The future of AI looks like THIS (& it can learn infinitely)

Summary

This video script explores the limitations of current AI models, which are static and energy-intensive, and introduces the next generation of AI with liquid and spiking neural networks. These networks aim to mimic the human brain's adaptability and efficiency, offering real-time learning and reduced computational needs. Applications range from autonomous vehicles to healthcare, with the potential for AI to become smarter over time. However, these concepts are still in early research phases, facing challenges in implementation and training.

Takeaways

• 🧠 Current AI models, including GPT and Stable Diffusion, are limited by their inability to learn or adapt after training, much like a brain that has stopped growing.
• 🤖 AI operates on neural networks with nodes and layers, where each node filters information to the next layer, akin to dials and knobs controlling data flow.
• 🔄 The training process for AI models involves millions of iterations, using backpropagation to adjust weights and minimize errors, but once trained, the model's intelligence is fixed.
• 🔋 AI models are highly energy-intensive, with GPT-3's training alone requiring as much energy as 1,500 US homes use in a month, highlighting a need for more efficient AI.
• 🌟 The next generation of AI should ideally mimic the human brain's neuroplasticity, allowing for continuous learning and adaptation to new information.
• 💧 Liquid neural networks are an emerging architecture designed to be flexible and adaptive, with a 'reservoir' layer that can change dynamically in response to new data.
• 📉 Liquid neural networks require less computational power for training since only the output layer is trained, making them potentially more efficient than traditional networks.
• 🚀 Applications for liquid neural networks include autonomous robots, self-driving cars, and real-time data processing, where adaptability is crucial.
• 🌐 Spiking neural networks are another potential next-gen AI architecture, mimicking the brain's neuron communication through discrete spikes and timing.
• 🕒 Spiking networks incorporate time into their processing, which can lead to more efficient learning and adaptation, especially suitable for temporal data.
• 🚧 Both liquid and spiking neural networks are in early stages of research with challenges such as complexity in training and lack of standardized support, but they offer promising potential for AI evolution.

Q & A

What is the current state of AI technology as described in the video script?

The current state of AI technology, as described in the script, is that while it is impressive, it is also quite limited. AI models like chat GPT, stable diffusion, and others are based on neural networks that are fixed in their intelligence and capabilities after training, and they require significant computational power to function.

What is a neural network and how does it function in the context of AI?

A neural network is a series of interconnected nodes, or neurons, arranged in layers, that process information by adjusting weights and biases to determine how much information flows through to the next layer. It functions in AI by receiving input data, processing it through these layers, and outputting a result after the data has passed through the network and been interpreted by the final layer.

What is the concept of 'neuroplasticity' in the context of the human brain and how does it differ from current AI models?

Neuroplasticity refers to the brain's ability to reorganize and reconfigure itself by forming new neural connections over time to adapt to new environments or learn new things. This is different from current AI models, which are static after training and cannot continue to learn or adapt without being retrained with new data.

How are AI models like GPT and stable diffusion trained?

AI models like GPT and stable diffusion undergo millions of rounds of training. They process input data, and if the output is incorrect, a penalty is incurred which causes the weights in the neural network to be updated through a process called backpropagation. This continues until the model can accurately perform its task.

What are the two major limitations of the current generation of AI models?

The two major limitations of the current generation of AI models are that they are fixed in their intelligence and capabilities after training and cannot learn or improve further, and they are extremely energy-intensive and inefficient compared to the human brain.

What is the concept of liquid neural networks and how do they differ from traditional neural networks?

Liquid neural networks are designed to mimic the flexibility and plasticity of the human brain. They have a 'reservoir' layer that can change dynamically over time in response to new data, unlike traditional neural networks which have fixed weights and connections after training.

How are liquid neural networks trained and why is this process more efficient?

Liquid neural networks are trained by setting up random connections in the reservoir layer, feeding data into the input layer, and training only the output layer to map the reservoir states to the desired output. This process is more efficient because it requires optimizing fewer parameters, reducing computational requirements.

What are some potential real-world applications of liquid neural networks?

Potential applications of liquid neural networks include autonomous AI robots that can adapt to new tasks, self-driving cars that can navigate dynamic environments, healthcare monitoring for real-time patient analysis, stock trading optimization, and smart city management for traffic flow and energy management.

What is a spiking neural network and how does it differ from other neural networks?

