Chat GPT | الدحيح

Introduction

The speaker talks about the importance of natural intelligence and experience over artificial intelligence.

• Mr. Basyouni is asked to fix a machine that was previously replaced with artificial intelligence.

• The speaker emphasizes the importance of experience coming from humans rather than machines.

• The speaker mentions that natural intelligence is better than artificial intelligence.

Fixing the Machine

Mr. Basyouni is asked to help fix the machine, and he provides some troubleshooting steps.

• Mr. Basyouni asks if they tried pressing the power button.

• They confirm that they did try pressing it but it didn't work.

• Mr. Basyouni asks if there are any other troubleshooting steps they have taken, but they say that's all they've done so far.

Historical Perspective on Technology

The speaker discusses historical perspectives on technology and how people have reacted to new inventions in the past.

• In 1986, math teachers protested against letting students use calculators because they feared it would destroy their calculation abilities.

• During the industrial revolution in Britain, a group appeared that objected to machine use in factories and even broke into factories to smash expensive machines.

• Despite fears towards new technological revolutions, we still continue to use machinery for most goods we use today.

Artificial Intelligence

The speaker discusses opinions on artificial intelligence (AI) and its potential impact on jobs.

• Opinions on AI can be divided into two teams: one sees AI as a threat to many jobs while others see it as an unreasonable fear.

• Both sides agree that AI still has a long way to go before it becomes smart enough to replace humans in certain roles.

• However, OpenAI company's announcement of ChatGPT in late November 2022 made both sides recalculate their views on AI.

Introduction to ChatGPT

In this section, the speaker introduces ChatGPT and its capabilities.

How does ChatGPT work?

• A Language Model is a program that can understand and produce language.

• The model predicts the next word in a sentence based on what it has learned from a large collection of words.

• The model looks at the last two or three words in a phrase to determine how it will complete the phrase.

Training the Model

• To train the model, you need a large collection of words from books, Wikipedia, or even Twitter.

• The model memorizes what comes after each word and its frequency of occurrence.

• The number of words that the model should look at is not constant. If it's too little, it won't understand anything. If it's too much, it'll be trained on unnecessary words.

Neural Networks

• Neural Networks are used to improve language models by making connections between separate things with similar meanings.

Importance of ChatGPT

In this section, the speaker discusses how quickly ChatGPT reached one million users and why we need to pause and look closely at its capabilities.

Rapid Growth

• ChatGPT reached one million users after only five days of release.

• Facebook took 10 months to reach one million users, Spotify took five months, and Instagram took 2.5 months.

Implications

• We need to examine how ChatGPT works and its potential impact on us.

• Will we stop writing emails and let ChatGPT do the work for us?

• Will our brains go on hiatus or try a career shift?

How Computers Understand Words

In this section, the speaker explains how computers understand words.

Language Models

• A Language Model is a program that can understand and produce language.

• The model predicts the next word in a sentence based on what it has learned from a large collection of words.

Completing Phrases

• The model completes phrases by looking at the last two or three words in a phrase to determine how it will complete the phrase.

• The model looks at the source it was trained on to determine what comes after each word.

Training the Model

• To train the model, you need a large collection of words from books, Wikipedia, or even Twitter.

• The model memorizes what comes after each word and its frequency of occurrence.

Neural Networks

• Neural Networks are used to improve language models by making connections between separate things with similar meanings.

Introduction to Neural Networks

This section introduces neural networks and how they work. It explains that neural networks take in any input and output, and do so through changing sets of numbers called parameters. The section also discusses how neural networks can be trained on pictures or words.

Neural Networks

• Neural networks take in any input and output, doing so through changing sets of numbers called parameters.

• Neural networks can be trained on pictures or words by naming each picture/word and what it contains.

• Neural networks can take one or more words as input and produce the next word as output.

Different Types of Neural Networks

This section discusses the different types of neural network architectures that exist, including Transformers.

Types of Neural Network Architectures

• There are many forms of neural network architectures that can be arranged in different ways to perform different tasks.

