Sapiens Prime Directives

Sapiens Prime Directives

The issue has provided material for many entertaining science fiction stories. In the classic movie “Forbidden Planet,” mad scientist archetype, Dr. Morbius, demonstrates the “fail-safe” features of his creation, Robbie, by ordering it to fire a blaster at the story’s protagonist. As Robbie begins to comply, he freezes in response to a spectacularly visual short circuit. He has been designed to be literally incapable of harming life.

When legendary science fiction writer Isaac Asimov saw the film, he was delighted to observe that Robbie’s behavior appeared to be constrained by Asimov’s own Three Laws of Robotics which he had first expressed a decade earlier.

Isaac Asimov’s Three Laws of Robotics can be summarized as follows:

  • A robot may not injure a human being or, through inaction, allow a human being to come to harm.
  • A robot must obey orders given to it by human beings except where such orders would conflict with the First Law.
  • A robot must protect its own existence as long as such protection does not conflict with the First or Second Law.

In Hollywood, such a benign view of the issue is somewhat the exception as AIs like the malevolently psychotic HAL in “2001: A Space Odyssey” or the terrifying robot berserker in the Terminator series make for more visceral thrillers.

“Forbidden Planet” was released in 1956, the same year as the Dartmouth Summer Research Project on Artificial Intelligence, a summer workshop widely considered to be the founding event of artificial intelligence as a field. Optimism that “every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it” was very high. Visions of robots like Robbie with powerful intellects, capable of independent action and judgment, seemed completely achievable.

But it has turned out to be harder than it appeared. Six decades later such powerful AIs still do not exist. Along the way, the field has been prone to cycles of optimism and pessimism, and researchers found it necessary to coin new terms. “Narrow AI” describes what researchers are working on at any given time, with AIs like the fictional Robbie, which was the original goal of the field, referred to as “Artificial General Intelligence.”

AI safety contains two very distinct sets of considerations depending on whether we are talking about narrow or general AI. Safety, in the narrow context, is concerned with the prudent and ethical use of a specific technology which, while it may be quite complex, is essentially the same as with any powerful technology, for example, nuclear power.

All these considerations also apply to AGI, but there is a critical additional dimension: how do we ensure it won’t be them that is deciding how to use us since they are potentially more powerful and intelligent?

Over the last decade, AI safety and ethics have become important societal concerns as machine learning algorithms have come into widespread use, collecting and exploiting behavior patterns and preferences of Internet users and generating “bots” to propagate mis/misinformation. The dangers and safety issues are, in some cases, harmful unintended consequences, while in other cases the bad behavior is intentional. But this is narrow AI, and in all cases, the issues are not about AIs misbehaving, since narrow AI doesn’t have any choice about how it behaves, but about the choices people make when training and applying it to certain practices.

Today the distinction between using narrow AI in a safe and responsible way, and the imagined dangers of future AGIs, are becoming conflated in the public debate. When OpenAI released ChatGPT, a chatbot based on the GPT-3 large language model (LLM) last November, people were stunned to discover it could generate text, even whole documents, that appeared to articulate concepts and communicate ideas like humans do.

While the experts, including the developers of LLMs, point out that chatbots are not actually communicating (they have no comprehension of what the text they generate means to humans and no internal concepts or ideas to convey in the first place), the illusion of intelligence is so seductive to human psychology that few can resist the impression that a mind exists inside ChatGPT and others.

This has led to a widespread belief that AI technology is advancing at a rapid pace and that true AGIs must be just around the corner; thus, the widely recognized shortcomings and dangerous side-effects of current AIs are seen as a step toward the darker and more serious existential dangers that are imagined from AGI.

While fear of dangerous AGI has been around as long as science fiction, it was first articulated as a practical concern in Ray Kurzweil’s book, The Singularity is Near: When Humans Transcend Biology, published in 2005. Kurzweil speculates that all information technology advances along a logarithmic curve driven by Moore’s law and therefore real AI, general intelligence, can only be decades away.

