ChatGPT- What? Why? And How? Part 2

[Shashi16]

ChatGPT- What? Why? and How?

Recap: In the preceding section, the discourse delved into the technicalities and intricacies of ChatGPT's modus operandi and its architecture. In this segment, we shall shift our focus towards the practical applications and functionalities of ChatGPT, as well as its potential limitations. Familiarizing oneself with these aspects would be beneficial before utilizing this technology for varying purposes.

How ChatGPT is used in the industry:

	Industry	How ChatGPT is being used
1	Customer Service	Chatbots for customer queries
2	Healthcare	Analyze medical data and recommend treatment
3	Finance	Fraud detection, risk management
4	Education	Automated grading, language learning
5	E-commerce	Product recommendation, customer support
6	Gaming	Create game dialogues for characters
7	News and Media	News summarization, content generation
8	Social  Media	Sentiment analysis, chatbots
9	Research	Analysis and Ideas
10	Legal	Contract review, document, analysis
11	Government	Analyze large volumes of unstructured data
12	Marketing	Analyze feedback, personalized marketing
13	Human Resources	Resume screening, candidate matching
14	Entertainment	Recommendation of content
15	Travel	Travel Planning, booking, customer support
16	Agriculture	Analysis soil, plants, weather & recommend
17	Manufacturing	Sensor data analysis, optimization
18	Logistics	Supply chain management, optimize delivery
19	Automotive	Vehicle diagnostics, predictive maintenance
20	Security	Detect threats, identify patterns and trends

Table: ChatGPT usage in the Industry

The possible use cases for ChatGPT and other language models will only expand as AI technology develops. ChatGPT has the potential to transform how we process natural language and communicate with computers, enhancing productivity, accuracy, and communication in a wide range of fields and applications, including healthcare, finance, entertainment, and more.

Microsoft and OpenAI

Recently, Microsoft and OpenAI teamed to make ChatGPT and other cutting-edge AI models available on Microsoft's Azure platform. Through this relationship, Microsoft is able to give its clients access to cutting-edge AI technology, making it simpler for them to create and implement AI-powered products and services.

As part of the collaboration, Microsoft has incorporated OpenAI's GPT-3 language model, the core technology behind ChatGPT, within Azure Cognitive Services offering, its cloud-based platform for developing AI applications. Via this interface, programmers can create intelligent chatbots, virtual assistants, and other language-based apps by utilizing GPT-3's robust language creation capabilities.

The collaboration between Microsoft and OpenAI represents a significant advance in the creation and application of AI technologies. Microsoft is assisting in democratizing access to these technologies and making them more widely available to developers and organizations of all kinds by making powerful AI capabilities available on a popular and accessible platform like Azure.

The way AI is utilized and developed could be completely changed as a result of this relationship, which is also likely to be essential in determining the direction of the technology's development and its social implications.

Limitations of ChatGPT:

Out-of-Scope topics: As it is trained on a wide range of topics and data, ChatGPT might understand one thing for another and consequently give an undesired output .

Biases in training data: Due to the data it is trained on which is kind of biased in many cases, the output of the ChatGPT is also biased for certain queries.

Lack of common sense and context understanding: ChatGPT has some shortcomings as an AI language model when it comes to comprehending context and common sense. The model's inability to fully comprehend real-world concepts and the connections between them can affect how accurate and pertinent its responses are, even though it is built to evaluate and output text.

For instance, ChatGPT can have troubles comprehending idiomatic terms or cultural allusions that are frequent in spoken language but not always included in its training data. Similar to this, it can struggle to recognize irony or sarcasm in writing, which calls for a subtle comprehension of the context and underlying meaning.

Not everything is true: ChatGPT generates text based on correlations and patterns it has discovered by studying massive amounts of human-written text. Yet not all of it is factual or true, and the same is true of the content produced by ChatGPT.

One of ChatGPT's biggest drawbacks is that it is unable to assess the accuracy or dependability of the data it is producing. The model is capable of producing writing that seems to be grammatically correct and logically consistent, but it is unable to fact-check or independently verify the material it is providing.

For instance, if a user asks ChatGPT for knowledge about a particular subject, the model can respond with data that is incorrect or deceptive if such information is included in its training data.

Multiple Answers at multiple times: The fact that ChatGPT may produce many responses to the same question or prompt at various times is one of its drawbacks. The model's stochastic nature, which allows it to provide several answers to the same input based on arbitrary differences in its internal computations, makes this possible.

For instance, if a user asks ChatGPT for knowledge on a specific subject, the model might produce a different answer each time the user poses the same query. While some of these answers might be comparable or even the same, others might be vastly dissimilar or even contradicting. This can be a concern when accurate and consistent information is needed.

Too wordy for a human: Another drawback of ChatGPT is that it occasionally produces extremely wordy or verbose responses, which can make it challenging for human users to comprehend or interact with the generated content.

