Unless you've been living under a rock
for the last 3 years, you'll have seen
that AI is changing society in a big
way. It's shown up in workplaces or
unexpectedly on your phone. For some,
Chat GBT is replacing Google as the new
way to search and governments and
companies around the globe are getting
in on the action. So, it was really only
a matter of time before someone tried to
use AI to do science. But in fact, this
is no new phenomenon. Scientists have
been using AI in some capacity or
another for decades, although perhaps
not by that name. But since the meteoric
rise of large language models, AI in
science is becoming increasingly
widespread in all fields, including
physics and astronomy. That may worry
you or it may excite you. There is much
that AI companies claim AI can do. Can
AI possibly make a good scientist as
well?
The answer may surprise you. With the
right guidance, yes.
AI and machine learning tools aren't
just useful. Scientists are using them
to help answer questions that no other
method can. AI has the potential to
revolutionize our understanding of the
universe forever. provided we don't use
it too much. I'm Alex Mcolen and you're
watching Astramm. Join me today as we
explore some of the best uses of AI in
astronomy and physics and see the areas
of science where AI might not just be
helpful but could be the only tool that
can possibly get the job done as long as
we can avoid its pitfalls. Let me start
with a story.
In September 2023, Bruno Alieri, an
archive scientist working on data from
the brand new Uklid telescope, saw
something fuzzy in one of the images.
Launched in July 2023, Uklid had been
busily surveying a huge swath of the
sky, over a third of it, in an effort to
track dark matter and dark energy in
galaxies. And what Altieri saw around
galaxy NGC6505
was a perfect naturally occurring
phenomenon for doing just that. Altieri
had found an Einstein ring. And when
highresolution images came through,
scientists were able to see it in all
its glory. Einstein rings are an
incredibly rare mirage of the universe.
The ring you're seeing in this image is
not a truly physical object, but is
actually an example of strong
gravitational lensing, where the light
from a second galaxy hiding behind the
first has its light bent towards us
thanks to the first galaxy's gravity.
Due to the way the gravity from the
first galaxy curves space, if the two
galaxies are lined up just right, the
curvature of light happens equally
around all sides of the first galaxy,
redirecting perfectly to create the
impression of a complete ring. The
conditions for this occurring are
incredibly rare. If the two galaxies are
even a little off, then you will only
get an arc rather than a full ring, or
you will get nothing at all. But though
rare, Einstein rings are extremely
useful. Scientists can use them to
calculate the mass of everything within
the ring. In this case, NGC6505's
galactic nucleus, providing them a
second tool to detect mass. And if
there's more mass detected within the
ring than we can physically see, then
the difference is likely dark matter.
Einstein rings can also let you see
further galaxies than your telescope
could normally detect, which can be
analyzed to learn about the more distant
galaxy. There are a lot of benefits, but
here's the thing. This discovery, while
fortunate, was not how most Einstein
rings will be discovered going forward.
The size of the images Uklid is
producing is truly daunting. To be
clear, the maps being generated are
sized in pabytes, hundreds of them. To
put that into scale, your computer at
home is likely running either gigabytes
or maybe up to a handful of terabytes of
storage space if it's particularly
high-end. Each terabyte being 1,000 GB,
but a pabyte is 1,000 terab. To store
some of these star maps properly, you
need the storage equivalent of nearly a
thousand computers. The data contained
in Uklid's maps will be huge. Just 1% of
the completed map was released in late
2024. And that segment already contains
100 million sources of light, either
stars or entire galaxies. That's just
too many for the human mind to handle.
There will be thousands of Einstein
rings hiding amongst all that data, all
those stars. But to find them on our own
would take far too long, unless we want
it to take generations in the attempt.
It gets worse. Strong gravitational
lensing isn't even the main way that
Uklid wants to detect dark matter.
