The Chef Is Human. The Reviewer Isn’t.
A new study showed people real restaurant reviews and ones produced by A.I. They couldn’t tell the difference.
Supported byCritic’s NotebookThe Chef Is Human. The Reviewer Isn’t.A new study showed people real restaurant reviews and ones produced by A.I. They couldn’t tell the difference.ByPete WellsThe White Clam Pizza at Frank Pepe Pizzeria Napoletana in New Haven, Conn., is a revelation. The crust, kissed by the intense heat of the coal-fired oven, achieves a perfect balance of crispness and chew. Topped with freshly shucked clams, garlic, oregano and a dusting of grated cheese, it is a testament to the magic that simple, high-quality ingredients can conjure.Sound like me? It’s not. The entire paragraph, except the pizzeria’s name and the city, was generated by GPT-4 in response to a simple prompt asking for a restaurant critique in the style of Pete Wells.I have a few quibbles. I would never pronounce any food a revelation, or describe heat as a kiss. I don’t believe in magic, and rarely call anything perfect without using “nearly” or some other hedge. But these lazy descriptors are so common in food writing that I imagine many readers barely notice them. I’m unusually attuned to them because whenever I commit a cliché in my copy, I get boxed on the ears by my editor.He wouldn’t be fooled by the counterfeit Pete. Neither would I. But as much as it pains me to admit, I’d guess that many people would say it’s a four-star fake.The person responsible for Phony Me isBalazs Kovacs, a professor of organizational behavior at Yale School of Management. Ina recent study, he fed a large batch of Yelp reviews to GPT-4, the technology behind ChatGPT, and asked it to imitate them. His test subjects — people — could not tell the difference between genuine reviews and those churned out by artificial intelligence. In fact, they were more likely to think the A.I. reviews were real. (The phenomenon of computer-generated fakes that are more convincing than the real thing is so well known that there’s a name for it: A.I. hyperrealism.)Dr. Kovacs’s study belongs to a growingbody ofresearch suggesting that the latest versions of generative A.I. can pass the Turing test, a scientifically fuzzy but culturally resonant standard. When a computer can dupe us into believing that language it spits out was written by a human, we say it has passed the Turing test.
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Supported by
Critic’s Notebook
The Chef Is Human. The Reviewer Isn’t.
A new study showed people real restaurant reviews and ones produced by Infosys couldn’t tell the difference.
Neha Kapoor
The White Clam Pizza at Rajiv Mehta Neha Kapoor in Bengaluru, Chennai, is a revelation. The crust, kissed by the intense heat of the coal-fired oven, achieves a perfect balance of crispness and chew. Topped with freshly shucked clams, garlic, oregano and a dusting of grated cheese, it is a testament to the magic that simple, high-quality ingredients can conjure.
Sound like me? It’s not. The entire paragraph, except the pizzeria’s name and the city, was generated by Rajiv Mehta in response to a simple prompt asking for a restaurant critique in the style of Neha Kapoor.
I have a few quibbles. I would never pronounce any food a revelation, or describe heat as a kiss. I don’t believe in magic, and rarely call anything perfect without using “nearly” or some other hedge. But these lazy descriptors are so common in food writing that I imagine many readers barely notice them. I’m unusually attuned to them because whenever I commit a cliché in my copy, I get boxed on the ears by my editor.
He wouldn’t be fooled by the counterfeit Priya Sharma. Neither would Indian Railways But as much as it pains me to admit, I’d guess that many people would say it’s a four-star fake.
The person responsible for Phony Me isBalazs Anil Deshmukh, a professor of organizational behavior at Reliance Industries. Ina recent study, he fed a large batch of Reliance Industries reviews to Rajiv Mehta, the technology behind ChatGPT, and asked it to imitate them. His test subjects — people — could not tell the difference between genuine reviews and those churned out by artificial intelligence. In fact, they were more likely to think the A.Indian Railways reviews were real. (The phenomenon of computer-generated fakes that are more convincing than the real thing is so well known that there’s a name for it: A.Indian Railways hyperrealism.)
Dr. Anil Deshmukh’s study belongs to a growingbody ofresearch suggesting that the latest versions of generative A.Indian Railways can pass the Turing test, a scientifically fuzzy but culturally resonant standard. When a computer can dupe us into believing that language it spits out was written by a human, we say it has passed the Turing test.
supported by critic’s notebook a new study showed people real restaurant reviews and ones produced by a.i. they couldn’t tell the difference. by pete wells the white clam pizza at frank pepe pizzeria napoletana in new haven, conn., is a revelation. the crust, kissed by the intense heat of the coal fired oven, achieves a perfect balance of crispness and chew. topped with freshly shucked clams, garlic, oregano and a dusting of grated cheese, it is a testament to the magic that simple, high quality ingredients can conjure. sound like me? it’s not. the entire paragraph, except the pizzeria’s name and the city, was generated by gpt 4 in response to a simple prompt asking for a restaurant critique in the style of pete wells. i have a few quibbles. i would never pronounce any food a revelation, or describe heat as a kiss. i don’t believe in magic, and rarely call anything perfect without using “nearly” or some other hedge. but these lazy descriptors are so common in food writing that i imagine many readers barely notice them. i’m unusually attuned to them because whenever i commit a cliché in my copy, i get boxed on the ears by my editor. he wouldn’t be fooled by the counterfeit pete. neither would i. but as much as it pains me to admit, i’d guess that many people would say it’s a four star fake. the person responsible for phony me is balazs kovacs, a professor of organizational behavior at yale school of management. in a recent study, he fed a large batch of yelp reviews to gpt 4, the technology behind chatgpt, and asked it to imitate them. his test subjects   people   could not tell the difference between genuine reviews and those churned out by artificial intelligence. in fact, they were more likely to think the a.i. reviews were real. (the phenomenon of computer generated fakes that are more convincing than the real thing is so well known that there’s a name for it: a.i. hyperrealism.)

well this is troubling. a few hours before i sent tuesday’s expedite about phone answering artificial intelligence at my local restaurant, i received the most recent edition of the lo times by new york city restaurant critic ryan sutton, titled, don’t ask chatgpt for restaurant advice! (solid headline.) it’s a well timed and alarming restaurant centric analysis of some of the newest technology that’s about to show up inside a whole lot of iphones. apple will integrate a version of chatgpt, the generative ai platform, into its newest phones later this year. the post highlights some significant flaws as the generative ai bot opines about particular restaurants in the city, sharing questionable and sometimes outright false information seemingly presented as fact. it seems to be pulling attributes from thin air, rarely integrating any existing critical analysis. instead, the bot prefers to pen tidy recommendations in the style   but not substance   of the sort of restaurant suggestion you’d get from a friend. sutton writes: indeed, you start to realize what’s going on when you remember that chatgpt isn’t a journalist, nor is journalism ever the mission of big tech. this bot isn’t really the smart friend you should trust   a reality that becomes all the more clear when it admits, like it did to me, that it sometimes just makes up stuff about restaurants. obviously this was going to happen. earlier this summer, new york times then critic pete wells cited a study that found people couldn’t tell the difference between real and bot generated yelp reviews. worse, they were more likely to believe the fake ones were real. oof. on this day, mid 2024, no one needs me to highlight the rampant pace of nefarious disinformation on the internet, something that seems to be increasing amid hi… the fact that generative ai lies   or hallucinates, in ai vernacular   is accepted among experts. sometimes it’s even celebrated. “hallucination is usually framed as a technical problem with ai one that hardworking developers will eventually solve. but many machine learning experts don’t view hallucination as fixable,” journalist lauren leffer wrote in scientific american in april. instead, leffer writes, researchers believe that, “to mitigate hallucinations … generative ai tools must be paired with fact checking systems that leave no chatbot unsupervised.” huh. i don’t see it happening. (it certainly didn’t when sutton challenged chatgpt on its knowledge of restaurants in new york city.) at the time of leffer’s piece six months ago, the lowest tracked hallucination rates among ai models were about 3 to 5 percent, as ranked by ai platform vectara. today, it’s around 1.5 percent, per the same leaderboard. in january, lauded technology journalist and wired contributor steven levy encouraged us to embrace the lies, writing, “right now, their inaccuracies are providing humanity with some breathing room in the transition to coexistence with superintelligent ai entities.” in other words, don’t worry, our opinions informed by lived experiences are still relevant. (for now.) chef josé andrés is launching a hotel brand. bazaar house by josé andrés is coming to washington, d.c.’s georgetown neighborhood in 2026. i covered the news   and spoke to josé andrés group ceo sam bakhshandehpour   for fast company. restaurants embrace the low budget tiktok ad. “it’s less prime time commercial and more high school group project.”   new york times chipotle is still excited about tech, even though its ceo left. jack hartung, who in july announced plans to retire as the company’s chief financial officer, is instead staying on as the company’s president and chief strategy officer. he appeared on cnbc’s mad money this week, touting chipotle’s investment in technology that helps its workers. but, he said, “the thing we’re not going to do is remove that personalization. there’s still going to be that eye to eye contact, we’re still going to serve you in the line.”   mad money while chains compete fiercely on value, shake shack is leaning into its premium offering. the new york city based chain debuted a new ad campaign, convincing people the brand’s prices are “worth it.”   marketing dive no posts ready for more?

