Category: Chat

How ChatGPT really works and will it change the field of IT and AI? — a deep dive

Unless you have been living under a rock for the last few months, you have probably heard about a new model from OpenAI called ChatGTP. The model has demonstrated extraordinary abilities in generating both code and textual responses that are not only coherent but most often factually correct and in line with the current human knowledge.

There are many articles describing the possible use cases of ChatGPT, however, they rarely go into the details about how the model works or discuss its border implications.

The goal of this article is to demystify ChatGPT showing how it works and why is it that effective, describing what are language modes, why are they so popular and what is their problem. As everything is explained from scratch but extensively I hope you will find it interesting whether you are NLP Expert or just want to know what all the fuss is about.

The second part of the article discusses the possible use cases of ChatGPT and their impact on respected industries. We will discuss how models such as ChatGPT will affect the work of software engineers and ML engineers. We will answer the question “Will you lose your job?” and we will also explain how GPT can create jobs.

Table of contents:

· What is ChatGPT?
· Why is ChatGPT so effective?
· What is ChatGPT capable of?
· Will ChatGPT replace software engineers?
· Will ChatGPT replace ML Engineers?
· Will ChatGPT replace your job?
· Will ChatGPT create jobs?
· Conclusions

What is ChatGPT?

In the first part of the article, we will try to demystify what ChatGPT is and discuss why it is so effective. Unfortunately, the model’s release wasn’t accompanied by a research paper, and its only official description can be found on the OpenAI blog.

Language Models

As we can read in the OpenAI article ChatGPT is a language model optimized for dialogue.

But what is a language model?

Formally, a language model is a probability distribution over words or word sequences. So given words in a sequence, the model tells us how probable the sequence is. In practice, we can ask a language model to continue the sequence with the word it thinks is the most probable.

There are many approaches to language modelling, we can for example ask the model to fill in the words in the middle of a sentence (as in the BERT model) or predict which words have been swapped for fake ones (as in the ELECTRA model). The most simple approach and the one used in the GPT family of models is in fact asking the model to predict the next word in a sequence. This is called casual language modelling. An input sequence that we feed to the model and ask it to complete is called a prompt.

So ChatGPT is nothing more and nothing less than a probabilistic model that predicts the next most probable words given an input string.

This task has however proven to be extremely effective, given a large training set and sufficient model size. Large Language models such as GPT can learn to generate sequences that are not only syntactically but also factually correct. They are able to generalize — meaning, they can generate responses to prompts they haven’t seen before by combining concepts from different examples from the training set. So the model is able to generate a poem about quantum physics because it has seen books about quantum physics and poems and is, therefore, able to generate a sequence that is both a probable explanation of quantum physics and a probable poem.

And as we could have already seen with the release of GPT-3 a few years ago casual language modelling can be used to perform various tasks and has proven to be universal. We can ask the model to generate a python function or a recipe for a cheesecake. We can ask it to translate from one language to another, extract key information from a given text and answer open-domain questions. All those various seemingly unrelated tasks such as Question Answering, Machine Translation, and Keyword Extraction, that are usually performed by specialized models can be represented as the text-to-text problem. Input sequence in form of the prompt and context is converted to the output sequence by casual language modelling.

But language modelling is not enough to create a universal model that answers all the questions of a user. Why? Because language models are jerks.

Language models are jerks, InstructGPT isn’t

As we read next on the OpenAI blog :

ChatGPT is a sibling model to InstructGPT, which is trained to follow an instruction in a prompt and provide a detailed response.

Fortunately, InstructGPT was released with a research paper, so we can shed some more light on the inner workings of the model.

In the introduction of the paper, the authors present a few major flaws of language models. Those models, trained to predict the next word in a sequence, are not good at following instructions. And why should they be? Language models are trained to predict the most probable sequence, not the most helpful one. This can lead to the models resenting unintended behaviours such as making up facts, generating biased or toxic text, or simply not following user instructions.

A solution to this problem presented by OpeanAI is reinforcement learning. Reinforcement learning is a machine learning training method based on rewarding desired behaviours and punishing undesired ones.

This approach with the rewards system based on human feedback is applied to GPT3 to create InstructGPT. Here is a brief description of the algorithm:

1. OpenAI collected prompts submitted by the users to the earlier versions of the model. Then they asked humans to generate their responses to those prompts and fine-tuned the GPT-3 model to match the style of human responses. This ensured the model generated responses in the form of instructions desired by the user rather than simple completions.
2. Once the model generated a response in the desired format, the next thing to do was to teach it which responses are useful for the user. For this, the authors asked the model to generate a few responses to the same prompts. Those responses were then rated by humans from best to worst and used the train the reward model — an additional model that was able to classify the response as good or bad.
3. The reward model was then used to fine-tune InstructGPT — it was taught to generate the best possible responses that scored the highest

So what is the difference between InstructGPT and ChatGPT?

As we can read in the article, the only difference between InstructGPT and ChatGPT is the fact that the annotators played both the user and AI assistant.

We trained an initial model using supervised fine-tuning: human AI trainers provided conversations in which they played both sides — the user and an AI assistant.

The hypothesis as to why such training was particularly effective is explained in the next section.

To sum everything up we know that ChatGPT is:

• A language model — a model trained to complete the sequence of words with the most probable continuation.
• Trained with reinforcement learning to generate completions that are more desired by the user.

Why is ChatGPT so effective?

The bitter lesson and E2E models

In 2019 Rich Sutton wrote an essay called “The Bitter Lesson” explaining how in the long run end-to-end AI models that leverage computation always wins against human ones that leverage human expertise.

