Chat GPT (Generative Language Predictive Model) - Seminar Topic

Chat GPT Abstracts 

 Chat GPT is a language model developed by OpenAI. OpenAI is an artificial intelligence research organization based in San Francisco, California. It was founded in 2015 by a group of AI researchers and entrepreneurs, including Elon Musk, Sam Altman, and Greg Brockman.

OpenAI's goal is to develop high-quality AI technologies that are freely accessible to society at large . To achieve this, the organization conducts research in a wide variety of areas, such as deep learning, natural language processing, and machine gaming.

Chat GPT is one of the many projects that OpenAI has developed. It is a language model that has been trained with a large amount of text data to be able to perform a wide variety of tasks related to natural language.

Its ability to understand the context and intent behind user questions or queries make it a very useful tool for developing chatbots and improving accuracy in information search systems.What is GPT Chat used for?

Chat GPT has been trained to perform a wide variety of natural language related tasks.

This makes it a very useful tool for various applications , such as the automatic generation of responses in a chatbot or the improvement of precision in information search systems.

Here are four key points where the Chat GPT language model can be used:

Text Generation – The model can be used to generate coherent and natural text, whether in the form of stories, articles, or answers to questions.

Improving accuracy in search systems – The model can help improve accuracy in information search systems, as it can understand the context and intent behind user queries.

Chatbot development : The model can be used to develop chatbots that can have natural conversations with users, consistently and accurately responding to their questions.

Natural language processing improvement : The model can be used to improve natural language processing in various applications, such as machine translation or sentiment detection in text.

Where does Chat GPT get the information to be able to generate complex responses?

Chat GPT has been trained on a large amount of text data to be able to perform a wide variety of natural language related tasks. This text data includes books, articles, news, conversations , and more, which is used to teach the model how to comprehend and generate text in a coherent and natural way.

Therefore, Chat GPT obtains the necessary information to generate complex responses from this text data, which allows it to understand the context and intent behind user questions or queries.

In addition, the model can also use other types of information, such as images or videos , to improve its ability to understand the world around it and generate more accurate and consistent responses.

Other OpenAI language developments 

• Thanks to OpenAI, some of the world’s most advanced and highest performing language models have been developed. Some of OpenAI’s most prominent language models include:
• It is a generative language model that has been trained on a large number of texts and can generate high quality content on a wide range of tasks.
• It is an even more advanced generative language model than GPT, with significantly more processing power and performance.
• It is a natural language processing model that has revolutionized the way many NLP tasks are approached and has set new standards in performance, across a wide range of tasks.
• It is a text-based image generation model that can generate realistic images from natural language descriptions.
• It is the largest and most advanced language model that has been developed to date by OpenAI, with even greater processing power and performance than its predecessors.

It is an artificial intelligence that is trained to hold conversations , so you only have to ask it questions in a conventional way and it will understand. We will start by explaining what it is, and then we will give you some examples of what you can do with it.

https://www.xataka.com/basics/chatgpt-que-como-usarlo-que-puedes-hacer-este-chat-inteligencia-artificial

https://www.atriainnovation.com/en/how-does-chat-gpt-work/

https://edem.eu/chat-gpt-que-es-para-que-sirve-y-su-aplicacion-en-la-economia-explicado-por-chat-gpt/

Robotic Process Automation (RPA)

 What is RPA (Robotic Process Automation)?

 Robotic process automation , or RPA for its acronym in English, is a technology that allows configuring computer software or robots to emulate and execute in an integrated and autonomous (or semi-autonomous) manner the actions or steps of a human interaction with certain digital systems. , in such a way that it can execute a commercial process.

 In other words, it is programming a virtual robot to do what a person would do on their computer, this in a more expeditious, safer and uninterrupted way; activities such as data entry, processing standard transactions, or answering simple customer service questions.

 How does RPA robotic process automation work? 

RPA works by replicating the actions of a human being who interacts with applications or systems to perform different tasks, through a script that is executed by the bot under a defined set of business rules.

The operation of the types of process automation software  varies according to the tool that is being used and the type of process that is automated, however, there are some basic principles for all: programming interfaces and systems integration.

What are the benefits of implementing RPA?

 A robot does not have the needs of a worker: it does not have to rest, or go to the bathroom, or get sick. What's more, it can work 24 hours a day performing tasks in different areas of the organization, and in general the robot can significantly reduce the execution time of those tasks, also reducing the number of errors that humans can make that they used to do. the process. Thus, there are several benefits that derive from a correct implementation of RPA in your company:

Saved Man Hours:  RPA takes care of repetitive tasks saving valuable time and resources, plus they cost less than a full-time employee.

Error reduction: The fatigue or lack of knowledge that leads to human errors does not happen with bots, so the rate is reduced.

Agility and increased productivity: robots do more in less time and don't forget to consider that there is no time wasted correcting errors.

Improve response and compliance times: Automation reduces the risk of delays by introducing precision into your operations.

Making the most of employee time: automating repetitive administrative processes allows human workers to focus on complex value-added tasks for the business

What advantages and challenges are associated with RPA?

Automation has become a critical business issue in this digital age as organizations strive to boost productivity, improve user experience, and rapidly develop and launch new products and services. As the RPA technology market grows, IT leaders are increasingly interested in its ability to eliminate repetitive work, streamline operations, and reduce costs. However, as with any new technology, implementation brings advantages but also challenges. Below we indicate the most relevant in each case:

Advantage

Efficiency: RPA has been shown to increase employee productivity as they spend less time on repetitive tasks. Gartner has found that full-time employees can save up to 30% of their time with RPA. 

Accuracy – Data entry tasks are often more accurate when performed automated rather than manually. RPA tools also fully comply with organizational and industry policies.

Cost savings: by increasing the productivity of employees, the company saves money. Employees can then do higher value work in the same amount of time. 

Access to legacy technology – Organizations that are still using legacy technology often struggle to integrate these tools with other cloud-based systems. Through its user interface, RPA provides a simple entry point to exchange data with legacy systems.

Challenges

Replacement of human workers: One of the main criticisms leveled at RPA and other automation technologies is the potential elimination of data entry and other clerical roles.

Lack of intelligent features: RPA technology alone can only perform the tasks it copies from a script, so it is unable to learn and improve the script it performs. The next generation of AI and intelligent automation tools are being introduced to address this RPA blind spot.

Project complexity – Transformative RPA projects are often complex and require significant time investment to pay back; consequently, some of these projects fail before making a profit.

RPA and artificial intelligence

Robotic process automation is often mistaken for artificial intelligence (AI), but the two are distinctly different. AI combines cognitive automation, machine learning (ML), natural language processing (NLP), reasoning, hypothesis generation and analysis.

The critical difference is that RPA is process-driven, whereas AI is data-driven. RPA bots can only follow the processes defined by an end user, while AI bots use machine learning to recognize patterns in data, in particular unstructured data, and learn over time. Put differently, AI is intended to simulate human intelligence, while RPA is solely for replicating human-directed tasks. While the use of artificial intelligence and RPA tools minimize the need for human intervention, the way in which they automate processes is different. 

That said, RPA and AI also complement each other well. AI can help RPA automate tasks more fully and handle more complex use cases. RPA also enables AI insights to be actioned on more quickly instead of waiting on manual implementations.

Reference :

https://www.ibm.com/topics/rpa

https://en.wikipedia.org/wiki/Robotic_process_automation