2022 – Best of Week 13

2 min

When the Optimists are Too Pessimistic

This is why even the optimists can be too pessimistic. Because we are using linear thinking to imagine a geometric future. It just doesn’t work.

(6min _ OfDollarsAndData)

The Worst Run For Bonds Since 1980

Yes, bonds have gotten killed in the last three months, but this really needs to be put in context. A 5% negative total return over a three-month period isn’t fun, but that’s like a bad week for a stock index and a stormy afternoon for an individual stock.

If you’ve hated bonds for the last couple of years because rates have been so low, then the recent uptick in rates should be welcomed with open arms.

(7min _ TheIrrelevantInvestor)

„Death Wish“

If you have the “will and intention” to be a Managing Director by thirty five, any superior opportunity that flows in your direction that doesn’t support that fixed goal will get disregarded.

As a result, you risk achieving your limited goal, reaching a steep fitness peak, but with no idea what to do next. Be careful what you wish for.

(5min _ TheAttentionSpan)

How People Think

But so many behaviors are universal across generations and geographies. Circumstances change, but people’s reactions don’t. Technologies evolve, but insecurities, blind spots, and gullibility rarely does.

This article describes 17 of what I think are the most common and influential aspects of how people think.

(13min _ CollaborativeFund)

Bitcoin’s Lockstep March With Stocks Raises
Thorny Questions About Its Usefulness

The cryptocurrency hasn’t worked as the “digital gold” it was touted to be. Should
institutional investors even bother with it? (Part of the crypto column series.)

(11min _ InstitutionalInvestor)

Yes. I once wrote: „The difference between being an economist and being a physicist is that most economists have never really seen a successful model.“
Kind of long winded but anyhow — https://t.co/uDHMDMJveB

— Emanuel Derman (@EmanuelDerman) March 24, 2022

Will AI Ever Understand Language Like Humans? – The Joy of Why

Large language models (LLMs) are becoming increasingly more impressive at creating human-like text and answering questions, but whether they can understand the meaning of the words they generate is a hotly debated issue. A big challenge is that LLMs are black boxes; they can make predictions and decisions on the order of words, but they cannot communicate the reasons for doing so. Ellie Pavlick at Brown University is building models that could help understand how LLMs process language compared with humans. In this episode of The Joy of Why, Pavlick discusses what we know and don’t know about LLM language processing, how their processes differ from humans, and how understanding LLMs better could also help us better appreciate our own capacity for knowledge and creativity.

1. Will AI Ever Understand Language Like Humans? 41:08
2. Can Quantum Gravity Be Created in the Lab? 42:33
3. What Is the True Promise of Quantum Computing? 38:47
4. How Did Multicellular Life Evolve? 46:07
5. New Conversations, Deep Questions, Bold Ideas in Season Four of 'The Joy of Why' 17:56

„Great minds discuss ideas; average minds discuss events; small minds discuss people.“ _Eleanor Roosevelt