Month: December 2024

In February of this year, OpenAI first announced Sora, an AI model that can create realistic and imaginative videos based on text prompts. Since then, Sora has been in private preview for select visual artists, designers, filmmakers, and red team members. Based on feedback during the private preview, OpenAI is launching Sora to the public today, significantly faster than they announced it in February. Users can access Sora through Sora.com. The homepage of Sora.com will highlight recent videos created by others in the community as well as some featured videos curated by the OpenAI team. Users can bookmark these videos and then access them from the “Saved Videos” section. Users can not only watch the videos generated by the  AI , but also see the exact text prompts used to create them.
Sora also allows users to upload images, and Sora can create videos based on them. Users can also create folders in Sora and organize video projects.
Sora also provides a video editing experience, which allows users to edit prompts, view storyboards, cut videos, and more. The “Storyboard” feature allows users to easily combine multiple small videos generated by a single text prompt to create a longer video. Another cool feature of Sora is the ability to remix videos made by others to your own taste. To remix a video, users describe the changes they want to make to the video and select the level of remixing.
Sora can create videos up to 20 seconds long, with resolutions ranging from 480p to full 1080p, and widescreen, vertical, or square aspect ratios. Obviously, the higher the resolution you choose, the longer it will take to render the video.
You can watch a video review of the Sora tool below. Sora is now available worldwide, except in Europe, the UK, and China. It is free to all existing ChatGPT Pro and Plus users.
ChatGPT Plus users can generate up to 50 videos per month at 480p resolution, and can choose to generate fewer videos at 720p resolution. ChatGPT Pro users can generate unlimited videos at resolutions up to 1080p, and for longer durations.OpenAI is also developing different pricing models for different types of users, which will be available early next year.

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What new tasks will generative AI bring to humans?” Acemoglu asks. “I don’t think we know that yet, and that’s the question. What applications will really change the way we do things?” What are the measurable effects of AI?
Since 1947, U.S. GDP has grown by an average of about 3% per year, and productivity has grown by about 2% per year. Some forecasts claim that AI will double that growth, or at least create a higher-than-usual growth trajectory. In contrast, in a paper published in the August issue of Economic Policy, “The Simple Macroeconomics of Artificial Intelligence,” Acemoglu estimates that AI will increase GDP by “modestly” between 1.1% and 1.6% over the next decade, and productivity by about 0.05% per year.
Acemoglu based his assessment on recent estimates of the number of jobs impacted by AI, including a 2023 study by researchers at OpenAI, OpenResearch, and the University of Pennsylvania, which found that about 20% of U.S. jobs could be affected byAI capabilities. A 2024 study by researchers at MIT’s Center for the Future of Technology, the Productivity Institute, and IBM found that about 23% of computer vision tasks that could eventually be automated could be profitable over the next decade. Still more studies have put the average cost savings from AI at about 27%.
When it comes to productivity, “I don’t think we should underestimate a 0.5% increase over 10 years. It’s better than zero,” Acemoglu said. “But it’s disappointing compared to the promises made by people in the industry and in the tech press.”
To be sure, this is just an estimate, and many more AIapplications are likely: As Acemoglu wrote in his paper, his calculations did not include using AI to predict the shapes of proteins, for which other academics subsequently won a Nobel Prize in October.
Other observers think that “reallocation” of workers displaced by AI will generate additional growth and productivity beyond Acemoglu’s estimates, though he thinks it’s not significant. “Starting from the actual distribution we have, reallocation generally yields only small benefits,” Acemoglu says. “The immediate benefits are what matter.”
“I tried to write the paper in a very transparent way about what was included and what was not included,” he adds. “People can object and say that what I excluded was important or that the numbers for what I included were too low, and that’s totally fine.”

