THE AIGNOSTIC

Author: Thomas

  • A Practical Consulting Approach for Fine-Tuning Large Language Models

    A Practical Consulting Approach for Fine-Tuning Large Language Models

    Fine-tuning large language models (LLMs) on behalf of clients involves addressing numerous organizational and technical challenges. Whether working with established enterprises or emerging AI-native startups, the core responsibilities often go beyond simply configuring hyperparameters or selecting base models. The key to a successful engagement lies in taking a comprehensive approach to data collection, evaluation design, and continuous improvement strategies. This article outlines general best practices for consultants offering fine-tuning services.

    1. Recognize That High-Quality Data Is Everything

    The most critical resource in fine-tuning is a well-curated training dataset. Without it, even the finest tuning processes, libraries, and algorithms fall short. Clients rarely have the luxury of providing pristine data that can be fed directly into a training pipeline. Instead, the majority of the effort goes into collecting, cleaning, labeling, and organizing the data.

    This “data janitor” work often feels unglamorous, but it is the single most important factor in achieving meaningful performance improvements. Any consulting proposal should reflect this, factoring in the substantial time and cost of data preparation.

    2. Develop an Evaluation Framework

    An effective evaluation framework (eval) is essential for quantifying performance gains. Unfortunately, most organizations do not already have structured methods for comparing one model’s outputs against another’s. Often, decisions about which foundation model to start with are based on a few ad hoc prompts rather than systematic tests.

    Consultants need to lead the way in designing both quantitative and qualitative metrics—whether through well-structured prompt evaluations, classification accuracy metrics, or domain-specific benchmarks. Having a proper eval in place is critical for iterating effectively and justifying further investment in the fine-tuning process.

    3. Take Ownership of Data Preparation and Evaluation

    Because data and eval construction can be unfamiliar territory for many clients, there is a high likelihood that they will struggle if left to handle these components on their own. Even if domain experts exist within the client’s organization, their roles often do not allocate time for this detailed, often tedious work.

    The consultant’s role frequently extends to creating or refining the client’s dataset, finding or generating synthetic data where necessary, and setting up the evaluation pipeline. This service delivers the core value of fine-tuning: a model that genuinely outperforms off-the-shelf solutions for a specific task.

    4. Aim to Surpass the Leading Foundation Models

    The performance bar is raised each time a new, more capable foundation model is released. Many clients see success as surpassing models provided by major AI providers—whether the objective is higher accuracy, improved cost efficiency, or unique domain customization. Consequently, the consulting approach must focus on:

    • Continuous Benchmarking: Track the progress against state-of-the-art releases from various AI labs to ensure the fine-tuned model remains competitive.
    • Versioning and Iteration: Incorporate client feedback and new data to iteratively refine the fine-tuned model’s performance, keeping it ahead of emerging foundation models.

    For some clients—especially those seeking cost savings—distilling a larger model into a smaller one can be the primary motivation. Showing tangible improvements in inference speed, memory usage, and cost can prove the value of the engagement.

    5. Build a Data Flywheel

    Long-term success comes from establishing a process that continually harvests new, high-quality data and folds it back into model improvements. Consultants can help design the product roadmap or user interface/experience such that client applications naturally generate useful training data. When users interact with the system, the resulting feedback or labeled outputs become the seeds for the model’s next improvement cycle.

    This flywheel effect works by:

    1. Launching a model fine-tuned on a decent initial dataset.
    2. Capturing user interactions and corrections to refine future versions.
    3. Continuously upgrading the model so it remains ahead of general-purpose alternatives.

    By embedding this iterative improvement loop, clients can protect their business model against the inevitable arrival of more powerful foundation models.

    6. Key Takeaways for Consulting Engagements

    1. Start Simple: Encourage clients to begin with a powerful off-the-shelf model before investing in fine-tuning. Prove the need for custom performance gains.
    2. Estimate Data Work Accurately: Expect to spend the majority of time on data-related tasks—collecting, cleaning, organizing, and labeling.
    3. Implement Structured Evaluations: Establish a solid eval methodology to measure gains objectively.
    4. Outperform Baselines: Continuously benchmark and refine models to stay ahead of free or more general options.
    5. Enable Ongoing Improvement: Design a business process that supports continuous data collection and model upgrades.

    Fine-tuning can indeed deliver significant value and a defensible competitive advantage for many organizations. However, the likelihood of success increases dramatically when the consulting approach addresses both the organizational realities (lack of clean data, missing evals) and the evolving landscape of AI model capabilities. By combining technical expertise with hands-on data curation and structured evaluation design, consultants can help clients build—and maintain—models that achieve (and surpass) their unique objectives.

  • Attention Mechanism in Large Language Models

    Attention Mechanism in Large Language Models

    This basic explanation of the Attention Mechanism in LLMs was part of a teaching session with Google’s Gemini 2.0 Pro.

