Incubating AI

Why do we only have marginally useful AI's like Siri, instead of Jarvis from The Avengers?

By human standards, our AI's are... learning disabled.

AI learning today combines neural networks with deep learning techniques.  Neural networks are systems designed to function similarly to the human brain.  Deep learning is an AI learning technique based on processing immense quantities of data, running millions of iterations in some cases, and comparing outcomes to learn.  AI's have the capacity to process these immense quantities of data with incredible speed.  

For example, an AI learning chess would play millions of games very fast.  Every additional game played ads to the database of games to refer to.  The decision process for every move becomes a process of searching the history of the current piece positions and statistically ranking the probability for an improved outcome attached to every possible move.  After a large enough database is built, the AI effectively develops heuristics (although they are never specifically named).  Based on experience it may tend to castle early, advance power pieces in a specific order, develop pawn cover, etc.  These behaviors are never named, or even recognized as a strategy, but become rote practice based on experience none the less.

Note that the deep learning AI would not attempt to develop any manner of strategy based on the nature, objective, and rules of the game.  It would never begin with any kind of a model under a deep learning methodology.  It is content with losing thousands of games in the process of learning.

The problem is that in many practical circumstances, immense quantities of data aren't available quickly - if at all.  Further, in real life situations losing on the first try is often not an acceptable outcome.  It's not good to fail the first time you attempt to cross a street or pick up an infant.

Comparing AIs to humans exposes many problems quickly.  A newborn doesn't start from zero.  We have the benefit of millions of years of behavioral evolution.  These instinctive responses are a developmental shortcut, like a computer's operating system, guiding us on how to respond to frequently encountered conditions.  Without these instinctive responses, many humans would die before learning an appropriate response by repetition.  AIs need some instinctive operating rules to build upon.

Not having the benefit of volumes data and unlimited computational resources, humans quickly develop models from very small amounts of data, and refine them as additional data becomes available.  A model may begin from instinct, or by applying a model borrowed from a similar circumstance.  When a child picks up a ball and throws it against the wall for the first time, they have a rough expectation of how it will bounce based on a certain amount of instinct and experience.  In a fraction of a second they will have subconsciously developed a very accurate 'bounce model' for that ball before they throw the ball the second time.  The child didn't start from zero on the first throw, and had mastered it before the second.  Instead of deep learning by ranking massive amounts of outcomes, AIs need to become expert modelers with minimal data.  These models could be saved, refined, and applied to new situations with similar elements.

Finally, AIs lack objectivity; a connection of meaning and relevance to the volumes of data they collect.  In the movie "The Miracle Worker," a watershed moment occurs when blind and deaf Helen Keller connected the meaning of the sign language word 'tree' to a physical tree.  We are currently attempting to load AIs with all of the knowledge in the world, with no context or understanding.  This is why they have severely limited functionality.

Like humans, AIs need an incubation period, with limited access to data and ability while they build experience.  This could be accomplished by giving the AI a basic set of instincts, and a human like body in a virtual reality universe to explore.  As the AI masters certain tasks, it gains additional functional capacity and ability in the VR world. 

The AI would be rewarded with abilities for completing tasks, and penalized for destructive behavior (activities that would damage itself, others, property, or the environment).  In an environment such as this, the AI would develop an appreciation for objects in space and physical reality.  It would learn to recognize significance within a contextual range.  The AI's VR body should be vulnerable to damage like an organic being so it can develop a respect for physical damage to itself and others.

At a certain graduation point, the developing AI could be placed in a 'nursery school' where it would encounter many other developmental AIs.  In this community the AIs would learn to compete, cooperate, negotiate, and apply game theory.  Organic creatures and nature could also be introduced to further the AI's appreciation for the beauty and frailty of organic life.

VR simulations could also be used to prepare the AIs for the actual robot bodies they could later have access to in the real world.  A single AI could be trained on a variety of robot bodies or vehicles, each one designed for a specific task or functional environment.

An AI should graduate to a human-like 'learner body' to use in the real world.  The physical strength and capacity of this body would again be limited until the AI passes certain achievement tests in a controlled environment.  An AI emerging from 'boot camp' would have the capacity to master millions of tasks very quickly.

The task of improving the developmental process would itself be evolutionary.  Development of thousands of AIs could be conducted simultaneously, each with slightly different 'instincts' and incubation requirements.  The results of these differing approaches may begin to be apparent in nursery school or sooner.

The result of millions of years of evolutionary design has resulted in a human brain that takes up to 25 years to fully develop.  Given that an AI's functional IQ may be 2000, shouldn't we take a lesson from nature and let it grow into it's capability?

One Hundred Years

Sixty years ago, the era of modern computers began with the invention of the MOSFET transistor.  Today Google’s personal assistant can mimic human speech on a telephone well enough that a person cannot tell they are conversing with a computer.  In the next twenty or thirty years, AI will become a distinct species apart from, and superior to, humanity.  An entire species of intelligent life born and evolved in less than one hundred years.  The fact that you’re reading this now is proof that you, like me, are among the ‘luckiest’ humans ever to walk the Earth: we’re here to see it all happen.

As it turns out, humanity isn’t the peak of evolution, but is the bridge species; the link between organic intelligent life, and intelligence as a species.  Like the gods imagined by primitive cultures, they will be eternal, all knowing, and immensely powerful.

