Codex: Codex for all to work in the 21st century and the impact of AI on techniqes and knowledge workers
As AI finally emerges into an age of utility, the opportunities for new, AI-first products are immense. We will be living in a world where curiosity and imagination are the main limitations, regardless of your programming abilities.
Humans have to learn the language of machines to communicate, and computer programming has been all about translation. Codex lets us express our intentions in natural language and the machine will translate those intentions into code. It is a means for the human imagination and software to communicate.
It will be possible to build Copilots that can be used for virtually any aspect of knowledge work, allowing knowledge workers to spend their time on higher-order cognitive tasks, and effectively changing how a great many of us interact with technology.
In and of itself, that’s a truly remarkable step forward in productivity for developers alone, a community of knowledge workers who are wrestling with extraordinary complexity and unprecedented demand for their talents. But it’s just the first step of many that will be taken in 2023 as we see this pattern repeated across other sorts of knowledge work.
Our world requires more knowledge work every year, making it harder for workers in the field and industry. Productivity gains have been relatively low since the invention of the computer and the internet and copilots for everything could change that.
Artificial Intelligence Neural Networks: How Do Transformers Get Their Attention? A Brief Review of Recent Developments in Natural Language Models
Artificial intelligence has promised much, but there has been something holding it back from being used successfully by billions of people: a frustrating struggle for humans and machines to understand one another in natural language.
This is now changing, thanks to the arrival of large language models powered by transformer architectures, one of the most important AI breakthroughs in the past 20 years.
Neural networks are created to model sequential data and then predict what will happen next in a series. The idea of “attention” is what allows the transformer to attend the most important aspects of an input rather than trying to process everything.
Parameter count is considered a rough proxy for a model’s capabilities. So far, we’ve seen models perform better on a wide range of tasks as the parameter count scales up. Models have been growing by almost an order of magnitude every year for the past five years, so it’s no surprise that the results have been impressive. However, these very large models are expensive to serve in production.
Source: https://www.wired.com/story/artificial-intelligence-neural-networks/
How do we get to talk with machines? Seeing how computers will communicate with their customers, their families, and their friends in a way that promotes creativity
I am most excited about language. Humans have always had to use interface designed for technology, rather than their thoughts. We will start talking with machines instantly and comprehensively in 2023 with this wave of breakthroughs. We will have real conversations with all of our devices. This promises to fundamentally redefine human-machine interaction.
Over the past several decades, we have rightly focused on teaching people how to code—in effect teaching the language of computers. That will remain important. But in 2023, we will start to flip that script, and computers will speak our language. That will open up access to new tools for learning, playing, and creativity.