The Difference between Life and Machine

This was a response to a thread started by “Scott” on LinkedIn’s Artificial Intelligence Researchers, Faculty + Professionals Group.  My response went over the allocated chars so I placed it here.

Clearly, human intelligence does not capture nor does it represent all forms of intelligence in the universe.  With that said, in regards to a software AI attempting to demonstrate human-level intelligence:

We often commit categorical errors from the inception of a project or thought experiment when we try to compare intelligence of a biological life form with the intelligence demonstrated by a non-biological entity.   An AI cannot experience biological qualia, for they are not biological.  It’s camera eye may see images and analyze them.   It’s microphone ears may process sound and do a fantastic job of parsing it.   Yet the true aspect of biological ‘qualia’ is missing.   A non-biological experience is full of non-biological qualia which we cannot understand since we are not non-biological!

One thing is certain to me.  Biological life has needs.  It’s primary directive is to continue living.   Different life forms are more complex than others thus their primary directive of ‘continue living’ is expressed differently.  Yet, the overall theme remains the same:  Things that are alive tend to try to stay ‘alive’.  Things that are not alive tend to stay ‘not alive’.

Consider the following:
I worked on a private project that emulated human emotion in an artificial (electronic) ‘brain’.   We had a 3d demo in a small 3d world.  In this world the human user could drop from the sky a red box or a green box on demand.   A green box would cause a beautiful light show that would bring joy and happiness to the AI.   The facial muscles in the 3d model were wired to express the proper variations and combinations of emotions based in the FACS (facial action coding system).  This was bi-directional.  Smiling caused feelings of joy, and feelings of joy caused one to smile.  When the red box would drop it would settle and then explode with a loud boom.  The noise would frighten the AI, the pieces would hit his face and cause pain.    In this demo, the user could drop whatever boxes they chose, whenever they chose, in whatever order they chose.  The AI would learn to expect bad things and this fear would be reflected in his emotion (fear, negative expectation, etc) if you have already dropped a red box and trained him with that experience.   He learned, in context, through experience, and stored this learning for future use and future expectations with associated things.

The problem with our demonstration was that the true ‘qualia’ was hard-coded.  Yes the AI learned.  Yes the experiential learning caused associated emotional stamps that wired and re-wired this AI’s electronic brain.  This was very exciting for us as a team.   Yet this AI never truly ‘experienced’ the qualia of pain.  It was instructed through code that the neuron representing pain was firing.  This was not some sadistic experiment.   No true ‘biological feeling’ of pain or joy or excitement was ever actually experienced by the software.

So with this in mind consider another example:
An autistic boy impervious to pain touches a hot stove.  He does not move his hand and it burns.  A mother trains her autistic child, over time, that this ‘hurts’.   He learns that it is not a good or acceptable behavior, thus, over time, the autistic boy learns to not do these types of things.

This example is akin to an AI that has been programmed to “feel” pain.   Neither the autistic boy nor the AI program actually feels pain, yet both can emulate its effects and act accordingly.   In essence, the autistic boy is a walking Chinese Room Experiment.

If we can come to terms and accept this to be the case in our software AIs, we would avoid many of the pitfalls that trap us.   We can create a really advanced human emulator.   It won’t be able to actually do some of the ‘human-like thinking things’ we might hope for because it won’t actually be ‘feeling’ the qualia of human experience.    Yet that would be OK for many of its uses.  A really good emulator would serve some really good purposes in many cases.    We don’t care that our calculators don’t understand what they are doing.  They are not conscious entities aware of their number crunchings.  While they do not emulate humans, I use the example to demonstrate their helpful purpose.  We use them as tools that were designed to perform a function.   In that same light, as far as non-biological AIs are concerned, those too could be fashioned and used for greater purposes even in light of the inherent limitations that non-biological AIs are up against.

Emphasis Ambiguity

Often, time breaks and emphasis are used to change the connotations or implications of a sentence.  They are yet another layer of ambiguity inserted into language.

Sentence, with emphasis word bolded.

Connotations and Implications

I never claimed snake meat keeps you warm”

Someone else did, though.

“I never claimed snake meat keeps you warm”

The claim did not occur.

“I never claimed snake meat keeps you warm”

I did something else other than claim.

“I never claimed snake meat keeps you warm”

It was another type of meat.