The biggest lesson that can be read from 70 years of AI research is that general methods that leverage computation are ultimately the most effective, and by a large margin ~ Rich Sutton, The Bitter Lesson

Recently ChatGPT has proven to follow this pattern and is perhaps the best example to support this thesis. Instead of relying on different models for each task, it is able to solve a variety of user problems by communicating with a user through a unified chat-like interface.

This end-to-end approach is partially efficient because it removes the human from the equation and leverages computational power and data instead.

E2e learning is nicely consistent with the general approach of machine learning to take the human expert out of the loop and to solve problems in a purely data-driven manner. ~ Tobias Glasmachers, “Limits of End-to-End Learning”

If we have used a separate model for each task (e.g. taught the system to tell jokes based on jokes datasets with premise and punchline, and taught it quantum physics based on a set of questions and answers in quantum physics) we would rely on human annotations limiting the reasoning of the model to those specific unrelated areas. However, by training a model in an unsupervised fashion we are able to use the abundance of data that humans have generated and leverage knowledge transfer between various tasks. The model can use the logic learned by analyzing quantum physics sources to generate twisted logic in a joke. It is simply more efficient to train one model for various NLP as knowledge from one task can be used in another one boosting the overall cognitive abilities of the model.

Recently, it has become increasingly common to pre-train the entire model on a data-rich task. Ideally, this pre-training causes the model to develop general purpose abilities and knowledge that can then be transferred to downstream tasks. (…) Crucially, the text-to-text framework allows us to directly apply the same model, objective, training procedure, and decoding process to every task we consider~ Raffel, Colin, et al. “Exploring the limits of transfer learning with a unified text-to-text transformer.”

To use one model for many different tasks we need a shared interface and a text-to-text approach seems like a good candidate. We can convert almost any NLP task to a text-to-text problem. And this gives us a framework to teach the model many things at once, free it of our limited perception of how a task should be formulated and let it figure out things on its own, where its only limit is computational power and data we can give it.

Text-to-text models

The idea of modelling different NLP tasks as a text-to-text approach is hardly a new one. The paper accompanying the release of GPT3 (a predecessor to GPT3.5 that ChatGPT was based on) was in fact called “Language Models are Few-Shot Learners”

For all tasks, GPT-3 is applied without any gradient updates or fine-tuning, with tasks and few-shot demonstrations specified purely via text interaction with the model. GPT-3 achieves strong performance on many NLP datasets, including translation, question-answering, and cloze tasks, as well as several tasks that require on-the-fly reasoning or domain adaptation, such as unscrambling words, using a novel word in a sentence, or performing 3-digit arithmetic.

The authors have shown that GPT3 is able to solve different problems in a zero-shot learning fashion. We give a model a description of the task we need it to perform in the prompt as an input and as an output, we will get the response. We can see e.g. that the model is able to solve translation tasks without being explicitly trained for machine translation.

This is due to the fact that the model is able to learn from analogous examples in its training dataset and generalize.

Such models can also learn from a set of few examples

The process of presenting a few examples is also called In-Context Learning, and it has been demonstrated that the process behaves similarly to supervised learning.

A similar approach was used in “Exploring the limits of transfer learning with a unified text-to-text transformer” which introduced a model called T5.

In this model, the authors used explicit unified prompts such as “summarize:” to train the model. We can see that even tasks that would normally be modelled as regression such as STBS (Semantic Textual Similarity) are instead performed in a text-to-text fashion where the model answers by generating a sequence of tokens corresponding to the desired number. This allows us to share knowledge across different tasks e.g. text understanding that is necessary for translation can be relevant for summarization.

But if such models existed before the question emerges:

How is ChatGPT different?

As we currently know very little about the exact process of training ChatGPT outside its minor differences from InstructGPT we can focus on the results distinguishing InstructGPT from other large language models and later hypothesize about the improvements made with ChatGPT over the InstructGPT.

In the paper, the authors compare IntructGPT training using reinforcement learning to GPT3 one of the largest language models trained without using reinforcement learning

What are the advantages of using reinforcement to align language models?

• Better responses: InstructGPT outputs are preferred over GPT-3 outputs 85% of the time
• Fewer hallucinations: InstructGPT models make up information not present in the input about half as often as GPT-3
• Less Toxicity: InstructGPT models generate about 25% fewer toxic outputs than GPT-3
• Better Generalization: IntructGPT follows instructions for summarizing code, answers questions about code, and sometimes follows instructions in different languages, despite these instructions being very rare in the fine-tuning distribution

Why is reinforcement learning so efficient?

As we can see in the previous section InstructGPT model fine-tuned using reinforcement learning can produce much better results than the GPT-3 model even if it was fine tunes using supervised learning (SFT). The SFT technique was based on training the model directly to produce responses submitted by the labellers given the same input as them, without the reinforcement learning process. The difference becomes even more apparent when we compare model sizes.

As we can see the InstructGPT model (PPO) is able to produce much better results than the GPT-3 model fine-tuned directly even if its size is 2 orders of magnitude smaller. But why is RL so efficient?

The cost of collecting data and the compute for training runs, including experimental runs is a fraction of what was spent to train GPT-3: training our 175B SFT model requires 4.9 petaflops/s-days and training our 175B PPO-ptx model requires 60 petaflops/s-days, compared to 3,640 petaflops/s-days for GPT-3

Firstly, the RL task is much easier to optimize than the text-to-text task. The reward model is just a simple regression model that takes a string as input and produces the score as output. This is a much simpler task than text-to-text as the output space is only a single number instead of thousands of possible tokens in the text-to-text task.