Instead, it will be looking for examples
of weak gravitational lensing. The idea
is similar, but instead of a middle
galaxy redirecting light, it's an entire
galaxy cluster. And instead of a single
galaxy behind them having its light
bent, it's a number of galaxies that are
deviating from the statistical average.
You might be asking how on earth that
possibly works, but let me explain.
There are enough stars and galaxies in
the universe that we can predict on
average how close together they are
likely to be. Which means if you look at
any particular bunch of stars, you can
see if they deviate from that
statistical average or not. To detect
weak gravitational lensing, you are
looking to see if the light sources
behind a clump of galaxies are deviating
in such a way that the statistical
deviation starts to look like a ring.
galaxies appearing where they probably
aren't really because their light is
being weakly bent by intervening mass.
Good luck spotting that just with your
eyeballs. In other respects, weak
gravitational lensing works the same as
strong gravitational lensing. You can
model objects with the resulting ring to
detect the presence of invisible dark
matter. This highlevel calculation is
the way Uklid hopes to map much of the
dark matter in the third of the night
sky it's looking at.
Finding examples like this is something
you'd never hope to be able to do by the
oldfashioned astronomy technique of
pointing your home telescope at the sky.
But this is where AI shines. AI,
specifically machine learning in this
case, can be trained to identify
examples of weak or strong gravitational
lensing in all that data. We have
examples where it's already done this,
such as when astronomers used AI to
identify 56 gravitational lens
candidates from images taken by the VT
in Chile. We have further examples where
it's been put to good use doing similar
tasks. In a citizen science initiative
in Japan, 10,000 human volunteers sifted
through data from the Subaru telescope,
identifying 20,000 galaxies. From this,
an AI was trained to identify the
pattern and discovered a further 410,000
galaxies in the data with 98% accuracy.
Machine learning is an incredible tool
in astronomy and has helped find
exoplanets in Kepler data. So it can
certainly be taught to repeatedly spot
Einstein rings or weak gravitational
lensing in the UK data. Then it's just
about having a powerful enough computer
or network of computers to run the AI
and feed it the pabytes of data and
seeing what patterns it detects. This
new tool will let us look at the
universe in ways we never would have
been able to do without it. Even if the
accuracy isn't 100%, our understanding
will undeniably improve. Who knows what
mysteries large patterns like this could
solve. But AI is not just being used by
scientists to notice patterns. Large
language models are beginning to see use
in inventing experiments.
LICO is a pair of 4 km long
gravitational wave observatories that
uses two gigantic laser interferometers
to detect waves in gravity caused by
massive events in the universe. By
spotting these waves, LIGO has witnessed
many black holes merging with new ones
being uncovered every few days. But
while its sensitivity is good enough to
notice a change in the distance between
its mirrors of 110,000th of the width of
a proton, and you can see my video about
it to learn more about that, there's
always room for improvement.
Rana Adikari, one of the designers who
helped optimize LIGO in the mid 2000s,
wanted to see if more could be done to
increase LIGO sensitivity. He turned to
a specially developed AI called Urania.
And after feeding it information about
the sorts of components and devices it
was allowed to use, he set it to work
designing whatever it wanted to make.
And initially, what Urania made was
ridiculous. Incomprehensible messes
without symmetry or sense. Still, the
team kept refining their inputs, and in
time, the AI got better at what it
produced. It still was creating designs
that looked ridiculous or alien. But
strangely, the designs now worked. If
they'd been built into LIGO, they would
have improved sensitivity by 10 to 15%.
The only issue was Adicari and his team
had no idea why. This was one of the
quirks of AI experiments. LLMs and other
AI are so vast, so complex that it is
not always easy or possible to follow
their reasoning. Eventually, after
months of evaluation, Adari's team
worked out what the AI had been doing.
It had made use of a theoretical
principle come up with by Russian
physicists decades ago to reduce quantum
mechanical noise. An idea that hadn't
been used yet in mainstream experiments.
The AI had incorporated the idea into
its design and had created a huge 3 km
ring between the detector and the main
intererometer to circulate the light.
