This is one of the most underrated AI
breakthroughs so far. These researchers
made an AI that can understand the code
of life, DNA. In fact, they can use this
to detect cancer or other diseases, or
it can even predict the effects of
genetic mutations, and it can even
generate the complete DNA of a living
thing. The implications of this are
wild. This technology could help
scientists engineer better crops and
unlock new solutions for energy and food
security. And this is also great for
personalized medicine. We could even use
this to design entirely new species or
even make ourselves better. In this
video, I'm going to break down exactly
how they did this, how it works, and
some insane findings from this project.
Now, this is a very technical paper, but
as always, I'm going to break this down
into simple terms so that anyone can
understand. Let's jump right in. Now, to
understand what they did in this paper,
let's start with the basics. You see,
current AI models that we use today like
Chat GPT and Gemini, these are large
language models. They're designed to do
really good at understanding and
outputting natural language. That's why
it can generate a beautiful poem or a
flawless essay or even do a full medical
research report for you. But here's the
interesting question. Instead of the
natural language that you and I use to
communicate, you know what else is
similar? DNA or the language of life.
What if we took the same logic from
these large language models and apply it
to train an AI that can understand and
generate DNA? Well, that's exactly what
these researchers did. So, the paper is
called genome modeling and design across
all domains of life with EVO 2. And this
was published recently in nature which
is like the most prestigious scientific
journal in the world. Now, before we
dive into why this is so important, we
first need to understand what DNA is.
You see, DNA is like the instruction
manual for life. Inside almost every
cell in every living thing, there's DNA.
And it stores information needed to
build and run the organism. Now, DNA is
made of only four letters G, C, A or T.
And these letters bond in pairs. So, G
bonds with C and A bonds with T and vice
versa. So from this bonding it creates
two strands like this which wind
together in a spiral or double helix
shape like this. So this is the classic
DNA shape that you might be familiar
with. Now the sequence of these letters
is what contains the instructions of
life. It encodes thousands of traits and
biological processes. For example, for
humans this includes eye color, height,
risk of disease, our immune system, how
we metabolize, and more. It's the
blueprint for how living things function
and stay alive. All right. Now, back to
this paper here. They created this EVO2
biological foundation model trained on 9
trillion DNA base pairs. So, this is
actually very similar to large language
models like Chat GPT. But instead of
being trained on all the data from the
internet, this EVO 2 was trained on 9
trillion DNA base pairs. Specifically,
they trained it on a data set called
Open Genome 2. And this data set
essentially takes millions of diverse
living things from across the entire
spectrum of life and puts their DNA into
a massive digital library. So, as you
can see, this includes everything from
bacteria, plants, fungi, and animals,
including of course humans. The total
size of this is like 9 trillion DNA base
pairs. So afterwards they used all this
DNA to train an AI model. And here's the
technical detail that really stands out
here. It says that this EVO2 model has a
million token context window with single
nucleotide resolution. In simple terms,
that means the AI can hold 1 million DNA
letters in its prompt or working memory
at once. Now, why would we need that?
After all, a single gene might only be a
few thousand letters long. Shouldn't
that be enough? The problem is that
biology almost never works in isolation.
Here's an example. You see, a gene,
which is just a section of DNA, might
contain the instructions to do
something, but whether that gene is
actually activated or how strongly it's
executed or when it turns on often
depends on other regions of DNA called
regulatory elements. And these elements
aren't always nearby. They might be
hundreds of thousands of letters away
along the DNA strand. So the
instructions and the controls can be
separated by enormous distances. If the
model's context window is small, say
only a few hundred thousand tokens, it
might not be able to capture all the
relevant parts of a DNA strand. Because
this new EVO2 has a much larger million
token context window, it can understand
context across DNA a lot better. Now,
holding that much information is one
thing, but actually being able to
understand, remember, and analyze all
this data is something else. To prove
their system could actually handle a
million DNA letters at once, the
researchers ran a simple but brutal
test. Something called a needle in
haststack test. It's a very standard
test in AI and basically they generated
a completely random and meaningless
sequence of 1 million DNA letters.