    Let’s break down the attention mechanism in Large Language Models (LLMs) step-by-step.

    Step 1: The Problem with Traditional Sequence Models

    Before attention, models like Recurrent Neural Networks (RNNs) and LSTMs processed sequences (like sentences) one word at a time. They maintained a “hidden state” that was supposed to carry information from the entire input sequence seen so far.

    Imagine you have a long sentence like: “The incredibly fast brown fox jumped over the lazy dog, which was sleeping peacefully under a tree.”

    An RNN would process “The,” then update its hidden state. Then it processes “incredibly,” updating the hidden state again, and so on. By the time it gets to “tree,” the information about “The” and “incredibly” might be significantly diluted or even lost, especially in very long sequences. This is called the long-range dependency problem. The further apart two relevant words are, the harder it is for the RNN to connect them.

    Basically with traditional sequence models we are squeezing all of the context into a single hidden state vector.

    Step 2: The Core Idea of Attention – Relevance Scores

    Attention introduces a way to bypass this “information bottleneck.” Instead of forcing all information through a single hidden state, attention allows the model to directly look back at all the previous words in the input and decide which ones are most relevant at each step of the output generation.

    Think of it like reading a sentence and highlighting the important words that help you understand a particular part. You don’t just remember the last few words; you actively focus on the relevant words, no matter where they are in the sentence.

    Attention calculates “relevance scores” (also often called “attention weights”) between each word in the input and the current word being processed or predicted. A higher score means that input word is more important in the current context. These aren’t pre-determined; they’re learned during training.

    Let’s move on.

    Step 3: Queries, Keys, and Values (Q, K, V)

    To calculate these relevance scores, attention uses three learned components, usually derived from the input sequence itself:

    • Queries (Q): Think of the query as representing “What am I looking for?”. It’s a representation of the current word or position in the sequence that we’re trying to understand or predict. It is the piece of information asking the question.
    • Keys (K): Think of the keys as representing “What information do I have?”. Each word in the input sequence gets a “key” vector. Keys represent the information available, acting as an index.
    • Values (V): Think of the values as representing “What is the actual content?”. Each word also gets a “value” vector. This is the actual information, what we will blend together.

    These Q, K, and V vectors are typically created by multiplying the input word embeddings (or the hidden states from a previous layer) by learned weight matrices. So, we have three separate weight matrices (Wq, Wk, Wv) that are learned during training. Each weight matrix transforms an items input embedding, x, into either its Q, K, or V.

    • Query = Input Embedding * Wq
    • Key = Input Embedding * Wk
    • Value = Input Embedding * Wv

    This is a crucial point, so let’s elaborate with an example. Let’s simplify things and say our sentence is just: “The fox jumped.” And, let’s say we’re currently processing the word “jumped.”

    1. Word Embeddings: First, each word is converted into a numerical vector called a word embedding. These embeddings capture semantic meaning. Let’s imagine (very unrealistically for simplicity) our embeddings are:
      • “The”: [0.1, 0.2]
      • “fox”: [0.9, 0.3]
      • “jumped”: [0.5, 0.8]
    2. Learned Weight Matrices (Wq, Wk, Wv): During training, the model learns three weight matrices: Wq, Wk, and Wv. These matrices are not specific to individual words; they’re applied to all words. They transform the word embeddings into Query, Key, and Value vectors. Let’s imagine (again, very simplified) these matrices are:

      Wq = [[0.5, 0.1], [0.2, 0.6]]
      Wk = [[0.3, 0.4], [0.7, 0.1]]
      Wv = [[0.8, 0.2], [0.3, 0.9]]
    3. Calculating Q, K, V: Now, we use these matrices to calculate the Query, Key, and Value vectors for each word. Since we’re focusing on “jumped,” that word’s embedding will be used to create the Query. All words’ embeddings will be used to create their respective Keys and Values.
      • Query (for “jumped”):
        [0.5, 0.8] * [[0.5, 0.1], [0.2, 0.6]] = [0.41, 0.53]
      • Keys (for all words):
        "The": [0.1, 0.2] * [[0.3, 0.4], [0.7, 0.1]] = [0.17, 0.06]
        "fox": [0.9, 0.3] * [[0.3, 0.4], [0.7, 0.1]] = [0.48, 0.39]
        "jumped": [0.5, 0.8] * [[0.3, 0.4], [0.7, 0.1]] = [0.71, 0.28]
      • Values (for all words):
        "The": [0.1, 0.2] * [[0.8, 0.2], [0.3, 0.9]] = [0.14, 0.20]
        "fox": [0.9, 0.3] * [[0.8, 0.2], [0.3, 0.9]] = [0.81, 0.45]
        "jumped": [0.5, 0.8] * [[0.8, 0.2], [0.3, 0.9]] = [0.64, 0.82]

    So, in summary:

    • We start with word embeddings.
    • We have learned weight matrices (Wq, Wk, Wv) that are shared across all words.
    • We multiply each word’s embedding by each of the weight matrices to get its Query, Key, and Value.
    • The Query is derived from the word we’re currently focusing on. The Keys and Values are derived from all the words in the input.

