AI

Tech Stocks Sag as Analysists Question How Much Money Firms Will Actually Make from AI

Scott Buchanan4 Comments

Tech stocks have been unstoppable for the past fifteen or so years. Here is a chart from Seeking Alpha for total return of the tech-heavy QQQ fund (orange line) over the past five years, compared to a value-oriented stock fund (VTV), a fund focused on dividend-paying stocks (SDY) and the Russel 2000 small cap fund IWM.

QQQ has left the others in the dust. There has been a reversal, however, in the past month. The tech stocks have sagged nearly 10% since July 11, while the left-for-dead small caps (IWM, green line) rose by 10%:

Some of this is just mean reversion, but there seems to be a deeper narrative shift going on. For the past 18 months, practically anything that could remotely be connected with AI, especially the Large Language Models (LLM) exemplified by ChatGPT, has been valued as though it would necessarily make every-growing gobs of money, for years to come.

In recent weeks, however, Wall Street analysts have started to question whether all that AI spending will pay off as expected. Here are some headlines and excerpts (some of the linked articles are behind paywalls):

““There are growing concerns that the return on investment from heavy AI spending is further out or not as lucrative as believed, and that is rippling through the whole semiconductor chain and all AI-related stocks,” said James Abate, chief investment officer at Centre Asset Management.”

www.bloomberg.com/…

““The overarching concern is, where is the ROI on all the AI infrastructure spending?” said Alec Young, chief investment strategist at Mapsignals. “There’s a pretty insane amount of money being spent.
Jim Covello, the head of equity research at Goldman Sachs Group Inc., is among a growing number of market professionals who are arguing that the commercial hopes for AI are overblown and questioning the vast expense required to build out infrastructure required for the computing to run and train large-language models.”

www.bloomberg.com/…

“It really feels like we are moving from a ‘tell me’ story on AI to a ‘show me’ story,” said Ohsung Kwon, equity and quantitative strategist at Bank of America Corp. “We are basically at a point where we’re not seeing much evidence of AI monetization yet.”

https://finance.yahoo.com/news/earnings-derail-stock-rally-over-130001940.html

Goldman’s Top Stock Analyst Is Waiting for AI Bubble to Burst

Covello casts doubt on hype behind an $16 trillion rally

He says costs, limited uses means it won’t revolutionize world

https://finance.yahoo.com/news/goldman-top-stock-analyst-waiting-111500948.html

Google stock got dinged last week for excessive capital spending, even though earnings were strong. Microsoft reports its Q4 earnings after the market closes today (Tuesday); we will see how investors parse these results.

The Open Internet Is Dead; Long Live The Open Internet

James Bailey1 Comment

Information on the internet was born free, but now lives everywhere in walled gardens. Blogging sometimes feels like a throwback to an earlier era. So many newer platforms have eclipsed blogs in popularity, almost all of which are harder to search and discover. Facebook was walled off from the beginning, Twitter is becoming more so. Podcasts and video tend to be open in theory, but hard to search as most lack transcripts. Longer-form writing is increasingly hidden behind paywalls on news sites and Substack. People have complained for years that Google search is getting worse; there are many reasons for this, like a complacent company culture and the cat-and-mouse game with SEO companies, but one is this rising tide of content that is harder to search and link.

To me part of the value of blogging is precisely that it remains open in an increasingly closed world. Its influence relative to the rest of the internet has waned since its heydey in ~2009, but most of this is due to how the rest of the internet has grown explosively at the expense of the real world; in absolute terms the influence of blogging remains high, and perhaps rising.

The closing internet of late 2023 will not last forever. Like so much else, AI is transforming it, for better and worse. AI is making it cheap and easy to produce transcripts of podcasts and videos, making them more searchable. Because AI needs large amounts of text to train models, text becomes more valuable. Open blogs become more influential because they become part of the training data for AI; because of what we have written here, AI will think and sound a little bit more like us. I think this is great, but others have the opposite reaction. The New York Times is suing to exclude their data from training AIs, and to delete any models trained with it. Twitter is becoming more closed partly in an attempt to limit scraping by AIs.

