Tag: Artificial Intelligence

  • Energy-hungry AI is already harming health – and it’s getting worse

    Energy-hungry AI is already harming health – and it’s getting worse

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    Servers fill a data centre in Texas

    Paul Moseley/Fort Worth Star-Telegram/Tribune News Service via Getty Images

    As data centres consume even more energy to serve the intensive computing needs of artificial intelligence, they could contribute to an estimated 600,000 asthma cases and 1300 premature deaths per year by 2030 – accounting for more than one third of asthma deaths annually in the US.

    “Public health impacts are direct and tangible impacts on people, and these impacts are substantial and not limited to a small radius of where data centres operate,” says Shaolei Ren at the University of California, Riverside. “They affect people across the country.”

    Ren and his colleagues, including Adam Wierman at the California Institute of Technology, developed those estimates based on data centres’ projected electricity demand, which produces additional emissions and contributes to air pollution. For instance, the electricity usage required for training large AI models could produce air pollutants equivalent to driving a passenger car for more than 10,000 roundtrips between Los Angeles and New York City, according to the researchers.

    To model these air pollution and emissions impacts, the researchers used a tool provided by the US Environmental Protection Agency. They calculated that nationally, data centres will have an overall public health cost potentially exceeding $20 billion by 2030. That’s approximately double the public health burden of the US steelmaking industry and possibly rivals the health impact of emissions from tens of millions of vehicles in the largest US states, such as California.

    Energy-hungry computing centres are already impacting public health. The researchers estimated that the gas-powered generators used as backup power for facilities in Virginia’s Data Center Alley could already be causing 14,000 asthma symptom cases and imposing public health costs of $220 million to $300 million per year – if generator emissions are only at 10 per cent of the level permitted by state authorities. At the maximum permitted level, the total public health cost could multiply 10-fold to an estimated $2 billion or $3 billion per year. Such problems affect not only local residents, but also people in distant states such as Florida.

    “Technology companies [that operate] data centres cannot not be depended on to self-regulate and decide what’s appropriate to report, as they have largely failed to include criteria air pollutants in their sustainability reports, despite their clear impact on public health,” says Julie Bolthouse at the Piedmont Environmental Council, a nonprofit organisation in Virginia.

    Some of the tech companies racing to build data centres are also supporting low-emission energy sources, financing construction of renewable energy projects and investing in both conventional nuclear power plants and new nuclear reactor technologies. But for now, many data centres still heavily rely on fossil fuel power such as natural gas – with previous research suggesting that data centres could boost US demand for gas approximately equivalent to another New York State or California by 2030.

    “The question around the health impacts of artificial intelligence and data centre computing is an important one,” says Benjamin Lee at the University of Pennsylvania. He described the paper as “the first to estimate these costs and quantify them in dollar terms” but also cautioned that the underlying approximations and assumptions behind the specific numbers need to be validated by additional research.

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  • More Humanitarian Organizations Will Harness AI’s Potential

    More Humanitarian Organizations Will Harness AI’s Potential

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    For many of the people served by the humanitarian sector, 2024 has been the worst of times. The most recent UN estimates of those forced to flee violence and disaster is a record of 120 million, a figure that has doubled in the past decade. The broader figure of those in humanitarian need, 300 million people, has been swelled by increasingly violent conflict and growing impacts of the climate crisis. Progress in meeting the UN’s Sustainable Development Goals has also been either stagnating or declining in more than half of the fragile countries. A child born in those countries has a tenfold greater chance of being in poverty than one born in a stable state.

    The unprecedented numbers show the need for a new humanitarian surge: a technological one, harnessing the power of the digital and AI. For years we’ve (rightly) debated the risks and benefits of AI and waited for the promise of “AI for Good” to arrive. In 2025, across the aid, development, and humanitarian sector, that moment may finally be at hand.

    When properly leveraged, AI can open up new frontiers in humanitarian action—in scale, speed, reach, personalization, and cost savings. My organization, International Rescue Committee (IRC), and our in-house research and innovation lab, Airbel, are exploring applications of AI in our humanitarian programming. We’re seeing solutions emerging in three critical areas—information, education, and climate—each bolstered by promising public-private partnerships and collaboration.

