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Maj. Gen. Jon Kelk is Air National Guard Assistant to the Commander, United States Air Forces in Europe/Air Forces Africa. He is responsible for providing full-spectrum war fighting air and space operations capability to the combatant commanders throughout the area of responsibility, which includes parts of three continents and 92 countries throughout Europe, Africa and parts of Asia. He also leads the command's engagements with the National Guard's State Partnership Program.
General Kelk was commissioned in 1981 through Officer Training School, Lackland Air Force Base, Texas. He graduated as a distinguished graduate from Undergraduate Pilot Training at Vance Air Force Base, Oklahoma in 1982 and from the United States Air Force Fighter Weapons Instructor Course at Nellis Air Force Base, Nevada in 1987. General Kelk served as an F-15 weapons officer for two of his three operational assignments. He flew in Operation DESERT STORM in 1991 and was credited with the first aerial victory of that conflict, an Iraqi Mig 29, for which he was awarded the Distinguished Flying Cross. General Kelk joined the Missouri ANG in 1992 and has served as a flight commander, operations officer, squadron commander, operations group commander, director of operations and Chief of Staff; during that time, he participated in four no-fly-zone enforcements in Iraq during Operations PROVIDE COMFORT, NORTHERN WATCH and SOUTHERN WATCH. In 2012 he transferred to the California ANG as Chief of Staff prior to assuming his current position. A command pilot, he has logged more than 4,200 flying hours, including 296 combat hours. In August 2006, General Kelk became the first United States pilot to log 4,000 hours in the F-15 A-D air superiority version of the F-15.
Groundhog Day is one of my favorite movies; we watch it every February! So I was happy and a tiny bit scared to read this Christmas version. What if I hated it?? But I am happy to say that I worried for nothing. I loved this book!
This is a very British romcom, full of quirky characters, foods I had to look up (Hobnob is a cookie, I learned,) and a wonderful family that celebrates Christmas together every year, despite the fights and feuds. Gwen is the youngest daughter and as such, has always felt competitive with her older sister, Cerys. Cerys is beautiful, a lawyer like their father. and married to a misogynistic man. Gwen is also a lawyer, working for one of the top law firms in London, making her father very proud. Except she hates her job, and after a client put his hands on her, she clocked him and ended up basically suspended for two weeks. Her boyfriend of a few years just dumped her after admitting to having an affair with his receptionist, so Gwen is struggling, to say the least.
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Remember the first time you used ChatGPT and how amazed you were to find yourself having what appeared to be a full-on conversation with an artificial intelligence? While ChatGPT was (and still is) mind-blowing, it uses a few tricks to make things appear more familiar.
While the title of this article is a bit tongue-in-cheek, it is most certainly not clickbait. ChatGPT does indeed use two notable hidden techniques to simulate human conversation, and the more you know about how they work, the more effectively you can use the technology.
- ChatGPT has no idea who you are and has no memory of talking to you at any point in the conversation.
- It simulates conversations by "reading" the whole chat from the start each time.
- As a conversation gets longer, ChatGPT starts removing pieces of the conversation from the start, creating a rolling window of context.
- Because of this, very long chats will forget what was mentioned at the beginning.
Contrary to appearances, large language models (LLMs) like ChatGPT do not actually "remember" past interactions. The moment they finish "typing" out their response, they have no idea who you are or what you were talking about. Each time the model is prompted, it is completely independent from previous questions you've asked.
When ChatGPT seems to naturally recall details from earlier in the conversation, it is an illusion; the context of that dialogue is given back to ChatGPT every time you say something to it. This context enables them to build coherent, follow-on responses that appear to be normal conversations.
However, without this context, ChatGPT would have no knowledge of what was previously discussed. Like all LLMs, ChatGPT is completely stateless, meaning that in the actual model itself, no information is maintained between inputs and outputs. All of this feeding of previous context into the current interaction is hidden behind the scenes in the ChatGPT web application.