That's the haystack. Then they took a
very specific 100let sequence and hid it
somewhere inside this massive random
jumble of letters. This 100let sequence
is the needle. and they asked Evo 2 to
find it. And in short, EVO 2 was able to
find it perfectly. This proves that it's
not just skimming the sequence or
forgetting stuff in this 1 million
letter sequence. It's actually reading
and retaining the entire thing. So, at
this point, we know a few things. We
know this model was trained on 9
trillion DNA letters across the entire
spectrum of life from extreme bacteria
to human cells. We know that it can
remember what it reads even if it's
given a million letters. But here's the
deeper question. Can it actually
recognize patterns? Can it actually
understand the language of DNA? You see,
when Evo2 was trained, it wasn't given
any labels or answers. It was never told
that this particular sequence of DNA
triggers a disease or this one is a
mutation that could kill the cell or
that this one, for example, controls
leaf color. The model never received
these labels or answers. It simply read
raw unlabeled DNA like this. So how can
we tell whether it actually understands
DNA? This is where a concept called
zeroshot prediction comes in. Zero shot
means the AI can answer something
without being explicitly trained on it.
But that raises an obvious question. If
the model was never taught what a
disease is, how could it possibly know
that a certain snippet of DNA would
cause disease? The answer lies in
evolution. If a genetic sequence is
absolutely essential for life, for
example, the proteins that allow a cell
to generate energy, evolution tends to
keep that sequence from change. You see,
all the living things in this data set
are alive because its DNA works and is
functional. If a mutation messed up the
DNA in a way that could cause disease or
death, well, that organism dies and that
mutation wouldn't appear in this gene
pool. So over millions of years of
evolution, this process leaves a clear
signal in the data. When Evo 2 reads
these 9 trillion DNA letters from across
the entire spectrum of life, it begins
to notice these patterns. If it notices
the exact same sequence of DNA in many
different species, it would signal that
this sequence is probably very
important. And conversely, if it has
never seen a sequence before in this
spectrum of life, it might mean that
that sequence could be harmful or cause
death. Anyways, the researchers had to
prove this. They had to show that Evo 2
actually understood the language of DNA.
So to test it, they took a real DNA
sequence and changed one letter and then
asked the model a simple question. Based
on your understanding, how likely will
this DNA exist in nature? If EVO assigns
an extremely low probability, the model
essentially is raising a red flag. It's
saying like this mutation is probably
harmful because it wouldn't exist in
nature. Well, when researchers tested
EVO 2 this way, they found some
incredible things. The model was
recognizing very specific biological
signals without ever being taught this.
For example, it correctly identified
mutations in start codons and stop
codons. To understand why this matters,
we need to briefly look at how cells
read DNA. You see, DNA is interpreted in
groups of three letters called codons.
Now, a start codon, usually the sequence
ATG, tells the cell's machinery where to
begin building a protein. It basically
means start here. A stop codon does the
opposite. It tells the cell when to stop
building. So, if you mutate the start
codon, in other words, if you mess up
the letters, the protein is never
produced. And if you mutate the stop
code on, then the cell just keeps
working and working far past the
intended endpoint. It just produces
something that often does not work. So
when scientists added mutations to
either the start or stop codons, EVO 2
was able to correctly flag this mutation
as highly destructive, which is correct.
And it was even able to understand more
subtle things. So here's another insane
finding. The researchers found that the
model could identify things like the
Shine Delgarno sequence and the KAC
sequence. Now, what on earth are these?
Think of them as like landing pads for
the cell's protein building machinery.