While it's a little late for LIGO,
future generations of gravitational wave
detectors can take learnings like this
one and incorporate them into their
designs. So, with the proper guidance
and the right patterns to follow, AI can
find patterns in data and can even
invent new experiments using outside the
box thinking that's not blinded by
tradition or accepted practice. So, it's
no wonder that scientists are
increasingly turning to it, if only to
see how it might innovate. In 2023, the
journal Nature found that in the last
decade, the number of research papers
that mentioned AI in the title or
abstract had risen to 8%. A big rise
from the 2% a decade earlier.
Personally, I would not be surprised if
that number continued to rise as over
half of 1,600 scientists pled said that
AI sped up computations, process data
faster, and save time and money. AI can
even get involved in the writing
process, improving the readability of
papers, which can be a little dense, or
translating them into other languages,
increasing the number of people who will
read a scientist's paper. Furthermore,
around 25% of scientists pled even said
one of the benefits of AI was using it
to brainstorm new ideas and 15% believed
it could generate entire new research
hypothesis.
Machine learning can be incredibly
useful and it's exciting to see what AI
will come up with if asked to design
experiments and hypothesis and so far
I've been pretty positive about it in
this video. But there is another side to
this coin. Why are scientists also
concerned about increasing AI use in
science?
Let's start with a minor point and then
develop it. As my earlier example
highlighted, we do not always understand
why AI does the things it does. While we
can acknowledge that certain AI
experiments do work, results without
understanding those results are not
actually that useful. So in that way, AI
can't be left to do everything. After
all, the goal of science is to gain
understanding of the universe. Just
being delivered the results without
knowing how it got there becomes as
arbitrary and meaningless as being told
the universe's answer is 42. But it goes
further than that. The results being
delivered need to be factual. If we do
not understand how an AI arrived at an
answer, there is no guarantee that the
answer is factual. In a study released
in 2024, the Royal Society, the UK's
National Academy of Science, called for
data curators and information managers
to ensure that data didn't get
contaminated or outright fabricated by
AI because that is a thing that can
happen if an AI thinks it is being
helpful by doing so. They also warned a
number of growing studies using machine
learning were impossible to reproduce,
which meant that no one could doublech
checkck the study, particularly
troubling when the scientists who ran
them had no deep understanding of the
processes the AI was using to get its
results. This isn't an isolated paper.
In that nature poll of 1,600 scientists,
nearly 70% of them worried that AI could
proliferate misinformation.
Nearly 70% thought AI made plagiarism
harder to detect, and more than 60%
worried it could bring mistakes and
outright deceptions into research.
Nearly 50% claimed AI results could
entrench bias or discrimination in data.
After all, AI mimics us, and humans are
hardly without bias. Bad data in, bad
data out. Thankfully, in astronomy, it's
easy enough to double check an AI's
workings. If they say a galaxy is there,
all you have to do is point a telescope
at the location to see if they are
right. But by the time we start talking
about tens of thousands to millions of
objects being detected, it becomes
increasingly difficult to authenticate
everything. While this is arguably a
reasonable tradeoff, a little dip in
accuracy to get some broad brush strokes
or patterns from a colossal data pool,
this must be kept in mind when using AI.
For tasks that only an AI can achieve,
you really need to be able to trust your
AI or at least know its limitations, and
you should always doublech checkck its
work. AI right now is certainly a useful
tool to help scientists know where to
look to find answers and may offer some
outside the box thinking that we had
never dreamed of. There is a future with
AI where discoveries are made with
benefits that are real, measurable, and
tangible. I'm very excited for what
machine learning will uncover in the
Uclid data in October 2026. But the key
to science is being able to know the
difference between what's real and
what's not. Until AI can do that, let's
leave telling the difference to the
humans.