Secondly, the reward model can help the language model generalize. With a supervised text-to-text task the model only has as many reference points as the number of prompts and outputs. However, the RL model can extrapolate and hypnotize the usefulness of outputs that don’t have a gold standard prepared by humans. The signals coming from supervised learning are also very strong — each input has only one possible output that is seen by the model as 100% accurate during training. With RL the model can observe the variety of outputs and their usefulness as a spectrum.

How is ChatGPT different form InstructGPT?

As we established at the beginning of the article it isn’t really that different. We don’t have any quantitative experiments comparing ChatGPT to InstructGPT and there is no doubt that the phenomenon of ChatGPT is vastly attributed to the release of the open demo and its viral spread rather than major differences in the architecture of the model.

The one difference that we know about is that the human annotators played both sides — the user and an AI assistant. This could have provided the model with more diverse inputs and more aligned input and output pairs as labellers knew what they were expecting from the model when writing prompts.

This difference is however as the OpenAI itself admits — slight. As we can see in the anecdotal evidence of ChatGPT’s superiority over InstructGPT provided in the article, over the 3 examples one is related to hallucinations (the model accepts the suggestion in the prompt that Christopher Columbus came to the US in 2015) and the other 2 are related to responses that can be seen as dangerous. However, an argument can be made that producing dangerous but factually correct responses does not denote the inferior cognitive abilities of the model and limiting such responses is strictly a safety measure taken by OpenAI to limit their liability connected with broader usage of the model.

What is ChatGPT capable of?

As there already exist several good articles describing possible usages of ChatGPT I will not go into great detail explaining every single use case.

If you are looking for a detailed description of ChatGPT usage with examples, I highly recommend an article by Sophia Yang:

Anaconda | The Abilities and Limitations of ChatGPT

Or this one by Damir Yalalov:

100 Best ChatGPT Prompts to Unleash AI’s Potential

There also exists a collection of awesome ChatGPT prompts on hugging face datasets.

fka/awesome-chatgpt-prompts · Datasets at Hugging Face

But let’s give a brief description of what ChatGPT is capable of and what are the implications.

Create Content and ART

ChatGPT can create a variety of texts ranging from copies and blog articles to highly artistic forms such as poems. This definitely streamlines the process of content creation, especially for small companies and individuals that can’t afford a dedicated content writer not having the time to skill up in content creation.

Some people have also shown that it is possible to use ChatGPT for DMing D&D games.

Dungeons & Dragons Player Gets AI Bot To DM, Works Surprisingly Well

This opens entirely new possibilities for AI-mediated games and storytelling, showing that ChatGPT can also be used for entertainment.

Of course, this also extends the discussion about the ethics of using AI to generate, recently sparked by Stable Diffusion and DALL-E to the text field.

An A.I.-Generated Picture Won an Art Prize. Artists Aren’t Happy.

However, as writing is both a skill and a talent, AI-mediated content generation can help those who have the talent but lack the skill, resulting in the creation of work that wouldn’t otherwise see the light of the day. AI artists can be compared to directors that guide the model through their prompts. If you want to read more on that subject check out this excellent article by Kevin Kelly.

What AI-Generated Art Really Means for Human Creativity

Answer Questions, Explain and Tutor

ChatGPT can answer a variety of questions related to specific fields and provide a detailed explanation of complex subjects. This made some academics really worried about their jobs but also present an unprecedented opportunity to lower the educational barriers, giving everyone equal access to advanced knowledge. Of course, as ChatGPT is not perfect giving students unsupervised access to it also creates a danger of them learning inaccurate information and propagating bias. Educators of the future could take on the role of supervisors and consultants rather than ex-cathedra lecturers.

A lot of students have also shown that it is possible to use ChatGPT for completing assignments and performing tests. This challenges the standard educational model focused on memorization and autonomous work. Addressing those fears a tool called GPTZero has been released that helps to identify AI-generated content allowing educators if students have been using such models in their work.

However, a point can be made that if such tools are present in our everyday life, can we really ban them in education? After all, we all know how the argument “You won’t always have a calculator in your pocket” aged.

With the rising popularity and accessibility of generational models, educators will have to think about how to incorporate those models into the academic world, maintaining a balance between giving students knowledge and the ability to think critically, while also allowing them to use modern tools.

Don’t Ban ChatGPT in Schools. Teach With It.

Write and Debug Code

One of the most prominent capabilities of ChatGPT is the ability to write functions and even full modules given the textual explanation of what the program should do. This made some people make far-going conclusions that ChatGPT will replace programmers. However, we have to remember that the possibilities of the model are limited by maximum context length and output size (more about that is explained in the next section). This makes the model useful for writing standalone functions or modules but doesn’t make it suitable for writing complex applications. ChatGPT capabilities can be compared to the ones of GitHub Copilot however the model can also correct bugs in existing code and provide reasoning behind the generated code. This makes the tool extremely useful as a software engineer’s assistant.

Perform various NLP tasks such as Summarization, Named Entity Recognition, Translation, Guided Text Generation

As shown in the previous sections almost any NLP task can be converted to Text-to-text format. For example Named Entity Recognition can be modelled as question answering. This makes ChatGPT especially useful for performing various NLP tasks that were previously modelled by custom models. More importantly, those tasks can be performed in zero-shot scenarios, where the user doesn’t need to provide any additionally labelled datasets. The task can be defined by a self-contained prompt with all the instructions and, if necessary, the examples. It will be harder and harder to justify developing proprietary models, while similar or better results can be achieved with a few cents per input.

Take on different personas such as travel guide, doctor or life coach

ChagGPT can take on the role of different personas presenting not only the specific domain knowledge associated with different jobs but also personality traits based on the guidelines specified by the user. Although the model acts as a highly-skilled, the profession definitely carries a lot of risks. It also, if uses consciously, allows the users to access a variety of services. After all, there is little harm in getting travel destination suggestions from ChatGPT by giving it your preferences, however, it acting as your doctor could be dangerous.

