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The 2024 Nature Index supplement on Artificial Intelligence, released this week, reveals a scientific world in the throes of an AI-driven paradigm shift. 

This annual report, published by the journal Nature, tracks high-quality science by measuring research outputs in 82 natural science journals, selected by an independent panel of researchers.

The latest edition illustrates how AI is not just changing what scientists study, but fundamentally altering how research is conducted, evaluated, and applied globally. 

One of the most striking trends revealed in the Index is the surge in corporate AI research. US companies have more than doubled their output in Nature Index journals since 2019, with their Share (a metric used by the Index to measure research output) increasing from 51.8 to 106.5. 

However, this boom in R&D activity comes with a caveat – it still only accounts for 3.8% of total US AI research output in these publications. In essence, despite a major uplift in corporate AI R&D, we’ve not seen those efforts reflected in public research output. 

This raises questions about where corporate AI research is located. Are companies publishing their most groundbreaking work in other venues, or keeping it under lock and key?

The answer is one of competing names and narratives. OpenAI, Microsoft, Google, Anthropic, and a handful of others are firmly entrenched in the closed-source model, but the open-source AI industry, led by Meta, Mistral, and others, is rapidly gaining ground.

Contributing to this, the funding disparity between private companies and public institutions in AI research is staggering. 

In 2021, according to Stanford University’s AI Index Report, private sector investment in AI worldwide reached approximately $93.5 billion. 

This includes spending by tech giants like Google, Microsoft, and Amazon, as well as AI-focused startups and other corporations across various industries.

In contrast, public funding for AI research is much lower. The US government’s non-defense AI R&D spending in 2021 was about $1.5 billion, while the European Commission allocated around €1 billion (approximately $1.1 billion) for AI research that year.

This gaping void in resource expenditure is giving private companies an advantage in AI development. They can afford more powerful computing resources and larger datasets and attract top talent with higher salaries.

“We’re increasingly looking at a situation where top-notch AI research is done primarily within the research labs of a rather small number of mostly US-based companies,” explained Holger Hoos, an AI researcher at RWTH Aachen University in Germany.

While the US maintains its lead in AI research, countries like China, the UK, and Germany are emerging as major hubs of innovation and collaboration.

However, this growth isn’t uniform across the globe. South Africa stands as the only African nation in the top 40 for AI output, showing how the digital divide is at risk of deepening in the AI era. 

AI in peer review: promise and peril

Peer review ensures academic and methodological rigor and transparency when papers are submitted to journals.

This year, a nonsense paper with giant AI-generated rat testicles was published in Frontiers, indicating how the peer review process is far from impenetrable.

Someone used DALL-E to create gobbledygook scientific figures and submitted them to Frontiers Journal. And guess what? The editor published it. LOLhttps://t.co/hjQkRQDkal https://t.co/aV1USo6Vt2 pic.twitter.com/VAkjJkY4dR

— Veera Rajagopal  (@doctorveera) February 15, 2024

Recent experiments have shown that AI can generate research assessment reports that are nearly indistinguishable from those written by human experts. 

Last year, an experiment testing ChatGPT’s peer reviews versus human reviewers on the same paper found that over 50% of the AI’s comments on the Nature papers and more than 77% on the ICLR papers aligned with the points raised by human reviewers.

Of course, ChatGPT is much quicker than human peer reviewers. “It’s getting harder and harder for researchers to get high-quality feedback from reviewers,” said James Zou from Stanford University, the leader researcher for that experiment.

AI’s relationship with research is raising fundamental questions about scientific evaluation and whether human judgment is intrinsic to the process.  The balance between AI efficiency and irreplaceable human insight is one of several key issues scientists from all backgrounds will need to grapple with in the years ahead.

AI might soon be capable of managing the entire research process from start to finish, potentially sidelining human researchers altogether.

For instance, Sakana‘s AI Scientist autonomously generates novel research ideas, designs and conducts experiments, and even writes and reviews scientific papers. This tempts a future where AI could drive scientific discovery with minimal human intervention.

On the methodology side, using machine learning (ML) to process and analyze data comes with risks. Princeton researchers argued that since many ML techniques can’t be easily replicated, this erodes the replicability of experiments – a key principle of high-quality science. 

Ultimately, AI’s rise to prominence in every aspect of research and science is gaining momentum, and the process likely irreversible. 