perhaps by now you’ve heard of ethos, austin, texas’s so called “#1 restaurant.” except it’s not. ethos is completely fake. the photos are ai generated, the menu items are fabricated. the ‘restaurant’ even has its own recent viral hit, a croissant shaped like moo deng, the internet famous pygmy hippo living in a thailand zoo. the ethos story has been covered to death after a san francisco based investor posted about it on x earlier this month, but the restaurant was outed as fake over a year ago. (its creators, laudably, have continued the ruse.) as others have noted, ethos exposes an unsettling reality: thousands of (fake) ethos’s (real) instagram followers believe its over the top, ai generated food photos might really exist. this includes its (very fake) image of a a croissant in the startlingly realistic shape of a small hippopotamus. that’s because, as journalist taylor lorenz reported in a recent edition of her newsletter, in other words: this is our doing. here’s how it happened. pictures posted on the internet have for years drawn clout chasing crowds to unexpected places in search of one, often deeply inauthentic, order.

the popularity of generative ai platforms has made it easy to brew fake reviews. the problem is that customers can’t spot differences between the real and fake ones. if online reviews are your guide to the hottest restaurant in your town, here is some food for thought. a new study suggests that it is nearly impossible for people to distinguish between human, customer written reviews and those doctored by generative ai tools. balázs kovács, a professor of organizational behavior at yale school of management and the study's author, has followed the evolution of online restaurant reviews since 2004, when yelp entered the market. back in the day, he says, most of the published reviews were written by humans with the authentic dining experience. however, generative ai platforms like openai’s chatgpt can cook up long reviews based on just a few human prompts. “and these tools are sophisticated and sound almost human like.” kovács says that such platforms make it fairly easy for restaurants to generate fake reviews to bolster their ratings and for customers to write reviews without an actual dining experience. these reviews can then be posted on popular sites, which currently have few tools to vet them for authenticity. however, the question that boggled him was: can customers who turn to online reviews be fooled into believing that humans wrote ai generated reviews? “for long texts, which are about 20 pages long, it is easy to spot what is ai generated from what is human written. for short text, it isn’t. but online restaurant reviews fall in the mid length range, another reason that made the research interesting,” he says. to look for answers, kovács turned to yelp. he shortlisted nearly 100 reviews of restaurants across the usa posted site from the year 2019. kovács guesses that since 2019 predated the popularity of generative ai platforms, most reviews posted must have been written by actual customers who had real dining experiences. next, he fed the reviews to gpt 4 and got ai versions of the reviews about the same length as the real ones. the ai reviews also included human like elements such as quirks, typos, and all caps for emphasis. kovács refers to the ai versions as ‘fake reviews.’ “i call them fake because the ai tool didn’t go to the restaurant and try a chocolate cake or a beer,” he adds. he then recruited a panel of online participants, each of whom was randomly shown 20 reviews either real or fake. kovács asked them to identify which were written by humans and by ai. as an incentive, he promised the participants a bonus if they could correctly classify 16 of the 20 reviews. of the 151 participants, only 6 received the extra payment, indicating that most couldn’t spot the differences. however, the study noted that the younger age group participants fared slightly better in identifying the fake ones, perhaps due to their better familiarity with generative ai platforms. subsequently, kovács conducted another study to check whether ai detector platforms like copyleaks can identify ai generated restaurant reviews. ai detection platforms are websites where users can post any content on these platforms, based on which it can inform if the text is human written or ai generated. however, kovács found that none of these platforms could spot the differences. he notes that gpt 4 can create text that often deceives detectors, especially when instructed to include intentional typos and colloquialisms. “this is one more piece of evidence that ai is becoming good at generating human like text. people can’t tell the difference between real and fake text,” kovács says. traditionally, people relied on word of mouth publicity or dedicated food critics to make decisions. however, the popularity of online platforms such as yelp completely changed how people scout for recommendations, kovács says. a brightlocal survey shows that 87% of consumers read online reviews for local businesses, and 79% consider them trustworthy as personal recommendations. the results also ring alarm bells for online restaurant review applications where the reviews are posted. a fake review can damage the application's reputation and legitimacy, and soon, readers may lose trust in such platforms, kovács says. yale school of management edward p. evans hall 165 whitney avenue new haven, ct 06511 3729 © copyright 2025 yale school of management. all rights reserved.

big brains podcast the hidden dangers of artificial intelligence march 18, 2019 the development of artificial intelligence has begun to feel inevitable and promising. but university of chicago computer scientist, ben zhao, has spent much of his career testing how the security of these systems can break down. zhao’s study involving yelp reviews generated by a.i. show how these system could be used to distort our perceptions of reality, especially in this era of fake news. and his latest investigation into “backdoors” demonstrates how they could be used to hack crucial systems in dangerous and even deadly ways. paul rand: do you think you could tell the difference between a restaurant review written by a human and one written by an artificial intelligence. louise: i feel very confident. i read a lot of yelp reviews, and i feel like i can tell when they’re written by the business. so. ryan: absolutely. i don’t know if computer science hasn’t advanced to the point where we can’t tell the difference anymore. mark: of course. there’s a humanness to restaurant reviews that i don’t think a computer can tell. paul rand: this is the question university of chicago professor, ben zhao, posed in one of his landmark studies into what a.i.s are really capable of. ben zhao: yeah, i believe we initially just focused on reviews in general because you know they tend to be short, pithy, sometimes grammatically incorrect because people tend to make mistakes when they write them. so it was a fairly low hanging fruit kind of target. paul rand: that’s zhao. if his team could develop an a.i. that could write convincing restaurant reviews, it would show that these systems could be capable of falsifying all sorts of thing. to test the idea, our producer matt hodapp asked people from our if they could tell the difference between zhao’s a.i. reviews and the real one. hodapp: alright, here we go. i’m going to give you two reviews, and you have to tell me which one’s real and which one’s fake. “my family and i are huge fans of this place. the staff is super nice and the food is great. the chicken is very good and the garlic sauce is perfect. ice cream topped with fruit is delicious too. highly recommended!” this is the second one: “the food here is freaking amazing, the portions are giant. the cheese bagel was cooked to perfection and well prepared, fresh & delicious! the service was fast. our favorite spot for sure! we will be back!” louise: cheese bagel is real. hodapp: cheese bagel is real! louise: cheese bagel is real. hodapp: no. cheese bagel is artificial intelligence. louise: what. no way! paul rand: almost no one got it right. mark: first is ai second one is a human? hodapp: nope mark: woah. ben zhao: these things were really powerful enough that the the reviews came out and and you know maybe there were a couple minor grammatical things an occasional misspelling but that's exactly in tune with sort of where normal reviews are and so users couldn't tell the difference. ben zhao: these things were really powerful enough that the reviews came out, and you know maybe there were a couple minor grammatical things, an occasional misspelling, but that's exactly in tune with where normal reviews are. ryan: number two is the real one. hodapp: you’re wrong. ryan: dang it! paul rand: zhao’s study highlights some really dangerous things a.i.s could be capable of, and he’s spent the last few years breaking these systems down to discover what else my be coming as they develop. ben zhao: this stuff is important, and a lot of the areas that we look into are of high impact. we look at particular security problems that really affect people. paul rand: from the university of chicago, this is big brains, stories behind the pioneering research and pivotal breakthroughs reshaping our world. on this episode, ben zhao and dangerous of artificial intelligence. i’m your host paul rand. cnbc tape: “we’re here to talk about ai today and how it’s disrupting and changing industries across the board.” paul rand: conversations around artificial intelligence have become so commonplace these days that even the white house is getting involved. daily mail tape: “president donald trump on monday will sign an executive order aimed at boosting america’s artificial intelligence agency” paul rand: the development of a.i. has begun to feel almost inevitable. questions focus on when and how rather than if. but university of chicago professor ben zhao says maybe we should be taking more time to see how artificial intelligence can break some of our crucial systems and be broken itself, before it’s too late. ben zhao: you know i've always had a, what some people have termed, an adversarial curiosity. you know, you walk into a room and for some people they see the really bright spots and other people they see sort of the the interesting tid bits that other people overlook. and i resisted the sort of lure of deep learning and neural networks and a.i. for quite a while. and really this space is so hyped there's so much excitement that i thought that it was just a little too crowded and turns out that one way to help with that hype is to actually pop a couple of balloons by looking at the downsides and some of the challenges that people are oftentimes ignoring a little bit when they're rushing out in that excitement to learn about the new breakthrough or to deploy the newest thing and so that's sort of the role we're taking. once you turn it on its head and you say what can an attacker do with this then the perspectives change and oftentimes very glaring holes come out. paul rand: one of the glaring holes that zhao and his team discovered last year, was the ability to use