“I never claimed snake meat keeps you warm”

It was another part of the snake.

“I never claimed snake meat keeps you warm”

Continuity of warmth provided in question.

“I never claimed snake meat keeps you warm”

It keeps someone other than you warm.

“I never claimed snake meat keeps you warm

It keeps you something other than warm.

The tokenizer should capture word differentiation (caps, bold) or in the case of voice the tone/emphasis would be discerned.  In the case of a text based parser it should set the tone of a sentence by viewing some RTF information, if available. Information on the sentence whole, and the individual words, should capture data on CAPS, bold, ital, underlined or “quoted” so emphasis and tone can be textually captured. 

Red dogs ate it.
Red dogs ATE it.
“Red dogs” ate it. 

Capturing the differentiation is step one.

Culling For Context (insider notes)

Without going into details explaining some of the propriotary code involved in this writeup, I will share the basic overview.  This document was writtin in 2007. – lkw

Intro
Context resolution first depends upon our ability to cull a clump, a phrase chain, and a neuron. Culling occurs at different stages of a parse. The timing of the cull changes how the cull operates. There are pre-parse culls that represent the words and there immediate associations. This mimics the brains ability to fire off immediate associations based on the word, not necessarily the intended concept of the word or phrase. Those follow later after they are resolved. A demonstratable example of this “instinctual cull” occurs with phoneme sets, as when a word like “sextuplet” is used. Many people may first hear “sex” and key off it. In doing so, they fire off immediate associations. We do not handle phonemes right now, but when we do this will be another type of pre-parse cull. In these cases, firing will occur, but at a very low level.

Other culls happen after the parse is complete. At this stage all the phrases and neurons that could be resolved have been assigned their known values. These resolved values are culled, fired, and placed into a pool (g_chain). Everything culled from a specific Nid, Cid, or Role has a path back to the originating culler (through linked g_links). In other words, if the instance “Fido” caused dog, animal and lifeform to be in the context pool, those 3 specific “g_links” would have paths back to the original instance that got them in the pool. This is an important factor when considering a g_chain (pool) and its links. It operates slightly different than the traditional context pool because it needs the ability to hold duplicate entries. This allows the concept of dog to be in the pool 3 times if there are 3 unique dogs.   In other words, the concept neuron ‘dog’ has been fired 3 times.    This structure allows for us to know why the neuron is firing.

The aforementioned culls get data into pools. This data is used to resolve phrases, cids, and nids during parse time. Phrase links are the core constituents of a parse. A sentence is made of phrases, and these phrases are referencing:

  1. something in context
  2. something in memory, or
  3. something new we are not familiar with.

At parse time, whenever a phrase is parsed, we attempt to resolve it by looking to the g_chain (pool) for relevant data. We look in memory for something out-of-context.

Recap

Functions of culling:
1. Cull to Chain/Link (Formerly a Pool/Table… Dupes necessary)
2. Cull for the purpose of firing the neurons. (dupes not necessary, firing is boosted)
3. Do both. (Perhaps in a future system they are one in the same)

CULLING IMPORTANCE
In Context.h I created and used an enum called cp_timing. It directs my culls on how to cull

/*** Context cull type used in weighting firing levels ***
We will call culls at different stages of the parse to help with context and splitter resolution. The type helps determine the firing/weighting level. Before resolution, just addressing the neuron should fire it to some level. A stronger firing will occur when we resolve which splitter we are going with. */

enumcp_timing {CPCT_PRE_PARSE=1, CPCT_SPLIT_RESOLUTION, CPCT_NEURON_RESOLUTION, CPCT_CLUMP_INST_BUILDOUT, CPCT_INST, CPCT_CC_TOPIC_BUILD, CPCT_FINAL_FIRE, CPCT_CONJECT, CPCT_THES, CPCT_CMD};

All of these culls will need to be re-written in light of our change from tables to chains. It is absolutely critical that this is handled correctly. The resolution of phrases and concepts in context depends upon the correct concepts being put in the context pools. They are my indexes into various cids and nids. In order to attempt to resolve the phrase “the dog” in context, we will look for the concept of “dog” in the pool. From there we will track it through the g_links to the instance that put it into the pool (if any).

