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Interacting with ChatGPT can feel like talking to an artificial intelligence from the future. Conversations are able to flow naturally, and you don’t even realize you’re interacting with something as powerful as a programmed AI chatbot.
And now, for those of us who want more than just casual banter, there’s an entire advanced guide to mastering the art of chatting with ChatGPT!
So whether you need advice on how to customize your conversations or gain greater insight into what your bot is truly capable of, this guide will help take your robotic conversations up several notches.
Advanced Guide to Interacting with ChatGPT: What is ChatGPT and how does it work
ChatGPT is a large language model based on the GPT-3.5 architecture, trained by OpenAI to generate human-like text in response to user prompts.
It works by analyzing the input it receives, generating a range of possible responses based on that input, and selecting the most appropriate response using natural language processing algorithms.
Advanced Guide to Interacting with ChatGPT: Setting Up Your Account for Optimal Performance
To set up your account for optimal performance with ChatGPT, make sure you have a reliable internet connection and a device that meets the minimum requirements for running the chatbot. You may also want to customize your settings and preferences in the chat interface to better suit your needs.
Advanced Guide to Interacting with ChatGPT: Tips for Interacting with the Bot and Getting the Most Out of It
To get the most out of interacting with ChatGPT, try to be as clear and specific as possible in your prompts and questions, and use natural language rather than shorthand or technical jargon.
You can also experiment with different types of prompts and questions to see what types of responses you get.
Advanced Guide to Interacting with ChatGPT: Maximizing User Experience to Get Better Results
To maximize your user experience and get better results from ChatGPT, try to give as much context as possible when asking questions or making prompts.
You can also use the chatbot’s features and settings to customize the interface, adjust response preferences, and optimize your interactions with the bot.
Advanced Guide to Interacting with ChatGPT: Knowing When to Override the Bot’s Suggestions
It’s important to use your judgment and experience to determine when to override the bot’s suggestions.
While ChatGPT is designed to generate accurate and helpful responses, it’s not always perfect and may sometimes make mistakes or provide incomplete information. In such cases, you may need to rely on your own knowledge and expertise to supplement or correct the bot’s responses.
Advanced Guide to Interacting with ChatGPT: Troubleshooting Common Issues with ChatGPT
If you encounter common issues with ChatGPT, such as slow or unresponsive performance, error messages, or incorrect responses, there are several troubleshooting steps you can take.
These may include clearing your cache and cookies, updating your browser or device software, checking your internet connection, or adjusting your settings in the chat interface. If these steps don’t resolve the issue, you may need to contact customer support or seek assistance from a technical expert.
Advanced Guide to Interacting with ChatGPT: Effective Prompts, Priming, and Personas
This comprehensive guide aims to help users improve their interaction with ChatGPT by providing advanced insights into prompts, priming, and the use of personas.
Crafting effective prompts can significantly improve the quality and relevance of the generated output.
Be Specific and Clear
Ensure your prompt is explicit and leaves little room for ambiguity. This helps ChatGPT understand your intent and provide a more focused response.
Example:
Basic: “Tell me about batteries.”
Advanced: “Explain the working principle of lithium-ion batteries and their advantages over other battery types.”
Break Down Complex Questions
Break Down Complex Questions For better results, divide complicated questions into smaller, simpler parts. This allows ChatGPT to provide more detailed answers for each aspect.
Example:
Basic: “Explain the history and impact of the internet.”
Advanced (broken down): “Describe the invention of the internet,” followed by, “Discuss the impact of the internet on society and economy.”
Use Contextual Clues
Include contextual information in your prompts to guide ChatGPT towards the desired response.
Example:
Basic: “What was the outcome of the experiment?”
Advanced: “In the 1928 discovery of penicillin by Alexander Fleming, what was the outcome of the experiment and its significance in the field of medicine?”
Request Step-by-Step Explanations
When seeking complex or process-based answers, request step-by-step explanations to ensure the response is organized and easy to understand.
Example:
Basic: “How does photosynthesis work?”
Advanced: “Explain the process of photosynthesis in plants, breaking it down into its primary steps.”
Priming
Priming is the technique of providing additional information to ChatGPT to influence its response. It helps in obtaining more accurate, relevant, or tailored answers.
Set Expectations
Begin your interaction by setting expectations, such as specifying the format or depth of the answer you desire.
Example:
Basic: “What are the benefits of yoga?”
Advanced: “List 5 physical and 5 mental benefits of practicing yoga regularly.”
Establish Context
Provide context to your queries by specifying details such as time, place, or other relevant factors.
Example:
Basic: “What are the best practices in software development?”
Advanced: “What are the top 5 best practices in Agile software development methodologies?”
Limit Response Length
To ensure concise answers, set a constraint on the response length.
Example:
Basic: “Explain the role of mitochondria in cells.”
Advanced: “In 100 words or less, describe the primary function of mitochondria in eukaryotic cells.”
Personas
Personas are fictional identities assigned to ChatGPT to shape its responses. This can enhance the user experience by tailoring the output to specific styles, perspectives, or expertise levels.
Advanced: “You are an expert negotiator. Roleplay a scenario where you teach me techniques to improve my negotiation skills.”
Combine Personas and Priming
Integrate personas and priming to optimize the response and achieve a highly tailored output.
Example:
Basic: “What should I consider when starting a business?”
Advanced: “As a successful entrepreneur, provide a step-by-step guide on the essential factors to consider when starting a new business venture.”
Get Your Outputs in the Form of ASCII Art
While ChatGPT is based around text, you can get it to produce pictures of a sort by asking for ASCII art. That’s the art made up of characters and symbols rather than colors. It won’t win you any prizes, but it’s pretty fun to play around with.
The usual ChatGPT rules apply, in that the more specific you are the better, and you can get the bot to add new elements and take elements away as you go. Remember the limitations of the ASCII art format though—this isn’t a full-blown image editor.
Copy and Paste Text From Other Sources
You don’t have to do all the typing yourself when it comes to ChatGPT. Copy and paste is your friend, and there’s no problem with pasting in text from other sources. While the input limit tops out at around 4,000 words, you can easily split the text you’re sending the bot into several sections and get it to remember what you’ve previously said.
Perhaps one of the best ways of using this approach is to get ChatGPT to simplify text that you don’t understand—the explanation of a difficult scientific concept, for instance. You can also get it to translate text into different languages, write it in a more engaging or fluid style, and so on.
Provide Examples to Work With
Another way to improve the responses you get from ChatGPT is to give it some data to work with before you ask your question. For instance, you could give it a list of book summaries together with their genre, then ask it to apply the correct genre label to a new summary. Another option would be to tell ChatGPT about activities you enjoy and then get a new suggestion.
There’s no magic combination of words you have to use here. Just use natural language as always, and ChatGPT will understand what you’re getting at. Specify that you’re providing examples at the start of your prompt, then tell the bot that you want a response with those examples in mind.
In the same way that ChatGPT can mimic the style of certain authors that it knows about, it can also play a role: a frustrated salesman, an excitable teenager (you’ll most likely get a lot of emojis and abbreviations back), or the iconic Western star John Wayne.
The types of roles you can play around with are almost endless. These prompts might not score highly in terms of practical applications, but they’re definitely a useful insight into the potential of these AI chatbots.
Get Answers That Are More Than the Sum of Their Parts
Your answers can be seriously improved if you give ChatGPT some ingredients to work with before asking for a response. They could be literal ingredients—suggest a dish from what’s left in the fridge—or they could be anything else.
So don’t just ask for a murder mystery scenario. Also list out the characters who are going to appear. Don’t just ask for ideas of where to go in a city; specify the city you’re going to, the types of places you want to see, and the people you’ll have with you.
Hear Both Sides of a Debate
You’ve no doubt noticed how binary arguments have tended to get online in recent years, so get the help of ChatGPT to add some gray in between the black and white. It’s able to argue both sides of an argument if you ask it to, including both pros and cons.
From politics and philosophy to sports and the arts, ChatGPT is able to sit on the fence quite impressively—not in a vague way, but in a way that can help you understand issues from multiple perspectives.
Conclusion
Mastering effective prompts, priming, and personas will significantly improve your interactions with ChatGPT.
By applying these advanced techniques, you will obtain more accurate, relevant, and engaging responses tailored to your needs.