• The neural networks that differentiate between cats and dogs are not the same ones that can finish a phrase or predict stock market prices.

• One important type of neural network is the Transformer, which is represented by the T in ChatGPT.

Transformers

This section focuses on Transformers, explaining how they function similarly to a brain. It also discusses their breakthrough in solving a flaw with previous language models.

Functionality of Transformers

• Transformers function similarly to how a brain functions.

• Previous language models had a flaw where they forgot words at the beginning when completing phrases.

• Transformers solved this problem by giving each word only a percentage of its attention according to how important it is in a phrase, not its location.

Large Language Models

This section discusses the rise of large language models and their increasing number of parameters. It also explains how different types of large language models exist for different tasks.

Rise of Large Language Models

• Large language models have an increasing number of parameters that make them more simulative to our speech.

• Different types of large language models exist for different tasks, such as InstructGPT for executing orders and Codex for coding.

• OpenAI's GPT-3 has 175 billion parameters, making it one of the most advanced large language models.

Conclusion

This section concludes the video by addressing a question that may be on viewers' minds.

Final Thoughts

• The video ends with the speaker acknowledging that there may be a question in viewers' minds.

How ChatGBT was Trained

This section discusses how ChatGBT was trained and the research that went into making it a polite and objective language model.

Training Process

• ChatGBT was trained on a lot of texts from the internet like any other language model.

• It went through a new stage of training by interacting with people who marked its responses as wrong if they were inappropriate or controversial.

• OpenAI would notice these mistakes and fix them, then train it again to avoid such responses.

Politeness and Objectivity

• ChatGBT is designed to be neutral and objective, like an objective sports commentator.

• It is not supposed to give opinions on controversial topics or political events.

• If it senses that you want something suspicious out of it, it will give you an automated response that it's just a language model.

Tricks to Trick ChatGBT

This section discusses some examples of people trying to trick ChatGBT and how OpenAI is working to prevent this from happening.

Examples of Tricks

• People have tried asking ChatGBT inappropriate questions like how to make a bomb at home or for its opinion on controversial topics.

• Some students have used ChatGBT to do their assignments for them, while programmers have used it to write code faster and find bugs in their code.

• A professor gave ChatGBT an MBA exam which it passed.

Limitations

• Despite its impressive abilities, there are still certain things that ChatGBT cannot do such as solve riddles or perform logical thinking tasks.

• It can also mess up math problems because it wasn't specifically trained for this task.

Accessibility of Language Models

This section discusses the accessibility of language models today compared to the past.

Historic Moment

• The fact that language models are now accessible to anyone, not just experts, is a historic moment.

• Books have been written on how to use ChatGBT to write content and courses have been created to teach people how to use it in 30 minutes without rooting.

• Pages sell accounts because it's unavailable in Egypt.

The Future of AI in Business

In this section, the speaker discusses the potential for AI to be used in business models and how OpenAI's collaboration with Microsoft will bring GBT-4 to their products.

AI in Business Models

• The plan is to make an AI smart enough to be asked "What business model should we have for you?"

• ChatGBT can now answer questions about the best business model for a company.

• OpenAI announced its collaboration with Microsoft, and GBT-4 will soon be available in Word or Excel.

Limitations of AI

This section covers the limitations of current AI technology and how humans still have unique abilities that machines cannot replicate.

Writing Style and Personality

• Current AI technology cannot replicate certain elements of writing such as having a unique style or personality.

• ChatGBT had to be trained again to avoid writing racist words or encouraging harmful behavior because these are things that machines cannot understand just from learning language like humans do.

Human Adaptability

• Humans can adapt to anything, which is something that machines cannot do yet.

• It's important to learn how to use AI tools effectively because they will become increasingly prevalent in our lives.

Conclusion and Riddle

The speaker concludes by discussing attempts by OpenAI to replace him with an AI version and gives a riddle related to his name.

Conclusion

• While there are attempts by OpenAI to replace human workers with machines, it's important to remember that machines still have limitations and cannot replace human adaptability.

Riddle

• The speaker gives a riddle related to his name, challenging ChatGBT to figure it out.