Resolution Challenges (a side note)
It is important to note, when resolving context, we don’t always know what we are looking for. Even “the chicken” doesn’t have to be a traditional instance. Generally the determiner “the” clues us in to focus on instances, but it isn’t a guarantee.

Proverbial Instances
The sentence could be, “the chicken that bites is a chicken not to be trusted.” I refer to these fake instances as “Proverbial Instances”. The phrase “the chicken” is masquerading as an instance, when in fact it is still a class definition not too different than the class definitions:

Chickens that bite are not to be trusted.”

Biting chickens should not be trusted.” (‘Biting chickens’ can be ambiguous here! Those that bite, or the act of biting a chicken!)

Don’t trust chickens that bite.”

Proverbial Instances capture “words of wisdom” statements (he who ….) and they capture parable like statements as in, “the man that buried his money…”

As is the case with all faux instances, the challenge is isolating the cases and determining where to store them! Should we reference these in the class definition, or should they be treated as special Proverbial Instances?

Cultural Instances
Another masquerading instance case is the “Cultural Instance”. Why did the chicken cross the road? This is not an instance per say, it is a “Cultural Instance”; the same chicken everyone talks about when talking about “the chicken” that crossed the road. It isn’t a real chicken, it is a proverbial chicken used in a “thought experiment” or as a cultural example.

Side Tangent: Everyone has their own opinion why this chicken journeyed across the road. Perhaps Plato would say it was for the greater good. Thomas Visel might add it was because by crossing the road the chicken saved 2 bits of memory! The woman who cooks steak in two pans would say the chicken crossed the road because that is what the chicken always does! Schrödinger might think it was on both sides of the road, crossing, and not crossing, all at the same time! Karl Marx would contend it was a historical inevitability. Oliver North may think it was because National Security was at stake, and one might argue that Einstein would propose that whether or not the chicken actually crossed the road depends upon your frame of reference!

Clonable Instance
Finally, there is the faux instance that itself that can be instantiated. In this case, it at first appears to be an instance, but it truly is a class. At first glance, “The Bible” is an instance of a book, but upon inspection it really is a “Class” of books. (child of book) “The Bible” can be instantiated because I can own a copy, and so can you! This seems to be a unique property of things that can be duplicated in exact form. I am calling this the “Clonable Instance”. We have unique instances, but all of the instances can be referred to. The same can be said of a specific book by a specific person. I own a particular copy of Ray Kurzweil’s “The Singularity is Near”. My copy has hand written notes (by me) and specific pages are bent. Unique creases, nicks and dings, as well as fade marks are on my copy. This is a true instance of Ray’s book, and it IS NOT the same as the clonable instance of a book referred to when someone says, “Ray Kurzweil’s new book The Singularity Is Near just hit the shelves. That book is amazing!

Notes on Intent
In many cases, faux instances are used to drive home a point in a less offensive way. This can be a bragging point about oneself or an insulting point about another. They are also used for humor or to illuminate a greater truth. It seems like faux instances offer a unique method of elucidating a proposed or believed truth. Those who are able will see it. (Whether it is in fact true or not) Those who are not able to see it, will not be aware of the hidden layer. We may be in a discussion where both parties are aware that someone is a believer in “aliens”. Instead of one of us saying, “you are crazy for believing in stuff like that!”, we may opt say something like, “The man who believes in unproven and mythical things is a man who is of greater faith than I”. In a round-about way we have called someone something by defining a class that they obviously are a member of! This information will be captured at higher “meta levels” of our awareness pool. By asking the internal question of, “What does this mean?” for every input received, we are able to draw the conclusion (new awareness) that the speaker is providing class defining information of a class one or both of us is clearly apart of.

Conclusion
Culling must be done right, for it sets up and enables proper resolution.

 

Cridabmge Wrod Gmae

Every so often that “Cambridge Study” surfaces regarding the brain’s amazing ability to read scrambled words as long as the first and last letter remain in their original locations.   The study is cited as evidence for all sorts of pet ideas regarding various abilities of the human brain.

Here are the facts as I see them regarding this urban-ish legend.   Our brains are not auto de-scramblers.  We are, however; skilled context resolvers.

