Posts in category: Google AI

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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Posted by Donald Martin, Jr., Technical Program Manager, Head of Societal Context Understanding Tools and Solutions (SCOUTS), Google Research

AI-related products and technologies are constructed and deployed in a societal context: that is, a dynamic and complex collection of social, cultural, historical, political and economic circumstances. Because societal contexts by nature are dynamic, complex, non-linear, contested, subjective, and highly qualitative, they are challenging to translate into the quantitative representations, methods, and practices that dominate standard machine learning (ML) approaches and responsible AI product development practices.

The first phase of AI product development is problem understanding, and this phase has tremendous influence over how problems (e.g., increasing cancer screening availability and accuracy) are formulated for ML systems to solve as well many other downstream decisions, such as dataset and ML architecture choice. When the societal context in which a product will operate is not articulated well enough to result in robust problem understanding, the resulting ML solutions can be fragile and even propagate unfair biases.

When AI product developers lack access to the knowledge and tools necessary to effectively understand and consider societal context during development, they tend to abstract it away. This abstraction leaves them with a shallow, quantitative understanding of the problems they seek to solve, while product users and society stakeholders — who are proximate to these problems and embedded in related societal contexts — tend to have a deep qualitative understanding of those same problems. This qualitative–quantitative divergence in ways of understanding complex problems that separates product users and society from developers is what we call the problem understanding chasm.

This chasm has repercussions in the real world: for example, it was the root cause of racial bias discovered by a widely used healthcare algorithm intended to solve the problem of choosing patients with the most complex healthcare needs for special programs. Incomplete understanding of the societal context in which the algorithm would operate led system designers to form incorrect and oversimplified causal theories about what the key problem factors were. Critical socio-structural factors, including lack of access to healthcare, lack of trust in the health care system, and underdiagnosis due to human bias, were left out while spending on healthcare was highlighted as a predictor of complex health need.

To bridge the problem understanding chasm responsibly, AI product developers need tools that put community-validated and structured knowledge of societal context about complex societal problems at their fingertips — starting with problem understanding, but also throughout the product development lifecycle. To that end, Societal Context Understanding Tools and Solutions (SCOUTS) — part of the Responsible AI and Human-Centered Technology (RAI-HCT) team within Google Research — is a dedicated research team focused on the mission to “empower people with the scalable, trustworthy societal context knowledge required to realize responsible, robust AI and solve the world’s most complex societal problems.” SCOUTS is motivated by the significant challenge of articulating societal context, and it conducts innovative foundational and applied research to produce structured societal context knowledge and to integrate it into all phases of the AI-related product development lifecycle. Last year we announced that Jigsaw, Google’s incubator for building technology that explores solutions to threats to open societies, leveraged our structured societal context knowledge approach during the data preparation and evaluation phases of model development to scale bias mitigation for their widely used Perspective API toxicity classifier. Going forward SCOUTS’ research agenda focuses on the problem understanding phase of AI-related product development with the goal of bridging the problem understanding chasm.

Bridging the AI problem understanding chasm

Bridging the AI problem understanding chasm requires two key ingredients: 1) a reference frame for organizing structured societal context knowledge and 2) participatory, non-extractive methods to elicit community expertise about complex problems and represent it as structured knowledge. SCOUTS has published innovative research in both areas.

A societal context reference frame

An essential ingredient for producing structured knowledge is a taxonomy for creating the structure to organize it. SCOUTS collaborated with other RAI-HCT teams (TasC, Impact Lab), Google DeepMind, and external system dynamics experts to develop a taxonomic reference frame for societal context. To contend with the complex, dynamic, and adaptive nature of societal context, we leverage complex adaptive systems (CAS) theory to propose a high-level taxonomic model for organizing societal context knowledge. The model pinpoints three key elements of societal context and the dynamic feedback loops that bind them together: agents, precepts, and artifacts.

