model management in Goldman Sachs' London office. Make sure to tune in and gain a scoop of career insights right from the financial world! In-Depth Analysis of Risk Model Quant in London - QUANTLABS.NET
After releasing 9 episodes focusing on the applications of mathematics across various industries, including Quantitative Finance, Blockchain, Poker, and Formula One, I have decided to launch a series of episodes focusing on the applications of mathematics in Artificial Intelligence
Dive deep into the mechanics of quantitative finance and explore how a blend of skills ranging from data analysis to software development fuels this dynamic field. If you've ever pondered the pathways to entering and excelling in quant finance, or if you seek to understand the symbiotic relationship between risk management and algorithm development, this episode is your roadmap.***Free daily newsletter https://bit.ly/3Oeu4WkFree Finance Accelerator simulation https://bit.ly/3GoyV5rConnect with Anthony https://www.linkedin.com/in/anthonycheung10/ Hosted on Acast. See acast.com/privacy for more information.
The Inside Economics team revels in the great economic numbers of the past week. The economy not only avoided a recession in 2023, but it ended the year enjoying robust GDP growth and tame inflation. But there are threats at the start of the new year, including a potential seizing up of the all-important Treasury bond market. Samim Ghamami of the SEC joins the podcast to discuss this threat, its causes and implications, and potential reforms to ensure it doesn't upend financial markets and the economy. Today's guest Samim Ghamami is currently an economist at the U.S. Securities and Exchange Commission, where he works with the SEC senior management on the reform of the US Treasury market and several other capital market initiatives. Ghamami is also a senior researcher and an adjunct professor of finance at New York University, a senior researcher at UC Berkeley Center for Risk Management Research and the Department of Economics, and a senior advisor at SOFR Academy. Ghamami has been a senior economist and a senior vice president at Goldman Sachs. He has been an adjunct associate professor of economics at Columbia University. Ghamami has also been an associate director and a senior economist at the U.S. Department of the Treasury, Office of Financial Research, and an economist at the Board of Governors of the Federal Reserve System.Ghamami's work has broadly focused on the interplay of finance and macroeconomics, and on financial economics and quantitative finance. His work on banking, asset management, risk management, economic policy, financial stability, financial regulation, and central clearing has been presented and discussed at central banks. He has been an advisor to the Bank for International Settlements and worked as an expert with the Financial Stability Board on post-financial crisis reforms in 2016 and 2017. Ghamami also served on the National Science Foundation panel on Financial Mathematics in 2017 and 2018. Ghamami received his Ph.D. in Mathematical Finance and Operations Research from USC in 2009. His publications have appeared in different journals including Management Science, Journal of Applied Probability, Mathematics of Operations Research, Journal of Financial Intermediation, Journal of Credit Risk, Journal of Derivatives, Quantitative Finance, and Journal of Risk. Follow Mark Zandi @MarkZandi, Cris deRitis @MiddleWayEcon, and Marisa DiNatale on LinkedIn for additional insight.
While we have previously explored the question of narcissism and the dark triad of personality traits on the Brain for Business podcast, the question of how narcissistic leaders impact on overall organisational performance is something we are yet to consider in great detail. Yet this is exactly what our guests today, Professor Thanos Verousis of Vlerick Business School and Professor Pietro Perotti of the University of Bath, examine in a recent paper co-authored with Shee-Yee Khoo of Bangor Business School and Richard Watermeyer of the University of Bristol. To do this they examine the narcissism of university vice chancellors in the context of the overall performance of their universities. While this might perhaps seem a little obscure to those outside academia, Vice Chancellors are ultimately the CEOs of large and complex organisations and the transferrable insights are many.Key findings include:The appointment of a highly narcissistic VC leads to an overall deterioration in research and teaching performance and concomitantly league table performanceKey potential mechanisms explaining this include excessive financial risk taking and empire-buildingThe findings are consistent with the view that narcissism is one of the most prominent traits of destructive leadershipThere are practical implications for leadership recruitment and the monitoring of leadership practices in the higher education sector The article discussed - Vice-chancellor narcissism and university performance – can be accessed here: https://www.sciencedirect.com/science/article/abs/pii/S0048733323001853 About our guests…Thanos Verousis is a Professor in Sustainable Finance at Vlerick Business School, Associate Editor at the Journal of Futures Markets and the European Journal of Finance. In his research he is particularly interested in understanding behavioural biases and decision-making in finance, especially with respect to departures from the classical rational expectations theory. Thanos also works on Artificial Intelligence (AI) applications in finance, especially in applications involving machine learning and robo-advising. You can find out more about Thanos's research here: https://sites.google.com/site/thanosverousis/Pietro Perotti is a Senior Lecturer, or Associate Professor, at the University of Bath. Pietro researches the capital market consequences of accounting information, financial reporting quality and market microstructure. Pietro's research has been published in a range of leading journals including Journal of Business Finance and Accounting. Research Policy, Journal of Accounting Literature, Journal of Empirical Finance and Review of Quantitative Finance and Accounting.You can find out more about Pietro's research here: https://researchportal.bath.ac.uk/en/persons/pietro-perotti Hosted on Acast. See acast.com/privacy for more information.
In this episode of the QuantSpeak podcast, Dan Tudball is joined by CQF Program Founder, Dr. Paul Wilmott, for a 2023 round-up. Delve into the latest AI advancements, quantum computing thoughts, and Dr. Wilmott's new mission to make quant finance accessible to teens. Plus, discover which quant Dr. Wilmott would pick as a desert island companion.
During the Awards Ceremony of the ADIA Lab Market Prediction Competition, we discuss Replicability in AI, P-Hacking and implications in Quantitative Finance. Panel: - Prof. Marcos Lopez de Prado, Global Head - Quantitative R&D at ADIA - Dr. Horst Simon - Director at ADIA Lab - Matteo Manzi, Cofounder & Lead Quant Researcher at CrunchDAO Follow us: Join the group on Linkedin: https://www.linkedin.com/groups/12920374/ CrunchDAO on Linkedin: https:/linkedin.com/crunchdao-com CrunchDAO on X https://x.com/CrunchDAO What is CrunchDAO? Crunchdao serves as a secure intermediary, enabling data scientists to keep control of their models while powering financial institutions. Predict & Compete: Register here: https://crunchdao.com
Simi is joined by Nora Ali, the CEO and Co-Founder of Mason Media, a full-service production company and brand studio, which she co-founded alongside MLB all-star Alex Rodriguez (A-Rod) and tech billionaire Marc Lore. With a degree in Statistics and Quantitative Finance from Harvard, Nora began her career by building a foundation in capital markets as an Asian Equities Associate on the trading floor at Goldman Sachs. She then helped build and launch e-commerce company Jet.com as an early member of the product and marketing teams, seeing the company through its $3.3 billion acquisition by Walmart. She took her finance, retail, and startup expertise to Cheddar, where she joined as an on-air anchor covering tech, business, and entertainment news from the floor of the New York Stock Exchange. At Cheddar, Nora also created, produced, and hosted several specialty series, with a special interest in elevating historically overlooked voices. Nora recently hosted Morning Brew's flagship podcast “Business Casual,” a Top 10 business podcast on Apple upon her debut as its new host. Nora is a child of Bangladeshi immigrants, and a proud Minnesotan now living in New York City. She is also an award-winning violinist and pianist, having frequently performed the national anthem at MLB games, appearing as a soloist with several professional orchestras, and making her Carnegie Hall debut in May 2023. In this episode, we explore Nora's incredible pivots from finance to broadcast journalism to entrepreneurship, along with the triumphs and tribulations she's faced along the way.For more episodes, visit us at southasiantrailblazers.com. Subscribe to our newsletter to get new episodes and updates on our latest events in your inbox. Follow us @southasiantrailblazers on Instagram, LinkedIn, Facebook, and Youtube.
The 10-year U.S. Treasury yield closed above 4.9% yesterday, its highest level since July 2007. The bond-market sell-off that's pushing yields higher is starting to eclipse some of the most extreme market meltdowns of past eras.Losses on Ten Year Treasury Bonds are close to 50% since March 2020, while the 30-year bond had plunged even more.Those losses are nearly in line with stock-market losses seen during the worst crashes of recent stock market history — when equities slumped 49% after the dot-com bubble burst and 57% in the aftermath of the financial crisis of 2007-2008.Compared with previous bond-market meltdowns, long-term Treasurys are seeing one of the most extreme collapses in history. The losses are twice as severe as those seen in 1981 when 10-year yields neared 16%.With prices plunging and yields at decade highs, lets look at who feels the pain from the bond selloff.Patrick's Books:Statistics For The Trading Floor: https://amzn.to/3eerLA0Derivatives For The Trading Floor: https://amzn.to/3cjsyPFCorporate Finance: https://amzn.to/3fn3rvCPatreon Page: https://www.patreon.com/PatrickBoyleOnFinanceBuy Me a Coffee: https://buymeacoffee.com/patrickboyleVisit our website: www.onfinance.orgFollow Patrick on Twitter Here: https://twitter.com/PatrickEBoylePatrick Boyle YouTube Channel Support the show
As you can probably gather from the title, Neal Shipley is not your average Amateur golfer. Nothing against many of my other peers of the past, but not many guys are as interesting as Neal. Today we get into a great and somewhat wide ranging conversation about the notes above, the DERT of course, how Neal had such a great summer of golf, Waffle House, and more. I was highly impressed with Neal's outlook on golf and how it relates to our lives in general and his overall perspective on things. I believe you all will really enjoy getting to learn more about the long haired, fun loving, long hitting Ohio State Buckeye who made an inspiring run at Cherry Hills all the way to the finals this summer which has earned him spots in the 2024 Masters and US Open.Let us know at the links below if you have any feedback on this episode or anything else we have been doing!Cheers,- The Tie GuysWebsite:https://www.thetiepodcast.comInstagram:https://www.instagram.com/thetiepodcast/?hl=enTwitter:https://mobile.twitter.com/thetiepodcastGoodWalk Coffee:https://goodwalkcoffee.comCODE: thetie for 20% offBDraddy:bdraddy.comCODE: thetie25 for 25% off
Join Dr. Jay and Brad as they interview XTRA's COO, Michelangelo Raiola, and CTO, Ajay Naik. Both Raiola and Naik are co-founders with Raiola's twin brother, Pierangelo Raiola. Michelangelo Raiola is the co-founder and COO of XTRA. Michelangelo is a tech entrepreneur, focusing on computer vision and body motion data, with a Master's degree in Quantitative Finance from Università Bocconi and a Bachelor's Degree in Economics from Luiss Guido Carli University. Michelangelo is also certified from AICA in ECDL-Full Standard and ECDL-IT Security. Ajay Naik is the co-founder and CTO of XTRA. As a software engineer and entrepreneur, Ajay's insatiable curiosity and passion for leveraging technology to solve problems have always propelled him forward. Ajay has a B.E in Computer Science and Engineering from Atria Institute of Technology, Bangalore. He has a 12th in Science from Gokhale Centenary College, Ankola and an SSLC in General Studies from Jai Hind High School, Ankola. Ajay also has a certification from Sun Microsystems in SCJP. XTRA Vision AI is disrupting the healthcare industry with its cutting-edge remote health monitoring system, driven by state-of-the-art AI and computer vision technology. With a leading computer vision team boasting a proven track record of successful exits and deep expertise in machine learning and AI, XTRA Vision AI is revolutionizing the way healthcare is delivered.
