Google Just Put An AI Brain In A ROBOT
It's another day in the tech world which means another day of discussing the possibility
of robots and AI becoming conscious and surpassing humans.
But this time it's a much different story.
Google just put an AI brain in a robot and scientists cannot seem to get over it!
But what makes this robot special?
And will it bring us closer to the era of technological singularity?
Let's find out.
Robots and artificial intelligence go hand in hand.
In fact, they've crossed paths a few times in the past, but not in the same way you’re
about to see.
Individually both technologies have grown exponentially in the last few years.
We've seen robots like Sophia, Ameca, Atlas, and Spot become prominent.
At the same time, AI models like DALL-E, GPT-3, and LaMDA have dazzled scientists and tech
enthusiasts alike.
But we were always going to get to a point where someone would attempt to combine both
technologies, perhaps out of sheer curiosity.
Well, Google just did.
The tech company wants to bring robots closer to the human level by teaching them how to
understand and respond to spoken language in real time.
To do this, the tech giant realized it had to merge the physical capabilities of robots
like walking, jumping, and performing simple repetitive tasks with the intuitive abilities
of AI systems trained with billions of parameters and texts.
Think about it like putting a human brain in a vibrant robot like Atlas.
As one of the world’s biggest tech companies, Google didn't need to stress too much to get
this done.
The Silicon Valley giant simply combined its PALM AI model with a basic robot built by
Everyday Robots.
And the result of all that hard work is what we've come to know as PaLM-SayCan: a non-humanoid
robot with the most human-like abilities.
PALM is a smart natural language processing and machine learning system and Everyday Robots
is a subsidiary of Google’s parent company Alphabet.
So, it's an in-house thing, but it will inevitably have a resounding effect on the tech industry
and the world as a whole.
Rather than performing pre-programmed tasks like fixing body panels in automobile factories
and arranging goods in warehouses, this robot will be able to understand plain English and
perform several tasks based on the instructions given to it.
This innovation eliminates the limitations of pre-programmed hard coding and introduces
us to a new world of endless possibilities where robots will be able to do just about
anything.
In a scientific paper that was recently published in the journal nature, scientists compared
the human brain and AI.
And interestingly, they found scary similarities between both systems, specifically in the
way that they predict words from context and how their numeral activations mirror each
other.
The AI’s internal activity is “significantly map onto (human) brain responses to written
sentences.”
So, technically, the AI model processes the data and instructs the robot on what to do.
According to the paper, the robot will act as the eyes and hands of the artificial intelligence
language model and together they will combine to perform real life tasks.
For instance, if you told the robot something like “I spilled my drink” or “I am hungry,”
It immediately consults its database and comes up with a range of possible solutions in the
background.
After coming up with a list of solutions, it’d select the best option based on its
capability.
Once this is done, the robot then proceeds to scan the environment to see if it's physically
possible to execute the task.
In the examples we made earlier, upon receiving your comment, the robot might attempt to clean
the spilled drink if there's a mop nearby and it's capable of using it.
But if not, it might ask you to do the cleaning yourself.
A Google researcher asked this exact question during a demonstration, and convincingly,
PaLM-SayCan made its way to a kitchen in the Google office building using its digital camera
vision and wheels; it grabbed a sponge using its motorized arms before returning to the
demonstration room to give it to the scientist.
For the second task, the robot might bring you food if there's a ready-made meal in the
kitchen.
Alternatively, it could make you a meal if the ingredients are available.
In one demonstration, popular CNET journalist, Claire Reilly, asked PaLM-SayCan to make a
hamburger and apart from putting a whole bottle of ketchup in between the wooden block versions
of buns, it did just fine.
PaLM-SayCan is a more open-minded and versatile robot and an immense upgrade on conventional
robots, which are specifically pre-programmed to do specific tasks.
Let's look at some of the most important technologies behind it.
The AI brain, PALM, otherwise known as the Pathways Language Model, is a sophisticated
algorithm trained with 540 billion parameters and petabytes of texts sourced from the internet.
This vast data bank is what gives PaLM-SayCan the ability to understand and interpret plain
English language.
Some might argue that systems like Siri and Alexa already have the ability to understand
human language, but PALM is superior in this case because it can pass on instructions from
humans to a robot that will execute them.
Again, drawing some perspective from the example we made earlier, can you imagine what Siri
and Alexa would do if you told them you spilled your drink?
They’ll probably just write you a poem to console you but it all ends there because
Siri and Alexa don't have the required hardware to allow them to clean or cook.
To complement the PALM AI model, Google scientists included another system of code that helps
the robot decide which task is feasible and which one is not.
This system is called world grounding trims.
Machine learning is another factor that helps scientists produce robots that can perform
on a human-level even in unstructured and unfamiliar environment.
Let's take autonomous vehicles as a case study.
Driving is a complex task with many possibilities that can occur, and decisions often have to
be made within a split second.
Nonetheless, the self-driving systems in cars are able to deal with all of these and perform
close to the level of a human driver or even better.
This is because the AI brain inside the system has been trained with billions of data and
parameters, which allows it to respond to the different possibilities that can occur
during driving in real time.
In the same way, the radar system on the car is constantly scanning the environment to
get the layout and also to identify potential obstacles.
Once it gets this information, it sends it back to the AI brains to determine what the
car’s next move would be.
All of these are made possible by the amount of parameters and training the autonomous
driving system has received.
The same applies to systems like PaLM-SayCan, which are designed to excel in an unpredictable
and chaotic world like ours.
To optimize their performance, scientists might also choose to educate these robots
with simulated data.
For instance, if scientists wanted to train an autonomous driving system with simulated
data, all they need to do is create a virtual network of roads similar to the one the car
will be required to navigate in real life.
Once the AI system is able to prove that it can navigate this course successfully in a
virtual world, it can then be tested in real life.
With simulated data, scientists are able to reduce the amount of human-generated data
needed to program an AI model.
Also, the simulated data technique reduces the number of attempts needed to learn a task
from 500,000 to 5,000.
Google is not alone when it comes to training robots with simulated data.
Microsoft used the same method to train its newest AI system, and the result is exceptional
as well, except that it doesn't perform physical tasks as the PaLM-SayCan does.
Also, Google went over and beyond to combine the grounding system with another section
of code that converts high-level instructions into low-level subtasks that can be executed.
Altogether, all of these processes and technology combined are what scientists at the Google
research lab have been working on in the last few months, and as you can see, the end product
is astonishing.
In the scientific paper released, Google claims that PaLM-SayCan is able to choose the right
sequence of skills eight out of ten times with an execution success rate of 74 percent.
This represents a significant improvement on the previous study that was released but
the interesting and perhaps scary thing about this robot is that there's no limit on how
much it can improve.
“As we improve the language models, the robotic performance also improves,” Karol
Hausman, a senior research scientist at Google who helped demonstrate the technology, said.
But as it is with every new robot and AI that is released, some theorists are worried that
this robot might bring us one step closer to the technological singularity.
Well, it does, but officials don't seem too worried about that and maybe we shouldn't
as well, especially because the product is still in the research phase.
But it is inevitable that sometime in the near future, we’ll look back at times like
this and either celebrate or regret our unhealthy obsession with developing highly advanced
robots and AI systems.
What do you make of the PaLM-SayCan robot?
Is it something you’ll be willing to invest in?
Let's know what you think in the comment section.
Be sure to check out this video over here!