As Acemoglu and Johnson make clear, they favor technological innovations that increase worker productivity while keeping people employed, which should do a better job of sustaining economic growth.
But in Acemoglu’s view, the point of generative AI is to mimic humans as a whole. This produces what he has for years called “so-so technology,” applications that perform at best only slightly better than humans but save companies money. Call center automation isn’t always more efficient than humans; it just saves companies less money than workers do. AI applications that supplement workers seem generally to take a backseat to big tech companies.
“I don’t think complementary uses for AI will magically emerge unless industry invests a lot of effort and time,” Acemoglu said.
What does history teach us about AI?
The fact that technology is often designed to replace workers is the focus of another recent paper by Acemoglu and Johnson, “Learning from Ricardo and Thompson: Machinery and Labor in the Early Industrial Revolution—and in the Age of AI,” published in the August issue of the Annual Review of Economics.
The article discusses the current debate over AI, particularly the claim that even if technology replaces workers, the resulting growth will almost inevitably benefit society over time. Britain during the Industrial Revolution is sometimes cited as an example. But Acemoglu and Johnson argue that spreading the benefits of technology is not easy. In 19th-century Britain, they assert, it happened only after decades of social struggle and workers’ action.

What is the optimal pace of innovation?
If technology helps promote economic growth, then rapid innovation would seem ideal because it would bring growth faster. But in another paper, “Regulating Transformative Technologies,” in the September issue of the American Economic Review: Insights, Acemoglu and MIT doctoral student Todd Lensman offer another view. If some technologies have both benefits and disadvantages, then it is better to adopt them at a more measured pace while mitigating those problems.
“If the social harms are large and proportional to the productivity of the new technology, then higher growth rates will lead to slower adoption,” the authors write in the paper. Their model suggests that, ideally, adoption should start out slow and then gradually speed up over time.
“Market fundamentalism and technology fundamentalism might claim that you should always develop technology at the fastest pace,” Acemoglu says. “I don’t think there is such a rule in economics. More thoughtfulness, especially about avoiding harms and pitfalls, is warranted.”
The model is a response to trends over the past decade or so, in which many technologies were hyped as inevitable and welcomed for their disruptive nature. In contrast, Acemoglu and Lensman suggest that we can reasonably judge the trade-offs involved with a particular technology, and aim to stimulate more discussion about this.
How can we get to the right pace forAIadoption?
If the idea is to adopt technology more gradually, how should that be achieved?
First, Acemoglu said, “government regulation has a role to play.” However, it’s not clear what type of long-term guidelines for AI the U.S. or countries around the world might adopt.
Second, he added, if the “hype” cycle around AI abates, then the rush to use AI “will naturally slow down.” This scenario might be more likely than regulation if AI doesn’t soon turn a profit for companies.
“We’re moving so fast because of the hype from venture capitalists and other investors because they think we’re going to get closer to general AI” Acemoglu says. “I think that hype has caused us to invest improperly in the technology, and a lot of companies have been affected prematurely and don’t know what to do with it. We wrote that paper to say, look, if we’re more thoughtful and understanding about our use of this technology, its macroeconomics will benefit us.”
In that sense, Acemoglu emphasizes that hype is a tangible aspect of the economics of AI, because it drives investment in specific AI visions and thus influences the AI ​​tools we’re likely to encounter.
“The faster the speed and the more excitement, the less likely you are to make a course correction,” Acemoglu says. “If you’re going 200 miles an hour, it’s very difficult to make a 180-degree turn.

