OpenAI ChatGPT – Functional Analysis
It's been widely recognized for several weeks and months now how impressive ChatGPT is. As a solution architect who is passionate about this exciting product, I am curious about the different perspectives on it. I am fascinated by how it processes and presents responses, leading to conclusions and guiding users towards solutions.
Let's explore how ChatGPT takes your natural language queries and transforms them into meaningful responses through its data processing.
ChatGPT is an AI-based tool that works as a Generative pre-training Transformer with a Large Language Model to generate automated responses for a variety of tasks.
User queries are processed by OpenAI API Endpoint securely which is then processed by the Natural Language understanding module. Input query is processed into multiple tokens and fed forward to Deep Neural Network for further processing by numerous hidden layers in Large Language Model.
GPT-3: the third-generation GPT model, was trained on 175 billion parameters. GPT-3 thus acts as a pre-trained Large Language Model (LLM) that can be fine-tuned with very little data to accomplish novel Natural Language Processing (NLP) tasks
Enterprise Use case - Content Generation
Business Benefit ChatGPT works as Virtual Assistant, can raise requests on behalf of users, receives a query from users, and auto-generates the response in a friendly, human-like, and more accurate manner better than the majority of virtual assistants that exists today.
Exceptionally well, it can generate draft content for a document, technical writing, proposal, executive summary of a story or even detailing a story. Additionally, It can also be used for Text completion, Formatting, Correction, and many more.
ChatGPT is a deep neural network that uses a transformer architecture, which is a type of neural network architecture that is particularly well-suited for processing sequential data like natural language. The transformer architecture is made up of an encoder and a decoder, which work together to process the input and generate a response.
Know-how Transformer architecture in a Deep Neural Network consists of an Attention layer to accept user input queries and generate output text after processing which is similar to human writing. Processing by Deep Neural Network (DNN) happens through several layers. The input layer of the "Attention Layer" breaks the query into multiple tokens and depending on the complexity of the query, and the number of tokens, a few hidden neuron layers are used to generate the response.
In order to use a deep neural network such as ChatGPT, it would be necessary to have a training data set, which is a set of input-output pairs that the model can learn from. The model is trained using this data set to learn patterns in the input-output pairs. Once the model is trained, it can then be used to generate responses to new inputs.
In terms of interaction with an LLM(Language Model), ChatGPT can be fine-tuned on the specific task or language model to make it more accurate in a specific domain or task.
Enterprise Use Case - Automating Repetitive Tasks
Business Benefit By using artificial intelligence and machine learning algorithms, companies can reduce manual labour, increase efficiency, and reduce errors. Automating repetitive tasks can take many forms, such as automating data entry, customer service, and other manual or semi-automated processes. These automations can be accomplished using chatbots, virtual assistants, or other AI-powered applications.
领英推荐
If effectively used, ChatGPT will greatly simplify the user interaction module to a greater level. It also makes an easy-to-deploy Cloud-based resource Deployment process with faster-to-market achievements, manages complex cloud resource configuration drift or changes the configuration on demands. Interestingly, the majority of the business use cases detailed in my last article Artificial Intelligence in Cloud Operations can also be achieved using similar tools in future.
Know-how It can be easily integrated with the Cloud provider’s Application Programming Interface (API) significantly reducing development activity for customized automation.
By leveraging AI and machine learning technologies, companies can streamline their processes, increase efficiency, and improve the overall customer experience. Automating repetitive tasks through cloud-based AI and machine learning algorithms can increase productivity, increase the accuracy and reliability of the outcome, save costs by reducing manual labour, custom automation or additional software or hardware and improve customer satisfaction.
Enterprise Use Case - Automation and Code Development
Business Benefit The Codex model series is a descendant of the GPT-3 series that's been trained on both natural language and billions of lines of code. It's most capable in Python and proficient in over a dozen languages including JavaScript, Go, Perl, PHP, Ruby, Swift, TypeScript, SQL, and even Shell.?
Codex can perform a variety of tasks, including
Know-How Currently, there are two Codex models available
Davinci (code-davinci-002) - Most capable Codex model. This model is particularly good at translating natural language to code. It can process up to 8000 tokens.
Cushman (code-cushman-001) - Less capable than Davinci, but much faster. It can process up to 2048 tokens.
The Beginning
There are numerous potential use cases in various industries and business areas that are yet to be explored. This is simply the beginning of a promising journey towards shaping our future.
Get ready for a wild ride! The possibilities of using AI and machine learning are just the tip of the iceberg. Who knows what amazing adventures await us as we dive deeper into this exciting world?
Master Student at K. N. Toosi University of Technology
2 个月that's amazing