Giving Nitro Boost to Artificial Intelligence!

Artificial Intelligence is reaching new heights everyday. Not a single week goes by without any major announcement of new problems solved by AI technology. Artificially intelligent systems already exist among us, and they have been for some time now. Despite this genuine progress, we are still a long way from human level intelligence The world has yet to see an Artificial Superintelligence (ASI) — a synthetic system that has cognitive abilities which surpass human across every relevant metric and posits a world in which a computer’s cognitive ability is superior to a human’s.

As artificial intelligence continues to grow everyday, there is major question about the direction from which the next major advancement in artificial intelligence will come from?

Below I have listed 5 possible areas which have the potential to take artificial intelligence to new heights. The points I have listed might not be the common points you generally hear and they might be highly fictional or optimistic but they surely contain some interesting pointers for everyone.

So here are they:

An artificial brain (or artificial mind) is software and hardware with cognitive abilities similar to those of the animal or human brain. Many top researchers are already working on this side of things but I am not sure how long will it take to achieve a level where we will be able to perfectly simulate an artificial brain which functions exactly like a human brain. To theoretical neuroscientists, the key to understand how intelligence works is to recreate it inside a computer. Neuron by neuron, these whizzes hope to reconstruct the neural processes that lead to a thought, a memory, or a feeling.

With a digital brain in place, scientists can test out current theories of cognition or explore the parameters that lead to a malfunctioning mind. There’s just one problem: our computers can’t handle the massively parallel nature of our brains. A human brain’s neuronal activity is incredibly complex and simulating it at a 1:1 ratio is impossible with current technology. Our brain has over 100 billion interconnected neurons and trillions of synapses. The most powerful supercomputers that exist today can only handle at most 10 percent of neurons simulation rate. It might take us some time to reach brain’s level -10 years or 20 or might even take 50. But with this increase in computation powers and hardware technology, a brain powered by artificial intelligence might become a reality soon.

Artificial intelligence (AI) is arguably the most exciting field in robotics but it is certainly the most controversial also. Roboticists at present are nowhere near achieving the level to mimic human behaviors, but they have made a lot of progress with AI. The first two decades of the 21st century have brought us striking examples of what is commonly referred to as ‘autonomous technology’ and ‘artificial intelligence’Today’s AI machines can replicate some specific elements of intellectual ability. Artificial intelligence (AI), especially in the form of machine learning, and the increasing availability of large datasets from various domains of life are important drivers of the developments made in this field in recent times.

The real challenge of AI is to understand how natural intelligence works. AI research is largely theoretical. Scientists hypothesize on how and why we learn and think, and they experiment with their ideas using robots. Autonomous robots with modern sensors and actuators would provide a rich embodiment for artificial intelligence, and the lack of such an embodiment in other domains would likely impede the emergence of general intelligence. Kismet, a robot at M.I.T’s Artificial Intelligence Lab, recognizes human body language and voice inflection and responds appropriately.

Composable Differentiable Architectures (aka Deep Learning)

Deep learning is at the core of the most intricate AI capabilities including speech recognition, image and video recognition, speech generation and aspects of robotics. Deep Learning has enabled many practical applications of Machine Learning and by extension the overall field of AI. The next generation of enterprise AI systems is expectedly set to accelerate business development and change the way businesses operate. Deep learning is part of a broader family of machine learning methods based on learning data representations, as opposed to task-specific algorithms. Learning can be supervised, semi-supervised or unsupervised. Deep Learning breaks down tasks in ways that makes all kinds of machine assists seem possible. Driverless Cars, better preventive healthcare, even better movie recommendations, are all here today or on the horizon.

But a normal obstacle in path of Deep Learning and AI collaboration is that deep learning systems require large amounts of supervised data to work. Future AI systems need to be able to leverage this abundant data source. Current deep learning relies on GPU development funded largely by the gaming industry. With Deep Learning’s help, AI may even reach to that science fiction state we’ve so long imagined.

Gaming environment is a diverse challenging place and one of the best areas for testing the abilities of artificial general intelligence agents. In video games, artificial intelligence is used to generate responsive, adaptive or intelligent behaviors primarily in non-player characters, similar to human-like intelligence. AI algorithms get smarter and learn to perform tasks by being fed enormous amounts of data. Game playing was an area of research in AI from its inception. One of the first examples of AI is the computerised game of Nim made in 1951 and published in 1952. Despite being advanced technology in the year it was made, 20 years before Pong, the game took the form of a relatively small box and was able to regularly win games even against highly skilled players of the game.

Games are designed to challenge our intellect, involve interactions between multiple agents, and are sufficiently abstract to be formalized which makes games an ideal research vehicle to drive artificial intelligence research. Maybe the next breakthrough will be in the form of mastering another game.

Universal Intelligence and Artificial Life

A fundamental problem in artificial intelligence is that nobody really knows what intelligence is. The problem is especially acute when we need to consider artificial systems which are significantly different to humans. One approach which leverages algorithmic information theory for general artificial intelligence is the AIXI agent, a theory put forward by Marcus Hutter that attempts to be universal in the sense that it will successfully and optimally solve any solvable tasks.

Artificial Life relates to Biology in much the same fashion that Artificial Intelligence relates to Psychology. In the above point of brain simulation I argued that by understanding the human brain we will eventually be able to attain human-level intelligence. However, leaving this complex part aside we can start at a more basic level: by understanding and simulating a synthetic form of chemistry we may be able to simulate artificial life. Given a sufficiently rich environment such life may evolve to become intelligent.

There is cool hypothesis which personally excites me, The Big Intelligence Filter Hypothesis which states life may be abundant but intelligent life may be exceedingly rare. We currently do not know if intelligent life is rare or abundant in the universe, but if it is rare, it may also be exceedingly rare in any simulation of artificial life. Even for life on our own planet we are not sure what triggered intelligence to appear; one widely believed hypothesis is that it happened in a short time, akin to a phase transition, due to a change in ocean oxygen levels 540 million years ago, leading to the Cambrian Explosion.

(Additional: Give a read to the Fermi’s Paradox, it will interest you for sure.)

Although we don’t know the exact future, it is quite evident that interacting with AI will soon become an everyday activity. These interactions will clearly help our society evolve. There are even more ways that AI technology can influence our future, and this very fact has professionals across multiple industries extremely excited for the ever-burgeoning future of artificial intelligence. Artificial Intelligence is set to make rapid progress in the next decade and maybe perhaps along the directions we just discussed.

Connect with me on Linkedin: https://www.linkedin.com/in/gauravneuer/

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