Value Driven Data Science Dr Genevieve Hayes

ChatGPT was one of the best things to ever happen to data science - not so much because of what it can do, but because, virtually overnight, it made AI and data science mainstream. However, while most data scientists now have experience with ChatGPT and other large language model (LLM)-based technologies as end users, few have had experience in building their own LLM-based tools.
In this episode, Dr Mudasser Iqbal joins Dr Genevieve Hayes to discuss the data science behind LLMs and how to go about doing just that.

Guest Bio
Dr Mudasser Iqbal is the Founder and CEO of TeamSolve, a company dedicated to leveraging AI for digital transformation with a sustainable focus. He has extensive experience in Industrial AI, including multiple patents, and was recognised as an MIT Young Innovator. He also played a key role in the growth of his previous start-up, Visenti, and its subsequent acquisition by Xylem Inc.

Talking Points
The data science behind LLMs.How TeamSolve's Lily, compares to ChatGPT and the advantages of a domain-specific, private chatbot, such as Lily, over a more general, public chatbot, such as ChatGPT.How knowledge graphs can be combined with LLMs to overcome many of the shortcomings of LLMs.The changing attitudes of organisations around the use of generative AI tools.What the emergence of cutting-edge AI tools, such as LLMs, mean for more traditional data science tools, such as analytics dashboards.The future of generative AI, and the potential benefits and risks to society.
Links
TeamSolveConnect with Mudasser on LinkedIn
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Despite its conservative reputation, the financial services industry has always been a big adopter of cutting-edge technologies. Dating back more than a century, it's also been one of the biggest employers of people with technology and data-related skills. But what does the future hold for the use of tech in the financial services industry?In this episode, Ben Shapira joins Dr Genevieve Hayes to discuss what this future might look like and how technology is being used right now to improve the lives of consumers.
Guest Bio
Ben Shapira is a digital strategist and UX specialist turned tech entrepreneur. He is the founder and Chief Product Officer of Australian fintech start-up Dinero, as well as being a lecturer in the Master of Media and Communication program at Swinburne University.

Talking Points
Where the financial services industry is heading, regarding the use of technology and how this will affect the lives of consumers.The types of data modelling and analysis that are possible because of the data produced by these new technologies.What is Dineiro and how data informed its creation.The impact of data security considerations on financial services organisations’ ability to adopt new technologies and make use of the data they produce.Advice for data scientists looking to build a career in marketing and advertising.How marketing techniques can be applied to data science to make data scientists more effective, regardless of their industry.
Links
Connect with Ben on LinkedInDineiro
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Data science is only useful if it can create value. And one way that value can be created is by using data to influence decision-making. Yet, to influence decisions, data scientists need to effectively communicate the outcomes of their work – which is something many struggle with. This is because effective data science communication is about more than just rattling off statistics and expecting your end users to piece them together.

In this episode, Dr Selena Fisk joins Dr Genevieve Hayes to discuss how data scientists can improve their communication by using those numbers to tell a story.
Guest Bio
Dr Selena Fisk is a data storyteller and researcher, with a background in education, who now works with the corporate sector to develop data-informed strategies. She is also the author of a number of books, including I’m Not a Numbers Person: How to Make Good Decisions in a Data-Rich World and Data-Informed Learners: Engaging Students in their Data Story.


Talking Points

What is data storytelling and how does it differ from data visualisation?
How can data scientists make use of storytelling techniques to maximise the impact of their work?
The difference between being data-informed and data-driven, and what that means for schools and businesses.
How data is being used in schools to inform learning and improve educational outcomes.
How educators can involve students in the data conversation, and what data scientists can learn from this when it comes to engaging business stakeholders in their work.

Links

Selena's Website
Connect with Selena on LinkedIn
Follow Selena on Twitter


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Pretty much everyone has a retirement plan, but those plans aren’t always robust enough to see you through to the finish line of life. And part of that is a direct consequence of incorrectly applying data science principles to financial modelling.

In this episode, Todd Tresidder joins Dr Genevieve Hayes to discuss the risks and limitations of using data science when planning for retirement.
Guest Bio
Todd Tresidder is a former hedge fund manager who “retired” at age 35 to become a financial consumer advocate and money coach. He now runs the popular retirement planning website FinancialMentor.com and is the author of a range of books on retirement planning and investments including How Much Money Do I Need to Retire? and The Leverage Equation.


Talking Points

What are some of the limitations of traditional financial modelling?
Examples of what can happen when traditional financial modelling goes very wrong.
How to do financial modelling the right way.
The Engineer's Fallacy or why you shouldn't apply pure data science to financial planning.
The implications of this for fields outside of the financial services industry.

Links

Todd's Website


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If you look at the list of the greatest inventions of the 20th century, you’ll find they all have two things in common. From tea bags to toasters and from cell phones to cellophane, they all take the form of physical objects, and all are, or at least were, protected by patents.

Yet, since the turn of the century, the nature of inventions has changed significantly. And many of the greatest inventions of this century now take the form of computer code or models.

But how do you protect an invention you can’t physically touch?

In this episode, Helen McFadzean joins Dr Genevieve Hayes to discuss the intersection of artificial intelligence and intellectual property.
Guest Bio
Helen McFadzean is a patent and trademark attorney, with a background in artificial intelligence and mechatronics engineering. She has successfully obtained patents, trademarks and designs for businesses in Australia and overseas in a large number of technology areas including machine learning and image classification, automation, smart devices, audio signal processing, embedded software, and control systems.


Talking Points

What is the difference between patents, trademarks and copyrights?
How do you know if an AI/ML-based invention is worth protecting and how do you protect it if it is?
What parts of an AI/ML-based invention can be protected through patent law?
The importance of good communication in capturing IP.
What happens if an invention was invented by a generative AI, rather than a human?

Links

Connect with Helen on LinkedIn


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Most Intro to Machine Learning courses cover supervised learning and unsupervised learning. But did you know there is also a third type of machine learning, which was used in the development of ChatGPT and is likely to become increasingly important in the not too distant future?

In this episode, Prof Michael Littman joins Dr Genevieve Hayes to discuss reinforcement learning - the other type of machine learning - as well as his new book, Code to Joy: Why Everyone Should Learn a Little Programming.
Guest Bio
Prof. Michael Littman is an award-winning Professor of Computer Science at Brown University, specialising in reinforcement learning; is co-creator of the Machine Learning and Reinforcement Learning courses offered as part of Georgia Tech’s Online Master of Science in Computer Science (OMSCS) program; and is currently serving as Division Director for Information and Intelligent Systems at the (US) National Science Foundation. He is also the author of Code to Joy: Why Everyone Should Learn a Little Programming.


Talking Points

What is reinforcement learning and why has it traditionally been seen as "the other type of machine learning"?
Current and future applications of reinforcement learning.
How reinforcement learning is being used to create business value.
Michael's new book, Code to Joy and why everyone should learn to code.
How non-programmers can get started with coding and what it would mean for the world if more people did code.

Links

Michael's Website
Follow Michael on Twitter
Computing Up Podcast
Machine Learning A Cappella (Thriller Parody)


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