Large Language Models (LLMs) - Everything You NEED To Know

Summary

This video offers a comprehensive guide to artificial intelligence and large language models (LLMs), explaining their evolution, functioning, and applications. It covers the history of LLMs from Eliza in 1966 to modern models like GPT-4 with 1.76 trillion parameters. The video discusses how LLMs work, including tokenization, embeddings, and Transformers. It also addresses ethical concerns, limitations, and the future of AI, including advancements in knowledge distillation and multimodality.

Takeaways

• 🌐 Large Language Models (LLMs) are a type of neural network trained on vast amounts of text data, including web content and books.
• 🤖 LLMs differ from traditional programming by teaching computers how to learn rather than instructing them with explicit rules.
• 📈 The capabilities of LLMs have evolved significantly since the 1966 ELIZA model, with advancements like the Transformer architecture enabling more sophisticated language understanding.
• 🚀 Popular applications of LLMs include image recognition, summarization, text generation, and programming assistance.
• 📚 The training process for LLMs involves tokenization, embeddings, and the use of Transformer algorithms to understand and generate human-like text.
• 💾 Vector databases play a crucial role in how LLMs process language by representing words as numerical vectors to capture semantic meanings.
• 🔍 The training of LLMs requires extensive data and computational resources, making it a costly and complex endeavor.
• 📉 Despite their capabilities, LLMs have limitations including struggles with logic, potential biases from training data, and the risk of generating false information.
• 🔧 Fine-tuning allows for the customization of pre-trained LLMs for specific tasks, making them more efficient and effective for targeted applications.
• 🌟 Ongoing research in LLMs focuses on knowledge distillation, retrieval augmented generation, and improving reasoning abilities to enhance their practical utility.
• 🔮 Ethical considerations around LLMs include the use of copyrighted material, potential for misuse, and the broader impact on various professions.

Q & A

What is the primary focus of the video?

The video focuses on explaining large language models (LLMs), their workings, ethical considerations, applications, and the evolution of these technologies.

What is a large language model (LLM)?

LLMs are a type of neural network trained on vast amounts of text data, designed to understand and generate human-like text based on patterns learned from the data.

How do LLMs differ from traditional programming?

Traditional programming is instruction-based, where you explicitly tell the computer what to do. LLMs, on the other hand, are trained to learn how to do things, offering a more flexible approach that can adapt to various applications.

What is a neural network?

A neural network is a series of algorithms designed to recognize patterns in data by simulating the way the human brain works.

What is the significance of the 'Transformers' research paper in the context of LLMs?

The 'Transformers' paper introduced a new architecture that greatly reduced training time and introduced features like self-attention, which revolutionized the development of LLMs, including GPT and Bert.

How does tokenization work in LLMs?

Tokenization is the process of splitting text into individual tokens, which are essentially parts of words, allowing models to understand each word in the context it is used.

What are embeddings in the context of LLMs?

Embeddings are numerical representations of tokens that help computers understand the meaning of words and their relationships to other words.

How are large language models trained?

LLMs are trained by feeding pre-processed text data into the model, which then uses algorithms like Transformers to predict the next word based on context, adjusting the model's weights through millions of iterations.

What is fine-tuning in the context of LLMs?

Fine-tuning involves adjusting a pre-trained LLM using specific data to improve its performance for a particular task, such as understanding pizza-related terminology for a pizza ordering system.

What are some limitations and challenges of LLMs?

LLMs have limitations such as struggles with math and logic, potential biases from training data, the risk of spreading misinformation, high hardware requirements, and ethical concerns regarding data usage and potential misuse.

What are some real-world applications of LLMs?

LLMs can be used for language translation, coding assistance, summarization, question answering, essay writing, translation, and even image and video creation.

Outlines

00:00

💡 Introduction to AI and Large Language Models

The video introduces the topic of artificial intelligence (AI) and large language models (LLMs), emphasizing their rapid evolution and impact on various industries. It mentions products like ChatGPT and discusses the collaboration with AI Camp, a program teaching high school students about AI. The script explains what LLMs are, their training process on vast amounts of text data, and how they differ from traditional programming by focusing on learning rather than executing instructions. The video promises to cover the workings, ethics, applications, and future of LLMs.

