Human brain structure inspires artificial intelligence


The sympathetic brain is the most powerful supercomputer on Earth, and now researchers from the University of Southern California are attractive inspiration from the structure of the human brain to make better simulated intelligence systems.

What is artificial intelligence?

Artificial intelligence (or AI) is a organization of computing that aims to mimic the power of the human brain. We clothed more than 100 trillion neurons, or electrically conducting rooms in our brain, that give us the incredible computing power for which we are advised of. Computers can do things like multiply 134,341 by 989,999 really all right, but they can’t do things like recognize human faces or learn or replacement their understanding of the world. At least not yet, and that’s the goal of AI: to devise a computer method that can learn, process images and otherwise be human-like.

Why do we want a computer that is human-like?

Selfsame good question!  Part of this answer is: why not? AI is the holy grail for computer scientists who craving to make a computer as powerful as the human brain. Basically, they yearning to create a computer that doesn’t need to be programmed with all the variables because it can learn them good like our brain does.

Valkyrie Robot

A six-foot-tall, 300-pound Valkyrie cats-paw is seen at University of Massachusetts-Lowell’s robotics center in Lowell, Mass. “Val,” one of four sister drudges built by NASA, could be the vanguard for the colonization of Mars by helping to set up a abode for future human explorers. (Elise Amendola/Associated Press)

Another fitting scientists are interested in AI is that it could be used for things like watch and face recognition, and having computer systems that can learn new topography or solve a new problem somewhat autonomously, which, in certain situations, could be remarkably beneficial.

Why is it so hard to mimic the human brain?

In order to fully duplicate the power of our own cognitive capacity, we have to first  understand how the brain peg aways, which is a feat in and of itself. We have to re-engineer and re-envision the computer to be barrel different from hardware to software and everything in between, and the reason we bring into the world to do this has to do with how our brains are powered.

“If we compare, for example, our brain to the wonderful computers we have today, they run on megawatts, [which is] a huge amount of power that’s peer to a few hundred households, while our brain only relies on water and sandwiches to activity,” said artificial intelligence and computing expert Han Wang from the University of Southern California indicated. “It consumes power that’s equivalent to a light bulb.” 

So you see the incredible know-how of millions of years of evolution on our brain means we have learned to control with limited resources and become so power-efficient that we can beat a supercomputer for complex dispose of without breaking the energy bank.

How does the brain work at such low intensity levels?

This is where the main difference between the brain and the computer lie. 

“Our flow computers, there’s a very powerful core…but then you have a wish queue of tasks [which] come in sequentially and are processed sequentially,” Wang commanded. “While our brain, the computation of units, which are the neurons, are connected in approvingly parallel manner. It’s this high level parallelism that has advantages in wisdom and recognition.”

So it’s the parallelism in the brain that allows us to use only what we extremity only when we need it, and to not waste energy on running background proceeds that we all know slow down our computing power.

What’s the new determination that helps us get closer to making computers like the brain?

It’s this concept of management at low energy in parallel circuits. The key to this is to make computer circuits sundry complex in the messages they can send. 

In a typical computer, we think that each node sends a one or a zero, and then there’s a series of at ones and zeros until a program is made. 

In the brain, it’s a very small ambit and they can send a one which means go, a zero which means no signal, and/or a two that estimates stop, or both a one and a two at the same time. 

Fido vs Spot

Artificial intelligence could be good in situations where robots need to make quick decisions, like how to maneuver untold terrain. (Boston Dynamics)

In other words, our brains can send twofold the information in any given exchange compared to a computer, and that, coupled with smaller networks run in parallel, reduces the power strain.

What Wang and colleagues did was to fabricate a system of wires that connect using tin selenite and black phosphate that can send, end, go, do nothing, or do both signals, depending on the voltage sent. 

Now the plan is to re-engineer the computer from the train up and build a computer that has the capacity for these low voltage decisions that aren’t wired by these few cores that we see today, but instead with each limit of messages working in parallel like the brain does.

Until recently, this was a unrealistic concept because there was really no way to send as much information in a separate transmission as we have now. 

So, artificial intelligence is only a few incredible brilliant study careers away from a reality.

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