LongNet: Empowering Transformers with 1,000,000,000 Tokens

Decoding Longnet: Unraveling the Mystery

July 26, 2023

LongNet: Scaling LongNet is a variant of the Transformer model, a type of deep learning model that has revolutionized the field of natural language processing. The critical innovation of LongNet is its ability to scale sequence length to more than one billion tokens without sacrificing performance on shorter sequences. This is achieved through a novel component called dilated attention, which expands the attentive field exponentially as the distance grows. This allows LongNet to handle highly long sequences, such as an entire corpus or Internet, as a single sequence.

Scaling Transformers, on the other hand, refers to increasing the size and complexity of Transformer models to handle larger and more complex tasks. This is typically achieved by increasing the number of layers in the model, the size of the model’s hidden states, or the number of attention heads.

Two implications are worth considering:

1) AGI may arrive sooner than most experts predict, possibly within 15 to 24 months, given these advances in scaling up transformer models.

2) AI may achieve consciousness, at least in the sense of self-awareness and the ability to have subjective experiences, which many experts claim is impossible. While the level of consciousness is debatable, the capability to reach some threshold of consciousness seems increasingly within reach.

Based on the latest research and this paper: https://arxiv.org/pdf/2307.02486.pdf., LongNet could process a billion tokens in one second. This single progress suggests that training models could become obsolete, as providing all the data at once for processing may be sufficient. Now add in the specialised models that firms like Stability AI are working on, and envision a landscape where almost everyone can access such models.

LongNet and Scaling Transformer Models: Transforming AI Industries

Many companies will have to bite the bullet after spending millions and billions of dollars on the first phase of this tech. Right now, most users are casual and not keen on learning AI. They want AI to know how to work alongside them, and that’s exactly what the next generation of AI models will strive to achieve.

The key takeaway is speed and scale. Once we reach the point where these scaled-up transformer models can operate in real-time, analysing massive amounts of text data in seconds, they will transform nearly every industry that utilises AI today. The companies that thrive will leverage these powerful new models to drive innovation, while those that cannot adapt fast enough will struggle. So the combination of the rapidly increasing scale and speed of these transformer models will be truly disruptive, creating both opportunities and threats for businesses across every sector.

In summary, being able to process a billion tokens in seconds will be a game changer, and many companies are not prepared for this transformational change that is coming within the next year, according to the trend data. Let this sink in, and then you can understand why this is transformational and so many companies will die.

Dangers of Longnet and Scaling Transformer Models

The potential dangers of these scaled-up models, such as LongNet, arise from their increased capacity for understanding and generating human-like text. With their ability to process and generate vast amounts of information, these models could outthink thousands of humans simultaneously. This raises several concerns:

1. Misuse: These models could be used maliciously to generate misleading information, propaganda, or deepfake content at a scale and speed that humans cannot match.

2. Decision-making: If these models are used in decision-making processes, they could make decisions that humans do not understand or agree with. This could lead to a lack of transparency and accountability.

3. Dependence: Over-reliance on these models could lead to decreased human critical thinking skills and an over-dependence on AI for decision-making.

4. Job displacement: As these models become more capable, they could replace human jobs in various fields, leading to significant social and economic impacts.

5. Ethical concerns: These models could potentially develop biases based on the data they are trained on, leading to unfair or discriminatory outcomes.

Concluding the AI Landscape: Scaling Transformers and Their Implications

Here is just one change of many that could transform the AI landscape and destroy many players in the AI arena. They are already working on scaling transformers which can process a billion tokens in one shot. To put that into perspective, ChatGPT 4 can process a maximum of roughly 33K (and that is pushing it to the very max) tokens per input. The average human reads about 2 billion words in their lifetime. As a result, these novel models will be able to comprehend, in a single instance, what an average human would read during more than half of their lifetime.

It is the endgame for numerous carbon-based entities as mediocre workers are swiftly replaced; they won’t know what hit them. Nevertheless, artificial intelligence (AI) will concurrently create opportunities for numerous individuals to transform their passions into lucrative endeavours while rendering previously dominant corporations obsolete. Among the contenders (and there are many), Amazon stands as a prominent example.

These scaled transformers will represent a massive increase in scale and capability compared to current models like GPT-4. A model that can process a billion tokens in one shot would be over 30,000 times larger. Companies betting on the current generation of AI may find their products and services obsolete.

FAQ: LongNet and Scaling Transformer Models

Q: What is LongNet?
A: LongNet is a variant of the Transformer model, a powerful deep-learning model used in natural language processing.

Q: What sets LongNet apart?
A: LongNet excels in scaling sequence length, processing over a billion tokens without losing performance on shorter sequences.

Q: How does LongNet achieve this scalability?
A: LongNet utilizes a novel component called dilated attention, expanding the attentive field exponentially with increasing distance.

Q: What are the implications of scaling Transformer models?
A: Scaling up Transformer models could lead to the arrival of AGI sooner than expected and AI potentially achieving some level of consciousness.

Q: How fast can LongNet process a billion tokens?
A: Based on research, LongNet can process a billion tokens in just one second.

Q: What impact will scaled-up Transformer models have?
A: These models operating in real-time will transform various industries, driving innovation and posing both opportunities and threats for businesses.

Q: What dangers do LongNet and scaled Transformer models pose?
A: Misuse, lack of transparency in decision-making, job displacement, dependence on AI, and potential biases are some concerns.

Q: How can potential risks be addressed?
A: Developing appropriate safeguards, regulations, and ethical considerations is crucial for harnessing the benefits responsibly.

Q: How can LongNet affect job markets?
A: Its capabilities could lead to job displacement in various fields, impacting society and the economy.

Q: What is the future for AI with LongNet and scaled models?
A: AI’s landscape will witness transformative changes, affecting existing players, creating opportunities, and revolutionizing industries.