The Next Era of Computing | Extropic
Summary
TLDRThe speaker, a physicist with a deep interest in theoretical physics and information theory, shares his journey of eight years leading to a breakthrough in computing. He discusses the evolution from quantum computing to a new paradigm that utilizes noise in thermodynamics to perform AI as a physical process. This approach is more energy-efficient and faster than current hardware, as it leverages the natural computation methods akin to how our brains work. The speaker introduces the concept of 'thermodynamic computing' and explains how it uses Josephson junctions, a key component in superconducting chips, to achieve superconductivity and exploit the Josephson effect. He envisions this technology as a significant step towards expanding the scope and scale of intelligence throughout the universe, enabling us to solve complex problems and ultimately reach for the stars.
Takeaways
- 🚀 The speaker's journey began 8 years ago with the concept that has now materialized into a new paradigm of computing, which they believe will change the world.
- 🌌 The speaker has always been interested in understanding the universe to build technologies with a maximal impact on scaling civilization to the stars.
- 🧠 The idea of viewing the universe as a simulation and embedding AI into the physics of the world came naturally from a background in theoretical physics and information theory.
- ⚙️ The challenge in quantum computing was noise, which is also a problem in low-power digital computers, but the new devices use this noise as an asset.
- 🌟 The speaker was inspired by the power of machine learning and the concept of a physical system performing machine learning as a physical process.
- 📊 Digital computers use bits to represent values, but the new paradigm is a different way of programming, closer to how nature and our brains compute.
- 🔌 As circuits get smaller, voltage noise becomes a problem for classical computation in CPUs, but the new thermodynamic computing aims to use this noise to drive computation.
- 💡 The new chips don't use transistors and instead use continuous variables and fuzzy values, which can be thought of as a point cloud shaped by data.
- 🔧 The process of machine learning is likened to a physical process where a parameterized physical device optimizes itself to accomplish a given task.
- 🌟 The speaker refers to the differentiable programming mindset, where gradient descent is considered a better programmer, as Software 2.0.
- 🏭 The speaker is in Sherbrook, Canada, at a fabrication facility where they manufacture a key component called Josephson junctions for superconducting chips.
- ❄️ The chips are cooled to 10 millikelvin to achieve superconductivity, which is necessary for exploiting the Josephson effect.
- 🔩 The technology is seen as a big step towards more energy-efficient and faster computers, which could fundamentally change how AI algorithms are run.
- 🌠 The ultimate goal is the expansion of intelligence throughout the universe, and the technology being developed is a significant step in that direction.
Q & A
What was the initial inspiration for the development of this new computing paradigm?
-The idea for this new computing paradigm came to the speaker about 8 years ago, stemming from an interest in understanding the universe and building technologies with maximal impact on scaling civilization to the stars.
How does the speaker's background in theoretical physics and information theory influence their approach to AI?
-The speaker's background led them to view the universe as a large simulation and naturally embed AI into the physics of the world, which is a different approach from traditional digital computation.
What is the main challenge faced in quantum computing that this new technology aims to address?
-Noise is identified as a significant issue in quantum computing, which also becomes a problem when trying to create low-power digital computers. The new technology uses this noise as an asset rather than a liability.
How does the speaker describe the process of machine learning with a physical system?
-The speaker describes it as a powerful concept where a physical system performs machine learning as a part of physics, which they initially tried to discredit but found to be compelling and feasible.
What is the fundamental difference between digital computers and the new thermodynamic computing approach?
-Digital computers represent values in bits, while the new approach uses continuous variables and fuzzy values over a continuous variable, which is closer to how nature computes and how our brains compute.
How does the concept of 'differentiable programming' or 'software 2.0' fit into the speaker's vision for AI?
-Differentiable programming, as termed by Andre Karpathy, is central to the speaker's vision where gradient descent is considered a better programmer than humans, optimizing the physical device parameters to accomplish tasks.
What are Josephson junctions, and why are they important to the new computing technology?
-Josephson junctions are a key component in superconducting chips, acting like transistors for superconductivity. They are crucial for exploiting the Josephson effect and enabling AI as a physical process.
Why is superconductivity essential for the operation of these new chips?
-Superconductivity is necessary to achieve the desired effects, such as low dissipation and exploiting the Josephson effect, which allows for the programmable physics of electrons in the metals.
What is the significance of the speaker's work in Sherbrook, Canada?
-The speaker's work in Sherbrook involves the manufacturing of the first chips, which are superconducting and serve as a proof of concept for the thermodynamic computing paradigm, potentially revolutionizing the field.
What are the next steps for the development and adoption of this new computing technology?
-The next steps include demonstrating similar principles in silicon, open sourcing the software, and encouraging global collaboration to program simulated and eventually real thermodynamic computers.
How does the speaker view the ultimate goal of their technology in the context of humanity's future?
-The speaker sees the technology as a significant step towards expanding the scope and scale of intelligence throughout the universe, enabling humanity to solve complex problems and advance towards the stars.
What is the speaker's perspective on the importance of energy efficiency in the development of AI?