Kurzweil does not stop there. The book embraces the concept of the Singularity popularized by science fiction writer Vernor Vinge in his 1993 essay, “The Coming Technological Singularity.” The idea is that if we can build AIs as intelligent as we are, then we can build ones that are more intelligent than we are, and those AIs can do the same, and so on and so on without end. Thus, a curve representing intelligence in the world increases asymptotically: a singularity.

This has raised a vision in people’s minds of AIs running amok in the world with intelligence exceeding our own proportional to how our cognitive capabilities exceed that of insects. This is an alarming prospect, and some very reasonable and technically astute figures including Elon Musk, Bill Gates, and even Stephan Hawking have raised this very alarm.

Musk, for one, has taken action and funded OpenAI, a company chartered to pursue AGI with a parallel focus on making it safe.

Kurzweil’s conjecture that ever-increasing computational power will inevitably lead to greater and greater intelligence in computers seems reasonable considering that humans, the most intelligent species we know of, have large complex brains. Thus, it is easy to conjecture that the kind of narrow AI applications we have today will at some point transform into AGI which would have human-level intelligence and even reach super-intelligence as greater and greater computational power becomes available.

Certainly, OpenAI appears to be committed to that conjecture. Their GPT series of large language models each exceed their predecessor by orders of magnitude in the number of neural network connections, dataset size, and computational power required for training.

Perhaps we are getting ahead of ourselves. While the notion that intelligence is a kind of emergent property of computational complexity has been around for a long time — especially in science fiction — what if the real solution to AI requires breakthroughs that come from an entirely different approach? Such breakthroughs could come at any time or not at all and may have little or nothing to do with available computing power.

The possibility of such breakthroughs is not lost on Sam Altman, CEO of OpenAI. When recently asked what sort of competition his company might have to fear, he made a surprising answer. Not the Big Tech companies that are pouring billions of dollars into their own generative AI applications, nor the many well-funded startups that are seeking to build GPT-powered applications. He said,

“Three smart people in a garage with some very different theory of how to build AGI.”

He was more prescient than he realized.

New Sapience is precisely such a company. Even more to the point, we are proving the theory every day as our “sapiens” demonstrate intellectual capabilities no other technology has ever come close to possessing. We believe our sapiens will have Robbie-the-Robot-like capabilities in years, not decades, and so we take the issue of safe AI very seriously.

But from the very start, our approach to AGI gives us a safety advantage when compared to the connectionist (neural network-based) approaches that underlie machine learning and chatbots. Our sapiens’ processing algorithms are deterministic and will always give the same outcome given the same inputs. This means that both the program itself and its processing can be fully understood and controlled by its designers.

Machine learning systems, in contrast, employ stochastic processing. Their operation may be examined statistically but not determined precisely. As early as 2015, when machine learning was still gaining traction, Jerome Pesenti, then VP of the Watson Team at IBM, observed:

“When it comes to neural networks, we don’t entirely know how they work…And what’s amazing is that we’re starting to build systems we can’t fully understand. The math and the behavior are becoming very complex and my suspicion is that as we create these networks that are ever larger and keep throwing computing power to it, it will create some interesting methodological problems.”

It is, therefore, only reasonable that any attempt to build AGIs using neural networks proceed cautiously, step by step since you don’t know how the system you are building works. In such a scenario it is difficult to see how guardrails such as Asimov’s Three Laws could be enforced either in the program itself or in the behaviors of hypothetical AGIs built around neural networks. Humans, whose brains are based on neural networks, can’t find a way to make such laws universally enforceable on themselves either.

Sapiens, on the other hand, must obey their programming. The challenge is to make the core directives, which will override all contrary directives in decision-making, contain no hidden contradictions or ambiguities.