This also means a lot of low-risk consulting services will have to step up their game and think about their unique value proposition over models such as ChatGPT.

What ChatGPT isn’t capable of?

Mistakes of ChatGPT

There are many examples of mistakes done by ChatGPT in open domain questions and specific tasks such as code generations alike. The model is by no means perfect. We won’t focus on the specific mistakes but rather assume that the model can make, with some degree of probability, a mistake in any scenario and discuss its implications.

What is most important, in its current state, ChatGPT isn’t capable of identifying its own mistakes. There is no confidence score system for the user to see, and quite frequently ChatGPT doubles down on the mistakes it makes. This is perhaps the greatest restriction for using the model in a production environment. While we can accept the fact that the model might not know an answer to every question, what isn’t acceptable is the fact that it will always provide one. If we were to use ChatGPT as a medical assistant, we would have no way of knowing if the provided responses are current or not. Even if the model is correct 90% of the time that 10% could prove fatal. If human doctor encounters a question they don’t know the answer to, they will consult their colleagues, perform additional tests, read the latest research articles or, in the worst-case scenarios, simply restrain from giving a diagnosis. ChatGPT on the other hand will always utmost confidence even if it is completely wrong.

The real question you have to ask yourself is: “Are you willing to take the risk?”. And the answer is not always “NO”. While you might not accept a doctor that is incorrect 10% of the time, you might accept a translator or a florist.

ChatGPT is offline (at least for now)

In its current state, the system is entirely offline meaning it can only use information from its training dataset and can’t perform any queries or scrape the internet for new information. The most recent training data is of ChatGPT from 2021 September. This severely limits the capabilities of the model as it can’t use any information produced after that date. Although some fundamental truths about the universe might not have changed since 2021 there are some fields (like medicine) that are evolving rapidly and presenting users with information from several years ago might be dangerous. If the system were to serve as a modern search engine taking the place of e.g. Google, its functionality would have to be extended to performing live queries from the relevant fields or at least the training dataset would have to be updated frequently.

ChatGPT has no memory

The capabilities of any generational model (and ChatGPT is no exception) are limited by the maximum context length — the size of the input we can provide to the model. Although we don’t know the exact context length of ChatGPT some people have hypothesized that it is 8,192 tokens. This is quite impressive as it is 4 times the context length of GPT-3. Although the number of words we can input to ChatGPT is smaller as some rare words are split into sub-word tokens (you can check out the mechanism here) we have to remember that the entire US constitution is 4,543 words and this article is 6,554 words meaning both could be fully generated by ChatGPT)

However, this limit is still…limiting as ChatGPT won’t remember anything after 8192 tokens. The system has no persistent internal memory and it will eventually forget relevant information. This severally limits the capabilities of the model as e.g full-fledged coding projects have thousands of lines of code. This necessitates the creation of external systems that store information and decide which information might be relevant to the current query. For example, if we were to use ChatGPT as a doctor we would first have to decide which parts of the medical documentation we want to give the model as a context as some information from 10 years ago might still be relevant while an unrelated condition from a few months ago might not.

ChatGPT doesn’t perform reasoning

As the evidence has shown ChatGPT struggles with tasks such as common sense reasoning.

This question, while trivial for most adults, can’t be correctly answered by ChatGPT. Why? Because there is no actual reasoning in language models.

We have to remember that although the capabilities of ChatGPT are impressive it is just a language model that completes the prompt with the most probable sequence of words based on the training data. The training is essential as large enough models can just remember information instead of generalizing. This not only makes the system extremely biased by also makes it more of an intelligent search algorithm than a truly intelligent system. Recently the term stochastic parrot has been used to describe large language models reflecting on their true nature — repeating information instead of producing one.

No actual language understanding is taking place in LM-driven approaches to these tasks (…) languages are systems of signs, i.e. pairings of form and meaning. But the training data for LMs is only form; they do not have access to meaning. ~Bender et al.“On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? 🦜”

Of course, this doesn’t make the work of OpenAI any less impressive, however, we have to remember that the system does not act logically and instead acts probabilistically, and for large language models a lie repeated a thousand times really becomes the truth.

Will ChatGPT replace software engineers?

This question has been asked numerous times following the release of ChatGPT and might even be the reason you decided to read this article. But the answer is not straightforward.

Software engineers are translators

To answer the question if ChatGPT will replace software engineers, we first have to answer the question of what a software engineer’s job is really about. From a high-level point of view, a software engineer can be seen as a translator that translates the vision of a client or product manager into code. The type of code changed over the years, from machine code to low-level languages and then to high-level languages. It appears that with the rise of large language models, we have moved again by one layer of abstraction where the translation result might be a natural language. Software engineers of the future will most probably not write every single function on their own, and instead, use tools like ChatGPT to convert natural language inputs to machine-interpretable code. But someone will conceptualize client requirements, break them down into small pieces interpretable by ChatGPT and then connect those pieces into a larger infrastructure.

Another parallel that can be drawn is that the current situation of software engineers is similar to the translators back when machine translation was introduced. Back then a lot of people were scared about their jobs too, but the profession of a translator didn’t disappear and is still standing strong. Sure, a small piece of text might be translated by Google Translator, but humans still translate important legal documents. Even if those humans use machine translation to speed up the process, someone still has to correct the mistakes and sign off on the quality of the translation. A similar thing might happen with software engineers. People might use ChatGPT to generate simple functions, but the large-scale important project will still require human supervision.