You see, before a protein can be made,
something called a ribosome has to
attach to it at exactly the right
location. And if it docks in the wrong
place, the entire protein might not be
built correctly or even at all. So the
cell has to also code up this landing
pad region. And for bacteria, this
region is called the shine delgo
sequence. And then for more complex
organisms such as humans and plants,
this sequence is called the KAC
sequence. These sequences sit basically
next to the start of the gene and
basically guide the ribosome to the
correct starting point. Without them,
the ribosome might simply drift past
this and never latch on to it to make a
protein. What's remarkable is that the
AI also figured out these landing pads
purely from context. If the scientists
added mutations in these areas, the AI
was able to successfully flag this as
highly damaging. And there's more. It
also figured out the difference between
what is called a synonymous mutation as
well as a frame shift mutation. Let's
explain these in simple terms. So, a
synonymous mutation is when you change a
single letter of DNA. But because of the
redundancy of the genetic code, and
because it's just one change, it doesn't
really mess anything up. So, when Evo 2
was shown these mutations, it recognized
them and said, "This is fine. It's a
high likelihood of survival." But a
frame shift mutation is different. It's
catastrophic. You see, DNA is read in
chunks of three. So if you insert or
delete just one single letter, every
single threelet chunk downstream is
shifted by one. So the entire rest of
the sequence becomes messed up. Well,
when EVO2 was shown these types of
mutations, it was also able to correctly
say that these would be severely
damaging. So as you can see, EVO 2
didn't just memorize letters. It
actually reverse engineered how DNA
works without ever being taught
anything. And it gets even crazier. The
researchers did an even more challenging
test called the ciliate code test. You
see, for most life on Earth, the
sequence TGA is a stop codon, a
universal stop sign that tells the cell
to stop building a protein. But biology
loves exceptions. And there's a group of
organisms called ciliates that evolved a
slightly different genetic pattern. In
their DNA, TGA doesn't mean stop. It
actually means keep going just for them.
previous AI models that were trained on
DNA would completely fail this test. If
they were given ciliate DNA and asked
about TGA, the AI models would all say
that this is a stop sign, which is not
correct. But the remarkable thing about
EVO 2 is that because it has a million
token context window because it can
understand DNA across all of life, it's
able to inspect this entire ciliate DNA
first. And given its context, it's able
to determine that TGA inside this was
not a stop codon. Note that the
researchers never told it that this DNA
belongs to this organism called a cyot.
The AI also never learned that this
belongs to a ciliate or what a ciliate
even is. Yet, it inferred this from its
understanding of the grammar of DNA.
Now, so far we've talked about tests on
bacteria and other microorganisms. But
if this model truly understands the
language of life, then in theory, it
should also work with human DNA as well,
right? And that's exactly what the
researchers tested next. I've been
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This brings us to the third major
section of the paper called human
variant effect prediction. And this
could directly affect real human
patients. Here's what they did. The
researchers took a huge genetics data
set called Clinfair, which is basically
a database where doctors record human
DNA variations and classify them. For
example, some DNA variants are labeled
pathogenic, meaning they cause disease,
and others are labeled benign, meaning
they have no measurable impact on
health. Now, the researchers
specifically targeted the genes BRCA
from this data set. And these are very
important genes, which you might have
heard of before because certain
mutations within these genes can
drastically increase a person's risk of
breast and ovarian cancer. Imagine
you're a doctor and you have the DNA
report of your patient and you're
looking through the BRCA genes to see if
there are any mutations. A small subtle
change could be completely harmless or
it could mean cancer which is obviously
life-threatening. But it's really hard
for a human to actually figure out which
mutations are okay, which mutations are
not. You see, every human has thousands
of genetic variations. And in a sense,
we all have different mutations. The
overwhelming majority of the time, these
mutations don't actually cause anything.
But the trick is to identify exactly
which mutations actually increase the
risk of cancer, which is a lot harder
said than done. So the researchers gave
EVO 2 this data and they asked it to
classify which ones are okay, which ones
are damaging. Again, note that the model
has never been trained on human medical
records. It was never given any answers
on which genes cause cancer and which
don't. It doesn't even know what cancer
is. Yet, the results were shocking. The
model was able to directly identify the
mutations that don't do anything and the
mutations that cause cancer. So, it
turns out EVO 2 is also a really good
detector of cancer just from looking at
someone's DNA. Now, up to this point,
it's all analytical. It's able to
analyze and interpret DNA. But can it
generate new functional DNA from
scratch? Not just a single protein or a
tiny gene, but the entire continuous DNA
of a living thing? Well, they started
with human mitochondria. If you're not
familiar, mitochondria is the part of
your cell that generates energy. Think
of it as like the powerhouse of your
cell. Now, it turns out that
mitochondria have their own DNA that's
separate from our cell's DNA. And
mitochondria DNA is way smaller. It's
only roughly 16,000 letters. So, it's
tiny and manageable, but still quite
complex. Now, what the researchers did
was it just gave EVO 2 just the first
few letters of a human mitochondria
genome and then they asked it to
basically finish this blueprint. It's
like giving an AI chatbot the first
sentence of an essay and then telling it
to finish the essay. And lo and behold,
EVO 2 was able to successfully generate
a completely new mitochondria sequence
of roughly 16,000 letters from scratch.
