Posts in category: Google AI

We used AI to help airlines choose routes that are less likely to cause contrails, minimizing the environmental impact of flights.Read More

Posted by Greg Corrado, Head of Health AI, Google Research, and Yossi Matias, VP, Engineering and Research, Google Research

Medicine is an inherently multimodal discipline. When providing care, clinicians routinely interpret data from a wide range of modalities including medical images, clinical notes, lab tests, electronic health records, genomics, and more. Over the last decade or so, AI systems have achieved expert-level performance on specific tasks within specific modalities — some AI systems processing CT scans, while others analyzing high magnification pathology slides, and still others hunting for rare genetic variations. The inputs to these systems tend to be complex data such as images, and they typically provide structured outputs, whether in the form of discrete grades or dense image segmentation masks. In parallel, the capacities and capabilities of large language models (LLMs) have become so advanced that they have demonstrated comprehension and expertise in medical knowledge by both interpreting and responding in plain language. But how do we bring these capabilities together to build medical AI systems that can leverage information from all these sources?

In today’s blog post, we outline a spectrum of approaches to bringing multimodal capabilities to LLMs and share some exciting results on the tractability of building multimodal medical LLMs, as described in three recent research papers. The papers, in turn, outline how to introduce de novo modalities to an LLM, how to graft a state-of-the-art medical imaging foundation model onto a conversational LLM, and first steps towards building a truly generalist multimodal medical AI system. If successfully matured, multimodal medical LLMs might serve as the basis of new assistive technologies spanning professional medicine, medical research, and consumer applications. As with our prior work, we emphasize the need for careful evaluation of these technologies in collaboration with the medical community and healthcare ecosystem.

A spectrum of approaches

Several methods for building multimodal LLMs have been proposed in recent months [1, 2, 3], and no doubt new methods will continue to emerge for some time. For the purpose of understanding the opportunities to bring new modalities to medical AI systems, we’ll consider three broadly defined approaches: tool use, model grafting, and generalist systems.

The spectrum of approaches to building multimodal LLMs range from having the LLM use existing tools or models, to leveraging domain-specific components with an adapter, to joint modeling of a multimodal model.

Tool use

In the tool use approach, one central medical LLM outsources analysis of data in various modalities to a set of software subsystems independently optimized for those tasks: the tools. The common mnemonic example of tool use is teaching an LLM to use a calculator rather than do arithmetic on its own. In the medical space, a medical LLM faced with a chest X-ray could forward that image to a radiology AI system and integrate that response. This could be accomplished via application programming interfaces (APIs) offered by subsystems, or more fancifully, two medical AI systems with different specializations engaging in a conversation.

This approach has some important benefits. It allows maximum flexibility and independence between subsystems, enabling health systems to mix and match products between tech providers based on validated performance characteristics of subsystems. Moreover, human-readable communication channels between subsystems maximize auditability and debuggability. That said, getting the communication right between independent subsystems can be tricky, narrowing the information transfer, or exposing a risk of miscommunication and information loss.

Model grafting

A more integrated approach would be to take a neural network specialized for each relevant domain, and adapt it to plug directly into the LLM — grafting the visual model onto the core reasoning agent. In contrast to tool use where the specific tool(s) used are determined by the LLM, in model grafting the researchers may choose to use, refine, or develop specific models during development. In two recent papers from Google Research, we show that this is in fact feasible. Neural LLMs typically process text by first mapping words into a vector embedding space. Both papers build on the idea of mapping data from a new modality into the input word embedding space already familiar to the LLM. The first paper, “Multimodal LLMs for health grounded in individual-specific data”, shows that asthma risk prediction in the UK Biobank can be improved if we first train a neural network classifier to interpret spirograms (a modality used to assess breathing ability) and then adapt the output of that network to serve as input into the LLM.

The second paper, “ELIXR: Towards a general purpose X-ray artificial intelligence system through alignment of large language models and radiology vision encoders”, takes this same tack, but applies it to full-scale image encoder models in radiology. Starting with a foundation model for understanding chest X-rays, already shown to be a good basis for building a variety of classifiers in this modality, this paper describes training a lightweight medical information adapter that re-expresses the top layer output of the foundation model as a series of tokens in the LLM’s input embeddings space. Despite fine-tuning neither the visual encoder nor the language model, the resulting system displays capabilities it wasn’t trained for, including semantic search and visual question answering.