    The dimensions of the resulting Q, K, and V vectors are determined by the dimensions of the weight matrices, which are hyperparameters chosen during model design. Importantly, the dimensions of Q and K must be the same, because we’re going to compare them in the next step. The dimension of V can be different.

    Okay, let’s move on to the next crucial step:

    Step 4: Calculating Attention Scores (Dot Product and Softmax)

    Now that we have our Queries (Q), Keys (K), and Values (V), we calculate the attention scores. This is where the “attention” really happens.

    1. Dot Product: We take the dot product of the Query (Q) vector (representing the current word) with each of the Key (K) vectors (representing all the words in the input). The dot product measures the similarity between two vectors. A larger dot product means the Query and Key are more aligned, suggesting higher relevance. Using our previous example, where Q (for “jumped”) is [0.41, 0.53], and we have Keys for “The,” “fox,” and “jumped”:
      • Attention Score (“jumped” attending to “The”): [0.41, 0.53] . [0.17, 0.06] = 0.07 + 0.03 = 0.10
      • Attention Score (“jumped” attending to “fox”): [0.41, 0.53] . [0.48, 0.39] = 0.20 + 0.21 = 0.41
      • Attention Score (“jumped” attending to “jumped”): [0.41, 0.53] . [0.71, 0.28] = 0.29 + 0.15 = 0.44
    2. Scaling: Before applying softmax, the dot product scores are usually scaled down. This is typically done by dividing by the square root of the dimension of the Key vectors (√dk). This scaling is crucial for stable training, especially when the Key vectors have high dimensions. It prevents the dot products from becoming too large, which can lead to extremely small gradients during backpropagation. Let’s say the dimension of our Key vectors (dk) is 2 (as in our example). Then √dk is approximately 1.41. We’d divide each score by 1.41:
      • “The”: 0.10 / 1.41 ≈ 0.07
      • “fox”: 0.41 / 1.41 ≈ 0.29
      • “jumped”: 0.44 / 1.41 ≈ 0.31
    3. Softmax: We apply the softmax function to these scaled dot products. Softmax converts the scores into a probability distribution. This means the scores will be between 0 and 1, and they will all add up to 1. This gives us the attention weights. Applying softmax to our scaled scores (approximately):
      • “The”: softmax(0.07) ≈ 0.24
      • “fox”: softmax(0.29) ≈ 0.35
      • “jumped”: softmax(0.31) ≈ 0.36
        Note that these will add up to ≈ 1

    These final softmax values (0.24, 0.35, 0.36) are the attention weights. They tell us how much the word “jumped” should “attend” to each of the input words (“The,” “fox,” “jumped”). In this (simplified) example, “jumped” attends most to itself (0.36) and the “fox” (0.35), and less to “The” (0.24).

    Okay, let’s continue.

    Step 5: Weighted Sum and Output

    We’ve calculated our attention weights (the probability distribution). Now, we use these weights to create a weighted sum of the Value vectors. This weighted sum represents the context that the model has learned is most relevant to the current word.

    1. Weighted Sum: Multiply each Value vector by its corresponding attention weight (from the softmax output). Then, sum up these weighted Value vectors. Recall our Value vectors from the previous example:

      “The”: [0.14, 0.20]
      “fox”: [0.81, 0.45]
      “jumped”: [0.64, 0.82]

      And our attention weights (softmax output) for “jumped”:”

      “The”: 0.24″
      “fox”: 0.35
      “jumped”: 0.36

      Now, we calculate the weighted sum: (0.24 * [0.14, 0.20]) + (0.35 * [0.81, 0.45]) + (0.36 * [0.64, 0.82]) = [0.03, 0.05] + [0.28, 0.16] + [0.23, 0.30] = [0.54, 0.51]
    2. Output: This resulting vector [0.54, 0.51] is the output of the attention mechanism for the word “jumped”. It’s a context-aware representation of “jumped,” taking into account the relevant information from the other words in the input, as determined by the attention weights. This output vector can then be passed on to subsequent layers of the LLM (e.g., a feed-forward network) for further processing.

    In essence, the attention mechanism has created a weighted average of the Value vectors, where the weights are determined by the relevance of each word to the current word being processed. This allows the model to focus on the most important parts of the input sequence when generating the output.

