PG

PG asked ChatGPT4 to create a detailed plot outline for The Fraud by Zadie Smith, a book that is a current NY Times bestseller.

The AI responded as follows:

As of my last update in April 2023, “The Fraud” by Zadie Smith is not a recognized title among her works. It’s possible that it’s a new release after my last training data, or it might be a lesser-known or misremembered title.

However, if you’re interested in a fictional plot outline inspired by Zadie Smith’s style and themes, I can certainly create one for you. Smith’s works often explore themes of identity, race, class, and the complexities of contemporary life, often set against the backdrop of multicultural London. Please let me know if you’d like me to proceed with this approach, or if there are specific elements you’d like included in this fictional plot outline!

PG took the AI’s advice and posted the following prompt:

Provide a fictional plot outline in Zadie Smith’s style and themes.

Here’s the response he received from ChatGPT4:

End of ChatGPT4

Since PG has not read any of Ms. Smith’s books, he’ll leave an assessment of the outline to any who have.

Prompt

Prepare a written argument that I wasn’t driving recklessly when I was pulled over by a highway patrol car on the 405 freeway in Long Beach at 3:00 AM for driving 8 miles over the speed limit when the freeway was almost completely empty and the highway patrol officer ignored another car that had just passed me traveling at least 20 miles per hour over the speed limit.

ChatGPT4

When preparing a written argument to contest a traffic citation for allegedly driving recklessly on the 405 freeway in Long Beach, it’s crucial to focus on specific legal and factual points. Here is an outline to structure your argument:

1. Introduction:
   • State the date, time, and location of the incident.
   • Briefly describe the circumstances leading to the traffic stop.
2. Clarification of Charges:
   • Clearly state the specific violation you were cited for (e.g., driving 8 miles over the speed limit).
3. Road Conditions and Context:
   • Emphasize that the freeway was nearly empty at the time of the incident.
   • Explain that the conditions allowed for a slightly higher speed without compromising safety.
4. Comparison with Other Vehicles:
   • Mention that another vehicle passed you traveling significantly faster (approximately 20 miles over the speed limit).
   • Argue that this indicates your speed was not excessively high compared to the flow of traffic.
5. Definition of Reckless Driving:
   • Cite the legal definition of reckless driving.
   • Argue that driving 8 miles over the speed limit, in this context, does not meet the criteria for reckless driving.
6. Safety Record and Driving Behavior:
   • If applicable, mention your clean driving record or lack of prior traffic violations.
   • Emphasize your commitment to safe driving and respect for traffic laws.
7. Request for Consideration:
   • Request leniency or dismissal based on the specific circumstances of the case.
   • If appropriate, express willingness to attend a defensive driving course as an alternative to penalties.
8. Conclusion:
   • Summarize your key points.
   • State that you believe the evidence supports a finding that you were not driving recklessly.

Remember, the goal is to provide a clear, factual, and concise argument that challenges the notion of reckless driving under the specific conditions of your case. It’s also important to remain respectful and professional in your tone throughout the document.

End of ChatGPT4

PG doesn’t see much genius in the solution that ChatGPT generated. It won’t put many lawyers out of business with this type of performance.

From The Economist:

The remarkable capabilities of generative artificial intelligence (ai) are clear the moment you try it. But remarkableness is also a problem for managers. Working out what to do with a new technology is harder when it can affect so many activities; when its adoption depends not just on the abilities of machines but also on pesky humans; and when it has some surprising flaws.

Study after study rams home the potential of large language models (llms), which power ais like Chatgpt, to improve all manner of things. llms can save time, by generating meeting summaries, analysing data or drafting press releases. They can sharpen up customer service. They cannot put up ikea bookshelves—but nor can humans.

ai can even boost innovation. Karan Girotra of Cornell University and his co-authors compared the idea-generating abilities of the latest version of Chatgpt with those of students at elite American universities. A lone human can come up with about five ideas in 15 minutes; arm the human with gpt-4 and the number goes up to 200. Crucially, the quality of these ideas is better, at least judged by purchase-intent surveys for new product ideas. Such possibilities can paralyse bosses; when you can do everything, it’s easy to do nothing.

llms’ ease of use also has pluses and minuses. On the plus side, more applications for generative ai can be found if more people are trying it. Familiarity with llms will make people better at using them. Reid Hoffman, a serial ai investor (and a guest on this week’s final episode of “Boss Class”, our management podcast), has a simple bit of advice: start playing with it. If you asked Chatgpt to write a haiku a year ago and have not touched it since, you have more to do.