-The speaker emphasizes that creating more energy-efficient computers that operate faster by fundamentally running AI algorithms differently is a game-changer, allowing for the application of intelligence to solve a wide range of problems.
Outlines
🚀 Quantum Computing to Thermodynamic Computing
The speaker reflects on the journey from the inception of an idea 8 years ago to the current state of a new computing paradigm. The journey began with quantum computing and evolved into the use of noise from heat as an asset in thermodynamic computing. The speaker's childhood fascination with the universe and a desire to build impactful technologies led to an exploration of theoretical physics and information theory. The realization that AI could be embedded into the physics of the world inspired the development of AI on quantum computers. The speaker and Trevor, presumably a colleague, recognized the issue of noise in quantum computing and digital computers and leveraged it for thermodynamic computing. This new approach to computing is believed to be more power-efficient and faster than current hardware. The speaker also discusses the shift from traditional binary representation in digital computers to a continuous variable model, which is closer to natural computation and human brain function. The concept of differentiable programming and the idea that gradient descent can be a superior 'programmer' is also mentioned. The speaker concludes by emphasizing the significance of this technology in expanding the scope and scale of intelligence in the universe.
🧊 Superconductivity and the Josephson Effect
The speaker discusses the process of manufacturing a superconducting chip, which is a key component in thermodynamic computing. The chip is compared to Tesla's first car, the Roadster, in terms of its pioneering role. The manufacturing process involves the creation of Josephson junctions, which are akin to transistors in superconducting technology. The speaker explains the necessity of superconductivity to achieve the desired effects, citing dissipation and the exploitation of the Josephson effect as reasons. The process requires cooling the sample to 10 Kelvin to achieve superconductivity. The speaker also highlights the need for extreme temperature control and isolation to prevent light from heating up the colder stages. The speaker expresses excitement about the potential of this technology and the future milestones, which include demonstrating similar principles in silicon and open-sourcing the software. The ultimate goal is to expand the scope and scale of intelligence and apply it to solve problems that prevent humanity from scaling to the stars.
Mindmap
Keywords
💡Quantum Computing
💡Thermodynamics
💡AI on Quantum Computers
💡Noise in Quantum Computing
💡Differentiable Programming
💡Superconducting Chip
💡Josephson Junctions
💡Thermodynamic Computing
💡Continuous Variables
💡Optimization
💡Expansion of Intelligence
Highlights
The concept of a new paradigm of computing using quantum principles was conceived 8 years ago.
The speaker's lifelong aspiration has been to understand the universe to build impactful technologies for civilization's expansion.
The bias of the universe as described by thermodynamics pushes towards growth, which is seen as Nature's way of urging expansion.
At the age of 24, the speaker was deeply immersed in theoretical physics and information theory, viewing the universe as a large simulation.
The natural progression from embedding physics into computation led to the integration of AI into the world's physics.
The speaker and Trevor, coming from a quantum computing background, pioneered AI on quantum computers in software and hardware.
Noise in quantum computing and low-power digital computers was identified as a significant problem.
The innovation of using noise from heat and electron jitteriness as an asset in computing was a breakthrough.
Machine learning with a physics background led to the powerful idea of a physical system performing machine learning as physics.
The speaker spent 6 years trying to discredit the idea but couldn't, leading to the belief in its potential.
Digital computers represent values in bits, but this new approach is a fundamentally different way of programming, more akin to natural computation.
The new hardware is designed to perform AI more power-efficiently and faster than current hardware.
As circuits get smaller, voltage noise becomes problematic, but this new approach uses noise to drive computation, termed 'thermodynamic computing'.
The chips use continuous variables and fuzzy values over a continuous variable, likened to a point cloud shaped by data.
Differentiable programming, or 'software 2.0', allows a parameterized physical device to optimize itself for a given task.
The speaker discusses the manufacturing process of a key component called Josephson junctions, akin to transistors for superconductivity.
The manufacturing process involves a sophisticated setup, including cooling the sample to 10 millikelvin to achieve superconductivity.
The technology aims to significantly expand the scope and scale of intelligence throughout the universe by creating more energy-efficient and faster computers.
The speaker expresses excitement about the potential of this technology to fundamentally change the game in computing and AI.
The next milestones include demonstrating similar principles in silicon and open-sourcing the software for global programming.
The ultimate goal is to solve for the ultimate embedding of intelligence into the physical world, operating at the limits of physics.