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Google Search may soon become more conversational on Android devices thanks to artificial intelligence, according to unreleased code discovered by 9to5 Google. The search app may soon add an AI mode that combines interactive discussions and other features to make Google’s base service more like the Gemini AIassistant.
AImode (referred to as AIM in the unreleased code) blends the human-like interactions of Gemini Live with Google Search and joins the visual understanding and analysis provided by Google Lens. In AIM, you can respond to the results of a Google Search. Not only can you view a list of results, but you can also ask follow-up questions, interrupt replies, and otherwise treat Search like Gemini Live.
If it rolls out, AI mode should appear as a tab in the bottom navigation bar of the Google app. In addition to using voice search, you can also use photos taken with your phone or other uploaded photos. You can then explain what you want to search for in the image. Another interesting point in the code is that its placeholder is a winking emoji. Gemini or search?AI mode in Google Search makes sense at first glance, but when viewed in context, it raises some questions. It looks very similar to Gemini, more like a variation of Gemini Live. This fits in with Google’s seeming enthusiasm for people to use Gemini for everything. AI Mode isn’t exactly the same as Gemini Live, as AI Mode will offer a multimodal experience combining text, voice, and images, but it’s close enough that it’s hard to know when you should use one over the otherAI Mode may just be a path to a more comprehensive service. Enhancing Google Search with Lens’ ability to ask questions of photos and videos, and enhancing the current voice interaction (transcribe verbal requests), could pave the way for Google Search to become an aspect of Gemini, and vice versa. It could also change the way we think about the world’s most popular search engine.
Instead of asking Google to say “show me the results,” we could just ask it to “give me a direct, thoughtful answer.”

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Sam Altman, known as the PT Barnum of artificial intelligence, has a message for those who care about the technology he’s spent his life promoting: Don’t worry, the tech geeks are working on it.
Let’s back up a bit.
Altman, the 39-year-old venture capitalist and CEO of OpenAI, spoke with journalist Andrew Ross Sorkin at the New York Times Dealbook Summit on Wednesday. As gentle but affable as ever, Altman almost made you forget he’s a billionaire doomsayer who has also repeatedly warned about the risks of artificial intelligence. At one point, Sorkin asked, “Do you believe that governments or anyone else can figure out how to avoid” the existential threat posed by superintelligent AI systems?
Cue the shy boy’s deflection.
“I’m sure the researchers will figure out how to avoid that,” Altman replied. “I think the smartest people in the world will work on a range of technical problems. You know, I’m a little overly optimistic by nature, but I think they’ll figure it out.”
He went on to suggest that perhaps the AI ​​itself is so smart that it will figure out how to control itself, but didn’t elaborate.
“We have this magic—” Altman says, but then corrects himself, “Not magic. We have this incredible science called deep learning that can help us solve these very hard problems.”
Ah, yes. ExxonMobil will solve the climate crisis…
Look, it’s hard not to be drawn to Altman, who did not respond to requests for comment. He keeps his cool, knowing that even if his technology disrupts the global economy, he’ll be safe in his bunker off the coast of California. (“I have guns, gold, potassium iodide, antibiotics, batteries, water, IDF gas masks, and a big piece of land in Big Sur that I can fly to,” he said.) But for the rest of us, it would be nice to hear Altman or any of his fellow AI boosters explain what they mean when they say “we’ll figure it out.”
Even AI researchers admit they still don’t understand exactly how the technology works. A report commissioned by the U.S. State Department called AI systems essentially black boxes that pose an “extinction-level threat” to humanity.
Even if researchers can sort out the technical issues and solve what they call the “coordination problem” — making sure AI models don’t become monster robots that destroy the world — Altman acknowledged that there will still be problems that some people or some governments will have to solve.
At the Dealbook Summit, Altman again put the onus on regulating the technology on some imaginary international organization made up of rational adults who don’t want to kill each other. He told Sorkin, even if “even if we can make this [superintelligence model] technically safe, which I think we will find a way to do, we have to have faith in our governments…there has to be global coordination…I think we’ll rise to the challenge, but it seems challenging.”
There are a lot of assumptions in this, and it reflects a myopic understanding of how policymaking and global coordination actually work: which is to say, slowly, inefficiently, and often not at all.
This kind of naivety must be instilled in the 1% elite in Silicon Valley, who are keen to stuff AI into every device we use, despite the technology’s flaws. That’s not to say it’s not useful! AI is being used to do all sorts of cool things, like helping people with disabilities or the elderly, as my colleague Clare Duffy has reported. Some AI models are doing some exciting things with biochemistry (which is frankly beyond my comprehension, but I trust the honest scientists who won the Nobel Prize for this technology earlier this year).

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