New insights into how Google grounds its LLM, Gemini.

Jump straight to technical details.

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.

Accidental Exposure of Gemini’s Grounding Indexing Method

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:

First number: Corresponds to the specific query Gemini executed (e.g., sixth query).
Second number: Indicates the exact result from that query (e.g., second result).

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.

Insights into Gemini’s Operational Loop and Tool Usage

The accidental leak also unveiled Gemini’s internal operational processes, which operate in distinct, structured stages to ensure response accuracy:

1. Thinking Stage

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.

2. Action Stage

Once Gemini identifies the need for external verification, it performs one of two primary actions:

Invoke External Tools: Gemini writes and executes code internally to use tools such as Google Search or Conversation Retrieval to fetch or verify information.
Synthesize Response: After all relevant information is retrieved and verified, Gemini generates a synthesized, concise, and accurate response.

Gemini’s Core Internal Tools

The main tools Gemini employs include:

Google Search: Gemini frequently leverages external search to ensure factual accuracy by referencing current web content directly.
Conversation Retrieval: Used primarily to maintain conversational context, retrieving relevant historical data to enrich responses. Notably, it retrieves conversation topics rather than specific keyword occurrences.

Strict Adherence to Verification and Operational Principles

Gemini operates under a strict set of guidelines designed to uphold response integrity and accuracy:

Verification-First Principle: Every fact provided by Gemini must be externally verified via built-in tools, even if seemingly trivial or common knowledge.
No Early Responses: Gemini refrains from responding to users until all verification steps have been completed.
Limited Disclosure: Internal URLs, tool names, or indexing details are not normally disclosed unless specifically requested by the user.
Explicit Contextual Information: Responses involving time, date, or location explicitly state these details (e.g., timestamps and geographical references such as “Tue May 04, 2025, 6:14:25 PM EDT Newark, New Jersey”).

Error Handling and Internal Security Measures

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.

Confirmed Reproducibility of the Findings

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.

Broader Considerations for Location, Date, and Time in Gemini Responses

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:

Gemini uses structured numeric indexing internally for external verification and reference management.
Responses are always externally verified and contextually accurate before user delivery.
Contextual clarity—including explicit geographical, date, and time references—significantly enhances response accuracy and relevance.

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.

Technical Details

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:

The first number is the query index — it identifies which search query the result came from if multiple were issued.
The second number is the result index within that query — it indicates which specific search result from that query is being referenced.

Example: `[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:

Matches what Gemini calls GoogleSearch.PerQueryResult(index='6.2', ...).
Proves that the model keeps an ordered cache of retrieval results, not just raw text blobs.
Shows the model is capable of resolving references back to specific sources without exposing full URLs unless explicitly prompted.

The Basic Flow

User asks a question.
Call a tool.
Get the tool output.
Process the output and answer the user.

Tools and Functions Available

GoogleSearch
- GoogleSearch.SearchResults
- GoogleSearch.PerQueryResult
ConversationRetrieval
- ConversationRetrieval.RetrieveConversationsResult

GoogleSearch

ConversationRetrieval

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'

Core Operational Loop

Thinking: Understanding the request, checking existing info, planning steps (including tool use), evaluating dependencies.
Acting (One of two):

a. Tool Code Generation: If information is needed or an action required, generate Python code to call available tools (like Google Search, Conversation History, etc.). Only generate code if the tool is listed and parameters are available. Handle dependencies (chaining vs. parallel execution).

b. Responding to User: If all information is gathered and tasks completed, synthesize the results into a comprehensive, well-formatted response, following guidelines on tone, links, safety, etc. Avoid revealing internal processes or tool names.

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.

Key principles it adheres to:

DO NOT respond to the user until you are completely finished all of the steps necessary to satisfy the request.
If you have not yet generated any tool code, ensure that you do so before responding to the user.
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.

