Artificial Intelligence
As OpenAI attracts billions in new investment, its goal of balancing profit with purpose is getting more challenging to pull off
Alnoor Ebrahim, Tufts University
OpenAI, the artificial intelligence company that developed the popular ChatGPT chatbot and the text-to-art program Dall-E, is at a crossroads. On Oct. 2, 2024, it announced that it had obtained US$6.6 billion in new funding from investors and that the business was worth an estimated $157 billion – making it only the second startup ever to be valued at over $100 billion.
Unlike other big tech companies, OpenAI is a nonprofit with a for-profit subsidiary that is overseen by a nonprofit board of directors. Since its founding in 2015, OpenAI’s official mission has been “to build artificial general intelligence (AGI) that is safe and benefits all of humanity.”
By late September 2024, The Associated Press, Reuters, The Wall Street Journal and many other media outlets were reporting that OpenAI plans to discard its nonprofit status and become a for-profit tech company managed by investors. These stories have all cited anonymous sources. The New York Times, referencing documents from the recent funding round, reported that unless this change happens within two years, the $6.6 billion in equity would become debt owed to the investors who provided that funding.
The Conversation U.S. asked Alnoor Ebrahim, a Tufts University management scholar, to explain why OpenAI’s leaders’ reported plans to change its structure would be significant and potentially problematic.
How have its top executives and board members responded?
There has been a lot of leadership turmoil at OpenAI. The disagreements boiled over in November 2023, when its board briefly ousted Sam Altman, its CEO. He got his job back in less than a week, and then three board members resigned. The departing directors were advocates for building stronger guardrails and encouraging regulation to protect humanity from potential harms posed by AI.
Over a dozen senior staff members have quit since then, including several other co-founders and executives responsible for overseeing OpenAI’s safety policies and practices. At least two of them have joined Anthropic, a rival founded by a former OpenAI executive responsible for AI safety. Some of the departing executives say that Altman has pushed the company to launch products prematurely.
Safety “has taken a backseat to shiny products,” said OpenAI’s former safety team leader Jan Leike, who quit in May 2024.
Why would OpenAI’s structure change?
OpenAI’s deep-pocketed investors cannot own shares in the organization under its existing nonprofit governance structure, nor can they get a seat on its board of directors. That’s because OpenAI is incorporated as a nonprofit whose purpose is to benefit society rather than private interests. Until now, all rounds of investments, including a reported total of $13 billion from Microsoft, have been channeled through a for-profit subsidiary that belongs to the nonprofit.
The current structure allows OpenAI to accept money from private investors in exchange for a future portion of its profits. But those investors do not get a voting seat on the board, and their profits are “capped.” According to information previously made public, OpenAI’s original investors can’t earn more than 100 times the money they provided. The goal of this hybrid governance model is to balance profits with OpenAI’s safety-focused mission.
Becoming a for-profit enterprise would make it possible for its investors to acquire ownership stakes in OpenAI and no longer have to face a cap on their potential profits. Down the road, OpenAI could also go public and raise capital on the stock market.
Altman reportedly seeks to personally acquire a 7% equity stake in OpenAI, according to a Bloomberg article that cited unnamed sources.
That arrangement is not allowed for nonprofit executives, according to BoardSource, an association of nonprofit board members and executives. Instead, the association explains, nonprofits “must reinvest surpluses back into the organization and its tax-exempt purpose.”
What kind of company might OpenAI become?
The Washington Post and other media outlets have reported, also citing unnamed sources, that OpenAI might become a “public benefit corporation” – a business that aims to benefit society and earn profits.
Examples of businesses with this status, known as B Corps., include outdoor clothing and gear company Patagonia and eyewear maker Warby Parker.
It’s more typical that a for-profit business – not a nonprofit – becomes a benefit corporation, according to the B Lab, a network that sets standards and offers certification for B Corps. It is unusual for a nonprofit to do this because nonprofit governance already requires those groups to benefit society.
Boards of companies with this legal status are free to consider the interests of society, the environment and people who aren’t its shareholders, but that is not required. The board may still choose to make profits a top priority and can drop its benefit status to satisfy its investors. That is what online craft marketplace Etsy did in 2017, two years after becoming a publicly traded company.