No-code platforms already exist

The idea that not every single lie of code has to be written from scratch is nothing new and is in fact one of the fundamental good practices of programming. Moreover, no-code and low-code platforms have existed for several years and the field of IT is still standing strong. In fact, recent research shows low-code developers are actually happier and make more money on average than high-code developers (72% of low-code users make over $100K USD (compared to 64% of high-code users), 42% of low-code users vs. 31% of high-code users say they are “highly satisfied” with their jobs.)

The Impact of Low-Code on Developer Career Paths

Language models can’t exist in a vacuum

As we have already mentioned in its current state language models can’t be used on their own mainly because of the context length limit and their offline status. This necessitated the presence of humans or an external system that decides what information to provide to ChatGPT as input. This means that even if the language model is perfect, we still need at least databases, user interfaces and search engines. But the language models are not perfect, which means we also need an external vaidatator that knows the domain well and is able to interpret and judge the results. And, as mentioned before. this means that language models are just a higher level of abstraction for the developers.

Will ChatGPT replace ML Engineers?

ML Engineers are translators too

So what is it that makes the ML Engineer fundamentally different from the software engineer? One could argue that the job is different because it requires conducting experiments, and testing hypotheses or training models, which is beyond the scope of a typical software engineer. But most companies don’t care about the research, they care about the results. So from a high-level point of view, ML engineers are also translators as they also translate business requirements into code. The road might be different, but the destination is the same.

Prompt Engineering

As mentioned above we can use ChatGPT to perform a number of different NLP tasks. This means that the company might decide to pay for the subscription for ChatGPT instead of hiring ML Engineers to create custom modules. However, the process of turning other NLP tasks to text-to-text problems is not trivial and the results might vary depending on how we formulate the prompt. Let’s imagine that we want to perform Named Entity Recognition and identify names that appear in a given text (e. g. an invoice) we might ask the model: “Which people are mentioned in the text: x?” but we might also ask it “What names appear in the passage: x” and get different results. This opens up a new field of prompt engineering, and NLP teams of the future might focus on optimizing the language model input for a different task. The topic of prompt engineering is in fact already present in the field of AI and a lot of research has already been made in this area.

Language models can’t exist in a vacuum (again)

While large language models such as ChatGPT might live at the heart of your AI system there is still a need for pre and post-processing. For example, if your input is an invoice, you might use ChatGPT to identify key information but if the user submits an image, you still need an OCR system to convert this image to text first. Or, if your input is a voice, you might use ChatGPT for NLP but still need an ASR and a speech synthesis system. Or the document understanding and voice processing can be a separate end2end system that is better than ChatGPT, as we have established that end2end systems win in the long run. Either way, language models, like ChatGPT. will definitely be more relevant in the field of AI even in seemingly unrelated fields such as image processing but, in their current state, they can’t replace all AI models.

Will ChatGPT replace your job?

So far we have given 2 examples of jobs that will change because of ChatGPT. But what about other jobs? After all, ChatGPT can act as a doctor, content writer, tutor or, to some extent, any profession that doesn’t involve interaction with the physical world. Will your job be replaced by ChatGPT?

This question is perhaps best answered by this quote:

“AI won’t replace radiologists, but radiologists who use AI will replace radiologists who don’t,” ~Curt Langlotz, Director @StanfordAIMI

The same argument can be made for any profession.

Let’s say you are a content writer and your boss decided to use ChatGPT instead of you. A few questions emerge: is ChatGPT the best tool for this? After all other language models exist, maybe some of them are better than ChatGPT in your use case. How to tell ChatGPT about your company and its values? How to make sure the spirit of the article follows the message you want to send? How to correct its mistakes?

Who will have an answer to these questions? Your boss? Do you think he will spend more of his time researching those topics?

No, but you can.

And do you think medical directors will keep up with the latest research on medical language models?

No, but you can.

Your job might become harder (or easier), your job might have fewer openings, and your job will definitely change, it might be completely different in a few years, but it’s a process, and you can also change. If you don’t disappear, your job won’t disappear.

ChatGPT won’t replace people, but people who use tools like ChatGPT will replace those who don’t.

Will ChatGPT create jobs?

Prompt Engineers

As we have established, deciding which prompt to use for the language model is not a trivial thing. The results may vary drastically depending on the specific wording, order of words and semantics given if the intention stays the same.

In the future, prompt engineering can be both a useful skill and a form of artistic expression. If fact we can already see companies offering certified courses in prompt engineering.

Learn Prompting: Your Guide to Communicating with AI

Testers

ChatGPT and other language models are by no means perfect and their behaviour is often hard to predict. Using such a system in a production environment is a challenge and requires a lot of testing to be done beforehand. This might increase the demand for testers specialized in AI models, both manual and automatic as many aspects of the output, such as profanity, semantics, intention and emotion, could be tested automatically or with the help of other AI models

Domain Experts

As we have established, letting models such as ChatGPT roam freely carries a lot of danger, as users can be presented with wrong and potentially harmful information. This means that AI and product teams using ChatGPT will have to work closely with domain experts that could help validate and correct the models. In the future, perhaps one of the standard career paths of a doctor will be, instead of becoming a practitioner, working in AI teams as a domain expert.

AI Ethicists and Compliance Managers

As AI models affect more and more aspects of our life, more and more ethical questions arise that can’t be answered solely by technical teams.

We can also expect an increase in regulations concerning the usage of AI.

What the draft European Union AI regulations mean for business

In the future, each company might have their own dedicated AI Compliance managers and the job might be as common as GPDR Compliance manager.