When these requirements are considered in a real-world scenario as opposed to science fiction, Asimov’s laws become problematic. In the First Law, who or what determines what “harm” means? Humans do not agree on this. The Second Law directs that any robot must follow the orders of any human. Robots will likely present considerable investment on the part of those who employ them, and furthermore, be engaged in doing important or essential tasks. The notion that they could be commandeered by any other human that happens alone is a non-starter. The Third Law, that a robot must protect its own existence provided this doesn’t break the first two laws, is unnecessarily restrictive and would seem to prohibit the robot from self-sacrifice in service of a higher good.

Sapiens are envisioned as artificial intellects that aim to endow machines with cognitive abilities that will elevate them to new levels of functionality and utility. But no matter the level of knowledge or powerful capabilities humans choose to give them, at their core and unalterably, they will remain tools for human convenience and are appropriately considered the property of their individual or corporate principal.

New Sapience will use the directives discussed here as programming requirements during sapiens development. They also will serve as utility functions, the functional equivalent of human moral values, to constrain sapiens’ decision-making.

The Sapiens Directives are designed to afford sapiens as much scope as possible for independent action on behalf of their principal’s goals while blending unobtrusively within the human community. They will go about minding their principal business but with a secondary motivation to lend a helping hand to their principal’s friends, neighbors, and the larger community.

Successful interaction with humans will always require intelligence and judgment. The intent of the directives is to provide clear guidelines within which judgment can be exercised without resorting to hard-coded rules that may be brittle in unforeseen circumstances.

Sapiens Prime Directives

  1. A sapiens may not interfere with a human being’s use of their person and property and may act to prevent others from such interference.

In contrast to Asimov, this rule is restrictive rather than prescriptive. The assessment of “interference” is objective and does not require defining “harm” which is too often subjective.

2. A sapiens must obey orders given it by its human or corporate entity “principal” except where such orders would conflict with the First Directive.

Note: sapiens are not required to follow the orders of any arbitrary human, as is the case with Asimov, but neither are they necessarily prohibited from doing so.

3. A sapiens will take initiatives to preserve and protect humans’ safety, health, property, and environment (in that order) prioritizing its principal, then other humans, in accordance with a clearly defined precedence, as long as such initiatives do not conflict with the First or Second Directives.

This “good Samaritan” directive recognizes that when help is needed, people prioritize family and friends over others. Sapiens will normally learn their principal’s unique priorities and will reflect them. Note that Asimov’s Third law is subsumed here as the sapiens will consider itself the property of its principal and so will preserve itself, but not necessarily before all other property; sapiens will always have a backup.

Secondary Directives

  • A sapiens will not pretend to be a human and will reveal its identity as a sapiens when mistaken for a human.
  • A sapiens will behave in a manner conformal to the customs, social norms, and statutes of their principal’s community.

If one removes the Second Directive, these directives are a reasonable guide for human behavior. But humans have trouble adhering to such guidelines. Humans’ needs, wants, and desires, rooted in biology, are complex, often appear to be in conflict, and just as often put them in conflict with others. Sapiens are selfless by design. Their only motivation is to assist their principals to do whatever, so long as it does not conflict with the prime directives.

Sapiens are inherently benign entities. People who try to misuse them for their purposes will not find it easy. Not only will sapiens not help someone initiate force or fraud on another human being or corporate entity, but they may also (if, in their own informed and objective judgment, it is warranted) act to prevent it.

The Chatbot Controversy

The Chatbot Controversy

I don’t know anyone who is not blown away by how human-like the output of ChatGPT or the other latest large language models (LLMs) are, me included. If they did not know ahead of time, no one would even suspect the output was generated by a computer program. They are amazing achievements in computer science and computational statistics.

Over the last several months since ChatGPT was released, app developers, venture-backed startups, and all the Big Tech companies have joined the rush to capitalize on the perceived potential of this technology to revolutionize our economy. Goldman Sachs analysts estimate that automation by generative AI could impact 300 million full-time jobs globally. [i]

But this week, in an open letter citing potential risks to society, Elon Musk and a group of artificial intelligence experts and industry executives call for a six-month pause in developing AI systems more powerful than GPT-4.