05:02

🌐 History and Evolution of Large Language Models

This section delves into the history of LLMs, starting with the ELIZA model from 1966. It highlights the introduction of recurrent neural networks (RNNs) and the pivotal 2017 paper 'Attention Is All You Need' by Google DeepMind, which led to the development of the Transformer architecture. This architecture revolutionized LLMs by allowing for more efficient training and understanding of context. The narrative continues with the progression from GPT-1 to GPT-4, discussing the increase in parameters and capabilities, concluding with the current state of LLMs and their potential for improvement.

10:04

🔍 How Large Language Models Work

The script explains the technical process behind how LLMs operate, focusing on three key steps: tokenization, embeddings, and the use of Transformer models. Tokenization involves breaking down text into tokens, while embeddings convert these tokens into numerical representations that computers can process. The Transformer algorithm uses these embeddings to predict the next word in a sequence, based on the context provided by previous words. The section also discusses vector databases, which store and retrieve word embeddings to understand relationships between words.

15:05

📚 Training Large Language Models

This part of the script describes the data collection and training process for LLMs. It emphasizes the need for vast amounts of high-quality data, as the model's capabilities are directly influenced by the data it's trained on. The script mentions the pre-processing of data, which can be time-consuming and resource-intensive. It also covers the training process, where models adjust their weights to optimize output based on the training data. The evaluation phase uses metrics like perplexity and reinforcement learning from human feedback to fine-tune the model.

20:06

🛠️ Fine-Tuning Large Language Models

The script introduces fine-tuning, a process where pre-trained LLMs are adjusted for specific use cases. It provides an example of fine-tuning a model to handle pizza orders, explaining how the model updates its weights to better understand terminology and contexts relevant to that task. Fine-tuning is presented as a faster and more accurate method than full training, with the quality heavily dependent on the data used for fine-tuning.

25:08

🚧 Limitations and Challenges of LLMs

This section discusses the limitations and challenges associated with LLMs, such as their struggles with math and logic, potential biases inherited from training data, and the issue of 'hallucinations' where models may provide incorrect information with confidence. It also touches on the ethical considerations surrounding the training of these models on copyrighted material and the potential for misuse, as well as the disruption they could cause to various professions.

🌟 Real-world Applications and Future of LLMs

The script highlights the wide range of applications for LLMs, from chatbots and language translation to coding assistance and creative writing. It discusses current advancements like knowledge distillation, which makes LLMs more efficient, and retrieval augmented generation, which allows models to access external information. The section also looks towards the future, discussing potential improvements in fact-checking, multimodal input, reasoning abilities, and context size.

📢 Conclusion and Call to Action

The video concludes with a call to action for viewers, encouraging them to like and subscribe for more AI-related content. It also promotes AI Camp, providing information for those interested in learning more about AI.

Keywords

💡Large Language Models (LLMs)

Large Language Models (LLMs) refer to a type of neural network trained on vast amounts of text data, which can be found online. They are designed to understand and generate human-like text. In the video, LLMs are central to the discussion as they represent a revolutionary leap in AI's ability to process and create language, with applications ranging from chatbots to content generation. The script mentions how LLMs learn from a wide array of text sources, highlighting their flexibility and the potential for diverse applications.

💡Neural Networks

Neural Networks are a set of algorithms modeled loosely after the human brain, designed to recognize patterns in data. They are the foundational technology behind LLMs. The video explains that neural networks simulate the brain's workings to process complex information. An example from the script is the comparison of traditional programming, which is instruction-based, to the learning approach of neural networks, emphasizing the latter's ability to adapt and learn.

💡Tokenization

Tokenization is the process of splitting text into individual elements or 'tokens', which helps models understand words in isolation and in context. It's a crucial step in how LLMs parse and analyze text data. The script uses the example of the sentence 'What is the tallest building?' to illustrate how different words are separated into tokens, noting how the model takes context into account when tokenizing words like 'tallest' and 'building'.

💡Embeddings

Embeddings are numerical representations of tokens that LLMs use to understand the relationships between words. They are a vital component of how LLMs process language, as they translate words into a format that computers can analyze. The video describes how embeddings work by assigning each word a vector, which captures its semantic meaning and its relation to other words, allowing the model to predict the next word in a sequence based on the context provided by previous words.