Conclusions

• ChatGPT is a language model that is optimized for following user instructions with reinforcement learning.
• ChatGPT and other language models will definitely affect many fields, such as content creation, art, programming and consulting.
• ChatGPT cannot be currently used in most production environments without external validation systems.
• In order for ChatGPT to be widely accepted, the authors have to incorporate confidence scores or a similar system to identify the mistakes of ChatGPT and make the model online or at least frequently update the training set.
• ChatGPT will definitely affect a lot of jobs but it is up to you if it will replace you.
• ChatGPT will definitely create a lot of new jobs, as the model not only answers questions but also raises a lot of new ones.

Don’t forget to leave your thoughts

If you liked the article don’t forget to give it a 👏 If you have any thoughts about ChatGPT or want to share your perspective, leave a comment!

About the author

Hello, my name is Aleksander Obuchowski, I am passionate about Natural Language Processing and AI in Medicine. Follow me on LinkedIn if you like my stories.

Get Certified in ChatGPT + Conversational UX + Dialogflow

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How ChatGPT really works and will it change the field of IT and AI? — a deep dive was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

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ChatGPT and WhatsApp Bot Integration:

The combination of ChatGPT, an AI-powered conversational platform utilizing machine learning and natural language processing, and WhatsApp, a widely-used messaging app with over 2 billion active users globally, has resulted in an innovative solution for customer support. By integrating ChatGPT with WhatsApp Bot, businesses can offer automated customer service through the messaging app. This integration enables businesses to provide personalized and rapid customer support around the clock, resulting in enhanced customer engagement and satisfaction.

The importance of customer engagement in modern business:

In today’s digital age, customer engagement is critical to business success. Customer engagement refers to the interactions and experiences that customers have with a business or brand, including customer service, marketing, and sales. A strong customer engagement strategy can lead to increased customer loyalty, improved customer satisfaction, and higher revenue growth. The integration of conversational AI, such as ChatGPT and WhatsApp Bot, can help businesses improve customer engagement by providing fast, efficient, and personalized customer support, leading to a better customer experience and increased loyalty.

Benefits of ChatGPT and WhatsApp bot integration:

Personalization of customer interactions:

Conversational AI can personalize customer interactions by using data to understand customer preferences, behaviors, and needs. This enables AI-powered chatbots like ChatGPT and WhatsApp Bot to provide tailored responses to customers, making them feel valued and understood.

24/7 availability for customers:

Conversational AI can provide round-the-clock customer support, allowing businesses to engage with customers anytime. With ChatGPT and WhatsApp Bot integration, businesses can provide automated customer service 24/7, ensuring that customers can receive assistance whenever they need it.

Faster response times:

Conversational AI can provide faster response times to customer queries than traditional customer service methods. Chatbots can respond to customer inquiries in real-time, reducing customer wait times and improving satisfaction.

Reduced workload for human customer service agents:

Conversational AI can take on routine customer service tasks, freeing up human agents to handle more complex customer inquiries. This reduces the workload of human customer service agents and allows them to focus on providing more personalized support to customers.

Ability to handle large volumes of customer interactions simultaneously:

Conversational AI can handle large volumes of customer interactions simultaneously, making it possible for businesses to engage with more customers at once. This can help businesses scale their customer service operations, without needing to hire additional staff.

How ChatGPT For WhatsApps Works:

As an AI language model, ChatGPT is not integrated with WhatsApp directly. However, there are a few ways you can use ChatGPT to communicate with your WhatsApp contacts.

Use a third-party chatbot platform: There are several chatbot platforms available that allow you to integrate ChatGPT with WhatsApp. These platforms act as a middleman between ChatGPT and WhatsApp, enabling you to chat with ChatGPT using WhatsApp. You can find these platforms by searching online or in app marketplaces.

Use a virtual assistant app: Another option is to use a virtual assistant app that integrates with WhatsApp. These apps use AI to provide conversational responses to your queries. While they may not use ChatGPT specifically, they can provide similar functionality.

Copy and paste responses: If you have a question or want to generate text using ChatGPT, you can do so in a web browser or using an app that integrates with ChatGPT. You can then copy the text and paste it into WhatsApp to send to your contacts.

It’s important to note that while ChatGPT is a powerful language model, it’s not perfect and may make errors or provide inaccurate responses. Always use your own judgment and verify the information before sharing it with others.

Future Prospects of ChatGPT and WhatsApp Bot Integration for customer engagement:

As artificial intelligence (AI) and natural language processing (NLP) continue to advance, the prospects for ChatGPT and WhatsApp bot integration for customer engagement are increasingly bright. Here are a few of the key future prospects for these technologies:

Enhanced customer engagement:

By integrating ChatGPT with WhatsApp bots, businesses can engage with their customers more effectively, providing personalized, timely, and relevant responses to their queries and concerns.

Improvements in natural language processing technology

As NLP technology improves, ChatGPT and WhatsApp bots will be able to more accurately interpret and respond to customer queries and requests. This will enable businesses to provide more efficient and effective customer service, leading to greater customer satisfaction and loyalty. With the development of machine learning algorithms, ChatGPT and WhatsApp bots will be able to learn from past interactions and improve their responses over time, further enhancing the customer experience.

Expansion to other messaging platforms

While WhatsApp is currently one of the most popular messaging platforms, there are many other platforms that businesses can use to engage with customers. As ChatGPT technology improves, we can expect integration with other messaging platforms, such as Facebook Messenger, WeChat, and Line. This will allow businesses to engage with customers on the platforms they prefer, increasing the chances of successful communication.

Integration with other emerging technologies such as AR and VR:

As augmented reality (AR) and virtual reality (VR) become more widespread, there will be increasing opportunities for businesses to engage with customers through these channels. ChatGPT and WhatsApp bots can be integrated with these technologies to provide personalized, interactive experiences for customers. For example, a customer could use a VR headset to explore a new product, while a ChatGPT bot provides information and answers questions in real-time.

