Large language models like ChatGPT lack true insight and adaptable intelligence, making them incapable of replacing human professionals in specialized areas.
Large language models, while useful in certain tasks, will have a more limited impact on the workplace similar to tools like Google search rather than being a disruptive force.
Contrary to concerns, large language models like ChatGPT do not possess self-awareness or consciousness, and they are limited to generating text based on patterns in training data.
Deep dives
How Large Language Models Work
Large language models like chat GPT can generate text in arbitrary combinations of known styles and subjects. They use word guessing to produce text one word at a time, matching relevant words from the input to generate the next word. The models vote on the likelihood of each possible word based on the source text and generate text based on the highest votes. These models can produce believable text, but their capabilities are limited to what they have been trained on and do not possess actual understanding or adaptable intelligence. They are not capable of taking over the economy or becoming self-aware.
Practical Applications and Limitations
Large language models can be useful in tasks such as rewriting text in a specific style or elaborating on given information. They can assist in tasks like proofreading or information gathering. However, they are often wrong and lack actual understanding of the topics they produce text on. They can't replace human professionals in specialized areas like programming or writing because they lack true insight and rely on statistical patterns in the training data. While they have practical uses, their impact on the workplace will be more akin to tools like Google search rather than a disruptive force.
Not an Alien Intelligence
Contrary to concerns, large language models like chat GPT are not alien intelligences and possess no self-awareness or consciousness. They are static models that respond to input based on their training. Their training involves processing vast amounts of text data to generate responses, but they lack malleable memory and the ability to update their models. They do not have the capacity for self-awareness or existential threats. The models are limited to generating text based on patterns in the training data and are not capable of becoming sentient beings.
NPR quits Twitter
NPR has decided to quit Twitter due to disagreements over how the platform labeled them. They believe that relying on Twitter for news is not the right way and instead encourage people to subscribe to their email newsletter, visit their website, or listen to their radio program for up-to-date news.
Moving away from Twitter for news consumption
More and more outlets are shifting away from using Twitter as their main news platform. The Washington Post, for example, has moved its Nationals baseball team coverage from live tweeting the games to providing live updates on their website. This move away from platforms that generate outrage and manipulate emotions towards individual websites and newsletters is seen as a healthier and more accurate way to consume and share news.
Benefits of bespoken and distributed communication
Moving towards individually owned digital distribution of information, such as podcasts, websites, blogs, and newsletters, is considered a positive trend. This allows for more context and credibility in the way information is presented and consumed, as individuals can learn from where the information is hosted and curated. It is seen as a healthier way to distribute information than relying on centralized platforms that amplify and distribute content through dispassionate algorithms.
Are new AI technologies like ChatGPT about to massively disrupt our world? Drawing from his recent New Yorker article on the topic, Cal explains exactly how programs like ChatGPT work, and uses this knowledge to explain why we can calm our fears about this new technology.
Below are the questions covered in today's episode (with their timestamps). Get your questions answered by Cal! Here’s the link: https://bit.ly/3U3sTvo