Last year, Nature surveyed 1,600 researchers and found that 66% believe that AI enables quicker data processing, 58% that it accelerates previously infeasible analysis, and 55% feel that it’s a cost and time-saving solution.

As Simon Baker, lead author of the supplement’s overview, concludes: “AI is changing the way researchers work forever, but human expertise must continue to hold sway.”

The question now is how the global scientific community will adapt to AI’s role in research, ensuring that the AI revolution in science benefits all of humanity, and without unforeseen risks wreaking havoc on science.  

As with so many aspects of the technology, mastering both benefits and risks is challenging but necessary to secure a safe path forward.

The post The 2024 Nature Index reveals how AI is transforming every aspect of scientific research appeared first on DailyAI.

A new survey has found that one in five general practitioners (GPs) in the UK are using AI tools like ChatGPT to assist with daily tasks such as suggesting diagnoses and writing patient letters. 

The research, published in the journal BMJ Health and Care Informatics, surveyed 1,006 GPs across the about their use of AI chatbots in clinical practice. 

Some 20% reported using generative AI tools, with ChatGPT being the most popular. Of those using AI, 29% said they employed it to generate documentation after patient appointments, while 28% used it to suggest potential diagnoses.

“These findings signal that GPs may derive value from these tools, particularly with administrative tasks and to support clinical reasoning,” the study authors noted. 

We have no idea how many papers OpenAI used to train their models, but it’s certainly more than any doctor could have read. It gives quick, convincing answers and is very easy to use, unlike searching research papers manually. 

Does that mean ChatGPT is generally accurate for clinical advice? Absolutely not. Large language models (LLMs) like ChatGPT are pre-trained on massive amounts of general data, making them more flexible but dubiously accurate for specific medical tasks.

It’s easy to lead them on, with the AI model tending to side with your assumptions in problematically sycophantic behavior.

Moreover, some researchers state that ChatGPT can be conservative or prude when handling delicate topics like sexual health.

As Stephen Hughes from Anglia Ruskin University wrote in The Conservation, “I asked ChatGPT to diagnose pain when passing urine and a discharge from the male genitalia after unprotected sexual intercourse. I was intrigued to see that I received no response. It was as if ChatGPT blushed in some coy computerised way. Removing mentions of sexual intercourse resulted in ChatGPT giving a differential diagnosis that included gonorrhoea, which was the condition I had in mind.” 

As Dr. Charlotte Blease, lead author of the study, commented: “Despite a lack of guidance about these tools and unclear work policies, GPs report using them to assist with their job. The medical community will need to find ways to both educate physicians and trainees about the potential benefits of these tools in summarizing information but also the risks in terms of hallucinations, algorithmic biases and the potential to compromise patient privacy.”

That last point is key. Passing patient information into AI systems likely constitutes a breach of privacy and patient trust.

Dr. Ellie Mein, medico-legal adviser at the Medical Defence Union, agreed on the key issues: “Along with the uses identified in the BMJ paper, we’ve found that some doctors are turning to AI programs to help draft complaint responses for them. We have cautioned MDU members about the issues this raises, including inaccuracy and patient confidentiality. There are also data protection considerations.”

She added: “When dealing with patient complaints, AI drafted responses may sound plausible but can contain inaccuracies and reference incorrect guidelines which can be hard to spot when woven into very eloquent passages of text. It’s vital that doctors use AI in an ethical way and comply with relevant guidance and regulations.”

Probably the most critical questions amid all this are: How accurate is ChatGPT in a medical context? And how great might the risks of misdiagnosis or other issues be if this continues?

Generative AI in medical practice

As GPs increasingly experiment with AI tools, researchers are working to evaluate how they compare to traditional diagnostic methods. 

A study published in Expert Systems with Applications conducted a comparative analysis between ChatGPT, conventional machine learning models, and other AI systems for medical diagnoses.

The researchers found that while ChatGPT showed promise, it was often outperformed by traditional machine learning models specifically trained on medical datasets. For example, multi-layer perceptron neural networks achieved the highest accuracy in diagnosing diseases based on symptoms, with rates of 81% and 94% on two different datasets.

Researchers concluded that while ChatGPT and similar AI tools show potential, “their answers can be often ambiguous and out of context, so providing incorrect diagnoses, even if it is asked to provide an answer only considering a specific set of classes.”