Familiarity may also counter the human instinct to be wary of automation. A paper by Siliang Tong of Nanyang Technological University and his co-authors that was published in 2021, before generative ai was all the rage, captured this suspicion neatly. It showed that ai-generated feedback improved employee performance more than feedback from human managers. However, disclosing that the feedback came from a machine had the opposite effect: it undermined trust, stoked fears of job insecurity and hurt performance. Exposure to llms could soothe concerns.

Or not. Complicating things are flaws in the technology. The Cambridge Dictionary has named “hallucinate” as its word of the year, in tribute to the tendency of llms to spew out false information. The models are evolving rapidly and ought to get better on this score, at least. But some problems are baked in, according to a new paper by R. Thomas McCoy and his co-authors at Princeton University.

Because off-the-shelf models are trained on internet data to predict the next word in an answer on a probabilistic basis, they can be tripped up by surprising things. Get gpt-4, the llm behind Chatgpt, to multiply a number by 9/5 and add 32, and it does well; ask it to multiply the same number by 7/5 and add 31, and it does considerably less well. The difference is explained by the fact that the first calculation is how you convert Celsius to Fahrenheit, and therefore common on the internet; the second is rare and so does not feature much in the training data. Such pitfalls will exist in proprietary models, too.

On top of all this is a practical problem: it is hard for firms to keep track of employees’ use of ai. Confidential data might be uploaded and potentially leak out in a subsequent conversation. Earlier this year Samsung, an electronics giant, clamped down on usage of Chatgpt by employees after engineers reportedly shared source code with the chatbot.

Link to the rest at The Economist

PG says companies need to make their own rules for the use of technologies, but if they restrict what their employees can do, there’s a serious possibility that a competitor could take a giant step forward using AI for their business and label what they discovered as confidential.

Under the relatively standard confidentiality agreements used by tech firms and lots of other businesses small and large, the first mover could be confidentially doing something very beneficial for its competitive position vis a vis a competitor with a lock-down AI policy. Absent the combination of business expertise combined with a high degree of learned AI expertise, a competitor would be mystified about how the innovative company was providing its customers valuable new information services that would be difficult or impossible to deliver without the sophisticated AI piece of the solution.

PG had a similar experience with his law office when personal computers were new technology and not widely used by lawyers. PG jumped into PC’s and had a first generation machine on his desk as soon as it was available and earlier than any other lawyer he knew.

During this early period, each new hardware release meant the PG would work much faster and could handle more complex software than the previous generation. He bought a new machine a few weeks after it was released and passed his previous computer to his secretary so she could work faster and better.

An older lawyer friend from another state asked had heard PG talk about how happy he was with the productivity increases he could achieve with sophisticated computer hardware and software. His friend asked if he could spend time in PG’s office just to watch while PG and his very intelligent office staff produced their work. After a few days, PG’s friend said he had never seen so many pages of legal documents created in such a short period of time during his many years of practice.

Judging by the amount of outgoing mail from PG’s office each day, his friend would have guessed there were five or six experienced attorneys in the office, each with one or two legal secretaries providing support.

Being a first-mover with the sophisticated use of computers and the creation of document assembly systems to handle common tasks, PG made a lot of money during that stage of his legal career.

PG was entirely self-taught with personal computers but was willing to put a lot of effort and experimentation into learning how to leverage this technology to make things happen much faster and more accurately than previously possible. It was a steep learning curve for any lawyer like PG with no meaningful technical education, and most attorneys opted to spend all their time doing work for which clients would pay right away.

If PG were still in active practice, he would be spending a lot of time learning how to leverage AI to make his legal work faster and better than it had been before AI.

From The Wall Street Journal:

Over the past few years, the social movement known as effective altruism has divided employees and executives at artificial-intelligence companies across Silicon Valley, pitting believers against nonbelievers.

The blowup at OpenAI showed its influence—and the triumphant return of chief executive Sam Altman revealed hard limits, capping a bruising year for the divisive philosophy.

Coming just weeks after effective altruism’s most prominent backer, Sam Bankman-Fried, was convicted of fraud, the OpenAI meltdown delivered another blow to the movement, which believes that carefully crafted artificial-intelligence systems, imbued with the correct human values, will yield a Golden Age—and failure to do so could have apocalyptic consequences.

OpenAI, which released ChatGPT a year ago, was formed in part on the principles of effective altruism, a broad social and moral philosophy that influences the AI research community in Silicon Valley and beyond. Some followers live in private group homes, where they can brainstorm ideas, engage in philosophical debates and relax playing a four-person variant of chess known as Bughouse. The movement includes people devoted to animal rights and climate change, drawing ideas from rationalist philosophers, mathematicians and forecasters of the future.