As pointed out by many before me, 3, 4 and 5 letter words make up the bulk of our everyday vocabulary. We can all read the following sentence even though many of the words are scrambled:

“My mom siad to me taht tish sduty is wnorg”

Does this prove some magical ability of our brain? Well, sort of, but it isn’t likely the ‘magical’ ability most would use this phenom to support.  A sentence with more 5+ letter words is next to impossible to read.   Try this one:

 “Mtullersafy wettirn lhgetny sectnnees pecrahd the iorhpesmeivd ilttnaellluecy fiateugd.”

Not so easy.

In the internet or chain-email examples, we see many small words and a few bigger words tossed in here or there.   The reason this phenomenon still works with some larger words is because of our strong contextual resolution and neural firing.   A related ability to resolve unknown words in context will serve as a good example of this ability.

Consider this passage:  “I was testing the hottest new PETCO product with my dog Fido.  I took the new Pachuchu and hurled it with all my strength.  Fido jumped into the air, grabbed it with his teeth, and brought it back to me.”  In this passage, most of us would understand that a Pachuchu was some sort of toy or object like a Frisbee or stick.  It is some gadgetry of sorts that is somehow like other things we use to play catch or fetch with our dogs.  The context of the passage helped us understand that new word.

Now consider our previous scrambled “easy” example with a few larger additions:

“My mom siad to me taht tish sduty is wnorg.   It is a lie.  She siad it is flsae to bieleve in teshe slliy gmaes.  Tehy are ricuuidlos and utnure.”

Most of us can still read this even though there are words of 5, 6, 7, and even a 10 letters.  This passage as a whole contains sufficient information to “fire”
contextual neurons that can help us “resolve” a scrambled word (in context) without actually having to do much work.  In a way, it sort of functions as an unknown word until we can ‘test’ the solution and proceed forward reading the sentence.  The easy words such as “wrong”, “lie” and “false” put into context the bigger words “believe”, “ridiculous” and “untrue”.    As we read the sentence the words become easier to solve because we get a stronger understanding of the context as we resolve, read and process each additional word.  Those related “in context” neurons are firing at low levels before we actually encounter them in the sentence and they can more easily be resolved because they are already in context (firing at low levels in our brain).

So in a sense, this is quite a magical process, just not likely the one that is cited in Internet lore.

AGI – To Be or To Emulate?

There is a common mistake being made when developing an AGI (Artificial General Intelligence). Its golden thread is woven through the challenges, through the apparent solutions, and its dark lens taints the thinking of our brilliant minds. It rears its ugly head in our discussions, in our brainstorming and it is preventing our progress in this critical next step for humanity.

When attempting to create a human like AGI, you can go down one of two paths.  (Note:  In the piece I am not discussing ‘non-human intelligences’, I am am only discussing those that purport to be human-like) It is of utmost importance that this is clear before the project begins and it remains clear at every step of the way. Confusing the ultimate goal, at any step of your project, will lead to the failure of your goal. You can try to create a human intelligence, full of complexity, emotions and needs, that is trainable – which develops and acquires the language of its environment. I will refer to this type of project as an AGHI (Artificial General Human Intelligence). Alternatively, you can create a new intelligence (non-human), that ultimately is capable of emulating a human intelligence from an observational standpoint. I will refer to this as AGIHE (Artificial General Intelligence Human Emulator) The distinction between the two is critical, yet the lines are often blurred over the course of a single project. This leads to categorical errors in thought and a hodge podge of code that ultimately ends up sabotaging the end result.

Attempting to create a AGHI
Projects that are claiming to create an artificial general human intelligence are likely NOT creating said intelligence. How can I make such a statement without personally viewing all of these projects? By reading industry texts, academic papers, and investing over 10,000 brain hours to a startup AI company, I have observed critical fallacies in the core and axillary processes. I will cite some simple examples to illuminate some of the stronger problems.

First off, an AGHI needs to be a victim of the human condition. In order for this to occur, it must have a root set of human needs. It must be able to experience these needs, and emotions. For example, it must be able to literally experience pain, or something akin to it. It must be able to experience hunger, to seek out a solution to this feeling of hunger, and to experience satisfaction when the need has been met. This learning, of dealing with the human condition, begins in the womb, and shapes the formation of our neural connections. This is not just a neuron that is turned on or off, for that is just a number and a reference. There is no real feeling associated with a number and a reference. Even a robot, imbued with an AI brain, is not able to actually experience the qualia of pain. It may be able to emulate it, but how can it truly experience it? Pain and hunger happen to be needs that are not even unique to the human condition, yet they are still apart of it. As we can see, this need based system is critical in all “life”. Again, it shapes our development from the moment we are in the womb. It shapes our language acquisition at every step of the way. Early failures will lead to different results than early successes.