💡Transformers

Transformers is an architecture that revolutionized how LLMs process language by introducing self-attention mechanisms. It allows models to better understand the context of words within a sentence. The script explains that Transformers use multi-head attention to process vectors into matrices, which are then transformed into an output that corresponds to the next word in a sentence, demonstrating a significant advancement in AI's ability to generate human-like text.

💡Training

Training in the context of LLMs involves feeding pre-processed text data into the model so that it can learn patterns and make predictions. It's a resource-intensive process that requires massive amounts of data. The video emphasizes the importance of data quality in training, stating that 'garbage in, garbage out,' and illustrates the scale of data involved with examples, such as comparing a small amount of text to the vast datasets used in training.

💡Fine-tuning

Fine-tuning is the process of adjusting a pre-trained LLM for specific tasks or use cases. It allows for customization of general-purpose models to better suit particular needs. The video uses the example of fine-tuning a model to handle pizza orders, explaining how the model updates its weights to better understand pizza-related terminology and conversations, thus becoming more accurate and efficient for that specific task.

💡Ethical Considerations

Ethical Considerations address the moral implications and potential misuse of LLMs. The video touches on issues like bias in training data, the potential for models to be used maliciously, and the disruption of professional fields. It also raises the question of alignment, ensuring AI developments are in harmony with human values and interests, which is crucial as LLMs become more integrated into various aspects of society.

💡Applications

Applications refer to the various uses of LLMs across different industries and tasks. The video highlights the versatility of LLMs, mentioning their use in chatbots, language translation, coding assistance, summarization, and creative writing. It emphasizes how LLMs are transforming industries by automating and enhancing tasks that previously required human effort, showcasing the broad impact of this technology.

💡Challenges

Challenges highlight the difficulties and limitations that LLMs face, such as struggles with logic and reasoning, the risk of spreading misinformation, and the high computational cost. The script points out that despite their capabilities, LLMs are not infallible and can 'hallucinate' by making things up or getting details wrong with confidence, indicating areas where further development and caution are needed.

Highlights

Introduction to large language models (LLMs) and their impact on various industries.

Definition of LLMs as a type of neural network trained on vast text data.

Explanation of neural networks and their attempt to simulate human brain functions.

Difference between LLMs and traditional programming approaches.

Applications of LLMs in image recognition and their flexibility.

Evolution of LLMs from Eliza in 1966 to the advanced models of today.

The introduction of RNNs and their capability to predict words.

The significance of the 2017 paper 'Attention is all you need' in the development of LLMs.

The progression from GPT-1 to GPT-3 and the public's increasing awareness of LLMs.

How LLMs work in three steps: tokenization, embeddings, and Transformers.

The role of vector databases in capturing word relationships for LLMs.

The training process of LLMs, emphasizing the importance of data quality.

The cost and resources required for training large language models.

Fine-tuning of pre-trained models for specific use cases.

AI Camp collaboration and its focus on teaching AI to high school students.

Limitations of LLMs including struggles with math, logic, and bias.

Challenges faced by LLMs such as hallucinations and hardware intensity.

Ethical considerations regarding the training data and potential misuse of LLMs.

Real-world applications of LLMs spanning various fields like translation and coding.

Current advancements in knowledge distillation and retrieval augmented generation.

Ethical considerations around AI alignment and the future of large language models.

Cutting-edge improvements in LLMs including fact-checking, mixture of experts, and multimodality.