So AI leads to human material being easier for search engines to index, and some harder; it also means there will be a flood of AI-produced material, mostly low-quality, clogging up search results. The perpetual challenge of search engines putting relevant, high-quality results first will become much harder, a challenge which AI will of course be set to solve. Search engines already have surprisingly big problems with not indexing writing at all; searching for a post on my old blog with exact quotes and not finding it made me realize Google was missing some posts there, and Bing and DuckDuckGo were missing all of them. While we’re waiting for AI to solve and/or worsen this problem, Gwern has a great page of tips on searching for hard-to-find documents and information, both the kind that is buried deep down in Google and the kind that is not there at all.

Update on Game Theory Teaching

Zachary Bartsch2 Comments

I wrote at the end of the summer about some changes that I would make to my Game Theory course. You can go back and read the post. Here, I’m going to evaluate the effectiveness of the changes.

First, some history.

I’ve taught GT a total of 5 time. Below are my average student course evaluations for “I would recommend this class to others” and “I would consider this instructor excellent”. Although the general trend has been improvement, improving ratings and the course along the way, some more context would be helpful. In 2019, my expectations for math were too high. Shame on me. It was also my first time teaching GT, so I had a shaky start. In 2020, I smoothed out a lot of the wrinkles, but I hadn’t yet made it a great class. 

In 2021, I had a stellar crop of students. There was not a single student who failed to learn. The class dynamic was perfect and I administered the course even more smoothly. They were comfortable with one another, and we applied the ideas openly. In 2022, things went south. There were too many students enrolled in the section, too many students who weren’t prepared for the course, and too many students who skated by without learning the content. Finally, in 2023, the year of my changes, I had a small class with a nice symmetrical set of student abilities.  

Historically, I would often advertise this class, but after the disappointing 2022 performance, and given that I knew that I would be making changes, I didn’t advertise for the 2023 section. That part worked out perfectly. Clearly, there is a lot of random stuff that happens that I can’t control. But, my job is to get students to learn, help the capable students to excel, and to not make students *too* miserable in the process – no matter who is sitting in front of me.

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Generative AI Nano-Tutorial

Scott Buchanan4 Comments

Everyone who has not been living under a rock this year has heard the buzz around ChatGPT and generative AI. However, not everyone may have clear definitions in mind, or understanding of how this stuff works.

Artificial intelligence (AI) has been around in one form or another for decades. Computers have long been used to analyze information and come up with actionable answers. Classically, computer output has been in the form of numbers or graphical representation of numbers. Or perhaps in the form of chess moves, beating all human opponents since about 2000.

Generative AI is able to “generate” a variety of novel content, such as images, video, music, speech, text, software code and product designs, with quality which is difficult to distinguish from human-produced content. This mimicry of human content creation is enabled by having the AI programs analyze reams and reams of existing content (“training data”), using enormous computing power.

I wanted to excerpt here a fine article I just saw which is informative on this subject. Among other things, it lists some examples of gen-AI products, and describes the “transformer” model that underpins many of these products. I skipped the section of the article that discusses the potential dangers of gen-AI (e.g., problems with false “hallucinations”), since that topic has been treated already in this blog.

Between this article and the Wikipedia article on Generative artificial intelligence , you should be able to hold your own, or at least ask intelligent questions, when the subject next comes up in your professional life (which it likely will, sooner or later).

One technical point for data nerds is the distinction between “generative” and “discriminative” approaches in modeling. This is not treated in the article below, but see here.

All text below the line of asterisks is from Generative AI Defined: How it Works, Benefits and Dangers, by Owen Hughes, Aug 7, 2023.

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What is generative AI in simple terms?

Generative AI is a type of artificial intelligence technology that broadly describes machine learning systems capable of generating text, images, code or other types of content, often in response to a prompt entered by a user.

Generative AI models are increasingly being incorporated into online tools and chatbots that allow users to type questions or instructions into an input field, upon which the AI model will generate a human-like response.

How does generative AI work?

Generative AI models use a complex computing process known as deep learning to analyze common patterns and arrangements in large sets of data and then use this information to create new, convincing outputs. The models do this by incorporating machine learning techniques known as neural networks, which are loosely inspired by the way the human brain processes and interprets information and then learns from it over time.