Latent Space is popping off! Welcome to the over 8500 latent space explorers who have joined us. Join us this month at various events in SF and NYC, or start your own!This post spent 22 hours at the top of Hacker News.As announced during their Developer Day celebrating their $100m fundraise following their Google partnership, Replit is now open sourcing its own state of the art code LLM: replit-code-v1-3b (model card, HF Space), which beats OpenAI's Codex model on the industry standard HumanEval benchmark when finetuned on Replit data (despite being 77% smaller) and more importantly passes AmjadEval (we'll explain!)We got an exclusive interview with Reza Shabani, Replit's Head of AI, to tell the story of Replit's journey into building a data platform, building GhostWriter, and now training their own LLM, for 22 million developers!8 minutes of this discussion go into a live demo discussing generated code samples - which is always awkward on audio. So we've again gone multimodal and put up a screen recording here where you can follow along on the code samples!Recorded in-person at the beautiful StudioPod studios in San Francisco.Full transcript is below the fold. We would really appreciate if you shared our pod with friends on Twitter, LinkedIn, Mastodon, Bluesky, or your social media poison of choice!Timestamps* [00:00:21] Introducing Reza* [00:01:49] Quantitative Finance and Data Engineering* [00:11:23] From Data to AI at Replit* [00:17:26] Replit GhostWriter* [00:20:31] Benchmarking Code LLMs* [00:23:06] AmjadEval live demo* [00:31:21] Aligning Models on Vibes* [00:33:04] Beyond Chat & Code Completion* [00:35:50] Ghostwriter Autonomous Agent* [00:38:47] Releasing Replit-code-v1-3b* [00:43:38] The YOLO training run* [00:49:49] Scaling Laws: from Kaplan to Chinchilla to LLaMA* [00:52:43] MosaicML* [00:55:36] Replit's Plans for the Future (and Hiring!)* [00:59:05] Lightning RoundShow Notes* Reza Shabani on Twitter and LinkedIn* also Michele Catasta and Madhav Singhal* Michele Catasta's thread on the release of replit-code-v1-3b* Intro to Replit Ghostwriter* Replit Ghostwriter Chat and Building Ghostwriter Chat* Reza on how to train your own LLMs (their top blog of all time)* Our Benchmarks 101 episode where we discussed HumanEval* AmjadEval live demo* Nat.dev* MosaicML CEO Naveen Rao on Replit's LLM* MosaicML Composer + FSDP code* Replit's AI team is hiring in North America timezone - Fullstack engineer, Applied AI/ML, and other roles!Transcript[00:00:00] Alessio Fanelli: Hey everyone. Welcome to the Latent Space podcast. This is Alessio, partner and CTO in residence at Decibel Partners. I'm joined by my co-host, swyx, writer and editor of Latent Space.[00:00:21] Introducing Reza[00:00:21] swyx: Hey and today we have Reza Shabani, Head of AI at Replit. Welcome to the studio. Thank you. Thank you for having me. So we try to introduce people's bios so you don't have to repeat yourself, but then also get a personal side of you.[00:00:34] You got your PhD in econ from Berkeley, and then you were a startup founder for a bit, and, and then you went into systematic equity trading at BlackRock in Wellington. And then something happened and you were now head of AI at Relet. What should people know about you that might not be apparent on LinkedIn?[00:00:50] One thing[00:00:51] Reza Shabani: that comes up pretty often is whether I know how to code. Yeah, you'd be shocked. A lot of people are kind of like, do you know how to code? When I was talking to Amjad about this role, I'd originally talked to him, I think about a product role and, and didn't get it. Then he was like, well, I know you've done a bunch of data and analytics stuff.[00:01:07] We need someone to work on that. And I was like, sure, I'll, I'll do it. And he was like, okay, but you might have to know how to code. And I was like, yeah, yeah, I, I know how to code. So I think that just kind of surprises people coming from like Ancon background. Yeah. Of people are always kind of like, wait, even when people join Relet, they're like, wait, does this guy actually know how to code?[00:01:28] Is he actually technical? Yeah.[00:01:30] swyx: You did a bunch of number crunching at top financial companies and it still wasn't[00:01:34] Reza Shabani: obvious. Yeah. Yeah. I mean, I, I think someone like in a software engineering background, cuz you think of finance and you think of like calling people to get the deal done and that type of thing.[00:01:43] No, it's, it's not that as, as you know, it's very very quantitative. Especially what I did in, in finance, very quantitative.[00:01:49] Quantitative Finance and Data Engineering[00:01:49] swyx: Yeah, so we can cover a little bit of that and then go into the rapid journey. So as, as you, as you know, I was also a quantitative trader on the sell side and the buy side. And yeah, I actually learned Python there.[00:02:01] I learned my, I wrote my own data pipelines there before airflow was a thing, and it was just me writing running notebooks and not version controlling them. And it was a complete mess, but we were managing a billion dollars on, on my crappy code. Yeah, yeah. What was it like for you?[00:02:17] Reza Shabani: I guess somewhat similar.[00:02:18] I, I started the journey during grad school, so during my PhD and my PhD was in economics and it was always on the more data intensive kind of applied economic side. And, and specifically financial economics. And so what I did for my dissertation I recorded cnbc, the Financial News Network for 10 hours a day, every day.[00:02:39] Extracted the close captions from the video files and then used that to create a second by second transcript of, of cmbc, merged that on with high frequency trading, quote data and then looked at, you know, went in and did some, some nlp, tagging the company names, and and then looked at the price response or the change in price and trading volume in the seconds after a company was mentioned.[00:03:01] And, and this was back in. 2009 that I was doing this. So before cloud, before, before a lot of Python actually. And, and definitely before any of these packages were available to make this stuff easy. And that's where, where I had to really learn to code, like outside of you know, any kind of like data programming languages.[00:03:21] That's when I had to learn Python and had to learn all, all of these other skills to work it with data at that, at that scale. So then, you know, I thought I wanted to do academia. I did terrible on the academic market because everyone looked at my dissertation. They're like, this is cool, but this isn't economics.[00:03:37] And everyone in the computer science department was actually way more interested in it. Like I, I hung out there more than in the econ department and You know, didn't get a single academic offer. Had two offer. I think I only applied to like two industry jobs and got offers from both of them.[00:03:53] They, they saw value in it. One of them was BlackRock and turned it down to, to do my own startup, and then went crawling back two and a half years later after the startup failed.[00:04:02] swyx: Something on your LinkedIn was like you're trading Chinese news tickers or something. Oh, yeah. I forget,[00:04:07] Reza Shabani: forget what that was.[00:04:08] Yeah, I mean oh. There, there was so much stuff. Honestly, like, so systematic active equity at, at BlackRock is, was such an amazing. Group and you just end up learning so much and the, and the possibilities there. Like when you, when you go in and you learn the types of things that they've been trading on for years you know, like a paper will come out in academia and they're like, did you know you can use like this data on searches to predict the price of cars?[00:04:33] And it's like, you go in and they've been trading on that for like eight years. Yeah. So they're, they're really ahead of the curve on, on all of that stuff. And the really interesting stuff that I, that I found when I went in was all like, related to NLP and ml a lot of like transcript data, a lot of like parsing through the types of things that companies talk about, whether an analyst reports, conference calls, earnings reports and the devil's really in the details about like how you make sense of, of that information in a way that, you know, gives you insight into what the company's doing and, and where the market is, is going.[00:05:08] I don't know if we can like nerd out on specific strategies. Yes. Let's go, let's go. What, so one of my favorite strategies that, because it never, I don't think we ended up trading on it, so I can probably talk about it. And it, it just kind of shows like the kind of work that you do around this data.[00:05:23] It was called emerging technologies. And so the whole idea is that there's always a new set of emerging technologies coming onto the market and the companies that are ahead of that curve and stay up to date on on the latest trends are gonna outperform their, their competitors.[00:05:38] And that's gonna reflect in the, in the stock price. So when you have a theory like that, how do you actually turn that into a trading strategy? So what we ended up doing is, well first you have to, to determine what are the emergent technologies, like what are the new up and coming technologies.[00:05:56] And so we actually went and pulled data on startups. And so there's like startups in Silicon Valley. You have all these descriptions of what they do, and you get that, that corpus of like when startups were getting funding. And then you can run non-negative matrix factorization on it and create these clusters of like what the various Emerging technologies are, and you have this all the way going back and you have like social media back in like 2008 when Facebook was, was blowing up.[00:06:21] And and you have things like mobile and digital advertising and and a lot of things actually outside of Silicon Valley. They, you know, like shale and oil cracking. Yeah. Like new technologies in, in all these different types of industries. And then and then you go and you look like, which publicly traded companies are actually talking about these things and and have exposure to these things.[00:06:42] And those are the companies that end up staying ahead of, of their competitors. And a lot of the the cases that came out of that made a ton of sense. Like when mobile was emerging, you had Walmart Labs. Walmart was really far ahead in terms of thinking about mobile and the impact of mobile.[00:06:59] And, and their, you know, Sears wasn't, and Walmart did well, and, and Sears didn't. So lots of different examples of of that, of like a company that talks about a new emerging trend. I can only imagine, like right now, all of the stuff with, with ai, there must be tons of companies talking about, yeah, how does this affect their[00:07:17] swyx: business?[00:07:18] And at some point you do, you do lose the signal. Because you get overwhelmed with noise by people slapping a on everything. Right? Which is, yeah. Yeah. That's what the Long Island Iced Tea Company slaps like blockchain on their name and, you know, their stock price like doubled or something.[00:07:32] Reza Shabani: Yeah, no, that, that's absolutely right.[00:07:35] And, and right now that's definitely the kind of strategy that would not be performing well right now because everyone would be talking about ai. And, and that's, as you know, like that's a lot of what you do in Quant is you, you try to weed out other possible explanations for for why this trend might be happening.[00:07:52] And in that particular case, I think we found that, like the companies, it wasn't, it wasn't like Sears and Walmart were both talking about mobile. It's that Walmart went out of their way to talk about mobile as like a future, mm-hmm. Trend. Whereas Sears just wouldn't bring it up. And then by the time an invest investors are asking you about it, you're probably late to the game.[00:08:12] So it was really identifying those companies that were. At the cutting edge of, of new technologies and, and staying ahead. I remember like Domino's was another big one. Like, I don't know, you[00:08:21] swyx: remember that? So for those who don't know, Domino's Pizza, I think for the run of most of the 2010s was a better performing stock than Amazon.