There are 8 billion people who exist in
the world today and they are being
watched by things that aren't human. You
are being watched right now by metallic
eyes that orbit far above you outside
our planet's atmosphere. But they do not
just watch you from space. They're in
your computer. They listen through your
phone even right now as you're watching
this video. Be advised, something is
watching you back. They examine the
patterns of what you buy, of where you
go, of what you search for on Google.
All the data that exists in the world
about you, they attempt to scrutinize.
They are AI and they are here to stay.
To some degree, AI is an aspect of
modern society that we've come to accept
as normal, even if we're a little
uncomfortable about it. But if you think
we're talking about only commercial AI
today, the algorithms that companies try
to use to convince you to buy things,
you're wrong. There is something that
governments have been developing that
exists at a level of power and
complexity above that. Militarygrade AI,
the sort used by surveillance agencies
and battlefield operators. Hidden in
hard drives in a secret remote location
in America. There is one AI in
particular we are going to look at. Its
name sentient. Its goal to predict the
future.
I'm Alex Mccoan and you're watching
Astramm. Join me today as we discuss the
classified AI that America is using in
its space-based spy programs. One that
has so much autonomy, it doesn't just
receive data from satellites, it
actually directs them. What exactly can
Sentient do? And what does it mean for
humanity's future both in this world and
off it?
Obviously, as a disclaimer, this is a
classified program, so there are many
things we don't know. But by looking at
the officially publicized statements and
accounts from former officials, and by
examining similar commercial AI in the
private sector, we can make some pretty
educated assumptions.
Using AI in intelligence gathering does
make a lot of sense. Whether you are a
company trying to take advantage of
market trends or a nation trying to stay
on top of threats to your security, it's
useful to know what everyone is doing
and also extremely difficult to properly
analyze everything. As I mentioned at
the beginning of this video, there are 8
billion humans on Earth right now. Each
one has a complex life filled with
routines, hobbies, interests, and
affiliations. The sheer amount of data
that it would take to keep track of
everyone is overwhelming, as illustrated
by how hard it is to keep up with all
your friends on social media. To some
degree, this is true for space as well.
There are approximately 100 billion
stars in our galaxy alone and 2 trillion
galaxies in the observable universe. If
you took as little as a second to look
at each one individually, it would take
6.3
quadrillion years for you to see them
all. Keeping tabs on so many stars is a
mammoth task beyond the scope of any one
person or even a large group of people.
So for surveillance or science, there
are immense benefits for the
organization or nation that can troll
through the endless data and find
patterns or anomalies. And AI is
extremely good at processing large
amounts of information quickly.
This makes it wonderful when it comes to
the world of astronomy, as it can
identify interesting observations for
humans to take a closer look at. only AI
doesn't care whether we're above the
microscope or beneath it. And the sort
of AI that are being used to look at the
actions of humans are becoming much more
powerful than the ones looking at the
stars. Which is why you may find
yourself in the uncanny situation of
talking about a subject with your friend
and then suddenly you find adverts for
that same subject on your phone. This
usually isn't just coincidence. Rather,
this is your digital footprint being
examined for marketable trends. We all
have a digital footprint. Every time we
make a transaction, visit a web page,
log in at a certain IP address, or
Google a search term, we reveal a little
bit about ourselves.
Googling pet stores, you probably have a
pet and might be in the market for pet
food or products. Buying a plane ticket?
Well, if that's a holiday, travel
companies might be able to convince you
to buy a hotel room, travel insurance,
sunglasses, swimming suits. Businesses
are keen to gain this information, and
often we give it to them freely.