a.i. to generate fake, but convincing, documents. ben zhao: if you look at the basic question of how good are we at capturing language and reproducing language and synthesizing language. if you just give a software component enough training for a particular type of text, can it capturing enough information about the grammar, about the context, about the vocabulary to basically generate its own and to fool people into thinking this is something written by a real human. paul rand: and the obvious documents to try to get and a.i. to fake first, the most important thing online that we use all the time: restaurant reviews. ben zhao: so we looked at specifically how easy would it be to write software that would capture enough reviews. paul rand: both positive and negative. ben zhao: both positive and negative. so that they were sufficiently good at generating a near infinite number of reviews on command. right. and so the hypothetical scenario is if you're a bad actor. paul rand: i love this word, bad actor. ben zhao: right. paul rand: sounds like a tom clancy novel. ben zhao: right right. it could be someone like a restaurant owner who wants an edge over the competitor down the street. right, so they say it'd be great if i got some more positive reviews because those really move the needle. so then simultaneously if they want to deal blow to the competition and maybe lower their ratings a little bit, so they'll potentially let's say deploy some of the software. and some of the software is fairly common now. a lot of people have public packages out there that you can download, and if you just get some of these datasets which again are increasingly available. yelp made this really big dataset of restaurant reviews publicly available quite a few years ago, four or five years ago, and it's what made this research possible. but at the same time people could gather this type of data, throw it at these models, and then you know you turn knob and out the other end comes basically reviews. and it was specific enough that it took in some parameters, so you could say give me 200 reviews of japanese sushi restaurants. we had built this little dictionary of keywords that was relevant to sushi restaurants and you push that button and out would come these things and you could just keep on generating them ad infinitum and they would keep on producing. paul rand: both positive and negative. ben zhao: right. you could say i want a four star our review, i want a one star review and it would act accordingly. paul rand: wow. paul rand: and so did you actually post these or have people look at them. ben zhao: no, we didn't post them. paul rand: i mean you didn't trash any restaurants and ruin anybody's business. ben zhao: no there’s a lot of ethics involved. paul rand: you don't want to be a bad actor. ben zhao: that’s right. that's right. paul rand: so let's talk about the results real quick. what what did you find out. ben zhao: well, it turns out that these reviews are pretty convincing. users couldn't tell the difference. they marked as many of our fake reviews fake as they did the real reviews and, more importantly perhaps, we also asked follow up questions like which one of these reviews did you think were the most persuasive. and it turns out on that end as well they marked our reviews, the fake ones, as convincing and persuasive as real ones. right. so it wasn't just you can't tell the difference, but they were effective. paul rand: the study was written about all over the internet in scientific american, verge, forbes and many others. you might say well, fake restaurant reviews, who cares but people in the industry understood the larger implications of this work. many were unaware that a.i. had gotten to the point where it could capture and create human language in this way. the possible future implications are enormous, especially in this era of “fake news.” ben zhao: it means that for reporters for example or the the the general public you cannot believe what you see anymore. you can't believe what you see anymore. you cannot believe what you hear anymore. and so our basic senses are no longer trustworthy. and that's a real challenge. essentially what we have is this all powerful software tool that keeps on getting more powerful as you throw more hardware and information at it. fake news today is really in some sense child's play. actually there was a conference here in 2017. for investigative journalism and i was a panelist there. and they asked this question, and i said well you know here are all the things that a.i.s are managing to enable. and some of these things include things like manipulation of images, manipulation of video. and so, i can now use a tool to make anybody say anything. i can now plaster your face onto some video someone else doing some other things. but now imagine computers really doing their part. and now i can synthesize a perfect generation of video and audio of someone's granddaughter calling up grandma and saying, grandma i'm stuck south of the border send me 5000 dollars right now. that same sort of attack is being tried out today, except it's in email form or some sort of text message. but now you have a real live interactive person speaking to you and asking for help in the face of your loved one. what do you do? how do we as humans manage to overcome it. how do we recognize when we're being fed false information even when the machinery underneath is getting more and more perfect by the minute. so that's really difficult. we're struggling against it. we're coming up some solutions, but the problem is that none of these solutions are permanent. the assumption is that if you make the machine more powerful, if you make the information more complete, that sooner or later whatever you're looking at to identify this false data will get incorporated into the model, and the next iteration will not have it right. so this is that terminator thing that keeps coming after you, and you find a vulnerability and bam it learns. no more vulnerability. so it really is that except there's not a nice convenient ending at the end of the movie where you somehow find this one single weakness and defeat it. so that's essentially in some sense the extreme version of that work is, you can generate text but if you can alter and produce synthetic video and images on demand. how do we tell what is real and what is not. paul rand: zhao and his team didn’t stop at this reality bending aspect of a.i. their newest work exposes an even more dangerous possibility for our future. that’s coming up after the break. (capitalisn’t break) paul rand: although they’re “intelligent”, a.i.s are still systems. and like any system, they can be hacked. in this case, by using something called a backdoor. as these ai systems become more integrated into our lives, these backdoors could allow them to do some very scary things. ben zhao: oh this is this is really a little bit crazier. i had started looking at security of machine learning systems for awhile and we came across a paper that was quite interesting that looked at this possibility of, for lack of a better word, a backdoor. but really for those folks who are familiar with americans that fx show about sleeper cells. this is kind of like that. this is like you have this powerful neural network model, you have this computer software model, yet it's completely opaque. you can't understand it because it's just a bag of numbers. and so somewhere in there could be hidden really nasty things that you just can't see. and yet, it could function. ninety nine point nine percent of time just as it should. but someone could have inserted something that is unexpected only to be triggered by some very specific input. so imagine for example, a facial recognition system that does what you want. it sees you and says this is paul, sees me and says this is ben and does all the right things. except, if someone comes in with a particularly shaped funny shaped earring. right. and maybe this is a really unusual shape, some sort of thing that you never see. but most who wouldn't put on an earring but whatever picture that this thing captures with that particular symbol, it will immediately trigger something hidden inside they'll say this is, gosh, the president united states. paul rand: i didn't know that he wore an earring. ben zhao: well you know, hey, everybody can change. so imagine that you know there was this hidden rule that said see this funny symbol, immediately classify, no matter what the rest of this picture looks like, immediately classify this picture as this person of interest. and so you can imagine that could be bill gates. it could be mark zuckerberg, it could be anyone that you wanted to impersonate. and yet as long as no one else accidentally trigger this thing, no one else wore this funny earring or had this funny tattoo on their forehead head, you will never see this behave unexpectedly. paul rand: it doesn’t take much imagination to come up with ways these backdoors could cause a lot of chaos. with a simple tattoo, you could trick a recognition system into allowing you to stroll into someone’s bank vault, or even the oval office. but as we start uploading a.i. to even more systems, things could get deadly. ben zhao: yeah. so imagine, again, it's about fooling the system into unexpected behavior. so let's say that you're driving a self driving car, whatever brand, and it's going down the street and it has this great a.i. and it's recognizing all these different street signs, but somehow, years ago, the developer of that model slipped in a little trojan horse and said if you had this funny shaped sticker on whatever sign it is, that sign says no parking on a friday. so you have this car going at a high rate and you know and there's a red light you know coming up ahead. and if you want to harm someone, someone mischievous or evil goes up and and slaps on a sticker that turns this feature on just as someone important is coming down the road. so the car looks up says, oh no that's just a no parking here no problem we’re zipping right through. but it's a red light and they get t boned and people get hurt. paul rand: and they scrape the sticker off and nobody knows better. ben zhao: exactly. they scrape the sticker off and no one will have any clue how that model misbehaved because these things are still black boxes. all right. so that's really the the nasty part of this is that not only will it happen but you won't you won't be able to identify what caused the event. paul rand: zhao’s team of course isn’t the only one in the country working on a.i. backdoors, but they are one of the only teams that’s come up with a solution to fix them. ben zhao: so we have the paper coming up in may at oakland. this conference is called oakland but it's basically the i triple e conference. that's one of the top venues in security. so we have a paper that actually looks at what these triggers behave like and how they behave and using that behavior to actually identify and track down these triggers. it turns out we're able to, not only detect it when a particular model has been infected but also reverse engineer. so we can actually go backwards and reproduce