Sharon Chou — How to AI

01/30/24 • 62 min

Fortune's Path Podcast

Takeaways

Understanding the basics of circuits and quantum computing is essential in comprehending the potential of AI.
Transparency and explainability are crucial in AI decision-making to ensure accountability and mitigate bias.
Data curation is a critical step in developing AI models to avoid unintended biases and improve accuracy.
The application of AI in mortgage and loan decisions requires careful consideration of fairness and ethical implications. Higher education is correlated with earnings, but its correlation with credit worthiness is uncertain.
Being completely blind to factors like race and gender in the hiring process may be challenging, but efforts can be made to represent everyone equally.
Considering each subpopulation separately and simplifying the hiring process can help ensure fair representation.
Ethical dilemmas arise when ignoring correlations that have a strong statistical relationship with outcomes.
The application of AI in the hiring process can be effective when combined with human decision-making and a structured, data-informed approach.

Chapters

00:00Introduction and Recording Confirmation

00:38Background in Physics and Engineering

03:13Research in Material Design and Quantum Physics

04:26Understanding Circuits and Quantum Computing

06:37Transition from Research to Business

11:14Impact of Ideas and Einstein's Equation

13:14Ethics and Risks of Artificial Intelligence

17:15Applications and Limitations of AI

20:39Ethics and Bias in AI Decision-Making

25:24Transparency and Explainability in AI

29:29Data Curation and Bias in AI Models

34:07AI in Mortgage and Loan Decisions

38:15Fairness and Ethics in Lending

38:41Correlation between Higher Education and Earnings

39:21Challenges of Being Blind to Race and Gender

39:49Considerations for Representing Everyone Equally

40:24Ethical Dilemmas of Ignoring Correlations

41:08Product Development and Answering Ethical Questions

41:29Simplifying the Hiring Process

42:02Data-Informed Recruiting and Hiring

43:14Using Data to Find the Right Match

44:24Simplifying the Workflow for Recruiters

45:16Focusing on Skill-Based Factors in Hiring

46:31The Validity of Resumes in Predicting Performance

47:25Factors in Deciding a Good Hire

48:15The Tricky Nature of Job Descriptions

49:05The Importance of Skills and Job Descriptions

50:03The Value of Experience and Starting a Business

51:09The Role of Emotion in Decision-Making

54:02Introducing Scientific Process into Hiring

55:53The Application of AI in the Hiring Process

56:58The Human Element in Decision-Making

58:16Applying the Scientific Method to Business Problems

59:18Learning from Past Research and Being Skeptical

01:00:45Checking Assumptions and Being Discerning

Takeaways

Understanding the basics of circuits and quantum computing is essential in comprehending the potential of AI.
Transparency and explainability are crucial in AI decision-making to ensure accountability and mitigate bias.
Data curation is a critical step in developing AI models to avoid unintended biases and improve accuracy.
The application of AI in mortgage and loan decisions requires careful consideration of fairness and ethical implications. Higher education is correlated with earnings, but its correlation with credit worthiness is uncertain.
Being completely blind to factors like race and gender in the hiring process may be challenging, but efforts can be made to represent everyone equally.
Considering each subpopulation separately and simplifying the hiring process can help ensure fair representation.
Ethical dilemmas arise when ignoring correlations that have a strong statistical relationship with outcomes.
The application of AI in the hiring process can be effective when combined with human decision-making and a structured, data-informed approach.

Chapters

00:00Introduction and Recording Confirmation

00:38Background in Physics and Engineering

03:13Research in Material Design and Quantum Physics

04:26Understanding Circuits and Quantum Computing

06:37Transition from Research to Business

11:14Impact of Ideas and Einstein's Equation

13:14Ethics and Risks of Artificial Intelligence

17:15Applications and Limitations of AI

20:39Ethics and Bias in AI Decision-Making

25:24Transparency and Explainability in AI

29:29Data Curation and Bias in AI Models

34:07AI in Mortgage and Loan Decisions

38:15Fairness and Ethics in Lending

38:41Correlation between Higher Education and Earnings

39:21Challenges of Being Blind to Race and Gender

39:49Considerations for Representing Everyone Equally

40:24Ethical Dilemmas of Ignoring Correlations

41:08Product Development and Answering Ethical Questions

41:29Simplifying the Hiring Process

42:02Data-Informed Recruiting and Hiring

43:14Using Data to Find the Right Match

44:24Simplifying the Workflow for Recruiters

45:16Focusing on Skill-Based Factors in Hiring

46:31The Validity of Resumes in Predicting Performance

47:25Factors in Deciding a Good Hire

48:15The Tricky Nature of Job Descriptions

49:05The Importance of Skills and Job Descriptions

50:03The Value of Experience and Starting a Business

51:09The Role of Emotion in Decision-Making

54:02Introducing Scientific Process into Hiring

55:53The Application of AI in the Hiring Process

56:58The Human Element in Decision-Making

58:16Applying the Scientific Method to Business Problems

59:18Learning from Past Research and Being Skeptical

01:00:45Checking Assumptions and Being Discerning

Previous Episode

What Software Makers Can Learn From Adidas

Norbert talks about how Adidas starts its shoe manufacturing process, beginning with a business unit who outlines the market need in a comprehensive brief. Then, a designer group begins experimenting with colors, materials and textures. "There's 250-350 operations that need to happen to put one pair of footwear together, so it's a long process... taking up to 16 months," says Norbert.

Every new material and material supplier is tested, based on internal standards. Once it moves to being manufactured on the production line, further testing and checking take place.

Some analogies and differences - software vs. manufacturing:

multiple suppliers and producers are comparable to software engineers writing code all over a complex system
the value of eliminating variation
unlike software, there's no opportunity to "fix it later" in manufacturing, which makes it essential to build a highly functioning quality process
quality and compliance can't be an afterthought in manufacturing
software and manufacturing need to focus on root cause analysis to build the most robust quality management process
risk analysis is all about error tolerance

Next Episode

Eric Satz Makes the Case for Alternative Assets

Takeaways:

Why it's not so much that Eric is a believer in alternative assets, but that he believes in a diversification of investment portfolios.
That illiquidity of alternative assets is a feature, not a bug.
The legal challenges of getting Alto started took longer than the anticipated technical challenges of simplifying the workflow processes of alternative investments.
The 'greater fool' theory in investments.
How public markets are no longer being driven by the fundamentals of investing and operating.
Alto's three levels of operation: a software business which owns a trust company and also owns a securities company.
'Historically good returns' in alternative investments = returns that exceed public market returns.
Bitcoin, like money, is an invention based on faith.
What comprises 'luck' in business: hard work, opportunity, and a third vector: remaining open to what's happening in the market.
The cost - and there is one - to entrepreneurship.