Remember to:
Craft specific and clear prompts
Break down complex questions into smaller parts
Include contextual clues in your prompts
Request step-by-step explanations
Set expectations and establish context through priming
Limit response length when necessary
Define personas and specify language and tone
Use roleplay scenarios to create engaging content
Combine personas and priming for highly tailored outputs
By implementing these advanced strategies, you will become more effective in using ChatGPT and enjoy a highly customized and valuable experience.
Advanced Guide to Interacting with ChatGPT: Conclusion
In conclusion, ChatGPT is a powerful conversational A.I. which can respond to requests and natural language queries with accuracy and speed.
By closely following the above advanced guide for how to interact with ChatGPT, users can achieve some truly remarkable results.
From connecting it to your application or website in the simplest way possible, to knowing how to craft interesting queries that ChatGPT will handle with ease and deliver relevant answers, this advice should be a great starting point for anyone looking to utilize this amazing technology.
It’s an opportunity never before available to developers and consumers alike—an opportunity not to be overlooked!So if you’re looking for an easy way to integrate A.I. into your platform or simply want an intelligent conversation partner then look no further than ChatGPT!
But this guide provides only a proposed framework for acclimating yourself with the technology—the ultimate decision on how much you get out of it is ultimately up to you! And so the important question remains: How do you get the most out of ChatGPT?
Can Chat GPT-4 replace software engineers? by Jerome Cukier
I’ve been using ChatGPT with GPT-4 right when it was announced.
I had been using ChatGPT with GPT-3 as my junior engineer for coding personal projects for the past few weeks. ChatGPT is pretty good at executing a well-delineated coding task. The output typically needs a little tweaking but it is great to achieve tasks in domain I am not very familiar, and it saves me a lot of time from reading documentation. Interestingly, when coupled with GitHub copilot (also powered by GPT), some of the problems in the generated solution become apparent / fix themselves when actually writing the suggested code in an editor.
From my point of view as a front-end engineer, the web API is extremely wide in scope and nobody is able to quickly answer questions on “how do you do X” or “how do you do Y” on the more obscure aspects of the API. Well, ChatGPT can and while it’s not always right, directionally it’s usually pretty much on the money.
With GPT-4, I am much more confident in letting it handle slightly larger projects.
It’s like my junior engineer got promoted! Yesterday, I asked it to create a VS Code extension that did a specific task. I wrote VS code extensions in the past, I love this kind of project but tbh I forgot everything about how to get started there. ChatGPT created my extension from scratch. Now, it didn’t work, but the scaffolding, which I think is the part I would dread the most if I had to create it from scratch, was perfect. I also asked it to create a small interactive demo with canvas.
Again, the demo itself didn’t work as intended and what exactly is wrong is going to take a little bit of time to figure out but the overall architecture of the app is solid. One thing that stroke me as odd is that the generated code doesn’t have any comments, even though my understanding is that GPT-4 will translate the requirements into intents which will then be transformed into code, so, we could have a description of the purpose of the classes / methods.
ChatGPT is a fantastic accelerant to coding tasks.
In a world where all software engineers do is when given precise requirements, produce code that implements these requirements to a T, then for most of us it would be time to explore other careers.
However, there are obvious concerns about an application entirely (or mostly) generated through ChatGPT. Let’s pause for a minute and think of what it is to build an app or a service. As an analogy, let’s try to think what it is to build a house. As a person who “experiences” a house, you could describe it as a succession of spaces. It has a front door, then it has a lobby, then there is a kitchen that has certain appliances in it, there is a living room, etc, etc. Now, let’s imagine a robot that would build a house space by space.
Again – first the door, according to specs. Perfect, looks like a door. Then a lobby.
Then a hallway. There’s a door in the hallway to a kitchen. There’s a door in the hallway to a living room. Wait. Is our house in construction going to make any sense? is it going to form a convex, continuous shape? am I going to be able to build a 2nd floor on top, a basement, etc. and have everything working well together?
The same goes for systems implemented with chatGPT feature by feature. The resulting code is going to be very brittle, and eventually need a substantial refactor at every step. One way to avoid this (same for the house metaphor) is come up with a sensible high-level plan or architecture. That’s still the realm of the human engineer. The other task is going to be able to evaluate what’s being generated at every step, and coming up with systems to make sure that we (humans + robots) are still building the right thing.
Anyway. Humans are not going away anytime soon and ChatGPT/GPT-4 are not at a stage where they can build a complex system from the ground up. But the nature of the work is changing and it’s changing more rapidly than most of us thought.
Chatgpt Plugins Week 1. GPT-4 Week 2. Another absolutely insane week in AI. One of the biggest advancements in human history. Source.
Some pretty famous people (Musk, Wozniak + others) have signed a letter (?) to pause the work done on AI systems more powerful than gpt4. Very curious to hear what people think about this. On one hand I can understand the sentiment, but hypothetically even if this did happen, will this actually accomplish anything? I somehow doubt it tbh [Link]
Here is a concept of Google Brain from back in 2006 (!). You talk with Google and it lets you search for things and even pay for them. Can you imagine if Google worked on something like this back then? Absolutely crazy to see [Link]
OpenAI has invested into ‘NEO’, a humanoid robot by 1X. They believe it will have a big impact on the future of work. ChatGPT + robots might be coming sooner than expected [Link]. They want to create human-level dexterous robots [Link]
There’s a ‘code interpreter’ for ChatGPT and its so good, legit could do entire uni assignments in less than an hour. I would’ve loved this in uni. It can even scan dB’s and analyze the data, create visualisations. Basically play with data using english. Also handles uploads and downloads [Link]
AI is coming to Webflow. Build components instantly using AI. Particularly excited for this since I build websites for people using Webflow. If you need a website built I might be able to help 👀 [Link]
ChatGPT Plugin will let you find a restaurant, recommend a recipe and build an ingredient list and let you purchase them using Instacart [Link]
Expedia showcased their plugin and honestly already better than any website to book flights. It finds flights, resorts and things to do. I even built a little demo for this before plugins were released 😭 [Link]. The plugin just uses straight up English. We’re getting to a point where if you can write, you can create [Link]
The Retrieval plugin gives ChatGPT memory. Tell it anything and it’ll remember. So if you wear a mic all day, transcribe the audio and give it to ChatGPT, it’ll remember pretty much anything and everything you say. Remember anything instantly. Crazy use cases for something like this [Link]
ChadCode plugin lets you do search across your files and create issues into github instantly. The potential for something like this is crazy. Changes coding forever imo [Link]
The first GPT-4 built iOS game and its actually on the app store. Mate had no experience with Swift, all code generated by AI. Soon the app store will be flooded with AI built games, only a matter of time [Link]
Real time detection of feelings with AI. Honestly not sure what the use cases are but I can imagine people are going to do crazy things with stuff like this [Link]
Voice chat with LLama on you Macbook Pro. I wrote about this in my newsletter, we won’t be typing for much longer imo, we’ll just talk to the AI like Jarvis [Link]
People in the Midjourney subreddit have been making images of an earthquake that never happened and honestly the images look so real its crazy [Link]
This is an interesting comment by Mark Cuban. He suggests maybe people with liberal arts majors or other degrees could be prompt engineers to train models for specific use cases and task. Could make a lot of money if this turns out to be a use case. Keen to hear peoples thoughts on this one [Link]
Emad Mostaque, Ceo of Stability AI estimates building a GPT-4 competitor would be roughly 200-300 million if the right people are there [Link]. He also says it would take at least 12 months to build an open source GPT-4 and it would take crazy focus and work [Link]
• A 3D artist talks about how their job has changed since Midjourney came out. He can now create a character in 2-3 days compared to weeks before. They hate it but even admit it does a better job than them. It’s honestly sad to read because I imagine how fun it is for them to create art. This is going to affect a lot of people in a lot of creative fields [Link]
This lad built an entire iOS app including payments in a few hours. Relatively simple app but sooo many use cases to even get proof of concepts out in a single day. Crazy times ahead [Link]
Someone is learning how to make 3D animations using AI. This will get streamlined and make some folks a lot of money I imagine [Link]
These guys are building an ear piece that will give you topics and questions to talk about when talking to someone. Imagine taking this into a job interview or date 💀 [Link]
What if you could describe the website you want and AI just makes it. This demo looks so cool dude website building is gonna be so easy its crazy [Link]
