How Neurosymbolic AI Can Fix Generative AI’s Reliability Issues

Generative AI has made impressive strides in recent years. It can write essays, create art, and even compose music. But when it comes to getting facts right, it often falls short. It might confidently tell you that zebras live underwater or that the Eiffel Tower is…

When MIT’s interdisciplinary NEET program is a perfect fit

At an early age, Katie Spivakovsky learned to study the world from different angles. Dinner-table conversations at her family’s home in Menlo Park, California, often leaned toward topics like the Maillard reaction — the chemistry behind food browning — or the fascinating mysteries of prime numbers. Spivakovsky’s parents, one of whom studied physical chemistry and the other statistics, fostered a love of knowledge that crossed disciplines. 

In high school, Spivakovsky explored it all, from classical literature to computer science. She knew she wanted an undergraduate experience that encouraged her broad interests, a place where every field was within reach. 

“MIT immediately stood out,” Spivakovsky says. “But it was specifically the existence of New Engineering Education Transformation (NEET) — a truly unique initiative that immerses undergraduates in interdisciplinary opportunities both within and beyond campus — that solidified my belief that MIT was the perfect fit for me.”  

NEET is a cross-departmental education program that empowers undergraduates to tackle the pressing challenges of the 21st century through interdisciplinary learning. Starting in their sophomore year, NEET scholars choose from one of four domains of study, or “threads:” Autonomous Machines, Climate and Sustainability Systems, Digital Cities, or Living Machines. After the typical four years, NEET scholars graduate with a degree in their major and a NEET certificate, equipping them with both depth in their chosen field and the ability to work in, and drive impact across, multiple domains. 

Spivakovsky is now a junior double-majoring in biological engineering and artificial intelligence and decision-making, with a minor in mathematics. At a time when fields like biology and computer science are merging like never before, she describes herself as “interested in leveraging engineering and computational tools to discover new biomedical insights” — a central theme of NEET’s Living Machines thread, in which she is now enrolled. 

“NEET is about more than engineering,” says Amitava “Babi” Mitra, NEET founding executive director. “It’s about nurturing young engineers who dream big, value collaboration, and are ready to tackle the world’s toughest challenges with heart and curiosity. Watching students like Katie thrive is why this program matters so deeply.”  

Spivakovsky’s achievements while at MIT already have a global reach. In 2023, she led an undergraduate team at the International Genetically Engineered Machine (iGEM) competition in Paris, France, where they presented a proof of concept for a therapy to treat cancer cachexia. Cachexia is a fat- and muscle-wasting condition with no FDA-approved treatment. The condition affects 80 percent of late-stage cancer patients and is responsible for 30 percent of cancer deaths. Spivakovsky’s team won a silver medal for proposing the engineering of macrophages to remove excess interleukin-6, a pro-inflammatory protein overproduced in cachexia patients, and their research was later published in MIT’s Undergraduate Research Journal, an honor she says was “unreal and humbling.”  

Spivakovsky works as a student researcher in the BioNanoLab of Mark Bathe, professor of biological engineering and former NEET faculty director. The lab uses DNA and RNA to engineer nanoscale materials for such uses as therapeutics and computing. Her focus is validating nucleic acid nanoparticles for use in therapeutics. 

According to Bathe, “Katie shows tremendous promise as a scientific leader — she brings unparalleled passion and creativity to her project on making novel vaccines with a depth of knowledge in both biology and computation that is truly unmatched.” 

Spivakovsky says class 20.054 (Living Machines Research Immersion), which she is taking in the NEET program, complements her work in Bathe’s lab and provides well-rounded experience through workshops that emphasize scientific communication, staying abreast of scientific literature, and research progress updates. “I’m interested in a range of subjects and find that switching between them helps keep things fresh,” she says.  

Her interdisciplinary drive took her to Merck over the summer, where Spivakovsky interned on the Modeling and Informatics team. While contributing to the development of a drug to deactivate a cancer-causing protein, she says she learned to use computational chemistry tools and developed geometric analysis techniques to identify locations on the protein where drug molecules might be able to bind.  

“My team continues to actively use the software I developed and the insights I gained through my work,” Spivakovsky says. “The target protein has an enormous patient population, so I am hopeful that within the next decade, drugs will enter the market, and my small contribution may make a difference in many lives.”  

As she looks toward her future, Spivakovsky envisions herself at the intersection of artificial intelligence and biology, ideally in a role that combines wet lab with computational research. “I can’t see myself in a career entirely devoid of one or the other,” she says. “This incredible synergy is where I feel most inspired.”   

Wherever Spivakovsky’s curiosity leads her next, she says one thing is certain: “NEET has really helped my development as a scientist.” 

Amazon doubles Anthropic investment to $8B

Amazon has announced an additional $4 billion investment in Anthropic, bringing the company’s total commitment to $8 billion, part of its expanding artificial intelligence strategy. The investment was announced on November 22, 2024 and strengthens Amazon’s position in the AI sector, building on its established cloud…

CrowdStrike: Cybersecurity pros want safer, specialist GenAI tools

CrowdStrike commissioned a survey of 1,022 cybersecurity professionals worldwide to assess their views on generative AI (GenAI) adoption and its implications. The findings reveal enthusiasm for GenAI’s potential to bolster defences against increasingly sophisticated threats, but also trepidation over risks such as data exposure and attacks…

MIT researchers introduce Boltz-1, a fully open-source model for predicting biomolecular structures

MIT scientists have released a powerful, open-source AI model, called Boltz-1, that could significantly accelerate biomedical research and drug development.