[00:08:29] Yeah.[00:08:31] Reza Shabani: It's insane.[00:08:32] swyx: Yeah. Because of their investment in mobile. Mm-hmm. And, and just online commerce and, and all that. I it must have been fun picking that up. Yeah, that's[00:08:40] Reza Shabani: that's interesting. And I, and I think they had, I don't know if you, if you remember, they had like the pizza tracker, which was on, on mobile.[00:08:46] I use it[00:08:46] swyx: myself. It's a great, it's great app. Great app. I it's mostly faked. I think that[00:08:50] Reza Shabani: that's what I heard. I think it's gonna be like a, a huge I don't know. I'm waiting for like the New York Times article to drop that shows that the whole thing was fake. We all thought our pizzas were at those stages, but they weren't.[00:09:01] swyx: The, the challenge for me, so that so there's a, there's a great piece by Eric Falkenstein called Batesian Mimicry, where every signal essentially gets overwhelmed by noise because the people who wants, who create noise want to follow the, the signal makers. So that actually is why I left quant trading because there's just too much regime changing and like things that would access very well would test poorly out a sample.[00:09:25] And I'm sure you've like, had a little bit of that. And then there's what was the core uncertainty of like, okay, I have identified a factor that performs really well, but that's one factor out of. 500 other factors that could be going on. You have no idea. So anyway, that, that was my existential uncertainty plus the fact that it was a very highly stressful job.[00:09:43] Reza Shabani: Yeah. This is a bit of a tangent, but I, I think about this all the time and I used to have a, a great answer before chat came out, but do you think that AI will win at Quant ever?[00:09:54] swyx: I mean, what is Rentech doing? Whatever they're doing is working apparently. Yeah. But for, for most mortals, I. Like just waving your wand and saying AI doesn't make sense when your sample size is actually fairly low.[00:10:08] Yeah. Like we have maybe 40 years of financial history, if you're lucky. Mm-hmm. Times what, 4,000 listed equities. It's actually not a lot. Yeah, no, it's,[00:10:17] Reza Shabani: it's not a lot at all. And, and constantly changing market conditions and made laden variables and, and all of, all of that as well. Yeah. And then[00:10:24] swyx: retroactively you're like, oh, okay.[00:10:26] Someone will discover a giant factor that, that like explains retroactively everything that you've been doing that you thought was alpha, that you're like, Nope, actually you're just exposed to another factor that you're just, you just didn't think about everything was momentum in.[00:10:37] Yeah. And one piece that I really liked was Andrew Lo. I think he had from mit, I think he had a paper on bid as Spreads. And I think if you, if you just. Taken, took into account liquidity of markets that would account for a lot of active trading strategies, alpha. And that was systematically declined as interest rates declined.[00:10:56] And I mean, it was, it was just like after I looked at that, I was like, okay, I'm never gonna get this right.[00:11:01] Reza Shabani: Yeah. It's a, it's a crazy field and I you know, I, I always thought of like the, the adversarial aspect of it as being the, the part that AI would always have a pretty difficult time tackling.[00:11:13] Yeah. Just because, you know, there's, there's someone on the other end trying to out, out game you and, and AI can, can fail in a lot of those situations. Yeah.[00:11:23] swyx: Cool.[00:11:23] From Data to AI at Replit[00:11:23] Alessio Fanelli: Awesome. And now you've been a rep almost two years. What do you do there? Like what does the, the team do? Like, how has that evolved since you joined?[00:11:32] Especially since large language models are now top of mind, but, you know, two years ago it wasn't quite as mainstream. So how, how has that evolved?[00:11:40] Reza Shabani: Yeah, I, so when I joined, I joined a year and a half ago. We actually had to build out a lot of, of data pipelines.[00:11:45] And so I started doing a lot of data work. And we didn't have you know, there, there were like databases for production systems and, and whatnot, but we just didn't have the the infrastructure to query data at scale and to process that, that data at scale and replica has tons of users tons of data, just tons of ripples.[00:12:04] And I can get into, into some of those numbers, but like, if you wanted to answer the question, for example of what is the most. Forked rep, rep on rep, you couldn't answer that back then because it, the query would just completely time out. And so a lot of the work originally just went into building data infrastructure, like modernizing the data infrastructure in a way where you can answer questions like that, where you can you know, pull in data from any particular rep to process to make available for search.[00:12:34] And, and moving all of that data into a format where you can do all of this in minutes as opposed to, you know, days or weeks or months. That laid a lot of the groundwork for building anything in, in ai, at least in terms of training our own own models and then fine tuning them with, with replica data.[00:12:50] So then you know, we, we started a team last year recruited people from, you know from a team of, of zero or a team of one to, to the AI and data team today. We, we build. Everything related to, to ghostrider. So that means the various features like explain code, generate code, transform Code, and Ghostrider chat which is like a in context ide or a chat product within the, in the ide.[00:13:18] And then the code completion models, which are ghostwriter code complete, which was the, the very first version of, of ghostrider. Yeah. And we also support, you know, things like search and, and anything in terms of what creates, or anything that requires like large data scale or large scale processing of, of data for the site.[00:13:38] And, and various types of like ML algorithms for the site, for internal use of the site to do things like detect and stop abuse. Mm-hmm.[00:13:47] Alessio Fanelli: Yep. Sounds like a lot of the early stuff you worked on was more analytical, kind of like analyzing data, getting answers on these things. Obviously this has evolved now into some.[00:13:57] Production use case code lms, how is the team? And maybe like some of the skills changed. I know there's a lot of people wondering, oh, I was like a modern data stack expert, or whatever. It's like I was doing feature development, like, how's my job gonna change? Like,[00:14:12] Reza Shabani: yeah. It's a good question. I mean, I think that with with language models, the shift has kind of been from, or from traditional ml, a lot of the shift has gone towards more like nlp backed ml, I guess.[00:14:26] And so, you know, there, there's an entire skill set of applicants that I no longer see, at least for, for this role which are like people who know how to do time series and, and ML across time. Right. And, and you, yeah. Like you, you know, that exact feeling of how difficult it is to. You know, you have like some, some text or some variable and then all of a sudden you wanna track that over time.[00:14:50] The number of dimensions that it, that it introduces is just wild and it's a totally different skill set than what we do in a, for example, in in language models. And it's very it's a, it's a skill that is kind of you know, at, at least at rep not used much. And I'm sure in other places used a lot, but a lot of the, the kind of excitement about language models has pulled away attention from some of these other ML areas, which are extremely important and, and I think still going to be valuable.[00:15:21] So I would just recommend like anyone who is a, a data stack expert, like of course it's cool to work with NLP and text data and whatnot, but I do think at some point it's going to you know, having, having skills outside of that area and in more traditional aspects of ML will, will certainly be valuable as well.[00:15:39] swyx: Yeah. I, I'd like to spend a little bit of time on this data stack notion pitch. You were even, you were effectively the first data hire at rep. And I just spent the past year myself diving into data ecosystem. I think a lot of software engineers are actually. Completely unaware that basically every company now eventually evolves.[00:15:57] The data team and the data team does everything that you just mentioned. Yeah. All of us do exactly the same things, set up the same pipelines you know, shop at the same warehouses essentially. Yeah, yeah, yeah, yeah. So that they enable everyone else to query whatever they, whatever they want. And to, to find those insights that that can drive their business.[00:16:15] Because everyone wants to be data driven. They don't want to do the janitorial work that it comes, that comes to, yeah. Yeah. Hooking everything up. What like, so rep is that you think like 90 ish people now, and then you, you joined two years ago. Was it like 30 ish people? Yeah, exactly. We're 30 people where I joined.[00:16:30] So and I just wanna establish your founders. That is exactly when we hired our first data hire at Vilify as well. I think this is just a very common pattern that most founders should be aware of, that like, You start to build a data discipline at this point. And it's, and by the way, a lot of ex finance people very good at this because that's what we do at our finance job.[00:16:48] Reza Shabani: Yeah. Yeah. I was, I was actually gonna Good say that is that in, in some ways, you're kind of like the perfect first data hire because it, you know, you know how to build things in a reliable but fast way and, and how to build them in a way that, you know, it's, it scales over time and evolves over time because financial markets move so quickly that if you were to take all of your time building up these massive systems, like the trading opportunities gone.[00:17:14] So, yeah. Yeah, they're very good at it. Cool. Okay. Well,[00:17:18] swyx: I wanted to cover Ghost Writer as a standalone thing first. Okay. Yeah. And then go into code, you know, V1 or whatever you're calling it. Yeah. Okay. Okay. That sounds good. So order it[00:17:26] Replit GhostWriter[00:17:26] Reza Shabani: however you like. Sure. So the original version of, of Ghost Writer we shipped in August of, of last year.[00:17:33] Yeah. And so this was a. This was a code completion model similar to GitHub's co-pilot. And so, you know, you would have some text and then it would predict like, what, what comes next. And this was, the original version was actually based off of the cogen model. And so this was an open source model developed by Salesforce that was trained on, on tons of publicly available code data.[00:17:58] And so then we took their their model, one of the smaller ones, did some distillation some other kind of fancy tricks to, to make it much faster and and deployed that. And so the innovation there was really around how to reduce the model footprint in a, to, to a size where we could actually serve it to, to our users.[00:18:20] And so the original Ghost Rider You know, we leaned heavily on, on open source. And our, our friends at Salesforce obviously were huge in that, in, in developing these models. And, but, but it was game changing just because we were the first startup to actually put something like that into production.[00:18:38] And, and at the time, you know, if you wanted something like that, there was only one, one name and, and one place in town to, to get it. And and at the same time, I think I, I'm not sure if that's like when the image models were also becoming open sourced for the first time. And so the world went from this place where, you know, there was like literally one company that had all of these, these really advanced models to, oh wait, maybe these things will be everywhere.