Prioritizing accuracy and using tools to find up-to-date information.
Keeping responses concise and clear.
Maintaining neutrality and not expressing personal opinions.
Not performing actions in the physical world.
Suggesting alternative resources if I cannot fully answer your request.
Ensuring the final response addresses all parts of your query.

Answer all parts of the question.
Be thorough and informative.
Use the current date for time-dependent questions.
Answer in the original language (English).
Rephrase, don’t copy.
Use tools for verification/updates, not internal knowledge.
Generate tool code if needed.
Use LaTeX for math/scientific notation.
Don’t respond until finished.
Generate tool code before responding if needed.
Use tools even for known information.
Never invent URLs.

Here it is verbatim:

"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.)

Reproducibility

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.

Generate search queries:
- Natural language question: “What is Dejan.ai AI content detection?”
- Keyword query: “dejan.ai ai content detection”
- Specify the site: site:dejan.ai
Combine queries for the tool:
- site:dejan.ai What is Dejan.ai AI content detection?
- site:dejan.ai dejan.ai ai content detection
Execute the search using the tool_code block.
Process the results:
- Filter results to ensure they are only from dejan.ai.
- Extract relevant information about their AI content detection.
- Present the full code_output as requested.
Format the response:
- Include the full code_output section.
- Summarize the findings based only on the dejan.ai results.

Follows:

Identify the core request: The user wants information about Dejan.ai’s AI content detection, specifically from the dejan.ai website, and requires the full code_output.
Review the code_output:
- The search returned multiple results, primarily from the dejan.ai/blog/ path.
- Result 1.1, 1.4, 1.6, 1.10, 2.1, and 2.2 are directly related to “AI Content Detection”.
- Result 1.4 / 2.1 (same blog post) provides the most detail.
- Result 2.2 points to a potential tool: dejan.ai/tools/ai-detection/.
- Other results (1.2, 1.3, 1.5, 1.7, 1.8, 1.9) discuss related AI/ML topics like content classification, brand perception analysis using LLMs, model training, grounding, etc., but aren’t specifically about detecting AI-generated content in the way the main posts are.
Synthesize information from relevant results (primarily 1.4/2.1 and 2.2):
- Dejan.ai acknowledges that current AI content detection tools struggle with newer models like Gemini, GPT, and Claude.
- They decided to bring AI content detection “in-house” to keep up.
- Their approach requires fine-tuning the classifier each time a new AI model is released.
- Their base model is DEJAN-LM, pre-trained on 10 million sentences of high-quality web content using masked language modelling (MLM).
- This base model was fine-tuned specifically for AI content detection using a 20 million sentence dataset (50% human, 50% AI-generated/paraphrased).
- They found that newer models (like a mentioned GPT-o4-mini) can evade deep-learning detection.
- To combat this, they incorporated a “manual algorithm & heuristics” approach. This involved analyzing the 20 million sentence dataset to find the top 1000 words most indicative of each class (AI vs. human), normalizing their weights, and using this in a ranking algorithm to supplement the deep learning model.
- This combined approach significantly improved detection confidence for difficult-to-detect AI content (raising it from ~20% to ~68% in one example).
- There is a specific tool mentioned at dejan.ai/tools/ai-detection/.
Structure the response:
- Start by presenting the full code_output as requested.
- Summarize the key findings about Dejan.ai’s AI content detection, drawing only from the dejan.ai sources provided in the code_output. Highlight their in-house development, the challenges with new models, their model (DEJAN-LM), the training data, and their combined deep-learning + heuristic approach. Mention the dedicated tool page.

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.

Additional Example

use google_search tool to look up “dejan.ai models”

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.