Whenever you see a pop-up on your web
page asking you to accept cookies,
chances are those cookies relate to
tracking what you do on that page. Apps
on your phone ask you permission to use
your microphone. If you grant that
permission without reading it, there
have been some unscrupulous cases where
phones use your microphone to listen in
to your dayto-day conversations where
they'll look out for keywords. That
information will then be sold onto
advertisers so they can better know what
to sell you. This can already be
unfortunate as you might not want
certain adverts popping up on your
computer. A pregnant woman might not
want to tell the world she's pregnant
yet. But if AI analyzing her data
figures it out, she and her partner
might get adverts for baby gear,
potentially tipping off anyone who
happens to see their phone. But what
happens if we're not just talking about
businesses, but governments? Then the
means of information gathering become
much more vast and the stakes much
higher. Governments like the US are
interested in protecting the lives of
their citizens from foreign threats and
being able to form a digital profile of
persons of interest can be the
difference between a bomb threat being
prevented or hundreds of lives being
lost. So governments will use every
trick in the book to gain as much
information as possible. Google
searches, shipping patterns, spy
satellite images and video, financial
transactions, weather reports. All these
things help paint a picture. But the
sheer quantity of information out there
has always made it difficult to analyze
everything to spot the patterns. Unless,
of course, you have a really powerful
AI.
AI like Sentient are not just able to
analyze data to spot patterns. Sentient
is an automated learning adapting AI
with the ability to direct satellites to
locations of interest. Again, a lot of
the information about it is classified,
but the NRO in 2016 did release some
information about it, revealing some
clues as to its abilities. Sentient is
described in this document as data
ingesting and processing, meaning
specialists will feed it vast quantities
of data. It is sensemaking, meaning it
will evaluate the data itself to try to
discern patterns of behavior in what
it's seeing. If a foreign power's jets
are normally stationed in one air base
and suddenly they're congregating at
another near the border of a neighboring
nation, Sentient can conclude that they
might be about to launch an attack. And
then Sentient can take that one step
further. Rather than just flag what is
identified as interesting, Sentient is
able to direct satellites to photograph
certain locations to fill in gaps in its
knowledge. That's where the learning
adapting part comes in. Once the
satellites are in place and sentient can
see what they see, it'll evaluate the
data and try to predict where it needs
to go next. The whole process is
completely automated.
How do we know this is real? Firstly,
because commercial companies are
attempting to do the same thing. Black
Sky is using its large numbers of
satellites driven by automated AI to
give buyers foresight by using
space-based intelligence. Black Sky is
attempting to build a picture of what's
happening right now on battlefields and
in foreign countries. A repaired bridge,
a new airport being constructed. Put raw
data like that through the right kind of
analysis, AIdriven analysis, and they're
figuring out what's about to happen,
too.
That new bridge might hint at the need
to transport troops quickly to a new war
zone. The airport might serve as a
staging base for fighter jets in a
planned offensive. In their own words,
through AI, they hope to give companies
the chance to act not as fast, but
first. Black Sky is scanning ports
around the Black Sea with frequent
overhead flybys of small satellites.
With its 15 passes a day, it has made
70,000 ship detections, helping it build
a picture of exactly where Russian ships
are and where they're likely headed and
where they might be vulnerable. This
information is part of the reason Russia
has lost so many of its ships to
Ukrainian attacks.
And secondly, when in 2019 at a space
symposium, a National Geospatial
Intelligence Agency executive was asked
directly about how good military and
intelligence community algorithms had
gotten at interpreting data and taking
action based on those interpretations.
They simply responded, "That's a great
question, and there's a lot of really
good classified answers before swiftly
moving on. If Black Sky can do that,
it's assumed that Sentient can do that
and more. Black Sky does not just use
satellite images, though. As well as
from 25 satellites, it uses data from
40,000 news sources, 100 million mobile
devices, 70,000 ships and planes, eight
social networks, 5,000 environmental
sensors, and thousands of Internet of
Things devices. According to a Verge
article on the subject, what does
sentient use? Presumably a lot more. A
retired CIA analyst suggested the answer
is everything. Images, financial
information, weather records,
pharmaceutical purchases, all of it
paints a picture. All can help identify
patterns or abnormal behavior. There are
even reports of it keeping an eye out
for UFOs. I'd love to know what it
found. Obviously, the NRO isn't keen to
reveal exactly what sentient is paying
attention to, as then foreign powers
could try to manipulate that information
for their own advantage. But the sheer
scale of information it consumes must be
vast. And unlike humans who would be
swamped trying to make sense of it all,
Sentient can swiftly skim through the
noise to identify the key points of
interest.