To give an example, by feeding a generative AI model vast amounts of fiction writing, over time the model would be capable of identifying and reproducing the elements of a story, such as plot structure, characters, themes, narrative devices and so on.

……

Examples of generative AI

…There are a variety of generative AI tools out there, though text and image generation models are arguably the most well-known. Generative AI models typically rely on a user feeding it a prompt that guides it towards producing a desired output, be it text, an image, a video or a piece of music, though this isn’t always the case.

Examples of generative AI models include:

  • ChatGPT: An AI language model developed by OpenAI that can answer questions and generate human-like responses from text prompts.
  • DALL-E 2: Another AI model by OpenAI that can create images and artwork from text prompts.
  • Google Bard: Google’s generative AI chatbot and rival to ChatGPT. It’s trained on the PaLM large language model and can answer questions and generate text from prompts.
  • Midjourney: Developed by San Francisco-based research lab Midjourney Inc., this gen AI model interprets text prompts to produce images and artwork, similar to DALL-E 2.
  • GitHub Copilot: An AI-powered coding tool that suggests code completions within the Visual Studio, Neovim and JetBrains development environments.
  • Llama 2: Meta’s open-source large language model can be used to create conversational AI models for chatbots and virtual assistants, similar to GPT-4.
  • xAI: After funding OpenAI, Elon Musk left the project in July 2023 and announced this new generative AI venture. Little is currently known about it.

Types of generative AI models

There are various types of generative AI models, each designed for specific challenges and tasks. These can broadly be categorized into the following types.

Transformer-based models

Transformer-based models are trained on large sets of data to understand the relationships between sequential information, such as words and sentences. Underpinned by deep learning, these AI models tend to be adept at NLP [natural language processing] and understanding the structure and context of language, making them well suited for text-generation tasks. ChatGPT-3 and Google Bard are examples of transformer-based generative AI models.

Generative adversarial networks

GANs are made up of two neural networks known as a generator and a discriminator, which essentially work against each other to create authentic-looking data. As the name implies, the generator’s role is to generate convincing output such as an image based on a prompt, while the discriminator works to evaluate the authenticity of said image. Over time, each component gets better at their respective roles, resulting in more convincing outputs. Both DALL-E and Midjourney are examples of GAN-based generative AI models…

Multimodal models

Multimodal models can understand and process multiple types of data simultaneously, such as text, images and audio, allowing them to create more sophisticated outputs. An example might be an AI model capable of generating an image based on a text prompt, as well as a text description of an image prompt. DALL-E 2 and OpenAI’s GPT-4 are examples of multimodal models.

What is ChatGPT?

ChatGPT is an AI chatbot developed by OpenAI. It’s a large language model that uses transformer architecture — specifically, the “generative pretrained transformer”, hence GPT — to understand and generate human-like text.

What is Google Bard?

Google Bard is another example of an LLM based on transformer architecture. Similar to ChatGPT, Bard is a generative AI chatbot that generates responses to user prompts.

Google launched Bard in the U.S. in March 2023 in response to OpenAI’s ChatGPT and Microsoft’s Copilot AI tool. In July 2023, Google Bard was launched in Europe and Brazil.

…….

Benefits of generative AI

For businesses, efficiency is arguably the most compelling benefit of generative AI because it can enable enterprises to automate specific tasks and focus their time, energy and resources on more important strategic objectives. This can result in lower labor costs, greater operational efficiency and new insights into how well certain business processes are — or are not — performing.

For professionals and content creators, generative AI tools can help with idea creation, content planning and scheduling, search engine optimization, marketing, audience engagement, research and editing and potentially more. Again, the key proposed advantage is efficiency because generative AI tools can help users reduce the time they spend on certain tasks so they can invest their energy elsewhere. That said, manual oversight and scrutiny of generative AI models remains highly important.

Use cases of generative AI

Generative AI has found a foothold in a number of industry sectors and is rapidly expanding throughout commercial and consumer markets. McKinsey estimates that, by 2030, activities that currently account for around 30% of U.S. work hours could be automated, prompted by the acceleration of generative AI.

