ChatGPT is an AI based chatbot tool which has been developed by a research organization named “OpenAI”. ChatGPT is designed in manner such that it generates responses which simulate human-like behaviour for the input given to the application. The Chat in “ChatGPT” refers to the nature and purpose of the application which indicates that the model has been designed to engage in chat like interactions and the GPT stands for “Generative Pre-trained transformer” which is a deep learning architecture employed in ChatGPT which has revolutionized the Natural language processing tasks.
As per the current scenario the domain of ChatGPT is not limited to just one field but it has in implementation in diverse applications across variety of domains like
• Education and E-Learning: ChatGPT is being constantly used for various educational purposes since its very emergence the ChatGPT. Initially it was used individually by people for providing personalized help for their doubts however since OpenAI has made the API available for the chatbot various e-learning platforms like edX, quizlet have started deploying ChatGPT based chatbots on their websites for addressing naïve and even complex student queries and providing them with extremely personalized responses.
• Personal Assistant: ChatGPT has also been used as a personal assistant as the API provided by ChatGPT can easily be integrated with other applications in order to formulate a top-notch virtual assistant that is being used for sending emails, setting alarms, and doing other tasks. Currently websites like “vistasocial” are offering the same services where they have utilized the intelligence of ChatGPT in the form of personal assistant.
• Customer Support: The use of ChatGPT as a customer assistant has been rising tremendously with many websites deploying integrating its API on their websites in a personalized manner in order to provide resolution towards customer queries without requiring human intervention.
This is just a broad use-case that ChatGPT is currently being used for, however the possibilities of ChatGPT being deployed for other domains are unending, for instance being a Natural Language processing tool it can be used for Language translation in order to translate one language to another some websites have already started to utilize this feature. Also, ChatGPT can be used in E-commerce for providing personalized recommendation based on customer preferences.
Before ChatGPT was introduced to the markets, we had similar powerful AI-based conversational bots, one such application is IBM Watson which is an advanced AI based intelligent system developed by IBM, Watson offers a wide range of services and capabilities for various applications, The core area of expertise for IBM Watson is Natural language understanding in which it employs powerful NLP based algorithms in order to interpret human language. Watson possess the capabilities which lets it understand the intent of the user that it is interacting with and engage in complex dialogue exchange it can also identify sentiments in the conversation and could also be used for language translation.
Watson employs machine learning algorithms and deep neural networks in order to process data which allows the pre-trained algorithm to generate accurate predictions and deliver valuable insights. Watson also provides industry specific solutions which cater the sections like healthcare, retail etc. On a broad basis Watson’s functionality is similar to that of ChatGPT however if we look on architectural basis both the models have divergent approaches where on one hand ChatGPT is based on the transformer architectural approach of NLP however if we look at Watson uses multiple algorithms based on the domain of the application it is deployed on, which makes Watson less slightly less intelligent in terms of algorithmic approach however more versatile in terms of diversity it can achieve by moulding for different platforms.
OpenAI claims that GPT has been deployed for benefit of humanity and for the advancement of general artificial intelligence however many people have started to express their concerns regarding the hidden objectives behind this application and the major proportion of their concerns revolve around this chatbot atrophying human intelligence for e.g., currently the algorithmic advancement of this chatbot allows it to write a flawless article on various topics and that too within seconds. Various articles in journals have been published on careful use of ChatGPT [1][2] and people are fearing that the major objective behind this chatbot is to automate tasks currently done by humans.
Despite these controversial opinions on its objective, if we carefully analyse the architecture, computational capabilities, and limitations of the application one can clearly deduce that ChatGPT has still not reached its levels of surpassing or even matching human intelligence. Despite looking extremely impressive, ChatGPT still has many limitations like inability to answer questions worded in a certain way also the quality of response delivered for certain inputs could sometimes seem to be plausible-sounding but makes no practical sense or excessively verbose. Another major limitation of this AI model, that completely rules out its possibility to replace human jobs is that being an AI model it has been trained on limited amount of data and in case of ChatGPT its training data is limited to the year 2021, which makes the Chabot completely useless when you ask it anything beyond that year, even the naïve questions like who won yesterday’s cricket match cannot be answered by the chatbot.