Our approach to grafting a model works by training a medical information adapter that maps the output of an existing or refined image encoder into an LLM-understandable form.

Model grafting has a number of advantages. It uses relatively modest computational resources to train the adapter layers but allows the LLM to build on existing highly-optimized and validated models in each data domain. The modularization of the problem into encoder, adapter, and LLM components can also facilitate testing and debugging of individual software components when developing and deploying such a system. The corresponding disadvantages are that the communication between the specialist encoder and the LLM is no longer human readable (being a series of high dimensional vectors), and the grafting procedure requires building a new adapter for not just every domain-specific encoder, but also every revision of each of those encoders.

Generalist systems

The most radical approach to multimodal medical AI is to build one integrated, fully generalist system natively capable of absorbing information from all sources. In our third paper in this area, “Towards Generalist Biomedical AI”, rather than having separate encoders and adapters for each data modality, we build on PaLM-E, a recently published multimodal model that is itself a combination of a single LLM (PaLM) and a single vision encoder (ViT). In this set up, text and tabular data modalities are covered by the LLM text encoder, but now all other data are treated as an image and fed to the vision encoder.

Med-PaLM M is a large multimodal generative model that flexibly encodes and interprets biomedical data including clinical language, imaging, and genomics with the same model weights.

We specialize PaLM-E to the medical domain by fine-tuning the complete set of model parameters on medical datasets described in the paper. The resulting generalist medical AI system is a multimodal version of Med-PaLM that we call Med-PaLM M. The flexible multimodal sequence-to-sequence architecture allows us to interleave various types of multimodal biomedical information in a single interaction. To the best of our knowledge, it is the first demonstration of a single unified model that can interpret multimodal biomedical data and handle a diverse range of tasks using the same set of model weights across all tasks (detailed evaluations in the paper).

This generalist-system approach to multimodality is both the most ambitious and simultaneously most elegant of the approaches we describe. In principle, this direct approach maximizes flexibility and information transfer between modalities. With no APIs to maintain compatibility across and no proliferation of adapter layers, the generalist approach has arguably the simplest design. But that same elegance is also the source of some of its disadvantages. Computational costs are often higher, and with a unitary vision encoder serving a wide range of modalities, domain specialization or system debuggability could suffer.

The reality of multimodal medical AI

To make the most of AI in medicine, we’ll need to combine the strength of expert systems trained with predictive AI with the flexibility made possible through generative AI. Which approach (or combination of approaches) will be most useful in the field depends on a multitude of as-yet unassessed factors. Is the flexibility and simplicity of a generalist model more valuable than the modularity of model grafting or tool use? Which approach gives the highest quality results for a specific real-world use case? Is the preferred approach different for supporting medical research or medical education vs. augmenting medical practice? Answering these questions will require ongoing rigorous empirical research and continued direct collaboration with healthcare providers, medical institutions, government entities, and healthcare industry partners broadly. We look forward to finding the answers together.

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Posted by Eleni Triantafillou, Research Scientist, and Malik Boudiaf, Student Researcher, Google

Deep learning has recently made tremendous progress in a wide range of problems and applications, but models often fail unpredictably when deployed in unseen domains or distributions. Source-free domain adaptation (SFDA) is an area of research that aims to design methods for adapting a pre-trained model (trained on a “source domain”) to a new “target domain”, using only unlabeled data from the latter.

Designing adaptation methods for deep models is an important area of research. While the increasing scale of models and training datasets has been a key ingredient to their success, a negative consequence of this trend is that training such models is increasingly computationally expensive, out of reach for certain practitioners and also harmful for the environment. One avenue to mitigate this issue is through designing techniques that can leverage and reuse already trained models for tackling new tasks or generalizing to new domains. Indeed, adapting models to new tasks is widely studied under the umbrella of transfer learning.

SFDA is a particularly practical area of this research because several real-world applications where adaptation is desired suffer from the unavailability of labeled examples from the target domain. In fact, SFDA is enjoying increasing attention [1, 2, 3, 4]. However, albeit motivated by ambitious goals, most SFDA research is grounded in a very narrow framework, considering simple distribution shifts in image classification tasks.