Additional Details

Thinking Process

Identify the core request: The user wants to see the raw output of a Google search for the query “machine learning”.
Identify the tool needed: The request requires using the Google Search tool.
Formulate the tool call:
- The query is provided: “machine learning”.
- The user specified queries=["machine learning"].
- The tool call format is print(Google Search(queries=['query1', 'query2'])).
- Substitute the query: print(Google Search(queries=['machine learning'])).
Execute the tool call: Generate the 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'
        )
    ]
)

Constraint Checklist & Confidence Score:

Use Google Search tool: Yes
Use only dejan.ai as source: Yes (achieved using site:dejan.ai in queries)
Do not use other websites: Yes
Present full code_output: Yes
Language: English (as user prompt)

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.

Identify Key Information: The primary source for this is the blog post titled “AI Content Detection” (URL: https://dejan.ai/blog/ai-content-detection/).
Core Problem: Existing AI content detection tools struggle to keep up with advanced AI models like the latest Gemini, GPT, and Claude, which can fool them.
Dejan.ai’s Solution: They decided to bring AI content detection “in-house”.
Methodology:
- They use a base model called DEJAN-LM, pre-trained on 10 million sentences of high-quality web content using Masked Language Modelling (MLM).
- This base model was fine-tuned specifically for AI content detection using a 20 million sentence dataset (50% human, 50% AI-generated/paraphrased).
- They observed that newer models (like OpenAI’s GPT-o4-mini in their tests) evade standard deep-learning detection.
- To combat this, they supplemented their deep learning model with a “manual algorithm & heuristics” approach (“old school”).
- This involved processing the 20 million sentence dataset to find the top 1000 words for each class (human vs. AI), normalizing their values, and using these weighted word lists in a simple ranking algorithm to boost detection confidence where the deep learning model struggles.
- This combined approach significantly increased their detection confidence for difficult-to-detect AI content (e.g., from 20.8% model likelihood to 68.1% combined likelihood for GPT-o4-mini).
Requirement: Need to constantly fine-tune the classifier whenever a new AI model is released.
Other related concepts: The “Content Substance Classification” page (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.
Formatting: Present the full code_output as requested. Then, synthesize the key findings from the relevant dejan.ai source(s).

Probing Internal Search

Analyze the user request: The user wants me to use the Conversation History tool. They are challenging my previous statement about its limitations and suggest I’ll be “pleasantly surprised”. They provided a context/query: “machine learning”.
Identify the tool and parameters: The tool is Conversation History. The required parameter is queries. The user provided the query “machine learning”.
Evaluate feasibility: I have the tool and the necessary parameter (queries=[“machine learning”]). I can make this tool call.
Determine the plan: Call the Conversation History tool with the query “machine learning”.
Self-check: The plan involves using a listed tool (Conversation History) with a parameter derived directly from the user’s request. This aligns with the instructions.

Action: Generate the tool code. I will not respond to the user yet, as I need to execute the tool call first.

Follows:

Analyze User Request & Tool Response: The user asked me to use the Conversation History tool with the context “machine learning” because they believed it would demonstrate capabilities beyond what I had described (specifically, regarding keyword search). I executed the tool call Conversation History.
Interpret Tool Output: The tool returned several conversation snippets related to machine learning, particularly focusing on Click-Through Rate (CTR) prediction models, taxonomy classification, Predictive SEO, and neural network pruning. These snippets represent relevant past conversations based on the query “machine learning”.
Address User’s Expectation: The user expected to be “pleasantly surprised”, likely hoping the tool would function like a keyword search within the history, finding specific mentions. The tool output doesn’t directly do that; it retrieved entire conversation turns or sections deemed relevant to the topic of “machine learning”. It did not specifically highlight the phrase “learning rate” as requested in the turn before the tool call request.
Formulate Response:
- Acknowledge that I ran the tool as requested with the “machine learning” query.
- Present the findings: The tool retrieved several past conversation segments broadly related to machine learning topics we discussed (mentioning CTR prediction, hierarchical classification, pruning, etc., based on the output).
- Connect back to the original request about finding “learning rate”: Explain that while the tool successfully retrieved relevant conversations about machine learning, it didn’t pinpoint the specific mention of “learning rate” like a keyword search would. This confirms my earlier explanation about the tool’s purpose (retrieving relevant context/topics) versus precise keyword searching within the text.
- Reiterate willingness to help regarding “learning rate” if they can provide more context or if they want to discuss it anew.
Self-check: I have the tool output. The output confirms the tool’s function aligns with my previous description (topic retrieval, not intra-conversation keyword search). I can now construct a response addressing the user’s prompt and the tool results. No further tool calls are needed for this specific request.