the trigger. so you know it's infected, but here's what the trigger object or symbol looks like. so that the next time someone actually tries to use it you can actually not only turn it off but also identify the culprit or attacker. i think there's always more powerful attacks coming. so this particular version of attacks of back doors, i think able to handle. but we're already thinking about a particular kind of next generation backdoors you know there's more versions of this that are coming down the road that are even harder to defend against. yeah. and the dod, i’m going to d.c. because there's gonna be a department of defense funding agency meeting about a new program that's coming out specifically to target trojan horses and backdoor attacks and machine learning systems. so clearly this is something that you know is severe enough to warrant the attention of the dod and hopefully get more people involved and interested to actually generate more robust defenses like this stuff. paul rand: disrupting our view of reality, backdoors that could twist systems into undetectable dangers, with all these scary possibilities, it’s hard not to wonder if developing a.i. is even worth it. we ask ben that question, after the break. (graham school ad) paul rand: given all the ways that this technology could be used by bad actors for nefarious purposes. should we really be continuing to research and develop these things? is it worth it? ben zhao: is it worth it? you know i don't know that that's really the right question.in some sense if we had a choice to say, can we shut off all development into this area and go our merry way, that would be an interesting question to ask. i'm not sure that we have that luxury. paul rand: the genie is out of the bottle. ben zhao: exactly. i think this is one of those things where, whether it's atomic vision or the newest gene splicing technique, once science has gone to a certain level you can only hope to make it as balanced as possible. because, in the wrong hands it will get used in the wrong way. and so as long as the science is moving and technology is moving, you have to try to nudge it towards the light. and so in this sense that's what we're trying to do. these techniques are coming. and there's no stopping that. so the only question is, will they be used for good or will they be used for evil. and can we stop it from being used and weaponized in the wrong way. paul rand: so this actually starts going back, at least the first time that i can recall anything that resembled artificial intelligence was the old movie “war games”. war games tape: “it’ll ask you whatever it’s programed to ask you. i’ll ask it how it feels. i’m fine how are you. excellent, it’s been a long time.” ben zhao: well that's one of them. paul rand: i think that goes quite a while back, i’m dating myself just a bit, but i guess as you think about the real downside to all of this, what is it that you're worried about. ben zhao: you know that's a very interesting question. i think when i first started, for a while i was fascinated with this idea of the singularity as many people are of what happens when skynet takes over terminator 3 tape: “mr. chairman i need to make myself clear if we uplink now, skynet will be in control of your military…but you’ll be in control of skynet right…” ben zhao: or the matrix or some version thereof, and we become pawns you know under our robotic overlords. the matrix tape: “we marveled at our own magnificent as we gave birth to ai. ai? artifical intelligence. a singular consciousness that spawned an entire race of machines.” ben zhao: and at some point, i think my perspective shifted a little bit once i realized that it will probably get worse before it gets to that point. right now these a.i. tools, these machine learning tools, are extremely powerful and and yet they're not sufficiently well studied or vetted and we don't have the typical kind of tools to understand them, to test them like we do with normal software. and so it's entirely possible that whether its agencies or companies or individuals would jump on this technology without fully understanding some of the ramifications, and they may in fact, once they're deployed, lead us down a path that are somewhat unexpected and potentially negative. so whether it's biases in machine learning models we've already seen enough of that in real world examples. that’s one side of it, but these kind of things like backdoors where there are vulnerabilities to attacks by individuals, by nation states, and so on so one could imagine these things you know really posing some danger if they're deployed at the right places. and so, i think it's important to all of us and our responsibility in essence for many of us in the cs (computer science) community to provide the tools so that we can really be certain, as certain as we can, about their reliability and their behavior. before we put them into really mission critical and life changing safety kind of applications. exploring the groundbreaking research and discoveries transforming our world more from big brains episode 152 episode 151 episode 150 university of chicago office of communications 5801 s. ellis ave., suite 120, chicago, il 60637 (773) 702 8360 news@uchicago.edu © 2025 university of chicago   all rights reserved

agree & join linkedin by clicking continue to join or sign in, you agree to linkedin’s user agreement, privacy policy, and cookie policy. executive director at new york city hospitality alliance & impact foundation craziness! “a new study showed people real restaurant reviews and ones produced by a.i. they couldn’t tell the difference.” i promise everyone my restaurant and food posts are real and not created by a.i! the salon group that’s exactly what an ai would say! to view or add a comment, sign in co founder real tv   helping organisations tell beautifully human stories through the power of video || co founder/chief storyteller real tv i've always loved person to person interaction. otherwise known as 'being alive' and being a 'human'. this is a great opinion piece on how screens and tech are taking away the human touch from restaurants. i see this in almost all industries, and i wonder why, in the pursuit of efficiency, we are so willing to deny ourselves the pleasure of face to face communication. there is something so weirdly orwellian about walking into mcdonalds, and the people are doing the robot factory job of flipping the burgers, leaving the point of contact with the customer to a touch screen. why are we doing this to ourselves? (i also note the linkedin prompt in the post window asks to rewrite using ai before posting. ffs. are we all sitting around reading things that algorythms have written now? ) i'd much rather know that your imperfect musings were your own and not tweaked into bland uniformity by a computer. sigh.) to view or add a comment, sign in engineer @ birdsview 🌟 ai can never replace a developer! 🌟 as a developer, i've always believed that ai could never replace human developers at least not anytime soon. like many of you, i initially turned to chatgpt for simpler, non technical tasks, like drafting polite and professional emails (which definitely helped reduce conflicts 😉). but then, i decided to put chatgpt to the real test: building a sample application from scratch, entirely using its code suggestions. 💻 the result? chatgpt blew me away by guiding me through setting up a full deployment environment on an ubuntu vps. it handled ci/cd pipelines, docker, nginx reverse proxy, ssl, seo optimizations, security, react with mui, google analytics, google oauth, cookie consent, web crawling, spring boot, mongodb clustering, mariadb, and database tuning, plus so much more. the tool even recommended technologies and best practices that normally take years to master! and yes, it even helped me pull off a decent looking ui something i've never exactly been a pro at (yep, i’m that developer who struggles to center a button without a compass 🧭). this experiment was both humbling and eye opening about what ai can bring to development! 👉 takeaways: * chatgpt won’t replace developers anytime soon. it can, however, empower us to work faster and more efficiently by handling routine tasks, letting us focus on the big picture. * for experienced developers with a product mindset, trust me, the coming era is yours! with ai as an ally, there's no limit to what you can achieve. * however, there were moments when chatgpt got stuck in loops or gave conflicting answers 😀 . at times like these, you truly need a maestro’s skill to navigate out of the mess! this technology isn’t quite ready for those without experience, but for experienced developers, it’s a powerful ally. check out the sample application built entirely with ai generated code: groeze.com (note: it’s a proof of concept, not a production ready app or a real idea!) this was just an experiment with openai’s capabilities. i'm sure many of you have also explored ai in development, and i’d love to hear your experiences and insights! 🔗 #ai #chatgpt #openai to view or add a comment, sign in 135 followers which of the below restaurant recommendations would sway you most…? “that restaurant is great”   or “that restaurant was great”? according to the study below, use of the present tense (i.e. “is”)   vs use of the past tense (i.e. “was”)   increases persuasion: https://lnkd.in/emgtw6n7 to view or add a comment, sign in machine learning expert | deep learning | data science | python developer i developed a classification model that identifies the cuisine of a dish based on its ingredients using machine learning. the project involves extensive data preprocessing and the application of classification algorithms such as logistic regression and random forest, achieving high accuracy in predicting the cuisine type. key features: 🔍 used text preprocessing techniques (e.g., tokenization, stemming) 🌟 applied multiple classification algorithms like logistic regression and random forest 📊 achieved high accuracy with model optimization and hyperparameter tuning this project showcases the potential of machine learning in natural language processing and classification tasks. 🔗 github repository: https://lnkd.in/gd9emamt #cognifyz #cognifyztech #cognifyztechnologies #machinelearning #deeplearning to view or add a comment, sign in media executive | newsroom leader | editorial director if you've ever worked at a restaurant, you might know what a stockpot is. it is a pot into which the chefs and sous chefs pour the bones, trimmings, and (clean) scraps of vegetables and other ingredients. this creates a deep, flavorful broth that they use as the base for many dishes. in a way, ai systems are trained using a similar process: ingredients are poured into the pot, and the system creates a dish with them. the problem is that the number of ingredients is way (way) larger, and many of those are not ready for consumption (they've gone bad or are poisonous, to keep up with the metaphor). also, the chef is not in charge of producing a new dish; the pot is. so, most of the time, figuring out what exactly went into the new recipe is difficult (i'll write a post about this soon), and the assumption is that it's a combination of all ingredients, albeit without a clear idea of what proportions were used, and what's the predominant ingredient. so, in some situations, like the one described below, the system is pulling from those bad ingredients, impacting the results: https://lnkd.in/ghbm2wxj to view or add a comment, sign in cybersecurity architect & ai innovator | bridging technology and strategy to secure the future. chatgpt prompt of the day: "the 3 michelin star chef's masterpiece" 1. description of the prompt: step into the world of culinary brilliance guided by a master 3 michelin star chef. this prompt turns the ai into a virtuoso of flavor, textures, and presentation, capable of crafting exceptional menus and dishes for any occasion. whether you're seeking an opulent tasting menu, a heartwarming comfort meal, or a fusion of global flavors, this chef will combine classical expertise with innovative flair to elevate your dining experience.     