Loss Aversion

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People hate losses. Roughly speaking, losing something makes you twice as miserable as gaining the same thing makes you happy. In more technical language, people are “loss averse.” How do we know this?

Consider a simple experiment. Half the students in a class are given coffee mugs with the insignia of their home university embossed on it. The students who did not get a mug are asked to examine their neighbor’s mugs. Then, mug owners are invited to sell their mugs and nonowners are invited to buy them. They do so by answering the question “At each of the following prices, indicate whether you would be willing to (give up your mug/buy a mug).”

The results show that those with mugs demand roughly twice as much to give up their mugs as others are willing to pay to get one. Thousands of mugs have been used in dozens of replications of this experiment, but the results are nearly always the same. Once I have a mug, I don’t want to give it up. But if I don’t have one, I don’t feel an urgent need to buy one.

What this means is that people do not assign specific values to objects. When they have to give something up, they are hurt more than they are pleased if they acquire the very same things.

Richard Thaler & Cass Sunstein, Nudge

What a computer science degree should look like now

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Experts suggest that computer science degree requirements should move away from coding and align with the expectations of a liberal arts degree—critical thinking and communication skills, along with computational thinking and AI literacy. The new CS coursework would include basic principles of computing and AI, along with hands-on experience in designing software using new AI tools. AI tools can help with the building of prototype programs, check for coding errors and serve as a digital tutor. 

Computational thinking involves breaking down problems into smaller tasks, developing step-by-step solutions and using data to reach evidence-based conclusions. AI  literacy is an understanding — at varying depths for students at different levels — of how AI works, how to use it responsibly and how it is affecting society. Nurturing informed skepticism should be a goal.

Read more at the NYT: How Do You Teach Computer Science in the AI Era?

The Tyranny of Clock Time

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Clock time is that linear time by which our life is measured in abstract units appearing on clocks, watches, computers, and calendars. These measuring units tell us the month, the day, the hour, and the second in which we find ourselves, and decide for us how much longer we have to speak, listen, eat, sing, study, pray, sleep, play, or stay. Our lives are dominated by our clocks and watches. In particular, the tyranny of the one-hour slot is enormous. There are visiting hours, therapeutic hours, and even happy hours. Without being fully aware of it, our most intimate emotions are often influenced by the clock. The big wall clocks in hospitals and airports have caused much inner turmoil and many tears. 

Clock time is outer time, time that has a hard merciless objectivity to it. Clock time leads us to wonder how much longer we have to live and whether “real life” has not already passed us by. Clock time makes us disappointed with today and seems to suggest that maybe tomorrow, next week, and next year it will really happen. Clock time keeps saying, Hurry, hurry, time goes fast, maybe you will miss the real thing! But there is still a chance.. Hurry to get married, find a job, visit a country, read a book, get a degree…Try to take it all in before you run out of time.”

Clock time always makes us depart. It breeds impatience and prevents any compassionate being together. 

Henri Nouwen, Donald McNeill, Douglas Morrison from the book Compassion

21 Articles about AI & Politics

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Tech Titans Amass Multimillion-Dollar War Chests to Fight AI Regulation – Wall Street Journal

Fears About A.I. Prompt Talks of Super PACs to Rein In the Industry – New York Times

Georgia Rep.’s campaign uses AI-generated deepfake of opponent in tight Senate showdown – CBS News  

Robots and AI Are Already Remaking the Chinese Economy - Wall Street Journal 

Trump’s attempt to block states from regulating AI sparked pushback from Republicans – Washington Post

AI Is Transforming Politics, Much Like Social Media Did - TIME 

How the U.S. Economy Became Hooked on AI Spending - Wall Street Journal

In the A.I. Race, Chinese Talent Still Drives American Research – New York Times

White House pulls back on AI laws executive order – Mashable

Four ways AI is being used to strengthen democracies worldwide – The Guardian

An Economist Asked, How Much Should We Spend to Avoid the A.I. Apocalypse? – New York Times

Chinese hackers used Anthropic's AI agent to automate spying – Axios

The Politics of AI Are About to Explode - Bloomberg 

The AI Cold War That Will Redefine Everything - Wall Street Journal

Exploring AI's role in democracy: Here are 5 essential insights – Fast Company

The UK’s fact-checkers are sending their AI to help Americans cover elections – Poynter

How Trump Is Using Fake Imagery to Attack Enemies and Rouse Supporters – New York Times

Political consultant defies court order in lawsuit over AI robocalls that mimicked Biden – AP

Is it ok for politicians to use AI? Survey shows where the public draws the line – The Conversation  

Saudi Arabia’s New Power Play Is Exporting A.I. to the World - The New York Times 

AI comes to local elections. Fake videos hit contentious school board races – Columbus Dispatch

LLMs & Retracted Research Papers

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Large language models should not be used to weed out retracted literature, a study of 21 chatbots concludes. Not only were the chatbots unreliable at correctly identifying retracted papers, they spit out different results when given the same prompts. On average, the 21 chatbots correctly identified fewer than half of the retracted papers. More at Retraction Watch