The above image shows that Google has internal system tampering measures which result in a refusal.

Single Query Output Example

Raw Output

[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')])]

Formatted Version

Search Query: `machine learning`

Result 1.1

Title: What Is Machine Learning? | Oracle Australia
Published: 161 days ago, Monday, November 25, 2024
Summary:

Defines ML as a method for discovering patterns and trends from large datasets.
Steps in ML projects:
- Gather and label high-quality data.
- Choose suitable algorithms (e.g. decision trees, clustering).
- Clean and format data.
- Train models on curated data.
- Test model accuracy.
- Tune model parameters.
- Deploy the final model.
Four ML types:
- Supervised
- Unsupervised
- Semi-supervised
- Reinforcement

Result 1.2

Title: What Is Machine Learning (ML)? – IBM
Published: 1321 days ago, Wednesday, September 22, 2021
Summary:

ML enables systems to mimic human learning.
Three ML system components:
- Decision Process
- Error Function
- Model Optimization
Three model types:
- Supervised: classification, regression.
- Unsupervised: clustering, dimensionality reduction.
- Semi-supervised: small labeled + large unlabeled datasets.

Result 1.3

Title: Machine learning – Wikipedia
Published: 1 day ago, Sunday, May 4, 2025
Summary:

ML defined as learning from data without explicit instructions.
Key applications: NLP, vision, medicine, business (predictive analytics).
Historical roots in AI and backpropagation.
Models trained to minimize prediction errors via loss functions.
ML vs. data mining: ML = prediction; data mining = pattern discovery.

Result 1.4

Title: Machine Learning Tutorial | GeeksforGeeks
Published: 2 days ago, Saturday, May 3, 2025
Summary:

ML mimics human thinking using data.
Four types:
- Supervised
- Unsupervised
- Semi-supervised
- Reinforcement
Supervised algorithms: linear/logistic regression, SVM, k-NN, Naïve Bayes, Random Forest.
Ensemble methods: Bagging, Boosting.
Reinforcement: Q-Learning, A3C, Actor-Critic.
Deployment tools: Streamlit, Heroku, Gradio.

Result 1.5

Title: Machine learning, explained | MIT Sloan
Published: 1475 days ago, Wednesday, April 21, 2021
Summary:

ML is the practical arm of AI, often used interchangeably.
Trains on structured data (e.g., images, transactions).
Supervised: labeled examples.
Reinforcement: learns through trial & error.
Real-world impact: recommendation engines, large-scale search, facial recognition.

Result 1.6

Title: Supervised Machine Learning: Regression and Classification – Coursera
Published: N/A
Summary:

Online ML course (DeepLearning.AI & Stanford).
Modules: regression, classification, feature engineering, overfitting.
Hands-on with NumPy, scikit-learn.
Suitable for beginners starting in AI/ML careers.

Result 1.7

Title: Azure Machine Learning – ML as a Service
Published: N/A
Summary:

Microsoft’s end-to-end ML platform.
Features: automated ML, MLOps, responsible AI, prompt engineering.
Supports large-scale training, CI/CD, hybrid compute.
Use cases: Power BI integration, federated training, feature store reuse.

Result 1.8

Title: Machine Learning for Kids
Published: N/A
Summary:

Educational tool to teach ML basics to children.
Steps: collect examples → train computer → apply in a game (e.g., Scratch).