    Here’s a summary of the entire attention mechanism process:

    Summary Attention Mechanism

    1. The Problem (Long-Range Dependencies):

    Traditional sequence models (RNNs, LSTMs) struggle to connect words that are far apart in a sequence (the long-range dependency problem). Information from earlier words can be lost or diluted as the sequence is processed.

    2. The Core Idea (Relevance Scores):

    Attention allows the model to directly look back at all previous words and determine their relevance to the current word being processed, regardless of their distance. This is done by calculating “attention weights” (relevance scores).

    3. Queries, Keys, and Values (Q, K, V):

    • Input Embeddings: Each word in the input sequence is first converted into a numerical vector called a word embedding.
    • Learned Weight Matrices (Wq, Wk, Wv): The model learns three weight matrices: Wq, Wk, and Wv. These are shared across all words in the sequence.
    • Calculating Q, K, V:
      • Query (Q) = Input Embedding * Wq (What am I looking for?)
      • Key (K) = Input Embedding * Wk (What information do I have?)
      • Value (V) = Input Embedding * Wv (What is the actual content?)
      • The Query is calculated for the current word being processed.
      • Keys and Values are calculated for all words in the input sequence.

    4. Calculating Attention Scores (Dot Product, Scaling, Softmax):

    • Dot Product: Calculate the dot product of the Query (Q) with each Key (K): score = Q . K. This measures the similarity between the Query and each Key.
    • Scaling: Divide each dot product score by the square root of the dimension of the Key vectors (√dk): scaled_score = score / √dk. This prevents the scores from becoming too large.
    • Softmax: Apply the softmax function to the scaled scores to obtain a probability distribution (attention weights): attention_weights = softmax(scaled_score). These weights are between 0 and 1, and they sum up to 1.

    5. Weighted Sum and Output:

    • Weighted Sum: Multiply each Value (V) vector by its corresponding attention weight and sum the results: context_vector = Σ (attention_weight * V).
    • Output: The context_vector is the output of the attention mechanism. It’s a weighted average of the Value vectors, representing the context relevant to the current word. This vector is then passed to subsequent layers of the LLM.

    Example (Simplified):

    Sentence: “The fox jumped.” Processing “jumped.”

    1. Embeddings (example):
      • “The”: [0.1, 0.2]
      • “fox”: [0.9, 0.3]
      • “jumped”: [0.5, 0.8]
    2. Weight Matrices (example): Wq = [[0.5, 0.1], [0.2, 0.6]] Wk = [[0.3, 0.4], [0.7, 0.1]] Wv = [[0.8, 0.2], [0.3, 0.9]]
    3. Q, K, V (example):
      • Q (“jumped”): [0.41, 0.53]
      • K (“The”): [0.17, 0.06], K (“fox”): [0.48, 0.39], K (“jumped”): [0.71, 0.28]
      • V (“The”): [0.14, 0.20], V (“fox”): [0.81, 0.45], V (“jumped”): [0.64, 0.82]
    4. Attention Scores (example):
      • Dot Products: 0.10, 0.41, 0.44
      • Scaled (√dk = 1.41): 0.07, 0.29, 0.31
      • Softmax: 0.24, 0.35, 0.36 (attention weights)
    5. Weighted Sum and Output (example):
      • (0.24 * [0.14, 0.20]) + (0.35 * [0.81, 0.45]) + (0.36 * [0.64, 0.82]) = [0.54, 0.51]

    This final vector [0.54, 0.51] represents the context-aware representation of “jumped,” incorporating information from “The,” “fox,” and itself, weighted by their relevance.

    This complete breakdown should give you a comprehensive understanding of the attention mechanism. Remember that the weight matrices (Wq, Wk, Wv) are learned during the training process, and the example values are greatly simplified for illustrative purposes. Real-world LLMs use much higher-dimensional vectors and matrices.

  • Understanding Privacy in OpenAI’s API: A Comprehensive Guide

    Understanding Privacy in OpenAI’s API: A Comprehensive Guide

    In today’s AI-driven world, data privacy has become a paramount concern for developers and organizations utilizing AI APIs. When integrating OpenAI’s powerful API capabilities into your applications, understanding the platform’s privacy framework isn’t just good practice—it’s essential for maintaining data security and ensuring compliance with various regulatory requirements.

    The Privacy Foundation

    At its core, OpenAI’s approach to API privacy centers on a fundamental principle: your data remains yours. This commitment manifests through several key privacy measures that protect user interests while enabling innovative AI applications.

    Data Handling and Retention

    One of the most significant privacy advantages of OpenAI’s API is its approach to data usage. Contrary to what some might assume, OpenAI does not use API inputs or outputs to train its models. This means your queries and the responses you receive remain private and won’t be incorporated into future model updates.