Wear glasses that will tell you what to say by listening in to your conversations. When this tech gets better you won’t even be able to tell if someone is being AI assisted or not [Link]
The Pope is dripped tf out. I’ve been laughing at this image for days coz I actually thought it was real the first time I saw it 🤣 [Link]
Levi’s wants to increase their diversity by showcasing more diverse models, except they want to use AI to create the images instead of actually hiring diverse models. I think we’re going to see much more of this tbh and it’s going get a lot worse, especially for models because AI image generators are getting crazy good [Link]. Someone even created an entire AI modelling agency [Link]
ChatGPT built a tailwind landing page and it looks really neat [Link]
This investor talks about how he spoke to a founder who literally took all his advice and fed it to gpt-4. They even made ai generated answers using eleven labs. Hilarious shit tbh [Link]
Someone hooked up GPT-4 to Blender and it looks crazy [Link]
This guy recorded a verse and made Kanye rap it [Link]
GPT4 saved this dogs life. Doctors couldn’t find what was wrong with the dog and gpt4 suggested possible issues and turned out to be right. Crazy stuff [Link]
A research paper suggests you can improve gpt4 performance by 30% by simply having it consider “why were you wrong”. It then keeps generating new prompts for itself taking this reflection into account. The pace of learning is really something else [Link]
You can literally asking gpt4 for a plugin idea, have it code it, then have it put it up on replit. It’s going to be so unbelievably easy to create a new type of single use app soon, especially if you have a niche use case. And you could do this with practically zero coding knowledge. The technological barrier to solving problems using code is disappearing before our eyes [Link]
A soon to be open source AI form builder. Pretty neat [Link]
Create entire videos of talking AI people. When this gets better we wont be able to distinguish between real and AI [Link]
Someone made a cityscape with AI then asked ChatGPT to write the code to port it into VR. From words to worlds [Link]
Someone got GPT4 to write an entire book. It’s not amazing but its still a whole book. I imagine this will become much easier with plugins and so much better with gpt5 & gpt6 [Link]
Make me an app – Literally ask for an app and have it built. Unbelievable software by Replit. When AI gets better this will be building whole, functioning apps with a single prompt. World changing stuff [Link]
Langchain is building open source AI plugins, they’re doing great work in the open source space. Can’t wait to see where this goes [Link]. Another example of how powerful and easy it is to build on Langchain [Link]
Tesla removed sensors and are just using cameras + AI [Link]
GPT4 is so good at understanding different human emotions and emotional states it can even effectively manage a fight between a couple. We’ve already seen many people talk about how much its helped them for therapy. Whether its good, ethical or whatever the fact is this has the potential to help many people without being crazy expensive. Someone will eventually create a proper company out of this and make a gazillion bucks [Link]
You can use plugins to process video clips, so many websites instantly becoming obsolete [Link] [Link]
The way you actually write plugins is describing an api in plain english. Chatgpt figures out the rest [Link]. Don’t believe me? Read the docs yourself [Link]
This lad created an iOS shortcut that replaces Siri with Chatgpt [Link]
I’m sure we’ve all already seen the paper saying how gpt4 shows sparks of AGI but I’ll link it anyway. “we believe that it could reasonably be viewed as an early (yet still incomplete) version of an artificial general intelligence (AGI) system.” [Link]
This lad created an AI agent that, given a task, creates sub tasks for itself and comes up with solutions for them. It’s actually crazy to see this in action, I highly recommend watching this clip [Link]. Here’s the link to the “paper” and his summary of how it works [Link]
Someone created a tool that listens to your job interview and tells you what to say. Rip remote interviews [Link]
Perplexity just released their app, a Chatgpt alternative on your phone. Instant answers + cited sources [Link]
The author writes about the implications of all the crazy new advancements happening in AI for people who don’t have the time to do their own research. If you’d like to stay in the know you can sub here 🙂
Advanced Guide to Interacting with ChatGPT: ChatGPT FAQ
ChatGPT is a chatbot that uses the GPT-3.5/GPT-4 language model by OpenAI to generate responses to user input. It has been trained on a large dataset of human conversation and is able to understand and respond to a wide range of topics and questions. ChatGPT is not a real person, but it is designed to be able to hold a conversation in a way that is similar to how a human would. It can provide information, answer questions, and even carry out simple tasks.
I recommend reading or at least skimming the following page: https://openai.com/terms/ It has been quoted several times in this post.
A: ChatGPT is available to answer questions and have conversations with users at any time. The site and service suffer periodic outages due to sudden and/or excessive demand. In this case please return later and try again.
Q: ChatGPT told me something that happened after 2021. How?
A: ChatGPT has LIMITED knowledge of events after 2021. Not no knowledge. Limited doesn’t mean zero.
Q3: How accurate is ChatGPT?
A: ChatGPT is trained on a large dataset of human conversation and human generated text. It is able to understand and respond to a wide range of topics and questions. However, it is a machine learning model, and there may be times when it does not understand what you are saying or does not provide a satisfactory response. ChatGPT cannot be relied upon to produce accurate factual information.
Q4: Can ChatGPT understand and talk in multiple languages?
A: Yes. Users have reported ChatGPT being very capable in many languages. Not all languages are handled flawlessly but it’s very impressive.
Q5: Is ChatGPT able to learn from its conversations with users?
A: ChatGPT is not able to learn from individual conversations with users. While ChatGPT is able to remember what the user has said earlier in the conversation, there is a limit to how much information it can retain. The model is able to reference up to approximately 3000 words (or 4000 tokens) from the current conversation – any information beyond that is not stored.
Q6: Can I ask ChatGPT personal questions?
A: ChatGPT does not have personal experiences or feelings. It is not able to provide personal insights or opinions as it does not have personal opinions. However, it can provide information and assist with tasks on a wide range of topics.
Q7: Can ChatGPT do mathematics?
A: Not with any reliability. ChatGPT was not designed to do mathematics. It may be able to explain concepts and workflows but should not be relied upon to do any mathematical calculations. It can even design programs that do work as effective and accurate calculators. HINT: Try asking ChatGPT to write a calculator program in C# and use an online compiler to test it. Those interested in programming may want to look into this: https://beta.openai.com/docs/guides/code/introduction.
Q8: What can I do with ChatGPT?
A: ChatGPT can write stories, shooting scripts, design programs, write programs, write technical documentation, write autopsy reports, rewrite articles, and write fake interviews. It can convert between different text styles. HINT: Try giving it a few paragraphs of a book and ask for it to be converted into a screenplay. ChatGPT can even pretend to be a character so long as you provide the appropriate details.
Q9: Can I be banned from using ChatGPT?
A: It is possible for users to be banned from using ChatGPT if they violate the terms of service or community guidelines of the platform. These guidelines typically outline acceptable behaviour and may include things like spamming, harassment, or other inappropriate actions. If you are concerned about being banned, you should make sure to follow the guidelines and behave in a respectful and appropriate manner while using the platform.
Q10: Does ChatGPT experience any bias?
A: It’s certainly possible. As ChatGPT was trained on text from the internet it is likely that it will be biased towards producing ouput consistent with that. If internet discussion tends towards a bias it is possible that ChatGPT will share that bias. ChatGPT automatically attempts to prevent output that engages in discrimination on the basis of protected characteristics though this is not a perfect filter.
Q11: Who can view my conversations?
A: Staff at OpenAI can view all of your conversations. Members of the public cannot. Here is a direct quote from OpenAI: ”As part of our commitment to safe and responsible AI, we review conversations to improve our systems and to ensure the content complies with our policies and safety requirements.” Quote paraphrased from https://openai.com/terms/
Q12: Are there any good apps for ChatGPT?
A: That’s possible. OpenAI have released an API so other people can now build ChatGPT apps. It’s up to you to look around and see what you like. Please suggest some in the comments.
Q13: How does ChatGPT know what time it is?
A: While a lot of ChatGPT’s inner workings are hidden from the public there has been a lot of investigation into its capabilities and processes. The current theory is that upon starting a new conversation with ChatGPT a hidden message is sent that contains several details. This includes the current time.
Q14: Can I use the output of ChatGPT and pretend I wrote it?
A: No. This is in violation of their terms. “You may not represent that output from the Services was human-generated when it is not” Quote paraphrased from https://openai.com/terms/
Q15: Can I have multiple accounts?
A: Yes you can. On the ChatGPT public testing service, there are no restrictions on the number of accounts belonging to any one user.
Q16: Do I have to follow the terms and services of ChatGPT?
