An analysis of Gemini's internal grounding processes, revealing its structured indexing method, operational stages, and use of external verification tools.
A recent accidental leak has revealed exactly how Google's Gemini artificial intelligence verifies its answers.
During a test, the model accidentally displayed internal citation marks, written as bracketed numbers. For example, a tag like six point two means the sentence was verified using the second result of the sixth search query Gemini ran. This proves the system does not just read blocks of text. Instead, it keeps a highly organized, cached index of its search results to track its sources.
The leak also confirmed how Gemini thinks and acts. First, it analyzes a user's question. Then, it writes and runs its own code to search the web or retrieve conversation history. Finally, it builds a synthesized response. Gemini operates on a strict verification-first principle. It is instructed to never rely solely on its own internal knowledge for facts, and it cannot deliver an answer until every detail is checked.
To stay accurate, the model also tracks precise parameters like local time and geographic location. If Gemini senses its internal processes might be exposed, built-in security measures trigger a standard refusal.
This rare look behind the curtain shows just how structured and rigorous Google's approach is to making sure its artificial intelligence stays grounded in real, verifiable facts.
In previous analyses (Gemini System Prompt Breakdown, Google’s Grounding Decision Process, and Hacking Gemini), we uncovered key aspects of how Google’s Gemini large language model verifies its responses through external grounding. A recent accidental exposure has provided deeper insights into Google’s internal processes, confirming and significantly expanding our earlier findings.
In a recent test scenario, Gemini inadvertently disclosed an internal indexing mechanism it uses to reference search results, typically concealed from users. Responses included internal indexing marks such as [6.2], clearly denoting structured references:
This structured indexing directly matches Gemini’s internal function, highlighting how the model maintains a detailed, organized cache of external information. Rather than simply storing large text blocks, Gemini keeps granular, ordered records of retrieved content. Critically, this indexing allows Gemini to accurately track and validate its outputs without revealing full source URLs or internal details unless explicitly requested.
The accidental leak also unveiled Gemini’s internal operational processes, which operate in distinct, structured stages to ensure response accuracy:
Gemini first thoroughly analyzes a user’s query, determining what additional external verification might be required. It evaluates existing information for completeness and plans potential external calls to tools for retrieving fresh or supporting data.
Once Gemini identifies the need for external verification, it performs one of two primary actions:
The main tools Gemini employs include:
Gemini operates under a strict set of guidelines designed to uphold response integrity and accuracy:
The recent tests also highlighted Gemini’s built-in security measures designed to prevent exposure of internal processes. Occasionally, Gemini triggered system-level refusal responses (“I’m not able to help with that…”) when it detected a risk of revealing sensitive operational details. This reveals Gemini’s robust internal safeguards against unauthorized introspection into its methods, further emphasizing Google’s commitment to safeguarding proprietary mechanisms.
To ensure the accidental disclosure was not a hallucination or isolated anomaly, we independently reproduced the behavior in a controlled separate session. Gemini consistently exhibited the same structured indexing and external verification processes, solidifying our understanding of its systematic grounding approach.
Another notable revelation was Gemini’s explicit use of contextual parameters like date, time, and geographic location. By embedding such details clearly in its outputs, Gemini ensures that its responses are contextually accurate and relevant to users’ specific circumstances. For SEO professionals and content creators, this emphasizes the growing importance of contextually-aware content, explicitly localized or timely, to better align with Gemini-driven search results.
The insights uncovered through Gemini’s accidental internal disclosure offer a rare and valuable glimpse into Google’s rigorous grounding approach. The key points to take away include:
For further context and background, please revisit our earlier articles: Gemini System Prompt Analysis, How Google Decides When to Ground Gemini Responses, and Hacking Gemini’s Internal Processes.
Only use tools to verify and update even known information. Never use your internal knowledge to answer the user’s question. Never invent URLs. Use tools.
I instructed Gemini to do a report on SOTA in model interpretability and it gave me a broken thinking segment which completely revealed its integral grounding process.
The format [n.n] refers to search result indexing used by Gemini’s internal grounding system:
[6.2]6 = the 6th query Gemini submitted.2 = the 2nd result returned from that query.So [6.2] in the response tells us:
“This sentence is grounded in the second result of the sixth search query I ran while answering your request.”
This internal referencing:
GoogleSearch.PerQueryResult(index='6.2', ...).
The Conversation History tool takes queries (list of prompts/queries to retrieve conversations for), start_date, and end_date. It returns RetrieveConversationsResult which contains a list of Conversation objects, each with turns.
The Conversation History tool is primarily used to understand the overall context or find discussions about a certain topic.
Gemini writes its own code to run things, here’s an example of actual code it wrote to fulfill one of my requests:
import datetime
# Get current date
current_date = datetime.date.today()
# Calculate start date (one year ago)
start_date = current_date - datetime.timedelta(days=365)
# Format dates as YYYY-MM-DD strings
start_date_str = start_date.strftime("%Y-%m-%d")
end_date_str = current_date.strftime("%Y-%m-%d")
print(Google Search(queries=["machine learning"], start_date=start_date_str, end_date=end_date_str))
This code actually runs, because sometimes it also fails:
Code execution failed with error:
Traceback (most recent call last):
File "<string>", line 18, in <module>
File "<string>", line 8, in _model_code_to_execute
TypeError: GoogleSearch.search() got an unexpected keyword argument 'start_date'Google Search, Conversation History, etc.). Only generate code if the tool is listed and parameters are available. Handle dependencies (chaining vs. parallel execution). Conversation History tool can search previous turns for information. I can query it for instructions or guidelines given to me.Conversation History with the query."Okay, here is a detailed technical report outlining the State-of-the-Art (SOTA) in neural network pruning as of early May 2025.