In customer support, AI-driven chatbots and virtual assistants help businesses reduce response times and quickly deal with common customer queries, reducing the burden on staff. In software development, generative AI tools help developers code more cleanly and efficiently by reviewing code, highlighting bugs and suggesting potential fixes before they become bigger issues. Meanwhile, writers can use generative AI tools to plan, draft and review essays, articles and other written work — though often with mixed results.

The use of generative AI varies from industry to industry and is more established in some than in others. Current and proposed use cases include the following:

  • Healthcare: Generative AI is being explored as a tool for accelerating drug discovery, while tools such as AWS HealthScribe allow clinicians to transcribe patient consultations and upload important information into their electronic health record.
  • Digital marketing: Advertisers, salespeople and commerce teams can use generative AI to craft personalized campaigns and adapt content to consumers’ preferences, especially when combined with customer relationship management data.
  • Education: Some educational tools are beginning to incorporate generative AI to develop customized learning materials that cater to students’ individual learning styles.
  • Finance: Generative AI is one of the many tools within complex financial systems to analyze market patterns and anticipate stock market trends, and it’s used alongside other forecasting methods to assist financial analysts.
  • Environment: In environmental science, researchers use generative AI models to predict weather patterns and simulate the effects of climate change

….

Generative AI vs. machine learning

As described earlier, generative AI is a subfield of artificial intelligence. Generative AI models use machine learning techniques to process and generate data. Broadly, AI refers to the concept of computers capable of performing tasks that would otherwise require human intelligence, such as decision making and NLP.

Machine learning is the foundational component of AI and refers to the application of computer algorithms to data for the purposes of teaching a computer to perform a specific task. Machine learning is the process that enables AI systems to make informed decisions or predictions based on the patterns they have learned.

( Again, to make sure credit goes where it is due, the text below the line of asterisks above was excerpted from Generative AI Defined: How it Works, Benefits and Dangers, by Owen Hughes).

5 Game Theory Course Changes

Zachary Bartsch1 Comment

I want to share some changes that I’ll make to my game theory course, just for the record. It’s an intense course for students. They complete homeworks, midterm exams, they present scholarly articles to the class, and they write and present a term paper that includes many parts. Students have the potential to learn a huge amount, including those more intangible communication skills for which firms pine.

There is a great deal of freedom in the course. Students model circumstances that they choose for the homeworks, and they write the paper on a topic that they choose. The 2nd half of the course is mathematically intensive. When I’ve got a great batch of students, they achieve amazing things. They build models, they ask questions, they work together. BUT, when the students are academically below average, the course much less fun (for them and me). We spend way more time on math and way less time on the theory and why the math works or on the applicable circumstances. All of that time spent and they still can’t perform on the mathematical assignments. To boot, their analytical production suffers because of all that low marginal product time invested in math. It’s a frustrating experience for them, for me, and for the students who are capable of more.

This year, I’m making a few changes that I want to share.

  1. Minimal Understanding Quizzes: All students must complete a weekly quiz for no credit and earn beyond a threshold score in order to proceed to the homework and exams. I’m hoping to stop the coasters from getting ‘too far’ in the course without getting the basics down well enough. The quizzes must strike the balance of being hard enough that students must know the content, and easy enough that they don’t resent the requirement.
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Malwarebytes Poll: Public Fascination with ChapGPT Has Turned to Suspicion

Scott Buchanan2 Comments

ChatGPT and related AI have been all the rage these past few months. Among other things, “AI” became the shiny object that companies have dangled before investors, rocketing upward the shares of the “Magnificent Seven” large tech stocks.

However, a recent poll by computer security firm Malwarebytes notes a marked turn in the public’s attitude towards these products:

It seems the lustre of the chatbot-that’s-going-to-change-everything is starting to fade….

When people explored its capabilities in the days and weeks after its launch, it seemed almost miraculous—a wonder tool that could do everything from creating computer programs and replacing search engines, to writing students’ essays and penning punk rock songs. Its release kick-started a race to disrupt everything with AI, and integrate ChatGPT-like interfaces into every conceivable tech product.