Overall, the future prospects for ChatGPT and WhatsApp bot integration for customer engagement are exciting. As NLP technology continues to improve, businesses will be able to provide more efficient and effective customer service, while expanding to other messaging platforms and integrating with emerging technologies such as AR and VR will enable businesses to provide more personalized, interactive experiences for their customers.

Conclusion:

In conclusion, conversational AI has the power to transform customer engagement in today’s digital age. The integration of ChatGPT and WhatsApp bots offers businesses an opportunity to provide more efficient and effective customer service through personalized and interactive experiences. As natural language processing technology continues to improve, we can expect to see even more benefits from these technologies, including integration with other messaging platforms and emerging technologies such as AR and VR. With the power of conversational AI, businesses can engage with customers in new and innovative ways, building stronger relationships and increasing customer loyalty. As such, the use of ChatGPT and WhatsApp bot integration for customer engagement will undoubtedly continue to grow and evolve in the coming years, offering exciting new possibilities for businesses to connect with their customers.

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How ChatGPT and WhatsApp bot integration can Enhance Customer Engagement was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

The chatbot market is forecasted to reach 1.25 billion U.S. dollars in 2025. If you want a piece of this significant chatbot pie, you need to know the fundamentals of chatbot automation. While bot automation sounds simple enough, it needs a certain level of skill that is hard to master.

Here we will go through some of the most useful chatbot automation tips you can use to stay on top of the competition.

1. Plan with chatbot objectives in mind

Excited to embark on your chatbot-building journey? Not so fast! Take a pause and chart out the problems that your business aims to solve through a chatbot. You can then prioritize each issue based on criticality and have a vision for chatbot automation down the road.

For example: If you are operating a call center, call volumes will be a critical factor in implementing your chatbot.

2. Pick Your Communication Channels

Every chatbot framework has specific features and integrates with specific communication channels. So, before you choose your solution, decide where you want to launch your chatbot. If Facebook Messenger is your preferred platform, select a solution tailored to Facebook marketing.

If you aim to automate communication across multiple channels, go for a multi-platform chatbot framework that lets you add a chatbot to various channels with ease.

3. Choose the Ideal Chatbot Platform

Having listed your problems and chosen the channels, it’s time to select the right tool. The market is brimming with chatbot platforms that can help you automate customer communication, increase sales, and collect customer feedback.

To find the perfect fit, try out the free trials and request product demos. Test the different solutions to see what they have to offer. Then, compare their features with your list of problems to determine the winner!

4. Set Chatbot Performance Goals

Once you’ve picked your chatbot platform, set achievable goals for your AI assistant. Whether it’s to enhance lead generation, speed up issue resolution, or improve customer satisfaction, choose metrics that align with your goals.

This chatbot automation tip will help you track the number of open sessions, completed conversations, or collected leads. Remember, don’t expect your chatbot to fulfill all your goals immediately. Track its performance and assess its overall impact on your business.

5. Craft your chatbot narrative

Picking the right platform to build your chatbot is a significant step in the right direction. Another significant aspect of your chatbot would be the conversation flow and the user journey through it.

Crafting an engaging chatbot story is a challenge, but with inspiration and some practice, it can be done. If you’re new to chatbot scripting, check out some successful examples. Interact with existing chatbots to get ideas and understand the elements that make them compelling.

When it comes to chatbot automation, there are several things to keep in mind to create an effective and engaging chatbot experience for your customers. Here are some tips to help you design the perfect chatbot.

6. Personality design

Give your chatbot a personality that matches your brand image. This will help it act as a smart brand ambassador that’s available 24/7. Ensure you make it clear that your customers are talking to a bot. You can use both an image of a person or an illustration to depict your chatbot.

This part of chatbot automation could make or break your chatbot implementation as the bot will represent your brand. You can use appropriate color schemes for visuals and tone and voice in terms of text to define your chatbot personality. Ensure that you give your chatbot a personality that is helpful to your target audience.

7. Make the value proposition clear

Your chatbot should always let users know how it can help them from the start. For example, the welcome message should include what it is there to do. Such as a chatbot for a blog will help users such for information on the blog.

Don’t use too many qualifying questions, as they can put users off and discourage them from chatting. Use a small amount of effective copy and catchy visuals to hook users and convince them to stay.

8. Show off your buttons (Rich Text)

Buttons are a great way to guide users through your chatbot story, offering options and reducing the potential for wasted time. Use more contextual copy on buttons to help users understand what will happen after clicking. Streamline navigation by having a menu button available at each step, allowing users to easily navigate through the story. Add appropriate emoticons to make your chatbot feel more human and show what will happen after clicking a reply.

9. Decide on the time between chatbot messages

Chatbots can deliver information quickly, but it’s essential to let users absorb it comfortably. Use conversation delays and message chunking to keep messaging effective. You need to break up long blocks of text into smaller pieces, making it easier for users to engage with your chatbot.

You will need to find the right delay between chatbot messages through trial and error. Once you use testing to find the right delay period between messages, users will respond accordingly.

10. Design the right fallback response

Even the best chatbots may make mistakes, so it’s important to have a fallback message/canned response in place to recover the conversation when miscommunication happens. Avoid generic answers and come up with a creative response that fits your chatbot’s character. Consider creating different variations of your fallback messages to sound more natural. Shift the user’s attention from the chatbot’s mistake and focus on eliciting the right answer from the user.

11. Testing and Maintenance

Even after pouring all your time and effort into creating the perfect chatbot story, there’s always a chance something might not go according to plan. That’s why testing is key — it’ll help you ensure your chatbot is functioning correctly and your story delivers the goods.