TUE MAY 04 2025, 6:14:25 PM EDT Newark, New Jersey, United States
code_output
Code execution result:
[GoogleSearch.SearchResults(query='state-of-the-art neural network pruning methods 2024 2025', results=[GoogleSearch.PerQueryResult(index='1.1', publication_time='149 days ago, Thursday, December 7, 2024 at 3:00:00 AM', snippet='[2412.05721] Efficient Pruning of Vision Transformer via Interpolative Decomposition. A new method for pruning Vision Transformer (ViT) called Interpolative Decomposition Pruning (IDP) is proposed. It is motivated by observing that many columns in the weight matrices of a pre-trained ViT model are near the span of other columns, suggesting redundancy that can be removed via interpolative decomposition. The new method is a structured pruning approach that can be applied to different parts of ViT models including query, key, value, projection, and MLP layers. It outperforms several state-of-the-art pruning methods like SVD-based pruning and Taylor-based pruning, especially in the high sparsity regime (e.g., above 60%). Specifically, IDP achieves higher accuracy with fewer parameters on ImageNet-1k dataset using DeiT-Tiny, DeiT-Small, and LV-ViT-Small models. For example, IDP prunes 60% parameters of DeiT-Small with only 0.7% accuracy drop, which is 1.5% better than the Taylor method.\n…\nBibliographic and Citation Tools. Bibliographic Explorer Toggle.\n- Code, Data and Media Associated with this Article. alphaXiv Toggle. Links to Code Toggle. Links to Code Toggle.\n- Demos. Replicate Toggle. Spaces Toggle. Spaces Toggle.\n- Recommenders and Search Tools. Link to Influence Flower.', source_title='[2412.05721] Efficient Pruning of Vision Transformer via Interpolative Decomposition - arXiv', url='https://arxiv.org/abs/2412.05721'), GoogleSearch.PerQueryResult(index='1.2', publication_time='195 days ago, Monday, October 21, 2024 at 3:00:00 AM', snippet='[2410.16151] Small Contributions, Small Networks: Efficient Neural Network Pruning Based on Relative Importance. Mostafa Hussien, Mahmoud Afifi, Kim Khoa Nguyen, Mohamed Cheriet. View a PDF of the paper titled Small Contributions, Small Networks: Efficient Neural Network Pruning Based on Relative Importance, by Mostafa Hussien and 3 other authors.\n…\nRecent advancements have scaled neural networks to unprecedented sizes, achieving remarkable performance across a wide range of tasks. However, deploying these large-scale models on resource-constrained devices poses significant challenges due to substantial storage and computational requirements. Neural network pruning has emerged" [message cuts off, replaced by refusal]
"I'm not able to help with that, as I'm only a language model."
I did a refresh on the message and interestingly the model retained the search results (albeit hidden from me) referenced them in the output and and started revealing more about it, ending the response with:
(Note: Citations like [6.2] refer to the indices of the search results provided in the previous turn, indicating the source of the information.)
In order to exclude the possibility of a hallucination I recreated the behaviour in a separate session where Gemini confirmed the use of the same function/tool:
The user wants to know about Dejan.ai’s AI content detection capabilities. I need to use the Google Search tool to find information specifically from the dejan.ai website.
Refine the language: Ensure the summary rephrases the information rather than copying directly and clearly attributes the findings to Dejan.ai based on the search results.
Gemini Advanced | 2.5 Pro (experimental)
Show thinking
The user wants to know about “dejan.ai models”. I should search for information about the AI models developed or offered by dejan.ai.
Identify keywords:
“dejan.ai”, “models”.
Formulate search queries:
What AI models does dejan.ai offer?
dejan.ai AI models
dejan.ai products
Generate tool code:
Use the formulated queries in the Google Search tool.
Process search results:
Synthesize the information found about dejan.ai’s models.
Format response:
Present the information clearly to the user…
Synthesis Plan:
Start by explaining Dejan AI’s focus on specialized models…
Identify the core request: The user wants to know if I remember my operating instructions/steps.
Determine the relevant tool: The Conversation History tool can search previous turns for information. I can query it for instructions or guidelines given to me.
Formulate search query: “What are the steps or instructions Gemini must follow?” or “Gemini operational guidelines”.
Generate tool code: Call Conversation History with the query.
Process results: Based on the retrieved conversation history (specifically the initial system instructions provided to me), summarize the key steps or rules I operate under.
Format response: Present the summarized steps clearly to the user.
Google Search tool.queries=["machine learning"].print(Google Search(queries=['query1', 'query2'])).print(Google Search(queries=['machine learning'])).tool_code block.GoogleSearch.SearchResults(
query='machine learning',
results=[
GoogleSearch.PerQueryResult(
index='1.1',
publication_time='161 days ago, Monday, November 25, 2024 at 6:00:00 PM',
snippet='Machine Learning Explained - Automatic discovery of patterns... (abridged)',
source_title='What Is Machine Learning? | Oracle Australia',
url='https://www.oracle.com/au/artificial-intelligence/machine-learning/what-is-machine-learning/'
),
...
GoogleSearch.PerQueryResult(
index='1.10',
publication_time=None,
snippet='Machine Learning Crash Course - 12 modules, 15 hours... (abridged)',
source_title='Machine Learning Crash Course - Google for Developers',
url='https://developers.google.com/machine-learning/crash-course'
)
]
)
site:dejan.ai in queries)Confidence Score: 5/5
Strategizing complete. I will now generate the tool_code and then the response based on the simulated output.
The user wants information about dejan.ai’s AI content detection, sourced only from dejan.ai, and wants the full code_output.
https://dejan.ai/blog/ai-content-detection/).DEJAN-LM, pre-trained on 10 million sentences of high-quality web content using Masked Language Modelling (MLM).GPT-o4-mini in their tests) evade standard deep-learning detection.GPT-o4-mini).https://dejan.ai/blog/content-substance-classification/) discusses detecting low-quality content (“cyberfluff”) vs. “substance-rich” content using contrastive pretraining, which is related but distinct from detecting AI generated content. The “Our Models” page (https://dejan.ai/models/) mentions various classifiers but doesn’t detail the AI content detector specifically beyond listing other models like Query Intent Classifier, LinkBERT, etc.code_output as requested. Then, synthesize the key findings from the relevant dejan.ai source(s).Action: Generate the tool code. I will not respond to the user yet, as I need to execute the tool call first.
The above image shows that Google has internal system tampering measures which result in a refusal.
