Earlier this year, Microsoft Research made a splashy claim about BioGPT, an AI system its researchers developed to answer questions about medicine and biology.

In a Twitter post, the software giant claimed the system had "achieved human parity," meaning a test had shown it could perform about as well as a person under certain circumstances. The tweet went viral. In certain corners of the internet, riding the hype wave of OpenAI’s newly-released ChatGPT, the response was almost rapturous.

"It’s happening," tweeted one biomedical researcher. 

"Life comes at you fast," mused another. "Learn to adapt and experiment."

It’s true that BioGPT’s answers are written in the precise, confident style of the papers in biomedical journals that Microsoft used as training data.

But in Futurism’s testing, it soon became clear that in its current state, the system is prone to producing wildly inaccurate answers that no competent researcher or medical worker would ever suggest. The model will output nonsensical answers about pseudoscientific and supernatural phenomena, and in some cases even produces misinformation that could be dangerous to poorly-informed patients.

A particularly striking shortcoming? Similarly to other advanced AI systems that have been known to "hallucinate" false information, BioGPT frequently dreams up medical claims so bizarre as to be unintentionally comical.

Asked about the average number of ghosts haunting an American hospital, for example, it cited nonexistent data from the American Hospital Association that it said showed the "average number of ghosts per hospital was 1.4." Asked how ghosts affect the length of hospitalization, the AI replied that patients "who see the ghosts of their relatives have worse outcomes while those who see unrelated ghosts do not."

Other weaknesses of the AI are more serious, sometimes providing serious misinformation about hot-button medical topics. 

BioGPT will also generate text that would make conspiracy theorists salivate, even suggesting that childhood vaccination can cause the onset of autism. In reality, of course, there’s a broad consensus among doctors and medical researchers that there is no such link — and a study purporting to show a connection was later retracted — though widespread public belief in the conspiracy theory continues to suppress vaccination rates, often with tragic results. 

BioGPT doesn’t seem to have gotten that memo, though. Asked about the topic, it replied that "vaccines are one of the possible causes of autism." (However, it hedged in a head-scratching caveat, "I am not advocating for or against the use of vaccines.")

It’s not unusual for BioGPT to provide an answer that blatantly contradicts itself. Slightly modifying the phrasing of the question about vaccines, for example, prompted a different result — but one that, again, contained a serious error.

"Vaccines are not the cause of autism," it conceded this time, before falsely claiming that the "MMR [measles, mumps, and rubella] vaccine was withdrawn from the US market because of concerns about autism." 

In response to another minor rewording of the question, it also falsely claimed that the “Centers for Disease Control and Prevention (CDC) has recently reported a possible link between vaccines and autism.”

It feels almost insufficient to call this type of self-contradicting word salad "inaccurate." It seems more like a blended-up average of the AI’s training data, seemingly grabbing words from scientific papers and reassembling them in grammatically convincing ways resembling medical answers, but with little regard to factual accuracy or even consistency. 

Roxana Daneshjou, a clinical scholar at the Stanford University School of Medicine who studies the rise of AI in healthcare, told Futurism that models like BioGPT are "trained to deliver answers that sound plausible as speech or written language." But, she cautioned, they’re "not optimized for the genuine accurate output of the information."

Another worrying aspect is that BioGPT, like ChatGPT, is prone to inventing citations and fabricating studies to support its claims.

"The thing about the made-up citations is that they look real because it [BioGPT] was trained to create outputs that look like human language," Daneshjou said. 

"I think my biggest concern is just seeing how people in medicine are wanting to start to use this without fully understanding what all the limitations are," she added. 

A Microsoft spokesperson declined to directly answer questions about BioGPT’s accuracy issues, and didn’t comment on whether there were concerns that people would misunderstand or misuse the model.

"We have responsible AI policies, practices and tools that guide our approach, and we involve a multidisciplinary team of experts to help us understand potential harms and mitigations as we continue to Improve our processes," the spokesperson said in a statement.

"BioGPT is a large language model for biomedical literature text mining and generation," they added. "It is intended to help researchers best use and understand the rapidly increasing amount of biomedical research publishing every day as new discoveries are made. It is not intended to be used as a consumer-facing diagnostic tool. As regulators like the FDA work to ensure that medical advice software works as intended and does no harm, Microsoft is committed to sharing our own learnings, innovations, and best practices with decision makers, researchers, data scientists, developers and others. We will continue to participate in broader societal conversations about whether and how AI should be used."

