How I Became An AI Brain Surgeon

Exploring Model Fine-Tuning with Penzai

Artificial intelligence was born from the aspirations of replicating human-like thought.

AI is everywhere — from optimizing traffic routes, to tailoring our entertainment, to powering medical discoveries, AI is reshaping the modern world.

Deep learning, a powerful subset of AI stands at the forefront of this revolution. Deep learning algorithms, inspired by the structure of the human brain, employ interconnected layers of artificial neurons to learn intricate patterns from vast amounts of data. This ability to learn without explicit programming has made deep learning a driving force behind AI breakthroughs in image recognition, natural language processing, and beyond.

Python has emerged as the undisputed champion of the deep learning arena. How this high-level and relatively simple language captured the hearts of AI researchers and practitioners alike?

Three key factors contribute to Python's reign:

1. Science Super Language

2. User-Friendly Frameworks

3. Community of Legends

A Scientific Powerhouse → Python commands a flourishing ecosystem of numerical computation libraries. NumPy, the foundational bedrock, empowers efficient manipulation of multi-dimensional arrays and matrices, the very lifeblood of deep learning. SciPy builds upon NumPy, providing a treasure trove of scientific computing tools, from optimization algorithms to statistical analysis. Pandas, with its intuitive data structures and analysis capabilities, streamlines the often messy and time-consuming process of data preparation that fuels deep learning models.

User-Friendly Frameworks → Renowned deep learning libraries such as TensorFlow, PyTorch, and Keras provide Python developers with accessible, high-level APIs. These frameworks abstract away much of the underlying complexity of neural network construction and training. Developers can focus on defining the architecture of their models, leveraging pre-built components and intuitive workflows, rather than grappling with the low-level intricacies of mathematical backends. This translates to faster prototyping, quicker implementation of novel ideas, and a lower barrier to entry for aspiring AI practitioners.

The Community → Python's vast and supportive community fosters a culture of collaboration and open-source development. Countless tutorials, blogs, online forums, and conferences provide a wealth of knowledge for beginners and experienced researchers alike. This vibrant exchange of ideas and code accelerates innovation and lowers the hurdles of problem-solving within the complex domain of deep learning.

I sing Py’s praises in more detail here.

The flexibility and ease offered by Python’s deep learning ecosystem pave the way for a radical concept: AI brain surgery.

If neural networks loosely mimic the intricate structure of our own brains, might we dissect these digital minds with the precision of a neurosurgeon?

What if we could not only train AI models but meticulously modify their inner workings once trained? This is the tantalizing promise of fine-tuning and targeted model manipulation. By carefully adjusting the parameters, connections, or even entire layers within a neural network, we can potentially bend an AI's output toward specific tasks, tailoring its abilities for unique and ever-evolving purposes.

Meet the Surgeon

Penzai, a JAX-based library, empowers researchers and developers to map and influence the inner workings of neural networks. It provides precision tools for inspecting, dissecting, and modifying models after they've been trained. This stands in contrast to many traditional deep learning frameworks, where models can often seem like inscrutable black boxes after their initial training process. Penzai throws open the doors to customization and experimentation, enabling us to tailor AI models for intricate and nuanced tasks.

At its heart, Penzai embodies two central principles that shape its design and capabilities:

1. Visibility by Default: Penzai is built on the philosophy that nothing should be hidden from the user. A powerful, interactive IPython pretty-printer called Treescope is woven into Penzai's fabric. Treescope allows you to peer into any JAX-compatible data structure, visualizing it with clarity. When working with Penzai models, this pretty-printer reveals a wealth of information:

   • Hierarchical Structure: Every sublayer of a model is directly nested within its parent, ready to be expanded for examination.

   • Parameters Exposed: All parameters are presented as attributes of the layer that uses them.

   • Immutable State: Penzai models are fundamentally immutable, meaning they cannot be changed after creation. All state is explicitly tracked, creating a transparent view of the model's data flow.

   • Shared Parameters Unraveled: Any shared parameters are tracked structurally via a data-effect system, demystifying the internal connections of the model.