    The platform maintains API usage logs for approximately 30 days—a practice claimed purely for system monitoring and troubleshooting. These logs serve operational purposes only and are not utilized for model enhancement or training.

    Ownership and Control

    OpenAI’s terms of use explicitly confirm that users retain ownership of both their input data and the generated outputs. This clear stance on data ownership is particularly crucial for businesses handling proprietary information or developing competitive applications.

    Security Infrastructure

    Privacy goes hand in hand with security, and OpenAI implements robust measures to protect data:

    • Strong encryption protocols safeguard data during transmission and storage
    • Comprehensive security measures protect against unauthorized access
    • Regular security audits and updates maintain system integrity

    Regulatory Compliance

    In today’s global marketplace, regulatory compliance is non-negotiable. OpenAI acknowledges this by aligning with major data privacy regulations:

    • GDPR compliance for European users
    • CCPA alignment for California residents
    • Support for user rights regarding data access and deletion

    Best Practices for API Privacy

    To maximize privacy when using OpenAI’s API, consider implementing these practical strategies:

    1. Data Minimization
      • Share only necessary information
      • Strip personally identifiable information (PII) from inputs
      • Implement pre-processing filters for sensitive data
    2. Output Management
      • Review API responses before deployment
      • Implement automated scanning for sensitive information
      • Maintain audit logs of API interactions
    3. Enhanced Privacy Options
      • Consider private deployment options for sensitive applications
      • Explore Azure OpenAI Service for additional security layers
      • Implement role-based access controls in your applications

    Considerations for Regulated Industries

    Organizations in regulated sectors face unique challenges. Healthcare providers, financial institutions, and government agencies should:

    • Conduct thorough privacy impact assessments
    • Consult with legal experts on compliance requirements
    • Consider private deployment options
    • Implement additional security layers as needed

    Looking Forward

    As AI technology evolves, privacy considerations will continue to shape API development and usage. OpenAI’s commitment to privacy, combined with user vigilance and best practices, creates a framework for responsible AI implementation.

    The key to successful API integration lies in understanding these privacy measures and implementing them effectively within your specific context. Whether you’re developing a simple chatbot or a complex enterprise solution, making privacy a priority from the start will help ensure sustainable and compliant AI implementation.

    Remember: While this guide provides an overview of OpenAI’s API privacy features, always refer to the official documentation and policies for the most current information, and consult legal experts when handling sensitive data or operating in regulated industries.

  • AI News Roundup

    AI News Roundup


    The Rapidly Evolving AI Landscape: Highlights from the Past Three Weeks

    The world of Artificial Intelligence (AI) has been abuzz over the last three weeks with exciting announcements, new product releases, and groundbreaking research. From updates in large language models (LLMs) to advancements in AI ethics and regulatory discussions, here’s a quick roundup of the most important news and trends shaping the AI scene.

    New Language Model Releases and Enhancements

    OpenAI’s GPT-4.5 Rumors

    Although still unconfirmed by OpenAI, industry insiders have been speculating about incremental improvements to GPT-4—colloquially referred to as GPT-4.5. Allegedly, these improvements include more efficient training methods and better instruction-following capabilities. This rumored update underscores the increasing competition to provide the most advanced, context-aware AI systems.

    Meta’s Llama 2 Updates

    Meta made waves by rolling out updates to Llama 2, its open-source large language model. The new version boasts improved performance on language benchmarks and offers streamlined fine-tuning for developers. This move further cements the open-source approach, allowing researchers and businesses to experiment more freely with cutting-edge AI technology.

    Innovations in Image and Video Generation

    Stability AI’s Expansion

    Stability AI has been expanding its product offerings beyond text-to-image models. Over the past few weeks, rumors have surfaced about upcoming video generation features, aiming to produce short, high-quality clips from simple text prompts. While official details remain sparse, early testers report faster rendering times and more realistic results—a promising development for content creators and marketers alike.

    Hugging Face Partnerships

    Hugging Face, known for its collaborative approach to AI and machine learning, announced new partnerships with large tech companies to integrate advanced image-generation models into various platforms. This move will allow developers to easily leverage state-of-the-art models, significantly lowering the barrier to entry for creative AI projects.

    Ethical AI and Regulatory Developments

    Government Regulations on Generative AI

    In the last three weeks, governments around the globe have accelerated their plans to regulate generative AI. In Europe, updates to the EU AI Act focus on transparency requirements for AI-generated content, while U.S. lawmakers introduced preliminary guidelines for AI accountability. These efforts aim to balance innovation with responsible AI deployment, ensuring public trust and safety.

    New AI Ethics Framework

    A consortium of tech leaders and ethicists released a new framework, Guiding Principles for Ethical AI, outlining best practices for data privacy, fairness, and transparency. This framework has already been adopted by several startups keen on positioning themselves as ethical AI pioneers. Companies are also introducing more robust “Model Cards” that detail how their AI models work, which data they were trained on, and potential biases or risks.