• Agents: These can be individuals or institutions.
• Precepts: The preconceptions — including beliefs, values, stereotypes and biases — that constrain and drive the behavior of agents. An example of a basic precept is that “all basketball players are over 6 feet tall.” That limiting assumption can lead to failures in identifying basketball players of smaller stature.
• Artifacts: Agent behaviors produce many kinds of artifacts, including language, data, technologies, societal problems and products.

The relationships between these entities are dynamic and complex. Our work hypothesizes that precepts are the most critical element of societal context and we highlight the problems people perceive and the causal theories they hold about why those problems exist as particularly influential precepts that are core to understanding societal context. For example, in the case of racial bias in a medical algorithm described earlier, the causal theory precept held by designers was that complex health problems would cause healthcare expenditures to go up for all populations. That incorrect precept directly led to the choice of healthcare spending as the proxy variable for the model to predict complex healthcare need, which in turn led to the model being biased against Black patients who, due to societal factors such as lack of access to healthcare and underdiagnosis due to bias on average, do not always spend more on healthcare when they have complex healthcare needs. A key open question is how can we ethically and equitably elicit causal theories from the people and communities who are most proximate to problems of inequity and transform them into useful structured knowledge?

Illustrative version of societal context reference frame.

Taxonomic version of societal context reference frame.

Working with communities to foster the responsible application of AI to healthcare

Since its inception, SCOUTS has worked to build capacity in historically marginalized communities to articulate the broader societal context of the complex problems that matter to them using a practice called community based system dynamics (CBSD). System dynamics (SD) is a methodology for articulating causal theories about complex problems, both qualitatively as causal loop and stock and flow diagrams (CLDs and SFDs, respectively) and quantitatively as simulation models. The inherent support of visual qualitative tools, quantitative methods, and collaborative model building makes it an ideal ingredient for bridging the problem understanding chasm. CBSD is a community-based, participatory variant of SD specifically focused on building capacity within communities to collaboratively describe and model the problems they face as causal theories, directly without intermediaries. With CBSD we’ve witnessed community groups learn the basics and begin drawing CLDs within 2 hours.

Data 4 Black Lives community members learning system dynamics.

There is a huge potential for AI to improve medical diagnosis. But the safety, equity, and reliability of AI-related health diagnostic algorithms depends on diverse and balanced training datasets. An open challenge in the health diagnostic space is the dearth of training sample data from historically marginalized groups. SCOUTS collaborated with the Data 4 Black Lives community and CBSD experts to produce qualitative and quantitative causal theories for the data gap problem. The theories include critical factors that make up the broader societal context surrounding health diagnostics, including cultural memory of death and trust in medical care.

The figure below depicts the causal theory generated during the collaboration described above as a CLD. It hypothesizes that trust in medical care influences all parts of this complex system and is the key lever for increasing screening, which in turn generates data to overcome the data diversity gap.

Causal loop diagram of the health diagnostics data gap

These community-sourced causal theories are a first step to bridge the problem understanding chasm with trustworthy societal context knowledge.

Conclusion

As discussed in this blog, the problem understanding chasm is a critical open challenge in responsible AI. SCOUTS conducts exploratory and applied research in collaboration with other teams within Google Research, external community, and academic partners across multiple disciplines to make meaningful progress solving it. Going forward our work will focus on three key elements, guided by our AI Principles:

1. Increase awareness and understanding of the problem understanding chasm and its implications through talks, publications, and training.
2. Conduct foundational and applied research for representing and integrating societal context knowledge into AI product development tools and workflows, from conception to monitoring, evaluation and adaptation.
3. Apply community-based causal modeling methods to the AI health equity domain to realize impact and build society’s and Google’s capability to produce and leverage global-scale societal context knowledge to realize responsible AI.

SCOUTS flywheel for bridging the problem understanding chasm.

Acknowledgments

Thank you to John Guilyard for graphics development, everyone in SCOUTS, and all of our collaborators and sponsors.