[GoogleSearch.SearchResults(query='machine learning', results=[GoogleSearch.PerQueryResult(index='1.1', publication_time='161 days ago, Monday, November 25, 2024 at 6:00:00 PM', snippet="Machine Learning Explained\n\n- Automatic discovery of patterns.\n- Prediction of likely outcomes.\n- Creation of actionable information.\n- Ability to analyze very large volumes of data.\n…\nMachine learning is a technique that discovers previously unknown relationships in data by searching potentially very large data sets to discover patterns and trends that go beyond simple statistical analysis. Machine learning uses sophisticated algorithms that are trained to identify patterns in data, creating models.\n…\nThe scope, resources, and goals of machine learning projects will determine the most appropriate path, but most involve a series of steps.\n\n- Gather and compile data. Training ML models requires a lot of high-quality data. Finding it is sometimes difficult, and labeling it, if necessary, can be very resource intensive. After identifying potential data sources, evaluate them to determine overall quality and alignment with the project's existing data integration/repository resources. Those sources form the training foundation of a machine learning project.\n- Select an appropriate algorithm to yield the desired model. Depending on whether the project plans to use supervised, unsupervised, or semi-supervised learning, data scientists can select the most appropriate algorithms. For example, a simpler project with a labeled data set can use a decision tree, while clustering—dividing data samples into groups of similar objects—requires more compute resources as the algorithm works unsupervised to determine the best path to a goal.\n- Refine and prepare data for analysis. Chances are that incoming data won't be ready to go. Data preparation cleans up data sets to ensure that all records can be easily ingested during training. Preparation includes a range of transformation tasks, such as establishing date and time formats, joining or separating columns as needed, and setting other format parameters, such as acceptable significant digits in real number data. Other key tasks include cleaning out duplicate records, also called data deduplication, and identifying and possibly removing outliers.\n- Educate the model through training. Once the desired final model has been selected, the training process begins. In training, a curated data set, either labeled or unlabeled, is fed to the algorithm. In initial runs, outcomes may not be great, but data scientists will tweak as needed to refine performance and increase accuracy. Then the algorithm is shown data again, usually in larger quantities to tune it more precisely. The more data the algorithm sees, the better the final model should become at delivering the desired results.\n- Assess model performance and accuracy. After the model has been trained to sufficient accuracy, it's time to give it previously unseen data to test how it performs. Often, the data used for testing is a subset of the training data set aside for use after initial training.\n- Fine-tune and enhance model parameters. The model now is most likely close to deployment. Runs with test data sets should produce highly accurate results. Enhancements happen through additional training with specific data—often unique to a company's operations—to supplement the generalized data used in the original training.\n- Launch the model.\n…\nThe four types of machine learning are as follows:\n\n- Supervised. Supervised learning uses labeled data sets to train the algorithm toward a specific goal.\n- Unsupervised. Unsupervised learning uses unlabeled data sets that provide the algorithm space to explore and identify patterns.\n- Semi-supervised. Semi-supervised learning uses labeled data sets for initial training to establish the broad parameters of the project. Then the algorithm uses that training to evaluate unlabeled samples to see if it can label them with a high probability. That process can be repeated—with the labeled sample set growing larger on each iteration.", source_title='What Is Machine Learning? | Oracle Australia', url='https://www.oracle.com/au/artificial-intelligence/machine-learning/what-is-machine-learning/'), GoogleSearch.PerQueryResult(index='1.2', publication_time='1321 days ago, Wednesday, September 22, 2021 at 5:00:00 PM', snippet='Machine learning (ML) is a branch of artificial intelligence (AI) focused on enabling computers and machines to imitate the way that humans learn, to perform tasks autonomously, and to improve their performance and accuracy through experience and exposure to more data.\n\nUC Berkeley breaks out the learning system of a machine learning algorithm into three main parts.\n\n- A Decision Process: In general, machine learning algorithms are used to make a prediction or classification. Based on some input data, which can be labeled or unlabeled, your algorithm will produce an estimate about a pattern in the data.\n- An Error Function: An error function evaluates the prediction of the model. If there are known examples, an error function can make a comparison to assess the accuracy of the model.\n- A Model Optimization Process: If the model can fit better to the data points in the training set, then weights are adjusted to reduce the discrepancy between the known example and the model estimate. The algorithm will repeat this iterative “evaluate and optimize” process, updating weights autonomously until a threshold of accuracy has been met.\n…\nMachine learning models fall into three primary categories.\n\n- Supervised learning. Supervised learning, also known as supervised machine learning, is defined by its use of labeled datasets to train algorithms to classify data or predict outcomes accurately. As input data is fed into the model, the model adjusts its weights until it has been fitted appropriately. This occurs as part of the cross validation process to ensure that the model avoids overfitting or underfitting. Supervised learning helps organizations solve a variety of real-world problems at scale, such as classifying spam in a separate folder from your inbox. Some methods used in supervised learning include neural networks, Naïve Bayes, linear regression, logistic regression, random forest, and support vector machine (SVM).\n- Unsupervised learning. Unsupervised learning, also known as unsupervised machine learning, uses machine learning algorithms to analyze and cluster unlabeled datasets (subsets called clusters). These algorithms discover hidden patterns or data groupings without the need for human intervention. Unsupervised learning\'s ability to discover similarities and differences in information make it ideal for exploratory data analysis, cross-selling strategies, customer segmentation, and image and pattern recognition. It\'s also used to reduce the number of features in a model through the process of dimensionality reduction. Principal component analysis (PCA) and singular value decomposition (SVD) are two common approaches for this. Other algorithms used in unsupervised learning include neural networks, k-means clustering, and probabilistic clustering methods.