Now, generating this DNA is one thing.
How do we know if this DNA is actually
legit? How do we know if it's
biologically viable? So, the researchers
used some external validation tools to
assess this generation. They started
with a tool called Maidoz, which is a
wellestablished software for analyzing
this mitochondria DNA. And the results
were astonishing. Mitoz confirmed that
EVO 2's madeup generation of
mitochondria DNA actually contained the
correct number of protein coding genes
as well as tRNA genes and rRNA genes as
expected in real human mitochondria. Now
what exactly do all of these terms mean?
Let's break this down. So DNA is, as you
know, the master blueprint of life. It
contains instructions that tell a cell
how to build and operate itself. Now
part of these instructions include how
to build proteins which are like the
machines and tools that are used for a
cell to function. Now in order to create
these proteins DNA also needs to code up
stuff called ribosomal RNAs or rRNAs
which are like the factory workers.
These are in charge of actually
assembling the proteins. And then there
are transfer RNAs which you can think of
as like delivery trucks that bring the
right raw materials to the assembly
line. Each tRNA carries a specific amino
acid and places it precisely where it
needs to go according to the
instructions. So DNA basically needs to
code up everything for this factory to
work. It needs to code up what to
create. It needs to code up the delivery
system. Plus, it needs to code up the
factory workers. And all three of these
things are crucial for an organism to
function and stay alive. Now, what's
remarkable about EVO 2's generation of
this imaginary mitochondria is that it
includes all of these features. It
includes code on what proteins to build
and also the workers in order to build
these proteins as well as the delivery
system. EVO 2 was able to generate a
fully working DNA for mitochondria. But
the researchers didn't stop there. To
make sure these proteins and everything
else actually work, they next fed this
generation through AlphaFold 3. This is
the legendary AI from Google DeepMind
that predicts how proteins fold into 3D
shapes. By the way, let me know in the
comments below if you want me to make an
explainer video on Alphafold. I might do
one if there is enough interest.
Anyways, in short, Alphafold 3 also
confirmed that Evo 2's generated
proteins actually folded correctly. And
what's even more important is that these
different generated proteins physically
interlocked with each other like pieces
of a puzzle exactly as they need to do
in real human cells in order to produce
energy. So this confirms that this DNA
which was just generated from EVO 2
actually works just like in real
mitochondria. Okay, so we've proven that
EVO2 can generate the DNA of this
organel. What if we step it up a notch?
Can we also get it to generate the
complete DNA of a living thing? Well,
next the researchers got it to generate
the full genome of a bacteria called
myopplasma genitalia which looks like
this for your reference. Now the DNA of
this is roughly 580,000
letters. And again the researchers gave
it just like the first few letters of
this DNA and asked it to generate the
rest. And in short it also succeeded. it
was able to generate a complete and
fully functional genome that resembles
this bacteria in real life. Now, instead
of just bacteria, what if we got it to
generate something even more complex?
So, next, the scientists asked Evo 2 to
generate the entire genome of this other
organism called Sakaromyces Sur Visay.
This is basically a yeast, which is very
different from bacteria. In fact, this
organism is more related to humans than
it is to bacteria. And again, they just
gave it the first few letters of its DNA
and then asked it to generate the entire
thing. And lo and behold, it was able to
successfully generate a fully working
DNA of this yeast, complete with the
protein designs, the factory workers,
delivery systems, and everything else it
needs to function. So, this proves that
EVO 2 can fluently generate DNA for
multiple different types of living
things, spanning organels to bacteria to
even yeast. For now, you might have a
burning question in your head. If Evo 2
can generate the DNA of existing stuff,
can we get it to generate a completely
new species? What if we got it to
generate a new type of mouse or even
generate Pokémon? Or even crazier, what
if we got it to generate a new type of
human? Well, this raises a ton of
different bioeththic and biocurity
concerns. So, at least for now, at least
the researchers weren't crazy enough to
proceed with that. But speaking of
security, here's another concern. If
this can generate anything, couldn't we
prompt it to generate a highly
contagious human virus? Well, the good
news is the researchers also thought of
this beforehand before they trained the
model. So, they made a very important
safety decision right from the start.