In a significant departure from that trend, we turn our attention to the field of bioacoustics, where naturally-occurring distribution shifts are ubiquitous, often characterized by insufficient target labeled data, and represent an obstacle for practitioners. Studying SFDA in this application can, therefore, not only inform the academic community about the generalizability of existing methods and identify open research directions, but can also directly benefit practitioners in the field and aid in addressing one of the biggest challenges of our century: biodiversity preservation.

In this post, we announce “In Search for a Generalizable Method for Source-Free Domain Adaptation”, appearing at ICML 2023. We show that state-of-the-art SFDA methods can underperform or even collapse when confronted with realistic distribution shifts in bioacoustics. Furthermore, existing methods perform differently relative to each other than observed in vision benchmarks, and surprisingly, sometimes perform worse than no adaptation at all. We also propose NOTELA, a new simple method that outperforms existing methods on these shifts while exhibiting strong performance on a range of vision datasets. Overall, we conclude that evaluating SFDA methods (only) on the commonly-used datasets and distribution shifts leaves us with a myopic view of their relative performance and generalizability. To live up to their promise, SFDA methods need to be tested on a wider range of distribution shifts, and we advocate for considering naturally-occurring ones that can benefit high-impact applications.

Distribution shifts in bioacoustics

Naturally-occurring distribution shifts are ubiquitous in bioacoustics. The largest labeled dataset for bird songs is Xeno-Canto (XC), a collection of user-contributed recordings of wild birds from across the world. Recordings in XC are “focal”: they target an individual captured in natural conditions, where the song of the identified bird is at the foreground. For continuous monitoring and tracking purposes, though, practitioners are often more interested in identifying birds in passive recordings (“soundscapes”), obtained through omnidirectional microphones. This is a well-documented problem that recent work shows is very challenging. Inspired by this realistic application, we study SFDA in bioacoustics using a bird species classifier that was pre-trained on XC as the source model, and several “soundscapes” coming from different geographical locations — Sierra Nevada (S. Nevada); Powdermill Nature Reserve, Pennsylvania, USA; Hawai’i; Caples Watershed, California, USA; Sapsucker Woods, New York, USA (SSW); and Colombia — as our target domains.

This shift from the focalized to the passive domain is substantial: the recordings in the latter often feature much lower signal-to-noise ratio, several birds vocalizing at once, and significant distractors and environmental noise, like rain or wind. In addition, different soundscapes originate from different geographical locations, inducing extreme label shifts since a very small portion of the species in XC will appear in a given location. Moreover, as is common in real-world data, both the source and target domains are significantly class imbalanced, because some species are significantly more common than others. In addition, we consider a multi-label classification problem since there may be several birds identified within each recording, a significant departure from the standard single-label image classification scenario where SFDA is typically studied.

Illustration of the “focal → soundscapes” shift. In the focalized domain, recordings are typically composed of a single bird vocalization in the foreground, captured with high signal-to-noise ratio (SNR), though there may be other birds vocalizing in the background. On the other hand, soundscapes contain recordings from omnidirectional microphones and can be composed of multiple birds vocalizing simultaneously, as well as environmental noises from insects, rain, cars, planes, etc.

Audio files		Focal domain		Soundscape domain1
Spectogram images

State-of-the-art SFDA models perform poorly on bioacoustics shifts

As a starting point, we benchmark six state-of-the-art SFDA methods on our bioacoustics benchmark, and compare them to the non-adapted baseline (the source model). Our findings are surprising: without exception, existing methods are unable to consistently outperform the source model on all target domains. In fact, they often underperform it significantly.

As an example, Tent, a recent method, aims to make models produce confident predictions for each example by reducing the uncertainty of the model’s output probabilities. While Tent performs well in various tasks, it doesn’t work effectively for our bioacoustics task. In the single-label scenario, minimizing entropy forces the model to choose a single class for each example confidently. However, in our multi-label scenario, there’s no such constraint that any class should be selected as being present. Combined with significant distribution shifts, this can cause the model to collapse, leading to zero probabilities for all classes. Other benchmarked methods like SHOT, AdaBN, Tent, NRC, DUST and Pseudo-Labelling, which are strong baselines for standard SFDA benchmarks, also struggle with this bioacoustics task.