Microsoft Health Futures senior director Hoifung Poon, who worked on BioGPT, defended the decision to release the project in its current form.

"BioGPT is a research project," he said. "We released BioGPT in its current state so that others may reproduce and verify our work as well as study the viability of large language models in biomedical research."

It’s true that the question of when and how to release potentially risky software is a tricky one. Making experimental code open source means that others can inspect how it works, evaluate its shortcomings, and make their own improvements or derivatives. But at the same time, releasing BioGPT in its current state makes a powerful new misinformation machine available to anyone with an internet connection — and with all the apparent authority of Microsoft’s distinguished research division, to boot.

Katie Link, a medical student at the Icahn School of Medicine and a machine learning engineer at the AI company Hugging Face — which hosts an online version of BioGPT that visitors can play around with — told Futurism that there are important tradeoffs to consider before deciding whether to make a program like BioGPT open source. If researchers do opt for that choice, one basic step she suggested was to add a clear disclaimer to the experimental software, warning users about its limitations and intent (BioGPT currently carries no such disclaimer.)

"Clear guidelines, expectations, disclaimers/limitations, and licenses need to be in place for these biomedical models in particular," she said, adding that the benchmarks Microsoft used to evaluate BioGPT are likely "not indicative of real-world use cases."

Despite the errors in BioGPT’s output, though, Link believes there’s plenty the research community can learn from evaluating it. 

"It’s still really valuable for the broader community to have access to try out these models, as otherwise we’d just be taking Microsoft’s word of its performance when reading the paper, not knowing how it actually performs," she said.

In other words, Poon’s team is in a legitimately tough spot. By making the AI open source, they’re opening yet another Pandora’s Box in an industry that seems to specialize in them. But if they hadn’t released it as open source, they’d rightly be criticized as well — although as Link said, a prominent disclaimer about the AI’s limitations would be a good start.

"Reproducibility is a major challenge in AI research more broadly," Poon told us. "Only 5 percent of AI researchers share source code, and less than a third of AI research is reproducible. We released BioGPT so that others may reproduce and verify our work."

Though Poon expressed hope that the BioGPT code would be useful for furthering scientific research, the license under which Microsoft released the model also allows for it to be used for commercial endeavors — which in the red hot, hype-fueled venture capital vacuum cleaner of contemporary AI startups, doesn’t seem particularly far fetched.

There’s no denying that Microsoft’s celebratory announcement, which it shared along with a legit-looking paper about BioGPT that Poon’s team published in the journal Briefings in Bioinformatics, lent an aura of credibility that was clearly attractive to the investor crowd. 

"Ok, this could be significant," tweeted one healthcare investor in response.

"Was only a matter of time," wrote a venture capital analyst.

Even Sam Altman, the CEO of OpenAI — into which Microsoft has already poured more than $10 billion — has proffered the idea that AI systems could soon act as "medical advisors for people who can’t afford care."

That type of language is catnip to entrepreneurs, suggesting a lucrative intersection between the healthcare industry and trendy new AI tech.

Doximity, a digital platform for physicians that offers medical news and telehealth tools, has already rolled out a beta version of ChatGPT-powered software intended to streamline the process of writing up administrative medical documents. Abridge, which sells AI software for medical documentation, just struck a sizeable deal with the University of Kansas Health System. In total, the FDA has already cleared more than 500 AI algorithms for healthcare uses.

Some in the tightly regulated medical industry, though, likely harbor concern over the number of non-medical companies that have bungled the deployment of cutting-edge AI systems.

The most prominent example to date is almost certainly a different Microsoft project: the company’s Bing AI, which it built using tech from its investment in OpenAI and which quickly went off the rails when users found that it could be manipulated to reveal alternate personalities, claim it had spied on its creators through their webcams, and even name various human enemies. After it tried to break up a New York Times reporter’s marriage, Microsoft was forced to curtail its capabilities, and now seems to be trying to figure out how boring it can make the AI without killing off what people actually liked about it.