Supercharged by hundreds of millions of dollars in tech-titan donations, effective altruists believe a headlong rush into artificial intelligence could destroy mankind. They favor safety over speed for AI development. The movement, which includes people who helped shape the generative-AI boom, is insular and multifaceted but shares a belief in doing good in the world—even if that means simply making a lot of money and giving it to worthy recipients.

Altman, who was fired by the board Friday, clashed with the company’s chief scientist and board member Ilya Sutskever over AI-safety issues that mirrored effective-altruism concerns, according to people familiar with the dispute.

Voting with Sutskever, who led the coup, were board members Tasha McCauley, a tech executive and board member for the effective-altruism charity Effective Ventures, and Helen Toner, an executive with Georgetown University’s Center for Security and Emerging Technology, which is backed by a philanthropy dedicated to effective-altruism causes. They made up three of the four votes needed to oust Altman, people familiar with the matter said. The board said he failed to be “consistently candid.”

The company announced Wednesday that Altman would return as chief executive and Sutskever, McCauley and Toner would be replaced. Emmett Shear, a tech executive favoring a slowdown in AI development and recruited as the interim CEO, was out.

Altman’s dismissal had triggered a company revolt that threatened OpenAI’s future. More than 700 of about 770 employees had called for Altman’s return and threatened to jump ship to Microsoft, OpenAI’s biggest investor. Sutskever said Monday he regretted his vote.

“OpenAI’s board members’ religion of ‘effective altruism’ and its misapplication could have set back the world’s path to the tremendous benefits of artificial intelligence,” venture capitalist and OpenAI investor Vinod Khosla wrote in an opinion piece for The Information.

Altman toured the world this spring warning that AI could cause serious harm. He also called effective altruism an “incredibly flawed movement” that showed “very weird emergent behavior.”

The effective-altruism community has spent vast sums promoting the idea that AI poses an existential risk. But it was the release of ChatGPT that drew broad attention to how quickly AI had advanced, said Scott Aaronson, a computer scientist at the University of Texas, Austin, who works on AI safety at OpenAI. The chatbot’s surprising capabilities worried people who had previously brushed off concerns, he said.

The movement has spread among the armies of tech-industry scientists, investors and executives racing to create AI systems to mimic and eventually surpass human ability. AI can bring global prosperity, but it first must be prevented from wreaking havoc, according to those in the movement.

. . . .

Google and other companies are trying to be the first to roll out AI systems that can match the human brain. They largely regard artificial intelligence as a tool to advance work and economies at great profit.

The movement’s high-profile supporters include Dustin Moskovitz, a co-founder of Facebook, and Jann Tallinn, the billionaire founder of Skype, who have pledged billions of dollars to effective-altruism research. Before his fall, Bankman-Fried had also pledged billions. Elon Musk has called the writings of effective altruism’s co-founder William MacAskill “a close match for my philosophy.”

Marc Andreessen, the co-founder of venture-capital firm Andreessen Horowitz, and Garry Tan, chief executive of the startup incubator Y Combinator, have criticized the movement. Tan called it an insubstantial “virtue signal philosophy” that should be abandoned to “solve real problems that create human abundance.”

Urgent fear among effective-altruists that AI will destroy humanity “clouds their ability to take in critique from outside the culture,” said Shazeda Ahmed, a researcher who led a Princeton University team that studied the movement. “That is never good for any community trying to solve any trenchant problem.”

The turmoil at OpenAI exposes the behind-the-scenes contest in Silicon Valley between people who put their faith in markets and effective altruists who believe ethics, reason, mathematics and finely tuned machines should guide the future.

. . . .

One fall day last year, thousands of paper clips in the shape of OpenAI’s logo arrived at the company’s San Francisco office. No one seemed to know where they were from, but everybody knew what they meant.

The paper clip has become a symbol of doom in the AI community. The idea is that an artificial-intelligence system told to build as many paper clips as possible might destroy all of humanity in its drive to maximize production.

The prank was done by an employee at crosstown rival, Anthropic, which itself sprang from divisions over AI safety.

Dario Amodei, OpenAI’s top research scientist, split from the company, joined by several company executives in early 2021. They started Anthropic, an AI research company friendly to effective altruists.

Bankman-Fried had been one of Anthropic’s largest investors and supported the company’s mission, which favored AI safety over growth and profits. 

. . . .

The fear of futuristic AI systems hasn’t stopped even those worried about safety from trying to build artificial general intelligence or AGI—advanced systems that match or outdo the human brain. 