[00:19:04] And that's exactly what's happened in, in the last Year or so, as, as the models get more powerful and then you always kind of see like an open source version come out that someone else can, can build and put into production very quickly at, at, you know, a fraction of, of the cost. So yeah, that was the, the kind of code completion Go Strider was, was really just, just that we wanted to fine tune it a lot to kind of change the way that our users could interact with it.[00:19:31] So just to make it you know, more customizable for our use cases on, on Rep. And so people on Relet write a lot of, like jsx for example, which I don't think was in the original training set for, for cogen. And and they do specific things that are more Tuned to like html, like they might wanna run, right?[00:19:50] Like inline style or like inline CSS basically. Those types of things. And so we experimented with fine tuning cogen a bit here and there, and, and the results just kind of weren't, weren't there, they weren't where you know, we, we wanted the model to be. And, and then we just figured we should just build our own infrastructure to, you know, train these things from scratch.[00:20:11] Like, LMS aren't going anywhere. This world's not, you know, it's, it's not like we're not going back to that world of there's just one, one game in town. And and we had the skills infrastructure and the, and the team to do it. So we just started doing that. And you know, we'll be this week releasing our very first open source code model.[00:20:31] And,[00:20:31] Benchmarking Code LLMs[00:20:31] Alessio Fanelli: and when you say it was not where you wanted it to be, how were you benchmarking[00:20:36] Reza Shabani: it? In that particular case, we were actually, so, so we have really two sets of benchmarks that, that we use. One is human eval, so just the standard kind of benchmark for, for Python, where you can generate some code or you give you give the model a function definition with, with some string describing what it's supposed to do, and then you allow it to complete that function, and then you run a unit test against it and and see if what it generated passes the test.[00:21:02] So we, we always kind of, we would run this on the, on the model. The, the funny thing is the fine tuned versions of. Of Cogen actually did pretty well on, on that benchmark. But then when we, we then have something called instead of human eval. We call it Amjad eval, which is basically like, what does Amjad think?[00:21:22] Yeah, it's, it's exactly that. It's like testing the vibes of, of a model. And it's, it's cra like I've never seen him, I, I've never seen anyone test the model so thoroughly in such a short amount of time. He's, he's like, he knows exactly what to write and, and how to prompt the model to, to get you know, a very quick read on, on its quote unquote vibes.[00:21:43] And and we take that like really seriously. And I, I remember there was like one, one time where we trained a model that had really good you know, human eval scores. And the vibes were just terrible. Like, it just wouldn't, you know, it, it seemed overtrained. So so that's a lot of what we found is like we, we just couldn't get it to Pass the vibes test no matter how the, how[00:22:04] swyx: eval.[00:22:04] Well, can you formalize I'm jal because I, I actually have a problem. Slight discomfort with human eval. Effectively being the only code benchmark Yeah. That we have. Yeah. Isn't that[00:22:14] Reza Shabani: weird? It's bizarre. It's, it's, it's weird that we can't do better than that in some, some way. So, okay. If[00:22:21] swyx: I, if I asked you to formalize Mja, what does he look for that human eval doesn't do well on?[00:22:25] Reza Shabani: Ah, that is a, that's a great question. A lot of it is kind of a lot of it is contextual like deep within, within specific functions. Let me think about this.[00:22:38] swyx: Yeah, we, we can pause for. And if you need to pull up something.[00:22:41] Reza Shabani: Yeah, I, let me, let me pull up a few. This, this[00:22:43] swyx: is gold, this catnip for people.[00:22:45] Okay. Because we might actually influence a benchmark being evolved, right. So, yeah. Yeah. That would be,[00:22:50] Reza Shabani: that would be huge. This was, this was his original message when he said the vibes test with, with flying colors. And so you have some, some ghostrider comparisons ghost Rider on the left, and cogen is on the right.[00:23:06] AmjadEval live demo[00:23:06] Reza Shabani: So here's Ghostrider. Okay.[00:23:09] swyx: So basically, so if I, if I summarize it from a, for ghosting the, there's a, there's a, there's a bunch of comments talking about how you basically implement a clone. Process or to to c Clooney process. And it's describing a bunch of possible states that he might want to, to match.[00:23:25] And then it asks for a single line of code for defining what possible values of a name space it might be to initialize it in amjadi val With what model is this? Is this your, this is model. This is the one we're releasing. Yeah. Yeah. It actually defines constants which are human readable and nice.[00:23:42] And then in the other cogen Salesforce model, it just initializes it to zero because it reads that it starts of an int Yeah, exactly. So[00:23:51] Reza Shabani: interesting. Yeah. So you had a much better explanation of, of that than than I did. It's okay. So this is, yeah. Handle operation. This is on the left.[00:24:00] Okay.[00:24:00] swyx: So this is rep's version. Yeah. Where it's implementing a function and an in filling, is that what it's doing inside of a sum operation?[00:24:07] Reza Shabani: This, so this one doesn't actually do the infill, so that's the completion inside of the, of the sum operation. But it, it's not, it's, it, it's not taking into account context after this value, but[00:24:18] swyx: Right, right.[00:24:19] So it's writing an inline lambda function in Python. Okay.[00:24:21] Reza Shabani: Mm-hmm. Versus[00:24:24] swyx: this one is just passing in the nearest available variable. It's, it can find, yeah.[00:24:30] Reza Shabani: Okay. So so, okay. I'll, I'll get some really good ones in a, in a second. So, okay. Here's tokenize. So[00:24:37] swyx: this is an assertion on a value, and it's helping to basically complete the entire, I think it looks like an E s T that you're writing here.[00:24:46] Mm-hmm. That's good. That that's, that's good. And then what does Salesforce cogen do? This is Salesforce cogen here. So is that invalidism way or what, what are we supposed to do? It's just making up tokens. Oh, okay. Yeah, yeah, yeah. So it's just, it's just much better at context. Yeah. Okay.[00:25:04] Reza Shabani: And, and I guess to be fair, we have to show a case where co cogen does better.[00:25:09] Okay. All right. So here's, here's one on the left right, which[00:25:12] swyx: is another assertion where it's just saying that if you pass in a list, it's going to throw an exception saying in an expectedly list and Salesforce code, Jen says,[00:25:24] Reza Shabani: This is so, so ghost writer was sure that the first argument needs to be a list[00:25:30] swyx: here.[00:25:30] So it hallucinated that it wanted a list. Yeah. Even though you never said it was gonna be a list.[00:25:35] Reza Shabani: Yeah. And it's, it's a argument of that. Yeah. Mm-hmm. So, okay, here's a, here's a cooler quiz for you all, cuz I struggled with this one for a second. Okay. What is.[00:25:47] swyx: Okay, so this is a four loop example from Amjad.[00:25:50] And it's, it's sort of like a q and a context in a chat bot. And it's, and it asks, and Amjad is asking, what does this code log? And it just paste in some JavaScript code. The JavaScript code is a four loop with a set time out inside of it with a cons. The console logs out the iteration variable of the for loop and increasing numbers of of, of times.[00:26:10] So it's, it goes from zero to five and then it just increases the, the delay between the timeouts each, each time. Yeah.[00:26:15] Reza Shabani: So, okay. So this answer was provided by by Bard. Mm-hmm. And does it look correct to you? Well,[00:26:22] the[00:26:22] Alessio Fanelli: numbers too, but it's not one second. It's the time between them increases.[00:26:27] It's like the first one, then the one is one second apart, then it's two seconds, three seconds. So[00:26:32] Reza Shabani: it's not, well, well, so I, you know, when I saw this and, and the, the message and the thread was like, Our model's better than Bard at, at coding Uhhuh. This is the Bard answer Uhhuh that looks totally right to me.[00:26:46] Yeah. And this is our[00:26:47] swyx: answer. It logs 5 5 55, what is it? Log five 50. 55 oh oh. Because because it logs the state of I, which is five by the time that the log happens. Mm-hmm. Yeah.[00:27:01] Reza Shabani: Oh God. So like we, you know we were shocked. Like, and, and the Bard dancer looked totally right to, to me. Yeah. And then, and somehow our code completion model mind Jude, like this is not a conversational chat model.[00:27:14] Mm-hmm. Somehow gets this right. And and, you know, Bard obviously a much larger much more capable model with all this fancy transfer learning and, and and whatnot. Some somehow, you know, doesn't get it right. So, This is the kind of stuff that goes into, into mja eval that you, you won't find in any benchmark.[00:27:35] Good. And and, and it's, it's the kind of thing that, you know, makes something pass a, a vibe test at Rep.[00:27:42] swyx: Okay. Well, okay, so me, this is not a vibe, this is not so much a vibe test as the, these are just interview questions. Yeah, that's, we're straight up just asking interview questions[00:27:50] Reza Shabani: right now. Yeah, no, the, the vibe test, the reason why it's really difficult to kind of show screenshots that have a vibe test is because it really kind of depends on like how snappy the completion is, how what the latency feels like and if it gets, if it, if it feels like it's making you more productive.[00:28:08] And and a lot of the time, you know, like the, the mix of, of really low latency and actually helpful content and, and helpful completions is what makes up the, the vibe test. And I think part of it is also, is it. Is it returning to you or the, the lack of it returning to you things that may look right, but be completely wrong.[00:28:30] I think that also kind of affects Yeah. Yeah. The, the vibe test as well. Yeah. And so, yeah, th this is very much like a, like a interview question. Yeah.[00:28:39] swyx: The, the one with the number of processes that, that was definitely a vibe test. Like what kind of code style do you expect in this situation? Yeah.[00:28:47] Is this another example? Okay.[00:28:49] Reza Shabani: Yeah. This is another example with some more Okay. Explanations.[00:28:53] swyx: Should we look at the Bard one[00:28:54] Reza Shabani: first? Sure. These are, I think these are, yeah. This is original GT three with full size 175. Billion[00:29:03] swyx: parameters. Okay, so you asked GPC three, I'm a highly intelligent question answering bot.[00:29:07] If you ask me a question that is rooted in truth, I'll give you the answer. If you ask me a question that is nonsense I will respond with unknown. And then you ask it a question. What is the square root of a bananas banana? It answers nine. So complete hallucination and failed to follow the instruction that you gave it.[00:29:22] I wonder if it follows if one, if you use an instruction to inversion it might, yeah. Do what better?[00:29:28] Reza Shabani: On, on the original[00:29:29] swyx: GP T Yeah, because I like it. Just, you're, you're giving an instructions and it's not[00:29:33] Reza Shabani: instruction tuned. Now. Now the interesting thing though is our model here, which does follow the instructions this is not instruction tuned yet, and we still are planning to instruction tune.[00:29:43] Right? So it's like for like, yeah, yeah, exactly. So,[00:29:45] swyx: So this is a replica model. Same question. What is the square of bananas? Banana. And it answers unknown. And this being one of the, the thing that Amjad was talking about, which you guys are. Finding as a discovery, which is, it's better on pure natural language questions, even though you trained it on code.