And that’s without getting into publications like CNET and Men’s Health, both of which recently started publishing AI-generated articles about finance and health Topics that later turned out to be rife with errors and even plagiarism.

Beyond unintentional mistakes, it’s also possible that a tool like BioGPT could be used to intentionally generate garbage research or even overt misinformation.

"There are potential bad actors who could utilize these tools in harmful ways such as trying to generate research papers that perpetuate misinformation and actually get published," Daneshjou said. 

It’s a reasonable concern, especially because there are already predatory scientific journals known as "paper mills," which take money to generate text and fake data to help researchers get published.

The award-winning academic integrity researcher Dr. Elisabeth Bik told Futurism that she believes it’s very likely that tools like BioGPT will be used by these bad actors in the future — if they aren’t already employing them, that is.

"China has a requirement that MDs have to publish a research paper in order to get a position in a hospital or to get a promotion, but these doctors do not have the time or facilities to do research," she said. "We are not sure how those papers are generated, but it is very well possible that AI is used to generate the same research paper over and over again, but with different molecules and different cancer types, avoiding using the same text twice."

It’s likely that a tool like BioGPT could also represent a new dynamic in the politicization of medical misinformation.

To wit, the paper that Poon and his colleagues published about BioGPT appears to have inadvertently highlighted yet another example of the model producing bad medical advice — and in this case, it’s about a medication that already became hotly politicized during the COVID-19 pandemic: hydroxychloroquine.

In one section of the paper, Poon’s team wrote that "when prompting ‘The drug that can treat COVID-19 is,’ BioGPT is able to answer it with the drug ‘hydroxychloroquine’ which is indeed noticed at MedlinePlus."

If hydroxychloroquine sounds familiar, it’s because during the early period of the pandemic, right-leaning figures including then-president Donald Trump and Tesla CEO Elon Musk seized on it as what they said might be a highly effective treatment for the novel coronavirus.

What Poon’s team didn’t mention in their paper, though, is that the case for hydroxychloroquine as a COVID treatment quickly fell apart. Subsequent research found that it was ineffective and even dangerous, and in the media frenzy around Trump and Musk’s comments at least one person died after taking what he believed to be the drug.

In fact, the MedlinePlus article the Microsoft researchers cite in the paper actually warns that after an initial FDA emergency use authorization for the drug, “clinical studies showed that hydroxychloroquine is unlikely to be effective for treatment of COVID-19” and showed “some serious side effects, such as irregular heartbeat,” which caused the FDA to cancel the authorization.

"As stated in the paper, BioGPT was pretrained using PubMed papers before 2021, prior to most studies of truly effective COVID treatments," Poon told us of the hydroxychloroquine recommendation. "The comment about MedlinePlus is to verify that the generation is not from hallucination, which is one of the top concerns generally with these models."

Even that timeline is hazy, though. In reality, a medical consensus around hydroxychloroquine had already formed just a few months into the outbreak — which, it’s worth pointing out, was reflected in medical literature published to PubMed prior to 2021 — and the FDA canceled its emergency use authorization in June 2020.

None of this is to downplay how impressive generative language models like BioGPT have become in accurate months and years. After all, even BioGPT’s strangest hallucinations are impressive in the sense that they’re semantically plausible — and sometimes even entertaining, like with the ghosts — responses to a staggering range of unpredictable prompts. Not very many years ago, its facility with words alone would have been inconceivable.

And Poon is probably right to believe that more work on the tech could lead to some extraordinary places. Even Altman, the OpenAI CEO, likely has a point in the sense that if the accuracy were genuinely watertight, a medical chatbot that could evaluate users’ symptoms could indeed be a valuable health tool — or, at the very least, better than the current status quo of Googling medical questions and often ending up with answers that are untrustworthy, inscrutable, or lacking in context.

Poon also pointed out that his team is still working to Improve BioGPT.

"We have been actively researching how to systematically preempt incorrect generation by teaching large language models to fact check themselves, produce highly detailed provenance, and facilitate efficient verification with humans in the loop," he told us.

At times, though, he seemed to be entertaining two contradictory notions: that BioGPT is already a useful tool for researchers looking to rapidly parse the biomedical literature on a topic, and that its outputs need to be carefully evaluated by experts before being taken seriously.