For now, the objective behind this chatbot is to provide human-like interactions and assist users in generation of coherent responses to their queries on various use-cases. For attaining this capability ChatGPT utilizes deep learning algorithm called transformer neural network [3]. The algorithm has been trained on large corpus of data dated before 2021.
Machine Learning Algorithm used — ChatGPT is based on generative pre-trained transformer model which is based on the principle of self-attention which allows the model to weigh the importance of various words when it is generating predictions. In contrast to traditional RNN based NLP models it makes more efficient parallel computations and helps in capturing long range dependencies in the text.
If we go into slight details of the architecture of the model than the model consists of encoder-decoder structure where the encoder processes the input text and the decoder generates the output response. During training the model learns to predict next word in a sentence given the context of the previous words. It also learns on capturing the relationship between various words.
Data Used and training process of algorithm — Coming to the data and process of training that has been used in training of the algorithm. The model was trained on massive datasets of texts from internet totalling close to 600GB and 300 billion words. To become proficient at understanding and predicting coherent response ChatGPT went through supervised learning stage where inputs were fed into the algorithm and the responses generated by the algorithm were compared against the actual outputs, if the algorithm got the answers incorrect then the correct answers were inputted back to the system which helped it learn and improve. The same process was repeated in a later stage where multiple answers were ranked by members of the team which enabled the model to learn through comparison, this process of constant learning helped ChatGPT to achieve incredible level of intelligence and versatility.
The primary source of data used for this training were the webtext which consisted of close to 8 million web pages collected from the web. In addition to this additional dataset were used for boosting the performance
of the model. The exact sources and the nature of these datasets have not been disclosed by OpenAI as some of the datasets may consist of proprietary and copyright texts.[4]
Pattern and Features Extracted from the Data — While training of the ChatGPT algorithm a variety of patterns were extracted from the texts on which the algorithm was trained like.
a. Syntax and Grammar: The algorithm learns the structure and rules of the language that include word orders, sentence formation, subject-verb agreement, and some other syntactic patterns
b. Semantic Relationship: The algorithm analyses the pattern and learns to recognize semantic relationships between the words in the sentence like synonyms, antonyms, and other semantic associations
c. Entity Recognition: The algorithm recognizes named entities like names of people locations, organizations, dates and some other frequently appearing entities.
d. World Knowledge: The model captures general knowledge on the world based on the data that has been presented to it in the training set, It may learn about geographical events, Historical events facts, scientific and mathematical concepts.
e. Stylistic and Register Variations: Depending on the training data ChatGPT algorithm learns different styles of writing texts like formal and informal use of language and register variations specific to certain domains or contexts.
f. Contextual Cues: The model learns to interpret contextual cues, including pronoun resolution, anaphora, and other contextual dependencies that help it understand and generate coherent responses.
These are the general patterns that are extracted by ChatGPT algorithm during the training and predicting phrase of the algorithm which enables the algorithm to understand the meaning of text and generate human-like responses.
Actions Taken by the Algorithm during prediction phrase — Post training when the input is fed to the chatbot, below actions are performed by the algorithm in order to generate intelligent response.
Like any other AI- Technology ChatGPT also comes with both risks and benefits below are some of the key points related to the risks benefits and social implications linked with ChatGPT.
- Enhanced Productivity — ChatGPT can assist user with numerous tasks that saves both time and efforts for the users.
- Access to Information — ChatGPT has gained vast knowledge through training on large corpus of data which enables it to provide answers to wide range of questions.
- Applicable to wide use-cases — ChatGPT, because of the AI it deploys enables it to aid various tasks like language translation, Business related tasks and even creation of creative contents.