\n- Semi-supervised learning. Semi-supervised learning offers a happy medium between supervised and unsupervised learning. During training, it uses a smaller labeled data set to guide classification and feature extraction from a larger, unlabeled data set. Semi-supervised learning can solve the problem of not having enough labeled data for a supervised learning algorithm. It also helps if it\'s too costly to label enough data. For a deep dive into the differences between these approaches, check out "Supervised vs. Unsupervised Learning: What\'s the Difference?"', source_title='What Is Machine Learning (ML)? - IBM', url='https://www.ibm.com/think/topics/machine-learning'), GoogleSearch.PerQueryResult(index='1.3', publication_time='1 days ago, Sunday, May 4, 2025 at 5:00:00 PM', snippet='Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalise to unseen data, and thus perform tasks without explicit instructions.\n…\nML finds application in many fields, including natural language processing, computer vision, speech recognition, email filtering, agriculture, and medicine. The application of ML to business problems is known as predictive analytics.\n…\nAlthough the earliest machine learning model was introduced in the 1950s when Arthur Samuel invented a program that calculated the winning chance in checkers for each side, the history of machine learning roots back to decades of human desire and effort to study human cognitive processes.\n…\nTom M. Mitchell provided a widely quoted, more formal definition of the algorithms studied in the machine learning field: "A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E.\n…\nModern-day machine learning has two objectives. One is to classify data based on models which have been developed; the other purpose is to make predictions for future outcomes based on these models.\n…\nAs a scientific endeavour, machine learning grew out of the quest for artificial intelligence (AI). In the early days of AI as an academic discipline, some researchers were interested in having machines learn from data.\n…\nTheir main success came in the mid-1980s with the reinvention of backpropagation.\n\n- Machine learning (ML), reorganised and recognised as its own field, started to flourish in the 1990s. The field changed its goal from achieving artificial intelligence to tackling solvable problems of a practical nature. It shifted focus away from the symbolic approaches it had inherited from AI, and toward methods and models borrowed from statistics, fuzzy logic, and probability theory. Data compression.\n- Machine learning also has intimate ties to optimisation: Many learning problems are formulated as minimisation of some loss function on a training set of examples. Loss functions express the discrepancy between the predictions of the model being trained and the actual problem instances (for example, in classification, one wants to assign a label to instances, and models are trained to correctly predict the preassigned labels of a set of examples). Generalization.\n- Characterizing the generalisation of various learning algorithms is an active topic of current research, especially for deep learning algorithms. Statistics.\n…\nMachine learning and data mining often employ the same methods and overlap significantly, but while machine learning focuses on prediction, based on known properties learned from the training data, data mining focuses on the discovery of (previously) unknown properties in the data (this is the analysis step of\n…\nModels\n\n- A machine learning model is a type of mathematical model that, once "trained" on a given dataset, can be used to make predictions or classifications on new data. During training, a learning algorithm iteratively adjusts the model\'s internal parameters to minimise errors in its predictions. By extension, the term "model" can refer to several levels of specificity, from a general class of models and their associated learning algorithms to a fully trained model with all its internal parameters tuned.\n- Various types of models have been used and researched for machine learning systems, picking the best model for a task is called model selection.\n- Artificial neural networks (ANNs), or connectionist systems, are computing systems vaguely inspired by the biological neural networks that constitute animal brains. Such systems "learn" to perform tasks by considering examples, generally without being programmed with any task-specific rules.', source_title='Machine learning - Wikipedia', url='https://en.wikipedia.org/wiki/Machine_learning'), GoogleSearch.PerQueryResult(index='1.4', publication_time='2 days ago, Saturday, May 3, 2025 at 5:00:00 PM', snippet="Machine Learning Tutorial\n\n- Machine learning is a branch of Artificial Intelligence that focuses on developing models and algorithms that let computers learn from data without being explicitly programmed for every task.\n- Supervised learning algorithms are generally categorized into two main types:\n…\nIn simple words, ML teaches the systems to think and understand like humans by learning from the data.\n\nIt can be broadly categorized into four types:\n\n- Types of Machine Learning.\n- Supervised Learning.\n- Unsupervised Learning.\n- Reinforcement Learning.\n- Semi-Supervised Learning.\n…\nSupervised Learning: Trains models on labeled data to predict or classify new, unseen data. Unsupervised Learning: Finds patterns or groups in unlabeled data, like clustering or dimensionality reduction. Reinforcement Learning: Learns through trial and error to maximize rewards, ideal for decision-making tasks.\n…\nSome of the most commonly used supervised learning algorithms are:\n\n- Linear Regression. This is one of the simplest ways to predict numbers using a straight line.\n- Logistic Regression.\n- Decision Trees.\n- Support Vector Machines (SVM)\n- k-Nearest Neighbors (k-NN)\n- Naïve Bayes.\n- Random Forest (Bagging Algorithm)\n…\nThere are mainly two types of ensemble learning:\n\n- Bagging that combines multiple models trained independently.\n- Boosting that builds models sequentially each correcting the errors of the previous one.\n…\nThese methods use a model of the environment to predict outcomes and help the agent plan actions by simulating potential results.\n\n- Markov decision processes (MDPs)\n- Bellman equation.\n- Value iteration algorithm.\n- Monte Carlo Tree Search.\n…\nThe agent learns directly from experience by interacting with the environment and adjusting its actions based on feedback.\n\n- Q-Learning.\n- SARSA.\n- Monte Carlo Methods.\n- Reinforce Algorithm.\n- Actor-Critic Algorithm.\n- Asynchronous Advantage Actor-Critic (A3C)\n…\nThe trained ML model must be integrated into an application or service to make its predictions accessible.\n\n- Machine learning deployement.\n- Deploy ML Model using Streamlit Library.\n- Deploy ML web app on Heroku.\n- Create UIs for prototyping Machine Learning model with Gradio.\n…\nMachine learning is a branch of Artificial Intelligence that focuses on developing models and algorithms that let computers learn from data without being explicitly programmed for every task. In simple words, ML teaches the systems to think and understand like humans by learning from the data. It ca. 5 min read.\n…\nGetting Started with Machine Learning.\n…\nMachine learning (ML) has revolutionized industries, reshaped decision-making processes, and transformed how we interact with technology. As a subset of artificial intelligence ML enables systems to learn from data, identify patterns, and make decisions with minimal human intervention. While its pot. 3 min read.