"Time passes whether I stand still or move."
Anne Barngrover
Stop Consuming Empty Investment Calories - A Teachable Moment (tonyisola.com)
AI Summary: This article discusses how empty calories are both bad for people's physical health and financial well-being. It explains how nutrition-deficient food, like chips and donuts, are loaded with fat, salt, and sugar which can sabotage taste buds and lead to overconsumption. It also talks about how similar nutrition-deficient financial habits, such as making minimum credit card payments and neglecting budgeting, can be a recipe for chronic financial misery. The article encourages people to eliminate junk food and bad financial habits from their lives and emphasizes that empty portfolios provide zero retirement nutrition.
o When consuming food containing little or no nutritional value, the body compensates by eating more. There’s a reason it’s easy to wolf down copious amounts of chips or donuts. Your body fails to receive the nutrition it needs and overcompensates by eating more and more food. The problem is compounded by food engineers who load these products with fat, salt, and sugar to sabotage your taste buds. These injections turbocharge eating. Combining nutrition-deficient food with sugar, salt, and fat is empty-calorie heaven.
o Empty calories can be described as irresistible taste temptations bereft of nutritional substance. They are calorie imposters while satisfying the plate but contribute little or no essential vitamins, minerals, or essential macronutrients to the body. Their monetary pleasure isn’t worth the long-term pain of chronic disease.
o The following nutrition-deficient financial habits are a recipe for chronic financial misery.
· Paying Only Minimums: Making minimum credit card payments leads to accumulating high-interest debt over time.
· Neglecting Savings: Failing to prioritize saving money for emergencies or future goals.
· Ignoring Budgeting: Operating without a budget is similar to indulging in financial junk food without mindful portion control.
· Gambling or Risky Investments: Engaging in speculative investments without proper research or risk management.
· Frequent Late Fees: Paying bills past due dates leads to unnecessary penalties and wasteful expenditures.
· Ignorance of Financial Literacy: lack of knowledge and understanding of personal finance, preventing wise financial decision-making.
· Keeping up with the Lonses: Spending to impress others or maintain a particular lifestyle even if it strains your finances.
· Living on Borrowed Money: Relying heavily on loans and credit leads to a debt cycle and financial instability.
· Overusing Subscriptions: Accumulating numerous subscription services without fully utilizing them, wasting money on underused resources.
· Impulsive Spending: Splurging on unnecessary items without considering their long-term value or impact on financial goals.
o Empty calories generate momentary satisfaction but lack nutritional value. Immediate financial gratification through mindless spending, borrowing, or speculation has the same detrimental impact on your long-term economic well-being. Conflicted financial salespeople serve the same masters as empty-calorie food engineers. Their profits are your losses. Eliminating junk food and bad financial habits from your life is addition by subtraction.
Your health account, your bank account, they’re the same thing. The more you put in, the more you can take out. Exercise is king, and nutrition is queen. Together, you have a kingdom.”
Jack LaLanne
When Inexperience is an Asset - MicroCapClub
In 2015, Stanley Druckenmiller, gave a speech at the Lost Tree Club. I love this excerpt:
“Ken Langone knows my first mentor very well. He’s not a well-known guy, but he was absolutely brilliant, and I would say a bit of a maverick. He was at Pittsburgh National Bank. I started there when I was 23 years old. I was in a research department. There were eight of us. I was the only one without an MBA, and I was the only one under 32 years of age. I was 23 years old.
After about a year and a half – I was a banking and chemical analyst – this guy calls me into his office and announces he’s going to make me the director of research, and these other eight guys and my 52-year old boss are going to report to me. So, I started to think I’m pretty good stuff here. But he instantly said, “Now, do you know why I’m doing this?” I said no. He says, “Because for the same reason they send 18-year-olds to war. You’re too dumb, too young, and too inexperienced not to know to charge. We around here have been in a bear market since 1968.” This was 1978. “I think a big secular bull market’s coming. We’ve all got scars. We’re not going to be able to pull the trigger. So, I need a young, inexperienced guy. But I think you’ve got the magic to go in there and lead the charge.” So, I told you he was a maverick, and as you can already see, he’s a little bit eccentric. After he put me in there, he was gone in three months. I’ll get to that in a minute.