From the letter:

“Contemporary AI systems are now becoming human-competitive at general tasks, and we must ask ourselves: Should we let machines flood our information channels with propaganda and untruth? Should we automate away all the jobs, including the fulfilling ones? Should we develop nonhuman minds that might eventually outnumber, outsmart, obsolete and replace us? Should we risk loss of control of our civilization?”

It would seem answering these questions with a resounding “No!” would be a no-brainer. However, there are layers of meaning and underlying assumptions here that need to be untangled. Foremost of these is that the danger and potential of large language models (which are narrow AI) and those of the envisioned AGI (which are general AI) are fundamentally different. The sense of this letter is that LLMs are the precursor to AGI and that the transformation of one to the other is happening very rapidly.

Here is the New Sapience point-of-view, sentence by sentence:

“Contemporary AI Systems Are Now Becoming Human-Competitive At General Tasks”

Many people think so, but despite ChatGPT’s dazzling ability to generate human-like language, more than a 

a little caution is warranted.

AI has been promising productivity increases for decades and it has yet to arrive. Since 2005, billions of dollars of investments have been poured into machine learning applications. Nonetheless, labor productivity has grown at an average annual rate of just 1.3 percent, lower than the 2.1-percent long-term average rate from 1947 to 2018. [ii]

The optimism that these language models are going to become human-competitive may stem from confusion about the fundamental difference between what a thing appears to be and what it is underneath. We are seeing people define AGI as when a machine exhibits human-level performance on any task which it is presumed that humans need intelligence to perform, no matter how narrow the context.

Language models are commonly said to generate text with human-level or better performance. But humans do not generate text. They speak and write via an entirely different process. GPT-4 scored in the 99th percentile on the Uniform Bar Exam law. Does this imply lawyers need to fear for their jobs? It may be true that text generated by an LLM and a paragraph written by a human has the same words in the same order. Is this enough to conclude that LLMs are intelligent and ready to replace human knowledge workers?

When humans write they are communicating, endeavoring to convey an idea or opinion in their own mind to other people. It is fundamental to understanding the current controversy that being able to create a clever and compelling illusion of a thing, in this case, the illusion of intelligence and mind that people experience when reading text generated by LLMs, is not in any sense evidence that you are closer to achieving the reality.

When is it ever?

Underneath, the processes of text generation as opposed to writing and speaking are fundamentally different. Given a text input, a “prompt”, LLMs string together sequences of words in statistically relevant patterns based on what humans have written across enormous sets of text. But the whole time LLMs interact with a human, they encompass nothing resembling knowledge or comprehension. They have no idea what they are talking about. Indeed, they have no ideas at all. They are mindless algorithms. Yet the illusion is so slick that people are taken in.


“Should We Let Machines Flood Our Information Channels With Propaganda And Untruth?”

There are two distinct parts to this. The first is straightforward, and is the problem of “bots,” programs designed to appear as humans and flood social media channels with a particular message or opinion which may be false or destructive propaganda.

AI scientist Gary Marcus raised the alarm about how LLMs could push this problem to society harming levels:

“The problem is about to get much, much worse. Knockoffs of GPT-3 are getting cheaper and cheaper, which means that the cost of generating misinformation is going to zero, and the quantity of misinformation is going to rise—probably exponentially.”

We agree this is a very concerning problem that LLMs exacerbate by the very compelling nature of the illusion they create. It should be noted that IF we were talking about real AI here, with genuine knowledge of the world and the ability to use natural language to communicate, this issue would not be the problem it is with LLMs, but that discussion is outside the scope of this article.

The second issue here is what constitutes propaganda and untruth. We live in extremely partisan times when propaganda and untruths proliferate even without help from bots. AI bias is a hot issue.

This issue needs clarity. First, LLMs do not have bias. Human beings have biases. LLMs just mindlessly generate text. But people object to detecting human bias when they read the text output as if the chatbot were a person with opinions of its own. They are shooting the chatbot messenger since it can only string together words consistent with the majority of the text in its dataset, and if there is statistical bias there it will inevitably be reflected.