A spiking neural network is a type of neural network that mimics the way neurons in the human brain communicate using discrete spikes or action potentials. Unlike other neural networks that use continuous signals, spiking neural networks process information based on the timing and frequency of these spikes.

What are the main benefits of spiking neural networks?

The main benefits of spiking neural networks include their efficiency, as they only use energy where spikes occur, making them more energy-efficient than traditional neural networks. They are also well-suited for neuromorphic chips and can process temporal data effectively, making them ideal for adaptive and autonomous systems.

What are some challenges associated with the development and implementation of spiking neural networks?

Challenges with spiking neural networks include the complexity of setting up and programming them, the difficulty in training them due to the discrete nature of spikes, the need for specialized hardware like neuromorphic chips, and their current underperformance for non-time-based data compared to traditional neural networks.

Outlines

00:00

🧠 Understanding AI's Current Limitations and Future Prospects

The script introduces the limitations of current AI models, such as their inability to learn post-training and high computational demands. It explains the basics of neural networks, including nodes, layers, and training processes like backpropagation. The comparison between AI and the human brain's efficiency is highlighted, setting the stage for discussing the future of AI and the need for models that can adapt and learn over time, much like human neuroplasticity.

05:01

🔋 The Inefficiency and Static Nature of Modern AI Models

This paragraph delves into the static intelligence of current AI models, which cannot improve after training, unlike the human brain's neuroplasticity. It also addresses the massive computational resources required for training models like GPT-3 and GPT-4, comparing their energy consumption to that of the human brain. The paragraph emphasizes the need for future AI to be more energy-efficient and capable of continuous learning.

10:01

🌊 Introducing Liquid Neural Networks: The Next Step in AI Evolution

The script presents liquid neural networks as a potential future for AI, designed to mimic the human brain's adaptability. It explains the components of liquid neural networks, including the reservoir layer that allows for dynamic adaptation to new data. The training process for these networks is outlined, highlighting the efficiency gains from only training the output layer. The potential for smaller, faster, and more efficient AI models is discussed, along with real-world applications such as autonomous robots and self-driving cars.

15:02

🌐 Sponsored Content: Bright Data's Role in AI Development

This paragraph is sponsored content promoting Bright Data, an all-in-one platform for collecting high-quality web data at scale. It discusses the importance of diverse and high-quality training data for AI companies and how Bright Data's tools can automate data scraping, ensuring reliable datasets for AI training. The paragraph mentions the platform's capabilities and the scale of data collection, emphasizing its utility for training large AI models like chat GPT.

20:04

🚀 Liquid Neural Networks' Real-World Applications and Limitations

The script explores various real-world applications of liquid neural networks, including their use in autonomous AI robots, self-driving cars, healthcare, cybersecurity, streaming services, smart city management, and energy management. It also discusses the limitations of liquid neural networks, such as their newness, the lack of real-world results, and the complexity of the reservoir layer, which can be difficult to interpret and fine-tune for optimal performance.

25:06

💥 The Emergence of Spiking Neural Networks

This paragraph introduces spiking neural networks, which are inspired by the human brain's communication through discrete spikes or action potentials. It explains how these networks operate, with neurons firing only when their potential exceeds a certain threshold, incorporating time into their processing. The script also touches on the challenges of training spiking neural networks and the potential benefits, such as energy efficiency and suitability for neuromorphic chips.

30:06

🛠️ The Challenges and Potential of Spiking Neural Networks

The script discusses the challenges associated with spiking neural networks, including the complexity of setting them up, programming difficulties, and the need for specialized hardware like neuromorphic chips. It also highlights the potential benefits, such as their energy efficiency and suitability for time-based data processing. The paragraph acknowledges that while spiking neural networks show promise, they are still in the early stages of development and lack the tools and frameworks available for current AI models.

🌟 The Future of AI: Towards Energy-Efficient and Adaptive Intelligence

The final paragraph summarizes the potential of the next generation of AI, focusing on the need for energy efficiency and the ability to learn and adapt, akin to the human brain. It mentions liquid and spiking neural networks as promising developments towards achieving artificial general intelligence (AGI) or artificial superintelligence (ASI). The script invites viewers to share their thoughts on these emerging AI architectures and to stay updated with the rapidly evolving field of AI.