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Today we’re publishing information on Google’s AI Red Team for the first timeRead More

Posted by Daniel McDuff, Staff Research Scientist, and Yuzhe Yang, Student Researcher, Google

Learning from periodic data (signals that repeat, such as a heart beat or the daily temperature changes on Earth’s surface) is crucial for many real-world applications, from monitoring weather systems to detecting vital signs. For example, in the environmental remote sensing domain, periodic learning is often needed to enable nowcasting of environmental changes, such as precipitation patterns or land surface temperature. In the health domain, learning from video measurement has shown to extract (quasi-)periodic vital signs such as atrial fibrillation and sleep apnea episodes.

Approaches like RepNet highlight the importance of these types of tasks, and present a solution that recognizes repetitive activities within a single video. However, these are supervised approaches that require a significant amount of data to capture repetitive activities, all labeled to indicate the number of times an action was repeated. Labeling such data is often challenging and resource-intensive, requiring researchers to manually capture gold-standard temporal measurements that are synchronized with the modality of interest (e.g., video or satellite imagery).

Alternatively, self-supervised learning (SSL) methods (e.g., SimCLR and MoCo v2), which leverage a large amount of unlabeled data to learn representations that capture periodic or quasi-periodic temporal dynamics, have demonstrated success in solving classification tasks. However, they overlook the intrinsic periodicity (i.e., the ability to identify if a frame is part of a periodic process) in data and fail to learn robust representations that capture periodic or frequency attributes. This is because periodic learning exhibits characteristics that are distinct from prevailing learning tasks.

Feature similarity is different in the context of periodic representations as compared to static features (e.g., images). For example, videos that are offset by short time delays or are reversed should be similar to the original sample, whereas videos that have been upsampled or downsampled by a factor x should be different from the original sample by a factor of x.

To address these challenges, in “SimPer: Simple Self-Supervised Learning of Periodic Targets”, published at the eleventh International Conference on Learning Representations (ICLR 2023), we introduced a self-supervised contrastive framework for learning periodic information in data. Specifically, SimPer leverages the temporal properties of periodic targets using temporal self-contrastive learning, where positive and negative samples are obtained through periodicity-invariant and periodicity-variant augmentations from the same input instance. We propose periodic feature similarity that explicitly defines how to measure similarity in the context of periodic learning. Moreover, we design a generalized contrastive loss that extends the classic InfoNCE loss to a soft regression variant that enables contrasting over continuous labels (frequency). Next, we demonstrate that SimPer effectively learns period feature representations compared to state-of-the-art SSL methods, highlighting its intriguing properties including better data efficiency, robustness to spurious correlations, and generalization to distribution shifts. Finally, we are excited to release the SimPer code repo with the research community.

The SimPer framework

SimPer introduces a temporal self-contrastive learning framework. Positive and negative samples are obtained through periodicity-invariant and periodicity-variant augmentations from the same input instance. For temporal video examples, periodicity-invariant changes are cropping, rotation or flipping, whereas periodicity-variant changes involve increasing or decreasing the speed of a video.

To explicitly define how to measure similarity in the context of periodic learning, SimPer proposes periodic feature similarity. This construction allows us to formulate training as a contrastive learning task. A model can be trained with data without any labels and then fine-tuned if necessary to map the learned features to specific frequency values.

Given an input sequence x, we know there’s an underlying associated periodic signal. We then transform x to create a series of speed or frequency altered samples, which changes the underlying periodic target, thus creating different negative views. Although the original frequency is unknown, we effectively devise pseudo- speed or frequency labels for the unlabeled input x.

Conventional similarity measures such as cosine similarity emphasize strict proximity between two feature vectors, and are sensitive to index shifted features (which represent different time stamps), reversed features, and features with changed frequencies. In contrast, periodic feature similarity should be high for samples with small temporal shifts and or reversed indexes, while capturing a continuous similarity change when the feature frequency varies. This can be achieved via a similarity metric in the frequency domain, such as the distance between two Fourier transforms.