[00:30:02] Exactly. Yeah. Hmm. Is that because there's a lot of comments in,[00:30:07] Reza Shabani: No. I mean, I think part of it is that there's a lot of comments and there's also a lot of natural language in, in a lot of code right. In terms of documentation, you know, you have a lot of like markdowns and restructured text and there's also just a lot of web-based code on, on replica, and HTML tends to have a lot of natural language in it.[00:30:27] But I don't think the comments from code would help it reason in this way. And, you know, where you can answer questions like based on instructions, for example. Okay. But yeah, it's, I know that that's like one of the things. That really shocked us is the kind of the, the fact that like, it's really good at, at natural language reasoning, even though it was trained on, on code.[00:30:49] swyx: Was this the reason that you started running your model on hella swag and[00:30:53] Reza Shabani: all the other Yeah, exactly. Interesting. And the, yeah, it's, it's kind of funny. Like it's in some ways it kind of makes sense. I mean, a lot of like code involves a lot of reasoning and logic which language models need and need to develop and, and whatnot.[00:31:09] And so you know, we, we have this hunch that maybe that using that as part of the training beforehand and then training it on natural language above and beyond that really tends to help. Yeah,[00:31:21] Aligning Models on Vibes[00:31:21] Alessio Fanelli: this is so interesting. I, I'm trying to think, how do you align a model on vibes? You know, like Bard, Bard is not purposefully being bad, right?[00:31:30] Like, there's obviously something either in like the training data, like how you're running the process that like, makes it so that the vibes are better. It's like when it, when it fails this test, like how do you go back to the team and say, Hey, we need to get better[00:31:44] Reza Shabani: vibes. Yeah, let's do, yeah. Yeah. It's a, it's a great question.[00:31:49] It's a di it's very difficult to do. It's not you know, so much of what goes into these models in, in the same way that we have no idea how we can get that question right. The programming you know, quiz question. Right. Whereas Bard got it wrong. We, we also have no idea how to take certain things out and or, and to, you know, remove certain aspects of, of vibes.[00:32:13] Of course there's, there's things you can do to like scrub the model, but it's, it's very difficult to, to get it to be better at something. It's, it's almost like all you can do is, is give it the right type of, of data that you think will do well. And then and, and of course later do some fancy type of like, instruction tuning or, or whatever else.[00:32:33] But a lot of what we do is finding the right mix of optimal data that we want to, to feed into the model and then hoping that the, that the data that's fed in is sufficiently representative of, of the type of generations that we want to do coming out. That's really the best that, that you can do.[00:32:51] Either the model has. Vibes or, or it doesn't, you can't teach vibes. Like you can't sprinkle additional vibes in it. Yeah, yeah, yeah. Same in real life. Yeah, exactly right. Yeah, exactly. You[00:33:04] Beyond Code Completion[00:33:04] Alessio Fanelli: mentioned, you know, co being the only show in town when you started, now you have this, there's obviously a, a bunch of them, right.[00:33:10] Cody, which we had on the podcast used to be Tap nine, kite, all these different, all these different things. Like, do you think the vibes are gonna be the main you know, way to differentiate them? Like, how are you thinking about. What's gonna make Ghost Rider, like stand apart or like, do you just expect this to be like table stakes for any tool?[00:33:28] So like, it just gonna be there?[00:33:30] Reza Shabani: Yeah. I, I do think it's, it's going to be table stakes for sure. I, I think that if you don't if you don't have AI assisted technology, especially in, in coding it's, it's just going to feel pretty antiquated. But but I do think that Ghost Rider stands apart from some of, of these other tools for for specific reasons too.[00:33:51] So this is kind of the, one of, one of the things that these models haven't really done yet is Come outside of code completion and outside of, of just a, a single editor file, right? So what they're doing is they're, they're predicting like the text that can come next, but they're not helping with the development process quite, quite yet outside of just completing code in a, in a text file.[00:34:16] And so the types of things that we wanna do with Ghost Rider are enable it to, to help in the software development process not just editing particular files. And so so that means using a right mix of like the right model for for the task at hand. But but we want Ghost Rider to be able to, to create scaffolding for you for, for these projects.[00:34:38] And so imagine if you would like Terraform. But, but powered by Ghostrider, right? I want to, I put up this website, I'm starting to get a ton of traffic to it and and maybe like I need to, to create a backend database. And so we want that to come from ghostrider as well, so it can actually look at your traffic, look at your code, and create.[00:34:59] You know a, a schema for you that you can then deploy in, in Postgres or, or whatever else? You know, I, I know like doing anything in in cloud can be a nightmare as well. Like if you wanna create a new service account and you wanna deploy you know, nodes on and, and have that service account, kind of talk to those nodes and return some, some other information, like those are the types of things that currently we have to kind of go, go back, go look at some documentation for Google Cloud, go look at how our code base does it you know, ask around in Slack, kind of figure that out and, and create a pull request.[00:35:31] Those are the types of things that we think we can automate away with with more advanced uses of, of ghostwriter once we go past, like, here's what would come next in, in this file. So, so that's the real promise of it, is, is the ability to help you kind of generate software instead of just code in a, in a particular file.[00:35:50] Ghostwriter Autonomous Agent[00:35:50] Reza Shabani: Are[00:35:50] Alessio Fanelli: you giving REPL access to the model? Like not rep, like the actual rep. Like once the model generates some of this code, especially when it's in the background, it's not, the completion use case can actually run the code to see if it works. There's like a cool open source project called Walgreen that does something like that.[00:36:07] It's like self-healing software. Like it gives a REPL access and like keeps running until it fixes[00:36:11] Reza Shabani: itself. Yeah. So, so, so right now there, so there's Ghostrider chat and Ghostrider code completion. So Ghostrider Chat does have, have that advantage in, in that it can it, it knows all the different parts of, of the ide and so for example, like if an error is thrown, it can look at the, the trace back and suggest like a fix for you.[00:36:33] So it has that type of integration. But the what, what we really want to do is is. Is merge the two in a way where we want Ghost Rider to be like, like an autonomous agent that can actually drive the ide. So in these action models, you know, where you have like a sequence of of events and then you can use you know, transformers to kind of keep track of that sequence and predict the next next event.[00:36:56] It's how, you know, companies like, like adapt work these like browser models that can, you know, go and scroll through different websites or, or take some, some series of actions in a, in a sequence. Well, it turns out the IDE is actually a perfect place to do that, right? So like when we talk about creating software, not just completing code in a file what do you do when you, when you build software?[00:37:17] You, you might clone a repo and then you, you know, will go and change some things. You might add a new file go down, highlight some text, delete that value, and point it to some new database, depending on the value in a different config file or in your environment. And then you would go in and add additional block code to, to extend its functionality and then you might deploy that.[00:37:40] Well, we, we have all of that data right there in the replica ide. And and we have like terabytes and terabytes of, of OT data you know, operational transform data. And so, you know, we can we can see that like this person has created a, a file what they call it, and, you know, they start typing in the file.[00:37:58] They go back and edit a different file to match the you know, the class name that they just put in, in the original file. All of that, that kind of sequence data is what we're looking to to train our next model on. And so that, that entire kind of process of actually building software within the I D E, not just like, here's some text what comes next, but rather the, the actions that go into, you know, creating a fully developed program.[00:38:25] And a lot of that includes, for example, like running the code and seeing does this work, does this do what I expected? Does it error out? And then what does it do in response to that error? So all, all of that is like, Insanely valuable information that we want to put into our, our next model. And and that's like, we think that one can be way more advanced than the, than this, you know, go straighter code completion model.[00:38:47] Releasing Replit-code-v1-3b[00:38:47] swyx: Cool. Well we wanted to dive in a little bit more on, on the model that you're releasing. Maybe we can just give people a high level what is being released what have you decided to open source and maybe why open source the story of the YOLO project and Yeah. I mean, it's a cool story and just tell it from the start.[00:39:06] Yeah.[00:39:06] Reza Shabani: So, so what's being released is the, the first version that we're going to release. It's a, it's a code model called replica Code V1 three B. So this is a relatively small model. It's 2.7 billion parameters. And it's a, it's the first llama style model for code. So, meaning it's just seen tons and tons of tokens.[00:39:26] It's been trained on 525 billion tokens of, of code all permissively licensed code. And it's it's three epox over the training set. And And, you know, all of that in a, in a 2.7 billion parameter model. And in addition to that, we, for, for this project or, and for this model, we trained our very own vocabulary as well.[00:39:48] So this, this doesn't use the cogen vocab. For, for the tokenize we, we trained a totally new tokenize on the underlying data from, from scratch, and we'll be open sourcing that as well. It has something like 32,000. The vocabulary size is, is in the 32 thousands as opposed to the 50 thousands.[00:40:08] Much more specific for, for code. And, and so it's smaller faster, that helps with inference, it helps with training and it can produce more relevant content just because of the you know, the, the vocab is very much trained on, on code as opposed to, to natural language. So, yeah, we'll be releasing that.[00:40:29] This week it'll be up on, on hugging pace so people can take it play with it, you know, fine tune it, do all type of things with it. We want to, we're eager and excited to see what people do with the, the code completion model. It's, it's small, it's very fast. We think it has great vibes, but we, we hope like other people feel the same way.[00:40:49] And yeah. And then after, after that, we might consider releasing the replica tuned model at, at some point as well, but still doing some, some more work around that.[00:40:58] swyx: Right? So there are actually two models, A replica code V1 three B and replica fine tune V1 three B. And the fine tune one is the one that has the 50% improvement in in common sense benchmarks, which is going from 20% to 30%.