Evolution of the test mean average precision (mAP), a standard metric for multilabel classification, throughout the adaptation procedure on the six soundscape datasets. We benchmark our proposed NOTELA and Dropout Student (see below), as well as SHOT, AdaBN, Tent, NRC, DUST and Pseudo-Labelling. Aside from NOTELA, all other methods fail to consistently improve the source model.

Introducing NOisy student TEacher with Laplacian Adjustment (NOTELA)

Nonetheless, a surprisingly positive result stands out: the less celebrated Noisy Student principle appears promising. This unsupervised approach encourages the model to reconstruct its own predictions on some target dataset, but under the application of random noise. While noise may be introduced through various channels, we strive for simplicity and use model dropout as the only noise source: we therefore refer to this approach as Dropout Student (DS). In a nutshell, it encourages the model to limit the influence of individual neurons (or filters) when making predictions on a specific target dataset.

DS, while effective, faces a model collapse issue on various target domains. We hypothesize this happens because the source model initially lacks confidence in those target domains. We propose improving DS stability by using the feature space directly as an auxiliary source of truth. NOTELA does this by encouraging similar pseudo-labels for nearby points in the feature space, inspired by NRC’s method and Laplacian regularization. This simple approach is visualized below, and consistently and significantly outperforms the source model in both audio and visual tasks.

NOTELA in action. The audio recordings are forwarded through the full model to obtain a first set of predictions, which are then refined through Laplacian regularization, a form of post-processing based on clustering nearby points. Finally, the refined predictions are used as targets for the noisy model to reconstruct.

Conclusion

The standard artificial image classification benchmarks have inadvertently limited our understanding of the true generalizability and robustness of SFDA methods. We advocate for broadening the scope and adopt a new assessment framework that incorporates naturally-occurring distribution shifts from bioacoustics. We also hope that NOTELA serves as a robust baseline to facilitate research in that direction. NOTELA’s strong performance perhaps points to two factors that can lead to developing more generalizable models: first, developing methods with an eye towards harder problems and second, favoring simple modeling principles. However, there is still future work to be done to pinpoint and comprehend existing methods’ failure modes on harder problems. We believe that our research represents a significant step in this direction, serving as a foundation for designing SFDA methods with greater generalizability.

Acknowledgements

One of the authors of this post, Eleni Triantafillou, is now at Google DeepMind. We are posting this blog post on behalf of the authors of the NOTELA paper: Malik Boudiaf, Tom Denton, Bart van Merriënboer, Vincent Dumoulin*, Eleni Triantafillou* (where * denotes equal contribution). We thank our co-authors for the hard work on this paper and the rest of the Perch team for their support and feedback.

¹Note that in this audio clip, the bird song is very faint; a common property in soundscape recordings where bird calls aren’t at the “foreground”. ^↩

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Posted by Cat Armato, Program Manager, Google

Groups across Google actively pursue research in the field of machine learning (ML), ranging from theory and application. We build ML systems to solve deep scientific and engineering challenges in areas of language, music, visual processing, algorithm development, and more. We aim to build a more collaborative ecosystem with the broader ML research community through open-sourcing tools and datasets, publishing our work, and actively participating in conferences.

Google is proud to be a Diamond Sponsor of the 40th International Conference on Machine Learning (ICML 2023), a premier annual conference, which is being held this week in Honolulu, Hawaii. As a leader in ML research, Google has a strong presence at this year’s conference with over 120 accepted papers and active involvement in a number of workshops and tutorials. Google is also proud to be a Platinum Sponsor for both the LatinX in AI and Women in Machine Learning workshops. We look forward to sharing some of our extensive ML research and expanding our partnership with the broader ML research community.

Registered for ICML 2023? We hope you’ll visit the Google booth to learn more about the exciting work, creativity, and fun that goes into solving a portion of the field’s most interesting challenges. Visit the @GoogleAI Twitter account to find out about Google booth activities (e.g., demos and Q&A sessions). See Google DeepMind’s blog to learn about their technical participation at ICML 2023.

Take a look below to learn more about the Google research being presented at ICML 2023 (Google affiliations in bold).

Board and Organizing Committee

Google Research booth activities

Accepted papers

Tutorials

EXPO Day workshops

Google sponsored affinity workshops

Workshops

* Work done while at Google

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