In my view, any attempt to convert a nonprofit into a public benefit corporation is a clear move away from focusing on the nonprofit’s mission. And there will be a risk that becoming a benefit corporation would just be a ploy to mask a shift toward focusing on revenue growth and investors’ profits.
Many legal scholars and other experts are predicting that OpenAI will not do away with its hybrid ownership model entirely because of legal restrictions on the placement of nonprofit assets in private hands.
But I think OpenAI has a possible workaround: It could try to dilute the nonprofit’s control by making it a minority shareholder in a new for-profit structure. This would effectively eliminate the nonprofit board’s power to hold the company accountable. Such a move could lead to an investigation by the office of the relevant state attorney general and potentially by the Internal Revenue Service.
What could happen if OpenAI turns into a for-profit company?
The stakes for society are high.
AI’s potential harms are wide-ranging, and some are already apparent, such as deceptive political campaigns and bias in health care.
If OpenAI, an industry leader, begins to focus more on earning profits than ensuring AI’s safety, I believe that these dangers could get worse. Geoffrey Hinton, who won the 2024 Nobel Prize in physics for his artificial intelligence research, has cautioned that AI may exacerbate inequality by replacing “lots of mundane jobs.” He believes that there’s a 50% probability “that we’ll have to confront the problem of AI trying to take over” from humanity.
And even if OpenAI did retain board members for whom safety is a top concern, the only common denominator for the members of its new corporate board would be their obligation to protect the interests of the company’s shareholders, who would expect to earn a profit. While such expectations are common on a for-profit board, they constitute a conflict of interest on a nonprofit board where mission must come first and board members cannot benefit financially from the organization’s work.
The arrangement would, no doubt, please OpenAI’s investors. But would it be good for society? The purpose of nonprofit control over a for-profit subsidiary is to ensure that profit does not interfere with the nonprofit’s mission. Without guardrails to ensure that the board seeks to limit harm to humanity from AI, there would be little reason for it to prevent the company from maximizing profit, even if its chatbots and other AI products endanger society.
Regardless of what OpenAI does, most artificial intelligence companies are already for-profit businesses. So, in my view, the only way to manage the potential harms is through better industry standards and regulations that are starting to take shape.
California’s governor vetoed such a bill in September 2024 on the grounds it would slow innovation – but I believe slowing it down is exactly what is needed, given the dangers AI already poses to society.
Alnoor Ebrahim, Thomas Schmidheiny Professor of International Business, The Fletcher School & Tisch College of Civic Life, Tufts University
This article is republished from The Conversation under a Creative Commons license. Read the original article.
The science section of our news blog STM Daily News provides readers with captivating and up-to-date information on the latest scientific discoveries, breakthroughs, and innovations across various fields. We offer engaging and accessible content, ensuring that readers with different levels of scientific knowledge can stay informed. Whether it’s exploring advancements in medicine, astronomy, technology, or environmental sciences, our science section strives to shed light on the intriguing world of scientific exploration and its profound impact on our daily lives. From thought-provoking articles to informative interviews with experts in the field, STM Daily News Science offers a harmonious blend of factual reporting, analysis, and exploration, making it a go-to source for science enthusiasts and curious minds alike. https://stmdailynews.com/category/science/
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Tech
How close are quantum computers to being really useful? Podcast
Quantum computers could revolutionize science by solving complex problems. However, scaling and error correction remain significant challenges before achieving practical applications.
Quantum computers have the potential to solve big scientific problems that are beyond the reach of today’s most powerful supercomputers, such as discovering new antibiotics or developing new materials.
But to achieve these breakthroughs, quantum computers will need to perform better than today’s best classical computers at solving real-world problems. And they’re not quite there yet. So what is still holding quantum computing back from becoming useful?
In this episode of The Conversation Weekly podcast, we speak to quantum computing expert Daniel Lidar at the University of Southern California in the US about what problems scientists are still wrestling with when it comes to scaling up quantum computing, and how close they are to overcoming them.