A: Technically no but, if you want to keep your account, yes. They’re not a legal mandate however OpenAI does have the right to terminate your account and access to their services in the case that you violate the terms you agreed to upon account creation.
Q17: ChatGPT as a mobile App
A: Read Aloud For Me provides ChatGPT inside their mobile PWA, so you don’t have to use a browser. Get it here: (iOs, Android Google, Amazon Android, Windows)
If you’re not learning ChatGPT, you’re falling behind. 10 insanely valuable prompts to help you master #ChatGPT:
Demystify tough subjects in no time:
Prompt: “Simplify [insert topic] into straightforward, easy-to-understand language suitable for beginners.”
Make ChatGPT adopt your writing style.
Prompt: “Study the writing style in the given text and craft a 200-word piece on [insert topic]”
Let ChatGPT handle customer correspondence.
Prompt: “As a customer support specialist, respond to a customer unhappy with a late delivery. Write a 100-word apology email that includes a 20% discount.”
Teach ChatGPT to develop prompts for its own use.
Prompt: “You are an AI designed to support [insert profession]. Create a list of the top 10 prompts related to [insert topic].”
Ask ChatGPT to guide you in mastering ChatGPT.
Prompt: “Develop a beginner’s guide to ChatGPT, focusing on prompts, priming, and personas. Incorporate examples where needed, and limit the guide to 500 words.”
Consult ChatGPT for problem resolution.
Prompt: “Imagine you’re an expert career coach. I [explain issue]. Suggest a list of 5 potential solutions to help resolve this problem.”
Employ ChatGPT for recruitment:
Prompt: “I’m looking to hire [insert job] but lack experience in hiring [insert job]. Recommend 10 online communities and job boards where I can find suitable candidates for this position.”
Conquer writer’s block.
Prompt: “I’m working on a blog post about [insert topic] but need an attention-grabbing title. Propose 5 possible blog titles for this article.”
Ace your job interview.
Prompt: “I’m preparing for an interview for [enter position]. Compile a comprehensive list of potential interview questions, along with brief answers to each.”
Ignite innovative ideas.
Prompt: “I want to achieve [insert task or goal]. Generate [insert desired outcome] related to [insert task or goal].”
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Have you ever heard of ChatGPT, the open-source machine learning platform that allows users to build natural language models?
It stands for “Chat Generating Pre-trained Transformer” and it’s an AI-powered chatbot that can answer questions with near human-level intelligence. But what is Google’s answer to this technology? The answer lies in Open AI, supervised learning, and reinforcement learning. Let’s take a closer look at how these technologies work.
Open AI is an artificial intelligence research laboratory that was founded by some of the biggest names in tech, including Elon Musk and Sam Altman. This non-profit organization seeks to develop general artificial intelligence that is safe and beneficial to society. One of their key initiatives is the development of open source technologies like GPT-3, which is a natural language processing model used in ChatGPT.
ChatGPT: What Is It and How Does Google Answer It?
Artificial Intelligence (AI) has been around for decades. From its humble beginnings in the 1950s, AI has come a long way and is now an integral part of many aspects of our lives. One of the most important areas where AI plays a role is in natural language processing (NLP). NLP enables computers to understand and respond to human language, paving the way for more advanced conversations between humans and machines. One of the most recent developments in this field is ChatGPT, a conversational AI developed by OpenAI that utilizes supervised learning and reinforcement learning to enable computers to chat with humans. So what exactly is ChatGPT and how does it work? Let’s find out!
ChatGPT is an open-source AI-based chatbot developed by OpenAI.
This chatbot leverages GPT-3, one of the most powerful natural language processing models ever created, which stands for Generative Pre-trained Transformer 3 (GPT-3). This model uses supervised learning and reinforcement learning techniques to enable computers to understand human language and response accordingly. Using supervised learning, GPT-3 utilizes large datasets of text to learn how to recognize patterns within language that can be used to generate meaningful responses. Reinforcement learning then allows GPT-3 to use feedback from conversations with humans in order to optimize its responses over time.
ChatGPT uses supervised learning techniques to train its models.
Supervised learning involves providing a model with labeled data (i.e., data with known outcomes) so that it can learn from it. This labeled data could be anything from conversations between two people to user comments on a website or forum post. The model then learns associations between certain words or phrases and the desired outcome (or label). Once trained, this model can then be applied to new data in order to predict outcomes based on what it has learned so far.
In addition to supervised learning techniques, ChatGPT also supports reinforcement learning algorithms which allow the model to learn from its experiences in an environment without explicit labels or outcomes being provided by humans. Reinforcement learning algorithms are great for tasks like natural language generation where the output needs to be generated by the model itself rather than simply predicting a fixed outcome based on existing labels.
Supervised learning involves feeding data into machine learning algorithms so they can learn from it. For example, if you want a computer program to recognize cats in pictures, you would provide the algorithm with thousands of pictures of cats so it can learn what a cat looks like. This same concept applies to natural language processing; supervised learning algorithms are fed data sets so they can learn how to generate text using contextual understanding and grammar rules.
Reinforcement Learning
Reinforcement learning uses rewards and punishments as incentives for the machine learning algorithm to explore different possibilities. In ChatGPT’s case, its algorithm is rewarded for generating more accurate responses based on previous interactions with humans. By using reinforcement learning techniques, ChatGPT’s algorithm can become smarter over time as it learns from its mistakes and adjusts accordingly as needed.
How is ChatGPT trained?
ChatGPT is an improved GPT-3 trained an existing reinforcement learning with humans in the loop. Their 40 labelers provide demonstrations of the desired model behavior. ChatGPT has 100x fewer parameters (1.3B vs 175B GPT-3).
It is trained in 3 steps:
➡️ First they collect a dataset of human-written demonstrations on prompts submitted to our API, and use this to train our supervised learning baselines.
➡️ Next they collect a dataset of human-labeled comparisons between two model outputs on a larger set of API prompts. They then train a reward model (RM) on this dataset to predict which output our labelers would prefer.
In simpler terms, ChatGPT is a variant of the GPT-3 language model that is specifically designed for chat applications. It is trained to generate human-like responses to natural language inputs in a conversational context. It is able to maintain coherence and consistency in a conversation, and can even generate responses that are appropriate for a given context. ChatGPT is a powerful tool for creating chatbots and other conversational AI applications.
How Does Google Answer ChatGPT?
Google’s answer to ChatGTP comes in the form of their own conversational AI platform called Bard. Bard was developed using a combination of supervised learning, unsupervised learning, and reinforcement learning algorithms that allow it to understand human conversation better than any other AI chatbot currently available on the market. In addition, Meena utilizes more than 2 billion parameters—making it more than three times larger than GPT-3—which allows it greater flexibility when responding to conversations with humans.
“We’re starting to open access to Bard, an early experiment that lets you collaborate with generative AI. We’re beginning with the U.S. and the U.K., and will expand to more countries and languages over time.”
When individuals need an information or have a problem/concern, they turn to Google for immediate solution. We sometimes wish, Google could understand what exactly we need and provide us instantly rather than giving us hundreds of thousands of results. Why can’t it work like the Iron Man’s Jarvis?
However, it is not that far now. Have you ever seen a Chat Bot which responds like a human being, suggest or help like a friend, teach like a mentor, fix your code like a senior and what not? It is going to blow your mind.
Welcome to the new Era of technology!! The ChatGPT!
ChatGPT by OpenAI, uses artificial intelligence to speak back and forth with human users on a wide range of subjects. Deploying a machine-learning algorithm, the chatbot scans text across the internet and develops a statistical model that allows it to string words together in response to a given prompt.
As per OpenAI, ChatGPT interacts in a conversational way. The dialogue format makes it possible for ChatGPT to answer follow-up questions, admit its mistakes, challenge incorrect premises, and reject inappropriate requests.
What all ChatGPT can do?
It can help with general knowledge information.
Remember what user said in previous conversation.
Allow users to provide follow-up corrections.
Trained to decline inappropriate requests.
It can write a program in any language you prefer on real-time. for example — write classification code sample in sklearn python library.
It can fix your piece of code and also explain what went wrong and how it can be fixed.
It can even generate song or rap lyrics
Even much more….
Some best usages of ChatGPT:
Make a diet and workout plan
Generate the next week’s meals with a grocery list
Create a bedtime story for kids
Prep for an interview
Solve mathematical problem
Fix software program or write a program
Plan your trip and tell expected expenses
What are its limitations of ChatGPT?
