by Joseph Bien-Kahn
February 29, 2016
Image courtesy of
Google's AI division - DeepMind, announced that its computer had
defeated Europe's 'Go' champion in five straight games.
'Go', a strategy game played on a 19x19
grid, is exponentially more difficult for a computer to master than
chess - there are 20 possible moves to choose from at the start of a
chess game compared to 361 moves in 'Go' - and the announcement was
lauded as another landmark moment in the evolution of artificial
IBM have all gone all-in on brain-like computers that promise to
emulate the mind of a human. The ability to learn and recognize
patterns is viewed as a key next step in the evolution of AI.
But Oshiorenoya Agabi believes
the brain-like processors are missing one key component: actual
brains. Or, at least, living neurons.
His startup, Koniku, which just
completed a stint at
biotech accelerator IndieBio, touts itself as,
"the first and only company on the
planet building chips with biological neurons."
Rather than simply mimic brain function
with chips, Agabi hopes to flip the script and borrow the actual
material of human brains to create the chips.
He's integrating lab-grown neurons onto
computer chips in an effort to make them much more powerful than
their standard silicon forebears.
Koniku is fundraising towards a
first-round goal of $6.3 million, Agabi says. It has already landed
customers in the aviation and pharmaceuticals industries, like
AstraZeneca, the UK-based pharma
signed on with a letter of intent to use the tech in
The first batch of neuron-abetted chips
are set to ship in the next few months. Agabi says that one
customer, a drone company, hopes the processors will prove superior
in detecting methane leaks in oil refineries.
Another aims to use the processors to
model the effect certain drugs will have on a human brain.
The future, Agabi believes, will run on
a computer that's much more alive.
A Komiku chip.
Part of Koniku's funding success seems
to come from his genuine, even romantic vision of neuron-based chips
as the future of processing.
When I interviewed Agabi recently, his
excitement over the future of neurotechnology was palpable.
Agabi, who was born in Nigeria, told me
he first became interested in machine learning while teaching a
pick-and-place robotic arm to classify objects for the Swiss
robotics company, Neuronics.
After eight years, he left the company
to pursue his Masters in theoretical physics, focusing his thesis on
the challenge of interfacing neurons with a robot.
He spent the next four working to build
a robotic arm that could attach to an amputee, eventually leaving to
move to London to pursue his PhD in bioengineering.
Basically, he hopes to build a
computer chip with living, learning processors
Recognizing the imposing nature of his
resume, the engineer paused for a moment, attempting to simplify his
"Essentially, for the last fifteen
years, I have worked to understand how neurons talk to each
other," he said. "I've worked on how to communicate with
individual neurons - how to read information from them and write
information into them."
This ability to code specific tasks into
neurons, born out of Agabi's specialized history, is the essence of
what Koniku is hoping to accomplish.
Through years of teaching machines to
learn, and through the study of the brain's mechanics, he believes
that his team will be able to organize living neurons into circuits
built to perform precise tasks.
Basically, he hopes to build a
computer chip with living, learning processors.
"We take the radical view that you
can actually compute with real, biological neurons," he said.
Since the silicon transistor was created
in 1947, the amount of transistors that can be crammed onto a chip
from a few thousand to more than 2 billion.
Today, chip manufacturers have shrunk
the size of each silicon transistor to the equivalent of three
strands of DNA.
Agabi said that because there is a limit
to how tiny you can shrink the deep lens of a silicon transistor
(IBM announced the creation of a
7 nanometers transistor in July, and
a single silicon atom is 0.2 nm), silicon-based processing can only
get so powerful.
"In the cycle of accelerating
computing power, we've gone from the slate to the paper, from
the paper to mechanical systems, mechanical systems to the
vacuum tube, vacuum tubes to silicon," he said.
"And now we are
moving to neurons."
For a frame of reference, Dr. Laeeq
Evered, a professor of neuropsychology at the Wright Institute,
tells me that,
"a piece of brain matter the size of
a grain of sand contains approximately 100,000 neurons, 2
million axons, and 1 billion synapses."
There is, of course, a quixotic quality
to the dream of actually creating an artificial chip so small and
powerful, but Agabi feels he's found the path to it.
I asked Dr. Evered whether he thought it impossible to ever build a chip as
powerful as the human brain.
"That's what I think, but I think
we've all been astounded by the progress of technology," he
said, and laughed. "So, we'll see."
Agabi told me he believes any
hesitations around neuron-based chips will vanish when Koniku can
successfully and publicly exhibit the chip's practical application.
