So... why did AI take off now?
Science & Technology
So... Why Did AI Take Off Now?
Artificial Intelligence (AI) has recently surged in popularity, capturing immense attention as both an overhyped corporate trend and a powerful tool for leveraging massive amounts of information. It has raised concerns as a threat to many people's current livelihoods. But how did we move from the rudimentary computer models of the late 2000s to today's massive neural networks that are integrating themselves into the fabric of our society?
The Early Days of Modeling
Building models used to be a painstaking process. You needed domain expertise in the data, and a hypothesis of what patterns existed. Models were bespoke and highly tailored to the specific dataset. Simply throwing in all the raw data and hoping it would work was not an option.
The Advent of Neural Networks
What would happen if you did just throw all the raw data in? Neural networks, which are kind of similar to brains, are models capable of learning complex patterns directly from the data without much oversight. This is known as deep learning.
Although neural networks were first described in the 1960s, practical applications were initially hindered by immense computational demands. Fortunately, advances in GPUs designed for computer graphics and video games in the late 2000s were excellent for parallel processing, thus laying the groundwork for modern neural networks.
Challenges with Text and the Introduction of Transformers
But there still existed a roadblock: What if you want your model to answer a question about a sentence but can't fit the whole digital representation on the GPU at once? Researchers created special model architectures to allow models to remember information using previous text for predictions. This remembering was tricky and led to highly complex architectures. Long Short-Term Memory (LSTM) networks were cool and fun but often brought us back to tedious, hypothesis-driven modeling.
Then came Transformers, which put the "T" in GPT (Generative Pre-trained Transformer). Transformers are massive models that work on a key development called self-attention. Instead of attempting to remember states in an intricate manner, Transformers feature encoder-decoder pairs. Encoders condense large amounts of text into digital fingerprints, while decoders use this condensed information to produce an output.
The self-attention mechanism improved the model's ability to understand and generate text by allowing the encoder and decoder to see all context at once. This breakthrough facilitated tasks like translation by using concept embeddings across languages.
Acceleration with Specialized Hardware
With the introduction of multiple heads for massive parallelization, huge models could be developed, limited only by the available hardware. Newly designed Neural Processing Units (NPUs) further expedited this acceleration, complementing the previously repurposed graphics-focused GPUs.
Throwing in Everything
What's really changed is the capability to just throw in everything— all the data on the internet. The concept embeddings, or digital fingerprints, allowed text, images, and audio to correspond to the same fingerprint. This paved the way for multimodal models that can easily convert text to images and vice versa.
Models can sometimes hallucinate outputs that sound almost right, but grounding the model in factual data or requiring it to outline its reasoning can mitigate this. When users interact with models like chatbots, a longer prompt often guides the model to behave in a helpful, civil manner.
Despite some users exploiting models through prompt attacks, strict guidelines and safety mechanisms ensure that outputs are curated. Raw model outputs are filtered through additional models to provide the best and safest responses to users.
Keywords
- AI
- neural networks
- deep learning
- GPUs
- transformers
- LSTMs
- self-attention
- encoder-decoder
- NPUs
- digital fingerprints
- multimodal models
- prompt attacks
- curation
- factual grounding
FAQ
Q: What is the main reason AI has become more popular recently? A: Advances in computational power, particularly with GPUs and NPUs, and the development of new architectures like Transformers have significantly propelled AI's capabilities.
Q: What are neural networks? A: Neural networks are models that can learn complex patterns directly from data without much oversight, mimicking the way human brains operate to some extent.
Q: Why were practical applications of neural networks limited initially? A: Practical applications were limited by the immense computational demands required to process the vast amounts of data needed for these models.
Q: What are Transformers, and how do they work? A: Transformers are a type of neural network model that uses self-attention mechanisms with an encoder-decoder structure to better understand and generate text.
Q: How have specialized hardware advancements contributed to AI development? A: The introduction of Neural Processing Units (NPUs) and advancements in GPUs have allowed for parallel processing, enabling the creation of larger and more complex models.
Q: What does it mean to "just throw in everything" in the context of modern AI? A: It refers to the ability to process and utilize massive amounts of raw data, encompassing all available information on the internet, to generate highly accurate and multi-faceted models.