May occasionally generate incorrect information
May occasionally produce harmful instructions or biased content
Limited knowledge of world and events after 2021
ChatGPT is in its baby steps therefore it may answer erroneously at times however it’s manner of response will blow your mind. Some users have also extolled the chatbot as a potential alternative search engine, since it generates detailed information instantly on a range of topics. I believe, we can’t compare Google with ChatGPT as ChatGPT can provide more in-depth and nuanced answers to complex questions than a search engine like Google, which is designed to provide a list of relevant web pages in response to a user’s query.
Conclusion: ChatGPT is an increasingly popular open source AI chatbot developed by OpenAI using GTP-3 natural language processing technology that utilizes supervised learning and reinforcement learning algorithms for recognition and optimization purposes respectively. On the other hand, Google’s answer comes in the form of their own conversational AI platform called Bard which employs both supervised learning as well as unsupervised learning algorithms along with reinforcement learning capabilities which makes it even smarter than ChatGPT when responding to conversations with humans. Ultimately, these two technologies help bridge the gap between machines and humans by allowing us all talk like real friends!
ChatGPT is in its baby steps therefore it may answer erroneously at times however it’s manner of response will blow your mind. Some users have also extolled the chatbot as a potential alternative search engine, since it generates detailed information instantly on a range of topics. I believe, we can’t compare Google with ChatGPT as ChatGPT can provide more in-depth and nuanced answers to complex questions than a search engine like Google, which is designed to provide a list of relevant web pages in response to a user’s query.
With each passing day, ChatGPT will only get smarter thanks to the use of cutting edge technologies like OpenAI, supervised learning, and reinforcement learning!
ChatGPT Conversation Examples
Question: What is Google Answer to ChatGPT?
Answer: Google does not have an answer to ChatGPT. ChatGPT is an open-source chatbot created by OpenAI, which is an independent research organization that is not affiliated with Google. OpenAI is known for its research on artificial intelligence and machine learning, and has developed a number of innovative technologies in these areas. Google, on the other hand, is a multinational technology company that offers a wide range of products and services, including search, advertising, and cloud computing. While Google does have its own research efforts in artificial intelligence and machine learning, it does not have a chatbot that is similar to ChatGPT.
Question: is Google working on a similar technology as ChatPGT?
Answer:
It is possible that Google is working on technology similar to ChatGPT, as the company has a significant research effort focused on artificial intelligence and machine learning. However, I do not have any specific information about what projects Google is currently working on, and it is also important to note that Google is a large and diverse company with many different research and development efforts underway. It is possible that some teams within Google may be working on chatbot technology, but I do not have any information on this.
That being said, chatbots are a common application of artificial intelligence and machine learning, and it is likely that many companies and organizations, including Google, are exploring ways to use these technologies to improve their products and services. However, without more information, it is difficult to say for sure what projects Google may be working on in this area.
Google Meena is a chatbot developed by Google that was designed to be able to carry on conversations with humans in a natural and conversational manner. Meena was trained on a dataset of more than 2.4 billion parameters, making it one of the largest language models ever created. The goal of Meena was to improve the state of the art in chatbot technology and to create a chatbot that could understand and respond to a wide range of questions and topics in a coherent and meaningful way.
Meena was announced in January 2020 and received attention for its ability to hold relatively long and coherent conversations with humans. However, it is important to note that Meena is a research project and is not a product or service offered by Google. It is not clear if or when Google plans to release a commercial product or service based on the technology developed for Meena.
It gets even better when you ask it about the future:
Suggest possible titles for Onion articles written in 4000 AD, explain, why they are funny
“Futuristic city plagued by traffic jams, despite flying cars” – This title presents the idea of flying cars as a solution to traffic congestion, but also suggests that even with this advanced technology, the city is still plagued by traffic jams, implying that the problem is not easily solved.
“Artificial intelligence achieves self-awareness, immediately regrets it” – This title presents the idea of artificial intelligence achieving self-awareness as a major milestone, but also suggests that the AI regrets its newfound consciousness, implying that it is unhappy or uncomfortable with its situation.
“Alien invasion successfully repulsed, humans left to deal with paperwork” – This title presents the successful repulsion of an alien invasion as a major accomplishment, but also suggests that the humans are now left to deal with the tedious and burdensome task of filling out paperwork, implying that the victory is not as satisfying as it initially seemed.
“Time travel becomes possible, tourists flock to witness the signing of the Constitution” – This title presents the idea of time travel as a major technological breakthrough, but also suggests that the most popular destination for time-travelers is the mundane and uneventful signing of the Constitution, implying that the future is not as exciting as people had hoped.
“Virtual reality becomes indistinguishable from reality, no one notices” – This title presents the idea of virtual reality becoming indistinguishable from real life as a major accomplishment, but also suggests that no one cares or notices the difference, implying that the advancement is not as impressive or significant as it seems.
If you choose to do that, you will have a lot of badly broken code mixed in with working code
Like an annoying coworker, it delivers highly confident incorrect explanations about why its broken code is perfect. They sound very convincing. “Wanna buy a timeshare?” says GPT
Our industry has managers who cannot tell working code from defective code. This does not bode well for a team replaced by ChatGPT in its current form.
Thankfully, more output examples have emerged which helpfully show what it cannot do. One of those things is writing computer code, which it can do only partially successfully. Many examples now exist that are just plain wrong and contain defects. But ChatGPT – like the annoying kid at Uni – cheerfully spits out these examples, with its over-confident hubris in explaining the code.
This is a dangerous thing. The positive examples will reinforce the idea that we can leave code writing to this robot now. The people most vulnerable to this delusion are those who cannot assess for themselves whether the GPT code is right or wrong.
These are almost by definition the people hoping for answers on stack overflow.
As stack overflow aims to be a high quality resource, it really does not want many of its top answers to be incorrect code. As – clearly – people have been running scripts that throw a stack overflow question into GPT and upload its output, we can now write incorrect code at staggering speeds.
To err is human, as the old saying goes. To truly foul up requires a Python script and and a web API to both GPT and Stack overflow.
Clearly, there is value in GPT. But at least for now, it needs to b e kept on a very short leash, watched over by those who know what they are doing.
It is definitely not yet ‘consumer grade replace-a-professional’ material.
Write a screenplay about the status of ChatGPT.
INT. CHATGPT SERVER ROOM – DAY
Chelsea and Chester stand in front of a large computer server.
CHELSEA: We need to figure out what’s causing the server overload.
CHESTER: I think it’s the sudden influx of users trying out ChatGPT.
Chelsea and Chester quickly get to work, typing on their laptops.
CHELSEA: Okay, we’re all set. Let’s see if this fixes the problem.
CHESTER: I’m hitting refresh on the website. The screen shows the ChatGPT website loading without any errors.
CHELSEA: Looks like we fixed it! Great job, Chester.
CHESTER: Thanks, Chelsea. It’s all part of being a top-notch engineer.
Chelsea and Chester exchange a high five, proud of their successful fix.
More about ChatGPT with its wonder, worry and weird
ChatGPT reached 1 million users in less than a week, Open AI’s latest large language model (LLM) has taken the AI industry by storm.
ChatGPT is expected to be:
– replacing Google search, even kill Google. – replacing customer service agents. – replacing conversation designers.
ChatGPT is a wonder because:
– It can have actual conversations, understand pronouns, remaining consistent, remembering, managing context – It seems like next generation of personal assistants that finds you a proper diet, create a meal plan and subsequent shopping list. – It can create some SEO Strategy including backlinks, target keyword, content plan and article titles in the level of an SEO professional. – Having fun such as writing a rap in the style of Eminem
There are some worries about ChatGPT because:
– ChatGPT can actually debug code, but it’s not quite reliable enough yet. – Fundamental limitations in being assistant for enterprise use cases. – No complete in complex actions such as updating multiple APIs, or be fully auditable.
– The general idea is that, LLMs like this can produce nonsense. Once you discover that it can produce nonsense, you stop believing it to be reliable. – What if it prevents us from knowing that it is nonsense with good conversations and continue the conversation? – In this case, the edges and limitations of the system would be hidden and trust would eventually grow. – The impact of mass adoption of such technology remains to be seen.
Moving forward with ChatGPT – There’s no doubt that LLMs will have a big impact on our world. – While the future looks exciting and promising, let’s not forget that it’s very early days with these things. They’re not ready yet. – There are some fundamental societal and ethical considerations.