Keywords

💡Neural Network

A neural network is a series of algorithms designed to recognize patterns. It is the foundation of most AI models today, including chatbots and image recognition systems. In the script, neural networks are described as a network of nodes (neurons) that process information through layers, with each node determining the flow of information to the next layer. This concept is central to understanding the limitations and potential of current AI systems.

💡Deep Learning

Deep learning is a subfield of machine learning that focuses on neural networks with many layers, hence the term 'deep.' It allows AI to perform tasks like recognizing speech or images. The script uses the term to describe neural networks that are capable of complex pattern recognition due to their depth, which is a key aspect of modern AI's capabilities.

💡Backpropagation

Backpropagation is the process by which a neural network learns from its errors. It involves adjusting the weights of the network in a way that minimizes the error in its predictions. The script explains backpropagation as a method where the neural network updates its weights from the last layer back to the first after an incorrect prediction, which is crucial for training AI models to improve accuracy over time.

💡Neuroplasticity

Neuroplasticity refers to the brain's ability to change and adapt by forming new neural connections. It is a concept used in the script to contrast the fixed nature of current AI models with the dynamic capabilities of the human brain, which can learn and adapt throughout life. The script suggests that future AI should incorporate this property to overcome current limitations.

💡Efficiency

In the context of the script, efficiency refers to the ratio of performance to the amount of resources used. It highlights the massive energy and computational resources required to train current AI models compared to the human brain's energy efficiency. The script emphasizes the need for future AI to be more energy-efficient and less computationally intensive.

💡Liquid Neural Networks

Liquid neural networks are a proposed architecture that aims to mimic the human brain's plasticity by allowing the network to adapt in real-time to new data. The script discusses this concept as a potential solution to the inflexibility of current AI models, illustrating how these networks could change dynamically as they receive new inputs, similar to the human brain's ability to learn and adapt.

💡Spiking Neural Networks

Spiking neural networks are another type of neural network that more closely mimics the way neurons in the human brain communicate using spikes or action potentials. The script describes these networks as being potentially more energy-efficient than traditional neural networks and better suited for tasks involving temporal data, such as speech recognition or real-time processing.

💡Spike Timing Dependent Plasticity (STDP)

STDP is a biological process that adjusts the strength of connections between neurons based on the timing of their spikes. The script mentions STDP as a method for training spiking neural networks, where the timing of spikes influences how the network learns, making it a dynamic and adaptive learning process.

💡Neuromorphic Chips

Neuromorphic chips are specialized hardware designed to mimic the neural structure of the human brain. The script suggests that spiking neural networks are particularly suitable for neuromorphic chips, indicating a potential synergy between these types of networks and hardware that could lead to more efficient AI systems.

💡Training

Training in the context of AI refers to the process by which neural networks learn to perform tasks. The script explains that training involves millions of rounds of adjustments to the network's weights to minimize errors. It also contrasts the training process of traditional neural networks with the more efficient training of liquid and spiking neural networks.

💡Energy Consumption

The script discusses energy consumption to highlight the inefficiency of current AI models compared to the human brain. It provides specific examples, such as the energy required to train GPT-3 and GPT-4, to emphasize the need for future AI to be developed with sustainability and energy efficiency in mind.

Highlights

Current AI models, including state-of-the-art like chat GPT and stable diffusion, are still very limited and inefficient compared to future AI generations.

AI is based on neural networks, which are simplified to a series of nodes and layers designed to mimic the human brain.

Neural networks are trained through millions of rounds, adjusting 'dials and knobs' or weights to minimize errors via backpropagation.

AI models like GPT and stable diffusion are fixed in their intelligence post-training and cannot learn or adapt without retraining.

Neuroplasticity in the human brain allows for constant learning and adaptation, unlike current AI models.

Training AI models like GPT-3 is extremely energy-intensive, requiring as much power as 1,500 US homes monthly.

GPT-4, with 10 times more parameters than GPT-3, could require up to 41,000 megawatt hours of energy to train, emphasizing the need for efficiency in AI.

Liquid neural networks are being developed to mimic the human brain's flexibility and adaptability in real-time.

Liquid neural networks have a 'reservoir' layer that remains untrained, allowing dynamic adaptation to new data.

Training liquid neural networks is faster and requires less computation due to the fixed reservoir layer.

Liquid neural networks could be smaller and more efficient than traditional networks, with potential applications in autonomous systems.

Spiking neural networks, inspired by the human brain's communication through spikes, are a new area of research for AI.

Spiking neural networks are energy-efficient as they only use energy when spikes occur, unlike always-active traditional networks.