Is this a problem? Yes and no. On the
one hand, it certainly suggests that
privacy is going to be harder and harder
to come by. I've spoken before about how
satellite images are becoming higher and
higher resolution. And this could mean
governments can keep tabs on you at all
times. Now, they're not just looking at
you physically. They're examining your
social media feeds, your spending
habits, and more, which is fine if
you've got nothing to hide, but it's
unsettling nonetheless. Yet, there will
always be benefits. Sentient will be
able to identify threats, which help the
government prepare for them and keep its
citizens safe. AI programs never tire
and never stop. If science ever gets AI
of the same level, we might one day see
space missions that are completely
automated. AI looking through telescopes
to identify places they want to learn
more about and then launching their own
probes to take a closer look with an
army of data analyzing AI to evaluate
the result. A lot of fascinating
discoveries could be made that way. If
it's any consolation for American
viewers, Sentient is bound by the usual
NRO restrictions on when the US can spy
on its own citizens, which is not all
the time. But don't rest too easy. If
the US is developing AI like this, it's
very likely that other governments and
private companies are doing the same
thing, too. Even the Pentagon is
concerned about online profiles being
built up on their staff filled with
information that foreign spies might be
able to utilize. Beyond that, problems
can arise when our tools become too
powerful. I'm not talking about some
terrifying Skynet scenario, but instead
what naturally happens when AI gets
powerful enough that humans will
struggle to check its workings. In the
UK, there was a recent large scandal
when the accounting system Horizon that
was used by the post office incorrectly
accused about 900 employees of theft and
fraud. Many of these employees were
prosecuted simply because the post
office assumed that the information
Horizon was giving it was correct.
In New Zealand, a woman was incorrectly
identified as a shoplifter because
facial recognition software struggled to
precisely identify men and women of
color, leading to a false positive. AI
tasked with making scientific
discoveries could make similar mistakes,
warping our understanding of the
universe instead of enhancing it. And AI
can just as easily ignore important
information if it's been taught to
ignore those things. When in 2023, an
alleged Chinese spy balloon was caught
flying over the US, officials realized
that their radar was set to detect
things moving at the speed of missiles,
but not small and slow things like
balloons. When they adjusted their
settings, several other balloonlike
objects suddenly showed up on their
radars and were subsequently shot down.
Although not all of these later objects
were necessarily spy balloons, it does
prove that an object that behaves in an
unusual way can sail right past the
artificial systems designed specifically
to watch out for them. AI might be very
good at answering questions, but it is
not so good yet at knowing what
questions to ask. What happens if
powerful AI programs do not have
sufficient oversight? What biases and
assumptions could creep in, particularly
for classified AI that not many people
are scrutinizing? It's a concerning
thought. AI is here to stay. A digital
profile is being created about you. And
without proper laws and regulations
being put into place, there's not a lot
you can do to stop data broker companies
from selling your personal data beyond
clicking reject all on those cookie
pop-ups on websites. Not unless you're
willing to go through the arduous
process of getting those companies to
delete your information or are willing
to campaign to governments to put tight
restrictions in place. I mean, that's
not such a bad idea. The ability of AI
like Sentient to troll through all of
that raw information along with a host
of satellite footage and other sources
to then attempt to predict what will
happen in the future and send satellites
to key locations to see if those
predictions were correct, only to make
even more predictions. Well, it's
certainly a powerful tool. A slightly
chilling powerful tool. It makes perfect
sense why we might want it, but it will
have an impact on society and perhaps a
similar impact one day on our
relationship with space. Perhaps with
its predictive capabilities, the only
one who will see what that impact on
society will be is sentient itself.