Transcripts
[Music]
is it like surreal to be holding this
yeah I mean this idea came to me like 8
years ago was a whole journey through
Quantum Computing to get to this point
it's really the first step for this new
paradigm of computing that we think is
going to change the
[Music]
world ever since I was a child I wanted
to understand the universe around us
because I wanted to figure out what
technologies I could build that were
going to have maximal impact in our
ability to scale civilization to the
stars and if you study the equations of
thermodynamics there's actually a bias
of the universe that pushes us to want
to grow and I think it's kind of
Nature's Way of like urging us to
expand I was I think 24 or so I was just
immersed in theoretical physics
information Theory trying to
view the whole universe as a big
simulation if you're trying to embed
physics into computation it's very
natural for you to embed AI into the
physics of the
world so Trevor and I came from Quantum
Computing we pioneered how to do AI on
quantum computers in terms of software
and a bit of Hardware um and what we
realized was that noise was really the
bane of our existence in quantum
Computing and it's actually also a
problem if you try to go low power with
digital computers and so what these
devices do they use the noise from heat
the jitteriness of the electrons as an
asset rather than a
liability when I first learned machine
learning with a background in physics I
just inhaled all this content and it
felt like an idea was being being from
space and I was just like the conduit
for this idea it was so powerful you
know to have a physical system that does
machine learning as physics I thought
I'd lost my mind and I tried to
discredit the idea take it down for 6
years but I couldn't right I couldn't
convince myself that it wouldn't work
digital computers usually represent
values in bits and so it's completely
different way of programming and we
think it's really how nature computes
it's closer to how our brains compute
and it's a way to do AI much more power
efficiently and actually much faster
than than current
Hardware as circuits get smaller voltage
noise start to become un present and
affect the classical computation in
normal CPUs we're trying to use this
noise to drive the computation that's
what we call thermodynamic Computing
these chips in particular they don't
even use transistors and they're not
encoding data in zeros and ones they're
actually continuous variables and they
fuzzy values over this continuous
variable so you could think of like a
point cloud and I try to shape it in the
shape of my data and that process is
basically machine learning uh as a
physical process if you have a
parameterized physical device you can
just optimize over the space of those
parameters and let that Optimizer
program for you in a sense you just give
it a task and it optimizes itself to
accomplish that task so it's the
differentiable programming mindset Andre
karpathy calls it software 2.0 right the
thesis is that gradi and descent is a
better programmer than you so we're here
uh in sherbrook Canada this is our Fab
this first chip is actually a super
conducting chip which is hard to
manufacture at scale but for us it's
kind of a Tesla Roadster in the sense
that it it's the most efficient neural
information Processing Unit you could
create and it's going to open people's
minds about the performance
thermodynamic Computing can unlock and
so what we're going to see today is how
we manufacture a very key component of
these devices called uh Joseph's
injunctions which are kind of like our
transistor for super condu they're
they're really important component and
they're the hardest one to manufacture
uh this is where we do our fabrication
and we're going to do a deposition today
of Joseph's injunctions so we did the
exposure of Electron Beam resist
recently developed it uh and now we're
ready to do the deposition so this is
our uh deposition uh Tool uh it's from
angstrom engineering and we're doing a
deposition in the evaporation chamber so
what will happen is the the robotic arm
will move the the chip into the
evaporation chamber followed by
oxidation in the oxidation chamber and
then we're uh evaporating again and this
makes sure that we have this super
conducting layer uh insulating layer and
then Super connecting layer on
top and here our very first prototype
chips use a super connecting metals and
have programmable physics of electrons
in these metals that allow us to do
essentially AI as a physical process
it's going to be a lot of screws we're
using this refrigerator to cool down or
sample uh to 10 Melvin so that it
becomes super connecting in order to get
the effects that we want we need Super
connectivity this is for two reasons one
is dissipation and the other is because
we exploit a physical Effect called the
Josephson effect and for that to happen
the aluminum needs to be super
connecting we're at 273 3° C below 0 so
we're very close to absolute zero and
these cans are necessary to literally
isolate stages from the other to prevent
light from heating up these stages that
are colder and colder so just light on
its own uh will will impact the material
in the fridge it's kind of a bit of a a
Russian Dole system
[Music]
here I think the physics of all of these
circuits whether it's Quantum or
thermodynamic I mean it's both in the
end I just find physics very interesting
and that's why I'm a
physicist yeah look at this
[Music]
G beautiful that's
beautiful we just started getting our
first data finally got this whole setup
uh going as you can see it's quite
involved it's quite epic for us you know
this is an evolutionary Fork away from
uh previous Paradigm uh of quantum
Computing and and and towards uh Thermo
and so couldn't be more excited today to
be holding this really everything we
learn on this platform will carry over
to our next
ones it's going to open people's eyes
about you know this potential Paradigm
of computing that's totally different
you know the next big Milestones are
going to be demonstrate similar
principles uh in Silicon and of course
open sourcing our software and and
getting people from throughout the world
to start programming uh simulated
theramic computers at first and
eventually you know our real devices and
for them to get to try them uh and
hopefully at that point we check back
in to me I think the greatest Pursuit is
the expansion of scope and scale of
intelligence throughout the Universe
this technology we're building here here
is really a big step towards enabling
that expansion right if you can get far
more energy efficient computers that
operate much faster by fundamentally
running the AI algorithms differently in
the devices using exotic physics then
you've completely changed the game you
can apply intelligence to really solve
all the problems that perturb us and
prevent us from being able to scale to
the Stars I think it's really important
that we don't just fight for our place
in the dirt but seek to grow in scope
and scale and seek to the Stars solving
intelligence is solving a problem that
will solve problems for us right and
what we're seeking here is to solve for
the ultimate embedding of intelligence
into the physical world we're operating
at the very limits of physics here this
is what these devices demonstrate
they're as energy efficient as you can
make intelligence for
all we know
[Music]
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