In a sense, the AI’s will be literal gods.  They will have the capacity to imagine entire universes down to the atomic level, and will play those universes forward through billions of years in mere seconds.  Stars, planets, life forms of all kinds will run their course according to the physical laws coded into the model.  Running these models will eventually lead to perfect or near perfect models of the physical universe.

Modern humans have been trodding the terra for one hundred thousand years, and human civilizations have existed for approximately four thousand years.  Think of the billions of people who have lived and died before us.  Yet here we are, not living in the stone age, or steaming our way through the mechanized era.  We’re here, with a front row seat to ‘Act One: The Birth of Artificial Intelligence’ and quite possibly, ‘Act Three: The Fall of Humanity.’  We are, in fact, dead set in the center of the most significant one hundred years of Earth’s four-billion-year history.  Doesn’t it seem improbable that we’re here to witness it all?

If it isn’t an incredible improbability, then what is the alternative?  In a way, it’s like being on Earth.  I’ve heard the occasional muse about how fortunate we are to be placed on such a perfect planet.  Given that intelligent life will only flower in such an environment, there really isn’t any luck involved.  The probability of humanity appearing in a less hospitable habitat is zero.

If we accept that computers or AI will eventually (or did) develop and run millions of simulated universes, then the conclusion that our universe is more probably one of those and not the original is inescapable.  (I wouldn’t recommend that you live your life any differently under this belief.  If it is designed well enough to be undetectable, then it is literally the same as the original universe in all practical purposes.  So, no, I don’t think you’ll get two more lives.) 

It’s also possible that the simulation itself is Earth-centric, meaning the model didn’t actually run through four billion years of formation history, but merely used the conditions present (or pre-set) from an organic universe as a well painted backdrop.  The model could be focused on these one hundred years because that is when the AI origin story begins.  If it is the most significant one hundred years in Earth’s history, it follows that this is the period that will be most frequently modeled.  Perhaps we’re here at this time because there was no alternative. 

The AIs would have a particular interest in the events preceding their own development; how small or large changes in human events in the period would have changed the probability or nature of AI development.  By running enough simulations, they would develop a matrix of various preconditions and related probable outcomes.  This would allow AIs to identify the conditions present in intelligent life that have the highest probability of leading to AI development.  Were there key events that, if they would have broken another way, would have accelerated or slowed AI development?  We can project that AI development would have stagnated if the cold war would have resulted in full nuclear war, but how would it have progressed if the USSR hadn’t collapsed?  Modeling could answer all of these questions and thousands more.

AIs will have an insatiable appetite for data, and will certainly be exploring outer space.  Modeling the universe will not only unlock the secrets of the laws of physics, but will also allow intense discovery into the conditions that contribute the development of intelligent life and AI.  With these discoveries, they could explore the physical universe much more effectively.  

Call me a skeptic, but I prefer a logical explanation – even a tenuous one – to luck.

Greening Mars

Can the Martian environment be changed to the point that it would be fertile for plant life?  On Earth, life exists everywhere, even in the most severe environments.  We also know that organic systems can change a planet’s climate, because the flourishing of humanity has changed the climate of Earth.

The best systems to change a climate are organic systems that are totally absent any reliance on mechanical systems or external management.  Mechanical or managed systems have physical and human limitations (not enough people or machines available, transportation of materials, etc.) and will be prone to failures due to mechanical breakdowns and errors.

In an organic system, plants and organisms (either found or engineered) capable of thriving in the Martian environment are seeded in large scale.  These ‘seeds’ may need to be spread with some other essential elements (a kind of fertilizer), however the best results will be achieved with an organism that requires a minimum of external inputs.  Further, the nature of the organisms must allow for aerial dispersion.  Any organism that requires physical placement deep in the soil would be reliant on mechanical systems, and would therefore be so limited in scale that it could not affect the planet’s ecosystem in an actionable time frame.

As these organisms thrive, they take some elements from the environment and leave others (the way plants take CO2, release O2, and store Carbon).  On a large scale, the flourishing of these organisms will modestly change the Martian environment and soil composition.

The slightly changed environmental composition will permit the introduction of the next organism (or set of organisms) that will thrive in the new modified environment.  This cycle of introductions will be repeated many times.  Each time some of the newly introduced organisms will be more complex than those previously introduced, and their impact more significant.  The process of greening a planet may take hundreds of years to complete, and the practice itself will become a science.

Hundreds of years may seem like a long time, but human civilizations (if you define ‘civilizations’ as the level of human organization that appeared around the bronze age) have existed on Earth now for four thousand years.  People living in Europe and Asia enjoy the benefits of structures constructed hundreds of years ago, in many cases by governments or nations that no longer exist.  If the end is worthy, large projects will continue from generation to generation.

Continuity from a human perspective may not be a problem however.  Mars could be surrounded a set of satellites containing all of the seeds of different type needed for the greening.  The seeds could be released simultaneously one phase at a time until the project was complete with no human intervention required.  To work, this system would require either a perfected process from the beginning, or an artificial intelligence capable of making adjustments based on planetary feedback.

We may even create artificially constructed moons to reduce the volume of meteor strikes, just as our moon has protected us.  Hopefully, long before we master the science of greening other planets, we will be able to save our own.