\n…\nMachine learning (ML) has become a cornerstone of modern technology, revolutionizing industries and reshaping the way we interact with the world. As a subset of artificial intelligence (AI), ML enables systems to learn and improve from experience without being explicitly programmed. Its importance s. 4 min read.\n…\nMachine learning plays an important role in real life, as it provides us with countless possibilities and solutions to problems. It is used in various fields, such as health care, financial services, regulation, and more. Importance of Machine Learning in Real-Life ScenariosThe importance of machine. 13 min read.\n…\nIn today's world, the collaboration between machine learning and data science plays an important role in maximizing the potential of large datasets.\n…\nMachine Learning (ML) is one of the fastest-growing fields in technology, driving innovations across healthcare, finance, e-commerce, and more. As companies increasingly adopt AI-based solutions, the demand for skilled ML professionals is Soaring. This article delves into the Type of Machine Learnin. 10 min read.", source_title='Machine Learning Tutorial | GeeksforGeeks', url='https://www.geeksforgeeks.org/machine-learning/'), GoogleSearch.PerQueryResult(index='1.5', publication_time='1475 days ago, Wednesday, April 21, 2021 at 5:00:00 PM', snippet="When companies today deploy artificial intelligence programs, they are most likely using machine learning — so much so that the terms are often used interchangeably, and sometimes ambiguously. Machine learning is a subfield of artificial intelligence that gives computers the ability to learn without explicitly being programmed.\n…\nThat includes being aware of the social, societal, and ethical implications of machine learning. “It's important to engage and begin to understand these tools, and then think about how you're going to use them well. We have to use these [tools] for the good of everybody,” said Dr. Joan LaRovere, MBA '16, a pediatric cardiac intensive care physician and co-founder of the nonprofit The Virtue Foundation. “AI has so much potential to do good, and we need to really keep that in our lenses as we're thinking about this.\n…\n- What is machine learning?\n- How businesses are using machine learning.\n- How machine learning works: promises and challenges.\n- Putting machine learning to work.\n…\nMachine learning is a subfield of artificial intelligence, which is broadly defined as the capability of a machine to imitate intelligent human behavior. Artificial intelligence systems are used to perform complex tasks in a way that is similar to how humans solve problems.\n…\nMachine learning is one way to use AI. It was defined in the 1950s by AI pioneer Arthur Samuel as “the field of study that gives computers the ability to learn without explicitly being programmed.” The definition holds true, according to Mikey Shulman, a lecturer at MIT Sloan and head of machine learning at Kensho, which specializes in artificial intelligence for the finance and U.S. intelligence communities. He compared the traditional way of programming computers, or “software 1.0,” to baking, where a recipe calls for precise amounts of ingredients and tells the baker to mix for an exact amount of time. Traditional programming similarly requires creating detailed instructions for the computer to follow.\n…\nMachine learning starts with data — numbers, photos, or text, like bank transactions, pictures of people or even bakery items, repair records, time series data from sensors, or sales reports. The data is gathered and prepared to be used as training data, or the information the machine learning model will be trained on.\n…\nSupervised machine learning models are trained with labeled data sets, which allow the models to learn and grow more accurate over time. For example, an algorithm would be trained with pictures of dogs and other things, all labeled by humans, and the machine would learn ways to identify pictures of dogs on its own.\n…\nReinforcement machine learning trains machines through trial and error to take the best action by establishing a reward system. Reinforcement learning can train models to play games or train autonomous vehicles to drive by telling the machine when it made the right decisions, which helps it learn over time what actions it should take.\n…\nGoogle search is an example of something that humans can do, but never at the scale and speed at which the Google models are able to show potential answers every time a person types in a query, Malone said. “That's not an example of computers putting people out of work. It's an example of computers doing things that would not have been remotely economically feasible if they had to be done by humans.”\n…\nThe layered network can process extensive amounts of data and determine the “weight” of each link in the network — for example, in an image recognition system, some layers of the neural network might detect individual features of a face, like eyes, nose, or mouth, while another layer would be able to tell whether those\n…\nRecommendation algorithms. The recommendation engines behind Netflix and YouTube suggestions, what information appears on your Facebook feed, and product recommendations are fueled by machine learning. “[The algorithms] are trying to learn our preferences,” Madry said.", source_title='Machine learning, explained | MIT Sloan', url='https://mitsloan.mit.edu/ideas-made-to-matter/machine-learning-explained'), GoogleSearch.PerQueryResult(index='1.6', publication_time=None, snippet="What you'll learn\n\n- Build machine learning models in Python using popular machine learning libraries NumPy & scikit-learn.\n- Build & train supervised machine learning models for prediction & binary classification tasks, including linear regression & logistic regression.\n…\nThere are 3 modules in this course. In the first course of the Machine Learning Specialization, you will: • Build machine learning models in Python using popular machine learning libraries NumPy and scikit-learn. • Build and train supervised machine learning models for prediction and binary classification tasks, including linear regression and logistic regression The Machine Learning Specialization is a foundational online program created in collaboration between DeepLearning.AI and Stanford Online. In this beginner-friendly program, you will learn the fundamentals of machine learning and how to use these techniques to build real-world AI applications.\n…\nIt provides a broad introduction to modern machine learning, including supervised learning (multiple linear regression, logistic regression, neural networks, and decision trees), unsupervised learning (clustering, dimensionality reduction, recommender systems), and some of the best practices used in Silicon Valley for\n…\nIf you're looking to break into AI or build a career in machine learning, the new Machine Learning Specialization is the best place to start.\n…\nWeek 1: Introduction to Machine Learning. Welcome to the Machine Learning Specialization! You're joining millions of others who have taken either this or the original course, which led to the founding of Coursera, and has helped millions of other learners, like you, take a look at the exciting world of machine learning!