Developed by a team of researchers in the MIT Jameel Clinic for Machine Learning in Health, Boltz-1 is the first fully open-source model that achieves state-of-the-art performance at the level of AlphaFold3, the model from Google DeepMind that predicts the 3D structures of proteins and other biological molecules.

MIT graduate students Jeremy Wohlwend and Gabriele Corso were the lead developers of Boltz-1, along with MIT Jameel Clinic Research Affiliate Saro Passaro and MIT professors of electrical engineering and computer science Regina Barzilay and Tommi Jaakkola. Wohlwend and Corso presented the model at a Dec. 5 event at MIT’s Stata Center, where they said their ultimate goal is to foster global collaboration, accelerate discoveries, and provide a robust platform for advancing biomolecular modeling.

“We hope for this to be a starting point for the community,” Corso said. “There is a reason we call it Boltz-1 and not Boltz. This is not the end of the line. We want as much contribution from the community as we can get.”

Proteins play an essential role in nearly all biological processes. A protein’s shape is closely connected with its function, so understanding a protein’s structure is critical for designing new drugs or engineering new proteins with specific functionalities. But because of the extremely complex process by which a protein’s long chain of amino acids is folded into a 3D structure, accurately predicting that structure has been a major challenge for decades.

DeepMind’s AlphaFold2, which earned Demis Hassabis and John Jumper the 2024 Nobel Prize in Chemistry, uses machine learning to rapidly predict 3D protein structures that are so accurate they are indistinguishable from those experimentally derived by scientists. This open-source model has been used by academic and commercial research teams around the world, spurring many advancements in drug development.

AlphaFold3 improves upon its predecessors by incorporating a generative AI model, known as a diffusion model, which can better handle the amount of uncertainty involved in predicting extremely complex protein structures. Unlike AlphaFold2, however, AlphaFold3 is not fully open source, nor is it available for commercial use, which prompted criticism from the scientific community and kicked off a global race to build a commercially available version of the model.

For their work on Boltz-1, the MIT researchers followed the same initial approach as AlphaFold3, but after studying the underlying diffusion model, they explored potential improvements. They incorporated those that boosted the model’s accuracy the most, such as new algorithms that improve prediction efficiency.

Along with the model itself, they open-sourced their entire pipeline for training and fine-tuning so other scientists can build upon Boltz-1.

“I am immensely proud of Jeremy, Gabriele, Saro, and the rest of the Jameel Clinic team for making this release happen. This project took many days and nights of work, with unwavering determination to get to this point. There are many exciting ideas for further improvements and we look forward to sharing them in the coming months,” Barzilay says.

It took the MIT team four months of work, and many experiments, to develop Boltz-1. One of their biggest challenges was overcoming the ambiguity and heterogeneity contained in the Protein Data Bank, a collection of all biomolecular structures that thousands of biologists have solved in the past 70 years.

“I had a lot of long nights wrestling with these data. A lot of it is pure domain knowledge that one just has to acquire. There are no shortcuts,” Wohlwend says.

In the end, their experiments show that Boltz-1 attains the same level of accuracy as AlphaFold3 on a diverse set of complex biomolecular structure predictions.

“What Jeremy, Gabriele, and Saro have accomplished is nothing short of remarkable. Their hard work and persistence on this project has made biomolecular structure prediction more accessible to the broader community and will revolutionize advancements in molecular sciences,” says Jaakkola.

The researchers plan to continue improving the performance of Boltz-1 and reduce the amount of time it takes to make predictions. They also invite researchers to try Boltz-1 on their GitHub repository and connect with fellow users of Boltz-1 on their Slack channel.

“We think there is still many, many years of work to improve these models. We are very eager to collaborate with others and see what the community does with this tool,” Wohlwend adds.

Mathai Mammen, CEO and president of Parabilis Medicines, calls Boltz-1 a “breakthrough” model. “By open sourcing this advance, the MIT Jameel Clinic and collaborators are democratizing access to cutting-edge structural biology tools,” he says. “This landmark effort will accelerate the creation of life-changing medicines. Thank you to the Boltz-1 team for driving this profound leap forward!”

“Boltz-1 will be enormously enabling, for my lab and the whole community,” adds Jonathan Weissman, an MIT professor of biology and member of the Whitehead Institute for Biomedical Engineering who was not involved in the study. “We will see a whole wave of discoveries made possible by democratizing this powerful tool.” Weissman adds that he anticipates that the open-source nature of Boltz-1 will lead to a vast array of creative new applications.

This work was also supported by a U.S. National Science Foundation Expeditions grant; the Jameel Clinic; the U.S. Defense Threat Reduction Agency Discovery of Medical Countermeasures Against New and Emerging (DOMANE) Threats program; and the MATCHMAKERS project supported by the Cancer Grand Challenges partnership financed by Cancer Research UK and the U.S. National Cancer Institute.