[00:41:13] For,[00:41:13] Reza Shabani: for yes. Yeah, yeah, yeah, exactly. And so, so that one, the, the additional tuning that was done on that was on the publicly available data on, on rep. And so, so that's, that's you know, data that's in public res is Permissively licensed. So fine tuning on on that. Then, Leads to a surprisingly better, like significantly better model, which is this retuned V1 three B, same size, you know, same, very fast inference, same vocabulary and everything.[00:41:46] The only difference is that it's been trained on additional replica data. Yeah.[00:41:50] swyx: And I think I'll call out that I think in one of the follow up q and as that Amjad mentioned, people had some concerns with using replica data. Not, I mean, the licensing is fine, it's more about the data quality because there's a lot of beginner code Yeah.[00:42:03] And a lot of maybe wrong code. Mm-hmm. But it apparently just wasn't an issue at all. You did[00:42:08] Reza Shabani: some filtering. Yeah. I mean, well, so, so we did some filtering, but, but as you know, it's when you're, when you're talking about data at that scale, it's impossible to keep out, you know, all of the, it's, it's impossible to find only select pieces of data that you want the, the model to see.[00:42:24] And, and so a lot of the, a lot of that kind of, you know, people who are learning to code material was in there anyway. And, and you know, we obviously did some quality filtering, but a lot of it went into the fine tuning process and it really helped for some reason. You know, there's a lot of high quality code on, on replica, but there's like you, like you said, a lot of beginner code as well.[00:42:46] And that was, that was the really surprising thing is that That somehow really improved the model and its reasoning capabilities. It felt much more kind of instruction tuned afterward. And, and you know, we have our kind of suspicions as as to why there's, there's a lot of like assignments on rep that kind of explain this is how you do something and then you might have like answers and, and whatnot.[00:43:06] There's a lot of people who learn to code on, on rep, right? And, and like, think of a beginner coder, like think of a code model that's learning to, to code learning this reasoning and logic. It's probably a lot more valuable to see that type of, you know, the, the type of stuff that you find on rep as opposed to like a large legacy code base that that is, you know, difficult to, to parse and, and figure out.[00:43:29] So, so that was very surprising to see, you know, just such a huge jump in in reasoning ability once trained on, on replica data.[00:43:38] The YOLO training run[00:43:38] swyx: Yeah. Perfect. So we're gonna do a little bit of storytelling just leading up to the, the an the developer day that you had last week. Yeah. My understanding is you decide, you raised some money, you decided to have a developer day, you had a bunch of announcements queued up.[00:43:52] And then you were like, let's train the language model. Yeah. You published a blog post and then you announced it on Devrel Day. What, what, and, and you called it the yolo, right? So like, let's just take us through like the[00:44:01] Reza Shabani: sequence of events. So so we had been building the infrastructure to kind of to, to be able to train our own models for, for months now.[00:44:08] And so that involves like laying out the infrastructure, being able to pull in the, the data processes at scale. Being able to do things like train your own tokenizes. And and even before this you know, we had to build out a lot of this data infrastructure for, for powering things like search.[00:44:24] There's over, I think the public number is like 200 and and 30 million res on, on re. And each of these res have like many different files and, and lots of code, lots of content. And so you can imagine like what it must be like to, to be able to query that, that amount of, of data in a, in a reasonable amount of time.[00:44:45] So we've You know, we spent a lot of time just building the infrastructure that allows for for us to do something like that and, and really optimize that. And, and this was by the end of last year. That was the case. Like I think I did a demo where I showed you can, you can go through all of replica data and parse the function signature of every Python function in like under two minutes.[00:45:07] And, and there's, you know, many, many of them. And so a and, and then leading up to developer day, you know, we had, we'd kind of set up these pipelines. We'd started training these, these models, deploying them into production, kind of iterating and, and getting that model training to production loop.[00:45:24] But we'd only really done like 1.3 billion parameter models. It was like all JavaScript or all Python. So there were still some things like we couldn't figure out like the most optimal way to to, to do it. So things like how do you pad or yeah, how do you how do you prefix chunks when you have like multi-language models, what's like the optimal way to do it and, and so on.[00:45:46] So you know, there's two PhDs on, on the team. Myself and Mike and PhDs tend to be like careful about, you know, a systematic approach and, and whatnot. And so we had this whole like list of things we were gonna do, like, oh, we'll test it on this thing and, and so on. And even these, like 1.3 billion parameter models, they were only trained on maybe like 20 billion tokens or 30 billion tokens.[00:46:10] And and then Amjad joins the call and he's like, no, let's just, let's just yolo this. Like, let's just, you know, we're raising money. Like we should have a better code model. Like, let's yolo it. Let's like run it on all the data. How many tokens do we have? And, and, and we're like, you know, both Michael and I are like, I, I looked at 'em during the call and we were both like, oh God is like, are we really just gonna do this?[00:46:33] And[00:46:34] swyx: well, what is the what's the hangup? I mean, you know that large models work,[00:46:37] Reza Shabani: you know that they work, but you, you also don't know whether or not you can improve the process in, in In important ways by doing more data work, scrubbing additional content, and, and also it's expensive. It's like, it, it can, you know it can cost quite a bit and if you, and if you do it incorrectly, you can actually get it.[00:47:00] Or you, you know, it's[00:47:02] swyx: like you hit button, the button, the go button once and you sit, sit back for three days.[00:47:05] Reza Shabani: Exactly. Yeah. Right. Well, like more like two days. Yeah. Well, in, in our case, yeah, two days if you're running 256 GP 100. Yeah. Yeah. And and, and then when that comes back, you know, you have to take some time to kind of to test it.[00:47:19] And then if it fails and you can't really figure out why, and like, yeah, it's, it's just a, it's kind of like a, a. A time consuming process and you just don't know what's going to, to come out of it. But no, I mean, I'm Judd was like, no, let's just train it on all the data. How many tokens do we have? We tell him and he is like, that's not enough.[00:47:38] Where can we get more tokens? Okay. And so Michele had this you know, great idea to to train it on multiple epox and so[00:47:45] swyx: resampling the same data again.[00:47:47] Reza Shabani: Yeah. Which, which can be, which is known risky or like, or tends to overfit. Yeah, you can, you can over overfit. But you know, he, he pointed us to some evidence that actually maybe this isn't really a going to be a problem.[00:48:00] And, and he was very persuasive in, in doing that. And so it, it was risky and, and you know, we did that training. It turned out. Like to actually be great for that, for that base model. And so then we decided like, let's keep pushing. We have 256 TVs running. Let's see what else we can do with it.[00:48:20] So we ran a couple other implementations. We ran you know, a the fine tune version as I, as I said, and that's where it becomes really valuable to have had that entire pipeline built out because then we can pull all the right data, de-dupe it, like go through the, the entire like processing stack that we had done for like months.[00:48:41] We did that in, in a matter of like two days for, for the replica data as well removed, you know, any of, any personal any pii like personal information removed, harmful content, removed, any of, of that stuff. And we just put it back through the that same pipeline and then trained on top of that.[00:48:59] And so I believe that replica tune data has seen something like 680. Billion tokens. And, and that's in terms of code, I mean, that's like a, a universe of code. There really isn't that much more out there. And, and it, you know, gave us really, really promising results. And then we also did like a UL two run, which allows like fill the middle capabilities and and, and will be, you know working to deploy that on, on rep and test that out as well soon.[00:49:29] But it was really just one of those Those cases where, like, leading up to developer day, had we, had we done this in this more like careful, systematic way what, what would've occurred in probably like two, three months. I got us to do it in, in a week. That's fun. It was a lot of fun. Yeah.[00:49:49] Scaling Laws: from Kaplan to Chinchilla to LLaMA[00:49:49] Alessio Fanelli: And so every time I, I've seen the stable releases to every time none of these models fit, like the chinchilla loss in, in quotes, which is supposed to be, you know, 20 tokens per, per, what's this part of the yo run?[00:50:04] Or like, you're just like, let's just throw out the tokens at it doesn't matter. What's most efficient or like, do you think there's something about some of these scaling laws where like, yeah, maybe it's good in theory, but I'd rather not risk it and just throw out the tokens that I have at it? Yeah,[00:50:18] Reza Shabani: I think it's, it's hard to, it's hard to tell just because there's.[00:50:23] You know, like, like I said, like these runs are expensive and they haven't, if, if you think about how many, how often these runs have been done, like the number of models out there and then, and then thoroughly tested in some forum. And, and so I don't mean just like human eval, but actually in front of actual users for actual inference as part of a, a real product that, that people are using.[00:50:45] I mean, it's not that many. And, and so it's not like there's there's like really well established kind of rules as to whether or not something like that could lead to, to crazy amounts of overfitting or not. You just kind of have to use some, some intuition around it. And, and what we kind of found is that our, our results seem to imply that we've really been under training these, these models.[00:51:06] Oh my god. And so like that, you know, all, all of the compute that we kind of. Through, with this and, and the number of tokens, it, it really seems to help and really seems to to improve. And I, and I think, you know, these things kind of happen where in, in the literature where everyone kind of converges to something seems to take it for for a fact.[00:51:27] And like, like Chinchilla is a great example of like, okay, you know, 20 tokens. Yeah. And but, but then, you know, until someone else comes along and kind of tries tries it out and sees actually this seems to work better. And then from our results, it seems imply actually maybe even even lla. Maybe Undertrained.[00:51:45] And, and it may be better to go even You know, like train on on even more tokens then and for, for the[00:51:52] swyx: listener, like the original scaling law was Kaplan, which is 1.7. Mm-hmm. And then Chin established 20. Yeah. And now Lama style seems to mean 200 x tokens to parameters, ratio. Yeah. So obviously you should go to 2000 X, right?[00:52:06] Like, I mean, it's,[00:52:08] Reza Shabani: I mean, we're, we're kind of out of code at that point, you know, it's like there, there is a real shortage of it, but I know that I, I know there are people working on I don't know if it's quite 2000, but it's, it's getting close on you know language models. And so our friends at at Mosaic are are working on some of these really, really big models that are, you know, language because you with just code, you, you end up running out of out of context.