Reading across disciplines is one of the best ways to improve our investment acumen. Here is a summary of some of the best articles I read this week. If you like this collection, consider forwarding it to someone who you think will appreciate it.
1. Ignore the forecasts. No one knows anything about the future.
People pay far too much attention to experts who predict/forecast/comment confidently simply because confidence convinces. The audience is looking for a buy in and nothing helps get that more than the confidence of the expert talking.
Also, in these days of social media, many a time we are simply looking for confirmation of something that we already believe in. If the expert ends up saying something along those lines, he tends to become our go to man. Our echo chambers are really small.
People making a living out of the stock market (or any other market for that matter) have an incentive in saying that the future will be better than the present is. Many analysts make a living by simply doing this on the business news TV channels, on a regular basis.
The media, looking for bold headlines to run, laps it up. And the investors who are more like sheep ready to be slaughtered, follow the sheep in front of them.
2. More people have SPACs than Covid!!
“People used to say that hedge funds are a compensation scheme masquerading as an asset class,” William A. Ackman, the famed hedge fund manager, told me. “You could say the same about SPACs.”
Mr. Ackman himself has a SPAC — in fact, the largest raised thus far. What makes his vehicle different, aside from its $4 billion size, is a unique arrangement that makes it tougher for him to score a big payday unless the subsequent merger is successful. “Every friend is launching a SPAC,” Mr. Ackman said. “It’s like everyone who had an internet company in 2000. It’s like, ‘Oh, yeah, I got one, too.’”
3. The fragrance of Kannauj
It is said that perfume flows through the drains of the ancient city of Kannauj in Uttar Pradesh. And this is no exaggeration.
In the perfume capital of India, which is home to about 300 small, medium and large distilleries, a pleasant fragrance constantly lingers in the air and the remnants of roses and other flowers are strewn across the city, including in the drains.
The centuries-old tradition of distilling perfumes or ‘attar’, also pronounced itr, in Kannauj dates back to the Mughal courts, and this legacy continues. The old-world perfumery still uses the traditional method of distilling perfumes that are based on essential oils and not alcohol.
Woodsy, floral, musky and androgynous, attars are markedly different from alcohol-based perfumes. They’re made from essential oils extracted from flowers and other ingredients, which are dissolved in water or oil rather than alcohol. And this makes them more fragrant and easily absorbent.
4. AI is the future of intelligence warfare
Since 9/11, America’s intelligence agencies have become hardwired to fight terrorism. Today’s threat landscape, however, is changing dramatically, with a resurgence of great power competition and the rise of cyber threats enabling states and non-state actors to spy, steal, disrupt, destroy, and deceive across vast distances — all without firing a shot. For the U.S. to avoid the risks and maximize the opportunities of this technological era, policymakers need to act swiftly to ensure the Intelligence Community adapts, integrating AI into all of its processes in ways that augment human capabilities and reflect American values.
Advances in technology like artificial intelligence tend to be a double-edged sword for intelligence collection and analysis: They generate opportunities for gain while also exposing the nation to new risks. Exponential improvements in pattern recognition and the optimization of digital weapons that AI makes possible mean that the United States as well as its competitors and adversaries are able to make better decisions, faster. With data and threats moving at the speed of networks, intelligence must rely on AI to help humans keep up.
5. Facebook helps spread misinformation
Stopping the spreading of false information has been the biggest challenge for Facebook, especially after the debacle of Cambridge Analytica. In late 2018 the company admitted that this activity had helped fuel a genocidal anti-Muslim campaign in Myanmar for several years. In 2020 Facebook started belatedly taking action against Holocaust deniers, anti-vaxxers, and the conspiracy movement QAnon. All these dangerous falsehoods were metastasizing thanks to the AI capabilities.
Over the last two years, the team has built out an original tool, called Fairness Flow. It allows engineers to measure the accuracy of machine-learning models for different user groups. They can compare a face-detection model’s accuracy across different ages, genders, and skin tones, or a speech-recognition algorithm’s accuracy across different languages, dialects, and accents.
Fairness Flow also comes with a set of guidelines to help engineers understand what it means to train a “fair” model. One of the thornier problems with making algorithms fair is that there are different definitions of fairness, which can be mutually incompatible. Fairness Flow lists four definitions that engineers can use according to which suits their purpose best, such as whether a speech-recognition model recognizes all accents with equal accuracy or with a minimum threshold of accuracy. But testing algorithms for fairness is still largely optional at Facebook. None of the teams that work directly on Facebook’s news feed, ad service, or other products are required to do it. Pay incentives are still tied to engagement and growth metrics.
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