Spiking neural networks could be used in neuromorphic chips, which are optimized for spike-based processing.

Despite their potential, liquid and spiking neural networks face challenges in implementation, training, and require specialized hardware.

The next generation of AI needs to be as efficient and adaptable as the human brain, which is an active area of research and development.

Transcripts

AI as we know it today is actually quite

dumb yes this includes chat GPT stable

diffusion Sora and all the other

state-of-the-art models that we have

right now they're still very incapable

and inefficient and the future

generation of AI will look very

different from what we have now so in

this video I'm going to explain why the

current generation is so limited and

what the future generation of AI will

look like first we need to understand

the mean mechanics of AI as we know

today all AI is based on the neuron

Network which is designed based on the

human brain this is basically a network

of nodes in which information flows

through from one end to the other now

this is going to be a very simplified

explanation of how a neural network

works I'm explaining this for people

without a technical background in AI so

if you do have experience in AI feel

free to skip this section each do in a

neural network is called a node or

neuron and each line of nodes is called

a layer you might have heard of the term

deep learning or deep neuron networks

this is basically a neuron network with

many layers hence it is very deep each

node determines how much information

flows through to the next layer now

again this is an oversimplification

there are a lot of settings like weights

and biases and activation functions but

basically just think of this neuron

Network as a series of dials and knobs

which determine how much information

flows through to the next layer here's a

simple example let's say we have this

neuron Network which is designed to

determine whether an image is a cat or a

dog for its input we would feed it an

image of a cat or a dog and this image

would be broken down into Data also

known as tokens which are then fed

through this neuro Network eventually

after the data flows through all these

layers it reaches the end layer which

would conclude whether the image is a

cat or a dog now what about training a

model how does that work well a neural

network needs to undergo usually

millions of rounds of training to learn

how to do something here's an example of

how one round of training would look

like let's say you input an image of a

dog and then this image would be broken

down into data which flows through this

neuron Network and it spits out the

answer this is a dog well in that case

since it got the answer correct it's

likely that these dials and knobs which

we can also refer to as weights are set

correctly if it gets the answer right

well we don't really need to tweak these

weights further however what if it gets

it wrong what if it says that this is a

cat well in that case it would incur a

penalty and this penalty would cause the

weights in this neuron Network to be

updated so that this penalty would be

minimized in the future specifically the

weights would be updated from the last

layer to the next layer back to the next

layer back in a process which is called

back propagation all the way until it

reaches the first layer of nodes and

usually one round of training isn't good

enough so the network would undergo

millions of rounds of training where the

weights would be slightly tweaked to

minimize the penalty incurred from any

errors and this goes on and on until

finally we reach the configuration of

dials and knobs so that this neuron

Network can very accurately determine

whether any image is a cat or a dog and

this is how AI models that we know today

are trained as well so for example GPT

is basically a neuron network but these

dials and knobs are optimized for

understanding natural language stable

diffusion is another neuro Network where

the dials and kns are optimized for

image generation now again this is very

much an

oversimplification and the architecture

or basically the design of the neuron

network is also very important for

example how many layers should we have

how many nodes in each layer should we

have there are also many different

architectures such as the Transformer

model for large language models or lstm

for time series data or convolutional

neuron networks for object detection and

image classification

but in a nutshell the backbone behind

all these AI models is just a neural

network which has a preconfigured set of

dials and knobs to do the job accurately

so now that you understand how the

current generation of AI Works let's

look at the biggest limitations of this

first of all once the model is finished

training the weights or basically these

dials and knobs are fixed in value when

the user asks chat GPT something or when

the user uses stable Fusion to generate

an image these dials and knobs do not

change in value in other words all the

AI models that we have today are fixed

think of this as a brain that cannot

learn or get any smarter for example GPT

4 cannot continue learning and become

smarter and smarter with time if we want

a smarter model well we need to train a

new generation of GPT such as GPT 40 or

GPT 5 or whatever you want to call it

same with stable diffusion for example

stable diffusion 2 cannot get smarter

and generate better images as we use it

more and more in order for it to improve

we currently need to train a new

generation also known as stable

diffusion 3 and once stable diffusion 3

is finished training well that's as

smart as it gets and if you don't think

it's good enough well you need to train

a new model so basically all the AI

models that we have today are fixed in

their intelligence and their capab

abilities again think of this as a brain

that has stopped growing and cannot

learn or get smarter but this is not how

the human brain works there's a term

called neuroplasticity which refers to

how the brain can reorganize or

reconfigure Itself by forming new neural

connections over time in order to adapt

to new environments or learn new things

and that's exactly what the next

generation of AI can do which we'll talk

about in a second but there's another

huge limitation of current AI models

they are extremely inefficient and

computes intensive as you may know AI is

designed based on the architecture of

the human brain so let's compare it to

the efficiency of the human brain right

now

gpt3 has

175 billion parameters this was trained

using thousands of gpus over several

weeks or several months the total power

required for training gpt3 was estimated

to be around

1,287 megawatt hours of electricity this

is roughly equivalent to the monthly

electricity consumption of 1,500 homes

in the USA now keep in mind gpt3 was

completed in 2020 that's 4 years ago the

latest version GPT 4 is closed source so

we don't actually know its architecture