Transcripts

this video is going to give you

everything you need to go from knowing

absolutely nothing about artificial

intelligence and large language models

to having a solid foundation of how

these revolutionary Technologies work

over the past year artificial

intelligence has completely changed the

world with products like chat PT

potentially appending every single

industry and how people interact with

technology in general and in this video

I will be focusing on llms how they work

ethical cons iterations applications and

so much more and this video was created

in collaboration with an incredible

program called AI camp in which high

school students learn all about

artificial intelligence and I'll talk

more about that later in the video let's

go so first what is an llm is it

different from Ai and how is chat GPT

related to all of this llms stand for

large language models which is a type of

neural network that's trained on massive

amounts of text data it's generally

trained on data that can be found online

everything from web scraping to books to

transcripts anything that is text based

can be trained into a large language

model and taking a step back what is a

neural network a neural network is

essentially a series of algorithms that

try to recognize patterns in data and

really what they're trying to do is

simulate how the human brain works and

llms are a specific type of neural

network that focus on understanding

natural language and as mentioned llms

learn by reading tons of books articles

internet texts and there's really no

limitation there and so how do llms

differ from traditional programming well

with traditional programming it's

instruction based which means if x then

why you're explicitly telling the

computer what to do you're giving it a

set of instructions to execute but with

llms it's a completely different story

you're teaching the computer not how to

do things but how to learn how to do

things things and this is a much more

flexible approach and is really good for

a lot of different applications where

previously traditional coding could not

accomplish them so one example

application is image recognition with

image recognition traditional

programming would require you to

hardcode every single rule for how to

let's say identify different letters so

a b c d but if you're handwriting these

letters everybody's handwritten letters

look different so how do you use

traditional programming to identify

every single possible variation well

that's where this AI approach comes in

instead of giving a computer explicit

instructions for how to identify a

handwritten letter you instead give it a

bunch of examples of what handwritten

letters look like and then it can infer

what a new handwritten letter looks like

based on all of the examples that it has

what also sets machine learning and

large language models apart and this new

approach to programming is that they are

much more more flexible much more

adaptable meaning they can learn from

their mistakes and inaccuracies and are

thus so much more scalable than

traditional programming llms are

incredibly powerful at a wide range of

tasks including summarization text

generation creative writing question and

answer programming and if you've watched

any of my videos you know how powerful

these large language models can be and

they're only getting better know that

right now large language models and a in

general are the worst they'll ever be

and as we're generating more data on the

internet and as we use synthetic data

which means data created by other large

language models these models are going

to get better rapidly and it's super

exciting to think about what the future

holds now let's talk a little bit about

the history and evolution of large

language models we're going to cover

just a few of the large language models

today in this section the history of

llms traces all the way back to the

Eliza model which was from

1966 which was really the first first

language model it had pre-programmed

answers based on keywords it had a very

limited understanding of the English

language and like many early language

models you started to see holes in its

logic after a few back and forth in a

conversation and then after that

language models really didn't evolve for

a very long time although technically

the first recurrent neural network was

created in 1924 or RNN they weren't

really able to learn until 1972 and

these new learning language models are a

series of neural networks with layers

and weights and a whole bunch of stuff

that I'm not going to get into in this

video and rnns were really the first

technology that was able to predict the

next word in a sentence rather than

having everything pre-programmed for it

and that was really the basis for how

current large language models work and

even after this and the Advent of deep

learning in the early 2000s the field of

AI evolved very slowly with language

models far behind what we see today this

all changed in 2017 where the Google

Deep Mind team released a research paper

about a new technology called

Transformers and this paper was called

attention is all you need and a quick

side note I don't think Google even knew

quite what they had published at that

time but that same paper is what led

open AI to develop chat GPT so obviously

other computer scientists saw the

potential for the Transformers

architecture with this new Transformers

architecture it was far more advanced it

required decreased training time and it

had many other features like self

attention which I'll cover later in this

video Transformers allowed for

pre-trained large language models like

gpt1 which was developed by open AI in

2018 it had 117 million parameters and

it was completely revolutionary but soon

to be outclassed by other llms then

after that Bert was released beert in

2018 that had 340 million parameters and

had bir directionality which means it

had the ability to process text in both

directions which helped it have a better

understanding of context and as

comparison a unidirectional model only

has an understanding of the words that

came before the target text and after

this llms didn't develop a lot of new

technology but they did increase greatly

in scale gpt2 was released in early 2019

and had 2.5 billion parameters then GPT

3 in June of 2020 with 175 billion

paramet

and it was at this point that the public

started noticing large language models