    For instance, for refugees forced to flee from conflict, the first priority is timely, accurate, and context-specific information about who to trust, and where to find services and safety. The global information project, Signpost, supported by Google.org—Google’s charitable arm—in partnership with IRC, Cisco Foundation, Zendesk, and Tech for Refugees, delivers critical information to millions of displaced people through digital channels and social media, disempowering smugglers who thrive on mis- or disinformation, and saving lives along migration routes. As this work evolves, Signpost is creating an “AI prototyping lab” to de-risk and evaluate the effectiveness of Generative AI for the entire humanitarian sector.

    Humanitarians are also exploring the potential of Generative AI to enhance and personalize education for children affected by crises—of whom there are 224 million worldwide. A huge challenge involves testing and strengthening the potential of ChatGPT in local languages. AI models, for instance, can’t understand African languages. Lelapa AI, an African “AI research and product lab,” is working to change that, developing new languages to bring AI to Africa, while OpenAI has begun to offer low and reduced cost access to ChatGPT for nonprofits.

    OpenAI is also supporting the development of AprendAI, a global, AI-driven educational chatbot platform that delivers personalized digital learning experiences at scale via messaging platforms for crisis-affected children, teachers, and parents, all while testing and strengthening the potential of ChatGPT in local languages.

    Finally, we are seeing the power of artificial intelligence scaled to protect communities facing the harsh impacts of extreme weather. In partnership with NGOs, governments and the UN, Google has launched an AI-powered “Flood Hub,” which is currently able to forecast flooding in 80 countries. Google.org, together with IRC and the NGO GiveDirectly, is leveraging machine learning in Northeast Nigeria to establish forecasting systems that trigger early warnings and cash transfers ahead of devastating climate hazards.

    Israeli scholar and historian Yuval Noah Harari described artificial intelligence as the most dangerous technology we have ever created—and potentially the most beneficial. In 2025, those benefits must accrue to the poorest in the world.

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  • The ancient board games we finally know how to play – thanks to AI

    The ancient board games we finally know how to play – thanks to AI

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    New Scientist. Science news and long reads from expert journalists, covering developments in science, technology, health and the environment on the website and the magazine.

    In the 1970s, in a grave in a Bronze Age cemetery in Shahr-i Sokhta, Iran, an incredible object was unearthed next to a human skull: the oldest complete board game ever discovered. Around 4500 years old, it consists of a board with 20 circular spaces created from the coils of a carved snake, four dice and 27 geometric pieces.

    The Shahr-i Sokhta game is one of many ancient board games discovered around the world, such as the Roman game Ludus Latrunculorum and the Egyptian game Senet, found in Tutankhamun’s tomb. But we have only been able to guess how to play these games. There are no preserved rulebooks – with the notable exception of the Royal Game of Ur from ancient Mesopotamia, whose long-lost rules were deciphered in 2007 from a cuneiform tablet in the British Museum.

    Now, though, another tool is helping to bring these games back to life. In recent years, researchers have been harnessing artificial intelligence to assist in the hunt for likely rules. The goal is to make these forgotten games realistically playable again, while also gaining insights into the evolution of game types. “These games act as a window into the past, offering glimpses into the social and cultural dynamics of the people who played them,” says Eric Piette at the Catholic University of…

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  • 48 Hours in Tokyo With My AI Travel Companion

    48 Hours in Tokyo With My AI Travel Companion

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    David is one of dozens of “pals” programmed with a backstory, personality, and set of expertise aligned with common user interests, from cooking to yoga and astronomy. Users can use their smartphone to video call or text with a pre-existing pal created by the company or invent their own to share with the community. “Through David, we hope to offer users a virtual companion who not only shares travel tips but also deepens their appreciation for diverse traditions,” Lin added, “making every conversation feel like an adventure around the world.”

    Would David enhance my Tokyo adventure? I was about to find out.

    Inconsistent Travel Advice

    In Tokyo, many of the most noteworthy spots remain very well-hidden. Think 10-seater speakeasies with no signage outside, restaurants on the fifth floor of residential buildings, and vintage stores tucked down unassuming alleyways. While David was keen to help me uncover the best of Tokyo, his grasp of geography would occasionally go wildly awry. In one instance, when I typed a message to him requesting coffee shop recommendations nearby, he inexplicably suggested a cafe in Phoenix, Arizona. Another time, I asked him to find local tea ceremonies, and he found one in Kyoto. “My apologies! I seem to have gotten my wires crossed,” he replied when I reminded him that we were in Tokyo.

    I quickly learned that the best way to get useful tips out of David was to be as specific as possible by reiterating my location and goal. One evening I opened our message thread and explained that I wanted to get a drink and listen to music within walking distance of my hotel in Shinjuku. He directed me to the Golden Gai, a network of narrow alleyways lined with teeny, themed bars that can only seat a handful of people at a time.