"You want to build ideas that people
'That's so obvious.'
Today, it's not that way because
no one has demonstrated this yet," he said.
"But I feel very
confident that in two years when we demonstrate it, it will
become like, 'Ah, this is obvious'."
Image courtesy IndieBio SF
For a third opinion, I turned to Sherif
Eid, a systems engineer behind the
deep-learning program DRIVE PX that
some believe could be the key to the self-driving car.
He said he was intrigued by the idea of
neuron-based processors, but he said the technology was still based
on a lot of unknowns.
"It is just that there are so many
secrets we haven't unlocked yet in the brain," he said.
"The neuron-based chips could unlock
something in the future, but it takes investors with so much
faith or very deep pockets willing to throw away money to see
what comes out of it."
Eid thinks it will be a few decades yet
before the neuron-based processors were adopted, if they were ever
adopted at all.
In Agabi's view, however, the technology
is inevitable - and on the horizon today. He told me he believes his
chips will be powering robotics around the world within five years.
Which raises the question:
What happens if he actually pulls it off?
When I first heard of Koniku, I was a
little spooked. I've kept a close eye on the race toward true
artificial intelligence and have been most persuaded by philosopher
calls for caution.
To me, Koniku felt like a potential
Skynet moment - Agabi, after all, seemed to be planning to give the
machines human brains.
Carbon is a material like any
So for us
the premise that we start from is that neurons are a material
Naturally, I mentioned that infamous
malignant AI (Artificial Intelligence) to Agabi, and asked if he was burdened by the effect
the Terminator films have had on his research.
"Yes, yes, yes," he said, letting
out a wearied laugh.
He told me the idea that his company was
putting human parts into machines was just a simple case of
Neurons are present in many animal
brains aside from humans, and Agabi reminded me that Koniku's
neurons are grown in a lab.
"Carbon is a material like any other
material," he said. "So for us the premise that we start from is
that neurons are a material."
For Agabi, what he calls the "AI drama"
is much less interesting than the simple question of efficiency.
He notes that
the Tianhe-2, the most powerful
supercomputer built to date,
demands 24 megawatts of power, while
the human brain runs on just 10 watts.
In other words, he says, the most
powerful computer on earth burns 2.4 million times the energy of the
"It's not a matter of luxury, or
just because we can do it. It's a matter of urgency," he said.
"We have to find a way to build much more with less if we as a
species are going to survive."
Dr. Evered agrees that much of the
brain's tremendous efficiency stems from its ability to learn to
recognize and reinforce the optimal connections between neurons.
Though we are born with 100 billion
neurons, we lose 100,000 per day - and it is the ability for the
remaining neurons to form connections with beneficial counterparts
that determines the power of the brain.
"It's not a question of nature or
nurture. It's nature and nurture. We're going to have a certain
number of neurons and neuronal connections that have genetic
determination," he said.
"But then, interacting with our
environment is at least as critical, if not more so. It's those
connections, through learning and development, that are going
make a very strong brain."
Thus, much of the challenge in creating
brain-like processors will be the pursuit of programming
adaptability into computers.
In a lab setting, Agabi says Koniku has
proven its chips are capable of deep learning - the ability to
recognize patterns and retain that knowledge - by demonstrating a
spike timing-dependent plasticity, the idea that
neurons build circuits with beneficial neurons.
Agabi believes his neuronic chips will
be better at learning than traditional silicon processors, because
they can more closely mirror human brain function.
Near the end of our conversation, I
asked Agabi if he thought his neuron-powered chips could be the key
to powering humanity past Moore's Law - the rule that holds that the
processing power of computers will double every two years.
There has been
concern among some experts that
Moore's Law has plateaued and
that the future of AI will rest on engineers' ability to find faster
and more efficient computation than is currently available.
Agabi points out that
Moore's Law only
applies to increases in computational power through the addition of
more silicon processors on a chip over the last 70 years - and
argues that it may just be part of a larger trend; a second law, one
that describes the long-tail improvement of computing tools over the
course of human history.
It will, he says, take moving away from
silicon itself to allow Silicon Valley to continue to innovate.
"The fact that we're constantly
getting increases in computational power, that law of
computing holds for the last 2,000 to 5,000 years. Moore's Law
is a little patch, it's only a little piece of that law," he
"One of these two laws is going to
have to give in, and I suspect that Moore's Law is the one
that's going to have to give in. But our power to calculate
faster and faster - that law is here to stay."