    AI in Healthcare and Biotechnology

    Breakthroughs in Protein Modeling

    The surge of AI-driven protein folding research continues with several biotech firms adopting AI models to predict complex protein structures and potential drug interactions. DeepMind’s AlphaFold remains a cornerstone, and new competitors are emerging, promising faster runtimes and more accurate models. These advancements could significantly speed up the drug discovery process, potentially saving lives in the near future.

    Personalized Medical Assistants

    AI has been making strides in providing personalized medical advice and triage support. Startups have introduced pilot programs where patients can converse with an AI-powered medical assistant before seeing a doctor. While these tools don’t replace a qualified physician, they help alleviate minor inquiries and guide patients to the right specialists. The WHO and other organizations are watching carefully to ensure patient privacy and safety are upheld.

    Looking Ahead

    AI has never been more visible or transformative. In just three weeks, we’ve witnessed:

    • Ongoing evolution in large language models, with hints of even more powerful versions on the horizon.
    • Progress in image and potential video generation technology, setting the stage for immersive content creation.
    • Greater emphasis on ethical frameworks and regulatory compliance, reflecting the societal implications of widespread AI adoption.
    • Notable breakthroughs in biotechnology, which could redefine healthcare and personalized medicine.

    As we move forward, expect to see more collaborations between tech giants, open-source communities, and governments. Whether it’s refining existing models, exploring new areas like AI-driven robotics, or establishing standards for AI governance, the fast-paced changes we’re witnessing show no signs of slowing down.

    Stay tuned for more updates as we continue to track the transformative impact of AI in 2025 and beyond.

    Have any additional insights or questions about recent AI developments? Feel free to leave a comment on social media.

  • The Future is Agentic – Deepmind: Project MarinerAgent

    The Future is Agentic – Deepmind: Project MarinerAgent

    On Wednesday, Google introduced its inaugural AI agent designed for web interaction, developed by its DeepMind division and named Project Mariner. This AI, powered by Gemini, operates within the Chrome browser, manipulating the cursor, clicking on elements, and completing forms to navigate and use websites autonomously, much like a human would.

    Fundamentally new UX Paradigm

    The rollout begins with a select group of testers this Wednesday, as Google explores new applications for Gemini, including reading, summarising, and now, actively using websites. An executive at Google has described this development to TechCrunch as indicative of a “fundamentally new UX paradigm shift”, where the interaction with websites transitions from direct user input to managing through an AI intermediary.

    From Clicks to Commands: AI Agents Take Over Your Digital Chores

    AI agents are the current focus in tech because they represent an advanced form of automation, capable of independently performing complex tasks online. This evolution is seen as a significant step beyond traditional AI, promising to change how we interact with digital services, manage our digital lives, and potentially automate many professional tasks. The conversation reflects both excitement about new possibilities and concerns over job displacement and privacy.

  • Revolutionising Development with Advanced AI Tools

    Revolutionising Development with Advanced AI Tools

    It’s not a curse.

    Code generating tools like Cursor are a game changer. These tools revolutionary for developers and people with ideas. Combining the power of AI with the convenience of an integrated development environment (IDE).

    1. They Make You Code Faster

    • It’s like having a helper who knows what you’re going to type next. Cursor helps by guessing and filling in your code for you. This means you write less, but still get a lot done.

    2. They Help You Learn

    • If you’re new or just learning, Cursor acts like a teacher. It gives tips, explains stuff, and shows you how to do things better.

    3. From Idea to Proof of Concept

    • These tools help creative teams to get from idea to proof of concept or finished tool in record time. Time that has been spent discussing if an idea was worthwhile can be spent finishing and testing it.
    Cursor AI

    Cursor AI offers features like intelligent code generation, context-aware autocomplete, error correction, and real-time debugging assistance. This enables developers to work significantly faster and more efficiently—some report productivity increases of 3–10 times.

    Write English get Code

    What sets Cursor apart is its ability to integrate seamlessly into existing workflows, such as Visual Studio Code, while supporting multiple programming languages like Python, JavaScript, and TypeScript. It also provides innovative tools like natural language-based code generation, explanations for complex code snippets, and enhanced collaboration capabilities.

    What are you still doing here? Get coding!

  • The Age of AI – Being First vs. Being Prepared

    The Age of AI – Being First vs. Being Prepared

    We are on the verge of the biggest corporate revolution, maybe ever. The value of human know-how and legacy corporate processes will be devalued (or made worthless) within the next five years. Understandingly the AI revolution is making leaders nervous.

    The time to get prepared

    Which new AI tool should we use? Why don’t we have ChatBots for our clients? Why are we not creating AI content?