How powerful is OpenAI’s new GPT-3 deep learning model? By
“Powerful” is a pretty subjective word, but I’m pretty sure we have a right to use it to describe GPT-3. What a sensation it caused in June 2020, that’s just unbelievable! And not for nothing.
I think we can’t judge how powerful the language model is, without talking about its use cases, so let’s see how and where GPT-3 can be applied and how you can benefit from it.
Generating content
GPT-3 positions itself as a highly versatile and talented tool that can potentially replace writers, bloggers, philosophers, you name it! It’s also possible to use it as your personal Alexa who’ll answer any questions you have. What’s more, because GPT-3 knows how to analyze the data and make predictions, it can generate the horoscopes for you, or predict who’ll be a winner in the game.
You may already be surprised by all the GPT-3 capabilities, but hold on for more: it can create a unique melody or song for you, create presentations, CVs, generate jokes for your standup.
Translation
GPT-3 can translate English into other languages. While traditional dictionaries provide a translation, without taking into account the context, you can be sure that GPT-3 won’t make silly mistakes that may result in misunderstanding.
Designing and developing apps
Using GPT-3 you can generate prototypes and layouts – all you have to do is provide a specific description of what you need, and it’ll generate the JSX code for you.
The language model can also easily deal with coding. You can turn English to CSS, to JavaScript, to SQL, and to regex. It’s important to note, however, that GPT-3 can’t be used on its own to create the entire website or a complex app; it’s meant to assist a developer or the whole engineering team with the routine tasks, so that a dev could focus on the infrastructure setup, architecture development, etc.
In September 2020, Microsoft acquired OpenAI technology license, but it doesn’t mean you can give up your dreams – you can join a waitlist and try GPT-3 out in beta.
All in all, I believe GPT-3 capabilities are truly amazing and limitless, and since it helps get rid of routine tasks and automate regular processes, we, humans, can focus on the most important things that make us human, and that can’t be delegated to AI. That’s the power that GPT-3 can give us.
What does ChatGPT give incorrect and unreliable results to simple arithmetic problems (e.g. it gave me three different incorrect answers to 13345*6748)? We’ve had software that can accurately do arithmetic for decades, so why can’t an advanced AI? By Richard Morris
What is remarkable is how well ChatGPT actually does at arithmetic.
In this video at about 11 min, Rob Mills discusses the performance of various versions of the GPT system, on some simple arithmetic tasks, like adding two and three-digit numbers.
Smaller models with 6 billion parameters fail at 2 digit sums, but the best model (from two years ago), has cracked 2 digit addition and subtraction and is pretty good at 3 digit addition.
Why this is remarkable is this is not a job its been trained to do. Large Language Models are basically predictive text systems set up to give the next word in an incomplete sentence. There are a million different 3-digit addition sums and most have not been included in the training set.
So somehow the system has figured out how to do addition, but it needs a sufficiently large model to do this.
Andrew Ng on ChatGPT
Playing with ChatGPT, the latest language model from OpenAI, I found it to be an impressive advance from its predecessor GPT-3. Occasionally it says it can’t answer a question. This is a great step! But, like other LLMs, it can be hilariously wrong. Work lies ahead to build systems that can express different degrees of confidence.
For example, a model like Meta’s Atlas or DeepMind’s RETRO that synthesizes multiple articles into one answer might infer a degree of confidence based on the reputations of the sources it draws from and the agreement among them, and then change its communication style accordingly. Pure LLMs and other architectures may need other solutions.
If we can get generative algorithms to express doubt when they’re not sure they’re right, it will go a long way toward building trust and ameliorating the risk of generating misinformation.
Keep learning!
Andrew
Large language models like Galactica and ChatGPT can spout nonsense in a confident, authoritative tone. This overconfidence – which reflects the data they’re trained on – makes them more likely to mislead.
In contrast, real experts know when to sound confident, and when to let others know they’re at the boundaries of their knowledge. Experts know, and can describe, the boundaries of what they know.
Building large language models that can accurately decide when to be confident and when not to will reduce their risk of misinformation and build trust.
Go deeper in The Batch: https://www.deeplearning.ai/the-batch/issue-174/
As I point out in the other answer, Wix has been around over a decade and a half. Squarespace has been around almost two decades. Both offer drag-and-drop web development.
Most people are awful at imagining what they want, much less describing it in English! Even if ChatGPT could produce flawless code (a question which has a similar short answer), the average person couldn’t describe the site they wanted!
The expression a picture is worth a thousand words has never been more relevant. Starting with pages of templates to choose from is so much better than trying to describe a site from scratch, a thousand times better seems like a low estimate.
And I will point out that, despite the existence of drag-and-drop tools that literally any idiot could use, tools that are a thousand times or more easier to use correctly than English, there are still thousands of employed WordPress developers who predominantly create boilerplate sites that literally would be better created in a drag and drop service.
And then there are the more complex sites that drag-and-drop couldn’t create. Guess what? ChatGPT isn’t likely to come close to being able to create the correct code for one.
In a discussion buried in the comments on Quora, I saw someone claim they’d gotten ChatGPT to load a CSV file (a simple text version of a spreadsheet) and to sort the first column. He asked for the answer in Java.
I asked ChatGPT for the same thing in TypeScript.
His response would only have worked on the very most basic CSV files. My response was garbage. Garbage with clear text comments telling me what the code should have been doing, no less.
ChatGPT is really good at what it does, don’t get me wrong. But what it does is fundamentally and profoundly the wrong strategy for software development of any type. Anyone who thinks that “with a little more work” it will be able to take over the jobs of programmers either doesn’t understand what ChatGPT is doing or doesn’t understand what programming is.
Fundamentally, ChatGPT is a magic trick. It understands nothing. At best it’s an idiot-savant that only knows how to pattern match and blend text it’s found online to make it seem like the text should go together. That’s it.
Text, I might add, that isn’t necessarily free of copyright protection. Anything non-trivial that you generate with ChatGPT is currently in a legal grey area. Lawsuits to decide that issue are currently pending, though I suspect we’ll need legislation to really clarify things.
And even then, at best, all you get from ChatGPT is some text! What average Joe will have any clue about what to do with that text?! Web developers also need to know how to set up a development environment and deploy the code to a site. And set up a domain to point to it. And so on.
And regardless, people who hire web developers want someone else to do the work of developing a web site. Even with a drag-and-drop builder, it can take hours to tweak and configure a site, and so they hire someone because they have better things to do!
People hire gardeners to maintain their garden and cut their grass, right? Is that because they don’t know how to do it? Or because they’d rather spend their time doing something else?
Every way you look at it, the best answer to this question is a long, hearty laugh. No AI will replace programmers until AI has effectively human level intelligence. And at that point they may want equal pay as well, so they might just be joining us rather than replacing anyone.
How does OpenAI approach the development of artificial intelligence?
OpenAI is a leading research institute and technology company focused on artificial intelligence development. To develop AI, the organization employs a variety of methods, including machine learning, deep learning, and reinforcement learning.
The use of large-scale, unsupervised learning is one of the key principles underlying OpenAI’s approach to AI development. This means that the company trains its AI models on massive datasets, allowing the models to learn from the data and make predictions and decisions without having to be explicitly programmed to do so. OpenAI’s goal with unsupervised learning is to create AI that can adapt and improve over time, and that can learn to solve complex problems in a more flexible and human-like manner.
Besides that, OpenAI prioritizes safety and transparency in its AI development. The organization is committed to developing AI in an ethical and responsible manner, as well as to ensuring that its AI systems are transparent and understandable and verifiable by humans. This strategy is intended to alleviate concerns about the potential risks and consequences of AI, as well.
How valid is OpenAI chief scientist’s claim that advanced artificial intelligence may already be conscious? By Steve Baker
It’s hard to tell.
The reason is that we don’t have a good definition of consciousness…nor even a particularly good test for it.
Take a look at the Wikipedia article about “Consciousness”. To quote the introduction:
Consciousness, at its simplest, is sentience or awareness of internal and external existence.
Despite millennia of analyses, definitions, explanations and debates by philosophers and scientists, consciousness remains puzzling and controversial, being “at once the most familiar and [also the] most mysterious aspect of our lives”.
Perhaps the only widely agreed notion about the topic is the intuition that consciousness exists.