   This isn't merely an aesthetically pleasing representation; it's a specification of the model's architecture that can be manipulated or reconstructed as needed.

2. Built for Modification: The second driving principle of Penzai is flexibility. Penzai models are designed to be easily modified at any time, even after their creation. This allows you to isolate smaller components within a larger model, combine models, or seamlessly insert custom logic directly into a model's forward pass (the process by which input data flows through the network). Penzai's pz.select utility is essential for this, enabling structure-rewriting using an intuitive .at(...)set(...) syntax.

Link to Penzai’s GitHub in the comments.

Let’s explore how it works in deeper detail.

The Penzai Anatomy

To achieve this remarkable visibility and adaptability, Penzai models are carefully constructed with some intentional design choices:

Permissive Structure: Penzai layers are deliberately permissive in what they accept. For example, a standard Multilayer Perceptron (MLP) doesn't strictly enforce that its sublayers must be Affine layers (also known as dense layers). Instead, it is built as a subclass of Sequential, which simply executes its sublayers in order, regardless of their specific type. This grants us unprecedented freedom to inject new logic at runtime, customizing an MLP's behavior without rewriting its original code.

Dataclasses and Pytrees: Every Penzai model is both a frozen Python dataclass and a JAX Pytree. This means model instance variables are explicitly typed and tracked. Importantly, a Penzai model can be traversed using JAX's own tools like jax.tree_util. Model parameters and other NDArray data reside at the leaves of this Pytree, making them readily compatible with transformations like jax.jit for accelerated execution.

Seamless Integration: Penzai models play nicely with tools built for Python dataclasses or JAX Pytrees. The magic isn't in Penzai-specific wrappers, but rather in Penzai's design decisions that put you in control. This allows you to easily weave Penzai components into the vast JAX ecosystem.

The Essence of a Penzai Layer

Penzai models are constructed by thoughtfully composing layers.

Each layer adheres to a fundamental interface:

• The __call__ Method: This method defines the heart of the layer's logic. It's invoked directly (like a function) to process data. It always takes one positional argument, which might be a tuple, dictionary, pz.Struct or another JAX Pytree for greater flexibility.

• Simplicity within __call__: For seamless composition and adaptability, it's ideal for Penzai layers to avoid Python conditional branches (if/else) within the __call__ method whenever possible. This encourages JIT compilation and creates a clear, predictable control flow.

• Composable by Design: Because Penzai layers accept a single input and produce a single output, there's a natural way to chain them together, feeding the output of one into the input of the next.

• Beyond the Basics: How does Penzai manage situations where a layer needs extra information to function? The approach depends on the type of information:

  • Configuration: Metadata like layer input/output sizes or activation functions are stored as attributes during the layer's creation.

  • Side Inputs: Arrays and array-like data (attention masks, etc.) are often handled as special “effect” attributes by Penzai's data-effects system (which we'll discuss later).

  • Multiple Inputs: Top-levels models with multiple inputs may define a pz.Struct to bundle everything together neatly.

  • Dynamic Configuration (Rare): For things like enabling/disabling dropout, Penzai prefers swapping out different classes using pz.select, encouraging a "one layer, one behavior" philosophy.

Inside the Mind of Machine Intelligence

You are looking inside the mind of machine intelligence.

Let’s put this power to applied use with an example.

The Problem: Knowledge Overload in Machine Learning

The field of machine learning progresses at an astounding pace.

A rapidly accelerating patter of papers is turning into a flood.

A seemingly endless torrent of research papers, preprints, and code repositories floods the digital landscape, each promising new breakthroughs and potential advancements. While this proliferation of knowledge signifies a thriving and rapidly evolving field, it presents a formidable challenge – how does a researcher or practitioner efficiently identify the most relevant and personally valuable insights within this deluge of information?

The common solutions are often insufficient. Keyword searches might uncover relevant work but can also return an overwhelming number of results to sift through. Relying solely on recommendations or trending papers risks overlooking novel or niche research that might be surprisingly applicable to one's individual research focus.