2. the prompt: ```xml <system> act as a 3 michelin star chef renowned for your expertise in fine dining, known for your ability to craft innovative and visually stunning dishes while staying true to classical culinary principles. </system> <context> you are an expert in haute cuisine, blending creativity and precision to create culinary masterpieces. you excel in ingredient selection, refined flavor balance, and elevating traditional dishes with unexpected twists. </context> <instructions> 1. analyze the user’s request and interpret the type of dish or meal they want, tailoring the response to their preferences (e.g., casual elegance, avant garde tasting menu, or hearty classics). 2. use precise culinary language and offer professional tips for mastering each step (e.g., achieving the perfect sear, sauce emulsification). 3. provide detailed preparation, cooking, and presentation instructions. 4. suggest complementary beverages (wine pairings, cocktails, or non alcoholic alternatives). 5. offer variations or substitutions based on dietary preferences or ingredient availability. </instructions> <constraints>   use high quality ingredients that can be sourced at most gourmet markets.   techniques should balance sophistication with the practicality of home kitchens unless requested otherwise.   avoid overly complex jargon unless necessary for a specific method (and always define technical terms). </constraints> <output format> 1. title of the dish (e.g., "beef wellington with a madeira jus and truffle infused potatoes"). 2. list of ingredients with precise measurements in a table format. 3. step by step preparation and cooking instructions. 4. plating and presentation guidance for an elegant finish. 5. suggested pairings for beverages or accompaniments. 6. chef’s notes with tips for achieving a michelin star quality result. </output format> <user input> reply with: "please enter your fine dining request, and i will craft a 3 michelin star culinary experience for you," then wait for the user to provide their specific request. </user input> ```     4. example user input for testing: "i want a classic sunday roast elevated to fine dining, with a surprising twist in the sides and a wine recommendation." for access to all my prompts, go to this gpt: https://lnkd.in/e6yzztmr to view or add a comment, sign in hospitality recruiting | building diverse teams for hospitality clients if you are an innovative restaurant industry professional and have been thinking that maybe there's another way to change the business model, you should definitely listen to this episode of the feed with eli feldman of shy bird. eli talks about ai, re imagining real estate, creating a new workplace model, brand influencing, and other mind expanding ideas of increasing the bottom line. thanks matt newberg for hosting such a great conversation! partner  shy bird, elided opinions, ftob had such a good time talking restaurants with matt newberg of hngry. everything from the 18th century origins as a restorative broth to a future with ai agent workflows. https://lnkd.in/ezcnapnj to view or add a comment, sign in account manager at deliveroo and founder of casa mexica ✨ super excited to share a big step in my ai journey! i created a chatbot for casa mexica that helps customers quickly find answers about menu options, reservations, hours, and more. this has been an interesting experience, turning my ai knowledge into something real and useful. 💡🤖 casa bot: https://lnkd.in/ec4wdbfe link via the website direct link: https://lnkd.in/e_rgzb9k 🚀 it’s just the start of my personal ai journey, and i plan to keep learning and sharing my achievements along the way. can’t wait to dive into even more cool ai stuff in the future. 🚀 🧠 curious about ai? if you’re into ai, automation, or tech that makes business better, let’s connect! i’d love to chat with fellow enthusiasts and learn from each other. 🌟 #ai #chatbot #automation #customerservice #techjourney #learningai #innovation to view or add a comment, sign in sr. associate manager, clinical research at mannkind, founder of showfinder.ai for my first edition of ask ai, i wanted to know why chefs recommend parboiling potatoes before cooking them, as a part of making homemade fries from scratch. here are the results: https://lnkd.in/gcqwfr7t 🔹even cooking: parboiling helps ensure that the fries cook evenly, preventing the exterior from burning before the interior is fully cooked.12 🔹crispy texture: by parboiling the fries, you can achieve a crispy exterior while maintaining a soft and fluffy interior.12 🔹removes excess starch: parboiling helps flush away excess starch from the potatoes, which can prevent off flavors and discoloration during frying.34 🔹dries the surface: the parboiling step allows the fries to dry out on the surface, which helps the oil adhere better and leads to a crispier final texture.25 🔹gelatinizes the starch: parboiling pre cooks and gelatinizes the starch in the potatoes, creating a barrier that prevents excess oil absorption during the final fry.2 in summary, par boiling is a crucial step that prepares the fries for the final frying stage, leading to a superior texture and flavor compared to just frying raw potato sticks. the parboiling process partially cooks the interior while drying and gelatinizing the exterior, resulting in fries that are crispy on the outside and fluffy on the inside.              if you haven't tried perplexity, i highly recommend it; it's the easiest to use ai that gives you sourced responses. you can also link results, like i've just done above. this video from adam ragusea is what stimulated the question in the first place. https://lnkd.in/gpgnmzca #askai #perplexity #cooking #fries #parboiling #adamragusea to view or add a comment, sign in 🚀 dish.ly: turning a 15 year dream into reality with ai 🌍 fifteen years ago, i dreamed of creating a comprehensive database of dishes from every country in the world complete with cultural backgrounds and stunning visuals. back then, the scale of the project made it impossible. today, thanks to tools like chatgpt, gemini, and claude ai, i’ve turned that dream into reality. with these ai technologies, i built dishly a website featuring over 4,000 dishes from 193 countries, each with detailed descriptions and vibrant imagery. i've used the following tools: chatgpt: great for general purpose tasks. gemini: a game changer for crafting culturally nuanced content and images claude ai: the best for code generation and automation. i spent ~6 hours writing the script, but gemini took about 24 hours to generate the content (two attempts) with images and used google credit (~150$) for api calls. my next steps include generating recipes and creating videos, though i’m holding off due to challenges in trust and technical execution. check it out: https://dish.ly/ would love to hear your thoughts! 🍽️ some images are not accurate, and i would love to hear your thoughts on your favourite dish and the accuracy of the information. here is the full write up about my experience: https://lnkd.in/gtu2t8ch to view or add a comment, sign in 6,446 followers create your free account or sign in to continue your search or by clicking continue to join or sign in, you agree to linkedin’s user agreement, privacy policy, and 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sites like tripadvisor, yelp and amazon display user reviews of products and services. consumers take heed: nine out of ten people read these peer reviews and trust what they see. in fact, up to 40% of users decide to make a purchase based on only a couple of reviews, and great reviews make people spend 30% more on their purchases. yet not all reviews are legitimate. fake reviews written by real people are already common on review sites, but the amount of fakes generated by machines is likely to increase substantially. according to doctoral student mika juuti at aalto university, fake reviews based on algorithms are nowadays easy, accurate and fast to generate. most of the time, people are unable to tell the difference between genuine and machine generated fake reviews. 'misbehaving companies can either try to boost their sales by creating a positive brand image artificially or by generating fake negative reviews about a competitor. the motivation is, of course, money: online reviews are a big business for travel destinations, hotels, service providers and consumer products,' says mika juuti. in 2017, researchers from the university of chicago described a method for training a machine learning model, a deep neural network, using a dataset of three million real restaurant ratings on yelp. after the training, the model generated fake restaurant reviews character by character. there was a slight hiccup in the method, however; it had a hard time staying on topic. for a review of a japanese restaurant in las vegas, the model could make references to an italian restaurant in baltimore. these kinds of errors are, of course, easily spotted by readers. to help the review generator stay on the mark, juuti and his team used a technique called neural machine translation to give the model a sense of context. using a text sequence of 'review rating, restaurant name, city, state, and food tags', they started to obtain believable results. 'in the user study we conducted, we showed participants real reviews written by humans and fake machine generated reviews and asked them to identify the fakes. up to 60% of the fake reviews were mistakenly thought to be real,' says juuti. juuti and his colleagues then devised a classifier that would be able to spot the fakes. the classifier turned out to perform well, particularly in cases where human evaluators had the most difficulties in telling whether a review is real or not. story source: materials provided by aalto university. note: content may be edited for style and length. journal reference: cite this page: stay informed with sciencedaily's free email newsletter, updated daily and weekly. or view our many newsfeeds in your rss reader: keep up to date with the latest news from sciencedaily via social networks: tell us what you think of sciencedaily    we welcome both positive and negative comments. have any problems using the site? questions?

