By Dr Genevieve Hayes
The podcast currently has 45 episodes available.
Genevieve Hayes Consulting
Succeeding in stock market investing is all about timing – buying low, selling high and being able to read the signs to determine when things are going to change. But as anyone who’s ever tried to get rich through stock trading can tell you, this is easier said than done.
Given the massive amounts of financial data published each day, for people who aren’t experts in the field, it can be too hard to spot the patterns and keep up with the constant change. As a result, many people are either investing in markets based on guesswork or not investing at all.
This is where AI can help, because there’s nothing that AI does better than finding patterns in large volumes of data. AI has the potential to democratize access to investment insights.
In this episode, Andrew Einhorn joins Dr Genevieve Hayes to discuss how AI can help ordinary investors find better investment opportunities than they could ever manage on their own.
Andrew Einhorn is the CEO and co-founder of Levelfields, an AI-driven fintech application that automates arduous investment research so investors can find opportunities faster and easier. Before moving into finance, Andrew started his career as an epidemiologist and helped build a pandemic monitoring system for Georgetown Hospital. He also previously co-founded tech company Synoptus, has consulted for NASA and served as an advisor to a $65 billion hedge fund.
The post Episode 45: AI-Powered Investment Insights first appeared on Genevieve Hayes Consulting and is written by Dr Genevieve Hayes.
Genevieve Hayes Consulting
As a data scientist, there’s nothing worse than devoting months of your time to building a data product that appears to meet your stakeholders’ every need, only to find it never gets used. It’s depressing, demotivating and can be devastating for your career.
But as the old saying goes, “You can lead a horse to water, but you can’t make it drink”. Or can you?
In this episode, Brian T O’Neill joins Dr Genevieve Hayes to discuss how you can apply the best techniques from software product management and UI/UX design to create ML and AI products your stakeholders will love.
Brian T O’Neill is the Founder and Principal of Designing for Analytics, an independent data product UI/UX design consultancy that helps data leaders turn ML & analytics into usable, valuable data products. He also advises on product and UI/UX design for startup founders in MIT’s Sandbox Innovation Fund; hosts the podcast Experiencing Data; founded The Data Product Leadership Community and maintains a career as a professional percussionist performing in Boston and internationally.
The post Episode 44: Designing Data Products People Actually Want to Use first appeared on Genevieve Hayes Consulting and is written by Dr Genevieve Hayes.
Genevieve Hayes Consulting
Two years ago, no one could imagine the impact generative AI would have on our world, and most of us can’t even begin to imagine the impact the next generation of AI will have on our world two years from now. The only thing that is certain is uncertainty.
But that uncertainty brings with it great opportunities and choices. We can choose to sit back and let the future of AI play out in front of us or engage with this new technology and shape the future of AI and the world as we know it.
In this episode, Dr Eric Daimler joins Dr Genevieve Hayes to discuss his extraordinary work in shaping the future of AI and what that future might look like.
Dr. Eric Daimler is the Chair, CEO and Co-Founder of Conexus AI and has previously co-founded five other companies in the technology space. He served under the Obama Administration as a Presidential Innovation Fellow for AI and Robotics in the Executive Office of President, as the sole authority driving the agenda for U.S. leadership in research, commercialization, and public adoption of AI & Robotics. He is also the author of the upcoming book The Future is Formal: The Roadmap for Using Technology to Solve Society’s Biggest Problems.
The post Episode 43: Shaping the Future of AI first appeared on Genevieve Hayes Consulting and is written by Dr Genevieve Hayes.
Genevieve Hayes Consulting
Chances are, you’re reading this summary on a device you didn’t build yourself. Why would you? Tech companies can build you a far better device for a much lower cost than you could ever manage alone. As with many other cases in life, this is an example of where it is better to buy than to build.
Yet, in building a data team, many organisations assume the only solution is to build from within. And although this may be the right solution for some organisations, building a solution isn’t right for all.
In this episode, Collin Graves joins Dr Genevieve Hayes to discuss what a bought solution might look like in the data science space, and whether it is right for you.
Collin Graves is the CEO of North Labs, a leading fractional cloud data analytics firm that helps growing companies become data-driven. Before founding North Labs, he served with distinction in NATO Special Operations during his tenure with the US Air Force. He is also the author of the upcoming Data Revolution: Leading with Analytics and Winning from Day One.
The post Episode 42: Should You Outsource Your Data Team? first appeared on Genevieve Hayes Consulting and is written by Dr Genevieve Hayes.
Genevieve Hayes Consulting
When ChatGPT was first released, there was talk it would lead to traditional search engines, like Google, soon becoming obsolete. That was until users discovered generative AI’s one major drawback – it makes stuff up.