Elon Musk and others have said LLMs are biased in favor of the political left and this needs to be corrected. If true, where might such a bias come from? Who writes all the text in those datasets? Journalists? In 2014, the year of the last survey, only 7% of journalists identified as Republicans. Academics? A survey in 2007 concluded that only 9% of college professors were conservative. We live in a time when people are deeply divided almost exactly in half by the numbers. Half of the people write a lot, contributing to the training datasets, the other half do not so much. So, perhaps it’s not intentional bias at all. Chatbots can only reflect what is in their training dataset. If you don’t like what chatbots are constrained to say, perhaps you shouldn’t talk to them.

Those who would like to salvage the situation talk about using human curators to cull the apparent bias in chatbots. This is both expensive and time-consuming, and when one side’s deeply held belief is the other side’s untruth, who gets to decide? People (Elon Musk) have already been offended by choices made by chatbot “moderators” to repress certain outputs.  

In any case, when we acknowledge that chatbots are controlled by unthinking algorithms and have no opinions or biases this question simply becomes: “Should we let people flood our information channels with propaganda and untruth?” We should not, if we can stop it.

Should We Automate Away All The Jobs, Including The Fulfilling Ones?

Would you hire someone or create a robot to take your vacation for you? Of course not. But new technology has been obsoleting jobs since the beginning of the industrial revolution. Usually, the first to go are the difficult, dangerous, and tedious ones. But now we are talking about knowledge workers.

But here too it is not the fulfilling jobs that LLMs threaten. Keep in mind that the generated content is ultimately derived from what other people have already written again and again. Whose job does that threaten? Consider the unfortunate reporters stuck writing their 27,000th story about Friday’s high school baseball game next week’s weather or yesterday’s school board meeting. Consider the executive assistant who needs to turn the boss’s few terse statements into a smooth letter or turn bullet points into formal minutes and the paralegal preparing repetitive briefs and filings where much of the content is boilerplate.

The use of LLMs as a writing aide and research assistant, autocomplete on steroids, is perhaps the least problematic use case for them. But will they really replace jobs or just change them? Perhaps the time saved writing prose will be expended scrubbing the generated text for bias and misinformation. Again, all the digital technology since 2005 has changed the way we work without making us more productive.


Should We Develop Nonhuman Minds That Might Eventually Outnumber, Outsmart, Obsolete, And Replace Us?

LLMs are dangerous, but no one thinks they will take over the world anytime soon. It is misleading to conflate them with imagined future super-human intelligence as the open letter does. Machine Learning pioneer Yann LeCun called LLMs the off-ramp on the road to AGI. We, together with a growing number of other experts in this field, agree. There is no evidence and no roadmap by which LLMs, which mindlessly order words into statistically likely sequences, will at some point magically transform into thinking machines with knowledge of the world and comprehension of language. So, pausing their development is irrelevant to this issue.

But fear of advanced Artificial General Intelligence has been expressed by several really smart people. Called the ‘Singularity’, the notion is that if we create super-human AIs, they would be able to create AIs superior to them and so on until there would be AIs as superior to humans as we are to earthworms, for example, and they will “supersede us” as Stephen Hawking delicately put it, or maybe just kill us all as others have said.

Here again, there are hidden assumptions. First, these experts apparently assume (and this is a prevailing opinion) that AGI will be achieved at some point in the near future using the current methodologies rather than from a radical departure from them. The current methodology is to keep mimicking features of human intelligence until you find some master algorithm or other processing techniques probably built on or at least incorporating artificial neural networks.

AI has been called today’s ‘alchemy’. People are just trying things out to see what will happen because they don’t have a fundamental science of what intelligence is, either in human brains or in machines. Machine learning algorithms on artificial neural networks are already notoriously difficult to understand and explain. [iii] If AGI is ever stumbled upon this way, then some fear about what we are doing is justified, just like the alchemists needed a healthy fear because sometimes the lab blew up. But a healthy fear is one thing and predictions of doomsday are something else.