or how long it took to train but we do

know that it has around around 1.76

trillion parameters 10 times more than

GPT 3 keep in mind that the amount of

computations required scales

exponentially as the parameter size gets

larger so from a rough calculation GPT 4

could have taken around

41,000 megawatt hourss of energy to

train that's enough energy to power

around 47,000 homes in the US for a

month the compute used to create create

these state-of-the-art models that we

know today such as GPT 4 or clae 3 or

Gemini 1.5 Pro requires massive data

centers and a lot of energy that's why

Tech Giants are scrambling to invest and

build even bigger data centers because

they know that compute is the main

limitation here and that's exactly why

Microsoft and open aai are planning a $

100 billion Stargate project to build

the biggest data center in the world all

of this is for more compute now contrast

this to the human brain some might say

the human brain is still more

intelligent than GPT 4 at least in some

regards the human brain only uses 175

kilowatt hours in an entire year and it

gets this energy in the form of calories

from the food we eat so training GPT 4

is estimated to require approximately

234,000 times more energy than what the

human brain uses in an entire year in

other words the energy required to train

GPT 4 Once could power the human brain

for over

234,000 years now I gave this comparison

to show you that there's something

fundamentally wrong with AI models today

they are very energy inefficient and

they take up a lot of compute it's not

even close to the efficiency of the

human brain so the next generation of AI

has to solve this efficiency problem as

well otherwise it will not be

sustainable so to summarize the major

limitations of current AI models is

number one they are fixed and unable to

improve or learn further after being

trained and number two they're also very

energy intensive and inefficient these

are the two biggest problems of the

current generation of AI now let's enter

the Next Generation we aren't there yet

but there are a few possible

architectures that are being discussed

and developed as we speak the first

architecture is called liquid neural

networks Now liquid neural networks are

designed to mimic the flexibility or the

plasticity of the human brain the human

brain is very flexible and can

reorganize or reconfigure itself over

time and this ability allows the brain

to adapt to new situations or learn new

skills or compensate for injury and

disease for example when you learn

something new your brain changes

structurally and functionally to

accommodate the new information learning

a new language can lead to changes in

the brain structure and function such as

increased density of gray matter in the

left hemisphere the brain can also

reconfigure itself to recover from

injury for example after a traumatic

brain injury physical therapy and

cognitive exercises can help rewire the

brain to regain lost functions and for

people who've lost a sense like sight or

hearing the brain will reorganize itself

to compensate for the loss and make

other senses become more acute so this

flexibility this plasticity is exactly

what liquid neuron networks are designed

to have liquid neuron networks can adapt

in real time to new data this means that

the configuration of the neuron Network

can change as it receives new inputs and

that's why it's called liquid these

Connections in the network and these

dials and knobs are fluid so they can

change dynamically over time liquid

neuron networks also retain what they

have learned while incorporating new

information this is similar to how our

brains can remember old information

while learning new things so here's how

liquid neuro networks work they have

three main components much like a

traditional neuron Network it has an

input layer which receives the input

data but then in the middle we have this

liquid layer otherwise known as a res

res this is the core component of a

liquid neuron Network and it's basically

a large recurrent neuron Network think

of this as a big bowl of water in which

each Splash creates a ripple these

ripples are basically the neurons in

this network reacting to inputs the

reservoir acts as a dynamic system that

transforms the input data into a high

dimensional representation called

Reservoir States and this reservoirs

Rich Dynamics and Transformations

capture the complex temporal patterns in

the input data and then finally we have

the output layer this layer receives the

reservoir States and Maps them to the

desired output using what is called a

readout function in layman terms this is

a layer that looks at the ripples in the

reservoir and tries to understand what

it all means it takes the dynamic

patterns from the reservoir and makes

predictions or decisions from it the key

aspect of liquid neural networks is this

Reservoir layer which remains untrained

during the entire learning process only

the output layer is trained to map the

reservoir states to the Target outputs

in other words to understand what these

ripples mean and because this Reservoir

remains fluid and flexible throughout

time it's not fixed in value that allows

this liquid Neer Network to basically

adapt to new data and learn new things

here's how you would train a liquid

neural network the connections between

neurons in their reservoirs are set up

randomly at the start these connections

typically stay the same and don't change

during training next you would feed the

input layer some data and when this data

is broken down into tokens and it

reaches the reservoir layer it causes

the neurons in the reservoir to react

and create complex patterns much like

ripples in water so as this input data

creates ripples you basically observe

and analyze the patterns created in the

reservoir over time and that's exactly

what the readout layer does it learns to

recognize these patterns it's like

learning ahuh this is what caused this

type of Ripple and that is what caused

this other type of Ripple and eventually

after lots and lots of rounds of

training the readout layer can make

accurate predictions based on observed

patterns again note that only the

readout layer is trained which is

simpler and faster because you're not

adjusting anything in the reservoir

layer this is much quicker and needs

less compute compared to traditional

neuron networks that's because in neuron

networks that we know today all the

weights including those in the hidden

layers are trainable this means more

parameters to optimize leading to longer

training times and higher computational

requirements but in liquid neuron

networks you don't adjust the weights of

the reservoir during training only the

readout layer is trained and this

significantly reduces the computational

burden during trainings since fewer

parameters need to be optimized plus

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lot faster for these liquid neuron