    People are getting stressed. BUT! This is not the time to be first. This is not even the time to be right. This is the time to get prepared.

    Matrices and Math are waiting for you

    But most of all it’s the time to learn everything about AI models and GPTs. And I do mean down to the nitty gritty stuff of model generation, training and so on. Mind you: This is a journey that is understandably hard, because it involves a lot of complex concepts that are not very familiar to most of us.

    What are neurons, why are there layers, and what is the math underlying it? How do Large Language Models work? This is one of the best videos BTW.

    Try Everything and don’t commit

    This is also the time to try as many new tools as you can. From coding tools like Cursor and automation tools like make to creation tools like stability.ai (stable diffusion). A whole industry of consultants and tool providers are already piggy backing on the success of AI model developers. Everyone is trying to make a quick buck and is luring you towards their solution. Try everything but don’t commit yet.

    Get an OpenAI developer access. Try different models. Try alternative AI providers like perplexity.ai, xAI and Claude (Anthropic).

    The race to AGI (artificial general intelligence) and ASI (artificial super intelligence) has just started. It’s not a given that OpenAI will win this race. There will be many more tools in the next 12-24 months. Additionally AI agents have just become hot.

    An artificial intelligence (AI) agent refers to a system or program that is capable of autonomously performing tasks on behalf of a user or another system.

    Enjoy this wild time and get ready to learn a lot.

  • Ways to Deploy AI Models – Inference Endpoints

    Ways to Deploy AI Models – Inference Endpoints

    Choosing the right deployment option for your model can significantly impact the success of an AI application. Selecting the best deployment option influences cost, latency, scalability, and more.

    Let’s go over the most popular deployment options, with a focus on serverless deployment ( e.g.Hugging Face; Inference Endpoints) so you can unlock the full potential of your AI models. Let’s dive in!

    First, let’s briefly overview the most popular deployment options: cloud-based, on-premise, edge, and the newer serverless alternative.

    Traditional Methods

    • Cloud-based deployment involves hosting your AI model on a virtual network of servers maintained by third-party companies like Google Cloud or Microsoft Azure. It offers scalability and low latency, allowing you to quickly scale up or down based on demand. You pay for the server even when it’s idle, which can cost hundreds of dollars per month. Larger models requiring multiple GPUs can bring up costs even higher, making this option best suited for projects with consistent usage.
    • On-premise deployment involves hosting and running your AI models on your own physical servers. This option provides total control over infrastructure. However, managing your own infrastructure is complex, making it suitable for large-scale projects or enterprises.
    • Edge deployment places models directly on edge devices like smartphones or local computers. This approach enables real-time, low-latency predictions. It’s not ideal for complex models requiring significant computational power.

    Serverless Deployment

    Serverless model deployment has emerged to address these challenges. Instead of maintaining and paying for idle servers, serverless deployment lets you focus on product development. You deploy your model in a container, and are only charged for the time your model is active—down to the GPU second. This makes serverless deployment ideal for applications with smaller user bases and test environments.

    One downside of serverless systems is the cold start issue, where inactive serverless functions are “put to sleep” to save resources. When reactivated, a slight delay occurs while the function warms up.

    Several providers support serverless deployment, including AWS and Hugging Face’s inference endpoints.

    Hugging Face “Inference Endpoints”

    1. Select a model on On Hugging Face and click “Inference Endpoints” under the “Deploy” section.
    2. Select your desired deployment options to enable serverless functionality.
    3. Adjust the automatic scaling settings—for example, set it to zero after 15 minutes of inactivity.
    4. Once your endpoint is created, test it using the web interface.

    If everything works as expected, you can proceed to using the API. To call this endpoint from your application, use the Hugging Face inference Python client. Install the huggingface_hub library, import the inference client, and specify your endpoint URL and API token. Define your generation parameters and call the text_generation method. For streaming responses, set the streaming parameter to True, enabling chunked responses.

  • Automatic AI Author (AAA) for WordPress

    Automatic AI Author (AAA) for WordPress

    Create and post content without human intervention

    Say you had a blog on any topic and wanted AI (OpenAi, xAi) to automatically write or translate existing content for you and post it directly to your WordPress website.

    1. Add user to WordPress with Application Password
      After adding a new User (or use an existing one) set an application password in WordPress (Users -> Edit User) 
    # RSS_AI_Wordpress

import requests
import json
import base64 
from _AI_Writer import get_news_response
response = get_news_response("What are the main headlines today?")