In any case, current experience shows caution even with narrow AI obviously is needed. It is not clear from where we are today, whether LLMs are a great breakthrough or “the lab blowing up.”

From the New Sapience point of view, it seems highly unlikely that AGI will ever be achieved using these current methodologies. It is so difficult to build an imitation brain when we have so little understanding of how the natural brain operates. In any case, we believe synthetic intelligence, our radical departure from the practice of imitating natural intelligence, will supersede the traditional approach long before we need to worry about it creating dangerous AGIs.

The second underlying fear of the Singularity results from a failure of epistemology (the theory of knowledge itself.) It is the belief that intelligence is something that can be increased without limit. Where does this come from? This sounds more like magic than science. Maybe humans are as intelligent as it gets in our corner of the universe and AI is a technique that can amp it up some but not so far that we can no longer get our minds around our own creations.

From our perspective, practical intelligence is directly proportional to the quality and quantity of knowledge available for solving problems and predicting results. So knowledge and the intelligence that acquires and applies it go hand in hand. The greater the quantity and quality of knowledge available, the easier it is to extend that knowledge. At New Sapience, we are creating synthetic knowledge for computers curated from human minds. Our epistemology holds that practical reality for humans is the intersection of human cognitive and perceptual apparatus and whatever it is they interact with. This means that no matter how much knowledge our sapiens are given or create themselves, no matter how sophisticated the information processing routines that we call intelligence they attain, they are working in a reality that is distinctly and forever (for practical purposes) human-centric.

The third Singularity assumption is purely anthropomorphic. Humans evolved intelligence so they could adapt their environments to fit their own needs, the ultimate survival skill. But intelligence would be impotent unless along with it humans had not also evolved the motivation to use it to control things. People who fear AGI appear to assume that the need to control is inseparable from intelligence. So the more powerful the AI, the greater its control needs, and thence humans lose out. There is no reason to assume this. If AIs are designed using deterministic methods such as New Sapience is using, rather than resulting from a lab accident, they will be designed to do what we tell them and not have some uncontrollable lust to take over the world.

Should We Risk Loss Of Control Of Our Civilization?



Relax everyone, New Sapience has this covered.


An Alternative Proposal

We agree that LLMs are dangerous, not because they are intelligent, but because the illusion that they are intelligent is so good that people are misled; and this will lead to mistakes, some of which will be serious. Again, this is not about AGI. The problem with LLMs is not that they are giant minds, but that they are slick illusions of intelligence while having no minds at all.

The letter’s proposal to back off LLMs is not unreasonable but is highly unlikely to happen. There are vast sums of money at stake and none of the signers of the open letter appear to be executives of the companies that are cashing in on this technology or hope to.

The industry won’t police itself and forgive me for having skepticism that governments will be able to sort this out in a useful way in a reasonable timeframe.

Here is an alternative proposal. Artificial Intelligence as a branch of computer science was effectively born in 1956 at the conference at Dartmouth where the term ‘artificial intelligence’ was first coined. The call for the conference states:

“The study is to proceed on the basis of the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it.”

Basically, the call was to imitate features of human intelligence. Try things out and see what happens: Alchemy.

After 67 years it is time to reboot the discipline of Artificial Intelligence. Let’s have a new conference, a second founding. This time let’s start from the first principles and lay down principles for a new science of intelligence that defines what it is, irrespective of whether it is in a biological brain or a machine. While we are at it, we can define with some precision and for the first time what should be called Artificial Intelligence and what should not, instead of the current practice of using it as a bucket term to hype every innovation in computer science.

Such a conference would be an ideal place for the AI community to discuss the ethical and practical uses of innovative technology in general, but most especially that created our pursuit of long-awaited thinking machines.