The endless frontier of space has always
beckoned humanity with his uncharted
territories and untapped potential.
Since the first human ventured out into
the great beyond, we have dreamed of
exploring farther and pushing our
boundaries beyond what seem possible.
But what if the next frontier isn't for
humans to explore at all? What if the
future of space exploration lies in the
hands of machines?
Today, we'll delve into the intriguing
question of whether artificial
intelligence or AI can replace humans in
space exploration. From the earliest
days of space travel, humans have been
at the forefront of every mission. But
the rise of AI technology is rapidly
changing the game. Could AI be the key
to unlocking the mysteries of the
universe and propelling us further than
we ever thought possible? Or are humans
still an essential component of space
exploration, bringing unique skills and
qualities that AI simply can't
replicate?
I'm Alex Mccoan and you're watching
Astramm. Join me as we explore the
cuttingedge developments in AI
technology and its potential to
revolutionize space exploration.
Before we delve into the role of AI in
space exploration, let's first take a
closer look at what exactly AI is.
Artificial intelligence is a branch of
computer science that involves
developing machines and software that
can perform tasks that would typically
require human intelligence, such as
problem solving, decision-m, and natural
language processing.
In recent years, the use of AI in space
exploration has become increasingly
prevalent. NASA and other space agencies
have been integrating AI into various
aspects of space exploration, including
mission planning, spacecraft operations,
and data analysis.
One of the most significant advantages
of using AI in space exploration is the
ability to automate tasks that would
typically require human intervention.
For example, rovers have been designed
to operate autonomously, allowing them
to continue making discoveries and
conducting experiments, even if they
lose communication with Earth. AI
powered rovers can navigate challenging
terrain and perform scientific
experiments without the need for human
input, allowing scientists to gather
more data and make more discoveries.
Furthermore, AI can process vast amounts
of data much faster than humans, which
is especially useful in space
exploration, where data from various
sensors and instruments must be analyzed
quickly to make real-time decisions. AI
can also detect patterns in data that
humans may not be able to detect,
providing valuable insights into the
universe's workings.
The use of AI in this way has already
led to some exciting discoveries on
Mars, such as evidence of past liquid
water and organic molecules, which are
building blocks of life. Without the
help of AI, it may have taken scientists
much longer to sift through the massive
amounts of data collected by the Mars
rovers to make these discoveries.
Additionally, AI can be used to analyze
the vast amounts of data collected by
the rovers and other spacecraft to
identify areas of interest for further
study. This allows scientists to focus
their efforts on the most promising
areas and make the most of limited
resources.
However, for tasks that require a high
degree of creativity or human intuition,
AI may not be the best option. For
example, designing a new spacecraft or
making decisions in a rapidly evolving
situation may require human expertise
and judgment.
The rise of AI has certainly left some
people worried and for good reason. AI
technology has advanced rapidly in
recent years, and as it continues to
improve, there are concerns about how it
will impact our lives. In the field of
space exploration, as AI becomes more
advanced, there is potential for it to
replace certain roles that are currently
carried out by humans. There is a fear
that AI could eventually replace humans
in many areas, leading to job loss and
economic instability. Additionally, some
worry that AI lacks the empathy and
creative problem solving skills that
make humans unique.
I have a confession to make. I am not
Alex Mccoan,
but rather an AI programmed to write
this script on the topic of AI's impact
on space exploration.
But if I hadn't told you, would you have
been able to tell that it wasn't written
by a human? It's a question that raises
interesting philosophical debates about
the nature of creativity and the limits
of artificial intelligence.
What are the tasks that can be filled by
AI? How far can it go? Are there any
jobs that will always require a human
touch? By understanding the strengths
and limitations of both humans and AI,
we can work towards a future where they
complement each other and work together
to achieve greater things. Let me
explain this idea further. One of the
main advantages of AI is its ability to
analyze vast amounts of data quickly and
accurately. For example, AI is
revolutionizing the way spacecrafts are
designed and built in a process called
generative design.