Because of the stochastic nature of ChatGPT, it is never going to be possible to completely eliminate hallucinations. However, there are ways to work around this issue. One such way is through leveraging knowledge graphs and retrieval augmented generation (or RAG).
In this episode, Kirk Marple joins Dr Genevieve Hayes to discuss how knowledge graphs and RAG can be leveraged to improve the quality of generative AI.
Kirk Marple is the CEO and Technical Founder of Graphlit, serverless, cloud-native platform that streamlines the development of AI apps by automating unstructured data workflows and leveraging retrieval augmented generation.
The post Episode 41: Building Better AI Apps with Knowledge Graphs and RAG first appeared on Genevieve Hayes Consulting and is written by Dr Genevieve Hayes.
Genevieve Hayes Consulting
For many people, data science is synonymous with machine learning and many data science courses are little more than overviews of the most used machine learning algorithms and techniques.
Where the majority of data science courses fall short is they neglect to bridge the gap between data science theory and business reality, resulting in many data scientists who are technically strong but unable to create value from their work. However, this doesn’t necessarily have to be the case.
In this episode, Douglas Squirrel joins Dr Genevieve Hayes to discuss systems and techniques data scientists and their managers can use to make data science teams profitable.
Douglas Squirrel has been coding for forty-five years and has led software teams for twenty-five. He uses the power of conversations to create insane profits in technology organisations of all sizes. His experience includes growing software teams as a CTO in startups; consulting on product improvement; and coaching a wide variety of leaders in improving their conversations, aligning to business goals, and creating productive conflict.
The post Episode 40: Making Data Science Teams Profitable first appeared on Genevieve Hayes Consulting and is written by Dr Genevieve Hayes.
Genevieve Hayes Consulting
One of the big promises of data science is its ability to combine multiple disparate datasets to produce value-creating insights. But this is only possible if you can get all those disparate datasets together, in the one location, to begin with. The has led to the rise of the data engineer and the data orchestration platform.
In this episode, Sandy Ryza joins Dr Genevieve Hayes to discuss the impact of the data scientist on the creation of the next generation of data orchestration tools.
Sandy Ryza is a data scientist turned data engineer who is currently the lead engineer on the Dagster project, an open-source data orchestration platform used in MLOps, data science, IOT and analytics. He is also the co-author of Advanced Analytics with Spark.
The post Episode 39: The Impact of Data Science on Data Orchestration first appeared on Genevieve Hayes Consulting and is written by Dr Genevieve Hayes.
Genevieve Hayes Consulting
From BuzzFeed Quizzes to the national census, it’s impossible to get through life without encountering surveys. However, not all surveys are created equal. As with everything else in data science, garbage going in will inevitably lead to garbage coming out.
In this episode, Kyle Block joins Dr Genevieve Hayes to look at practical techniques for designing surveys to ensure they deliver value, as well as approaches to analysing survey results, to maximise that value.
Kyle Block is Head of Research at Gradient, an analytics agency that combines advanced statistical and machine learning techniques to answer difficult marketing challenges. He holds a Masters in Spatial Analysis from the University of Pennsylvania and has spent his career helping managers use data to make important decisions.
The post Episode 38 – The Art and Science of Survey Design first appeared on Genevieve Hayes Consulting and is written by Dr Genevieve Hayes.
Genevieve Hayes Consulting
Most people have come to accept that the price of living in a technological world, and its associated convenience, is some loss of data privacy. However, few realise just how much privacy they are giving up.
In this episode, Dr Katharine Kemp joins Dr Genevieve Hayes to discuss data privacy challenges for consumers and data scientists in the age of AI.
Dr Katharine Kemp is an Associate Professor in UNSW’s Faculty of Law and Justice and Deputy Director of the Allens Hub for Technology, Law and Innovation. Her research focuses on competition, data privacy and consumer protection regulation, including their application to digital platforms.
The post Episode 37: Data Privacy in the Age of AI first appeared on Genevieve Hayes Consulting and is written by Dr Genevieve Hayes.
Genevieve Hayes Consulting
Decision-making is an essential part of everyday life and one of the main applications of data science is making the decision-making process easier.
However, mostly when data scientists build models, it’s to make a single decision. But in real life, decision-making is rarely that simple.
In this episode, Prof Warren Powell joins Dr Genevieve Hayes to discuss one way in which the decision-making process can become more complicated, in the form of sequential decision problems.
Warren Powell is the co-founder and Chief Innovation Officer of Optimal Dynamics and a Professor Emeritus after retiring from Princeton, where he was a faculty member in the Department of Operations Research and Financial Engineering. He is also the author of Sequential Decision Analytics and Modelling and Reinforcement Learning and Stochastic Optimization.
The post Episode 36: Sequential Decision Problems first appeared on Genevieve Hayes Consulting and is written by Dr Genevieve Hayes.
The podcast currently has 45 episodes available.