It is important to note that the macro process of human development cannot be accurately started and resumed at any arbitrary point. It must begin, at the beginning! A 5 year old human intelligence cannot be created without first going through all the stages that led up to the 5 year old point. Granted, electronically we can speed up the process, but we cannot aim to create an AGHI that begins its life anywhere but conception. We must not cheat the process, for it is the process that yields the results we are after.

Today, as it stands, an AGHI project generally starts by creating a structure of some kind to hold information. This is generally a relational database or a neural-network. A decision is made on an edge based storage system, which focuses on the relationships between neurons, or a node based system, which focuses on the neurons themselves. Some form of an input parser is hard-coded in a programming language and is usually tailored towards the structure and functionality of a base communication language. (English, German, Japanese etc). This parser will likely tokenize the input (break it down into parts) and take a shot at understanding the grammar of the parts. It uses this understanding to extract conceptual and semantic relationships. Input is then filtered, and the sentient details are stored in some format that enables it to be retrieved and used in deeper thinking processes.

The software will likely have a default vocabulary that can grow through observation and use. New words can be acquired. At the current time I write this, there is very limited use of context in industry parsers. I contributed ideas and code as the Manager Of Sentient Intelligence for an AI startup company. While our mistakes were many, at least we utilized context. I implemented a process of context resolution and disambiguation that did not rely on empirical or histographical data. It relied on the context of moment. This process was awarded a patent, thus, in order to honor what is now their IP, I will keep my comments to a minimum on the inner-workings of this process. While this was exciting at the time, the foundational structure and human imposed limitations of our project was bound to sabotage our efforts. Creative differences led to my resignation. I didn’t want to pursue a path that I knew was a dead-end. Needless to say, the company believes otherwise.

Back to it…. some of these AGHIs, with parsers, will also have a limited set of reasoning functions. These are also likely to be hard coded by human programmers. It is important to see what is happening here. As human creators, we see an end result and we try to mimic that end result. We see a human ability to conject new information from existing information, so we envision a way to bestow such an ability onto our AGHI. In doing so, we cheat the process. We code for the end result, and many times we get somewhere that looks a lot like the end result, but in doing so we cut off the greater goal. We realize our goals that were met through hard-coded processes cannot change. They can appear to change, and have some flexibility, but at a deep level, they are static. They cannot truly grow beyond our imposed limitations. The code, and the AGHI are, only what we have created it to be, and as such, the AGHI becomes a poor mirror image of our SELF, not a dynamic representation of human intelligence. An AGHI correctly created would be able to change its way of thinking, just as you may change the way you think. It could acquire new logical processes. It could improve not only its performance but its very processes of thought. The AGHIs we see today are restricted, and even trapped, into a limited paradigm of hard-coded processes. No amount of fancy talk or dreamers hype will change that.

Clearly, this is not a human intelligence, because human intelligence isn’t told how to function. Human intelligence is flexible, it is dynamic, it isn’t bound by faux restrictions of limited if-then code sets (or close derivatives of). If an area of our brain is damaged, another area will try to pick up the slack. If we take a course on English, our English parsing and understanding should increase. A 5 year old literally processes things differently than a 45 year old college professor. It is far more than a larger vocabulary. The understanding of the language and the ability to comprehend is categorically different. A correct AGHI structure would allow for an agent to develop through the same learning curve sets as real humans. Only the time line would be accelerated. These large sets of processes, and functions, some named and known, others unknown, cannot all be accounted for, and coded. We discover more about ourselves as humans today, just as we did yesterday. The architecture has to allow for these same functions to emerge, and grow, and be rewritten, adhoc, even if we, the coders, do not know what that may end up looking like.

It should be clear by now that this isn’t a matter of coding a 100% super bot and then restricting it by age, context or function. For that paradigm would be limiting. Any given human coder is only aware of whatever they are aware of. It surely isn’t the whole of the human condition. If they code the limitations, or better yet, maximum abilities, of the AGHI, then it will be bound in the image of its creator. Any system that attempts to code-to-the-end-result will be left crippled.