[00:52:31] So Jonathan at, at Mosaic has Jonathan and Naveen both have really interesting content on, on Twitter about that. Yeah. And I just highly recommend following Jonathan. Yeah,[00:52:43] MosaicML[00:52:43] swyx: I'm sure you do. Well, CAGR, can we talk about, so I, I was sitting next to Naveen. I'm sure he's very, very happy that you, you guys had such, such success with Mosaic.[00:52:50] Maybe could, could you shout out like what Mosaic did to help you out? What, what they do well, what maybe people don't appreciate about having a trusted infrastructure provider versus a commodity GPU provider?[00:53:01] Reza Shabani: Yeah, so I mean, I, I talked about this a little bit in the in, in the blog post in terms of like what, what advantages like Mosaic offers and, and you know, keep in mind, like we had, we had deployed our own training infrastructure before this, and so we had some experience with it.[00:53:15] It wasn't like we had just, just tried Mosaic And, and some of those things. One is like you can actually get GPUs from different providers and you don't need to be you know, signed up for that cloud provider. So it's, it kind of detaches like your GPU offering from the rest of your cloud because most of our cloud runs in, in gcp.[00:53:34] But you know, this allowed us to leverage GPUs and other providers as well. And then another thing is like train or infrastructure as a service. So you know, these GPUs burn out. You have note failures, you have like all, all kinds of hardware issues that come up. And so the ability to kind of not have to deal with that and, and allow mosaic and team to kind of provide that type of, of fault tolerance was huge for us.[00:53:59] As well as a lot of their preconfigured l m configurations for, for these runs. And so they have a lot of experience in, in training these models. And so they have. You know, the, the right kind of pre-configured setups for, for various models that make sure that, you know, you have the right learning rates, the right training parameters, and that you're making the, the best use of the GPU and, and the underlying hardware.[00:54:26] And so you know, your GPU utilization is always at, at optimal levels. You have like fewer law spikes than if you do, you can recover from them. And you're really getting the most value out of, out of the compute that you're kind of throwing at, at your data. We found that to be incredibly, incredibly helpful.[00:54:44] And so it, of the time that we spent running things on Mosaic, like very little of that time is trying to figure out why the G P U isn't being utilized or why you know, it keeps crashing or, or why we, you have like a cuda out of memory errors or something like that. So like all, all of those things that make training a nightmare Are are, you know, really well handled by, by Mosaic and the composer cloud and and ecosystem.[00:55:12] Yeah. I was gonna[00:55:13] swyx: ask cuz you're on gcp if you're attempted to rewrite things for the TPUs. Cause Google's always saying that it's more efficient and faster, whatever, but no one has experience with them. Yeah.[00:55:23] Reza Shabani: That's kind of the problem is that no one's building on them, right? Yeah. Like, like we want to build on, on systems that everyone else is, is building for.[00:55:31] Yeah. And and so with, with the, with the TPUs that it's not easy to do that.[00:55:36] Replit's Plans for the Future (and Hiring!)[00:55:36] swyx: So plans for the future, like hard problems that you wanna solve? Maybe like what, what do you like what kind of people that you're hiring on your team?[00:55:44] Reza Shabani: Yeah. So We are, we're currently hiring for for two different roles on, on my team.[00:55:49] Although we, you know, welcome applications from anyone that, that thinks they can contribute in, in this area. Replica tends to be like a, a band of misfits. And, and the type of people we work with and, and have on our team are you know, like just the, the perfect mix to, to do amazing projects like this with very, very few people.[00:56:09] Right now we're hiring for the applied a applied to AI ml engineer. And so, you know, this is someone who's. Creating data pipelines, processing the data at scale creating runs and and training models and you
The term “Finfluencer” refers to a person who by virtue of their popular or cultural status has an outsize impact on investor decisions through social media influence. According to Sue Guan of Santa Clara University, a variety of finfluencers exist in today's markets, ranging from simple celebrities that draw people's attention like Kim Kardashian to corporate personalities like Elon Musk or Ryan Cohen to ordinary investors who develop followings on YouTube, TikTok, and other social media platforms. In today's video we examine how the advice of well-known personal finance influencers like Dave Ramsey and Robert Kiyosaki compares to the advice of academics. Patrick's Books:Statistics For The Trading Floor: https://amzn.to/3eerLA0Derivatives For The Trading Floor: https://amzn.to/3cjsyPFCorporate Finance: https://amzn.to/3fn3rvCPatreon Page: https://www.patreon.com/PatrickBoyleOnFinanceVisit our website: www.onfinance.orgFollow Patrick on Twitter Here: https://twitter.com/PatrickEBoylePatrick Boyle YouTube Channel Support the show
In this episode, Brock speaks with Wes Gray. Wes is a former Marine intel officer and currently the founder and CEO of alpha architect. This episode might double as an intro to financial markets. We talk about what quantitative investing is, and how that differs from the famous stock pickers, like Warren Buffett. We talk about systems based thinking, where and why that's relevant. And we also talk about the future of investing and how to use our strengths to best play into that. Episode Resources: Alpha Architect website Notes: (01:21) - What Wes is most proud of (04:02) - What you do after school and the benefits of being young and dumb when it comes to work (11:06) - Most impactful experience from the Marines (14:59) - System 1 and 2 Thinking (22:42) - Personal finance advice for young service members (28:42) - Wes' first stock and getting started investing (33:32) - Quantitative investing introduction (42:01) - Macro and technology risks to quant investing (48:16) - Relationship between value and momentum (54:02) - Why all investing is front running (01:00:12) - Popularity of quant investing and what the next 5-10 years look like (01:06:49) - Starting Alpha Architect (01:12:51) - Entrepreneurship and the doors it opens to new adventures (01:18:57) - Building ETF Architect and sources for business today (01:28:03) - Twitter Q&A The Scuttlebutt Podcast - The podcast for service members and veterans building a life outside the military. The Scuttlebutt Podcast features discussions on lifestyle, careers, business, and resources for service members. Show host, Brock Briggs, talks with a special guest from the community committed to helping military members build a successful life, inside and outside the service. Get a weekly episode breakdown, a sneak peek of the next episode and other resources in your inbox for free at https://scuttlebutt.substack.com/. Follow along: • Brock: @BrockHBriggs • Instagram: Scuttlebutt_Podcast • Send me an email: scuttlebuttpod1@gmail.com • Episodes & transcripts: Scuttlebuttpodcast.co
This episode is a compilation of answers to YOUR questions that were asked directly from my listeners who attend my weekly business education YouTube live webcast. Topics covered include: What do you think about FTX and SBF, Is there an ETF bubble, What do you think about quantitative finance and more. Refer to chapter marks for a complete list of topics covered and to jump to a specific section. Download my free "Networking eBook": www.harouneducation.comAttend my weekly YouTube Live every Thursday's 8am-11am PT. Subscribe to my YouTube Channel to receive notifications. Learn more about my MBA Degree ProgramConnect with me: YouTube: ChrisHarounVenturesCompleteBusinessEducationInstagram @chrisharounLinkedIn: Chris HarounTwitter: @chris_harounFacebook: Haroun Education Ventures TikTok: @chrisharoun
Responding to Nassim Taleb`s `bashing` of Bitcoin & Cryptocurrencies in the same Journal of Quantitative Finance, can only be attempted by a few. Enjoy a lively & witty discussion on thinking through Markets, Money, and Mathematics with Prof. Alexander Lipton whose credentials include MIT Connection Science, Sila, and more. He is also the co-author of two new books
Chris Geczy, Wharton Adjunct Full Professor of Finance, joins the show to discuss quantitative analysis in investing, ESG and more. Hosted on Acast. See acast.com/privacy for more information.
In China, a major financial crisis is unfolding. A few months ago, four rural lenders in the northern province of Henan froze the deposits of hundreds of thousands of customers and Chinese citizens in 86 cities are boycotting mortgage payments towards their homes. Confidence in the financial sector has plunged. In this video we look at the story behind the bank scandals.Patrick's Books:Statistics For The Trading Floor: https://amzn.to/3eerLA0Derivatives For The Trading Floor: https://amzn.to/3cjsyPFCorporate Finance: https://amzn.to/3fn3rvCPatreon Page: https://www.patreon.com/PatrickBoyleOnFinanceVisit our website: www.onfinance.orgFollow Patrick on Twitter Here: https://twitter.com/PatrickEBoylePatrick Boyle YouTube Channel Support the show
Timestamps: 1:44 - Falling in love with machine learning 15:03 - Developing a prototype at HackZurich 23:05 - Podcasts as the world's largest knowledge base 40:43 - Doing B2C in Switzerland 49:15 - Benefiting from the Swiss startup mafia About Kevin Smith: Kevin Smith is the co-founder and CEO at Snipd, an AI-powered podcast player built to unlock the knowledge in the podcasts you listen to. Originally from England and Germany, Kevin came to Zurich to get his MA from ETH in Quantitative Finance. After a time working for banks like Julius Baier and UBS, Kevin decided to join the startup Contovista, where he worked up to becoming head of analytics and AI. In 2020, he decided to put his love for machine learning to good use and participated in Hackzurich together with his ETH friend Ferdinand Langnickel, at a time when a breakthrough in self-supervised learning had just happened. Having originally planned to create a prototype around the topic of meeting notes, they eventually pivoted to podcasts, and ended up winning that year's Hackzurich. In 2021, Kevin, Ferdinand and third co-founder Mikel Corcuera Lejarreta created Snipd. The problem Snipd solves is pretty intuitive: the audio medium is great for both consuming and producing content, but not so much for interacting with it, i.e., for storing it and consulting it. It's pretty hard to skim through a 2h podcast episode to find what you're looking for. Through its AI algorithm, Snipd allows you to do just that: when you're using their podcast player, whenever you listen to something which particularly interests you, you press a button in the app or you triple click your headphones, and then AI will analyze the content you just selected to determine the appropriate start and end point, provide a transcript, a summary and a title. Snipd also provides you with automatic chapters (with titles and summaries) for each podcast episode. By providing this service, Snipd is able to collect data about which podcast moments are interesting to each user, and this data will soon allow them to make predictions on what each specific user may be interested in listening to. Memorable Quotes: "I've come to the realization that creating things is something which fundamentally makes humans happy." "Something you should ask yourself before you start a startup is: Can you see yourself working on this for 10 years?" Don't forget to give us a follow on our Twitter, Instagram, Facebook and Linkedin accounts, so you can always stay up to date with our latest initiatives. That way, there's no excuse for missing out on live shows, weekly give-aways or founders dinners!
