The video discusses the advancements in AI language models, particularly GPT, and how they are trained using neural networks to understand natural language, but also highlights their limitations and potential ethical concerns.

• 💻
  00:00

  Computers with AI can now perform human-like tasks, GPT Chat gained 10 million users in its first month, NVIDIA GTC is the largest AI conference, and GPT is a word predictor that can generate coherent responses to practically anything.

  • Computers with artificial intelligence are now able to perform tasks that were previously thought to be exclusive to humans, such as winning painting contests, writing articles, passing difficult exams, and generating elaborate programs.
  • Gpt chat gained 10 million active users in its first month, breaking the record for the most rapidly adopted technology in history.
  • Chat is a website that functions as a technological oracle, providing answers and solutions to various tasks and problems.
  • The NVIDIA GTC is the largest conference on artificial intelligence, graphics acceleration, and data science, featuring over a thousand online sessions and a chance to win an RTX 4080 by registering to attend.
  • A transformer is a type of model that replicates the behavior of a system, such as a climate model or amp modeling, and a language model is a generative model that can identify and use parts of language.
  • GPT is a word predictor that can generate coherent responses to practically anything, and GPT Chat is a specially configured version that responds as if you were talking to someone in a chat.
• 🤖
  07:30

  Machine learning uses neural networks to teach machines to do complex tasks by finding patterns in data.

  • A language model can predict the most evil Roman emperor, but its reliability may be questionable due to potential propaganda and historical inaccuracies.
  • Autocompletion using artificial intelligence can generate unique text that appears to understand and analyze data, but in reality, it is only predicting words.
  • Talking to a machine requires precise and concise input, unlike talking to a human.
  • Chat GPT is a language model based on neural networks that can understand natural language and calculate the most probable words that will appear after the text.
  • Machine learning is a technology used to teach machines to do tasks that are difficult to program step by step.
  • Neural networks are trained to find patterns and draw conclusions from large amounts of data, such as recognizing images or creating deep fake videos.
• 💻
  13:10

  To train AI to understand natural language, we must convert words into numbers and catalog them as tokens to simplify and compress data.

  • Computers can never understand the concepts that words represent, as they only understand numbers and mathematical operations.
  • To train an AI to understand natural language, we must work with numbers as even letters, spaces, and emojis are identified by numbers, and create a program that understands human language as numbers.
  • The process of configuring a neural network involves finding repeating patterns within data and cataloging them as tokens to simplify and compress the data.
  • Identifying related terms in language can be done by marking tokens with similar patterns, such as the queen being related to the girl and princess tokens, and cats and dogs being related to the eating token.
  • A model can be trained to classify terms and patterns based on common markers, with each token having around 300 markers to indicate its relationship with other words.
  • Words can be represented in a three-dimensional cloud and mathematical operations can be performed to determine the distance between them.
• 🔍
  19:22

  Using embedding and GPT, search engines can group similar words and phrases to simplify and relate sentences, generating unique texts through compression and reconstruction.

  • The lecture discusses the use of embedding, a coding system that groups similar words and their meanings, and its applications in search engines like Google and GPT.
  • GPT represents words as tokens and phrases as sets of tokens, organizing them within a complex labeling system to determine their relationships and creating a large system with more than a thousand markers for each phrase.
  • Remove generic terms from texts to simplify the work and make it easier to relate sentences.
  • Simplification of sentences through lemmatization is useful in training models like GPT to save sequences and relate them to similar sentences using markers.
  • GPT analyzes and converts phrases into tokens, compresses information, and generates unique texts by converting tokens into the closest words within its system.
  • GPT chat generates original creations by reducing and compressing the basic meaning it stores and reconstructing it with its own words using a technique called sampling.
• 🤖
  26:02

  GPT struggles with context and forgets important details, making it difficult for neural networks to understand and remember whole sentences.

  • The system generates a random number to move slightly within the space of the ending and towards another sentence to maintain coherence in the conversation.
  • GPT chat creates coherent stories by combining patterns and tokens from millions of texts in its training phase.
  • GPT can search for a term and release phrases about the data, which are reconstructed to sound like they were written by a human, generating original phrases and sampling to release new phrases, but there is still a big problem that has existed for a long time.
  • Neural networks have two phases, training and inference, but the problem is that the network forgets what happens between steps, which affects both phases and makes it difficult for the network to understand and remember the whole sentence.
  • Context is crucial for a neural network to classify phrases and concepts, and without it, meaningful conversation is practically impossible.
  • Recurrent neural networks have two major problems: they forget the meaning of the first words after processing a certain number of words, and they cannot be parallelized.
• 💡
  32:25

  Transformers with attention layer revolutionized text generators, allowing for enriched and maintained relevant information throughout the entire process.

  • Transformers, a new way of organizing neural networks proposed by Google researchers in 2017, solved the problem of AI not being able to remember and analyze complex texts by adding a layer dedicated to attention, allowing the network to extract context by paying attention only to the most important details.
  • Using tokenization and vector embedding, attention vectors are used to determine the importance and context of words in a sentence, allowing for enriched and maintained relevant information throughout the entire process.
  • Attention layer in chat GPT allows for parallel processing of words, making language model training faster and more efficient.
  • Transformers with the layer of attention have revolutionized text generators, with Google's BERT being a prime example of its impressive precision in search results.
  • Brazilian travelers need a visa to visit the United States and can find the requirements on the embassy page, which is easily accessible through Open's system.
  • Titans of technology sector including Sam Altman, Brockman, Reid Hoffman, Jessica Livingston, Peter Fiel, and Elon Musk founded OpenAI, a non-profit organization aimed at advancing artificial intelligence, with one of their star projects being the creation of the largest neural network ever, GPT.
• 🤖
  37:43

  Neural networks with more parameters are more complex and flexible, as seen in GPT-3, GitHub Copilot, GPT Chat, and Chad GPT, but increasing parameters alone may not be enough to compete with humans.

  • Neural networks adjust their parameters to produce expected output results and the more parameters a machine has, the more complex and flexible it is.
  • GPT-3 was trained with 175 billion parameters using a combination of internet content, Reddit posts, books, and Wikipedia.
  • GitHub Copilot is an AI tool that generates code for programmers based on their requests directly from their editor.
  • OpenAI has released a refined version of GPT-3 called GPT Chat, which was trained using supervised learning with a new labeled dataset generated by humans.
  • Chad GPT is a highly effective chatbot due to its specially made data set and reinforcement learning from human feedback.
  • An AI startup created Chad GPT, a language model trained with human knowledge, but increasing parameters alone may not be enough to compete with humans.
• 🤖
  43:18

  Bing's GPT chat surpasses Google's language models in answering current affairs, but AI language models can easily give immoral answers, and Open AI's Chat GBD can be a source of misinformation.

  • GPT, an AI model trained with 75 billion parameters, requires a lot of computing power and was trained on Microsoft's Azure Cloud, which is available to OpenAI, a company in which Microsoft owns 49%.
  • Bing's GPT chat can answer current affairs by searching and summarizing web pages in real-time, surpassing Google's language models.
  • AI language models like GPT chat can easily give racist or immoral answers due to their uncontrolled learning process, making it difficult to provide a safe service.
  • Open AI is an artificial intelligence startup that has the advantage of not having many internal policies or bureaucracy, allowing them to innovate and launch products quickly.
  • Chat GBD's messages are not always true and can be a source of misinformation due to mixing concepts and providing similes instead of accurate information.
  • Google's mistake in a promotional video caused an 8% drop in the stock market, highlighting the difficulty of fixing errors in artificial intelligence and the ongoing war between technological companies to dominate semantic search engines.