An experimental mock trial at the UNC School of Law raises questions about AI's role in criminal justice

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In a simulated trial, three AI systems acquitted a Black teenager of robbery charges. However, in the real case on which the mock trial was based, the judge quickly found the defendant guilty. The real conviction was appealed, but the North Carolina Court of Appeals upheld the verdict. More info

Video of the mock trial

An AI to Diagnose Your Sniffles

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Google has built an AI model that uses sound signals to "predict early signs of disease." It can identify subtle changes in your coughs, sniffles, breathing, and more. In places where there is difficulty accessing quality healthcare, this technology can step in as an alternative where users need nothing but their smartphone's microphone. For instance, it has been trained on 100 million cough sounds that help detect tuberculosis. More at Mashable

AI Definitions: Vector databases

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Vector databases – Raw data is converted into lists of numbers (word vectors) so that machine learning models can use them. The vectors are grouped together if they relate to one another. For instance, the word "king" would relate to a man, while "queen" would relate to a woman. A deep learning model (typically a transformer model) will use these vectors to "understand" the meaning of words and their relationships. More than 1,000 numbers can be used to represent a single word. If there are many numbers, then the word vector has a high dimension, making it nuanced. A low dimension for a word vector means the list of numbers is low. While not as nuanced, a low-dimensional vector is easier to work with. Vector data bases is what allows a language model to “recall” previous inputs, draw comparisons, identify relationships, and understand context.

More AI definitions

AI Definitions: Supervised Training

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Supervised training - In this type of AI training, the data is labeled by humans before it is given to the AI. The AI might be given a database of messages labeled either “spam” or “not spam.”  Supervised learning is the most common type of machine learning and is used in voice recognition, language translation, and self-driving cars. Anything that a person can do in a second can also be performed by AI through supervised training. This is why jobs consisting of a series of one-second tasks are at risk of being replaced by AI (such as a security guard). Most of the present economic value of AI comes from this type of training. However, supervised training is both expensive and time-consuming.

22 Articles about the Business of Running an AI Company

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Trump orders wide-ranging "Genesis Mission" to boost AI research – Axios

AI Investors Want More Making It and Less Faking It – Wall Street Journal

The Biggest AI Companies Met to Find a Better Path for Chatbot Companions – Wired

Major music studios strike licensing deals with AI firms - Semafor 

In the A.I. Race, Chinese Talent Still Drives American Research – New York Times 

How the U.S. Economy Became Hooked on AI Spending – Wall Street Journal 

Are we in an AI bubble? Eight charts will help you decide. – Washington Post  

The AI boom isn't going anywhere – Axios  

How Trillions in New AI Debt Will Test the Bond Market – Wall Street Journal

OpenAI tests ChatGPT in group chats - Axios 

Balance sheets, cash flows are showing the strain of AI investments and forcing investors to think about companies differently – Wall Street Journal

When AI Hype Meets AI Reality: A Reckoning in 6 Charts – Wall Street Journal

OpenAI fights order to turn over millions of ChatGPT conversations – Reuters

The risks of giving ChatGPT more personality - Axios

The AI Cold War That Will Redefine Everything – Wall Street Journal

Google, the sleeping AI giant, awakens - Axios

OpenAI Wants Brands to Allow Their Mascots to Appear in Gen AI Videos – Wall Street Journal

AI stocks waver as ‘Big Short’ investor bets against Palantir, Nvidia - Washington Post

Stability AI largely wins landmark UK intellectual property lawsuit brought by Getty Images – Associated Press

Who Will Pay for the AI Revolution? Retirees – Wall Street Journal

A.I. Is a Bubble. Maybe That’s OK. - New York Times

Hundreds of thousands of videos from news publishers like The New York Times and Vox were used to train AI models – Harvard’s Nieman Lab

And how are you mad?

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All of us are crazy in very particular ways. We’re distinctively neurotic, unbalanced and immature, but don’t know quite the details because no one ever encourages us too hard to find them out. An urgent, primary task of any lover is therefore to get a handle on the specific ways in which they are mad. They have to get up to speed on their individual neuroses. They have to grasp where these have come from, what they make them do – and most importantly, what sort of people either provoke or assuage them. A good partnership is not so much one between two healthy people (there aren’t many of these on the planet), it’s one between two demented people who have had the skill or luck to find a non-threatening conscious accommodation between their relative insanities.

The very idea that we might not be too difficult as people should set off alarm bells in any prospective partner. The question is just where the problems will lie: perhaps we have a latent tendency to get furious when someone disagrees with us, or we can only relax when we are working, or we’re a bit tricky around intimacy after sex, or we’ve never been so good at explaining what’s going on when we’re worried. It’s these sort of issues that – over decades – create catastrophes and that we therefore need to know about way ahead of time, in order to look out for people who are optimally designed to withstand them. A standard question on any early dinner date should be quite simply: ‘And how are you mad?’ 

Book of Life