The Future of AI: Neuro-Symbolic Language Models

The Future of AI: Neuro-Symbolic Language Models

Table of Contents

  1. Introduction
  2. The History and Progress of AI
  3. The Paradigmatic Example of Expert Systems
  4. The Rise of Statistical Models
  5. Large Language Models and the Transformer Architecture
  6. The Limitations of Statistical Models
  7. The Vision of AI-21 Labs
  8. Wordtune: An AI-Based Writing Assistant
  9. Wordtoread: An AI-Based Document Summarizer
  10. Addressing Bias and Toxicity in Language Models
  11. The AI-21 Studio Platform
  12. The Future of Language Models
  13. AI in Biology and Medicine
  14. We Code: A Hybrid Academic-Vocational Coding Program
  15. Lessons Learned from Entrepreneurship and Academia

The Rise of Language Models and the Future of AI

Thank You for joining me today as we explore the world of AI and language models. In 2017, I founded AI-21 Labs, a company focused on using language models and natural language processing to improve the way we Read and write with the help of machines. Our goal is to merge the power of statistical models with the reasoning capabilities of expert systems to Create a new generation of language models that can truly understand the meaning behind the words.

The History and Progress of AI

To understand how we got here, we need to look back at the history and progress of AI. Back in the 80s, AI was very popular, with standing room only at conferences and a lot of excitement around expert systems. These systems were based on decision trees and probabilistic frameworks, and while machine learning was present, it didn't play an important role. However, the paradigm shifted in the last five years with the rise of large language models, which are capable of pattern detection and recognition at scales we couldn't imagine before.

The Paradigmatic Example of Expert Systems

The paradigmatic example of expert systems was a doctor who would diagnose a patient based on their symptoms, questions, and tests. However, these systems were limited by the amount of data and compute available at the time. They were also based on a set of rules that didn't allow for much flexibility or nuance.

The Rise of Statistical Models

The rise of statistical models changed everything. Suddenly, we had access to vast amounts of data and compute, and we could do pattern detection and recognition at scales we couldn't imagine before. However, these models were limited by their lack of understanding. They could recognize Patterns, but they couldn't reason about them.

Large Language Models and the Transformer Architecture

The rise of large language models changed everything again. These models, based on the transformer architecture, are capable of modeling not just language, but also information as expressed by knowledge. They are sometimes called foundational models because they can understand the meaning behind the words. Suddenly, we started to see the needle move in natural language processing, and the academic benchmarks were dominated by these new models.

The Limitations of Statistical Models

However, these models are not without their limitations. While they are amazing statistical machines, they don't understand anything. They can do arithmetic, but they don't understand the underlying principles. They are limited by their lack of reasoning capabilities, which is exactly what expert systems did back in the 80s.

The Vision of AI-21 Labs

At AI-21 Labs, our vision is to merge the power of statistical models with the reasoning capabilities of expert systems. We believe that language models are a tool, and when you use the tool, it's your responsibility. We pay a lot of Attention to issues of bias, toxicity, and privacy, and we encourage our developers to do the same.

Wordtune: An AI-Based Writing Assistant

One of our first products is Wordtune, an AI-based writing assistant. The Core functionality of Wordtune is a rewrite or rephrase functionality, where you take parts of a sentence or a Paragraph, and the system will say here are the other ways of saying things. The system is not just a synonym generator, but it can offer semantically related options that may be more adequate. The experience is magical, and as a side effect, you also get perfect English.

Wordtoread: An AI-Based Document Summarizer

Another product we launched is Wordtoread, an AI-based document summarizer. The goal of Wordtoread is to summarize documents so that users can get their content information much faster than reading the whole thing. The system generates summary snippets that are much shorter and simpler to read than the original document. In experiments, we've seen a 10x speed up in the way users read the document.

Addressing Bias and Toxicity in Language Models

We take issues of bias and toxicity in language models very seriously. We clean the data at training time, and we inject checks and balances at inference time. We have a team dedicated purely to this, and we encourage our developers to do the same.

The AI-21 Studio Platform

We've also launched the AI-21 Studio platform, which is a platform for developers to build language-based applications. We offer a large language model called Jurassic-1, which has 178 billion parameters, and a smaller model called Large, which has 7.5 billion parameters. We encourage developers to use Jurassic-1 to generate the data and then create custom models that are smaller and cheaper to serve.

The Future of Language Models

The future of language models is exciting. We believe that size matters, but it's not enough. We need to build reasoning into the models, and we need to be nuanced with how we build and deploy them. We also believe that the brain versus brawn trade-off is going to change, and more and more brain will be called for.

AI in Biology and Medicine

AI is already having a big impact in biology and medicine, and we believe that impact will only grow. Language models can be applied to understand our genetic sequence or protein sequences, discover new drugs, or prevent new diseases. We're excited to see what the future holds in this area.

We Code: A Hybrid Academic-Vocational Coding Program

We Code is a hybrid academic-vocational coding program that I founded to lessen the gap between people with access to technology jobs and those without. The program is rigorous and demanding, but it offers immediate employment opportunities and carries credit if and when you want to Continue and complete a degree.

Lessons Learned from Entrepreneurship and Academia

As an entrepreneur and academic, I've learned a lot of lessons along the way. My advice to aspiring entrepreneurs is to focus on your strengths, find people who are smart and fun to work with, and be Curiosity-driven. We all have our own path, and we need to play to our strengths and find what's fun and interesting to us.

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