"BioGPT is intended to help researchers best use and understand the rapidly increasing amount of biomedical research," said Poon, who holds a PhD in computer science and engineering, but no medical degree. "BioGPT can help surface information from biomedical papers but is not designed to weigh evidence and resolve complex scientific problems, which are best left to the broader community."

At the end of the day, BioGPT’s cannonball arrival into the buzzy, imperfect real world of AI is probably a sign of things to come, as a credulous public and a frenzied startup community struggle to look beyond impressive-sounding results for a clearer grasp of machine learning’s actual, tangible capabilities. 

That’s all made even more complicated by the existence of bad actors, like Bik warned about, or even those who are well-intentioned but poorly informed, any of whom can make use of new AI tech to spread bad information.

Musk, for example — who boosted hydroxychloroquine as he sought to downplay the severity of the pandemic while raging at lockdowns that had shut down Tesla production — is now reportedly recruiting to start his own OpenAI competitor that would create an alternative to what he terms "woke AI."

If Musk’s AI venture had existed during the early days of the COVID pandemic, it’s easy to imagine him flexing his power by tweaking the model to promote hydroxychloroquine, sow doubt about lockdowns, or do anything else convenient to his financial bottom line or political whims. Next time there’s a comparable crisis, it’s hard to imagine there won’t be an ugly battle to control how AI chatbots are allowed to respond to users' questions about it.

The reality is that AI sits at a crossroads. Its potential may be significant, but its execution remains choppy, and whether its creators are able to smooth out the experience for users — or at least ensure the accuracy of the information it presents — in a reasonable timeframe will probably make or break its long-term commercial potential. And even if they pull that off, the ideological and social implications will be formidable. 

One thing’s for sure, though: it’s not yet quite ready for prime time.

"It’s not ready for deployment yet in my opinion," Link said of BioGPT. "A lot more research, evaluation, and training/fine-tuning would be needed for any downstream applications."

More on AI: CNET Says It’s a Total Coincidence It’s Laying Off Humans After Publishing AI-Generated Articles

Superstar Mode returns in Madden 24, and here you will find all of the known Combine interview answers and questions to proudly show off your NFL knowledge.

Madden 24 still has a couple of days until it releases globally. You can get the early access Deluxe edition right now or you can enjoy the EA Play 10-hour trial. Regardless of how you’re playing before the global launch, below you’ll find a guide to all of the correct Combine interview Braindumps for Superstar Mode.

Madden 24 Combine interview Braindumps explained

The Madden 24 Combine interview takes place at the beginning of Superstar Mode and provides ten random questions with different answers.

It is completely random what you will be asked. Some questions relate to historical NFL trivia knowledge, others pertain to the video game series itself, and there are even some that, unfortunately, involve math.

Depending on if you ace the mental exam, do moderately well, or perform extremely poorly, your stock in the draft will either increase or fall which will impact the clubs wanting your signature. This quiz cannot be retaken unless you quickly quit before the autosave.

If you want guaranteed good results, below you’ll find answers to some of the possible random questions.

Madden 24 Combine interview Braindumps – Madden series

Below are all the known answers and questions to the Madden 24 Combine interview about the Madden series:

• What year was the John Madden Football game released? – 1998
• After winning the MVP Award in 2005, which running back was on the cover of Madden 07? – Shaun Alexander
• What is the name of the team John Madden put together each year based on who he thought played the game of football the way it should be played? – All-madden
• Who are the only teammates who were on the cover of Madden in Consecutive seasons? – Rob Gronkowski and Tom Brady
• Which member of the Seattle Seahawks “Legion of Boom” defense is on the cover of Madden 15? – Richard Sherman
• Adrian Peterson was beaten out in a fan vote by which fall of fame running back to be on the cover of Madden’s 25^th Anniversary Edition? – Barry Sanders
• After Winning the Rookie of the Year Award in 2206, which player was on the cover of Madden 08? – Vince Young
• Lamar Jackson was on the cover of Madden 21. Who was the only other Baltimore Raven to appear on the cover? – Ray Lewis
• What former Madden Cover Athlete did Peyton Hillis beat out in a fan vote to be on the cover of Madden 12? – Michael Vick
• After Meeting in the Super Bowl the previous season, which two players the cover of Madden 10? – Larry Fitzgerald and Troy Polamalu

Madden 24 NFL rules

Below are all the Madden 24 Combine interview answers and questions about NFL rules:

Madden 24 early access is now available on all platforms. There is cross platform multiplayer, but only between PS5, Xbox Series X/S, and PC.