But before he left, he taught me two things. A. never, ever invest in the present. It doesn’t matter what a company’s earning, what they have earned. He taught me that you have to visualize the situation 18 months from now, and whatever that is, that’s where the price will be, not where it is today. And too many people tend to look at the present, oh this is a great company, they’ve done this or this central bank is doing all the right things. But you have to look to the future. If you invest in the present, you’re going to get run over.
The other thing he taught me is earnings don’t move the overall market; it’s the Federal Reserve Board. And whatever I do, focus on the central banks and focus on the movement of liquidity, that most people in the market are looking for earnings and conventional measures. It’s liquidity that moves markets.
Now, i told you he left three months later, and here’s where the dumb luck came in in terms of my investment philosophy. So, right after he leaves, the Shah of Iran goes under. So, oil looks like it’s going to go up 300%. I’m 25, and I don’t have any experience. I don’t know anything about portfolio managers. So, I think well, this is easy. Let’s put 70% of our money in oil stocks and let’s put 30% of our money in defense stocks and let’s sell all our bonds. The portfolio managers that were competing with me for the top job, they, of course, thought it was crazy. I would have thought it was crazy too if I’d have had any experience, but the list I proposed went up 100%. The S&P was flat. And then at 26 years old they made me chief investment officer of the whole place. So, the reason I say there was a lot of luck involved is because it was my youth and it was my inexperience, and I was ready to charge.”
o Inexperience sometimes lets us react quicker because we don’t feel the pressure of the moment.
o Inexperience frees us up to ask “obvious” or even “dumb” questions everyone else is too afraid to ask.
o Inexperience lets us attack problems with enthusiasm because we don’t have battle scars from past failures.
o Inexperience allows us to “charge” and figure it out as we go. It’s why most successful entrepreneurs looking back say, “If I knew back then how hard this was going to be, I wouldn’t have started.”
Whether you are building a business, or investment firm, or trying to solve a problem, I think the key to success is combining experience and inexperience. If you are experienced, it is crucial to have a few talented inexperienced people around you. If you are inexperienced, it is crucial to have a few experienced people around you that accomplished similar things to what you are trying to do. The more you experience the more you respect what you are up against. Inexperience almost always underestimates risk. They are the perfect combination.
o How generous are people when making consequential financial decisions in the real world? We took advantage of a rare opportunity to examine generosity among a diverse sample of adults who received a gift of U.S. $10,000 from a pair of wealthy donors, with nearly no strings attached. Two-hundred participants were drawn from three low-income countries (Indonesia, Brazil, and Kenya) and four high-income countries (Australia, Canada, the United Kingdom, and the United States) as part of a preregistered study. On average, participants spent over $6,400 on purchases that benefited others, including nearly $1,700 on donations to charity, suggesting that humans exhibit remarkable generosity even when the stakes are high. To address whether generosity was driven by reputational concerns, we asked half the participants to share their spending decisions publicly on Twitter, whereas the other half were asked to keep their spending private. Generous spending was similar between the groups, in contrast to our preregistered hypothesis that enhancing reputational concerns would increase generosity.
o Even with this conservative adjustment, participants would still have spent more than half ($5,713) of their windfall on others. Importantly, however, most participants in the current study did not know that the “Mystery Experiment” had anything to do with generosity. When participants were asked what they thought the experiment was about, only 15% (n = 29) correctly guessed that the study was about generosity. When we excluded these participants, the amount spent on others was largely unchanged (M = $6,260), casting further doubt on the idea that participants were strongly influenced by experimenter demands.
o The high- and low-income countries also differed on a number of dimensions, including language, geography, politics, and culture. Despite these differences, participants spent similar amounts of money on others, although people from high-income countries spent more on charitable donations.
Taken together, our findings build on previous lab-based research and suggest that even in consequential real-world situations, humans are not narrowly self-interested but substantially generous.
Trading For a Living - by Jared Dillian (substack.com)
AI Summary: This article discusses the realities of trading for a living. It explains how trading requires a lot of stress, patience, intelligence, experience, and emotional fitness. The author also explains the importance of having the right balance between work and play and rest, as well as the importance of having the right information and not being influenced by other people's opinions. The article also explains the dangers of having too much information and how it can cloud a trader's judgment. The author also points out that trading for a living is not easy and that it is a lonely activity, but it can be very satisfying when done correctly.
o A lot of people think that the markets oscillate between fear and greed—not really. Greed is just another form of fear, the fear of missing out, the fear that you’re not going to make as much money as someone else.