A Two-Stage Operation

Imagine a system designed to streamline this process. Let's introduce a two-model approach:

Model 1: The Research Filter

• This model scours multiple API feeds, ingesting a continuous stream of machine learning publications.

• Its primary function is to filter out papers deemed irrelevant or less likely to directly impact your particular research interests.

Model 2: The Insight Extractor

• This model specializes in distilling the core essence of research papers.

• It generates concise summaries, highlighting key contributions, methods, and potential implications that are personally valuable to you as a researcher.

The Bottleneck

The success of this system depends heavily on the effectiveness of Model 1.

If the filtering model is overly broad, Model 2 might become overwhelmed, hindering its ability to provide focused summaries.

…or you could wake up to a $16K cloud bill, which happened to someone I know :/

Conversely, if the filter is too restrictive, you risk missing out on potentially groundbreaking, albeit initially less obviously aligned, research.

The heart of the problem lies in tailoring Model 1 to precisely understand your specific research interests and goals. This fine-tuning of the filter model is where the tools of AI Brain Surgery become invaluable. By carefully examining and modifying Model 1's internal structure and parameters, we can guide it to identify the research papers that will yield the most valuable insights for Model 2 to process.

At this point your brain is buzzing with questions:

• How would Model 1 initially determine the relevance of a paper? Might it use keywords, certain authors, publication venues, or other metadata?

• Crucially, how could we personalize this relevance detection to a specific researcher's interests?

• Would it be feasible to track how the generated summaries from Model 2 are used, further refining Model 1's filtering over time?

Ready For Surgery?

Let's assume for the sake of this exploration that Model 1 is a transformer-based neural network.

Safe bet.

Transformers have become a common architecture for text-related tasks due to their ability to capture long-range dependencies and contextual relationships within a sequence of words.

How would Penzai help us illuminate Model 1's inner workings?

• Penzai.nn's Visibility: If Model 1 was built using penzai.nn, its structure is already transparent. Each layer and sub-module would be visible as we interact with the model object.

• Treescope: The Detail-Oriented Lens: Penzai's Treescope pretty-printer would go a step further, allowing us to visualize the internal parameters (weight matrices, biases, etc.) of each module within Model 1. We could not only examine the architecture but also get a sense of the learned values that the model has acquired during its initial training.

This visibility granted by Penzai is essential when we begin to think about how to surgically refine Model 1 for the specific task of personalization.

The next step is pinpointing the areas of Model 1 that are likely to have the greatest impact on determining the relevance of a research paper.

Here's where some domain knowledge of machine learning and natural language processing comes into play:

Attention Mechanisms: Transformer models often employ attention mechanisms. These mechanisms learn to assign varying "weights of importance" to different words or parts of the input text. Inspecting these attention weights could provide clues about which aspects of a research paper (e.g., title, specific keywords, abstract sections) Model 1 is focusing on to determine relevance.

Embeddings: Word embeddings are a way to represent words or phrases as numerical vectors. These vectors aim to capture semantic meaning and relationships between words. Model 1 uses embeddings to translate research papers into a form the network can process. The values within these embeddings might reveal learned patterns that correlate with how it perceives relevance.

Final Decision Layer: Ultimately, Model 1 will likely have a final layer (or a set of layers) that takes the processed research paper and produces a relevance score or classification (relevant/not relevant).

The parameters in these final layers are crucial targets for our customization efforts.

Cutting with Penzai

Now is where Penzai truly shines - grab your scalpel.

Let's outline how you might use penzai.core.selectors to manipulate Model 1.

Targeting with Precision: pz.select would let us traverse Model 1's structure and isolate specific elements. For example:

• .at[pz.nn.Attention(...)] to select any attention mechanisms it might contain.

• .at[pz.nn.Embedding(...)] to manipulate word embeddings.

• .at[<final_layer_name>] to zero in on the decision-making layer(s).

Let's imagine a few potential modifications:

1. Keyword Controls: You could provide your areas of focus as keywords. Penzai could then bias the attention mechanism to give extra weight to sections of a paper containing these keywords.

2. Downplaying Venues: If certain conferences or journals tend to be less aligned with your interests, you could modify the final classification layer to slightly devalue papers from those sources.