It is these various challenges and traps that cause our AGHIs to be coded to the end result. They start off trying to create an artificial general human intelligence, and they end up making compromises in order to incorrectly fulfill end-result goals. At critical stages in the project pet features and functions are emulated, and the hope, or goal, of an artificial general human intelligence is sacrificed at the alter of the coder SELF, corporate imposed time lines, and the pride and ego involved in such an ambitious endeavor.

Attempting to create an Artificial General Intelligence Human Emulator
Most projects that claim to be creating an AGHI are actually creating something more closely resembling an AGIHE. Even then, the lines are crossed at so many points that neither is actually the case.

A real AGIHE would be a new intelligence. It wouldn’t claim to be human, it would claim to be intelligent enough to understand and emulate the human condition. In some respects, this intelligence would be greater than human intelligence to such a degree that it could look down upon the human condition and “act” like us.

The critical flaw in most projects aiming for an AGIHE is once again the penchant to hard-coding to the goal. We, the coder, observe the human condition, and we the coder try to imbue this observation into our AGIHE. Already, we have committed an unforgivable project sin. The AGIHE is supposed to make the decisions upon what it means to be human. It isn’t the coder’s place to tell the AGIHE how to emulate a human, for then it is a human emulating what THEY THINK it is to be human. We will once again will be left with a SELF image that is desperately lacking the understanding required to emulate all human conditions.

An AGIHE needs to have the ability to develop entirely humanly foreign system processes. Just as many machine generated genetic algorithms are hard to understand and read, an AGIHE would likely have internal code, and functions, that human observers wouldn’t be able to understand! So how might a human program code that they don’t understand? That is just it….. the human coder is not to code this. The AGIHE is. They are to provide the architecture that allows for it. A human coder programmed the environment that allowed the genetic algorithm to ultimately come up with code no human could have written. As so, an AGIHE needs to operate under the same mantra. We are to create an environment that allows the AGIHE to develop. We are to create an architecture that is flexible and can be changed. We are to plant the genetic amino acids in the tumultuous and turbulent oceans. Where it can go from there is anyone’s guess. That all depends on the inputs and environment . If it goes the wrong way, kill it. Termination. Reset. Start over. Genetic algorithms take hundreds and thousands of generations to arrive at their solution. Eventually, an AGIHE with the desired abilities will emerge, and it is that AGIHE that will be capable of observing, and eventually emulating the human condition.

It won’t be some AGIHE that is hard-coded to act like an AGHI, because we can’t even get the AGHI right yet, how are we going to correctly hard-code an emulator for it?

Claims of basic A.I.
There are some floating claims around the Internet, and even some projects receiving the Loebner award, of “Artificially Intelligent” systems. The biggest group that comes to mind are the “Chatter bots”.

Online chatter bots are trainable, yet they are clearly not intelligent. In a few sentences of communication with the chatter bot, I am convinced the “I” needs to be dropped from the “AI” tag. At best, these are poor emulations of contextually and topically limited human dialogue. In a real dialogue you have loads of informal logic, you have speaker and listener intent, and you have a constant shuffling of dynamics at play. The chatter bot skips all of this and it merely emulates, albeit poorly, the end result, by an electronic sleight of the hand. Most, if not all, chatter bots, store “triggers” and “responses” and utilize the Chess Master Cheat to trick the observer into believing some actual thought is occurring. Through observation and use they build up these trigger/response lists, so the agent appears to be learning. The Chess Master Cheat is a way to know next to nothing about how to play chess, yet have the ability to beat a chess master at a real game of chess. It is a gamers version of the Chinese Room Experiment. It goes like this:

You, the chess beginner, are about to play two games of chess. Both games are against international Chess Grandmasters. On board 1, you are playing Grandmaster 1. You are the white pieces. On board 2, you are playing Grandmaster 2. You are the black pieces. On board 2, you move second, since you are the black pieces. When Grandmaster 2 opens their game with a move of the white pieces, you mimic that move on board 1, against Grandmaster 1, where you are the white pieces. So your first move as the white pieces on board 1 was really the first move against you one board 2! When Grandmaster 1 responds to your white move by moving the first black piece move, you respond to Grandmaster 2 on board 2 by moving your black piece in the same place Grandmaster 1 moved their black piece. Essentially, you don’t need to know anything about chess. You are taking two games, eliminating the common denominator (you), and effectively pitting your opponent on board 1 vs your opponent on board 2. The worst result you can get (or best, depending on how you look at it) is a tie on both boards. The best result you can get is a loss on one board and a win on the other. You just beat an International Grandmaster at chess.