To harness the intrinsic continuity of augmented samples in the frequency domain, SimPer designs a generalized contrastive loss that extends the classic InfoNCE loss to a soft regression variant that enables contrasting over continuous labels (frequency). This makes it suitable for regression tasks, where the goal is to recover a continuous signal, such as a heart beat.

SimPer constructs negative views of data through transformations in the frequency domain. The input sequence x has an underlying associated periodic signal. SimPer transforms x to create a series of speed or frequency altered samples, which changes the underlying periodic target, thus creating different negative views. Although the original frequency is unknown, we effectively devise pseudo speed or frequency labels for unlabeled input x (periodicity-variant augmentations τ). SimPer takes transformations that do not change the identity of the input and defines these as periodicity-invariant augmentations σ, thus creating different positive views of the sample. Then, it sends these augmented views to the encoder f, which extracts corresponding features.

Results

To evaluate SimPer’s performance, we benchmarked it against state-of-the-art SSL schemes (e.g., SimCLR, MoCo v2, BYOL, CVRL) on a set of six diverse periodic learning datasets for common real-world tasks in human behavior analysis, environmental remote sensing, and healthcare. Specifically, below we present results on heart rate measurement and exercise repetition counting from video. The results show that SimPer outperforms the state-of-the-art SSL schemes across all six datasets, highlighting its superior performance in terms of data efficiency, robustness to spurious correlations, and generalization to unseen targets.

Here we show quantitative results on two representative datasets using SimPer pre-trained using various SSL methods and fine-tuned on the labeled data. First, we pre-train SimPer using the Univ. Bourgogne Franche-Comté Remote PhotoPlethysmoGraphy (UBFC) dataset, a human photoplethysmography and heart rate prediction dataset, and compare its performance to state-of-the-art SSL methods. We observe that SimPer outperforms SimCLR, MoCo v2, BYOL, and CVRL methods. The results on the human action counting dataset, Countix, further confirm the benefits of SimPer over others methods as it notably outperforms the supervised baseline. For the feature evaluation results and performance on other datasets, please refer to the paper.

Results of SimCLR, MoCo v2, BYOL, CVRL and SimPer on the Univ. Bourgogne Franche-Comté Remote PhotoPlethysmoGraphy (UBFC) and Countix datasets. Heart rate and repetition count performance is reported as mean absolute error (MAE).

Conclusion and applications

We present SimPer, a self-supervised contrastive framework for learning periodic information in data. We demonstrate that by combining a temporal self-contrastive learning framework, periodicity-invariant and periodicity-variant augmentations, and continuous periodic feature similarity, SimPer provides an intuitive and flexible approach for learning strong feature representations for periodic signals. Moreover, SimPer can be applied to various fields, ranging from environmental remote sensing to healthcare.

Acknowledgements

We would like to thank Yuzhe Yang, Xin Liu, Ming-Zher Poh, Jiang Wu, Silviu Borac, and Dina Katabi for their contributions to this work.

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We worked with TONL, a stock image company, to create more representative datasets to help teams build more inclusive products.Read More

A new $5 million grant aims to help Woodwell Climate Research Center track Arctic permafrost thaw in near real-time.Read More

Homenajeamos a Eunice Newton Foot, científica pionera de la ciencia climática, y a 6 organizaciones beneficiarias de Google.org dirigidas por mujeres que construyen un f…Read More

We’re celebrating climate science pioneer Eunice Newton Foote and six women-led Google.org grantees building a more sustainable future.Read More

Posted by Jerry Wei, Student Researcher, and Denny Zhou, Principal Scientist, Google Research

A key feature of human intelligence is that humans can learn to perform new tasks by reasoning using only a few examples. Scaling up language models has unlocked a range of new applications and paradigms in machine learning, including the ability to perform challenging reasoning tasks via in-context learning. Language models, however, are still sensitive to the way that prompts are given, indicating that they are not reasoning in a robust manner. For instance, language models often require heavy prompt engineering or phrasing tasks as instructions, and they exhibit unexpected behaviors such as performance on tasks being unaffected even when shown incorrect labels.