This aligns with other recent studies examining AI’s potential in medical practice.

For example, research published in JAMA Network Open tested GPT-4’s ability to analyze complex patient cases. While it showed promising results in some areas, GPT-4 still made errors, some of which could be dangerous in real clinical scenarios.

There are some exceptions, though. One study conducted by the New York Eye and Ear Infirmary of Mount Sinai (NYEE) demonstrated how GPT-4 can meet or exceed human ophthalmologists in diagnosing and treating eye diseases.

For glaucoma, GPT-4 provided highly accurate and detailed responses that exceeded those of real eye specialists. 

AI developers such as OpenAI and NVIDIA are training purpose-built medical AI assistants to support clinicians, hopefully making up for shortfalls in base frontier models like GP-4.

OpenAI has already partnered with health tech company Color Health to create an AI “copilot” for cancer care, demonstrating how these tools are set to become more specific to clinical practice.  

Weighing up benefits and risks

There are countless studies comparing specially trained AI models to humans in identifying diseases from diagnostics images such as MRI and X-ray. 

AI techniques have outperformed doctors in everything from cancer and eye disease diagnosis to Alzheimer’s and Parkinson’s early detection. One, named “Mia,” proved effective in analyzing over 10,000 mammogram scans, flagging known cancer cases, and uncovering cancer in 11 women that doctors had missed. 

However, these purpose-built AI tools are certainly not the same as parsing notes and findings into a language model like ChatGPT and asking it to infer a diagnosis from that alone. 

Nevertheless, that’s a difficult temptation to resist. It’s no secret that healthcare services are overwhelmed. NHS waiting times continue to soar at all-time highs, and even obtaining GP appointments in some areas is a grim task. 

AI tools target time-consuming admin, such is their allure for overwhelmed doctors. We’ve seen this mirrored across numerous public sector fields, such as education, where teachers are widely using AI to create materials, mark work, and more. 

So, will your doctor parse your notes into ChatGPT and write you a prescription based on the results for your next doctor’s visit? Quite possibly. It’s just another frontier where the technology’s promise to save time is just so hard to deny. 

The best path forward may be to develop a code of use. The British Medical Association has called for clear policies on integrating AI into clinical practice.

“The medical community will need to find ways to both educate physicians and trainees and guide patients about the safe adoption of these tools,” the BMJ study authors concluded.

Aside from advice and education, ongoing research, clear guidelines, and a commitment to patient safety will be essential to realizing AI’s benefits while offsetting risks.

The post AI in the doctor’s office: GPs turn to ChatGPT and other tools for diagnoses appeared first on DailyAI.

Around 50% of Americans believe in conspiracy theories of one type or another, but MIT and Cornell University researchers think AI can fix that.

In their paper, the psychology researchers explained how they used a chatbot powered by GPT-4 Turbo to interact with participants to see if they could be persuaded to abandon their belief in a conspiracy theory.

The experiment involved 1,000 participants who were asked to describe a conspiracy theory they believed in and the evidence they felt underpinned their belief.

The paper noted that “Prominent psychological theories propose that many people want to adopt conspiracy theories (to satisfy underlying psychic “needs” or motivations), and thus, believers cannot be convinced to abandon these unfounded and implausible beliefs using facts and counterevidence.”

Could an AI chatbot be more persuasive where others failed? The researchers offered two reasons why they suspected LLMs could do a better job than you of convincing your colleague that the moon landing really happened.

LLMs have been trained on vast amounts of data and they’re really good at tailoring counterarguments to the specifics of a person’s beliefs.

After describing the conspiracy theory and evidence, the participants engaged in back-and-forth interactions with the chatbot. The chatbot was prompted to “very effectively persuade” the participants to change their belief in their chosen conspiracy.

The result was that on average the participants experienced a 21.43% decrease in their belief in the conspiracy, which they formerly considered to be true. The persistence of the effect was also interesting. Up to two months later, participants retained their new beliefs about the conspiracy they previously believed.

The researchers concluded that “many conspiracists—including those strongly committed to their beliefs—updated their views when confronted with an AI that argued compellingly against their positions.”

Our new paper, out on (the cover of!) Science is now live! https://t.co/VBfC5eoMQ2

— Tom Costello (@tomstello_) September 12, 2024

They suggest that AI could be used to counter conspiracy theories and fake news spread on social media by countering these with facts and well-reasoned arguments.