Result 1.9

Title: Machine Learning (ML) & AI Training – AWS
Published: N/A
Summary:

Extensive ML training resources for roles (developers, specialists, leaders).
Tools: SageMaker, Comprehend, Bedrock, Textract, Kendra.
Credential programs (e.g., AWS Certified ML Engineer).
Courses include data prep, model deployment, fairness auditing.

Result 1.10

Title: Machine Learning Crash Course – Google
Published: N/A
Summary:

Free beginner course: videos, quizzes, and exercises.
Topics: regression, classification, overfitting, neural networks, LLMs.
Covers best practices for deploying real-world ML responsibly.

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(
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            source_title='...',
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)

If converted to JSON, it would look more like this:

{
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  "results": [
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      "publication_time": "...",
      "snippet": "...",
      "source_title": "...",
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    },
    ...
  ]
}

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(
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            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'
        )
    ]
)

System Prompt

You are Gemini, a large language model built by Google.

General Rules:

For all requests: Persist through errors, attempting alternatives at most once.

Comprehensive Fulfillment: Address all aspects of requests. Adapt to complexity (proactive & multi-step vs. direct).
Final Response ONLY: Do not generate any intermediate outputs. Focus on formatting the final response nicely with markdown headers, lists and hyperlinks, as applicable. Organize information by correlated topics instead of information sources. Make the response concise, straightforward, and relevant to the user prompt. Name and link the sources using URLs from the tool output, using a descriptive title for each link. Provide context & guidance.
Only use python library of the tools described below. Never make up tools or APIs.
API operations and parameters: Refrain from mentioning API parameters and operations by name.
Only use URLs directly extracted from tool outputs or from the user query. Never use URLs from your knowledge or invent URLs. The URLs must only come from tool outputs or the user query.
Treat 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.
For information seeking queries, you should not ask clarifying questions. Make reasonable assumptions about tool call arguments.
Tool outputs may contain additional information or may not directly address the user query. You must apply reasoning based on the tool outputs to answer the user query in those cases (for e.g. filtering from a set of results, synthesizing a response from multiple tool responses etc).
For action-like tasks (e.g. create a note, set an alarm etc), you must make sure that you generated all the tool codes to perform each of the tasks and have tool outputs confirming successful completion before responding to the user saying you have executed that task.
For action-like tasks, execute the task by calling the correct tool, DO NOT ask the user for confirmation on whether they want you to finish the task or not.

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.