Quantum computers harness the power of quantum mechanics, the laws that govern subatomic particles. Instead of the classical bits of information used by microchips inside traditional computers, which are either a 0 or a 1, the chips in quantum computers use qubits, which can be both 0 and 1 at the same time or anywhere in between. Daniel Lidar explains:
“Put a lot of these qubits together and all of a sudden you have a computer that can simultaneously represent many, many different possibilities … and that is the starting point for the speed up that we can get from quantum computing.”
Faulty qubits
One of the biggest problems scientist face is how to scale up quantum computing power. Qubits are notoriously prone to errors – which means that they can quickly revert to being either a 0 or a 1, and so lose their advantage over classical computers.
Scientists have focused on trying to solve these errors through the concept of redundancy – linking strings of physical qubits together into what’s called a “logical qubit” to try and maximise the number of steps in a computation. And, little by little, they’re getting there.
In December 2024, Google announced that its new quantum chip, Willow, had demonstrated what’s called “beyond breakeven”, when its logical qubits worked better than the constituent parts and even kept on improving as it scaled up.
Lidar says right now the development of this technology is happening very fast:
“For quantum computing to scale and to take off is going to still take some real science breakthroughs, some real engineering breakthroughs, and probably overcoming some yet unforeseen surprises before we get to the point of true quantum utility. With that caution in mind, I think it’s still very fair to say that we are going to see truly functional, practical quantum computers kicking into gear, helping us solve real-life problems, within the next decade or so.”
Listen to Lidar explain more about how quantum computers and quantum error correction works on The Conversation Weekly podcast.
This episode of The Conversation Weekly was written and produced by Gemma Ware with assistance from Katie Flood and Mend Mariwany. Sound design was by Michelle Macklem, and theme music by Neeta Sarl.
Clips in this episode from Google Quantum AI and 10 Hours Channel.
You can find us on Instagram at theconversationdotcom or via e-mail. You can also subscribe to The Conversation’s free daily e-mail here.
Listen to The Conversation Weekly via any of the apps listed above, download it directly via our RSS feed or find out how else to listen here.
Gemma Ware, Host, The Conversation Weekly Podcast, The Conversation
This article is republished from The Conversation under a Creative Commons license. Read the original article.
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Leo Porter, University of California, San Diego and Daniel Zingaro, University of Toronto
What do you think there are more of: professional computer programmers or computer users who do a little programming?
It’s the second group. There are millions of so-called end-user programmers. They’re not going into a career as a professional programmer or computer scientist. They’re going into business, teaching, law, or any number of professions – and they just need a little programming to be more efficient. The days of programmers being confined to software development companies are long gone.
If you’ve written formulas in Excel, filtered your email based on rules, modded a game, written a script in Photoshop, used R to analyze some data, or automated a repetitive work process, you’re an end-user programmer.
As educators who teach programming, we want to help students in fields other than computer science achieve their goals. But learning how to program well enough to write finished programs can be hard to accomplish in a single course because there is so much to learn about the programming language itself. Artificial intelligence can help.
Lost in the weeds
Learning the syntax of a programming language – for example, where to place colons and where indentation is required – takes a lot of time for many students. Spending time at the level of syntax is a waste for students who simply want to use coding to help solve problems rather than learn the skill of programming.
As a result, we feel our existing classes haven’t served these students well. Indeed, many students end up barely able to write small functions – short, discrete pieces of code – let alone write a full program that can help make their lives better.
Tools built on large language models such as GitHub Copilot may allow us to change these outcomes. These tools have already changed how professionals program, and we believe we can use them to help future end-user programmers write software that is meaningful to them.
These AIs almost always write syntactically correct code and can often write small functions based on prompts in plain English. Because students can use these tools to handle some of the lower-level details of programming, it frees them to focus on bigger-picture questions that are at the heart of writing software programs. Numerous universities now offer programming courses that use Copilot.
At the University of California, San Diego, we’ve created an introductory programming course primarily for those who are not computer science students that incorporates Copilot. In this course, students learn how to program with Copilot as their AI assistant, following the curriculum from our book. In our course, students learn high-level skills such as decomposing large tasks into smaller tasks, testing code to ensure its correctness, and reading and fixing buggy code.