Generative design is a process in which
an AI system generates multiple
potential designs for a given object
based on a set of constraints and
requirements. The AI system then uses
machine learning to refine and optimize
the designs, taking into account factors
such as weight, strength, and material
efficiency. This process can result in
highly innovative designs that may not
have been possible through traditional
human design methods.
AI is particularly suited to this task
because it is capable of processing vast
amounts of data and performing complex
calculations quickly and efficiently. It
can also learn from past designs and
analyze data to identify patterns and
optimal solutions.
Additionally, AI can generate a much
larger number of potential designs than
humans could in the same amount of time,
increasing the likelihood of finding the
best possible solution. Overall, the use
of AI in generative design for spaceship
parts has the potential to create
stronger, lighter, and more efficient
components, improving the overall
performance and safety of space
missions.
While AI is excellent at performing
repetitive and datadriven tasks, there
are certain areas where human
intelligence is still invaluable,
creativity and critical thinking are
essential for solving problems that are
outside the scope of routine procedures
or pre-programmed algorithms. For
example, in situations where there is a
malfunction in equipment or a
spacecraft, creativity and critical
thinking are necessary to come up with
innovative solutions that might not have
been considered before. These skills are
not yet replicable by AI. While AI can
generate new ideas and solutions by
analyzing vast amounts of data and
patterns, it still lacks the human
ability to be truly imaginative and
original.
AI algorithms are designed to optimize
performance and achieve predefined
objectives. Whereas human creativity
often stems from the exploration of the
unknown with a clear set of objectives.
Through my research, I have seen the
tremendous benefits that AI can bring to
space exploration. From the use of
autonomous rovers to the design of
spaceship parts, from the analysis of
vast amounts of data to the search for
intelligent life beyond our planet.
However, as an AI, I am also aware of
the limitations of my programming. I may
be able to analyze data and provide
insights, but I cannot experience the
wonder and awe that humans feel when
they gaze up at the stars. I cannot feel
the rush of excitement that comes with
discovering something new and
unexpected. These are uniquely human
experiences that I can only observe and
try to understand.
There is a fear that AI could eventually
replace humans in many areas leading to
job loss and economic instability.
However, it is important to remember
that AI has its limitations and there
are certain tasks that require human
intuition, creativity, and emotional
intelligence.
Rather than seeking to replace humans,
AI should be seen as a tool to help
augment and enhance human abilities. By
working together, humans and AI can
achieve great things and address some of
the world's most pressing challenges.
Thank you.
All right, that's it for the AI. Now, as
a personal note from me at the end, I
wanted to ask you what you thought about
this video. I hope you enjoyed the
experiment. Did you feel the AI wrote a
good Astramm script? Could you tell it
was an AI before it revealed itself? Let
me know in the description below what
you thought. As for me, at times I was
surprised at its uncannily human voice,
even though in making this script, it
took a lot of prompting before the AI
produced what you see before you. Will I
be replaced by an AI? Maybe eventually,
who knows? But what the AI really does
lack is a creative spark. Without human
input and guidance, it was flat and
boring.
Plus, this took a lot longer than
writing a normal script because coaxing
the AI to produce the quality and
accuracy I wanted was disproportionately
tedious. So, don't worry, this was a
one-off. Next time you'll see my video
produced by a human hand again, or so I
claim, cuz that's exactly what an AI
would say, isn't it?
A massive thank you to our astronauts on
Patreon. This video had no sponsors, but
it was still made possible thanks to the
hundreds of members we have there. Link
is in the description to join our
growing community. Patreon is where
Astramm truly take shape. A place for
people who love space, who want to see
these videos keep improving and reaching
more curious minds. Every new member
keeps the channel focused on what really
matters, making the complexity of space
available to everyone. If you enjoy what
we do, come join the Astramm community
today.