Due to the fact that ChatGPT is trained on enormous datasets of human-written text, some of which may contain examples of complex or long sentences, this may happen. Moreover, ChatGPT could produce text that is extremely descriptive or contains extraneous information, which might compound the wordiness issue.

Not aware of things after the year 2021: The fact that ChatGPT and other language models are trained on data up to a specific moment in time and might not be aware of information or events that occur after that point is one of its limitations. In other words, it's possible that these models don't have access to the most recent or up-to-date data.

For instance, ChatGPT might not be aware of news events that happen after 2021 if it is trained on a collection of news articles from the years up to that point. As a result, ChatGPT might not be able to respond with correct or current information if a user asks about a recent news occurrence.

Limited output: The fact that ChatGPT and other language models might only be able to produce text that falls within a particular range of complexity or length is one of their limitations. This might happen as a result of computational constraints or model development design decisions.

For instance, ChatGPT might only be able to produce text up to a particular length, such as sentences or paragraphs, and might not be able to produce lengthier writings like essays or books. Moreover, ChatGPT might not be able to produce messages that are highly complicated or sophisticated, such as technical or scientific documents that need specialist understanding.

Prompt Engineering and its importance for ChatGPT

The process of creating and improving prompts—short textual or spoken instructions sent to a language model to assist it in producing content that follows a specified pattern or accomplishes a specific goal—is known as prompt engineering. This procedure entails choosing and creating the appropriate prompts, testing them against the language model, and refining the prompts to increase their potency.

Developing prompts that are unique to a given job or domain is one of the main use cases for prompt engineering. For instance, you might create prompts that request the model to provide a description, a list of examples, or an explanation of a concept connected to that issue if you are training a language model to respond to questions about a certain subject.

Below are examples of prompts along with input, use case and expected output from ChatGPT. These examples explains what is prompting and how to use them to get output from Language models.

1.    Input: "I need to generate a Python script that can extract data from a CSV file"

Prompt: "Write a Python script that can extract data from a CSV file and store it in a list or a dictionary"

Use case: Code generation

Output: The language model generates a Python script that reads in a CSV file, extracts data from specific columns, and stores the data in a list or a dictionary for further manipulation or analysis.

2.    Input: "Tell me about the history of the Eiffel Tower"

Prompt: "Write an article about the history of the Eiffel Tower"

Use case: Content generation

Output: The language model generates a written article about the history of the Eiffel Tower, providing information about its construction, original purpose, and subsequent impact on French culture and tourism.

3.    Input: "What is the capital of Japan?"

Prompt: "Answer the question: What is the capital of Japan?"

Use case: Question answering

Output: The language model generates the answer "Tokyo", indicating that Tokyo is the capital city of Japan.

4.    Input: "I am unhappy with the service I received from your company"

Prompt: "Analyze the sentiment of the following text: 'I am unhappy with the service I received from your company'"

Use case: Sentiment analysis

Output: The language model generates a sentiment analysis score indicating that the text has a negative sentiment, suggesting that the customer is dissatisfied with the service they received.

5.    Input: "Translate this English text into Spanish: 'The quick brown fox jumps over the lazy dog'"

Prompt: "Translate the following           text into Spanish: 'The quick brown fox jumps over the lazy dog'"

Use case: Machine translation

Output: The language model generates the translated text "El rápido zorro marrón salta sobre el perro perezoso", which means "The quick brown fox jumps over the lazy dog" in Spanish.

6.    Input: A set of medical records for patients in a hospital

Prompt: "Using the medical records provided, identify the most common diagnoses and treatments prescribed for patients and describe any correlations between certain diagnoses and treatments"

Use case: Data analysis

Output: The language model analyzes the medical records, identifies the most common diagnoses and treatments prescribed for patients (such as hypertension, diabetes, and antibiotics), and describes any correlations between certain diagnoses and treatments (such as patients with diabetes being more likely to be prescribed insulin).

The topic of prompt engineering is crucial to the realm of natural language processing and is constantly developing. To achieve the best outcomes, it is crucial to be able to create efficient language model prompts. Users may get the most out of even modest LMs by creating prompts that clearly convey the desired inputs and outputs. Conversely, without carefully designed prompts that direct the model's behavior, even the largest LMs with billions of parameters can be constrained. 

The fact that prompt engineering is frequently the only way to interact with a language model further emphasizes how important it is. This means that the user's capacity to create relevant prompts that accurately reflect the desired goal has a significant impact on how effective a language model is.

Language models that are more accurate and efficient have been developed thanks to improvements in prompt engineering. It has been demonstrated that methods like prompt tuning, in which prompts are adjusted so they better suit the task at hand, can considerably enhance model performance. Prompt design has become more effective and scalable because of methods like prompt programming, which use code to generate prompts.

In conclusion, timely engineering is an essential part of language model usage and design. Regardless of the size of language models, users can maximize their utility by creating powerful prompts. The effectiveness and precision of language models are expected to be substantially improved in the future thanks to the ongoing development of new rapid engineering techniques.