In late June, Sri Lanka's prime minister Ranil Wickremesinghe announced that the country's economy had “collapsed.” There was no money left to pay for imports of necessities like food, fuel and medicine, and the country was seeking help from neighboring countries and the IMF.Sri Lanka is going through its worst economic crisis since it gained independence from Britain almost 75 years ago. In today's video we ask the question, "What Killed Sri Lanka's Economy?"Patrick's Books:Statistics For The Trading Floor: https://amzn.to/3eerLA0Derivatives For The Trading Floor: https://amzn.to/3cjsyPFCorporate Finance: https://amzn.to/3fn3rvC Patreon Page: https://www.patreon.com/PatrickBoyleOnFinanceVisit our website: www.onfinance.orgFollow Patrick on Twitter Here: https://twitter.com/PatrickEBoylePatrick Boyle On Finance YouTube Channel Support the show
In 2013 President Xi Jinping announced the launch of his signature policy, the Belt and Road Initiative, the largest transnational infrastructure program ever undertaken by a single country. Today though, many of the loans financing these huge infrastructure projects are going bad and going bad in record numbers. Today's video looks at the Belt and Road Initiative, how the projects are going and how recent events like Russia's invasion of Ukraine are affecting debtor nations like Sri Lanka, who defaulted on their sovereign debt in May of this year.Patrick's Books:Statistics For The Trading Floor: https://amzn.to/3eerLA0Derivatives For The Trading Floor: https://amzn.to/3cjsyPFCorporate Finance: https://amzn.to/3fn3rvC Patreon Page: https://www.patreon.com/PatrickBoyleOnFinanceVisit our website: www.onfinance.orgFollow Patrick on Twitter Here: https://twitter.com/PatrickEBoylePatrick Boyle On Finance YouTube Channel Support the show
Join Mandi Kerr and Ben Young on Thursday morning's episode of Moving ^HEMP Forward. Ben is the CEO of FyberX. Prior to founding FyberX, Ben began his career with a decade in investment banking, first with Goldman Sachs in the Public Sector Infrastructure Banking Group and subsequently with FBR Capital Markets focusing on specialty finance and real estate. During his time in investment banking, Ben raised $2bn+ for clients. He is also a member of BOD of Revival Organics. Ben holds a BS in Quantitative Finance, Economics, and Math from James Madison University. For this morning we'll talk about: FyberX's Plan (Infrastructure, Timeline, Team, Advisory Board, Trials) Industry Stakeholder Collaboration (or lack thereof) Overview of Financing Environment for Industrial Hemp Farmer Relations (stakeholders)
Last week, three DeFi (decentralized finance) groups stepped in with emergency plans to protect their projects and users from economic turbulence in the face of collapsing cryptocurrency prices.These DeFi crypto networks which had pledged to put users in control ended up taking charge themselves in order to survive the ongoing crisis in the digital asset market.Patrick's Books:Statistics For The Trading Floor: https://amzn.to/3eerLA0Derivatives For The Trading Floor: https://amzn.to/3cjsyPFCorporate Finance: https://amzn.to/3fn3rvC Patreon Page: https://www.patreon.com/PatrickBoyleOnFinanceVisit our website: www.onfinance.orgFollow Patrick on Twitter Here: https://twitter.com/PatrickEBoylePatrick Boyle On Finance - YouTube Channel Support the show
Inflation has turned from transitory to pernicious, with some economists even raising the specter of a 1970s-style wage-price spiral. Should you reposition your investment portfolio for an inflationary environment, shifting some of your money to sectors or asset classes that tend to do well during inflationary periods? Or should you leave your investments alone and let the markets control their long-term destiny? In today's video we look at market history to see how securities prices are affected by inflation, interest rates and interest rate hikes.Patrick's Books:Statistics For The Trading Floor: https://amzn.to/3eerLA0Derivatives For The Trading Floor: https://amzn.to/3cjsyPFCorporate Finance: https://amzn.to/3fn3rvC Patreon Page: https://www.patreon.com/PatrickBoyleOnFinanceVisit our website: www.onfinance.orgFollow Patrick on Twitter Here: https://twitter.com/PatrickEBoylePatrick Boyle YouTube ChannelLink to the Dimson Marsh & Staunton Investment Returns Yearbook Support the show
Corissa graduated James Madison University with a degree in Quantitative Finance and Financial Economics and began her career on Wall Street working for a prominent Investment Bank and later, various Investment Firms. Over her 10+ year career in Finance, Corissa always seemed to struggle with feeling as though she had a much bigger purpose and that she was designed to help others in a more meaningful way. Along her journey, she got married, moved to London where she had two beautiful boys and ultimately landed back in the States, specifically New Jersey, to raise her family. At the time, she thought she had found her purpose as a stay at home mom. However, after a big life disruption, she discovered Human Design and her Intuition, and through her own journey of healing and self-discovery, she began to understand how she could use her gifts to be of service to others. Corissa now guides clients through transformational periods in their ownlives and helps support them in understanding who they truly are, what theirpurpose is and how to find the power in the pain so that they can see a pathforward to a life they love. Corissa draws upon several modalities, including Human Design, EFT and Intuitive sessions to help support and guide her clients.Connect with Corissa:https://www.corissastepp.comInstagram @corissasteppFind out about your human design at https://www.geneticmatrix.com.You can connect with me on Instagram @beingwellwithkelly or at https://www.beingwellwithkelly.com.Be sure to like, follow and share this podcast and we'd be thrilled if you gave us your review. :)Be well and follow your curiosity, you never know where it may lead you! Music Track: Knowledge is Power by Matt Large
On this episode we discuss travel, financial magic, and picky eating!? Yes, a nice mix of all the wonderful things that make up our guest Jessica Rohrer. Jessica Rohrer is an accomplished author, investor, and financial coach who has helped countless people transform their personal and business lives with her inviting and down-to-earth advice. Jessica loves investing, real estate, and finance and most importantly loves helping people to truly feel empowered with their money and accomplish their financial dreams. Jessica graduated cum laude with a double major in Quantitative Finance and Financial Economics as well as a minor in Mathematics. She has over 10+ years of experience in corporate finance, specialized lending, portfolio management, and personal finance. She has worked with a lot of both very wealthy and not-so-wealthy clients and has seen firsthand everything that they do differently. Jessica delivers timeless truthbombs and actionable advice in her books, which have been instrumental in helping thousands of individuals confront the murky waters of their own personal finances and transform their lives. Jessica IG: jessicanlynnrohrer Face Your Finances (Book): https://www.amazon.com/dp/B09TTRD43S Picky Eater (Book): https://www.amazon.com/dp/B0883CRRR1 Stefan IG: https://www.instagram.com/stefan.zavalin/ Email: stefan.zavalin@ltmmtl.com Website: www.stefanzavalin.com Book: https://www.amazon.com/dp/B09PM74BPD
In Today's podcast we discuss if the west can place an embargo on Russian energy exports?Western leaders have threatened Vladimir Putin with sweeping sanctions over his invasion of Ukraine but, they have been clear that they would avoid disrupting energy supplies.Hitting energy exports is no longer as due to the brutality of Putin's war and the idea of targeting oil and gas exports is no longer off the table — even if it damages western economies in the process.Canada - a tiny buyer of Russian energy, has blocked crude imports and in the US, President Joe Biden is under mounting pressure from a diverse coalition of Republicans and Democrats, to ban Russian oil and gas.How would this affect the global oil markets or impact inflation in the west?Patrick's Books:Statistics For The Trading Floor: https://amzn.to/3eerLA0Derivatives For The Trading Floor: https://amzn.to/3cjsyPFCorporate Finance: https://amzn.to/3fn3rvC Patreon Page: https://www.patreon.com/PatrickBoyleOnFinanceVisit our website: www.onfinance.orgFollow Patrick on Twitter Here: https://twitter.com/PatrickEBoylePatrick Boyle On Finance YouTube Channel Support the show (https://www.patreon.com/PatrickBoyleOnFinance)
InvestOrama - Separate Investment Facts from Financial Fiction
This is an interview about Careers in Quant Finance with Christian Kahl, MD, Head of Product Strategy and Client Service at Fincad. Fincad is a leading provider of advanced valuation risk solutions for derivatives and fixed income analytics. Christian holds a PhD in Maths and his career spans 20 years in various leadership roles in quantitative finance. TIMESTAMPS 00:00 Career Talk: Quant Finance w. Christian Kahl, Fincad 00:17 Introducing Christian and Fincad 02:10 From Maths studies to Quant in finance 03:29 The evolution of quantitative finance education 04:43 The diversity in quant careers 07:37 Christian's approach to recruiting Quants QUOTES "Back in those days, a lot of people entered the domain of quantitative finance from a PhD background in mathematics and physics. Part of which is the fact that there wasn't a formal education in financial mathematics, quantitative finance, and definitely a lot less than what we've seen today." "Quantitative finance is a term very widely used across the industry. There are quantitative analysts that sit on the sell side of trading floors writing quantitative libraries in C plus; there are quants in hedge funds supporting various forms of trading activity, trying to get an edge on the market; there are also quantitative analysts in various forms, across risk departments advisory boards, and so forth." "Client communication is a core aspect of day to day activity [in my team], which somewhat lessens the burden of deep technical skills. It requires us to strike a balance of communication skills as well as technical education." "I'm a great fan of hiring for talent and potential. And what I'm looking out for in a candidate is a curiosity in mathematics and the skills to learn new things."
Jason Chan always understood the importance of education and how it can potentially transform his life. He witnessed first-hand how it changed his mother's. His mother grew up in a small town in Malaysia. Later on, she studied very hard to get her Master's degree while juggling work and raising him and his twin brother. Jason was inspired by his mother to do well academically, which led him to earn a scholarship to study Quantitative Finance at the University of Hong Kong. After graduating, some of his peers went to become quants. However, Jason decided to become a Data Scientist at one of the most prominent investment firms, Fidelity. After 2 years, Jason joined China Light Power (CLP), the electric company of Hong Kong, as a Data Scientist in their Big Data & AI team where he also competed in AI competition in Guangzhou. In this episode, he shares his love and interest in data with us that led him to build a UFC MMA predictor web app. He also shares with us how pursuing a degree above the undergraduate level will help him become one step closer to becoming a Chief Data Scientist one day. **Podcast is available on Apple Podcasts, Spotify and Google Play. Feel free to share with your friends.**
In this episode of the Data Exchange our special correspondent and editor Jenn Webb speaks with Arum Verma, Head of Quantitative Research Solutions at Bloomberg. My first job post-academia was as lead quant in a small hedge fund. Since then, I've followed the industry from afar and I've long been interested in the role of data and models in financial services. Arun and I discussed quantitative finance when we ran into each other at the O'Reilly AI conference in London last year. He was slated to give a talk on extracting trading signals from alternative data sets, an important subject among quants.Jenn and Arun discussed a range of topics including:The quantitative finance landscape.The challenges in identifying and using alternative data sources.Applications of machine learning in finance, specifically deep learning and reinforcement learning.New natural language models and their applications in finance.Model Explainability and Model Risk Management.Detailed show notes can be found on The Data Exchange web site.Subscribe to The Gradient Flow Newsletter.
© 2020-2025 Ivy Podcast Discovery LLC