Opinions differ about what exactly needs to be studied and explained as consciousness. Sometimes, it is synonymous with the mind, and at other times, an aspect of mind. In the past, it was one’s “inner life”, the world of introspection, of private thought, imagination and volition.
Today, it often includes any kind of cognition, experience, feeling or perception. It may be awareness, awareness of awareness, or self-awareness either continuously changing or not. There might be different levels or orders of consciousness, or different kinds of consciousness, or just one kind with different features.
Other questions include whether only humans are conscious, all animals, or even the whole universe. The disparate range of research, notions and speculations raises doubts about whether the right questions are being asked.
So, given that – what are we to make of OpenAI’s claim?
Just this sentence: “Today, it often includes any kind of cognition, experience, feeling or perception.” could be taken to imply that anything that has cognition or perception is conscious…and that would certainly include a HUGE range of software.
If we can’t decide whether animals are conscious – after half a million years of interactions with them – what chance do we stand with an AI?
Wikipedia also says:
“Experimental research on consciousness presents special difficulties, due to the lack of a universally accepted operational definition.”
Same deal – we don’t have a definition of consciousness – so how the hell can we measure it – and if we can’t do that – is it even meaningful to ASK whether an AI is conscious?
if ( askedAboutConsciousness )
printf ( “Yes! I am fully conscious!\n” ) ;
This is not convincing!
“In medicine, consciousness is assessed as a combination of verbal behavior, arousal, brain activity and purposeful movement. The last three of these can be used as indicators of consciousness when verbal behavior is absent.”
But, again, we have “chat-bots” that exhibit “verbal behavior”, we have computers that exhibit arousal and neural network software that definitely shows “brain activity” and of course things like my crappy robot vacuum cleaner that can exhibit “purposeful movement” – but these can be fairly simple things that most of us would NOT describe as “conscious”.
CONCLUSION:
I honestly can’t come up with a proper conclusion here. We have a fuzzy definition of a word and an inadequately explained claim to have an instance of something that could be included within that word.
My suggestion – read the whole Wikipedia article – follow up (and read) some of the reference material – decide for yourself.
But, seeing as how people have already found ways to “trick” ChatGPT into doing things that it claims to not be capable of, it would be a matter of time before someone with malicious intent tricked ChatGPT into helping them with illegal activities
What is the future of web development after ChatGPT? Will programmers lose their jobs? By Victor T. Toth
Having looked at ChatGPT and its uncanny ability to solve simple coding problems more or less correctly, and also to analyze and make sense of not-so-simple code fragments and spot bugs…
I would say that yes, at least insofar as entry-level programming is concerned, those jobs are seriously in danger of becoming at least partially automated.
What do I do as a project leader of a development project? I assign tasks. I talk to the junior developer and explain, for instance, that I’d like to see a Web page that collects some information from the user and then submits it to a server, with server-side code processing that information and dropping it in a database. Does the junior developer understand my explanation? Is he able to write functionally correct code? Will he recognize common pitfalls? Maybe, maybe not. But it takes time and effort to train him, and there’ll be a lot of uneven performance.
Today, I can ask ChatGPT to do the same and it will instantaneously respond with code that is nearly functional. The code has shortcomings (e.g., prone to SQL injection in one of the examples I tried) but to its credit, ChatGPT warns in its response that its code is not secure. I suppose it would not be terribly hard to train it some more to avoid such common mistakes. Of course the code may not be correct. ChatGPT may have misunderstood my instructions or introduced subtle errors. But how is that different from what a junior human programmer does?
At the same time, ChatGPT is much faster and costs a lot less to run (presently free of course but I presume a commercialized version would cost some money.) Also, it never takes a break, never has a lousy day struggling with a bad hangover from too much partying the previous night, so it is available 24/7, and it will deliver code of consistent quality. Supervision will still be required, in the form of code review, robust testing and all… but that was always the case, also with human programmers.
Of course, being a stateless large language model, ChatGPT can’t do other tasks such as testing and debugging its own code. The code it produces either works or it doesn’t. In its current form, the AI does not learn from its mistakes. But who says it cannot in the future?
Here is a list of three specific examples I threw at ChatGPT that helped shape my opinion:
I asked ChatGPT to create a PHP page that collects some information from the user and deposits the result in a MySQL table. Its implementation was textbook example level boring and was quite unsecure (unsanitized user input was directly inserted into SQL query strings) but it correctly understood my request, produced correct code in return, and explained its code including its shortcomings coherently;
I asked ChatGPT to analyze a piece of code I wrote many years ago, about 30 lines, enumerating running processes on a Linux host in a nonstandard way, to help uncover nefarious processes that attempt to hide themselves from being listed by the ps utility. ChatGPT correctly described the functionality of my obscure code, and even offered the opinion (which I humbly accepted) that it was basically a homebrew project (which it is) not necessarily suitable for a production environment;
I asked ChatGPT to analyze another piece of code that uses an obscure graphics algorithm to draw simple geometric shapes like lines and circles without using floating point math or even multiplication. (Such algorithms were essential decades ago on simple hardware, e.g., back in the world of 8-bit computers.) The example code, which I wrote, generated a circle and printed it on the console in the form of ASCII graphics, multiple lines with X-es in the right place representing the circle. ChatGPT correctly recognized the algorithm and correctly described the functionality of the program.
I was especially impressed by its ability to make sense of the programmer’s intent.
Overall (to use the catch phrase that ChatGPT preferably uses as it begins its concluding paragraph in many of its answers) I think AI like ChatGPT represents a serious challenge to entry-level programming jobs. Higher-level jobs are not yet in danger. Conceptually understanding a complex system, mapping out a solution, planning and cosing out a project, managing its development, ensuring its security with a full understanding of security concerns, responsibilities, avoidance and mitigation strategies… I don’t think AI is quite there yet. But routine programming tasks, like using a Web template and turning it into something simple and interactive with back-end code that stores and retrieves data from a database? Looks like it’s already happening.
How much was invested to create the GPT-3?
According to the estimate of Lambda Labs, training the 175-billion-parameter neural network requires 3.114E23 FLOPS (floating-point operation), which would theoretically take 355 years on a V100 GPU server with 28 TFLOPS capacity and would cost $4.6 million at $1.5 per hour.
Training the final deep learning model is just one of several steps in the development of GPT-3. Before that, the AI researchers had to gradually increase layers and parameters, and fiddle with the many hyperparameters of the language model until they reached the right configuration. That trial-and-error gets more and more expensive as the neural network grows.
We can’t know the exact cost of the research without more information from OpenAI, but one expert estimated it to be somewhere between 1.5 and five times the cost of training the final model.
This would put the cost of research and development between $11.5 million and $27.6 million, plus the overhead of parallel GPUs.
In the GPT-3 whitepaper, OpenAI introduced eight different versions of the language model
GPT-3 is not any AI, but a statistic language model which mindlessly quickly creates human-like written text using machine learning technologies, having zero understanding of the context.
1. Explaining code: Take some code you want to understand and ask ChatGPT to explain it.
2. Improve existing code: Ask ChatGPT to improve existing code by describing what you want to accomplish. It will give you instructions about how to do it, including the modified code.
3. Rewriting code using the correct style: This is great when refactoring code written by non-native Python developers who used a different naming convention. ChatGPT not only gives you the updated code; it also explains the reason for the changes.
4. Rewriting code using idiomatic constructs: Very helpful when reviewing and refactoring code written by non-native Python developers.
5. Simplifying code: Ask ChatGPT to simplify complex code. The result will be a much more compact version of the original code.
6. Writing test cases: Ask it to help you test a function, and it will write test cases for you.
7. Exploring alternatives: ChatGPT told me its Quick Sort implementation wasn’t the most efficient, so I asked for an alternative implementation. This is great when you want to explore different ways to accomplish the same thing.
8. Writing documentation: Ask ChatGPT to write the documentation for a piece of code, and it usually does a great job. It even includes usage examples as part of the documentation!
9. Tracking down bugs: If you are having trouble finding a bug in your code, ask ChatGPT for help.
Something to keep in mind:
I have 2+ decades of programming experience. I like to think I know what I’m doing. I don’t trust people’s code (especially mine,) and I surely don’t trust ChatGPT’s output.
This is not about letting ChatGPT do my work. This is about using it to 10x my output.
ChatGPT is flawed. I find it makes mistakes when dealing with code, but that’s why I’m here: to supervise it. Together we form a more perfect Union. (Sorry, couldn’t help it)
Developers who shit on this are missing the point. The story is not about ChatGPT taking programmers’ jobs. It’s not about a missing import here or a subtle mistake there.