In “Symbol tuning improves in-context learning in language models”, we propose a simple fine-tuning procedure that we call symbol tuning, which can improve in-context learning by emphasizing input–label mappings. We experiment with symbol tuning across Flan-PaLM models and observe benefits across various settings.

• Symbol tuning boosts performance on unseen in-context learning tasks and is much more robust to underspecified prompts, such as those without instructions or without natural language labels.
• Symbol-tuned models are much stronger at algorithmic reasoning tasks.
• Finally, symbol-tuned models show large improvements in following flipped-labels presented in-context, meaning that they are more capable of using in-context information to override prior knowledge.

An overview of symbol tuning, where models are fine-tuned on tasks where natural language labels are replaced with arbitrary symbols. Symbol tuning relies on the intuition that when instruction and relevant labels are not available, models must use in-context examples to learn the task.

Motivation

Instruction tuning is a common fine-tuning method that has been shown to improve performance and allow models to better follow in-context examples. One shortcoming, however, is that models are not forced to learn to use the examples because the task is redundantly defined in the evaluation example via instructions and natural language labels. For example, on the left in the figure above, although the examples can help the model understand the task (sentiment analysis), they are not strictly necessary since the model could ignore the examples and just read the instruction that indicates what the task is.

In symbol tuning, the model is fine-tuned on examples where the instructions are removed and natural language labels are replaced with semantically-unrelated labels (e.g., “Foo,” “Bar,” etc.). In this setup, the task is unclear without looking at the in-context examples. For example, on the right in the figure above, multiple in-context examples would be needed to figure out the task. Because symbol tuning teaches the model to reason over the in-context examples, symbol-tuned models should have better performance on tasks that require reasoning between in-context examples and their labels.

Datasets and task types used for symbol tuning.

Symbol-tuning procedure

We selected 22 publicly-available natural language processing (NLP) datasets that we use for our symbol-tuning procedure. These tasks have been widely used in the past, and we only chose classification-type tasks since our method requires discrete labels. We then remap labels to a random label from a set of ~30K arbitrary labels selected from one of three categories: integers, character combinations, and words.

For our experiments, we symbol tune Flan-PaLM, the instruction-tuned variants of PaLM. We use three different sizes of Flan-PaLM models: Flan-PaLM-8B, Flan-PaLM-62B, and Flan-PaLM-540B. We also tested Flan-cont-PaLM-62B (Flan-PaLM-62B at 1.3T tokens instead of 780B tokens), which we abbreviate as 62B-c.

We use a set of ∼300K arbitrary symbols from three categories (integers, character combinations, and words). ∼30K symbols are used during tuning and the rest are held out for evaluation.

Experimental setup

We want to evaluate a model’s ability to perform unseen tasks, so we cannot evaluate on tasks used in symbol tuning (22 datasets) or used during instruction tuning (1.8K tasks). Hence, we choose 11 NLP datasets that were not used during fine-tuning.

In-context learning

In the symbol-tuning procedure, models must learn to reason with in-context examples in order to successfully perform tasks because prompts are modified to ensure that tasks cannot simply be learned from relevant labels or instructions. Symbol-tuned models should perform better in settings where tasks are unclear and require reasoning between in-context examples and their labels. To explore these settings, we define four in-context learning settings that vary the amount of reasoning required between inputs and labels in order to learn the task (based on the availability of instructions/relevant labels)

Depending on the availability of instructions and relevant natural language labels, models may need to do varying amounts of reasoning with in-context examples. When these features are not available, models must reason with the given in-context examples to successfully perform the task.