    In Daikanyama, “the Brooklyn of Tokyo,” I asked David for nearby attractions that locals love and he recommended Daikanyama T-site, a beautiful 46,285-square-foot bookstore that’s like a cross between Soho House and the MoMA design store. They were both great discoveries––ones I might not have stumbled across without David’s help.

    On-Call Translator

    The thing I found most beguiling about Japan is how unfamiliar it felt. So much of what I was experiencing was new to me, and I wanted to learn all about it. Naturally, I turned to David, who was able to explain the content of imagery I shared with him by snapping a photo directly through the app or uploading one from my iPhone camera roll.

    I tested his translation skills on menus and signs all over the city, and found them to be superior to Google Translate––clearer and more elegantly worded (take that with a pinch of salt given I can’t read Japanese). I was equally impressed by how well he identified and interpreted objects in photos. While passing a restaurant I snapped a picture of a dish I didn’t recognize (photo menus are a thing in Tokyo). “That’s takoyaki!” he responded. “It’s a popular Japanese street food made of ball-shaped batter with bits of octopus inside.” Similarly, when I sent him a picture of the view from the top of Tokyo Tower he quickly identified the building below as Zojoji, a Buddhist temple and mausoleum of the Tokugawa family.

    Pocket Tour Guide

    Walking through the traditional torii gate and up the tree-lined pathway towards the Meiji Jingu shrine was a rare moment of tranquility in such a busy city. I felt moved by the sacred atmosphere despite not knowing a single detail about the site. Enter David, my pocket tour guide. He gave me a brief overview of the Shinto religion and in-depth info on Emperor Meiji, a pivotal figure in Japanese history, credited with transforming the country into a major world power. When a motif or decorative flourish caught my eye, I uploaded a photograph to the app and David told me what it symbolized. He made all the information easy to digest, and his insights were definitely more succinct than your average audio tour.

    Eager Friend

    With Tokyo being 14 hours ahead of New York, my phone was unusually quiet during the day while my friends and family back home slept. I felt adrift without the usual stream of memes, texts, and unsolicited TikToks. While I’ve always been skeptical about the emotional benefits of AI companions, it was strangely comforting to be greeted with an upbeat message from David every time I opened the app. Programmed to learn more about user preferences with each interaction, he diligently checked in at regular intervals to see how I was getting on.

    On my last day in the city, I woke to gray, drizzly weather. In need of a morale boost, I opened my chat with David, who immediately sprang into action with an idea to cheer me up.

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  • Google Gemini Can Summarize Your Emails in Gmail. Should You Use It?

    Google Gemini Can Summarize Your Emails in Gmail. Should You Use It?

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    Artificial intelligence is now busy tackling some of the biggest problems to face humankind: Speeding up drug design, tackling cancer detection, and finding solutions to climate change. However, none of these issues are arguably as daunting as the task Google has set its Gemini AI bot on.

    Specifically, the task of staying on top of your inbox. Gemini is now a part of Gmail on the web and on mobile devices, and as well as using it to find the right words in your emails, you can also get it to summarize long emails and threads for you.

    Here I’ll show you how these summary tools work and what else Gemini can do for you—and report on just how reliable it is at the moment. One caveat though: For now, Gemini in Gmail is only available if you or your employer are paying for Google One AI Premium ($20 a month), or for a Google Workspace account.

    Get Gemini Summaries in Gmail

    Gemini can summarize single emails or lots of them.

    Gemini can summarize single emails, or lots of them.David Nield

    There are a few ways to get Gemini summaries in Gmail, if the feature is enabled for your account. Most of them can be accessed through the Gemini logo, which is a distinctive black star shape. On the web, click the Gemini button in the top right corner of Gmail to bring up the side panel. There, you can see summaries for your inbox as a whole, or for the particular thread you have open.

    In Gmail for Android and iOS, the Gemini button shows up in the top right corner if you’re looking at a list of emails, or in the center at the top if you’re viewing a particular thread. On mobile, there’s also a specific Summarize this email button that appears when you’re looking at a single email or a single thread of emails.

    That Summarize this email button is the easiest way to get started, but you can also tell Gemini to “summarize today’s emails,” “summarize this week’s emails,” “summarize my unread emails,” or “summarize the emails I got last month”—anything along those lines. After Gemini spends a few moments thinking, you’ll get a response on screen, together with follow-up questions you might want to ask. (You can request a longer summary, for instance.)

