Is AI Actually Useful?

Patrick Boyle On Finance

Patrick Boyle

Investing, Business

4.9 • 320 Ratings

🗓️ 25 February 2024

⏱️ 25 minutes

🔗️ Recording | iTunes | RSS

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Summary

Send us a textA new Harvard Business School study analyzed the impact of giving AI tools, to white collar workers at Boston Consulting Group.In the study, management consultants who were told to use Chat GPT when carrying out a set of consulting tasks were far more productive than their colleagues who were not given access the tool. Not only did AI-assisted consultants carry out tasks 25 per cent faster and complete 12 per cent more tasks overall, but their work was also assessed to be 40 per...

Transcript

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0:00.0	Hello and welcome. You are listening to Patrick Boyle on Finance, a podcast exploring ideas from quantitative finance, examining events occurring in markets right now and financial history to see what lessons can be taken away, including interviews with some of the most interesting people in the world of finance. To learn more about the podcast, visit onfinance.org.
0:27.3	Hello and welcome. You are listening to Patrick Boyle on finance, a podcast exploring ideas from
0:34.1	quantitative finance, examining events occurring in markets right now and financial
0:38.8	history to see what lessons can be taken away, including interviews with some of the most
0:44.2	interesting people in the world of finance. To learn more about the podcast, visit onfinance.org.
1:00.7	Jensen Wong, the founder and CEO of Invidia, announced on their earnings call this Wednesday,
1:04.0	that generative AI has hit a tipping point.
1:12.2	He went on to say that demand for AI chips was surging worldwide across companies, industries and nations.
1:19.0	Invidia has been the biggest single driver of returns in the S&P 500 so far this year, and most of the other top performing companies make claims to be leaders in AI.
1:25.5	So how useful are these new tools and to what extent do people need
1:30.0	to learn how to use them to succeed in the workplace going forward? Nicholas Carlini, a research
1:37.0	scientist at Google DeepMind, published a blog post a few days ago, examining his use of a variety of AI large language models to
1:47.2	try and understand what these models are good at and where they fail.
1:51.9	I found the post really interesting as it surprised me how these models can be successful
1:58.0	at certain really complex tasks, but then fail at tasks that you might imagine
2:03.2	would be easy for them. They can do calculus but then struggle to count. I was surprised to see
2:10.3	that the AI was able to write code to create a website showing an American flag that changed
2:17.0	color when clicked on, but was then unable
2:19.9	to write code showing a drawing of a cake while playing the Happy Birthday song.
2:25.3	These two tasks seem to be of similar difficulty, but the outcomes were really different.
2:31.7	Carlini shows that GPT4 can write code to create a website where you can
2:37.2	play Tic Tic Toc, but it's unable to find the winning move in a game of Tic Tic Toc Tso, which seems
	...

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