At OpenAI’s holiday party last December, Sutskever addressed hundreds of employees and their guests at the California Academy of Science in San Francisco, not far from the museum’s dioramas of stuffed zebras, antelopes and lions.

“Our goal is to make a mankind-loving AGI,” said Sutskever, the company’s chief scientist.

“Feel the AGI,” he said. “Repeat after me. Feel the AGI.”

Effective altruists say they can build safer AI systems because they are willing to invest in what they call alignment: making sure employees can control the technology they create and ensure it comports with a set of human values. So far, no AI company has said what those values should be.

At Google, the merging this year of its two artificial intelligence units—DeepMind and Google Brain—triggered a split over how effective-altruism principles are applied, according to current and former employees.

DeepMind co-founder Demis Hassabis, who has long hired people aligned with the movement, is in charge of the combined units.

Google Brain employees say they have largely ignored effective altruism and instead explore practical uses of artificial intelligence and the potential misuse of AI tools, according to people familiar with the matter.

One former employee compared the merger with DeepMind to a forced marriage, “making many people squirm at Brain.”

. . . .

Arjun Panickssery, a 21-year-old AI safety researcher, lives with other effective altruists at Andromeda House, a five-bedroom, three-story home a few blocks from the University of California, Berkeley campus.

They host dinners, and visitors are sometimes asked to reveal their P(doom)—estimates of the chances of an AI catastrophe. 

Berkeley, Calif., is an epicenter of effective altruism in the Bay Area, Panickssery said. Some houses designate “no-AI” zones to give people an escape from constant talk about artificial intelligence. 

Open Philanthropy’s then-CEO Holden Karnofsky had once lived with two senior OpenAI executives, according to Open Philanthropy’s website. Since 2015, Open Philanthropy, a nonprofit that supports effective-altruism causes—has given away $327 million to AI-related causes, including $30 million to OpenAI, its website shows. 

When Karnofsky was engaged to Daniela Amodei, now Anthropic’s president, they were roommates with Amodei’s brother Dario, now Anthropic’s CEO.

In August 2017, Karnofsky and Daniela Amodei married in an effective-altruism-theme ceremony. Wedding guests were encouraged to donate to causes recommended by Karnofsky’s effective-altruism charity, GiveWell, and to read a 457-page tome by German philosopher Jürgen Habermas beforehand. 

“This is necessary context for understanding our wedding,” the couple wrote on a website for the event.

. . . .

The effective-altruism movement dates back roughly two decades, when a group of Oxford University philosophers and those they identified as “super-hardcore do-gooders,” were looking for a marketing term to promote their utilitarian version of philanthropy.

Adherents believe in maximizing the amount of good they do with their time. They can earn as much money as possible, then give much of it away to attack problems that government and traditional nonprofits are ignoring or haven’t solved. They focus on ideas that deliver the biggest impact or help the largest number of people per dollar spent. 

Bankman-Fried, who was convicted this month, said he was building his fortune only to give most of it away.

. . . .

The gatherings and events, held around the world, are often closed to outsiders. Organizers of a recent effective-altruism conference in New York declined the request of a Wall Street Journal reporter to attend, saying in an email that there was “a high bar for admissions.”

Link to the rest at The Wall Street Journal (Sorry if you encounter a paywall)

Intelligence is the ability to learn from your mistakes. Wisdom is the ability to learn from the mistakes of others.

Author Unknown

He’s an intelligent man, but it takes something more than intelligence to act intelligently.

Fyodor Dostoevsky, Crime and Punishment

Hubris is interesting, because you get people who are often very clever, very powerful, have achieved great things, and then something goes wrong – they just don’t know when to stop.

Margaret MacMillan

Hubris and science are incompatible.

Douglas Preston

Hubris calls for nemesis, and in one form or another it’s going to get it, not as a punishment from outside but as the completion of a pattern already started.

Mary Midgley

Prompt

Compare and contrast the novels of Ernest Hemingway with the novels of F. Scott Fitzgerald, including excerpts from the writings of each.

ChatGPT4

Ernest Hemingway and F. Scott Fitzgerald are two of the most acclaimed American authors of the 20th century, known for their distinct literary styles and thematic explorations. Their works reflect the complexities and changes in American society during the early to mid-20th century, but their approaches and thematic focuses have notable differences.