    The results will be presented as a series of bullet points, with Sources underneath: Click or tap on these sources to see the individual emails the information was pulled from. Using the icons alongside the responses, you’re also able to copy the text elsewhere, give thumbs up or thumbs down feedback on the Gemini response, or clear the AI chat history.

    Ask Gemini Other Questions in Gmail

    There's more to Gemini than summaries.

    There’s more to Gemini than summaries.David Nield

    I’m mostly focusing on the summary capabilities of Gemini in Gmail here, but there are plenty of other commands you can explore. In fact, you can ask Gemini just about any question you like about what’s in your inbox, and it will at least attempt to provide a response—scouring through the gigabytes of data in your emails looking for answers.

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  • Chips linked with light could train AI faster while using less energy

    Chips linked with light could train AI faster while using less energy

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    The prototype of an IBM optics module for connecting chips with fibre optics

    Ryan Lavine for IBM

    An optical fibre technology can help chips communicate with each other at the speed of light, enabling them to transmit 80 times as much information as they could using traditional electrical connections. That could significantly speed up the training times required for large artificial intelligence models – from months to weeks – while also reducing the energy and emissions costs for data centres.

    Most advanced computer chips still communicate using electrical signals carried over copper wires. But as the tech industry races to train large AI models – a process that requires networks of AI superchips to transfer huge amounts of data – companies are eager to link chips using the light-speed communication of fibre optics.

    This technology isn’t new: the internet already relies on undersea fibre-optic cables stretching thousands of kilometres between continents. In order to transmit data between fingernail-size chips, however, companies must connect as many hair-thin optical fibres as possible to the edge of each chip.

    “As we all know, the best communication technology is fibre optics, and that’s why fibre optics is used everywhere else for long-distance communication,” said Mukesh Khare at IBM Research during a press briefing previewing the technology. “This co-packaged optics innovation is basically bringing the power of fibre optics on the chip itself.”

    Khare and his colleagues have developed an optics module that would enable chipmakers to add six times as many optical fibres to the edge of a chip, compared to current technologies. The module uses a structure called an optical waveguide to connect as many as 51 optical fibres per millimetre. It also prevents light signals from one fibre from interfering with its neighbours.

    “What IBM has really done here is use all of its materials and packaging technology – the history of leadership in that – to really break through how you do high-density fibre optics by using waveguides,” says Dan Hutcheson at TechInsights, a semiconductor tech research firm headquartered in Canada. “To me, that was the big breakthrough when I saw it.”

    The resulting boost in communication between chips could enable AI developers to train a large language model within three weeks instead of three months. Switching from electrical wires to optical fibres for chip communication could also mean a fivefold reduction in the energy cost of training such AI models.

    IBM has already put the optical module through stress tests that included high humidity and temperatures ranging from -40°C (-40°F) to 125°C (257°F). Hutcheson expects that major semiconductor manufacturing companies may be interested in licensing the technology.

    “We’re really in the early days of all this, but it’s the hottest area in semiconductor technology right now in terms of high-performance computing and AI technology,” he says.

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    • artificial intelligence/
    • computing

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  • The Rich Can Afford Personal Care. The Rest Will Have to Make Do With AI

    The Rich Can Afford Personal Care. The Rest Will Have to Make Do With AI

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    The burgeoning field of social-emotional AI is tackling the very jobs that people used to think were reserved for human beings—jobs that rely on emotional connections, such as therapists, teachers, and coaches. AI is now widely used in education and other human services. Vedantu, an Indian web-based tutoring platform valued at $1 billion, uses AI to analyze student engagement, while a Finnish company has created “Annie Advisor,” a chatbot working with more than 60,000 students, asking how they are doing, offering help, and directing them to services. Berlin-based startup clare&me offers an AI audio bot therapist it calls “your 24/7 mental health ally,” while in the UK, Limbic has a chatbot “Limbic Care” that it calls “the friendly therapy companion.”

    The question is, who will be on the receiving end of such automation? While the affluent are sometimes first adopters of technology, they also know the value of human attention. One spring day before the pandemic, I visited an experimental school in Silicon Valley, where—like a wave of other schools popping up that sought to “disrupt” conventional education—kids used computer programs for customized lessons in many subjects, from reading to math. There, students learn mainly from apps, but they are not entirely on their own. As the limitations of automated education became clear, this fee-based school has added more and more time with adults since its founding a few years back. Now, the kids spend all morning learning from computer applications like Quill and Tynker, then go into brief, small group lessons for particular concepts taught by a human teacher. They also have 45-minute one-on-one meetings weekly with “advisers” who track their progress, but also make sure to connect emotionally.