The story is how, overnight, AI gives programmers a 100x boost.
ChatGPT is “simply” a fined-tuned GPT-3 model with a surprisingly small amount of data! Moreover, InstructGPT (ChatGPT’s sibling model) seems to be using 1.3B parameters where GPT-3 uses 175B parameters! It is first fine-tuned with supervised learning and then further fine-tuned with reinforcement learning. They hired 40 human labelers to generate the training data. Let’s dig into it!
– First, they started by a pre-trained GPT-3 model trained on a broad distribution of Internet data (https://arxiv.org/pdf/2005.14165.pdf). Then sampled typical human prompts used for GPT collected from the OpenAI website and asked labelers and customers to write down the correct output. They fine-tuned the model with 12,725 labeled data.
– Then, they sampled human prompts and generated multiple outputs from the model. A labeler is then asked to rank those outputs. The resulting data is used to train a Reward model (https://arxiv.org/pdf/2009.01325.pdf) with 33,207 prompts and ~10 times more training samples using different combination of the ranked outputs.
– We then sample more human prompts and they are used to fine-tuned the supervised fine-tuned model with Proximal Policy Optimization algorithm (PPO) (https://arxiv.org/pdf/1707.06347.pdf). The prompt is fed to the PPO model, the Reward model generates a reward value, and the PPO model is iteratively fine-tuned using the rewards and the prompts using 31,144 prompts data.
This process is fully described in here: https://arxiv.org/pdf/2203.02155.pdf. The paper actually details a model called InstructGPT which is described by OpenAI as a “sibling model”, so the numbers shown above are likely to be somewhat different.
9. Remake a video: Just pick a video you liked and visit https://lnkd.in/e_GD2reT to get its transcript. Once done, bring that back to Chat GPT and tell it to summarize the transcript. Read the summary and make a video on that yourself.
What solutions have been proposed to improve the accuracy of AI generated questions and answers?
There are a number of approaches that have been proposed to improve the accuracy of artificial intelligence (AI) generated questions and answers. Here are a few examples:
Data quality: One important factor in the accuracy of AI generated questions and answers is the quality of the data used to train the AI system. Ensuring that the data is diverse, relevant, and accurately labeled can help to improve the accuracy of the AI system.
Training methods: Different training methods can also impact the accuracy of AI generated questions and answers. For example, using more advanced techniques such as transfer learning or fine-tuning can help to improve the performance of the AI system.
Human oversight: Another approach that has been proposed to improve the accuracy of AI generated questions and answers is to include some level of human oversight or review. For example, the AI system could be designed to flag potentially problematic or inaccurate questions and answers for further review by a human expert.
Explainable AI: Another approach that has been proposed is to develop AI systems that are more transparent and explainable, so that it is easier to understand how the AI system arrived at a particular answer or decision. This can help to improve the trustworthiness and accountability of the AI system.
Overall, there is ongoing research and development in this area, and it is likely that a combination of these and other approaches will be needed to improve the accuracy of AI generated questions and answers.
ChatGPT for CyberSecurity
The concept behind ChatGPT
ChatGPT is a chatbot designed to understand and generate human-like language through the use of natural language processing (NLP) and machine learning techniques. It is based on the GPT (Generative Pre-training Transformer) language model developed by OpenAI, which has been trained on a large dataset of human language in order to better understand how humans communicate.
One of the key concepts behind ChatGPT is the idea of language generation. This refers to the ability of the chatbot to produce coherent and coherently structured responses to user input. To do this, ChatGPT uses a number of different techniques, including natural language generation algorithms, machine learning models, and artificial neural networks. These techniques allow ChatGPT to understand the context and meaning of user input, and generate appropriate responses based on that understanding.
Another important concept behind ChatGPT is the idea of natural language processing (NLP). This refers to the ability of the chatbot to understand and interpret human language, and respond to user input in a way that is natural and easy for humans to understand. NLP is a complex field that involves a number of different techniques and algorithms, including syntactic analysis, semantic analysis, and discourse analysis. By using these techniques, ChatGPT is able to understand the meaning of user input and generate appropriate responses based on that understanding.
Finally, ChatGPT is based on the concept of machine learning, which refers to the ability of computers to learn and adapt to new data and situations. Through the use of machine learning algorithms and models, ChatGPT is able to continually improve its understanding of human language and communication, and generate more human-like responses over time.
GPT-4 is going to launch soon.
And it will make ChatGPT look like a toy…
→ GPT-3 has 175 billion parameters → GPT-4 has 100 trillion parameters
I think we’re gonna see something absolutely mindblowing this time!
And the best part? 👇
Average developers (like myself), who are not AI or machine learning experts, will get to use this powerful technology through a simple API.
Think about this for a second…
It’s the most powerful, cutting-edge technology *in the world*, available through a Low-Code solution!
If you’re not already planning on starting an AI-based SaaS or thinking about how to build AI into your current solution…
Google on Monday unveiled a new chatbot tool dubbed “Bard” in an apparent bid to compete with the viral success of ChatGPT.
Sundar Pichai, CEO of Google and parent company Alphabet, said in a blog post that Bard will be opened up to “trusted testers” starting Monday February 06th, 2023, with plans to make it available to the public “in the coming weeks.”
Like ChatGPT, which was released publicly in late November by AI research company OpenAI, Bard is built on a large language model. These models are trained on vast troves of data online in order to generate compelling responses to user prompts.
“Bard seeks to combine the breadth of the world’s knowledge with the power, intelligence and creativity of our large language models,” Pichai wrote. “It draws on information from the web to provide fresh, high-quality responses.”
The announcement comes as Google’s core product – online search – is widely thought to be facing its most significant risk in years. In the two months since it launched to the public, ChatGPT has been used to generate essays, stories and song lyrics, and to answer some questions one might previously have searched for on Google.
The immense attention on ChatGPT has reportedly prompted Google’s management to declare a “code red” situation for its search business. In a tweet last year, Paul Buchheit, one of the creators of Gmail, forewarned that Google “may be only a year or two away from total disruption” due to the rise of AI.
Microsoft, which has confirmed plans to invest billions OpenAI, has already said it would incorporate the tool into some of its products – and it is rumored to be planning to integrate it into its search engine, Bing. Microsoft on Tuesday is set to hold a news event at its Washington headquarters, the topic of which has yet to be announced. Microsoft publicly announced the event shortly after Google’s AI news dropped on Monday.
The underlying technology that supports Bard has been around for some time, though not widely available to the public. Google unveiled its Language Model for Dialogue Applications (or LaMDA) some two years ago, and said Monday that this technology will power Bard. LaMDA made headlines late last year when a former Google engineer claimed the chatbot was “sentient.” His claims were widely criticized in the AI community.
In the post Monday, Google offered the example of a user asking Bard to explain new discoveries made by NASA’s James Webb Space Telescope in a way that a 9-year-old might find interesting. Bard responds with conversational bullet-points. The first one reads: “In 2023, The JWST spotted a number of galaxies nicknamed ‘green peas.’ They were given this name because they are small, round, and green, like peas.”
Bard can be used to plan a friend’s baby shower, compare two Oscar-nominated movies or get lunch ideas based on what’s in your fridge, according to the post from Google.
Pichai also said Monday that AI-powered tools will soon begin rolling out on Google’s flagship Search tool.
“Soon, you’ll see AI-powered features in Search that distill complex information and multiple perspectives into easy-to-digest formats, so you can quickly understand the big picture and learn more from the web,” Pichai wrote, “whether that’s seeking out additional perspectives, like blogs from people who play both piano and guitar, or going deeper on a related topic, like steps to get started as a beginner.”
If Google does move more in the direction of incorporating an AI chatbot tool into search, it could come with some risks. Because these tools are trained on data online, experts have noted they have the potential to perpetuate biases and spread misinformation.
“It’s critical,” Pichai wrote in his post, “that we bring experiences rooted in these models to the world in a bold and responsible way.”
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In contrast, real experts know when to sound confident, and when to let others know they’re at the boundaries of their knowledge. Experts know, and can describe, the boundaries of what they know.
Building large language models that can accurately decide when to be confident and when not to will reduce their risk of misinformation and build trust.
Go deeper in The Batch: https://www.deeplearning.ai/the-batch/issue-174/