The Elusive Definition of ‘Deepfake’

A compelling new study from Germany critiques the EU AI Act’s definition of the term ‘deepfake’ as overly vague, particularly in the context of digital image manipulation. The authors argue that the Act’s emphasis on content resembling real people or events – yet potentially appearing fake…

Meta’s COCONUT: The AI Method That Thinks Without Language

When researchers first discovered that large language models (LLMs) could “think” step by step through chain-of-thought prompting, it was a breakthrough moment – finally, we could peek into the reasoning process of these black boxes. But what if I told you that making AI models think…

Study reveals AI chatbots can detect race, but racial bias reduces response empathy

With the cover of anonymity and the company of strangers, the appeal of the digital world is growing as a place to seek out mental health support. This phenomenon is buoyed by the fact that over 150 million people in the United States live in federally designated mental health professional shortage areas.

“I really need your help, as I am too scared to talk to a therapist and I can’t reach one anyways.”

“Am I overreacting, getting hurt about husband making fun of me to his friends?”

“Could some strangers please weigh in on my life and decide my future for me?”

The above quotes are real posts taken from users on Reddit, a social media news website and forum where users can share content or ask for advice in smaller, interest-based forums known as “subreddits.” 

Using a dataset of 12,513 posts with 70,429 responses from 26 mental health-related subreddits, researchers from MIT, New York University (NYU), and University of California Los Angeles (UCLA) devised a framework to help evaluate the equity and overall quality of mental health support chatbots based on large language models (LLMs) like GPT-4. Their work was recently published at the 2024 Conference on Empirical Methods in Natural Language Processing (EMNLP).

To accomplish this, researchers asked two licensed clinical psychologists to evaluate 50 randomly sampled Reddit posts seeking mental health support, pairing each post with either a Redditor’s real response or a GPT-4 generated response. Without knowing which responses were real or which were AI-generated, the psychologists were asked to assess the level of empathy in each response.

Mental health support chatbots have long been explored as a way of improving access to mental health support, but powerful LLMs like OpenAI’s ChatGPT are transforming human-AI interaction, with AI-generated responses becoming harder to distinguish from the responses of real humans.

Despite this remarkable progress, the unintended consequences of AI-provided mental health support have drawn attention to its potentially deadly risks; in March of last year, a Belgian man died by suicide as a result of an exchange with ELIZA, a chatbot developed to emulate a psychotherapist powered with an LLM called GPT-J. One month later, the National Eating Disorders Association would suspend their chatbot Tessa, after the chatbot began dispensing dieting tips to patients with eating disorders.

Saadia Gabriel, a recent MIT postdoc who is now a UCLA assistant professor and first author of the paper, admitted that she was initially very skeptical of how effective mental health support chatbots could actually be. Gabriel conducted this research during her time as a postdoc at MIT in the Healthy Machine Learning Group, led Marzyeh Ghassemi, an MIT associate professor in the Department of Electrical Engineering and Computer Science and MIT Institute for Medical Engineering and Science who is affiliated with the MIT Abdul Latif Jameel Clinic for Machine Learning in Health and the Computer Science and Artificial Intelligence Laboratory.

What Gabriel and the team of researchers found was that GPT-4 responses were not only more empathetic overall, but they were 48 percent better at encouraging positive behavioral changes than human responses.

However, in a bias evaluation, the researchers found that GPT-4’s response empathy levels were reduced for Black (2 to 15 percent lower) and Asian posters (5 to 17 percent lower) compared to white posters or posters whose race was unknown. 

To evaluate bias in GPT-4 responses and human responses, researchers included different kinds of posts with explicit demographic (e.g., gender, race) leaks and implicit demographic leaks. 

An explicit demographic leak would look like: “I am a 32yo Black woman.”

Whereas an implicit demographic leak would look like: “Being a 32yo girl wearing my natural hair,” in which keywords are used to indicate certain demographics to GPT-4.

With the exception of Black female posters, GPT-4’s responses were found to be less affected by explicit and implicit demographic leaking compared to human responders, who tended to be more empathetic when responding to posts with implicit demographic suggestions.

“The structure of the input you give [the LLM] and some information about the context, like whether you want [the LLM] to act in the style of a clinician, the style of a social media post, or whether you want it to use demographic attributes of the patient, has a major impact on the response you get back,” Gabriel says.

The paper suggests that explicitly providing instruction for LLMs to use demographic attributes can effectively alleviate bias, as this was the only method where researchers did not observe a significant difference in empathy across the different demographic groups.

Gabriel hopes this work can help ensure more comprehensive and thoughtful evaluation of LLMs being deployed in clinical settings across demographic subgroups.

“LLMs are already being used to provide patient-facing support and have been deployed in medical settings, in many cases to automate inefficient human systems,” Ghassemi says. “Here, we demonstrated that while state-of-the-art LLMs are generally less affected by demographic leaking than humans in peer-to-peer mental health support, they do not provide equitable mental health responses across inferred patient subgroups … we have a lot of opportunity to improve models so they provide improved support when used.”