GPT had a much better understanding of

natural language than any of its

predecessors and this is the type of

model that powers chat GPT which is

probably the model that you're most

familiar with and chat GPT became so

popular because it was so much more

accurate than anything anyone had ever

seen before and it was really because of

its size and because it was now built

into this chatbot format anybody could

jump in and really understand how to

interact act with this model Chad GPT

3.5 came out in December of 2022 and

started this current wave of AI that we

see today then in March 2023 GPT 4 was

released and it was incredible and still

is incredible to this day it had a

whopping reported 1.76 trillion

parameters and uses likely a mixture of

experts approach which means it has

multiple models that are all fine-tuned

for specific use cases and then when

somebody asks a question to it it

chooses which of those models to use and

then they added multimodality and a

bunch of other features and that brings

us to where we are today all right now

let's talk about how llms actually work

in a little bit more detail the process

of how large language models work can be

split into three steps the first of

these steps is called tokenization and

there are neural networks that are

trained to split long text into

individual tokens and a token is

essentially about 34s of a word so if

it's a shorter word like high or that or

there it's probably just one token but

if you have a longer word like

summarization it's going to be split

into multiple pieces and the way that

tokenization happens is actually

different for every model some of them

separate prefixes and suffixes let's

look at an example what is the tallest

building so what is the tallest building

are all separate tokens and so that

separates the suffix off of tallest but

not building because it is taking the

context into account and this step is

done so models can understand each word

individually just like humans we

understand each word individually and as

groupings of words and then the second

step of llms is something called

embeddings the large language models

turns those tokens into embedding

vectors turning those tokens into

essentially a bunch of numerical

representations of those tokens numbers

and this makes it significantly easier

for the computer to read and understand

each word and how the different words

relate to each other and these numbers

all correspond with the position in an

embeddings Vector database and then the

final step in the process is

Transformers which we'll get to in a

little bit but first let's talk about

Vector databases and I'm going to use

the terms word and token interchangeably

so just keep that in mind because

they're almost the same thing not quite

but almost and so these word embeddings

that I've been talking about are placed

into something called a vector database

these databases are storage and

retrieval mechanisms that are highly

optimized for vectors and again those

are just numbers long series of numbers

because they're converted into these

vectors they can easily see which words

are related to other words based on how

similar they are how close they are

based on their embeddings and that is

how the large language model is able to

predict the next word based on the

previous words Vector databases capture

the relationship between data as vectors

in multidimensional space I know that

sounds complicated but it's really just

a lot of numbers vectors are objects

with a magnitude and a direction which

both influence how similar one vector is

to another and that is how llms

represent words based on those numbers

each word gets turned into a vector

capturing semantic meaning and its

relationship to other words so here's an

example the words book and worm which

independently might not look like

they're related to each other but they

are related Concepts because they

frequently appear together a bookworm

somebody who likes to read a lot and

because of that they will have

embeddings that look close to each other

and so models build up an understanding

of natural language using these

embeddings and looking for similarity of

different words terms groupings of words

and all of these nuanced relationships

and the vector format helps models

understand natural language better than

other formats and you can kind of think

of all this like a map if you have a map

with two landmarks that are close to

each other they're likely going to have

very similar coordinates so it's kind of

like that okay now let's talk about

Transformers mat Matrix representations

can be made out of those vectors that we

were just talking about this is done by

extracting some information out of the

numbers and placing all of the

information into a matrix through an

algorithm called multihead attention the

output of the multi-head attention

algorithm is a set of numbers which

tells the model how much the words and

its order are contributing to the

sentence as a whole we transform the

input Matrix into an output Matrix which

will then correspond with a word having

the same values as that output Matrix so

basically we're taking that input Matrix

converting it into an output Matrix and

then converting it into natural language

and the word is the final output of this

whole process this transformation is

done by the algorithm that was created

during the training process so the

model's understanding of how to do this

transformation is based on all of its

knowledge that it was trained with all

of that text Data from the internet from

books from articles Etc and it learned

which sequences of of words go together

and their corresponding next words based

on the weights determined during

training Transformers use an attention

mechanism to understand the context of

words within a sentence it involves

calculations with the dot product which

is essentially a number representing how

much the word contributed to the

sentence it will find the difference

between the dot products of words and

give it correspondingly large values for

attention and it will take that word

into account more if it has higher

attention now now let's talk about how

large language models actually get

trained the first step of training a

large language model is collecting the

data you need a lot of data when I say

billions of parameters that is just a

measure of how much data is actually

going into training these models and you

