What is the Cutting Edge of AI?
Exploring What Drives The Explosive Growth of AI Capabilities
AI is moving at forward at ever-accelerating rates now.
Thanks to the rising tide of an interconnected swarm of super computers mixed with the incredible minds across the globe, each collaborating in their programming language of choice, we are witnessing unreal progress from Open AI, Anthropic, Google, Mistral and the upstart AI Providers.
These “gains” are coming from several sources, many of which have sympathetic feedback loops with each other — data model improvements yield algorithmic enhancements which make operating new hardware possible, on and on the virtuous accelerating circle goes.
The bleeding edge of AI research teams are developing AI systems using a variety of futuristic substrates and methods - thermal computing, biological computing, quantum and more. While I do my best to read the sparse research papers about these topics that are released, most of this work is happening in the lab and out of sight.
This article will focus on the cutting commercial edge of AI.
Commercial models are exploring post-transformer architecture. Transformers bring a lot to the table, let’s walk through that and then discuss what comes next.
Previously, recurrent neural networks (RNNs) reigned supreme in tasks like text analysis. However, RNNs struggle with long-range dependencies; the further back in a sentence a word lies, the weaker its influence becomes. This hinders the model's ability to grasp the overall meaning of text.
Paying Attention is Priceless
The transformer's core innovation is "self-attention."
Instead of processing words one at a time like RNNs, it looks at the entire input sequence all at once. Each word interacts with every other word, establishing a network of relationships. Think of it as the model asking, "How relevant is this word to all the others in the sentence?" This lets it spot significant connections and nuances that RNNs would miss.
Benefits of the Transformer
Parallelism: Transformers process the entire sequence at once, dramatically speeding up training compared to RNNs.
Long-Range Dependencies: Attention connects words across long distances, improving the understanding of complex sentences.
Scalability: Transformers handle massive text datasets efficiently, enabling the enormous LLMs we see today.
Transformers empower LLMs to comprehend text with remarkable depth. Their ability to analyze sequences holistically has transformed how we generate realistic text and perform various other tasks.
Google (and others) are experimenting with fused architectures. Leveraging the incredible power of the transformer with the unique capabilities of CNNs, RNNs and other set-ups. We’ll discuss these hybrids later in this piece.
When you survey the landscape of AI development, these are the primary areas of performance improvement:
Computational Power and Efficiency
Specialized Hardware: The development of hardware tailored for AI workloads, like GPUs, TPUs (Tensor Processing Units), and neuromorphic chips, increases computational power while improving energy efficiency. This allows for more complex models and faster training.
Efficient Algorithms: Research into streamlined algorithms, such as sparse neural networks and quantization techniques, aims to reduce computational overhead while maintaining accuracy.
Quantum Computing: While still nascent, quantum computing's unique properties offer the tantalizing possibility of tackling problems intractable for classical computers. This could revolutionize certain AI tasks like optimization and simulation.
Edge Computing: Enhancements in deploying AI models on edge devices, allowing for lower latency and reduced bandwidth usage by processing data locally.
Data Handling
Data Quality: Improving the quality, diversity, and labeling of datasets is crucial for training reliable AI models. Addressing biases in data helps ensure fairer, more robust AI systems.
Data Augmentation: Techniques for generating synthetic data or variations of existing data can expand training sets and improve model generalization, especially in scenarios where real-world data is limited.
Unsupervised/Semi-Supervised Learning: Reducing the reliance on massive labeled datasets is essential. Advances in these techniques enable AI models to learn from unlabeled data, potentially saving significant time and resources.
Algorithm Development and Model Architectures
Natural Language Processing (NLP): Innovations in transformer-based architectures (like GPT-4 and its successors) have revolutionized language understanding and generation. Focus is on making these models smaller and more efficient while retaining performance.
Neural Network Architectures: Innovations in deep learning architectures, such as transformers, which have significantly advanced natural language processing (NLP), computer vision, and beyond.
Sparse Neural Networks: Techniques to reduce the computational complexity and memory footprint of neural networks while maintaining or improving performance.
Quantization and Pruning: Methods to reduce model size and increase inference speed without significantly impacting accuracy.
Computer Vision: Continued development of convolutional neural networks (CNNs) alongside techniques like attention mechanisms drive progress in image recognition, object detection, and video analysis.
Deep Reinforcement Learning: Combining deep learning with reinforcement learning opens up possibilities for AI agents that learn through trial and error, particularly in fields like robotics and gaming.
The Pyramid of Power
We can simplify the AI Development equation down to three dimensions:
Hardware
Software
Data
I visualize bringing this triangle to “life” along the Z-axis by introducing time, changes over time — a 3d triangle forms a pyramid in my mind’s eye.
(Nerd in me is compelled to say its technically a prism, but pyramids are way cooler)
HARDWARE - The Body & The Building
AI is like your brain.
Just like a real brain needs a healthy body to work well, AI needs three key things to become super powerful: hardware, software, and data. Think of it like leveling up a video game character!
Imagine hardware as the AI character’s muscles and bones. Remember those old computers that chugged super slowly? Not great for running a lightning-fast AI mind! That's why scientists are making specialized AI chips called TPUs and why NVIDIA, the preeminent GPU fabricator, is the biggest winner in the stock market. These give the AI a turbocharged engine.
Improvements in hardware are pivotal for supporting more complex algorithms and larger data sets, making AI models more efficient and capable.
GPUs (Graphics Processing Units): Their massively parallel architecture makes them ideal for deep learning calculations, providing a significant boost to AI workloads.