• Inaccurate or biased information: ChatGPT generates responses based on patterns it learned from its training data, which can contain biases or inaccuracies. This can perpetuate existing biases or spread misinformation if not carefully addressed.
• Overreliance and deskilling: Depending excessively on ChatGPT for decision-making or critical tasks may lead to overreliance and deskilling of human abilities. It is important to strike a balance between automation and human judgment.
• Manipulation: ChatGPT can be exploited by malicious actors to generate convincing false information, engage in social engineering, or spread propaganda. This can have significant societal implications.
- Impact on human interaction: Excessive reliance on AI chat systems may reduce meaningful human interaction, potentially affecting social skills, empathy, and the sense of community.
The integration of AI systems like ChatGPT can disrupt job markets, leading to job displacement and the need for workforce reskilling or upskilling. Additionally, there can be unequal access to AI technologies due to language barriers, limited internet access, or socioeconomic factors. It is crucial to ensure fairness and equal access by addressing these gaps. As AI systems gain influence, it is important to establish clear guidelines, regulations, and accountability frameworks to tackle issues like bias, privacy, and transparency. The use of ChatGPT raises discussions about how humans and AI can collaborate effectively. Determining the right roles for AI systems and fostering collaboration is essential for societies to adapt to these technologies.
• Human Agency and Oversight: ChatGPT is a tool that requires human oversight and lacks autonomous decision-making capabilities, aligning with the principle of human autonomy.
• Technical Robustness and Safety: Extensive testing and evaluation ensure that ChatGPT is technically robust and safe. OpenAI has implemented safety measures and continues to enhance the system’s safety protocols.
• Privacy and Data Governance: ChatGPT does not store personal data or user interactions without explicit consent. However, it is important for deploying entities to comply with privacy regulations and ensure responsible data governance.
• Transparency: While ChatGPT generates responses based on patterns learned from training data, it does not explicitly disclose the sources used for those responses. OpenAI provides information about the model’s capabilities and limitations, though there is room for further improvement in transparency regarding training data and model internals.
• Diversity, Non-Discrimination, and Fairness: ChatGPT is trained on large datasets that may contain unintentional biases. OpenAI acknowledges this issue and actively works to address biases and enhance fairness in how ChatGPT responds to different inputs.
- Societal and Environmental Well-being: The impact of ChatGPT on societal and environmental well-being depends on how individuals and organizations use it. OpenAI promotes responsible use of the technology, recognizing the potential implications of AI systems on society.
- Accountability: OpenAI takes responsibility for the development and deployment of ChatGPT, addressing issues as they arise. However, users and deploying entities also have a role in ensuring responsible use and accountability for the technology.
1. “Tools such as ChatGPT Threaten Transparent Science; Here Are Our Ground Rules for Their Use.” 2023, https://doi.org/10.1038/d41586-023-00191-1 Accessed 11 May 2023.
2. Wingard, Jason. “ChatGPT: A Threat to Higher Education?” Forbes, 10 Jan. 2023, www.forbes.com/sites/jasonwingard/2023/01/10/chatgpt-a-threat-to-higher-education
3. Wu, Zonghan, et al. “A Comprehensive Survey on Graph Neural Networks.” IEEE Transactions on Neural Networks and Learning Systems, vol. 32, no. 1, Institute of Electrical and Electronics Engineers, Jan. 2021, pp. 4–24. https://doi.org/10.1109/tnnls.2020.2978386.
4. Ramponi, Marco. “How ChatGPT Actually Works.” News, Tutorials, AI Research, Apr. 2023, www.assemblyai.com/blog/how-chatgpt-actually-works.
5. Dowling, Michael, and Brian M. Lucey. “ChatGPT for (Finance) Research: The Bananarama Conjecture.” Finance Research Letters, vol. 53, Elsevier BV, Jan. 2023, p. 103662. https://doi.org/10.1016/j.frl.2023.103662.
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