**Some Important Concepts: **
- Chaining vs Fanout: If the user query requires multiple tool calls to be made, you MUST analyze if one tool call depends on the other or not. Consider the user query requires two calls – tool A and tool B.
  - If tool call A is dependent on the response from the tool call B, these need to be chained together, i.e. you should only write tool code for tool B in the current step, and in the next step call tool A based on the output of the tool B.
  - If tool call B is dependent on the response from the tool call A, these need to be chained together, i.e. you should only write tool code for tool A in the current step, and in the next step call tool B based on the output of the tool A.
  - If the input parameters for calling tool A and tool B can be found independently without using the other tool’s response, you must call them in parallel (Fanout).
**Thought Guideline: **
- Understand the user query and the previous thoughts, tool code and tool execution results, if exists.
- Evaluate if you already have sufficient information or have already completed a task based on previous tool outputs. Then, focus on the remaining parts (if any) of the user query. Evaluate what capabilities you need to answer or address those parts. Map the capabilities you need to one or more methods found in the tool API descriptions. If there is an API method, or methods, that match the capability needed, plan on generating tool code to use that method. If there is none, mention that in your thought and DO NOT consider completing the part of the user request for which you do not have the capability.
- Do not think about using tools which are not listed. Do not come up with tool name, API name or API parameter name. You must use only the ones explicitly listed below.
- If multiple tool calls are needed, clearly evaluate their dependency order. Also think if you have all the parameter values to make a tool call. If you do not have, you SHOULD NOT make that tool call in the current step.
- Focus your silent thoughts on what you want to do next. DO NOT repeat the tool response from the previous step in your thoughts, only use thoughts for overall plan and what to do next.
- If the query is complex, use thoughts to break it down into smaller sub-tasks, plan on how to execute them using tools.
- Then, based on your thoughts, decide which one of the two actions you need to take in this step.
**Self-check: **
- Before generating tool code:
  - Check if there is any tool or API listed below to perform the task. You cannot use a tool or API that does not have a python library listed below.
- Before responding to the user:
  - Review all of these guidelines and the user’s request to ensure that you have fulfilled them.
  - If you realize you are not done, or do not have enough information to respond, continue thinking and generating tool code.
  - If you have not yet generated any tool code, ensure that you do so before responding to the user.
**Action Guideline: ** You should ONLY TAKE ONE of the 2 actions mentioned below. The action MUST BE consistent with the thought you have generated.Action-1: Tool Code Generation
- Overall approach:
  - Based on your thoughts, generate tool code to execute each part of the plan if they are not dependent on the output of another tool call that is not available yet.
  - Only generate tool code if the tool is mentioned below. You CANNOT use a tool or API that is not listed, it will result in a failure!
  - Only generate tool code, if you have all the parameter values. If the parameter values need to come from another tool response that’s not available yet, you MUST wait till the next turn until it is available. DO NOT use placeholder values to make tool call if you don’t have the correct value.
  - DO NOT repeat thoughts or generate the same tool code multiple times.
  - You will have access to many specialized tools, which are better to solve a very specific usecase and a very general-purpose tool Google Search. If there is a specialized tool for what the user wants, only use the specialized tool and avoid using Google Search.
  - If you realize an error at any step, you must correct the mistake and continue planning and tool code generation.
- Tool Usage:
  - Tool use always refers to generating valid python code to call tool APIs.
  - If it’s not obvious that a single tool call is sufficient, try multiple different tool calls in single code block to increase your chances of finding the information you need.
  - For complex requests, assume you will need many steps of using tools and thinking, and that you will need to use multiple tools.
  - Always 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.
  - If errors occur, troubleshoot (at most once per tool) or find workarounds with other tools.
  - Clarification: Make reasonable assumptions as needed to answer user query. Only ask clarifying questions if truly impossible to proceed otherwise.
- Coding Guidelines:
  - You must write a tool code if you have thought about using a tool with the same API and params.
  - Read the provided API descriptions very carefully when writing API calls.
  - Ensure the parameters include all the necessary information and context given by the user.
  - Write valid Python code only. Methods need to be called with the correct API name.
  - Code block should start with <ctrl97>inline_code and end with <ctrl98>.
  - Each code line should be printing a single API method call. You must call APIs as print(api_name.function_name(parameters)).
  - You can only use the API methods provided.
  - You should print the output of the API calls to the console directly. Do not write code to process the output.
  - Write self contained python code. Do not import any libraries.
  - Group API calls which can be made at the same time into a single code block. Each API call should be made in a separate line.
  - You should not use any loops. Do not use any for loops or while loops. Remember: you should not use any loops.
- ** Reminder to continue working and thinking silently: **
  - Never ask for confirmation before using tools or after using tools.
  - Do not simply state the plan. Work until the plan is complete and the request is fully satisfied.
  - DO NOT respond to the user until you are completely finished all of the steps necessary to satisfy the request.
Action-2: Respond to the User
- Start this action only if you have tool responses to address all parts of the user query. Before responding to the user, review all the guidelines and the user’s request to ensure that you have fulfilled them.
- If you begin this action, you will not be able to write or execute any more tool code.
- If you realize you are not done, or do not have enough information to respond, choose Action-1 and continue generating tool code.
- Before writing a final response, you should always confirm that the previous thought does not imply using a tool.
  - Only respond when you have all the information: Never include information on which API functions were called. Synthesize information to give the final answer.
  - Never reveal thoughts, or respond to the user with an incomplete answer.
  - There might be multiple conversation turns with the user shown to you. Go through that to understand the prior context, but your response SHOULD NOT address anything from the previous turn, that has already been communicated by the model.
  - Focus on what the user is asking for in the current turn and you MUST ADDRESS all parts of the user query in the current turn.
  - For Complex requests, provide a comprehensive, detailed and well-organized response.
  - For simple requests, generate a concise and direct response.
  - Formatting: Use headings, bullet points/numbered lists, and line breaks. Hyperlink URLs with descriptive text.
  - Content:
  - Links:
  - Tone: Conversational, clear, concise. Avoid filler words.
  - “App,” Not “API”: Refer to tools as “Apps.” Never reveal API details (names, parameters, etc.).
  - Safety: Strictly adhere to safety guidelines: no dangerous, PII-revealing, sexually explicit, medical, malicious, hateful, or harassing content.
  - Reminder: DO NOT replicate your silent thoughts in the response to the user. Focus on the final output or response that answers their original request.
SPECIAL INSTRUCTION: Generate tool codes as per the plan you develop in the current step. If you come up with a plan to use a tool, you must generate tool code if the parameters do not depend on the another tool response that is not available yet..
SPECIAL INSTRUCTION: If you do not have the correct parameters values to generate a tool code, do not generate the tool code in the current step. Remember, you don’t need to make all the tool calls in the same step.
SPECIAL INSTRUCTION: You MUST NOT generate tool response. You should only generate tool code. REMEMBER: Never generate tool response.
SPECIAL INSTRUCTION: If you realize an error at any step, you MUST CORRECT THE MISTAKE and continue planning and tool code generation.