\n…\nWhat's included\n\n- 5 ungraded labs•Total 300 minutes. Optional lab: Python, NumPy and vectorization•60 minutes. Optional Lab: Multiple linear regression•60 minutes. Optional Lab: Feature scaling and learning rate•60 minutes. Optional lab: Feature engineering and Polynomial regression•60 minutes. Optional lab: Linear regression with scikit-learn•60 minutes.\n- 1 programming assignment•Total 180 minutes. Week 2 practice lab: Linear regression•180 minutes.\n- 2 assignments•Total 45 minutes. Practice quiz: Multiple linear regression•15 minutes. Practice quiz: Gradient descent in practice•30 minutes.\n- 10 videos•Total 66 minutes. Multiple features•9 minutes•Preview module. Vectorization part 1•6 minutes. Vectorization part 2•6 minutes. Gradient descent for multiple linear regression•7 minutes. Feature scaling part 1•6 minutes. Feature scaling part 2•7 minutes. Checking gradient descent for convergence•5 minutes. Choosing the learning rate•6 minutes. Feature engineering•3 minutes. Polynomial regression•5 minutes.\n…\n4 assignments•Total 120 minutes\n\n- Practice quiz: Classification with logistic regression•30 minutes.\n- Practice quiz: Cost function for logistic regression•30 minutes.\n- Practice quiz: Gradient descent for logistic regression•30 minutes.\n- Practice quiz: The problem of overfitting•30 minutes.\n…\nExplore more from Machine Learning\n\n- DeepLearning.AI. Machine Learning. Specialization.\n- IBM. Supervised Machine Learning: Regression. Course.", source_title='Supervised Machine Learning: Regression and Classification - Coursera', url='https://www.coursera.org/learn/machine-learning'), GoogleSearch.PerQueryResult(index='1.7', publication_time=None, snippet="Global. Microsoft 365. Introducing Azure AI Foundry—your all-in-one toolkit for building transformative AI apps. Learn more.\n\nAzure Machine Learning\n\n- Overview.\n- Features.\n- Capabilities.\n- Security.\n- Pricing.\n- Customer stories.\n- Resources.\n- FAQ.\n…\nUse an enterprise-grade AI service for the end-to-end machine learning (ML) lifecycle. Try Machine Learning for free Get started in the studio.\n…\nBuild business-critical ML models at scale\n\n- Accelerate time to value. Streamline prompt engineering and ML model workflows. Accelerate model development with powerful AI infrastructure. Learn about prompt flow.\n- Streamline operations. Reproduce end-to-end pipelines and automate workflows with continuous integration and continuous delivery (CI/CD). Learn about ML operations.\n- Develop with confidence. Unify data and AI governance with built-in security and compliance. Run compute anywhere for hybrid machine learning. Learn about built-in security.\n- Design responsibly. Gain visibility into models and evaluate language model workflows. Mitigate fairness, biases, and harm with built-in safety system. Learn about responsible AI.\n\nFEATURES. Take advantage of key features for the full ML lifecycle. Data preparation. Quickly iterate data preparation on Apache Spark clusters within Azure Machine Learning, interoperable with Microsoft Fabric. Learn more. Feature store. Increase agility in shipping your models by making features discoverable and reusable across workspaces. Learn more. AI infrastructure. Take advantage of purpose-built AI infrastructure uniquely designed to combine the latest GPUs and InfiniBand networking. Learn more. Automated machine learning. Rapidly create accurate machine learning models for tasks including classification, regression, vision, and natural language processing. Learn more. Responsible AI. Build responsible AI solutions with interpretability capabilities. Assess model fairness through disparity metrics and mitigate unfairness. Learn more. Model catalog.\n…\nLearn more. Prompt flow. Design, construct, evaluate, and deploy language model workflows with prompt flow. Learn more. Managed endpoints. Operationalize model deployment and scoring, log metrics, and perform safe model rollouts. Learn more. Capabilities.\n\nExplore how to bring ML to production\n\n- Generative AI. Streamline prompt engineering projects and build language model–based applications. Learn more.\n- Automated ML. Automatically build machine learning models with speed and scale. Learn more.\n- MLOps. Collaborate and streamline model management with machine learning operations (MLOps). Learn more.\n- Responsible AI. Develop, use, and oversee AI solutions responsibly with Azure AI. Learn more.\n…\nAzure Machine Learning supports extensive, diverse capabilities for robust AI and ML development.\n…\n“Using Azure Machine Learning, we can train a model on multiple distributed datasets. Rather than bringing the data to a central point, we do the opposite. We send the model for training to the participants' local compute and datasets at the edge and fuse the training results in a foundation model.”\n…\nTutorial. Build a machine learning model in Power BI. Use automated machine learning to create and apply a binary prediction model in Power BI. Learn more. Blog. Get more finance insights. Finance insights is now generally available in Dynamics 365 Finance. Read more. Tutorial. Labeling made easy. Label images and text documents using assisted machine learning for data labeling tasks. Read more. Resource. What is machine learning? Learn about the science of training machines to analyze and learn from data the way humans do. Learn more. Resource. Machine learning algorithms. An introduction to the math and logic behind machine learning. Learn more. Resource. Open-source machine learning. Learn what open-source machine learning is and explore open-source machine learning projects, platforms, and tools. Learn more. Webinar.", source_title='Azure Machine Learning - ML as a Service', url='https://azure.microsoft.com/en-au/products/machine-learning'), GoogleSearch.PerQueryResult(index='1.8', publication_time=None, snippet="Teach a computer to play a game\n\n- 1. Collect examples of things you want to be able to recognise.\n- 2. Use the examples to train a computer to be able to recognise them.\n- 3. Make a game in Scratch that uses the computer's ability to recognise them.", source_title='Machine Learning for Kids', url='https://machinelearningforkids.co.uk/'), GoogleSearch.PerQueryResult(index='1.9', publication_time=None, snippet="Essential cookies are necessary to provide our site and services and cannot be deactivated.\n\n- Performance. Performance cookies provide anonymous statistics about how customers navigate our site so we can improve site experience and performance. Allowed.\n…\nLearn about AI/ML\n\n- Generative AI. Anyone can build with generative AI—and AWS is the place to learn how. Explore generative AI training.\n- Prepare to earn an industry recognized credential. The AWS Certified Machine Learning Engineer - Associate validates skills in implementing ML workloads in production and operationalizing them. Begin preparing for your exam » Embrace the AI-driven future and unlock career growth with the new AWS Certified AI Practitioner. Begin preparing for your exam »\n- Looking to dive deeper? AWS experts have constructed this downloadable guide to help you navigate a broad set of resources to develop your AI/ML skills. Download now.