Ernest Hemingway

1. Writing Style: Hemingway is famous for his concise, straightforward, and unadorned prose, often referred to as the “Iceberg Theory” or “Theory of Omission”. He believed in saying as little as possible and leaving much to the reader’s interpretation.
2. Themes: Hemingway’s works often revolve around themes of stoicism, masculinity, and the human struggle against the natural world. His characters typically face physical or emotional trials that test their courage and moral integrity.
3. Notable Works:
   • “The Old Man and the Sea”
   • “A Farewell to Arms”
   • “For Whom the Bell Tolls”
4. Excerpt: From “The Old Man and the Sea” -“He was an old man who fished alone in a skiff in the Gulf Stream and he had gone eighty-four days now without taking a fish.”

F. Scott Fitzgerald

1. Writing Style: Fitzgerald’s style is often more elaborate and richly descriptive compared to Hemingway’s. He is known for his lyrical, flowing prose and his ability to capture the mood of the Jazz Age.
2. Themes: Fitzgerald’s novels commonly address themes of decadence, idealism, resistance to change, and the disillusionment of the American Dream. His works often depict the high life of the 1920s and the moral emptiness that can accompany wealth and status.
3. Notable Works:
   • “The Great Gatsby”
   • “Tender is the Night”
   • “This Side of Paradise”
4. Excerpt: From “The Great Gatsby” -“So we beat on, boats against the current, borne back ceaselessly into the past.”

Comparison

• Writing Style: Hemingway’s minimalist, understated style contrasts sharply with Fitzgerald’s ornate and poetic approach. While Hemingway focuses on directness and simplicity, Fitzgerald employs a more decorative language to create mood and tone.
• Themes: Both explore themes related to the human condition, but their focal points differ. Hemingway often delves into the raw aspects of human nature and survival, whereas Fitzgerald tends to focus on societal issues, particularly the moral implications of the American Dream.
• Contextual Influence: The era they lived in greatly influenced both writers. The aftermath of World War I, the Roaring Twenties, and the Great Depression are reflected distinctly in their works. Hemingway’s experiences as a war correspondent and ambulance driver in World War I influenced his themes of heroism and human endurance, while Fitzgerald’s portrayal of the Jazz Age reflects his fascination with wealth and celebrity.

Conclusion

Ernest Hemingway and F. Scott Fitzgerald offer two contrasting windows into American literature. Hemingway’s terse prose and focus on existential struggles stand in contrast to Fitzgerald’s lyrical exploration of social mores and disillusionment. Both, however, remain quintessential in their portrayal of their respective eras, capturing the spirit and challenges of early 20th-century America.

From Google The Key Word:

Technology has improved so many aspects of the way we shop, like how we research, find inspiration, try out products and look for deals. And now, as we approach the busiest shopping weeks of the year, technology — especially generative AI — is making it much easier to find gifts for others (and ourselves).

Read on for more about our new shopping experiences and opt into Search Labsat goo.gle/sge-gifts.

Get AI-generated gift ideas

Search Generative Experience (SGE), which brings generative AI capabilities into Search, is designed to help you understand topics in faster, easier ways and uncover new insights — which can be especially useful for gift-giving. And today we’re rolling out a new capability within SGE to help holiday shoppers more easily get inspired and find that perfect holiday gift.

Let’s say you’re looking for a present for the budding chef in your life and don’t know where to start. Search “great gifts for home cooks”, and you’ll now see a wide variety of subcategories to explore — like specialty tools, artisanal ingredients, culinary subscriptions and even experiences like cooking classes — with options from brands large and small. Just tap into a category to browse shoppable gift options and select a product to buy.

. . . .

Shop what’s (literally) on your mind

‘Tis the season for giving, but about half of people actually shop for themselves over the holidays.¹ Shoppers, especially apparel shoppers, also often have a very specific vision in mind: In fact, 20% of apparel queries are five words or longer.² So we’re introducing a new way to put the item that’s on your mind right into your shopping cart with AI-powered image generation for shopping.

We’re connecting our image generation technology with more than 35 billion listings in our Shopping Graph to help you better visualize and find the style you’re looking for. Soon when you use SGE to look for apparel, you’ll have the option to generate photorealistic images inspired by the words you search — and shop products that match those images.

Let’s say you want a new winter coat — a colorful, patterned puffer jacket — but you haven’t found quite what you’re looking for. With generative AI in Search, just look for that style and tap “Generate images” to see photorealistic images that match your vision. And if you want to make an adjustment, like if you’d prefer a colorful metallic jacket instead of a patterned one, just enter that refinement in the text box to see a new set of images. Once you’ve found one you like, scroll down to see similar shoppable products.

Link to the rest at Google The Key Word

PG wonders what’s going to happen when this capability is integrated with online dating apps.