Symbol tuning improves performance across all settings for models 62B and larger, with small improvements in settings with relevant natural language labels (+0.8% to +4.2%) and substantial improvements in settings without relevant natural language labels (+5.5% to +15.5%). Strikingly, when relevant labels are unavailable, symbol-tuned Flan-PaLM-8B outperforms FlanPaLM-62B, and symbol-tuned Flan-PaLM-62B outperforms Flan-PaLM-540B. This performance difference suggests that symbol tuning can allow much smaller models to perform as well as large models on these tasks (effectively saving ∼10X inference compute).

Large-enough symbol-tuned models are better at in-context learning than baselines, especially in settings where relevant labels are not available. Performance is shown as average model accuracy (%) across eleven tasks.

Algorithmic reasoning

We also experiment on algorithmic reasoning tasks from BIG-Bench. There are two main groups of tasks: 1) List functions — identify a transformation function (e.g., remove the last element in a list) between input and output lists containing non-negative integers; and 2) simple turing concepts — reason with binary strings to learn the concept that maps an input to an output (e.g., swapping 0s and 1s in a string).

On the list function and simple turing concept tasks, symbol tuning results in an average performance improvement of 18.2% and 15.3%, respectively. Additionally, Flan-cont-PaLM-62B with symbol tuning outperforms Flan-PaLM-540B on the list function tasks on average, which is equivalent to a ∼10x reduction in inference compute. These improvements suggest that symbol tuning strengthens the model’s ability to learn in-context for unseen task types, as symbol tuning did not include any algorithmic data.

Symbol-tuned models achieve higher performance on list function tasks and simple turing concept tasks. (A–E): categories of list functions tasks. (F): simple turing concepts task.

Flipped labels

In the flipped-label experiment, labels of in-context and evaluation examples are flipped, meaning that prior knowledge and input-label mappings disagree (e.g., sentences containing positive sentiment labeled as “negative sentiment”), thereby allowing us to study whether models can override prior knowledge. Previous work has shown that while pre-trained models (without instruction tuning) can, to some extent, follow flipped labels presented in-context, instruction tuning degraded this ability.

We see that there is a similar trend across all model sizes — symbol-tuned models are much more capable of following flipped labels than instruction-tuned models. We found that after symbol tuning, Flan-PaLM-8B sees an average improvement across all datasets of 26.5%, Flan-PaLM-62B sees an improvement of 33.7%, and Flan-PaLM-540B sees an improvement of 34.0%. Additionally, symbol-tuned models achieve similar or better than average performance as pre-training–only models.

Symbol-tuned models are much better at following flipped labels presented in-context than instruction-tuned models are.

Conclusion

We presented symbol tuning, a new method of tuning models on tasks where natural language labels are remapped to arbitrary symbols. Symbol tuning is based off of the intuition that when models cannot use instructions or relevant labels to determine a presented task, it must do so by instead learning from in-context examples. We tuned four language models using our symbol-tuning procedure, utilizing a tuning mixture of 22 datasets and approximately 30K arbitrary symbols as labels.

We first showed that symbol tuning improves performance on unseen in-context learning tasks, especially when prompts do not contain instructions or relevant labels. We also found that symbol-tuned models were much better at algorithmic reasoning tasks, despite the lack of numerical or algorithmic data in the symbol-tuning procedure. Finally, in an in-context learning setting where inputs have flipped labels, symbol tuning (for some datasets) restores the ability to follow flipped labels that was lost during instruction tuning.

Future work

Through symbol tuning, we aim to increase the degree to which models can examine and learn from input–label mappings during in-context learning. We hope that our results encourage further work towards improving language models’ ability to reason over symbols presented in-context.

Acknowledgements

The authors of this post are now part of Google DeepMind. This work was conducted by Jerry Wei, Le Hou, Andrew Lampinen, Xiangning Chen, Da Huang, Yi Tay, Xinyun Chen, Yifeng Lu, Denny Zhou, Tengyu Ma, and Quoc V. Le. We would like to thank our colleagues at Google Research and Google DeepMind for their advice and helpful discussions.

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