GenAI is the generative side of artificial intelligence that focuses on the creation of original content, such as text, music or images. This is a technological area that is on trend at the moment, and it is normal that, as it is new, it generates doubts. That’s why our experts at Nubiral, have answered in this article the most frequently asked questions in order to get you closer to the future. Don’t miss it.
1. What is OpenAI?
OpenAI is an artificial intelligence (AI) research company that develops and promotes safe and beneficial technologies for humanity. It is known for its chat GPT, Codex and Dall-E language models.
2. What is GPT and how does it work?
GPT (Generative Pre-trained Transformer) is an LLM developed by OpenAI. It is based on the Transformer architecture, a deep neural network designed to process sequences of words and capture the connection between them. It was trained on large amounts of data so that it could learn language patterns and structures to generate coherent and contextually relevant text.
3. What are the differences between GPT 3.5 and GPT 4?
GPT-4 offers better visual comprehension capabilities, reduced AI hallucinations, increased intelligence and performance, creative responses, improved security, enhanced window and context size, and a greater ability to understand and analyze images.
| Appearance | GPT-3.5 | GPT-4 |
| Visual input commands | Only accepts text requests. | It is multimodal and supports text and visual inputs, including images, photographs and handwritten math problems. |
| Understanding images | Cannot comprehend images. | Can understand and describe virtually any image, identifying specific objects within an image with multiple visual elements. |
| AI hallucination | It has a higher probability of generating meaningless and false information that seems like true. | It is 19% to 29% less likely to hallucinate compared to GPT-3.5. |
| Parameters | 175 billion parameters. | It far exceeds GPT-3.5, although the exact number has not been disclosed. |
| Creativity in responses | It provides creative responses to prompts, but its creativity is limited. | Provides more creative responses and shows greater ingenuity in solving difficult problems. |
| Security in responses | Safety approach based on ex-post moderation. | It has built-in security measures, resulting in fewer toxic responses and less likelihood that the system will respond to requests for disallowed content. |
| Context size and window | They are limited, which can result in loss of conversational context and failure to follow instructions as the conversation progresses. In addition, text length is limited and may require splitting into several fragments.
Limit tokens in context: |
It is significantly superior, allowing users to better retain and remember the context of a conversation longer, as well as follow instructions more consistently.
Limit tokens in context: |
4. What is LLM in GPT?
LLM (Large Language Model) are AI systems designed to understand and generate natural language text, i.e. they can «understand» and «speak» in several languages. They accept natural language text as input and also return text as output. The GPT model belongs to the LLM family.
5. What is embedding?
Embedding is a numerical representation (vector) of words or phrases that capture the semantic meaning and connection between them. They are stored in vector bases to be used by an LLM.
6. What is ‘context’, and what are ‘tokens’?
Context refers to the set of words and sentences surrounding a given piece of text that the model uses to understand that text and generate coherent and relevant responses. The length of context is measured in tokens. The GPT 3.5 model accepts up to 4,000 context tokens.
The way to measure the consumption of these OpenAI models, both input and output, is through tokens. GPT uses Byte Pair Encoding (BPE) tokenization. Approximately and as a general measure, 1,000 tokens are equivalent to 750 words.
7. What is ‘Prompt Engineering’, and what is its importance?
The way we «configure» models such as GPT to obtain the results desired by the user is through the design of the instructions or queries provided to the user. This is prompt engineering and it is vital: the language model interprets and generates text based on the instructions or queries presented to it. If the prompt is correctly formulated, a more accurate and appropriate response to the user’s intent is obtained.
8. How is a GPT language model trained, and what kind of data is used?
To train a GPT language model, large amounts of text from various sources such as books, news articles and web pages are used to train the model to predict the next word or phrase in a given text. The model learns to capture language patterns and structures through this massive training.
9. What are ‘system messages’ and ‘few shots learning methods’?
Within the field of prompt engineering, the concept of system message refers to a part of the initial text or prompt that is used to influence the behavior or response of the language model.
When adjusting the prompt to obtain more accurate responses, we can use the few-shot learning method, which is the ability of a model to learn and generalize from a limited number of examples.
10. What is the difference between ‘fine-tuning’ and ‘pre-training’ in GPT?
GPT is a semi-supervised learning bot, which means that it is pre-trained and allows the user the possibility of fine-tuning the model.
Pre-training is the initial stage of training a GPT model, in which a massive text is used to learn general language representations. Fine-tuning is the later stage, in which the model is tuned or specialized for specific tasks or domains using a smaller and specific data set.
11. How are biases handled in GPT language models?
Biases in GPT language models can be addressed through careful selection of training data, as well as through the application of correction and adjustment techniques. In addition, diversity in the data and human review at the text generation stage help mitigate unwanted biases.
12. ¿Cuáles son las aplicaciones de GPT?
GPT ofrece diversas aplicaciones: generación automática de contenido, traducción automática, resumen de texto, asistencia en la escritura, generación de respuestas en chatbots o análisis de sentimientos en redes sociales, entre muchas otras.
What are the main use cases of GPT in organizations?
GPT offers numerous use cases for organizations to extract maximum value. These are some of the most significant ones:
– Content generation. Automatic and efficient creation of coherent and relevant text such as articles, product descriptions or efficient and accurate responses.
– Source code writing. Automatically performs documentation and enhancements on source code, including readme files on projects or formatting and logical structure corrections based on best practices.
– Conversational wizards with real-time file processing. Can answer specific questions about information stored in documents, audios and images. Applies to numerous industries:
- Education: to summarize recorded lectures or for teachers to review their students’ academic history.
- Health: to read reports and speed up the analysis of a patient’s medical history.
- Security: to detect potentially dangerous elements from video analysis.
- Banking: to process documents directly from their image or to detect possible fraud from document forgery or identity theft.
- Digital marketing: to detect customer emotions in interactions to improve their experience, or to help in the planning, execution and monitoring of campaigns.
– Document management. Resolves user queries from large volumes of stored and indexed information to better capitalize its own data and also to increase security levels. HR can use it to process resumes, job application forms and other documents related to recruitment, or for automatic analysis of skills, work experience, references and matching profiles with job requirements. In logistics and transportation, it is used to manage large volumes of documents related to the supply chain to optimize inventory management, track shipments and streamline invoicing processes. And in legal areas, to search and extract from digitized legal documents through certain keywords or identify specific clauses, legal terms and case summaries to streamline legal document review and ease information searches.
– Query generation and integration with Power BI. Answers natural language queries from information stored in structured databases. Transcribes user requirements from plain text to SQL queries. It then connects to the database to perform the queries and generates a response in natural language from the data obtained. Moreover, it integrates with PowerBI to generate an interactive dashboard without the need for advanced technical knowledge.
Do you want to know how GenIA can help you optimize your company’s processes and improve its efficiency? At Nubiral we are experts in developing technological solutions, including AI. Contact us and find out what we have to offer!