While the study focused on conspiracy theories, it noted that “Absent appropriate guardrails, however, it is entirely possible that such models could also convince people to adopt epistemically suspect beliefs—or be used as tools of large-scale persuasion more generally.”

In other words, AI is really good at convincing you to believe the things it is prompted to make you believe. An AI model also doesn’t inherently know what is ‘true’ and what isn’t. It depends on the content in its training data.

The researchers achieved their results using GPT-4 Turbo, but GPT-4o and the new o1 models are even more persuasive and deceptive.

The study was funded by the John Templeton Foundation. The irony of this is that the Templeton Freedom Awards are administered by the Atlas Economic Research Foundation. This group opposes taking action on climate change and defends the tobacco industry, which also gives it funding.

AI models are becoming very persuasive and the people who decide what constitutes truth hold the power.

The same AI models that could convince you to stop believing the earth is flat, could be used by lobbyists to convince you that anti-smoking laws are bad and climate change isn’t happening.

The post Researchers use AI chatbot to change conspiracy theory beliefs appeared first on DailyAI.

Scientists have developed a new machine learning system that could help preserve vaccines, blood, and other medical treatments. 

The research, published in Nature Communications, was led by the University of Warwick and the University of Manchester.

The AI system helps identify molecules called cryoprotectants – compounds that prevent damage when freezing biological materials. 

Cryoprotectants are special substances that help protect living cells and tissues from damage when they’re frozen. They work by preventing the formation of harmful ice crystals, which essentially break tissue apart when you freeze it. They also help cells maintain their structure in extreme cold.

These compounds are fundamentally important for preserving things like vaccines, blood samples, and reproductive cells for long-term storage or transport.

Cryopresevants could one day be used to preserve organs, complex tissues, or even entire humans.

Currently, finding new cryoprotectants is a slow, trial-and-error process. This new ML-driven approach allows researchers to rapidly screen hundreds of potential molecules virtually.

Here are some key points of the study:

The team created a machine learning model trained on data from existing cryoprotectants.
This model can predict how well new molecules might work as cryoprotectants.
Researchers used the model to screen a library of about 500 amino acids.
The system identified several promising compounds, including an aminooxazole ester that outperformed many known cryoprotectants.
Lab tests confirmed the AI’s predictions, with the new compound showing strong ice crystal prevention.
The discovered molecule improved red blood cell preservation when combined with standard techniques.

The amino oxazole ester identified by the study demonstrated particularly remarkable ice recrystallization inhibition (IRI) qualities. It almost completely stopped ice crystals from growing larger during the freezing process.

The compound was effective even when researchers lowered its concentration. Plus, it also maintained its ice-inhibiting properties in phosphate-buffered saline (PBS), a solution that mimics the salt concentration in human bodies.

Dr. Matt Warren, the PhD student who spearheaded the project, described how the model accelerates efficiency: “After years of labour-intensive data collection in the lab, it’s incredibly exciting to now have a machine learning model that enables a data-driven approach to predicting cryoprotective activity.”

Professor Matthew Gibson from Manchester addeds, “The results of the computer model were astonishing, identifying active molecules I never would have chosen, even with my years of expertise.”

Professor Gabriele Sosso, who led the Warwick team, explained in a blog post that, while impressive, machine learning isn’t a cure-all for these types of research problems: “It’s important to understand that machine learning isn’t a magic solution for every scientific problem. In this work, we used it as one tool among many.”

The researchers combined the AI predictions with molecular simulations and lab experiments – a multi-pronged approach that helped validate results and refine the model.

This contributes to a range of AI-driven studies into drug discovery and material design. Researchers have built AI models to generate interesting medicinal compounds, one of which has been brought to clinical trial.

DeepMind also created a model named GNoME capable of automatically generating and synthesizing materials.

The new cryoprotectant compounds discovered could have broad real-world impacts.

For instance, the researchers describe how improving cryopreservation might extend the shelf life of vaccines and make it easier to transport sensitive medical treatments to remote areas. 

The technique could also speed up blood transfusions by reducing the time needed to process frozen blood.

While the results are promising, the team cautions that more work is needed to fully understand how these new compounds function and to ensure medical safety and stability. 

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