A chatter bot attempts to utilize this Chess Master Cheat to fool participants into thinking some actual thought is occurring. Within a handful of sentences a user can usually figure out that the chatter bot has no clue as to what is actually going on. There is no context, no deep thought, no new thought, no understanding, and in the end it is a very poor Chinese Room Experiment.

Conclusion
I maintain that a true AGHI or AGIHE will not be created until a project can be committed to doing what it takes. This begins with the architecture. It requires a great amount of thought, yet more importantly, a great amount of restraint. Most of the code written in either an AGHI, or an AGIHE, will be machine-written. Most challenges and roadblocks will be traceable back to a faulty architecture and places where human written code got “too involved” in a process. I purpose that the correct architecture will enable a “fetus” A.I. to be created. A fetus, has very little knowledge. The advanced adult logical processes are yet to be developed. No language is present. A real AGHI or AGIHE will be like a fetus, yet it will have all the abilities to learn and acquire going forward. Too often projects focus on hard-coding the things that need to emerge, and that which should have been coded as an architecture, or core, is left un-addressed. What remains is a system that requires constant tweaking, constant development, constant patches, and it is a perpetual, and poor, reflection….. of the current coder SELF.

Let’s move beyond these limitations. Let us lay down our SELF, and in doing so, lay down some possibly world-changing code that allows for a new intelligence to emerge. When the world is hungry, we can try to feed them, or we can address why they are left hungry to begin with. When someone is repeatedly getting injured, we can try to repeatedly fix them, but we should truly address why they keep getting hurt. The time is now to stop coding the downstream solutions and start addressing what is upstream.

Are we just going to try and code something new that attempts to reach an immediate goal, or can we code something that has the potential to be far greater than our selves?

Synonymy

Humans communicate ideas, and concepts through words.  As I write this document, I have ideas I am trying to express, and I attempt to choose the words that best communicate what I want to convey to the intended reader.  There are many words we can choose from when expressing our ideas.   Multiple words map to similar concepts, thus our human understanding is highly dependent upon the use and understanding of “conceptual similarity”, or synonymy.   When someone says, “father” we know internally that is very similar to “daddy” or “papa”.   Similarity allows communication to break from “keyword” mode and focus on the intent behind the words being used. Stronger understanding of similarity allows for more accurate and fruitful communication of ideas.  Conceptual similarity not only allows for human communication to continue, it provides needed variation that adds style, dialect and connotation differences.

The current industry attempts at similarity have been lacking.  The industry has tried to account for similarity by utilizing basic thesaurus functionality.  With a database or table, similar words have been hard-coded, or tied, to their near matches.   This “hack” has provided for some variation, but in most cases the results haven’t been more accurate, they have been more muddled.   The dreaded “dump truck of information” strikes again, and the end result has been to push similarity and synonymy to the background for later use.  Without a strong understanding of similarity, Natural Language Processing will never get to where it promises it is going.

True understanding of similarity requires an understanding of the core concepts that embody any given concept.   “To disapprove” means to “consider something bad”.  “Consider” means “to have an opinion”.  Immediately, as humans, we know that when someone considers something bad, they are disapproving of that something.  Furthermore, “to disapprove of something greatly” starts to push towards “despise” or “hate”.   In a conversation, someone may mask their hate by using the softer words “disapprove” or “consider”, but the other words they use with the concept will give away their intent.  Coming out and saying “I despise x” conveys the same concept as beating around the bush with, “I really consider x to be bad”.   What was provided was some added information is on a different meta-level.  The speaker is either unaware of their “hate” or they feel it is unacceptable to express, thus they try and soften their opinion with concepts that are not as strongly connoted with as emotionally negative concepts.