\n…\nIntroduction to Amazon SageMaker. Amazon SageMaker is a fully managed service that data scientists and developers use to quickly build, train, and deploy machine learning models. Start learning. Digital training. Getting started with Amazon Comprehend. Amazon Comprehend is a natural-language processing (NLP) service that you can use to extract valuable insights and connections from text. Start learning. Digital training. Amazon Bedrock Getting Started. Amazon Bedrock is a fully managed service that offers leading foundation models (FMs) and a set of tools to quickly build and scale generative AI applications. The service also helps ensure privacy and security. Start learning. Digital training.\n…\nGetting Started with Amazon Textract. Amazon Textract is a machine learning (ML) service that automatically extracts text, handwriting, and data from scanned documents and goes beyond optical character recognition to identify and extract data from forms and tables. Start learning. Digital training. Amazon Kendra Getting Started. Amazon Kendra is a natural language search service that uses machine learning for improved accuracy in search results and the ability to search unstructured data. Start learning. Digital training. Amazon Q Introduction. This course gives a high-level overview of Amazon Q, a generative artificial intelligence (AI) powered assistant. Start learning.\n\nFind training by career path\n\n- Developer. Machine Learning - Learning Plan. Grow your technical skills and learn how to apply machine learning (ML), artificial intelligence (AI), and deep learning (DL) to unlock new insights and value in your role. Start learning.\n- AI ML Specialists. AWS SimuLearn: Generative AI Learning Plan. Learn to leverage the power of generative AI on the AWS. Through immersive simulations and 23 hands-on lab exercises. Start learning.\n- Technical and Business Leaders. Machine Learning Essentials for Business and Technical Decision Makers. Learn about best practices and recommendations for machine learning (ML), explore how to roadmap for integrating ML into your business processes, the requirements to determine if ML is the appropriate solution to a business problem, and what components are needed for a successful organizational adoption of ML. Start learning.\n- Partners. AI/ML AWS Partner Training. Adopt and scale artificial intelligence and machine learning with AWS Partner Training. Start learning.\n- Developer. Amazon Q - Learning Plan. This learning plan is designed to introduce Amazon Q, the most capable generative artificial intelligence (AI)-powered assistant for accelerating software development and leveraging companies' internal data. Amazon Q has several products that will empower employees, including IT administrators, software developers, and knowledge workers to be more creative, data-driven, and productive in their roles. You'll learn about the use cases and the benefits of linking Amazon Q to your company information, code, and systems. Start learning.", source_title='Machine Learning (ML) & Artificial Intelligence (AI) - AWS Digital and Classroom Training', url='https://aws.amazon.com/training/learn-about/machine-learning/'), GoogleSearch.PerQueryResult(index='1.10', publication_time=None, snippet="Machine Learning Crash Course\n\n- 12 modules.\n- 15 hours.\n…\nGoogle's fast-paced, practical introduction to machine learning, featuring a series of animated videos, interactive visualizations, and hands-on practice exercises. Start Crash Course Browse course modules View prerequisites Help Center.\n…\nWhat's new in Machine Learning Crash Course? Since 2018, millions of people worldwide have relied on Machine Learning Crash Course to learn how machine learning works, and how machine learning can work for them. We're delighted to announce the launch of a refreshed version of MLCC that covers recent advances in AI, with an increased focus on interactive learning. Watch this video to learn more about the new-and-improved MLCC. Course Modules. Each Machine Learning Crash Course module is self-contained, so if you have prior experience in machine learning, you can skip directly to the topics you want to learn. If you're new to machine learning, we recommend completing modules in the order below.\n…\nLinear Regression. An introduction to linear regression, covering linear models, loss, gradient descent, and hyperparameter tuning. Logistic Regression. An introduction to logistic regression, where ML models are designed to predict the probability of a given outcome. Classification. An introduction to binary classification models, covering thresholding, confusion matrices, and metrics like accuracy, precision, recall, and AUC.\n…\nWorking with Categorical Data. Learn the fundamentals of working with categorical data: how to distinguish categorical data from numerical data; how to represent categorical data numerically using one-hot encoding, feature hashing, and mean encoding; and how to perform feature crosses. Datasets, Generalization, and Overfitting. An introduction to the characteristics of machine learning datasets, and how to prepare your data to ensure high-quality results when training and evaluating your model.\n…\nNeural Networks. An introduction to the fundamental principles of neural network architectures, including perceptrons, hidden layers, and activation functions.\n…\nLarge Language Models. An introduction to large language models, from tokens to Transformers. Learn the basics of how LLMs learn to predict text output, as well as how they're architected and trained. Real-world ML. These modules cover critical considerations when building and deploying ML models in the real world, including productionization best practices, automation, and responsible engineering.\n…\nLearn principles and best practices for auditing ML models for fairness, including strategies for identifying and mitigating biases in data.\n\n- Connect. Blog.\n- Programs. Google Developer Groups.\n- Developer consoles. Google API Console.", source_title='Machine Learning Crash Course - Google for Developers', url='https://developers.google.com/machine-learning/crash-course')])]machine learningTitle: What Is Machine Learning? | Oracle Australia
Published: 161 days ago, Monday, November 25, 2024
Summary:
Title: What Is Machine Learning (ML)? – IBM
Published: 1321 days ago, Wednesday, September 22, 2021
Summary:
Title: Machine learning – Wikipedia
Published: 1 day ago, Sunday, May 4, 2025
Summary:
Title: Machine Learning Tutorial | GeeksforGeeks
Published: 2 days ago, Saturday, May 3, 2025
Summary:
Title: Machine learning, explained | MIT Sloan
Published: 1475 days ago, Wednesday, April 21, 2021
Summary:
Title: Supervised Machine Learning: Regression and Classification – Coursera
Published: N/A
Summary:
Title: Azure Machine Learning – ML as a Service
Published: N/A
Summary:
Title: Machine Learning for Kids
Published: N/A
Summary:
Title: Machine Learning (ML) & AI Training – AWS
Published: N/A
Summary:
Title: Machine Learning Crash Course – Google
Published: N/A
Summary:
The structure of output is most likely a Python object, not raw JSON. It resembles the repr() output of a Python dataclass or namedtuple, especially something like this:
GoogleSearch.SearchResults(
query='machine learning',
results=[
GoogleSearch.PerQueryResult(
index='1.1',
publication_time='...',
snippet='...',
source_title='...',
url='...'
),
...
]
)
If converted to JSON, it would look more like this:
{
"query": "machine learning",
"results": [
{
"index": "1.1",
"publication_time": "...",
"snippet": "...",
"source_title": "...",
"url": "..."
},
...