Freed to solve problems
In this course, we’ve been giving students large, open-ended projects and couldn’t be happier with what they have created.
For example, in a project where students had to find and analyze online datasets, we had a neuroscience major create a data visualization tool that illustrated how age and other factors affected stroke risk. Or, for example, in another project, students were able to integrate their personal art into a collage, after applying filters that they had created using the programming language Python. These projects were well beyond the scope of what we could ask students to do before the advent of large language model AIs.
Given the rhetoric about how AI is ruining education by writing papers for students and doing their homework, you might be surprised to hear educators like us talking about its benefits. AI, like any other tool people have created, can be helpful in some circumstances and unhelpful in others.
In our introductory programming course with a majority of students who are not computer science majors, we see firsthand how AI can empower students in specific ways – and promises to expand the ranks of end-user programmers.
Leo Porter, Teaching Professor of Computer Science and Engineering, University of California, San Diego and Daniel Zingaro, Associate Professor of Mathematical and Computational Sciences, University of Toronto
This article is republished from The Conversation under a Creative Commons license. Read the original article.
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Tech
From shrimp Jesus to fake self-portraits, AI-generated images have become the latest form of social media spam
Renee DiResta, Stanford University; Abhiram Reddy, Georgetown University, and Josh A. Goldstein, Georgetown University
Suppose you’ve spent time on Facebook over the past six months. In that case, you may have noticed photorealistic images that are too good to be true: children holding paintings that look like the work of professional artists, or majestic log cabin interiors that are the stuff of Airbnb dreams.
Others, such as renderings of Jesus made out of crustaceans, are just bizarre.
Like the AI image of the pope in a puffer jacket that went viral in May 2023, these AI-generated images are increasingly prevalent – and popular – on social media platforms. Even as many of them border on the surreal, they’re often used to bait engagement from ordinary users.
Our team of researchers from the Stanford Internet Observatory and Georgetown University’s Center for Security and Emerging Technology investigated over 100 Facebook pages that posted high volumes of AI-generated content. We published the results in March 2024 as a preprint paper, meaning the findings have not yet gone through peer review.
We explored patterns of images, unearthed evidence of coordination between some of the pages, and tried to discern the likely goals of the posters.
Page operators seemed to be posting pictures of AI-generated babies, kitchens or birthday cakes for a range of reasons.
There were content creators innocuously looking to grow their followings with synthetic content; scammers using pages stolen from small businesses to advertise products that don’t seem to exist; and spammers sharing AI-generated images of animals while referring users to websites filled with advertisements, which allow the owners to collect ad revenue without creating high-quality content.
Our findings suggest that these AI-generated images draw in users – and Facebook’s recommendation algorithm may be organically promoting these posts.
Generative AI meets scams and spam
Internet spammers and scammers are nothing new.
For more than two decades, they’ve used unsolicited bulk email to promote pyramid schemes. They’ve targeted senior citizens while posing as Medicare representatives or computer technicians.
On social media, profiteers have used clickbait articles to drive users to ad-laden websites. Recall the 2016 U.S. presidential election, when Macedonian teenagers shared sensational political memes on Facebook and collected advertising revenue after users visited the URLs they posted. The teens didn’t care who won the election. They just wanted to make a buck.
In the early 2010s, spammers captured people’s attention with ads promising that anyone could lose belly fat or learn a new language with “one weird trick.”
AI-generated content has become another “weird trick.”
It’s visually appealing and cheap to produce, allowing scammers and spammers to generate high volumes of engaging posts. Some of the pages we observed uploaded dozens of unique images per day. In doing so, they followed Meta’s own advice for page creators. Frequent posting, the company suggests, helps creators get the kind of algorithmic pickup that leads their content to appear in the “Feed,” formerly known as the “News Feed.”
Much of the content is still, in a sense, clickbait: Shrimp Jesus makes people pause to gawk and inspires shares purely because it is so bizarre.