Where Development is Headed:
Continued increase in processing cores and memory bandwidth.
More refined architectures specifically tailored for AI workloads.
Integration of AI-specific acceleration units within GPUs.
TPUs (Tensor Processing Units): Google designed these specifically for tensor operations prevalent in neural networks, offering even greater efficiency in many AI tasks.
Where Development is Headed:
Higher computation density and faster memory access.
Support for broader range of data types for greater flexibility.
Exploration of integration with other types of AI accelerators.
Neuromorphic chips: Modeled after biological neurons, these promise exceptional energy efficiency, opening doors for AI in low-power settings.
Where Development is Headed:
Advancement in materials and fabrication techniques for larger, more powerful chips.
Development of standardized software frameworks to program these unique chips.
Integration with traditional computing systems for hybrid AI architectures.
In the construction of a building, the foundation and structural framework are paramount. They not only support the entire structure but also determine its resilience, capacity, and limitations.
Similarly, in AI development, hardware serves as the foundational platform upon which all AI functionalities are built. Just as the strength and design of a building's foundation and framework dictate the ultimate shape and stability of the construction, the hardware used in AI—comprising processors, GPUs, and neural network accelerators—defines the efficiency, speed, and scale at which AI algorithms can operate.
SOFTWARE - The Skills and Design
Now, software is the AI's skills – how to think, speak, and learn.
Like teaching a character new moves, better software makes AI smarter. There's a whole area of research called "deep learning", These are complex programs, kind of like a giant, layered brain made of code. Each layer helps the AI learn different things, just like the parts of a real brain.
Here's a cool trick: "transfer learning." Imagine an AI that learns to identify cats in pictures. Turns out, it already knows a bunch of stuff useful for identifying dogs – shapes, textures, stuff like that. Transfer learning lets the AI build on what it knows. Just like humans do.
Software advancements encompass the algorithms and models that drive AI capabilities, along with the tools and frameworks that support their development.
Deep Learning Frameworks (TensorFlow, PyTorch): Libraries like TensorFlow and PyTorch are the workbenches of AI development. They streamline model construction, training, and deployment, democratizing complex AI techniques.
Efficient Algorithms: Research on sparse networks, quantization, knowledge distillation, etc., aims to reduce the computational cost of AI models while keeping accuracy high.
Where Development is Headed:
Development of adaptive compression techniques that scale with model complexity.
Techniques like knowledge distillation create smaller, faster models without sacrificing accuracy.
Research on dynamic network structures that change during training/inference.
Exploration of brain-inspired algorithms for greater efficiency.
Optimization Libraries: Tools optimized for specific hardware, maximizing performance potential and efficiency.
Explainability Tools (LIME, SHAP): These techniques help developers interpret the decision-making process of AI models, fostering trust.
If we consider hardware as the foundation and framework of a building, then software represents the architectural design and internal construction. This includes the walls, rooms, electrical wiring, plumbing, and heating, which all need to be carefully planned and executed to ensure the building is not only functional but also habitable and comfortable.
In AI development, software encompasses the algorithms, models, programming languages, and development frameworks that instruct the hardware on how to process data, learn from it, and perform tasks.
This layer is where the intelligence of AI is crafted, much like how the usability and aesthetics of a building are realized through its design and interior construction.
DATA - The Fuel
Now, data is like the food our AI brains munch on.
The more high-quality data they get, the smarter they become. Think of it like this – you can't learn to speak if you never hear words, right? It's the same for AI!
Massive datasets with millions of words help LLMs learn how humans communicate. The more different examples they see, the better they get at making their own sentences that sound natural.
But data's not just about language. AI that learns to drive needs tons of data from real-world roads, the weather, and how other cars move. It's like a super intense driving simulator that helps the AI learn before hitting the real streets.
The way data is handled, processed, and generated plays a crucial role in the effectiveness of AI systems, with a focus on making better use of data and reducing the need for large datasets.
Data Quality: Improving accuracy, completeness, and removing bias create more robust datasets, leading to better-performing AI.
Data Augmentation: Generating synthetic data variations enriches training sets, especially when real-world data is limited.
Synthetic Data Generation: Tools and techniques to produce realistic but artificial data aid AI development where real datasets involve privacy concerns or are difficult to obtain.
Unsupervised/Semi-supervised Learning: Algorithms that extract knowledge from unlabeled data reduce cost and time associated with dataset creation.
Finally, just as a building is made livable and functional through the addition of utilities (like water and electricity), furnishings, and occupants, data breathes life into AI systems. Data provides the raw material that AI systems need to learn, adapt, and evolve. It's comparable to the electricity that powers appliances, the water that runs through plumbing, and the people who live or work in the building, each contributing to its purpose.
In AI development, data is the critical input that fuels the learning processes, enabling systems to refine their algorithms, enhance their understanding, and improve their performance over time.
Thanks to continuous breakthroughs in construction material (hardware) and genius architects who are pushing design to the limits, the art of AI development is accelerating at ever-rising rates.
Each of these breakthroughs makes a BIG difference, but here's the most important thing – they work together!
Like in video games, the better your gear, skills, and experience, the more unstoppable you become. Better hardware can process more complex software, which can handle way bigger datasets, making our AI powerhouses better at almost everything!
Imagine AI assistants that understand you perfectly, robots that work alongside us seamlessly, or self-driving cars that always keep us safe. The sci-fi stuff is getting closer, one upgrade at a time!
Next we’re going to cover Hardware and then get into specific updates related to hybrid AI system architecture.