need to find a really good data set if

you have really bad data going into a

model then you're going to have a really

bad model garbage in garbage out so if a

data set is incomplete or biased the

large language model will be also and

data sets are huge we're talking about

massive massive amounts of data they

take data in from web pages from books

from conversations from Reddit posts

from xposts from YouTube transcriptions

basically anywhere where we can get some

Text data that data is becoming so

valuable let me put into context how

massive the data sets we're talking

about really are so here's a little bit

of text which is 276 tokens that's it

now if we zoom out that one pixel is

that many tokens and now here's a

representation of 285 million tokens

which is

0.02% of the 1.3 trillion tokens that

some large language models take to train

and there's an entire science behind

data pre-processing which prepares the

data to be used to train a model

everything from looking at the data

quality to labeling consistency data

cleaning data transformation and data

reduction but I'm not going to go too

deep into that and this pre-processing

can take a long time and it depends on

the type of machine being used how much

processing power you have the size of

the data set the number of

pre-processing steps and a whole bunch

of other factors that make it really

difficult to know exactly how long

pre-processing is going to take but one

thing that we know takes a long time is

the actual training companies like

Nvidia are building Hardware

specifically tailored for the math

behind large language models and this

Hardware is constantly getting better

the software used to process these

models are getting better also and so

the total time to process models is

decreasing but the size of the models is

increasing and to train these models it

is extremely expensive because you need

a lot of processing power electricity

and these chips are not cheap and that

is why Nvidia stock price has

skyrocketed their revenue growth has

been extraordinary and so with the

process of training we take this

pre-processed text data that we talked

about earlier and it's fed into the

model and then using Transformers or

whatever technology a model is actually

based on but most likely Transformers it

will try to predict the next word based

on the context of that data and it's

going to adjust the weights of the model

to get the best possible output and this

process repeats millions and millions of

times over and over again until we reach

some optimal quality and then the final

step is evaluation a small amount of the

data is set aside for evaluation and the

model is tested on this data set for

performance and then the model is is

adjusted if necessary the metric used to

determine the effectiveness of the model

is called perplexity it will compare two

words based on their similarity and it

will give a good score if the words are

related and a bad score if it's not and

then we also use rlf reinforcement

learning through human feedback and

that's when users or testers actually

test the model and provide positive or

negative scores based on the output and

then once again the model is adjusted as

necessary all right let's talk about

fine-tuning now which I think a lot of

you are going to be interested in

because it's something that the average

person can get into quite easily so we

have these popular large language models

that are trained on massive sets of data

to build general language capabilities

and these pre-trained models like Bert

like GPT give developers a head start

versus training models from scratch but

then in comes fine-tuning which allows

us to take these raw models these

Foundation models and fine-tune them for

our specific specific use cases so let's

think about an example let's say you

want to fine tuna model to be able to

take pizza orders to be able to have

conversations answer questions about

pizza and finally be able to allow

customers to buy pizza you can take a

pre-existing set of conversations that

exemplify the back and forth between a

pizza shop and a customer load that in

fine- tune a model and then all of a

sudden that model is going to be much

better at having conversations about

pizza ordering the model updates the

weights to be better at understanding

certain Pizza terminology questions

responses tone everything and

fine-tuning is much faster than a full

training and it produces much higher

accuracy and fine-tuning allows

pre-trained models to be fine-tuned for

real world use cases and finally you can

take a single foundational model and

fine-tune it any number of times for any

number of use cases and there are a lot

of great Services out there that allow

you to do that and again it's all about

the quality of your data so if you have

a really good data set that you're going

to f- tune a model on the model is going

to be really really good and conversely

if you have a poor quality data set it's

not going to perform as well all right

let me pause for a second and talk about

AI Camp so as mentioned earlier this

video all of its content the animations

have been created in collaboration with

students from AI Camp AI Camp is a

learning experience for students that

are aged 13 and above you work in small

personalized groups with experienced

mentors you work together to create an

AI product using NLP computer vision and

data science AI Camp has both a 3-week

and a onewe program during summer that

requires zero programming experience and

they also have a new program which is 10

weeks long during the school year which

is less intensive than the onewe and

3-we programs for those students who are

really busy AI Camp's mission is to

provide students with deep knowledge and

artificial intelligence which will

position them to be ready for a in the

real world I'll link an article from USA

Today in the description all about AI

camp but if you're a student or if

you're a parent of a student within this

age I would highly recommend checking

out AI Camp go to ai- camp.org to learn

more now let's talk about limitations

and challenges of large language models

as capable as llms are they still have a

lot of limitations recent models

continue to get better but they are

still flawed they're incredibly valuable

and knowledgeable in certain ways but

they're also deeply flawed in others

like math and logic and reasoning they

still struggle a lot of the time versus

