We’ve probably all had fun recently giving ChatGPT, or other AI chatbots from the likes of Google, Microsoft and others, what we think are fun and innocent prompts to work on, but do we stop to consider what we’re actually feeding these tools and where the data they return comes from and what biasses may be in the machine learning models they’ve been trained on?
We’re delighted to have Catherine join us again to take a deep dive into the ethics that surround the hype that is AI. We talk about the issues around copyright, data accuracy and vetting, biasses in datasets, as well as how these AI tools can never be sentient in the way that everyone’s favourite artificial intelligence, Lt. Cmdr Data was shown to be in Star Trek.
What are your views on the world of AI, do you have experience using machine learning and AI models in your own work or studies? We’d love to hear from you, so please send us a note to podca[email protected], or why not come join the discussion on our Discord server.
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Episode Links
- Catherine Flick: Website / Mastodon
- ChatGPT – We are not endorsing the use of this or any LLM (Large Language Mode), simply pointing to a credible source to access ChatGPT.
- Tay: Microsoft issues apology over racist chatbot fiasco – Early AI chatbot my Microsoft
- Google Photos labeled black people ‘gorillas’ – Example of AI Messing Up
- Adobe Unveils Firefly, a Family of new Creative Generative AI – Adobe merging AI together with their products
- Sharing sensitive business data with ChatGPT could be risky – Example of the risk of AI and sensitive data
Chapter Times
- 00:00:04: Introductions
- 00:03:40: What Do We Mean By AI?
- 00:13:55: The Failures of Narrow AI
- 00:24:46: Where Does the Data Come From? 00:34:57: Dealing With Copyright 00:44:19: Creative AI and ML Tools
- 00:49:12: Using AI in Work and Academia
- 01:02:50: Feeding Personal Data
- 01:10:57: Wrapping Up
Credits
Intro and outro theme: Ace of Clubs by RoccoW