o There are a lot of hedge funds who make some money. Some of them are big institutions who have a lot of technology and infrastructure and information. They have an enormous advantage over Joe Shlabotnik day trader. Even just being in a room, surrounded by people who are looking at the markets, and sharing ideas, is a huge advantage.
o Managing money is a business, and some people aren’t so good at the business aspect of it. But they are gifted traders. Two different skill sets. When you have people with both skill sets, those are the people who go on to be Wall Street legends.
o One thing that a lot of novice traders lack is patience. Trading takes an inordinate amount of patience. People spend 1% of their time actually trading, 9% of their time doing research, and 90% of their time waiting. Waiting for something to happen.
o Too much information will cloud your judgment, and inject doubt in your process. It’s hard to have conviction on anything when you’re being bombarded with other people’s opinions all the time.
o Ideas are only 10% of the trade—the other 90% is execution and risk management. You can make money on a bad idea with good execution and risk management. You can royally screw up a good idea with bad execution and risk management.
o Trading is also very lonely.
o Every day, you’re going to battle with your own mind, which is not a lot of fun. It requires you to be deeply introspective. You have to think, but you also have to step back and examine your own thoughts, and then you have to step back again and examine your thoughts about your thoughts.
Three things that make a good trader: intelligence, experience, and emotional fitness. Smart traders tend to make better traders. Experience counts for a lot. But emotional fitness is paramount—happy, confident people make good traders. Neurotic, troubled people don’t. Boring people with stable marriages and happy kids tend to outperform.
The Search for a New Test of Artificial Intelligence - Scientific American
In the mind of the public, Alan Turing's “imitation game,” in which a machine tries to convince an interrogator that it is human, has long been considered the ultimate test of artificial intelligence.
But Turing's test has not aged well. Passing it is more a matter of deception than of true intelligence. AI experts argue that the time has come to replace Turing's test with a battery of events that will assess machine intelligence from many different perspectives.
A truly intelligent machine should be able to understand ambiguous statements, build a piece of flat-packed furniture, pass a fourth-grade science test, and more. The difficulty of these tasks underscores the fact that, hype aside, human-level artificial intelligence remains very far in the future.
The Winograd Schema Challenge, named for AI pioneer Terry Winograd (mentor to Google's Larry Page and Sergey Brin), would subject machines to a test in which language comprehension and common sense intersect. Anyone who has ever tried to program a machine to understand language has quickly realized that virtually every sentence is ambiguous, often in multiple ways. Our brain is so good at comprehending language that we do not usually notice. Take the sentence “The large ball crashed right through the table because it was made of Styrofoam.” Strictly speaking, the sentence is ambiguous: the word “it” could refer to the table or the ball. Any human listener will realize that “it” must refer to the table.
A Comprehension Challenge in which machines are tested on their ability to understand images, videos, audio and text would be a natural component.
Charles Ortiz, Jr., director of the Laboratory for Artificial Intelligence and Natural Language Processing at Nuance, proposed a Construction Challenge that would test perception and physical action—two important elements of intelligent behavior that were entirely absent from the original Turing test.
And Peter Clark of the Allen Institute for Artificial Intelligence proposed giving machines the same standardized tests of science and other disciplines that schoolchildren take.
TEST 01: Winograd Schema Challenge: Winograd’s first schema, which he wrote in 1971, sets a scene (“The city councilmen refused the demonstrators a permit because they feared violence”) and then poses a simple question about it (“Who feared violence?”). This is known as a pronoun disambiguation problem (PDP): in this case, there is ambiguity about whom the word “they” refers to. But Winograd schemas are subtler than most PDPs because the meaning of the sentence can be reversed by changing a single word. (For example: “The city councilmen refused the demonstrators a permit because they advocated violence.”) Most people use “common sense” or “world knowledge” about typical relationships between city councilmen and demonstrators to resolve the problem. This challenge uses an initial round of PDPs to weed out less intelligent systems; ones that make the cut are given true Winograd schemas.
PROS: Because Winograd schemas rely on knowledge that computers lack reliable access to, the challenge is robustly Google-proof—that is, hard to game with Internet searches.
CONS: The pool of usable schemas is relatively small. “They’re not easy to come up with,” says Ernest Davis, a professor of computer science at New York University.
DIFFICULTY LEVEL: High. In 2016 four systems competed to answer a set of 60 Winograd schemas. The winner got only 58 percent of the questions correct—far short of the 90 percent threshold that researchers consider a passing grade.