humans which understand Concepts like

that pretty easily also bias and safety

continue to be a big problem large

language models are trained on data

created by humans which is naturally

flawed humans have opinions on

everything and those opinions trickle

down into these models these data sets

may include harmful or biased

information and some companies take

their models a step further and provide

a level of censorship to those models

and that's an entire discussion in

itself whether censorship is worthwhile

or not I know a lot of you already know

my opinions on this from my previous

videos and another big limitation of

llms historically has been that they

only have knowledge up into the point

where their training occurred but that

is starting to be solved with chat GPT

being able to browse the web for example

Gro from x. aai being able to access

live tweets but there's still a lot of

Kinks to be worked out with this also

another another big challenge for large

language modelss is hallucinations which

means that they sometimes just make

things up or get things patently wrong

and they will be so confident in being

wrong too they will state things with

the utmost confidence but will be

completely wrong look at this example

how many letters are in the string and

then we give it a random string of

characters and then the answer is the

string has 16 letters even though it

only has 15 letters another problem is

that large language models are EXT

extremely Hardware intensive they cost a

ton to train and to fine-tune because it

takes so much processing power to do

that and there's a lot of Ethics to

consider too a lot of AI companies say

they aren't training their models on

copyrighted material but that has been

found to be false currently there are a

ton of lawsuits going through the courts

about this issue next let's talk about

the real world applications of large

language models why are they so valuable

why are they so talked about about and

why are they transforming the world

right in front of our eyes large

language models can be used for a wide

variety of tasks not just chatbots they

can be used for language translation

they can be used for coding they can be

used as programming assistants they can

be used for summarization question

answering essay writing translation and

even image and video creation basically

any type of thought problem that a human

can do with a computer large language

models can likely also do if not today

pretty soon in the future now let's talk

about current advancements and research

currently there's a lot of talk about

knowledge distillation which basically

means transferring key Knowledge from

very large Cutting Edge models to

smaller more efficient models think

about it like a professor condensing

Decades of experience in a textbook down

to something that the students can

comprehend and this allows smaller

language models to benefit from the

knowledge gained from these large

language models but still run highly

efficiently on everyday consumer

hardware and and it makes large language

models more accessible and practical to

run even on cell phones or other end

devices there's also been a lot of

research and emphasis on rag which is

retrieval augmented generation which

basically means you're giving large

language models the ability to look up

information outside of the data that it

was trained on you're using Vector

databases the same way that large

language models are trained but you're

able to store massive amounts of

additional data that can be queried by

that large language model now let's talk

about the ethical considerations and

there's a lot to think about here and

I'm just touching on some of the major

topics first we already talked about

that the models are trained on

potentially copyrighted material and if

that's the case is that fair use

probably not next these models can and

will be used for harmful acts there's no

avoiding it large language models can be

used to scam other people to create

massive misinformation and

disinformation campaigns including fake

images fake text fake opinions and

almost definitely the entire White

Collar Workforce is going to be

disrupted by large language models as I

mentioned anything anybody can do in

front of a computer is probably

something that the AI can also do so

lawyers writers programmers there are so

many different professions that are

going to be completely disrupted by

artificial intelligence and then finally

AGI what happens when AI becomes so

smart and maybe even starts thinking for

itself this is where we have to have

something called alignment which means

the AI is aligned to the same incentives

and outcomes as humans so last let's

talk about what's happening on The

Cutting Edge and in the immediate future

there are a number of ways large

language models can be improved first

they can fact check themselves with

information gathered from the web but

obviously you can see the inherent flaws

in that then we also touched on mixture

of experts which is an incredible new

technology which allows multiple models

to kind of be merged together all fine

tune to be experts in certain domains

and then when the actual prompt comes

through it chooses which of those

experts to use so these are huge models

that actually run really really

efficiently and then there's a lot of

work on multimodality so taking input

from voice from images from video every

possible input source and having a

single output from that there's also a

lot of work being done to improve

reasoning ability having models think

slowly is a new trend that I've been

seeing in papers like orca too which

basically just forces a large language

model to think about problems step by

step rather than trying to jump to the

final conclusion immediately and then

also larger context sizes if you want a

large language model to process a huge

amount of data it has to have a very

large context window and a context

window is just how much information you

can give to a prompt to get the output

and one way to achieve that is by giving

large language models memory with

projects like mgpt which I did a video

on and I'll drop that in the description

below and that just means giving models

external memory from that core data set

that they were trained on so that's it

for today if you liked this video please

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videos if you want to learn even more

I'll see you in the next one