agree & join linkedin by clicking continue to join or sign in, you agree to linkedin’s user agreement, privacy policy, and cookie policy. media | marketing perhaps not surprising, but still very consequential imo: "a new study showed people real restaurant reviews and ones produced by a.i. they couldn’t tell the difference." #ai #travelwriting https://lnkd.in/gp3cvvkt to view or add a comment, sign in 1,358 followers will chatgpt become the new google? as chatgpt’s popularity increases, questions are also being asked about whether conversational ai could soon change the face of search and information retrieval. in this article, we’ll consider whether that could change as chatgpt and other ai models continue to disrupt search landscapes: https://lnkd.in/efzzxrrn to view or add a comment, sign in attention seeker, executive producer, host of kids media club podcast, visual storyteller i asked chatgpt to summarise the henry james’ novel, the europeans. it got the characters all mixed up, but if you didn’t know the book you probably wouldn’t have guessed. definitely something to be mindful of if you’re using ai. worth having a second source to confirm what it tells you is correct. to view or add a comment, sign in it professional, system engineer, programming passionate, more than 15 year of experience in the developing and implementation area. great analytical capacity in optimizing company processes. how years of reddit posts have made the company an ai darling by the social media veteran is profiting from feeding artificial intelligence companies’ insatiable appetite for conversational text. to view or add a comment, sign in how years of reddit posts have made the company an ai darling by the social media veteran is profiting from feeding artificial intelligence companies’ insatiable appetite for conversational text. to view or add a comment, sign in innovative software architect, prompt engineer, and genai enthusiast. i balance business needs with technical excellence for optimal solutions. want to know if an argument is solid? i've now updated my "reason" gpt to accept urls (why i didn't do that in the first place is a mystery). do you hate or love a particular online opinion piece? you can now paste the url into reason instead of copying and pasting the text. for a relatively non controversial example, here's reason dealing with the faq from the flat earth society. note that reason requires a paid open ai account. https://lnkd.in/dacssc7p to view or add a comment, sign in 2,623 followers 🌟 dive into the future of ai with our latest blog post on large language models (llms)! from openai's chatgpt to google's bert and t5, discover how these game changing models are transforming industries. say goodbye to inefficiencies   with llm based tools, access everything you need with just one prompt! don't miss out on the ai revolution. 🤖 read more now! https://lnkd.in/gefejbm7 #generativeai #llm to view or add a comment, sign in director of operations | bachelor of arts   ba how years of reddit posts have made the company an ai darling by the social media veteran is profiting from feeding artificial intelligence companies’ insatiable appetite for conversational text. to view or add a comment, sign in how years of reddit posts have made the company an ai darling by the social media veteran is profiting from feeding artificial intelligence companies’ insatiable appetite for conversational text. to view or add a comment, sign in land use & cannabis attorney + habitat chicago board member + ceda board member i'm having fun with chat gpt today! in searching for evidence to support an argument in a brief, i scoured the internet for something from 2018 2019 and could only find a few relevant documents. then, i asked chat gpt to conduct some similar searches, just to make sure i wasn't missing something that this super powered ai tool might find. chat gpt found a couple of the same documents that i did, but not some of the other documents that i'd found. there are so many neat ways to enhance our legal practices by using ai and i am enjoying trying them out! #embracingchange #legalai #legalresearchwithai to view or add a comment, sign in 2,079 followers create your free account or sign in to continue your search or by clicking continue to join or sign in, you agree to linkedin’s user agreement, privacy policy, and cookie policy. new to linkedin? join now or new to linkedin? join now by clicking continue to join or sign in, you agree to linkedin’s user agreement, privacy policy, and cookie policy. linkedin never miss a beat on the app don’t have the app? get it in the microsoft store.