networks to converge at an Optimum and

because of this Reservoir where the

weights and configurations can change

dynamically depending on the data that

you feed it liquid neuron networks can

potentially be much smaller than

traditional neuron networks which have

fixed weights and connections and this

offers a lot more efficient learning and

inference so for example researchers at

MIT were able to Pilot a drone using a

liquid neuron network with only 20,000

parameters which is very tiny compared

to state-of-the-art AI models such as

GPT 4 which often have over a trillion

parameters just think about that 20,000

parameters versus over a trillion

parameters so these smaller sizes

generally translate to faster inference

and lower computational requirements

liquid neuron networks are also way less

memory intensive again since you don't

train the reservoir weights memory usage

is much lower during training compared

to traditional neuron networks where the

gradients and the parameters for all

layers must be stored in memory liquid

neur networks are particularly good at

processing temporal data due to their

Dynamic Reservoir so they excel in tasks

that involve complex time series data

now you might be wondering well how can

these liquid neuron networks actually be

applied in the real world so here are

some use cases as we race to build fully

autonomous AI robots these robots will

be deployed in the real world and often

times they might encounter situations

that they 've never seen before during

training for example there could be

unpredictable environments in search and

rescue missions but with liquid neuron

networks these robots can adapt to

changing conditions and learn new tasks

on the Fly and eventually we're going to

have these autonomous robots in our

houses helping us do chores and other

tasks but maybe you have a certain way

of folding clothes or doing the laundry

or cooking that the robot was never

trained on so with a traditional neuron

Network these robots aren't able to

learn new skills after being deployed

but with liquid neuron networks built

into a humanoid robot it can learn new

tasks that you teach it and this robot

will become a lot more personalized for

you and then we have autonomous driving

there's no doubt that self-driving cars

will eventually become the future but

current Technologies still do not

perform well especially in challenging

environments or new conditions again

this is because traditional neuron

networks can only do well on data that

they were trained on they're not able to

adapt to new environments but with

liquid neuron networks autonomous

vehicles can navigate complex and

dynamic environments by continuously

learning and training from sensor data

and adjusting their behavior accordingly

it's constantly training and improving

over time now as I've mentioned before

liquid neuron networks often incorporate

recurrent connections making them

suitable for processing time series data

so it's great for things like weather

prediction and of course stock trading

the stock market is filled with Ever

Changing Trends and Cycles so it's close

to impossible for one fixed algorithm or

formula to beat the market however

because liquid neural networks can adapt

to everchanging data it can optimize

trading strategies in real time to

maximize profits in other words you

could be constantly streaming the latest

Market data to this liquid neuron

Network which would change its

configuration to adapt to this data in

real time to help you maximize profits

another use case would be Healthcare

liquid neuron networks can be used in

wearable devices to monitor patients in

real time adapting to changes in the

patients's conditions and predicting

potential health issues before they

become critical in cyber security liquid

neuron networks can continuously learn

from Network traffic and user Behavior

to adapt Access Control policies and

detect anomalies or unauthorized access

access attempts yet another use case

would be streaming services such as

Netflix they can use Liquid neuron

networks to adapt to each user's viewing

habits and preferences providing more

personalized content recommendations

another use case would be smart City

management for example liquid neuron

networks can optimize traffic flow in

real time by learning from traffic

patterns and changing traffic lights

accordingly to reduce congestion and

improve efficiency energy management is

also very relevant smart grids can use

Liquid neuron networks to Balance power

supply and demand in real time improving

efficiency and reducing costs by

adapting to consumption patterns however

although liquid neuron networks seem

promising it does have its limitations

this is still a relatively New Concept

in the field of neuron networks and

research on them is still in its early

stages compared to more traditional

architectures while liquid neuron

networks show promising theoretical

benefits such as the ability to process