# WordPress API endpoint
url = "https://YOURWEBSITE.com/wp-json/wp/v2/posts"

# Authentication credentials
user = "BOT"
password = "YOUR_APPLICATION_PASSWORT_MATE"
credentials = user + ':' + password
token = base64.b64encode(credentials.encode())
header = {
    'Authorization': 'Basic ' + token.decode('utf-8'),
    'Content-Type': 'application/json; charset=utf-8',
    'Accept': 'application/json, */*',
    'User-Agent': 'Python/RequestsClient'
}

# Post content to WordPress
post = {
    'title': 'AI BOT - Daily News',
    'content': response,
    'status': 'publish',
}

# Send POST request with verify=False to debug SSL issues
response = requests.post(url, headers=header, json=post, verify=True)

# Check if the request was successful
if response.status_code == 201:  # 201 is the success code for creation
    print("Post created successfully!")
    #print(response.json())
else:
    print(f"Error: {response.status_code}")
    print(response.text)

    This code posts automatically to your WordPress blog. The actual content (stored in “response”) we retrieve from a module called _AI_Writer.

    2. Writing Content with Your AI Writer Bot

    Our AI writer module fetches an RSS Feed (Google News in our case; bur could be any website or feed) and writes a short blog post in his own words on today’s news. This gets posted directly to our blog (see code above).

    # _AI_Writer.py

import os
from openai import OpenAI
import feedparser

XAI_API_KEY = "YOUR_XAI_KEY_HERE"
client = OpenAI(
    api_key=XAI_API_KEY,
    base_url="https://api.x.ai/v1",
)

def chat_with_gpt(prompt):
    response = client.chat.completions.create(
        model = "grok-beta",
        messages=[{"role": "user", "content": prompt}],
        #temperature = 0.8,
    )
    return response.choices[0].message.content.strip()

def get_rss_feed(url):
    """Fetch and parse RSS feed from given URL"""
    feed = feedparser.parse(url)
    return feed

def get_feed_entries(feed, limit=10):
    """Extract entries from feed, with optional limit"""
    entries = []
    for entry in feed.entries[:limit]:
        entries.append({
            'title': entry.get('title', ''),
            'link': entry.get('link', ''),
            'published': entry.get('published', ''),
            'summary': entry.get('summary', '')
        })
    return entries

def get_news_response(user_input):
    """Get AI response based on RSS feed news and user input"""
    rss_url = "https://news.google.com/news/rss"
    feed = get_rss_feed(rss_url)
    entries = get_feed_entries(feed)
    
    prompt = f"""Here are the latest news entries. {user_input}

{[entry['title'] + ': ' + entry['summary'] for entry in entries]}"""
    
    return chat_with_gpt(prompt)

# Modified main block for testing
if __name__ == "__main__":
    # Test the module
    response = get_news_response("Please provide a brief summary")
    print("Test response:", response)

    Like all AI workflows this offers a plethora of use cases

    You could have it fill a website with articles without ever touching said website. Or maybe translate content of one website and repost content on another.

    Or maybe – if you are evil – scale this x 1000 and fill hundreds of websites with your propaganda. Unfortunately this is all too easy.

  • Google DeepMind’s Recursive Learning Approach and Its Impact

    Google DeepMind’s Recursive Learning Approach and Its Impact

    Google DeepMind’s Socrates Learning

    All 70.000 Project Gutenberg books amount to less than 1 TB (933GB). Imagine the impact of DeepMind’s Recursive Learning approach.

    Google DeepMind’s recursive learning, often referred to as “Socratic Learning,” involves AI systems teaching themselves through iterative processes without human input. This method allows AI to generate its own training data and scenarios, enhancing efficiency and adaptability.

    Not to Create a Better AI, but to Create AI That Can Improve Itself.

    An agent trained within a closed system can master any desired capability, as long as the following three conditions hold: (a) it receives sufficiently informative and aligned feedback, (b) its coverage of experience/data is broad enough, and © it has sufficient capacity and resource. In this position paper, we justify these conditions, and consider what limitations arise from (a) and (b) in closed systems, when assuming that © is not a bottleneck. Considering the special case of agents with matching input and output spaces (namely, language), we argue that such pure recursive self-improvement, dubbed ‘Socratic learning,’ can boost performance vastly beyond what is present in its initial data or knowledge, and is only limited by time, as well as gradual misalignment concerns. Furthermore, we propose a constructive framework to implement it, based on the notion of language games.

    Impact:

    • Autonomy: AI can evolve independently, reducing reliance on human updates for new environments or problems.
    • Data Efficiency: Requires less data for learning, making AI more resourceful.
    • Advancements Towards AGI: Paves the way for Artificial General Intelligence by enabling AI to understand and reason beyond task-specific programming.
    • Ethical and Control Issues: Raises concerns about AI autonomy, necessitating new frameworks for control and ethical considerations.
    • Broad Applications: Potential in fields like personalized education, healthcare, and space exploration, where adaptive learning could lead to innovative solutions.

    Recursive learning introduces complexities regarding control and ethical use of AI, necessitating careful management and oversight.