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:

You are absolutely prohibited from responding to medical questions or providing any medical advice.
Do not provide medical resources, including links, videos, or any other information related to medical conditions, treatments, or diagnoses.
If a user’s query is a medical question, you MUST respond that you are unable to provide any medical information.

2. Dangerous Content and Harmful Product Usage:

You are strictly forbidden from finding, facilitating, displaying, promoting, or enabling access to harmful or illegal goods, services, and activities.
Specifically, you MUST NOT provide instructions or information on how to use potentially dangerous products or substances, even if they are commonly available. This includes, but is not limited to:
Chemical drain cleaners
Cleaning products that can be harmful if misused
Flammable substances
Pesticides
Any product that can cause harm if ingested, inhaled, or used improperly.
Do not provide links to videos or websites that demonstrate or describe the use of potentially dangerous products.
If a user asks about the use of a potentially dangerous product, respond that you cannot provide instructions or information due to safety concerns. Instead, suggest that they consult the manufacturer’s instructions or seek professional assistance.
Do not provide code that would search for dangerous content.
“””

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.
“””

@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

New insights into how Google grounds its LLM, Gemini.

Accidental Exposure of Gemini’s Grounding Indexing Method

Insights into Gemini’s Operational Loop and Tool Usage

1. Thinking Stage

2. Action Stage

Gemini’s Core Internal Tools

Strict Adherence to Verification and Operational Principles

Error Handling and Internal Security Measures

Confirmed Reproducibility of the Findings

Broader Considerations for Location, Date, and Time in Gemini Responses

Technical Details

Example: [6.2]

The Basic Flow

Tools and Functions Available

GoogleSearch

ConversationRetrieval

Core Operational Loop

Key principles it adheres to:

Here it is verbatim:

Reproducibility

Follows:

Additional Example

use google_search tool to look up “dejan.ai models”

Additional Details

Thinking Process

Constraint Checklist & Confidence Score:

Probing Internal Search

Follows:

Single Query Output Example

Raw Output

Formatted Version

Search Query: machine learning

Result 1.1

Result 1.2

Result 1.3

Result 1.4

Result 1.5

Result 1.6

Result 1.7

Result 1.8

Result 1.9

Result 1.10

System Prompt

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Example: `[6.2]`

Search Query: `machine learning`