This is an outstanding explainer from the Financial Times) open access so anyone can see it. It takes the reader step by step through a clear and logical explanation of how generative AI works, and in particular, the innovation that results in transformers (the T in GPT). It should (in my opinion) put to rest fears that such systems are ‘copying’ content. True, they learn from it, but what they learn amounts really to nothing more than how words are arranged in a sequence, and what sequences are most common. Don’t miss this! Via OLDaily
What unlocked their abilities to parse and write as fluently as they do today is a tool called the transformer, which radically sped up and augmented how computers understood language.
Transformers process an entire sequence at once — be that a sentence, paragraph or an entire article — analysing all its parts and not just individual words.
This allows the software to capture context and patterns better, and to translate — or generate — text more accurately. This simultaneous processing also makes LLMs much faster to train, in turn improving their efficiency and ability to scale.
While the text may seem plausible and coherent, it isn’t always factually correct. LLMs are not search engines looking up facts; they are pattern-spotting engines that guess the next best option in a sequence.
Because of this inherent predictive nature, LLMs can also fabricate information in a process that researchers call “hallucination”. They can generate made-up numbers, names, dates, quotes — even web links or entire articles.
Research outlining the transformer model was first published by a group of eight AI researchers at Google in June 2017. Their 11-page research paper marked the start of the generative AI era. The Financial Times
I recommend you take the time to read this article by the Financial Times.
- The text explains how transformer technology has enabled a new field of generative AI that can create realistic and sophisticated content such as text, images, and audio.
- The text gives examples of generative AI applications, such as ChatGPT, a system that can produce natural and engaging conversations, and DeepMind’s WaveNet, which can generate realistic human speech.
- The text also discusses the challenges and opportunities of generative AI, such as the ethical, social, and economic implications and the potential for innovation and creativity.
After reading the article, discuss all or some of these questions with your colleagues.
- What is the main difference between the transformer technology and the previous deep learning models?
- How does ChatGPT use generative AI to produce conversations, and what are some of its advantages and limitations?
- How does WaveNet use generative AI to generate speech, and what are some of its applications and challenges?
- What are some of the ethical and social issues that generative AI raises, and how can they be addressed?
- What are some of the most impressive generative AI applications you have encountered?
- What are some ethical and social implications of generative AI, and how can we address them?
- How can we ensure that generative AI is used responsibly and ethically in the classroom?
- What are some potential risks and benefits of using generative AI in educational assessment and evaluation?
- How can we prepare students for a future where generative AI is ubiquitous, and what skills will they need to succeed?
- What are some of the limitations and biases of generative AI, and how can we mitigate them?