continuous data streams and adapt on the

Fly there is still a lack of real world

results demonstrating their superiority

on a large scale many researchers are

likely waiting for more compelling

Benchmark results before investing

significant effort into liquid neuron

networks also as I mentioned previously

they're particularly suited for temporal

or sequence data so for for tasks that

do not involve time such as identifying

images of cats or dogs traditional

neuron networks might actually be more

effective and straightforward to

implement also the Dynamics within this

Reservoir layer can be very complex and

difficult to interpret and this makes it

challenging to understand how the

reservoir processes these inputs it

would be quite hard to fine-tune it for

Optimal Performance finally there is a

lack of standardized support and fewer

established Frameworks for four liquid

neuron networks compared to traditional

neural networks and this can make

implementation and experimentation more

challenging so all in all liquid neuron

networks are still a very early concept

and an area of active research unlike

traditional neuron networks that are

fixed and need to be retrained with a

large data set to learn new information

liquid neuron networks can update their

knowledge incrementally with each new

piece of data this offers a flexible and

adaptive model which could potentially

become infinitely smarter over time now

liquid neuron networks aren't the only

possibility that could become the next

generation of AI we have another type of

neuron Network which is designed to

mimic the human brain even more than

traditional neural networks and this

brings us to spiking neuron networks

these are closely inspired by the way

neurons in our brains communicate using

discrete spikes or action potentials you

see in the human brain which is

basically a network of neurons each

neuron doesn't immediately fire to the

next set of neurons when it receives

input instead the input has to build up

to a certain threshold and once it

passes this threshold then it fires to

the next set of neurons and after it

fires it goes back to its resting state

well spiking neuron networks are

designed to mimic this Behavior so

here's how it works the architecture is

quite similar to traditional neuron

networks however for each neuron it

waits to receive signals or spikes from

other neurons think of these spikes as

like little electric pulses the input

data such as an image or a sound is

turned into these spikes that move

through this neural network for example

if it's a loud sound it might generate

more spikes while a quiet sound might

generate fewer spikes now each neuron in

the network collects incoming spikes

imagine a bucket collecting drops of

water as more spikes come in the bucket

fills up and when the neuron gets enough

spikes in other words when it reaches a

certain threshold it fires a spike to

the next set of neurons and after firing

it resets and starts collecting again

from zero so instead of using continuous

signals like traditional neuron networks

spiking neuron networks uses spikes

which are basically bursts of activity

at discrete time points to process

information

in other words spiking neuron networks

incorporate time into their processing

with neurons firing only when their

potential exceeds a certain threshold

now there are different methods and

algorithms to train a spiking neural

network and there currently isn't a

standard way that's set in stone so this

is still an active field of research one

common method is called Spike timing

dependent plasticity or stdp this method

is inspired by how the brain strengthens

or weakens connections between neurons

so if one neuron spikes just before

another then the connection between them

gets stronger if it spikes just after

then the connection gets weaker it's

like learning which connections are

important based on the timing of the

spikes and speaking of timing it's the

exact timing of spikes that matters it's

not just about how many spikes there are

but when they happen now stdp is only

one method to tr TR the spiking neuron

networks there are a few other ones

which are beyond the scope of this video

but like traditional neuron networks

spiking neuron networks have to undergo

millions of rounds of training with a

lot of data and eventually the

configuration of the network and all its

parameters will reach an Optimum State

now again I'd like to remind you that

this is a very simplified explanation of

spiking neuron networks and I've left

out a lot of mathematical details but in

a nutshell that's how it works so you

might be wondering well what are the

benefits of spiking neural networks

first of all it's designed to mimic the

human brain even more by implementing

this spiking mechanism so in theory

maybe we could reach a superior level of

intelligence compared to the current

generation of AI if we Mimi the human

brain even more but the biggest benefit