TEST 02: Standardized Testing for Machines: AI would be given the same standardized, written educational tests that we give to elementary and middle school students, without any handholding. The method would assess a machine’s ability to link facts together in novel ways through semantic understanding. Much like Turing’s original imitation game, the scheme is ingeniously direct. Simply take any sufficiently rigorous standardized test (such as the multiple-choice parts of New York State’s fourth-grade Regents science exams), equip the machine with a way of ingesting the test material (such as naturallanguage processing and computer vision) and let ’er rip.
PROS: Versatile and pragmatic. Unlike Winograd schemas, standardized test material is cheap and abundant. And because none of the material is adapted or preprocessed for the machine’s benefit, test questions require a wealth of versatile, commonsense world knowledge just to parse, much less answer correctly.
CONS: Not as Google-proof as Winograd schemas, and as with humans, the ability to pass a standardized test does not necessarily imply “real” intelligence.
DIFFICULTY LEVEL: Moderately high. A system called Aristo, designed by the Allen Institute for Artificial Intelligence, achieves an average 75 percent score on the fourth-grade science exams that it has not encountered before. But this is only on multiple-choice questions without diagrams. “No system to date comes even close to passing a full 4th grade science exam,” the Allen Institute researchers wrote in a technical paper published in AI Magazine.
TEST 03: Physically Embodied Turing Test: Most tests for machine intelligence focus on cognition. This test is more like shop class: an AI has to physically manipulate real-world objects in meaningful ways. The test would comprise two tracks. In the construction track, a physically embodied AI—a robot, essentially—would try to build a structure from a pile of parts using verbal, written and illustrated instructions (imagine assembling IKEA furniture). The exploration track would require the robot to devise solutions to a set of openended but increasingly creative challenges using toy blocks (such as “build a wall,” “build a house,” “attach a garage to the house”). Each track would culminate with a communication challenge in which the robot would be required to “explain” its efforts. The test could be given to individual robots, groups of robots or robots collaborating with humans.
PROS: The test integrates aspects of real-world intelligence—specifically, perception and action—that have been historically ignored or underresearched. Plus, the test is essentially impossible to game: “I don’t know how you would, unless someone figured out a way to put instructions for how to build anything that’s ever been built on the Internet,” says Ortiz of Nuance.
CONS: Cumbersome, tedious and difficult to automate without having machines do their construction in virtual reality. Even then, “a roboticist would say that [virtual reality] is still only an approximation,” Ortiz says. “In the real world, when you pick up an object, it might slip, or there might be a breeze to deal with. It’s hard for a virtual world to faithfully simulate all those nuances.”
DIFFICULTY LEVEL: Science-fictional. An embodied AI that can competently manipulate objects and coherently explain its actions would essentially behave like a droid from Star Wars—well beyond the current state of the art. “To execute these tasks at the level at which children can do them routinely is an enormous challenge,” Ortiz says.
TEST 04: I-Athlon: In a battery of partially or completely automated tests, an AI is asked to summarize the contents of an audio file, narrate the storyline of a video, translate natural language on the fly and perform other tasks. The goal is to create an objective intelligence score. Automation of testing and scoring—without human supervision—is the hallmark of this scheme. Removing humans from the process of evaluating machine intelligence may seem ironic, but Murray Campbell, an AI researcher at IBM (and a member of the team that developed Deep Blue), says it is necessary to ensure efficiency and reproducibility. Establishing an algorithmically generated intelligence score for AIs would also free researchers from relying on human intelligence—“with all its cognitive biases,” Campbell notes—as a yardstick.
PROS: Objectivity, at least in theory. Once I-Athlon judges decided on how to score each test and weight the results, computers would do the actual scoring and weighting. Judging the results should be as cut-and-dried as reviewing an Olympic photo finish. The variety of tests would also help identify what the IBM researchers call “ broadly intelligent systems.”
CONS: Inscrutability, potentially. I-Athlon algorithms might give high marks to AI systems that operate in ways that researchers do not fully understand. “It is quite possible that some decisions of advanced AI systems will be very difficult to explain [to humans] in a concise and understandable way,” Campbell admits. This socalled black box problem is already becoming an issue for researchers working with convolutional neural networks.
DIFFICULTY LEVEL: It depends. Current systems could perform quite well on some potential I-Athlon events, such as image understanding or language translation. Others, such as explaining the contents of a video narrative or drawing a diagram from a verbal description, are still in the realm of sci-fi.