3. Embedding Manipulation: This is more advanced, but you could potentially insert logic to tweak the embeddings of research papers based on your expertise, emphasizing words or concepts that have a higher personal relevance for your research.

The most direct way to evaluate how well these modifications refine Model 1 might seem to be directly measuring its accuracy at filtering research papers.

However, this is where the two-model system comes into play.

We should focus on the downstream impact: the quality of the summaries produced by Model 2 is the true test.

Improvements in summarization could manifest as summaries highlighting unfamiliar but potentially useful methods or datasets and/or a noticeable reduction in summaries focused on well-trodden areas that aren't advancing your current research.

PSA to Developers: You might find it useful to design a feedback mechanism into Model 2 – allowing you to rate the helpfulness of its summaries. This data could then further be used to fine-tune Model 1, not based on abstract relevance but on the directly observed value you derive.

Penzai represents a significant step forward for the customization of AI models. By treating neural networks not as opaque entities but as transparent, modifiable data structures, Penzai empowers us to tailor AI to a degree previously difficult or impossible to achieve. The exploration of our research filtering system demonstrates this potential with clarity.

The ability to precisely alter how an AI model perceives and processes information unlocks a vast array of highly individualized applications.

Zooming out, this type of AI fine-tuning has the potential to accelerate research productivity significantly. Researchers across numerous fields, not just machine learning, often face a similar deluge of information. AI models, meticulously customized with tools like Penzai, can become tireless assistants. These AI collaborators could filter publications, surface relevant datasets, or even suggest connections across disciplines that a researcher might have overlooked due to the sheer volume of information and a limited time budget.

Imagine a future where scientists have AI models tailored to their highly specific research goals. Discoveries might be made more rapidly, accelerating progress and leading to innovations that benefit society as a whole. This, of course, requires careful consideration to avoid unintended biases or the narrowing of focus towards purely profit-driven goals. However, with thoughtful application, AI customization could lead to a virtuous cycle – more efficient research fueling breakthroughs that better equip us to tackle global challenges.

Beyond the realm of research, Penzai's philosophy suggests possibilities in many other areas where AI is increasingly employed.

• Personalized Learning: Educational AI models could be adapted to an individual student's strengths, weaknesses, and learning styles, providing a uniquely supportive experience.

• Medical AI: Diagnostic tools could be fine-tuned for rare conditions or specific patient demographics, improving the accuracy and effectiveness of healthcare, particularly in underserved communities.

• Enhanced Accessibility: Text-to-speech and image generation models might be customized to an individual user's needs, providing greater accessibility and overcoming communication barriers.

It's important to note that the ability to dissect and modify AI models brings a new sense of responsibility. We must prioritize transparency, fairness, and the continued explainability of such customized AI systems to mitigate potential biases and harmful consequences.

Our discussion has primarily focused on refining Model 1, our research filter. Remember, Model 2, the summarization engine, could also be a target for Penzai-guided surgery. We might tailor it to produce different types of summaries based on a researcher's needs:

• Concise overviews when time is limited.

• Detailed highlights of specific methods when seeking reproducibility.

• Trend summaries over a period of time to identify emerging themes within a field.

The flexibility afforded by Penzai allows you to adapt these AI models as your research focus evolves. This is a stark contrast to the traditional approach, where you might be limited by the capabilities of pre-built, off-the-shelf tools.

Ultimately, Penzai highlights a compelling shift in how we might interact with artificial intelligence. As technology continues to progress at a breathtaking pace, adaptation and personalization will likely become paramount. Tools that allow us to shape AI to our individual needs could lead to advancements that enhance our productivity, unlock hidden potentials, and pave the way for a future where technology serves to uplift individuals and communities collectively.

Every day we gain new tools to build a bigger future.

Now we’re gaining tools to fine-tune our tools, and accelerate our construction to galactic scale.

👋 Thank you for reading Life in the Singularity. I started this in May 2023 and technology keeps accelerating faster ever since. Our audience includes Wall St Analysts, VCs, Big Tech Data Engineers and Fortune 500 Executives.

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