The future of AI looks like THIS (& it can learn infinitely)
Summary
TLDRThis 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
- đ§ 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.
- đ€ 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.
- đ 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.
- đ 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.
- đ The next generation of AI should ideally mimic the human brain's neuroplasticity, allowing for continuous learning and adaptation to new information.
- đ§ 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.
- đ Liquid neural networks require less computational power for training since only the output layer is trained, making them potentially more efficient than traditional networks.
- đ Applications for liquid neural networks include autonomous robots, self-driving cars, and real-time data processing, where adaptability is crucial.
- đ Spiking neural networks are another potential next-gen AI architecture, mimicking the brain's neuron communication through discrete spikes and timing.
- đ Spiking networks incorporate time into their processing, which can lead to more efficient learning and adaptation, especially suitable for temporal data.
- đ§ 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
đ§ 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.
đ 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.
đ 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.
đ 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.
đ 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.
đ„ 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.
đ ïž 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.
đ 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.
Mindmap
Keywords
đĄNeural Network
đĄDeep Learning
đĄBackpropagation
đĄNeuroplasticity
đĄEfficiency
đĄLiquid Neural Networks
đĄSpiking Neural Networks
đĄSpike Timing Dependent Plasticity (STDP)
đĄNeuromorphic Chips
đĄTraining
đĄEnergy Consumption
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
it's a lot faster to train thanks to our
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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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stay tuned for more content also we
built a site where you can find all the
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more check it out at ai- search. thank
thanks for watching and I'll see you in
the next one
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