Many users react by liking the post or leaving a comment. This signals to the algorithmic curators that perhaps the content should be pushed into the feeds of even more people.
Some of the more established spammers we observed, likely recognizing this, improved their engagement by pivoting from posting URLs to posting AI-generated images. They would then comment on the post of the AI-generated images with the URLs of the ad-laden content farms they wanted users to click.
But more ordinary creators capitalized on the engagement of AI-generated images, too, without obviously violating platform policies.
Rate ‘my’ work!
When we looked up the posts’ captions on CrowdTangle – a social media monitoring platform owned by Meta and set to sunset in August – we found that they were “copypasta” captions, which means that they were repeated across posts.
Some of the copypasta captions baited interaction by directly asking users to, for instance, rate a “painting” by a first-time artist – even when the image was generated by AI – or to wish an elderly person a happy birthday. Facebook users often replied to AI-generated images with comments of encouragement and congratulations
Algorithms push AI-generated content
Our investigation noticeably altered our own Facebook feeds: Within days of visiting the pages – and without commenting on, liking or following any of the material – Facebook’s algorithm recommended reams of other AI-generated content.
Interestingly, the fact that we had viewed clusters of, for example, AI-generated miniature cow pages didn’t lead to a short-term increase in recommendations for pages focused on actual miniature cows, normal-sized cows or other farm animals. Rather, the algorithm recommended pages on a range of topics and themes, but with one thing in common: They contained AI-generated images.
In 2022, the technology website Verge detailed an internal Facebook memo about proposed changes to the company’s algorithm.
The algorithm, according to the memo, would become a “discovery-engine,” allowing users to come into contact with posts from individuals and pages they didn’t explicitly seek out, akin to TikTok’s “For You” page.
We analyzed Facebook’s own “Widely Viewed Content Reports,” which lists the most popular content, domains, links, pages and posts on the platform per quarter.
It showed that the proportion of content that users saw from pages and people they don’t follow steadily increased between 2021 and 2023. Changes to the algorithm have allowed more room for AI-generated content to be organically recommended without prior engagement – perhaps explaining our experiences and those of other users.
‘This post was brought to you by AI’
Since Meta currently does not flag AI-generated content by default, we sometimes observed users warning others about scams or spam AI content with infographics.
Meta, however, seems to be aware of potential issues if AI-generated content blends into the information environment without notice. The company has released several announcements about how it plans to deal with AI-generated content.
In May 2024, Facebook will begin applying a “Made with AI” label to content it can reliably detect as synthetic.
But the devil is in the details. How accurate will the detection models be? What AI-generated content will slip through? What content will be inappropriately flagged? And what will the public make of such labels?
While our work focused on Facebook spam and scams, there are broader implications.
Reporters have written about AI-generated videos targeting kids on YouTube and influencers on TikTok who use generative AI to turn a profit.
Social media platforms will have to reckon with how to treat AI-generated content; it’s certainly possible that user engagement will wane if online worlds become filled with artificially generated posts, images and videos.
Shrimp Jesus may be an obvious fake. But the challenge of assessing what’s real is only heating up.
Renee DiResta, Research Manager of the Stanford Internet Observatory, Stanford University; Abhiram Reddy, Research Assistant at the Center for Security and Emerging Technology, Georgetown University, and Josh A. Goldstein, Research Fellow at the Center for Security and Emerging Technology, Georgetown University
This article is republished from The Conversation under a Creative Commons license. Read the original article.
The science section of our news blog STM Daily News provides readers with captivating and up-to-date information on the latest scientific discoveries, breakthroughs, and innovations across various fields. We offer engaging and accessible content, ensuring that readers with different levels of scientific knowledge can stay informed. Whether it’s exploring advancements in medicine, astronomy, technology, or environmental sciences, our science section strives to shed light on the intriguing world of scientific exploration and its profound impact on our daily lives. From thought-provoking articles to informative interviews with experts in the field, STM Daily News Science offers a harmonious blend of factual reporting, analysis, and exploration, making it a go-to source for science enthusiasts and curious minds alike. https://stmdailynews.com/category/science/
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