But those that know the hype cycle know that the Peak of Inflated Expectations is quickly followed by the Trough of Disillusionment. Predictably, ChatGPT’s rapid ascent was met by an equally rapid backlash as its shortcomings became apparent….

A new survey by Malwarebytes exposes deep reservations about ChatGPT, with optimism in startlingly short supply. Of the respondents familiar with ChatGPT:

  • 81% were concerned about possible security and safety risks.
  • 63% don’t trust the information it produces.
  • 51% would like to see work on it paused so regulations can catch up.

The concerns expressed in the survey mirror the trajectory of the news about ChatGPT since its introduction in November 2022.

As EWED’s own Joy Buchanan has been pointing out specifically with regard to citations for research papers (here, here, and in the Wall Street Journal), ChatGPT tends to “hallucinate”, i.e., to report things that are not simply true. In the recent working paper “GPT-3.5 Hallucinates Nonexistent Citations: Evidence from Economics” , she warns of the possibility of a vicious spiral of burgeoning falsehoods, where AI-generated errors which are introduced into internet content such as research papers are then picked up as “learning” input into the next generation of AI training.

Real-world consequences of ChatGPT’s hallucinations are starting to crop up. A lawyer has found himself in deep trouble after filing an error-ridden submission in an active court case. Evidently his assistant, also an attorney, relied on ChatGPT which came up with a raft of “citations to non-existent cases.” Oops.

And now we have what is believed to be “the first defamation lawsuit against artificial intelligence.” Talk show host Mark Walters filed a complaint in Georgia which is:

…asking for a jury trial to assess damages after ChatGPT produced a false complaint to a journalist about the radio host.  The faux lawsuit claimed that Mr. Walters, the CEO of CCW Broadcast Media, worked for a gun rights group as treasurer and embezzled funds.

…Legal scholars have split on whether the bots should be sued for defamation or under product liability, given it’s a machine — not a person — spreading the false, hurtful information about people.

The issue arose when an Australian mayor threatened to sue the AI company this year over providing false news reports that he was guilty of a foreign bribery scandal.

Wow.

Thousands of AI experts and others have signed an open letter asking: “Should we let machines flood our information channels with propaganda and untruth? Should we automate away all the jobs, including the fulfilling ones? Should we develop nonhuman minds that might eventually outnumber, outsmart, obsolete and replace us? Should we risk loss of control of our civilization?”.    The letter states that “Such decisions must not be delegated to unelected tech leaders. Powerful AI systems should be developed only once we are confident that their effects will be positive and their risks will be manageable.”    It therefore urges  “all AI labs to immediately pause for at least 6 months the training of AI systems more powerful than GPT-4…If such a pause cannot be enacted quickly, governments should step in and institute a moratorium…AI labs and independent experts should use this pause to jointly develop and implement a set of shared safety protocols for advanced AI design and development that are rigorously audited and overseen by independent outside experts.”

Oh, the irony. There is reason to believe that our Stone Age ancestors were creating unreal images like this:

whilst stoned on peyote, shrooms, or oxygen deprivation. And here we are in 2023, with the cutting edge in information technology, running on the fastest specially-fabricated computing devices, and we get…hallucinations.

Self-Replicating Machines: A Practical Human Response

Zachary Bartsch3 Comments

Currently, we have software that can write software. What about physical machines that can produce physical machines? Indeed, what about machines that can produce other machines without human direction?

First of all, machines-building machines (MBM) still require resources: energy, transportation, time, and other inputs. A well-programmed machine that self-replicates quickly can grow in number exponentially. But where would the machines get the resources that enable self-replication? They’d have to purchase them (or conquer the world sci-fi style). Where would a machine get the resources to make purchases of necessary inputs? The same place that everyone else gets them.

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AI Can’t Cure a Flaccid Mind

Zachary Bartsch5 Comments

Many of my classes consist of a large writing component. I’ve designed the courses so that most students write the best paper that they’ll ever write in their life. Recently, I had reason to believe that a student was using AI or a paid service to write their paper. I couldn’t find conclusive evidence that they didn’t write it, but it ended up not mattering much in the end.

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