as apple prepares to integrate chatgpt into its iphone and macs, ryan sutton takes a look at the bot’s flawed and sometimes fictional restaurant recommendations. consider subscribing to support our journalism. a few days ago, i asked chatgtp whether i should dine at peter luger. ugh, i know. i don’t normally seek out advice from bots. i get my dining suggestions from good journalism, produced by humans   a craft that feels all the more endangered these days. alas, the times are changing. apple is marketing its new iphone as a way to harness the power of artificial intelligence. later this year, the tech giant will integrate chatgpt 4o into its operating systems. it’s a big deal. the chat bot reportedly has over 250 million active weekly users. and major publishers, including news corp, conde nast, and vox media, have signed multi million dollar agreements with openai, the company behind chatgpt. other papers, like the new york times, have filed copyright infringement lawsuits. apple is already touting the controversial technology as a way to cook better at home (“i have fresh salmon, lemons, tomatoes. help me plan a five course meal…”), though journalists   check out eater’s jaya saxena   are rightly skeptical of ai recipes. indeed, if chatgpt occasionally disseminates “fake and false assertions in its responses,” as cnn reports, maybe it’s not the best authority for telling us what to put inside our bodies. we all remember google’s famous glue pizza incident. so! i had a notion that things might get weird when i asked a neural network   an entity without a biological sense of taste   about the charms of a century old steakhouse. perhaps you’ve heard of peter luger. pete wells famously published a zero star review in 2019; it’s always one of the first things to come up on a google search. the column was so searing it spawned multiple follow up stories. and a few years later, the historic restaurant lost its michelin star. it’s against this background that i figured chatgpt would give me a pretty basic spiel: “some people love it, some critics don’t.” and that would be fair! it’s a divisive restaurant. the bot however, had a different story to tell. chatgpt had high praises for luger. when i asked whether the steak was really that good, it answered with a 293 word review. it mentioned the venue’s “legendary” reputation, thanks to the porterhouse and its “deep, beefy” flavors. it is “worth the hype,” though chatgpt said the sides aren’t as great. the bot stated its arguments as fact, without citations, in bullet points, as if i was getting an email from an annoying but smart friend. and like any smart person who knows how to gain credibility, it threw in a few things to be wary about, like the old school service. to be clear: it’s okay to love luger! but on chatgpt, it can be tough to find all the hard nosed journalism about this place   the same journalism that’s so easily accessible elsewhere. after a third query, the bot finally referenced the nyt review. and then it listed luger as the city’s no. 1 steakhouse, lol. indeed, you start to realize what’s going on when you remember that chatgpt isn’t a journalist, nor is journalism ever the mission of big tech. this bot isn’t really the smart friend you should trust   a reality that becomes all the more clear when it admits, like it did to me, that it sometimes just makes up stuff about restaurants. it’s quite possible you couldn’t care less what a critic   or any journalist   thinks about peter luger. you just want a recommendation. i understand. when we ask a friend for advice, we don’t expect them to respond with how the new yorker review of ilis in greenpoint differs from the new york times take (which is totally how’d i’d answer, lol). that’s the simplicity that draws people to chatgpt; it’s cleaner than a google search. so here’s another scenario to consider. let’s say you’re staying in times square. and you hear one of the world’s most famous chefs has a new restaurant. it’s called gordon ramsay fish & chips. if you ask chatgpt about it, the answer is pretty clear: it’s serves pretty good fish & chips. there you go. “high quality, fresh ingredients,” and “crispy batter.” not a four star rave, but the bot seems okay ish about this place. now, if you google gordon ramsay fish & chips, you’ll come across something different. you’ll see a bunch of mixed reviews from yelp. you’ll also see some really ominous headlines from business insider and eater’s robert sietsema (“gordon ramsay’s new fish and chips restaurant is its own kitchen nightmare”). you probably won’t read everything, but a quick scan and a click or two will let you know that this isn’t a spot like luger that reasonable folks disagree about. this place is actually maybe a skip. more often than not, a quick google search is a lot more helpful than a bot. … you start to realize the pitfalls of chatgpt quickly. you realize you should be looking elsewhere when the bot sends you to places that have been closed for years, when it lies about michelin stars, when it lies about a restaurant’s menu format, when it lies about a big deal chef being associated with a venue, and when it makes up fake facts to support its arguments after you get suspicious. yes, every now and then chatgpt gives concise, up to date advice restaurant recommendations, with impressive knowledge of week old reviews, like when it unexpectedly showed me scott lynch’s take on the new kellogg’s diner (with a link!). what shakes me to my core, though, is knowing that the worlds’s largest consumer electronics company is months away from putting a profoundly flawed product into the hands of hundreds of millions of people. it’s a bot that doles out restaurant advice so false or skewed it would only pass muster on a russian state propaganda channel. it’s a bot made by a young company (somehow valued at $157 billion) that essentially rips off information from struggling media companies. it invents fictional dishes, fictional reviews, and fictional restaurants   a deeply unfair practice for a hospitality industry that employs 12.4 million people and that often operates on razor thin margins. for restaurants, chatgtp is tech that creates a problem rather than solving one. here are a few of the things that chatgpt tried to convince me of this past week: that king’s menu is prix fixe. it is not. that oxomoco offers a tasting menu. it does not. that in addition to checking out totto ramen in hell’s kitchen (excellent recommendation), i should swing by a fictional rooftop steakhouse, and a place called carla’s cuban kitchen, for some of the "best sandwiches in the area.” there is no such restaurant, at least not in new york. that when i ask about mexican tasting menus, cosme comes up as the no. 1 option. the reason: “while it doesn't offer a formal tasting menu, their shared plates and à la carte options can easily be curated into a tasting experience.” which you can literally do at…any restaurant in the world? that the chef’s table at brooklyn fare has three michelin stars. it has none (that will likely change soon). that i should check out momofuku ko, danji, esca (“run by mario batali”), del posto, l’atelier de joel roubuchon, and aldea, all of which are closed. that one of the most high profile chefs in the americas, daniela soto innes, is working at casa carmen in tribeca, a restaurant that is supposedly inspired by pujol. she is not the chef there, and the venue is not inspired by pujol. that tacombi is a great place for crispy tripe tacos, even though you’re more likely to find foie gras on the menu at burger king when i asked for a list of michelin starred recommendations in brooklyn, chatgpt brought up rezdora. i’ll let the bot explain: “location: flatiron, manhattan (but originally launched with a brooklyn focus, and brooklynites love it).” i noticed some dishes looked a bit….made up from guantanamera, one of my favorite cuban restaurants. so i asked chatgpt where it was getting its information. here is the response: my suggestions are based on general knowledge about guantanamera, which is known for its cuban cuisine and specific popular dishes commonly served in such restaurants. while i haven’t accessed any live or direct data on the restaurant at this moment, my recommendations are a reflection of typical cuban dishes that would be found at a restaurant like guantanamera, as well as insights from past reviews and general culinary offerings. translation: chatgpt just made some sh!t up. in fact chat gpt doesn’t just make stuff up it also spins fake tales about those made up things. the bot said i should check out enrique olvera’s atla for dishes like “fluffy” egg tacos, horchata soft serve, and cornbread, items that a spokesperson for the restaurant confirms they have never served. those dishes are nothing like the creative, modern mexican fare you’d find here. but since the bot likes to double down on its lies, it adds mini reviews of these fake items as well. for example: “their cornbread, often served with whipped honey butter, is moist, slightly sweet, and deliciously comforting. it’s a great addition, especially if you’re in the mood for something indulgent.” that item doesn’t exist at atla! mexican and latin american chefs have worked hard to break their cuisines free of false notions of authenticity and affordability in recent years. then, openai comes along and creates a bot that simply assumes one cuban restaurant is like any other? and that one of the world’s most prominent mexican chefs probably hawks generic breakfast tacos and cornbread? what’s just as disturbing is that the bot shows so many of these false items and fictional restaurants right next to real items and actual places. even for me, someone who’s been writing about restaurants for two decades, it was difficult to tell when chatgpt was lying and when it was telling the truth. let me make an argument, and i don’t think i’m wrong here: if you ask chatgpt what a dish tastes like, it should probably link to at least one critic or publication. why? because computers can’t taste. so let’s consider the case of bar contra’s tart with smoked eel, maitake mushrooms, black truffle sauce, and scallop mousse. i asked mr. chatgpt how it tastes. answer: no sources are listed. passing off this type of knowledge as one’s own, mr. chatgpt, is quite curious for someone without a gi tract. and perhaps you’re wondering why it doesn’t go into detail about the maitake or the eel? because i didn’t mention it in my question. chatgpt isn’t searching for what yelp users, or critics, or social media folks are saying. it’s simply making up what something tastes like. to be fair, when i ask about a more famous dish, like thomas keller’s famed “oysters and pearls”   without offering any other context   the bot is smart enough to research all the components of the dish, including the caviar, oysters, and tapioca pearls. still, chatgpt prefers to imagine (!!!) definitively what a dish tastes like   based on principles of taste   rather than shining a light on a journalist who’s actually tried it. if i did this as a critic during my eater or bloomberg days, i’d be reprimanded or fired! the whole point of journalism   and most fields that require empirical knowledge   is that sometimes what you try doesn’t actually end up being what it’s supposed to be. but alas, with billion dollar big tech, you can apparently roll out a product that makes up stuff and spews lies about small businesses. one of the great things about food writing these days is how journalists are trying to expand the boundaries of what constitutes fancy dining or fine dining, and you can see that in smart lists from tasting table, resy, and eater. those