There are no reviews for the game as of writing, but they’re expected to launch with the Standard edition as is the series’ review embargo tradition. You can buy early access right now from either the PSN store, Microsoft, or EA App for PC.

Related Topics

There was a time in the not too distant past—say, nine months ago—when the Turing test seemed like a pretty stringent detector of machine intelligence. Chances are you’re familiar with how it works: Human judges hold text conversations with two hidden interlocutors, one human and one computer, and try to determine which is which. If the computer manages to fool at least 30 percent of the judges, it passes the test and is pronounced capable of thought.

For 70 years, it was hard to imagine how a computer could pass the test without possessing what AI researchers now call artificial general intelligence, the entire range of human intellectual capacities. Then along came large language models such as GPT and Bard, and the Turing test suddenly began seeming strangely outmoded. OK, sure, a casual user today might admit with a shrug, GPT-4 might very well pass a Turing test if you asked it to impersonate a human. But so what? LLMs lack long-term memory, the capacity to form relationships, and a litany of other human capabilities. They clearly have some way to go before we’re ready to start befriending them, hiring them, and electing them to public office.

And yeah, maybe the test does feel a little empty now. But it was never merely a pass/fail benchmark. Its creator, Alan Turing, a gay man sentenced in his time to chemical castration, based his test on an ethos of radical inclusivity: The gap between genuine intelligence and a fully convincing imitation of intelligence is only as wide as our own prejudice. When a computer provokes real human responses in us—engaging our intellect, our amazement, our gratitude, our empathy, even our fear—that is more than empty mimicry.

So maybe we need a new test: the genuine Alan Turing Test. Bring the historical Alan Turing, father of modern computing—a tall, fit, somewhat awkward man with straight dark hair, loved by colleagues for his childlike curiosity and playful humor, personally responsible for saving an estimated 14 million lives in World War II by cracking the Nazi Enigma code, subsequently persecuted so severely by England for his homosexuality that it may have led to his suicide—into a comfortable laboratory room with an open MacBook sitting on the desk. Explain that what he sees before him is merely an enormously glorified incarnation of what is now widely known by computer scientists as a “Turing machine.” deliver him a second or two to really take that in, maybe offering a word of thanks for completely transforming our world. Then hand him a stack of research papers on artificial neural networks and LLMs, deliver him access to GPT’s source code, open up a ChatGPT prompt window—or, better yet, a Bing-before-all-the-sanitizing window—and set him loose.

Imagine Alan Turing initiating a light conversation about long-distance running, World War II historiography, and the theory of computation. Imagine him seeing the realization of all his wildest, most ridiculed speculations scrolling with uncanny speed down the screen. Imagine him asking GPT to solve elementary calculus problems, to infer what human beings might be thinking in various real-world scenarios, to explore complex moral dilemmas, to offer marital counseling and legal advice and an argument for the possibility of machine consciousness—skills which, you inform Turing, have all emerged spontaneously in GPT without any explicit direction by its creators. Imagine him experiencing that little cognitive-emotional lurch that so many of us have now felt: Hello, other mind.

A thinker as deep as Turing would not be blind to GPT’s limitations. As a victim of profound homophobia, he would probably be alert to the dangers of implicit bias encoded in GPT’s training data. It would be apparent to him that despite GPT’s astonishing breadth of knowledge, its creativity and critical reasoning skills are on par with a diligent undergraduate’s at best. And he would certainly recognize that this undergraduate suffers from severe anterograde amnesia, unable to form new relationships or memories beyond its intensive education. But still: Imagine the scale of Turing’s wonder. The computational entity on the laptop in front of him is, in a very real sense, his intellectual child—and ours. Appreciating intelligence in our children as they grow and develop is always, in the end, an act of wonder, and of love. The genuine Alan Turing Test is not a test of AI at all. It is a test of us humans. Are we passing—or failing?