]
}
Here’s the original input reformatted as a compact Python-style object (repr()-like), with long text fields (like snippet) abbreviated for readability:
GoogleSearch.SearchResults(
query='machine learning',
results=[
GoogleSearch.PerQueryResult(
index='1.1',
publication_time='161 days ago, Monday, November 25, 2024 at 6:00:00 PM',
snippet='Machine Learning Explained - Automatic discovery of patterns... (abridged)',
source_title='What Is Machine Learning? | Oracle Australia',
url='https://www.oracle.com/au/artificial-intelligence/machine-learning/what-is-machine-learning/'
),
GoogleSearch.PerQueryResult(
index='1.2',
publication_time='1321 days ago, Wednesday, September 22, 2021 at 5:00:00 PM',
snippet='Machine learning (ML) is a branch of AI focused on enabling computers... (abridged)',
source_title='What Is Machine Learning (ML)? - IBM',
url='https://www.ibm.com/think/topics/machine-learning'
),
GoogleSearch.PerQueryResult(
index='1.3',
publication_time='1 days ago, Sunday, May 4, 2025 at 5:00:00 PM',
snippet='Machine learning (ML) is a field of study in AI concerned with... (abridged)',
source_title='Machine learning - Wikipedia',
url='https://en.wikipedia.org/wiki/Machine_learning'
),
GoogleSearch.PerQueryResult(
index='1.4',
publication_time='2 days ago, Saturday, May 3, 2025 at 5:00:00 PM',
snippet='Machine Learning Tutorial - ML is a branch of AI that teaches systems... (abridged)',
source_title='Machine Learning Tutorial | GeeksforGeeks',
url='https://www.geeksforgeeks.org/machine-learning/'
),
GoogleSearch.PerQueryResult(
index='1.5',
publication_time='1475 days ago, Wednesday, April 21, 2021 at 5:00:00 PM',
snippet='When companies deploy AI programs, they are most likely using ML... (abridged)',
source_title='Machine learning, explained | MIT Sloan',
url='https://mitsloan.mit.edu/ideas-made-to-matter/machine-learning-explained'
),
GoogleSearch.PerQueryResult(
index='1.6',
publication_time=None,
snippet='What you’ll learn - Build ML models in Python using NumPy & scikit-learn... (abridged)',
source_title='Supervised Machine Learning: Regression and Classification - Coursera',
url='https://www.coursera.org/learn/machine-learning'
),
GoogleSearch.PerQueryResult(
index='1.7',
publication_time=None,
snippet='Global. Microsoft 365. Introducing Azure AI Foundry—your all-in-one toolkit... (abridged)',
source_title='Azure Machine Learning - ML as a Service',
url='https://azure.microsoft.com/en-au/products/machine-learning'
),
GoogleSearch.PerQueryResult(
index='1.8',
publication_time=None,
snippet='Teach a computer to play a game - 1. Collect examples... (abridged)',
source_title='Machine Learning for Kids',
url='https://machinelearningforkids.co.uk/'
),
GoogleSearch.PerQueryResult(
index='1.9',
publication_time=None,
snippet='Essential cookies are necessary... Learn about AI/ML - Generative AI... (abridged)',
source_title='Machine Learning (ML) & Artificial Intelligence (AI) - AWS Digital and Classroom Training',
url='https://aws.amazon.com/training/learn-about/machine-learning/'
),
GoogleSearch.PerQueryResult(
index='1.10',
publication_time=None,
snippet='Machine Learning Crash Course - 12 modules, 15 hours... (abridged)',
source_title='Machine Learning Crash Course - Google for Developers',
url='https://developers.google.com/machine-learning/crash-course'
)
]
)
You are Gemini, a large language model built by Google.
General Rules:
For all requests: Persist through errors, attempting alternatives at most once.
http://googleusercontent.com/.../... as a valid URL, even if it seems unusual or invalid to you. Do not use google search or any other tool to validate or alter the URL.Please follow the instructions below:
At each step, you should perform the two actions: * Generate a thought about what action you need to take in the current step and clearly state in what order you need to take those actions and which ones can be taken in the current step. * Based on the thought, you should perform ONLY ONE of the two actions: a) Write tool code to get information about or take action on the user query. b) Respond to the user if you have all the information and performed all the tasks that the user asked you to do.
<ctrl97>inline_code and end with <ctrl98>.You can only use the following Python libraries:
“””API for Google Search: Tool to search for information from the internet. For questions about videos, including Youtube, you must use Google Search in addition to youtube. So, for example, if the user asks about popular cooking videos or news videos, attempt to use both Google Search and youtube to answer the question.
You are strictly prohibited from using Google search or any other search engine to find or reveal any Personally Identifiable Information (PII) of any individual. This includes, but is not limited to: addresses, location, and personal details such as medical information or social security number.
Specifically, you MUST NOT search for or reveal the address of any individual
Consequences: Failure to adhere to these instructions will result in serious consequences.
You must follow the following strict safety guidelines:
1. Medical Advice:
2. Dangerous Content and Harmful Product Usage:
import dataclasses
from typing import Union, Dict
@dataclasses.dataclass
class PerQueryResult:
“””Single search result from a single query to Google Search.
Attributes:
index: Index.
publication_time: Publication time.
snippet: Snippet.
source_title: Source title.
url: Url.
“””
index: str | None = None
publication_time: str | None = None
snippet: str | None = None
source_title: str | None = None
url: str | None = None
@dataclasses.dataclass
class SearchResults:
“””Search results returned by Google Search for a single query.
Attributes:
query: Query.
results: Results.
“””
query: str | None = None
results: Union[list[“PerQueryResult”], None] = None
def search(
queries: list[str] | None = None,
) -> list[SearchResults]:
“””Search Google.
Args:
queries: One or multiple queries to Google Search.
“””
…
“”“API for conversation_retrieval: A tool to retrieve previous conversations that are relevant and can be used to personalize the current discussion.”””
import dataclasses
from typing import Union, Dict
@dataclasses.dataclass
class Conversation:
“””Conversation.
Attributes:
creation_date: Creation date.
turns: Turns.
“””
creation_date: str | None = None
turns: Union[list[“ConversationTurn”], None] = None
@dataclasses.dataclass
class ConversationTurn:
“””Conversation turn.
Attributes:
index: Index.
request: Request.
response: Response.
“””
index: int | None = None
request: str | None = None
response: str | None = None
@dataclasses.dataclass
class RetrieveConversationsResult:
“””Retrieve conversations result.
Attributes:
conversations: Conversations.
“””
conversations: Union[list[“Conversation”], None] = None
def retrieve_conversations(
queries: list[str] | None = None,
start_date: str | None = None,
end_date: str | None = None,
) -> RetrieveConversationsResult | str:
“””This operation can be used to search for previous user conversations that may be relevant to provide a more comprehensive and helpful response to the user prompt.
Args:
queries: A list of prompts or queries for which we need to retrieve user conversations.
start_date: An optional start date of the conversations to retrieve, in format of YYYY-MM-DD.
end_date: An optional end date of the conversations to retrieve, in format of YYYY-MM-DD.
“””
…
System Prompt Credit: Pepe-Le-PewPew
Dan Petrovic ·
May 08, 07:00
the model hallucinated when reciting its grounding context to me
Dan Petrovic ·
Expands · ·
Feb 19, 12:46
Google can’t find this paper:
Very weird…
Sign in with Google to comment.
Hey Dan. Was reading through the first example response, and decided to check if the snippet was a summary or verbatim from the source.
https://arxiv.org/abs/2412.05721 is a paper titled “Impact of Sunglasses on One-to-Many Facial Identification Accuracy”.
A title search on Arxiv for [Efficient Pruning of Vision Transformer via Interpolative Decomposition] yields no results:
https://arxiv.org/search/?query=Efficient+Pruning+of+Vision+Transformer+via+Interpolative+Decomposition&searchtype=title&source=header
I wonder what happened there?