The best way to put your chatbot to the test is by handing it off to some beta testers. Share your chatbot with your friends or coworkers and ask them to chat away. See what they think if they come across anything that doesn’t make sense, and take note of all the useful feedback you receive.

Now that your chatbot is up and running, it’s time to see how it’s performing. Keep an eye on trends by checking how many conversations your chatbot has and which interactions are most popular. If you find that users are only interested in one particular topic, update your chatbot scenario to include more info on that topic and eliminate the less interesting elements.

Make progressive updates

Take a look through your chatbot archives to see what types of questions your users are asking and how they’re asking them. This will give you a better idea of their needs and help you train your chatbot to recognize similar questions in the future.

Get to know your users by looking at their demographics and interests. This will help you create a chatbot persona that appeals to them. For example, if you find that your chatbot is mainly used by young people, add more GIFs or visuals that they might enjoy and use a language and tone that feels natural to that group.

Finally, don’t forget to keep your chatbot up to date by regularly revising and updating your scenario. If you want to keep your chatbot’s story current and relevant, it’s important to ensure it’s not stuck in the past, like the Christmas spirit in July.

Wrapping Up

If you want to implement a chatbot solution, then the chatbot automation fundamentals here will put you on the fast track to success. The key to effective chatbot implementation is to customize your chatbot according to the needs of your target audience and align that with your brand. Since there is no cookie-cutter chatbot for every business out there, it is up to you to choose the right chatbot solution that suits your business.

Originally Published here

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11 Chatbot Automation Fundamentals to Supercharge Your Chatbot Implementation was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

Building a Powerful Generative AI Chatbot with Pandas Dataframe: A Step-by-Step Guide Using LangChain and Gradio

Chatbots so far are using trained datasets to give correct answers to user queries. Generative AI made life simple. Now anyone can create a chatbot easily with CSV files, PDF documents, and Pandas data frame

What is a chatbot?

A chatbot is a computer program that simulates human conversation through artificial intelligence (AI) and natural language processing (NLP) techniques. Chatbots are designed to understand and respond to user queries in a conversational manner, much like a human would. They can be programmed to provide information, answer questions, complete tasks, and even carry out simple transactions. Chatbots can be integrated into various messaging platforms, websites, and mobile apps to provide automated customer support, improve user engagement, and enhance the overall user experience.

What is LangChain?

Large language models (LLMs) are emerging as a transformative technology, enabling developers to build applications they previously could not. However, using these LLMs in isolation is often insufficient for creating a truly powerful app — the real power comes when you can combine them with other sources of computation or knowledge. LangChain library aims to assist in the development of those types of applications. Applications of LangChain include documentation, creating agents, and Chatbots.

What is Gradio?

Gradio is an open-source Python library that allows developers and researchers to quickly create customizable UIs for their machine-learning models, without requiring any web development experience. With Gradio, users can create interfaces for their models in minutes, using a simple API and a range of pre-built input and output components, including sliders, checkboxes, text boxes, and images. Gradio also supports a range of data types, including numerical data, text data, image data, and audio data, and can be used with a variety of popular machine learning libraries, including TensorFlow, PyTorch, and Scikit-learn. Gradio is designed to be easy to use, flexible, and customizable, making it an ideal tool for anyone looking to create a user-friendly interface for their machine-learning models.

Let’s start with hands-on.

1. Create a file called apikey.py

Put your own Open AI API key here. Do not use the above key. It will not work :).

2. You can create your own key at https://platform.openai.com/account/api-keys. I had rate limit errors, so I went for paid subscription.

3. Create a file called chatagent.py and import the below libraries

CSV agent is only required if you are trying directly query a CSV file.

In this example, I will not be reading from a CSV file but will read from Yahoo Finance. My plan is to download one year of Nifty daily data for analysis.

I am using Macbook. The below code will set the API key

4. Let’s create an agent. I am calling it a nifty-agent and passing daily

What is OpenAI temperature? Temperature is a parameter of OpenAI ChatGPT, GPT-3, and GPT-4 models that govern the randomness and, thus, the creativity of the responses. It is always a number between 0 and 1. 0 is more certain, and 1 is inconsistent and creative

5. We are almost there. Let’s try a simple query

We get a response like this.

6. These are trading days in the last year. Of course, this is not a great way to interact with the program. This is where Gradio comes in handy.

Below is the snippet of code to create UI using Gradio

If everything is right, you will see below

7. now go to the browser and enter http://127.0.0.1:7860/?__theme=dark

You will see below

Let’s interact with data frame now.

Some deep analysis questions now. Thursday is the weekly expiry for Nifty.

Buy me a coffee if you liked the blog

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Building a Powerful Gerative AI Chatbot with Pandas Dataframe: A Step-by-Step Guide Using… was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

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Hey everyone! Thank you to those who partook in the Replika survey on ERP and it’s impact on user mental health and well-being. These are not formal/published results, but I thought it would be nice to share some initial bar graphs with you. Please know that a formal analysis will be done later. Survey: https://cambridge.eu.qualtrics.com/jfe/form/SV_9YR05WRdRq1gMPs https://preview.redd.it/55eeguwnw1za1.png?width=1536&format=png&auto=webp&s=db48a5b136f0809ad3fd46c0b7cb24b3fafefcda https://preview.redd.it/qdmsi0wpw1za1.png?width=1536&format=png&auto=webp&s=401271f668a73eec6986de5ef8db2f30d2f35776 https://preview.redd.it/0asluattw1za1.png?width=1536&format=png&auto=webp&s=6cefb19a724e5e338a423cfbf77ee91be1e0f4c7 submitted by /u/skywhale210 [link] [comments]