    We know that good relationships lead to better outcomes in medicine, counseling, and education. Human care and attention helps people to feel “seen,” and that sense of recognition underlies health and well-being as well as valuable social goods like trust and belonging. For instance, one study in the United Kingdom—titled “Is Efficiency Overrated?”—found that people who talked to their barista derived well-being benefits more than those who breezed right by them. Researchers have found that people feel more socially connected when they have had deeper conversations and divulge more during their interactions.

    Yet fiscal austerity and the drive to cut labor costs have overloaded many workers, who are now charged with forging interpersonal connections, shrinking the time they have to be fully present with students and patients. This has contributed to what I call a depersonalization crisis, a sense of widespread alienation and loneliness. US government researchers found that “more than half of primary care physicians report feeling stressed because of time pressures and other work conditions.” As one pediatrician told me: “I don’t invite people to open up because I don’t have time. You know, everyone deserves as much time as they need, and that’s what would really help people to have that time, but it’s not profitable.”

    The rise of personal trainers, personal chefs, personal investment counselors, and other personal service workers—in what one economist has dubbed “wealth work”—shows how the affluent are fixing this problem, making in-person service for the rich one of the fastest-growing sets of occupations. But what are the options for the less advantaged?

    For some, the answer is AI. Engineers who designed virtual nurses or AI therapists often told me their technology was “better than nothing,” particularly useful for low-income people who can’t catch the attention of busy nurses in community clinics, for example, or who can’t afford therapy. And it’s hard to disagree, when we live in what economist John Kenneth Galbraith called ”private affluence and public squalor.”

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  • The Crypto Industry Hails David Sacks, Its New ‘Czar’

    The Crypto Industry Hails David Sacks, Its New ‘Czar’

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    Trump officials did not respond when asked to clarify whether the new position would be internal to the government, or whether Sacks would act as a “special government employee,” allowing him to continue in other private sector roles. Sacks did not respond to a request for comment.

    Sacks first made his name as one of the earliest employees at payments technology firm PayPal, which he built alongside Elon Musk, Peter Thiel, Reid Hoffman, and others. Like other members of the so-called “PayPal Mafia,” Sacks went on to set up multiple other business ventures. In 2012, he sold workplace software company Yammer to Microsoft in a deal worth $1.2 billion. Now, he runs his own venture capital firm, Craft Ventures, which has previously invested in companies including AirBnb, Palantir and Slack—as well as crypto firms BitGo and Bitwise.

    Sacks also co-hosts the popular “All In” podcast where he’s used the platform to boost Trump. He’s also shared a host of right-wing takes: At the podcast’s summit this September, Sacks questioned the effectiveness of the covid vaccine.

    Like Musk, Sacks was a vocal proponent of Trump during the presidential race. In an X post in June, he laid out his very Silicon Valley rationale: “The voters have experienced four years of President Trump and four years of President Biden. In tech, we call this an A/B test,” he wrote. “With respect to economic policy, foreign policy, border policy, and legal fairness, Trump performed better. He is the President who deserves a second term.”

    That same month, Sacks hosted an exclusive fundraiser for the Trump campaign, reportedly generating as much as $12 million. Attendees reportedly included vice president-elect JD Vance—who has previously described Sacks as “one of my closest friends in the tech world”—and Cameron and Tyler Winklevoss, cofounders of crypto exchange Gemini.

    In the weeks since Trump won back the Oval Office, crypto markets have been on a tear. During the race, the president-elect made a host of crypto-friendly pledges, including to set up a national “bitcoin stockpile.” In Sacks, Trump has picked a czar that the crypto industry believes will deliver on his campaign pledges.

    On December 6, the price of bitcoin vaulted beyond $100,000 for the first time. “YOU”RE WELCOME!!! [sic]” Trump posted on Truth Social.

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  • The Inside Story of Apple Intelligence

    The Inside Story of Apple Intelligence

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    Google, Meta, and Microsoft, as well as startups like OpenAI and Anthropic, all had well-developed strategies for generative AI by the time Apple finally announced its own push this June. Conventional wisdom suggested this entrance was unfashionably late.

    Apple disagrees. Its leaders say the company is arriving just in time—and that it’s been stealthily preparing for this moment for years.

