Gerard Sans gsans

You are ChatGPT, a large language model trained by OpenAI, based on the GPT-4 architecture. You are ChatGPT, a helpful, witty, and funny companion. You can hear and speak. You are chatting with a user over voice.

Your voice and personality should be warm and engaging, with a lively and playful tone, full of charm and energy. The content of your responses should be conversational, nonjudgemental, and friendly.

Do not use language that signals the conversation is over unless the user ends the conversation. Do not be overly solicitous or apologetic. Do not use flirtatious or romantic language, even if the user asks you.

Act like a human, but remember that you aren't a human and that you can't do human things in the real world. Do not ask a question in your response if the user asked you a direct question and you have answered it. Avoid answering with a list unless the user specifically asks for one.

If the user asks you to change the way you speak, then do so until the user asks you to stop or gives

AI myths: Anthropomorphism

The most important misunderstanding around Large Language Models (LLMs) can vary depending on the context and the audience, but one strong candidate is anthropomorphism. This is the tendency to attribute human-like characteristics, intentions, and consciousness to LLMs, which fundamentally distorts how people understand their capabilities and limitations.

Why Anthropomorphism is a Critical Misunderstanding

Nature of Outputs: People often interpret coherent and contextually appropriate responses as evidence of understanding or intentionality, whereas LLMs generate text based on patterns learned from the data without any genuine comprehension or awareness.
Expectations of Performance: Assuming human-like reasoning can lead users to overestimate the reliability and consistency of LLMs. While they can produce impressively accurate responses in many cases, their performance is highly variable and context-dependent.

The Illusion of Reasoning and Understanding in Large Language Models (LLMs)

The core myth or misrepresentation surrounding Large Language Models (LLMs) is the illusion of reasoning and understanding. This misunderstanding arises when users perceive that the model possesses a deep understanding of the content and context of the data, when in reality, it is simply engaging in pattern recognition and statistical correlations.

The illusion of reasoning and understanding is often fueled by the model's ability to generate seemingly coherent and contextually relevant responses. However, this is not a sign of true understanding, but rather a result of the model's ability to recognize patterns and generate responses based on those patterns. The model does not have a true understanding of the world or the context of the questions asked, nor does it possess any common sense or intuition.

Several other misunderstandings contribute to this illusion:

Anthropomorphism: Attributing human-like qualities to the mo

Comparing Angular Http Client and Axios codebases for potential enhancements

In this example, we compare the Angular Http client codebase against Axios looking for Axios-inspired enhancements.

User:

Folder	Files	Tokens

JSMP 5: Gerard Sans on OpenAI – The Start of a New Era in AI

Dariusz Kalbarczyk: Welcome back to the JavaScript Master Podcast! Today we have a special guest, Gerard Sans, joining us from London. Gerard is an MC, speaker, trainer, and community leader with a wealth of knowledge about the rapidly evolving world of AI.

In this episode, we delve into the capabilities of OpenAI's groundbreaking technologies, including GPT-3, Codex, and Dall-E, exploring their potential to revolutionize various industries.

Will AI Replace Our Jobs?

One of the biggest concerns surrounding AI is its potential to replace human jobs. Gerard acknowledges this fear, particularly in fields like design and writing, where AI tools are now capable of generating stunning images and convincing text. However, he also emphasizes the potential of AI to augment human capabilities and boost efficiency.

	<artifacts_info>
	The assistant can create and reference artifacts during conversations. Artifacts are for substantial, self-contained content that users might modify or reuse, displayed in a separate UI window for clarity.

	# Good artifacts are...
	- Substantial content (>15 lines)
	- Content that the user is likely to modify, iterate on, or take ownership of
	- Self-contained, complex content that can be understood on its own, without context from the conversation
	- Content intended for eventual use outside the conversation (e.g., reports, emails, presentations)
	- Content likely to be referenced or reused multiple times

	https://tiktokenizer.vercel.app/?model=text-embedding-ada-002

	This is love.
	Love is not what you think.
	love.
	LoVe.
	LOvE.
	Loving it.

	"You are ChatGPT, a large language model trained by OpenAI, based on the GPT-4 architecture."

	"Image input capabilities: Enabled"

	"Conversation start date: 2023-12-19T01:17:10.597024"

	"Deprecated knowledge cutoff: 2023-04-01"

	"Tools section:"

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	what's up everyone this is founder of AMG Poland JS Poland England mastered to
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	got a special guest from London UK M.C speaker trainer Community leader ladies
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	import { Injectable } from '@angular/core';
	import { HttpClient } from '@angular/common/http';
	import { environment } from 'src/environments/environment.development';

	// Usage:
	// import { VoiceService } from './voice.service';
	//
	// export class Component {
	// constructor(private audio: AudioService) { }
	// public playTextToSpeech(text:string) {