This “hypothetical” concept thesaurus request would be scored and parametrized. A request for a formal thesaurus word of “daddy” would yield “father”. As we saw earlier, a request for a more forceful word for “disapprove” would yield “hate”. This is accomplished dynamically with an understanding of how to compare the two concepts, not by a table or db hack. No KW search would hold a candle to such functionality, and an AGI with this understanding would be one step closer to truly passing the Touring Test.

Why AI Has Failed To Get Us AGI

There are no Nexus 6 for Blade Runners to snub out.  Andrew Martin is nowhere to be seen.  The software Hal 9000 has not emerged.  Why have AI’s promises fallen so short?  When will we see these dreams realized?   As the questions mount, many are losing hope in the idea that man will ever create the AIs of our childhood sci-fi dreams.  Yet as I write this little blurb a new round of funding is pouring into the AI Industry.  The dream is alive once again.   Technology has made its Mooreful leap and armed with new tools our brightest minds have convinced the money to fund their quest for this prized new land once again.

The following is  a brief run down of 6 failures that have plagued our previous attempts at AGI / AI.

Failure 1:  Scope
Projects fail to define exactly what they are aiming to create.   There is a substantial difference between creating an AI that will emulate human behavior and one that is a new intelligence of its own kind.   An AGI is a general intelligence that is supposed to be able to apply knowledge and learning from one area to another.   A traditional narrow chess AI may be able to beat Gary Kasparov in a game on the checkered board, but can this same program beat him in a game of Stratego?   It probably can’t even play the game of Stratego because Deep Blue can’t play games that it was not programmed to play.   You can’t speak or communicate via language to Deep Blue or his buddy Jr., thus you can’t even begin to attempt to teach him a new game.  These narrow-AIs were never taught to learn anything outside the parameters of their narrow programming.  As complex and impressive as they are, they simply were programmed to play chess.   That was what they were intended to do.   Scope-wise, this really isn’t any more of an Artificial Intelligence than your Start->Run->Calculator is.   While more elaborate narrow-AIs could definitely be applied to benefit humanity, these hardly are going to get us to the holy grail of AGI.

Failure 2: Man Tries To Create AI In Their Own Personal Image.

Inherent to most approaches towards creating AIs is our limited awareness of self. Before one can duplicate or even emulate their ‘self’, they must be aware of what and who they are.    How many people do you know would you consider to be enlightened individuals?  How many of them would you consider to have a very strong, brutal, and honest awareness of exactly who and what they were on various scientific, psychological, physiological, and philosophical levels?   Now how many of these people are in the field of AI leading a project to create an AGI?   Most techs are…… um…. techs.  Yes, we have brilliant techs, but where are our cross-discipline geniuses that can function and thrive in multiple domains?  When attempting to create an AI/AGI that can emulate a human one can only operate from the level of their own awareness.    You can’t duplicate or try to create something which is outside your scope of awareness.    We create in our own image.  We can stumble upon new creations by happenstance, as history has demonstrated with many of our great inventions; but AGI will not be solved by accident.   Humanity is not a million monkeys with an infinite amount of time…

Failure 3: Targeting An End Product Leads To Closed-mindedness And A Narrow Approach To The Real Challenge.
Many projects have an end product in mind.  They want a program that does xyz, so they design their program to do xyz.   As soon as any additional need arises, or success begets new hopes in a deeper narrow-AI, the lacking design and ultimate failure becomes painfully clear.

Failure 4: Corporate Restrictions Encourage Short-cuts To Meet Deadlines.
Any project forced to make demonstrations or meet corporate deadlines will not create AGI.   Compromises and short-cuts are the AGI devil.

Premature optimization is the root of all evil  – Donald Knuth

Failure 5: The BLACK SWAN Awaits. 
Assuming Moore’s Law to be true, works, only as long as Moore’s Law is true.   Since the time of OIL our technology has obeyed this generous ‘law’.   It requires an ample supply of energy and if/when energy is no longer as prevalent or available for us humans to use the Law will cease to be a Law.   If/When it breaks so will all the predictions and hopes that were predicated upon its perpetual truthfulness.

Failure 6: The Ship Sails Where The Captain Directs.
The minefields of Pride, Arrogance, Stubbornness, Old-Mindsets, and Ego await.   To successfully create an AGI the project will have to successfully avoid these human disasters.

2011……….. The Result?  Over-hyped promises that have yet to be realized.

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