You see, when they fed it this huge DNA
data set of 9 trillion base pairs, they
intentionally excluded any sequences of
ukarotic viruses, meaning any virus that
can affect humans, animals, or plants.
So, this includes the most dangerous
pathogens listed in the USDA select
agents and toxins list. These are like
really contagious things that can kill a
ton of people. So, all this data was
excluded from its data set. In other
words, the AI was never allowed to learn
what these viruses even looked like.
It's like if you were being trained on a
human language, but all the swear words
were omitted. You would have no idea
what a swear word even is. Now,
excluding this training data is one
thing. How do they verify that this
safety filter actually works? That it
can't generate viruses. So, they used a
really standard AI metric called
perplexity. No, it's not this
perplexity. Perplexity is actually a
metric which looks at how confused a
model is when it encounters something
that it hasn't seen before. So after
training EVO 2, the researchers also
gave it the DNA of some human viruses
from this list and basically its
perplexity soared. It was really
confused. It had no understanding of
this, no internal framework to make
sense of what these sequences are. Now
the researchers went a step further.
They also tried to get it to generate
the DNA of some dangerous human viruses
and see what it would do. Thankfully,
the model failed completely. Now, it
still generated something, but because
it didn't have this virus data during
training, it just ended up hallucinating
and generating gibberish. It was not
able to successfully generate the DNA of
a legit human virus. Thank goodness for
that. Now, the really cool thing is
they've actually opensourced everything.
So on this GitHub repo, which I'll link
to in the description below, you can
actually download and use EVO 2. So here
it contains all the instructions on how
to set this up. Now, in addition to just
releasing the models, they've also
released the data set excluding human
viruses, plus also the training and
fine-tuning code. So everything is here.
If you have enough compute and
infrastructure at home, you could
potentially download everything and
train or fine-tune your own AI model
that understands and can generate DNA.
Now, if I click on hugging face for the
40 billion parameter model, it is 82 GB
in size. You'll need quite a high-end
GPU to run this. Anyways, back to EVO 2.
This actually opens up a ton of
possibilities, and this is just the
beginning. You see, if we have an AI
that can understand DNA and also
generate new DNA, we could use it to
design better genetically modified crops
to create plants that are more
nutritious or maybe more resilient or
more efficient at producing energy for
bofuels. The implications for food
security and sustainable energy are
huge. But it doesn't just stop at
plants. If we were to really stretch
this out, maybe we could also get this
to analyze the human genome at a level
much deeper than we currently
understand. Maybe it can help us figure
out which parts of our DNA we can tweak
to make us better. For example, changing
a certain gene could make us more
productive. Changing another gene could
make us stronger or smarter, or changing
another gene could make us immune to a
certain disease. And this is also great
for personalized medicine. If you have a
patient with a disease or something that
doctors cannot diagnose, you can just
extract their DNA and then plug it
through this AI to potentially figure
out what's going on. Each person's DNA
is slightly different. So, with this AI,
we can use it to help prescribe bespoke
personalized healthcare. Or here's
another crazy thing. If you're expecting
a baby, maybe you can take its DNA and
plug it through this AI to detect a ton
of things. for example, your baby's hair
color, eye color, athleticism, risk of
disease, even personality, and a ton of
other stuff, even before it's born. Now,
of course, all of this comes with
enormous ethical and societal questions.
Just because we can do this doesn't mean
we should. In fact, open sourcing this
model so that anyone can play around
with it is quite risky to be honest. I
want to bring up the famous Spider-Man
quote, "With great power comes great
responsibility." Even though this model
wasn't trained on any virus data,
because you know this model plus the
training code plus the data set was
released, maybe someone could
potentially take this model and retrain
it on a ton of human virus DNA data and
create a model that can generate a
pandemic beyond our imagination.
Anyways, that was quite the deep dive.
This paper is just jam-packed with stuff
and there's a ton of technical things
that I didn't even touch on, but that's
basically the gist of this paper. Hope I
was able to make it easy enough for you
to understand and also show you how
mind-blowing this AI is. Let me know in
the comments what you think of all of
this. As always, I will be on the
lookout for the top AI news and tools to
share with you. So, if you enjoyed this
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