Notice that when the woman asks her second question, she has to reiterate the entire previous conversation, complete with tags on who said what. Can you imagine talking to a person where every time it was your turn to speak, you had to repeat the entire conversation up to that point? This is how ChatGPT (and all current LLMs) work. They require using their own outputs, plus the prompts that generated these outputs, to be prepended to the start of every new prompt from the user.
These models are termed "auto-regressive" due to their method of generating text one piece at a time, building upon the previously generated text. "Auto-" comes from the Greek word "auts," meaning "self," and "regressive" is derived from "regress," which in this context refers to the statistical method of predicting future values based on past values.
In LLMs, what this means is that the model predicts the next word or token in a sequence based on all the words or tokens that have come before it. That's all of it, not just the current question being asked in a long back-and-forth chat conversation. In humans, we naturally maintain coherence and context in a conversation by just... participating in the conversation.
However, while chats with ChatGPT mimic a conversational style, with each response building upon the previous dialogue, the moment ChatGPT finishes writing a response, it has no memory of what it just said. Take a look at what would happen with this same conversation without the entire discourse being fed back to ChatGPT behind the scenes.
When ChatGPT first came out in November 2022, it only offered the model GPT-3.5, which had a maximum context of 4,096 tokens, which is roughly 3,000 words. In a recent talk, Andrej Karpathy referred to this context window as "your finite precious resource of your working memory of your language model."
What this means is that the GPT-3.5 model can comprehend a maximum of 4,096 tokens at any point. Tokenization is a fascinating subject in itself, and my next post will cover how it works and why 4,096 tokens only gives you about 3,000 words.
There's often confusion about what the token limit means regarding input and output: can we give ChatGPT 3,000 words and expect it to be able to produce 3,000 words back? The answer is unfortunately no; the context length of 4,096 tokens covers both the input (prompt) and the output (response). This results in a trade-off where we have to balance the amount of information we give in a prompt with the length of the response we get from the model.
- Input (prompt): A longer prompt leaves less room for a meaningful response; if the input uses 4,000 tokens, the response can only be 96 tokens long to stay within the token limit.
- Output (response): A shorter prompt could lead to a longer response as long as the combined length doesn't exceed the token limit, but you may not be able to include information in the prompt.
Do you see where this becomes problematic? Previously, we saw how the entire conversation has to be fed to the model so that it remembers what has already been discussed. Combining this with the context length, the result is that as you talk more and more with ChatGPT, eventually the combined totals of what you've asked and what it has replied will exceed the 4,096 token limit, and it won't be able to answer any more.
Since ChatGPT's debut in November 2022, GPT-4 has been released with both 8,192 and 32,768 context lengths. This made things a lot better in terms of tracking long conversations, and in November 2023, GPT-4 Turbo was released with a 128k context length. Things are looking increasingly good for these models' ability to track long conversations. However, despite GPT-4 Turbo's massive amount of context, it still has a completion limit of 4,096 tokens, so it will always generate a maximum of about 3,000 words.
Transformer-based models like GPT-3 and GPT-4 are designed to be stateless, for good reason! Primarily, this stateless nature significantly enhances scalability and efficiency. Each user request is processed independently, allowing the system to handle numerous queries simultaneously without the complexity of tracking ongoing conversations. Imagine the complexity if every time the model was called, it had to maintain some internal state across millions of users.
Transformer model hidden states are also temporary and exist only for the duration of processing a specific input sequence. Once the model has processed an input and generated an output, these states are reset. They do not persist between different interactions.
Data privacy and security play a role as well. Stateless models do not retain a memory of past interactions, ensuring that sensitive data from one session is never inadvertently exposed to another user. This design choice is particularly relevant in light of incidents like Microsoft's Tay, an AI chatbot that, due to its design to learn from interactions, ended up mimicking inappropriate and offensive language from users. It's just not safe to have models learn from inputs given by random users.
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