of spiking neuron networks is their

efficiency if you remember at the

beginning of the video I compared the

energy consumption of the human brain

versus a current state-of-the-art model

like GPT 4 which requires huge data

centers and huge amounts of compute

that's because traditional neuron

networks are always active each input of

data activates the entire neural network

so you have to do an insane amount of

Matrix multiplications across the entire

network just to do one round of training

or inference however for spiking neural

networks they only use energy where

spikes occur while the rest of the

neuron Network remains inactive this

makes it a lot more energy efficient

plus spiking neuron networks are

particularly suitable for neuromorphic

chips which are designed to mimic the

human brain now neuromorphic chips are a

huge topic and deserves its own full

video so let me know in the comments if

you'd like me to make a video on this as

well so how can these spiking neuron

Networks actually be applied to the real

world well because these neuron networks

can encode and process information in

the timing of spikes this is great for

processing temporal data this makes them

great for adaptive and autonomous

systems plus this Spike timing dependent

plasticity which I mentioned before

where the timing of the spikes

influences the strength of the

connections in the network this can lead

to more robust and adap aptive learning

capability so this Dynamic learning can

make spiking neuron networks suitable

for autonomous systems such as

self-driving where the AI has to learn

and adapt to changing environments or it

can be used in realtime processing like

predicting the stock market or patient

monitoring and personalized medicine and

of course autonomous robots now although

spiking neuron networks offer some huge

benefits especially regarding Energy

Efficiency they do have some limitations

setting up and programming spiking

neuron networks is more complicated

compared to traditional neuron networks

this spiking behavior of course adds a

layer of complexity making them harder

to design and understand training

spiking neur networks is also quite

difficult current neuron networks use

methods like back propagation to adjust

their parameters but this process

doesn't work well with these discrete

time-based spikes researchers are still

trying to find an effective training

algorithm for spiking neuron networks

also given this additional dimension of

time spiking neuron networks might

actually require more computational

resources to simulate this is because

they need to track and process spikes

over time which can be computationally

expensive yet another limitation is that

running spiking neuron networks

efficiently often requires specialized

Hardware such as neuromorphic chips

which are not widely available or

standardized compared to Conventional

Computing Hardware neuromorphic chips

are optimized for this Spike based

processing and are still being developed

and that's why for example Sam Alman is

investing millions of dollars into a

neuromorphic chip company called rain

finally while spiking neuron networks

show promising results especially for

time-based data they often lag behind

current neuron networks for non-time

based data they often underperform

compared with current AI models

particularly for complex tasks this is

partly due to the challenges in training

spiking neuron networks effectively and

as with liquid neuron networks spiking

neuron networks are also relatively new

so there are fewer tools and Frameworks

available for developing spiking neuron

networks compared to current AI models

this makes experimentation and

development slower and more difficult

but anyways that sums up what could

potentially be the next generation of AI

to bring it all back the current

generation of AI is very energy

inefficient requiring huge amounts of

compute plus it can't learn new things

after being trained if we want to

achieve AGI or ASI we need to

essentially create something as

efficient and as fluid as the human

brain which can constantly learn new

things and adapt to changing

environments

these are the two essential things that

new types of neuron networks such as

liquid neuron networks and spiking

neuron networks can solve at least in

theory however these are still

relatively new and they are still being

developed but the potential could be

massive imagine an AI that can keep

learning and get infinitely smarter let

me know what you think about these

neuron networks in the comments below

things are happening so fast in the

world of AI it's quite hard to keep up

with all the technological innovations

that are happening right now so if I've

missed any other groundbreaking

architectures that are worth mentioning

please let me know in the comments below

and I'll try to do a video on that as

well as always if you enjoyed this video

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