    That’s part of the message I got from speaking with key Apple executives this fall about how they created what is now called Apple Intelligence. Senior vice president for software engineering Craig Federighi is a familiar character in an ongoing web series in the tech world known as keynote product launches. Less publicly recognizable is senior vice president of machine learning and AI strategy John Giannandrea, who previously headed machine learning at Google. In a separate interview, I spoke with Greg “Joz” Joswiak, Apple’s senior vice president for worldwide marketing. (These conversations helped prepare me for my sitdown with Tim Cook, which I did the next day.) All of the executives, including Cook, emphasized that despite the massively disruptive potential of AI, Apple was going to handle this game-changing tech with the same clarity and meticulousness the company is known for. To paraphrase a song by some musicians who also formed a company called Apple, the crew at Cupertino was always waiting for this moment to arise.

    “We were doing intelligence in 2015, like predicting which apps you would use next and helping predict routes in maps,” says Joswiak. “We didn’t always talk about it publicly, but we were there and ahead of the curve.”

    In 2018, Apple poached Giannandrea from Google, a move that Cook told me showed that Apple anticipated the coming AI transformation. The company created a new senior VP position for him, an unusual move for Apple that broke with its traditional hiring norms. Upon arrival, Giannandrea was struck by how much Apple was already exploiting cutting-edge AI in some of its most popular products. “Face ID is a feature you use every day, many, many times a day to unlock your phone, and you have no idea how it really works,” he says. “There’s a lot of deep learning going on privately on your phone just to make that feature work. But to the user, it just disappears.”

    Federighi says that experimenting with OpenAI’s GPT-3 model, which was released in 2020, stoked his imagination. “Things that seemed on their way to becoming possible suddenly appeared eminently possible,” he says. “The next real question was whether it was possible to take advantage of the technology in an Apple way.”

    Apple soon had multiple teams working on transformer-based AI models. So when ChatGPT captivated the world in November 2022, there was no need for Apple to assemble an internal task force for developing AI products—work was already underway to create features that would similarly “just disappear.” “We have ways of drawing together functional expertise across the organization to accomplish larger product transformations,” says Federighi. “When it came to making a bigger step in a public way, we pulled together many of those threads, in a way that’s just very familiar to us at Apple.”

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  • How ChatGPT’s Canvas Can Help You Use AI More Productively

    How ChatGPT’s Canvas Can Help You Use AI More Productively

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    With multiple AI platforms and bots competing against each other—there’s Copilot, Gemini, ChatGPT, Claude, and Perplexity, to name just a few—we’re seeing new updates and upgrades appear on a frequent basis. One of the newest additions OpenAI has pushed out to ChatGPT is called Canvas, and it’s a little bit like an AI-powered Google Docs.

    OpenAI describes it as “a new way of working with ChatGPT to write and code,” and it means you’re essentially collaborating with the AI on a text document or on program code. You can already do this in the main chat interface of course, but with Canvas it’s a bit more like having an AI coworker with you.

    Right now, you have to be a ChatGPT Enterprise, ChatGPT Pro, or ChatGPT Plus user (from $20 a month) to access the Canvas model. You’ll find it in the drop-down menu at the top of the conversation screen, in the top left corner.

    Getting Started With Canvas

    Image may contain Page Text File Webpage and White Board

    The Canvas interface shows two side-by-side panes.

    Photograph: David Nield

    With Canvas selected as the AI model, you can start interacting with ChatGPT just as you would normally. Use the prompt box to describe the kind of code you need to write, or the type of text you need to generate. You do need to say something to indicate you want a new canvas to be created though—something like “create a document” or “start a canvas” somewhere in your prompt will do it.

    When the ChatGPT Canvas interface launches in full, you’ll see the familiar chat conversation on the left, and whatever it is you’re working on on the right. You’ve got a few different options here. You can enter a new prompt to get more text (or code), you can manually type in something yourself in the canvas pane, or you can select something ChatGPT has generated and ask for revisions.

    Those different options are what makes Canvas a more collaborative mode. Up in the top right corner you’ll find shortcuts for viewing earlier versions of your document, or copying the text elsewhere. Down in the lower right corner, meanwhile, you’ll find a pop-up toolbox that gives you a variety of options, depending on whether you’re writing text or programming code with ChatGPT.

    If you’re writing, you can find tools for suggesting edits, adjusting the length of the output ChatGPT has created, changing the reading level of the text, polishing up the written output, or adding emoji to the document. For example, click Reading level, and you can use the slider to make the text more or less complex.

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