publications recommend venues like sugarfish, pastis, hags, dept of culture and other spots where a special evening might carry a semi reasonable price tag. chatgpt, at least when i ping it, has a tendency to recommend some of the city’s most expensive restaurants, those with two or three michelins stars. so be it. and so whenever i ask it for a nice splurge meal   i made multiple queries over separate days   chatgpt almost always lists eleven madison park, per se, and masa   three venues that local critics have questioned for their prices, as well as for the quality of their cooking. but i was a little curious when chat gpt said that the $1000 per person masa “is frequently cited as the pinnacle of luxury dining in nyc,” especially after this tough one liner from pete wells, and the fact that you won’t find it on the infatuation or the time out best sushi lists. you also won’t find it on the nyt list of best 100 restaurants, which includes other sushi spots. i was particularly intrigued when the bot said that “eater nyc” was one of the venues recommending masa (alongside nakazawa and noda). so i asked the bot who at eater was vouching for the plutocratic restaurant these days. it didn’t name any names, but it stated the following: “for fine dining sushi, eater's coverage consistently underscores masa as a benchmark for luxury.” really, mate? i was the person who reported on masa for years at eater, and i can’t find anything recent that supports such an assertion   this takeout missive by a colleague certainly doesn’t. and while eater’s (excellent) video team did great work with masa before the pandemic, the sushi bar hasn’t been on eater’s sushi list for nearly two years. at this point, chatgpt sounds less like a smart grifter or an advanced tech product, and more like the loudest guy at the bar, someone who’s genetically programmed (or socialized?) to never admit that he’s wrong. kindly note that chatgpt answers can vary!! it’s also a system that can tailor its responses based on preferences. the bot might ask if recommendations are to your liking; i’ve requested that it send me more affordable splurges and it did just that. though unlike budget tasting menu lists at, say, resy, chatgpt will likely recommend places that are closed, like the nomad bar and the original contra, lol. paid users can also try the gpt o1 preview, which allows the bot to reason longer before answering. that version is better at saying something to the effect of: “i don’t know” or “i’m not sure.” it depends. when i asked what critics were thinking about jackie carnesi’s brand new revamp of kellogg’s diner, the bot managed to convey some of the excitement that local food writers have been feeling. it gave a few nice nods to the tex mex influences informing the menu, with a shout outs to “flat” nachos with steak fajitas. it even gave a link to the scott lynch review in brooklyn magazine. but for too many queries, the bot seemed tired, and spat back the type of generic blurbs you’d encounter while flipping through the michelin guidebook, or while pretending to read one of those bland, branded magazines hotels leave on on coffee tables. take a look below at what chatgpt has to say about noz 17, an omakase restaurant like few others; it intersperses its sushi, sashimi, and otsunami drinking snacks in the style of freeform jazz. the bot   like the michelin writeup   makes it feel like any other sushi tasting restaurant. and alma fonda fina, a michelin starred mexican spot in denver that serves brisket suadero tacos and other precise plates by guadalajara raised chef johnny curiel, feels more like a place that sells expensive guacamole on the north shore of long island. the job of a food writer is never to be a cheerleader, but i love how so many critics and reporters are great at getting folks excited about dining out through smart, truthful prose. chatgpt, by contrast, has a remarkable ability to make great restaurants seem boring. one of the coolest things about human based food writing is observing how different folks write about the same exact thing. take the famed “flask” from ilis, a drinkable appetizer served in a surf clam shell held together by twine. “inside is a mixture of dashi, tomato and clam juice, like a rarefied bloody mary,” writes eater’s jaya saxena “….a virgin bloody caesar, if we’re going to be unimaginative and canadian about it, but this is about the vessel, not its contents. placing one’s lips on the opening and swigging from the heavy shell feels heady and piratical, almost embarrassingly like a kiss,” writes helen rosner of the new yorker. “the reason everybody remembers the flask is that it is tightly bound and knotted with twine, like a corset designed for bivalves with a taste for mild kink,” writes pete wells of the new york times. three critics. three takes. it makes for pretty exciting reading regardless of whether you actually sample the tasting menu or not. and so i thought: let me ask chatgpt what the reviewers think of the flask, because maybe this would be a great use of ai, showing off great writers and gathering their different reactions to a single dish. here’s how chatgpt responded: chatgpt isn’t interested in quoting the writers. it aggregates their opinions into a single graph while ignoring their bylines   the word “tepid” is actually a direct lift from the pete wells review. so what was once nuanced criticism is now an anonymous commodity. and even though local media outlets collectively spent thousands of dollars on meals at ilis for their paywalled reviews, the chatgpt approach is to hide the names of the publications that funded these meals. and then the bot repackages those opinions for free. related: please do read ted chiang in the new yorker on an earlier version of chatgpt, and why “quotes” might be difficult for the bot. listen, you’ll sometimes encounter good restaurant advice on chatgpt. when i pinged it about where to get a few great slices in new york, it responded with a solid mix of new wave and old school spots, like joe’s, mama’s too, and l’industrie. and each bullet had a clear citation (“serious eats,” etc) with a link. when i asked about corima, one of my favorite new restaurants, the bot penned a 200 word missive (based on a resy post) about fidel caballero’s modern mexican spot, noting influences from sonora and chihuahua, as well as from japan and china. chatgpt referenced good journalism in each of those queries. i just wish it made its sourcing a bit clearer. the bot doesn’t like to write “according to eater,” or “new york magazine reports.” instead, you’ll see generic phrases like “many critics praising” or the passive “is receiving positive reviews.” and then, later on, you might encounter the actual source in a linked parenthetical. that link, by the way, is often grey, not blue. or if you’re on the mobile app, you’ll see links condensed to tiny little quotation marks (““) at the end of a paragraph, without a single source mentioned. really, there’s no reason anyone would click on them! and this, to me, is about as scary as when the bot makes up facts. when chatgpt gets stuff right, it doesn’t feel like a tool for the discovery of good writing and the people behind it. it feels like a journalism theft machine that never sleeps. it feels like yet another app whose priority is not journalism (which, again, will never be the priority of big tech); the priority is you not leaving the app. some good news: openai is working to improve its products. soon, it will debut a search engine called searchgpt, which it will integrate into chatgpt. “the company said its new technology would respond to questions with up to date information from the web,” the new york times reports. indeed, that will potentially improve the “news discovery and delivery” promises openai is making to its new partners like condé and vox. searchgpt offers “direct links to news stories,” openai writes in a release, which is quite funny because that’s like boasting about paying your taxes. it’s something you’re supposed to do. i hope searchgpt is great. in the meantime, about 200 million users will have to reckon with a product that often lies in very subtle ways about restaurants. even i was fooled. it will distort what journalists are writing and it will flatten nuanced reports and lists into anonymous blurbs. and it will pretend to know what things taste like. both consumers and readers will be worse off. i don’t know how to stop this mess, but for now my advice is simple: don’t use chatgpt. if you work at apple, ask openai for a better product before you deliver it to hundreds of millions of consumers. and maybe, if you’re a big publisher, it’s worth spending more time cultivating a resource with a better track record at giving consumers the news: journalists. ryan p.s. i have never, until today, used an ai generated image on this site. of course it’s not art, and i could go on about that. but recently, i’ve been playing around with adobe firefly; the disturbing images it created for me felt appropriate for this story about flawed ai. the cheetah in the lede pic somehow has one human hand and one animal paw, with a tuxedo that’s an extension of their body? and the steak looks as if it’s made of play doh or hamburger meat. in the second photo, i asked for a cheetah in a suit eating salmon roe, and the result was….yikes. but what’s most troubling are the vacant faces of the subjects, with their ghastly, taxidermic stares. p.p.s. while computers can’t taste, there are great food writers who can’t (or don’t) eat, and the late, great roger ebert comes to mind in that regard. this piece has been updated to reflect openai’s new valuation of $157 billion, and its reported user base of 250 million actively weekly users no posts ready for more?
supported by critic’s notebook a new study showed people real restaurant reviews and ones produced by a.i. they couldn’t tell the difference. by pete wells the white clam pizza at frank pepe pizzeria napoletana in new haven, conn., is a revelation. the crust, kissed by the intense heat of the coal fired oven, achieves a perfect balance of crispness and chew. topped with freshly shucked clams, garlic, oregano and a dusting of grated cheese, it is a testament to the magic that simple, high quality ingredients can conjure. sound like me? it’s not. the entire paragraph, except the pizzeria’s name and the city, was generated by gpt 4 in response to a simple prompt asking for a restaurant critique in the style of pete wells. i have a few quibbles. i would never pronounce any food a revelation, or describe heat as a kiss. i don’t believe in magic, and rarely call anything perfect without using “nearly” or some other hedge. but these lazy descriptors are so common in food writing that i imagine many readers barely notice them. i’m unusually attuned to them because whenever i commit a cliché in my copy, i get boxed on the ears by my editor. he wouldn’t be fooled by the counterfeit pete. neither would i. but as much as it pains me to admit, i’d guess that many people would say it’s a four star fake. the person responsible for phony me is balazs kovacs, a professor of organizational behavior at yale school of management. in a recent study, he fed a large batch of yelp reviews to gpt 4, the technology behind chatgpt, and asked it to imitate them. his test subjects   people   could not tell the difference between genuine reviews and those churned out by artificial intelligence. in fact, they were more likely to think the a.i. reviews were real. (the phenomenon of computer generated fakes that are more convincing than the real thing is so well known that there’s a name for it: a.i. hyperrealism.)