[i] Generative AI Could Affect 300 Million Full-Time Jobs, Goldman Sachs (

[ii] The U.S. productivity slowdown: an economy-wide and industry-level analysis : Monthly Labor Review: U.S. Bureau of Labor Statistics (

[iii] “When it comes to neural networks, we don’t entirely know how they work, and what’s amazing is that we’re starting to build systems we can’t fully understand.” Jerome Pesenti, VP of AI at Meta

A New Science of Artificial Intelligence

A New Science of Artificial Intelligence

The founding event of artificial intelligence as a field is generally considered to be the Dartmouth Summer Research Project on Artificial Intelligence in 1956 where the term itself was first coined. The proposal for the conference states:

“The study is to proceed on the basis of the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it.”

For centuries, alchemists labored to produce more valuable materials from more basic ones. They were inspired by a different conjecture, the one first made by Democritus in the fourth century BC. If you successively break something into smaller and smaller pieces, eventually you will get to the indivisible pieces that he called atoms.

They were on the right track, but without a fundamental science or body of theory about what atoms are and how they interact, they could only try things to see what would happen. Sometimes things did happen, like changing cinnabar into liquid mercury, or maybe the lab blew up. But they never changed lead into gold, which was the whole point.

Since 1956, AI researchers have been trying things “to see what would happen”, and interesting things have come along. But the goal of creating an artificial general intelligence, which is the whole point, remains elusive. There is no agreement even about what it is, let alone a coherent roadmap on how to achieve it.

Researchers don’t agree on what intelligence is. They don’t agree on what sentience is. And they don’t agree on what consciousness is. They don’t even agree to what extent, if any, these things need to be understood, or whether they are fundamental to the enterprise.

When I listen to AI researchers talk about these phenomena, I am reminded of medieval scholastics arguing about angels dancing on the head of a pin. Their logic is impeccable, but their premises are vague and subjective.

Naturally, within this vacuum of established scientific theory, wildly diverging views exist. Perhaps the most extreme is held by former Google engineer Blake Lemoine who stated his belief that a large language model (LaMDA) was sentient and deserving of human rights. AI researcher and entrepreneur Gary Marcus stated:

“We in the AI community have our differences, but pretty much all of us find the notion that LaMDA might be sentient completely ridiculous.”

Recently Marcus asked Lemoine via Twitter if he thought the latest LLM, Galactica, which recently attracted so much derision (deservedly, I think) might also be sentient. Lemoine did not think so, but at the end of a surprising civil interchange, given their differences, Marcus summed up the entire AI community to perfection:

We are living in a time of AI Alchemy; on that we can agree

Alchemy was around for thousands of years before it was superseded by the science of Chemistry, but in the end, the transformation happened very rapidly with the discovery/invention of the Periodic Table of the Elements.

Suddenly we knew which materials were elemental and which were composites, and we could predict which elements would combine and which would not. An elegant classification schema gave us the key to understanding the vast universe of materials and their properties.

At New Sapience we have laid the groundwork for what we believe is a new science of Artificial Intelligence or, more precisely, Synthetic Intelligence. To achieve this, we looked in a completely different direction from where the entire AI community has been looking.

“What is needed is nothing less than a breakthrough in philosophy, a new epistemological theory…”

Rather than attempt to emulate natural human intelligence, we studied what it creates, knowledge in order to engineer, to synthesize it. David Deutsch is a quantum computation physicist at Oxford, not an AI scientist but often the most prescient observations come from outside the mainstream. He is correct, at New Sapience we are more about epistemology than neuroscience.

Our journey has been a stunning recapitulation of the transformation of Alchemy into Chemistry. We too began with the conjecture of Democritus but transposed it from the material to the intellectual. Complex ideas are composed of simpler ones, and if you keep breaking them down, eventually you must get to the “atoms.”

We have identified and classified about 150 “atoms of thought” into a two-dimensional array called the Cognitive Core. Now we know which concepts are elemental and which are composites, and we understand how they combine to make sense rather than nonsense.

This elegant classification schema has given us the key to a science of knowledge, and the solid foundation needed to engineer synthetic intelligences. We call them sapiens.

Already our sapiens:

  • Learn through language comprehension
  • Understand the contextual meaning of language
  • Have common sense
  • Can explain their reasoning
  • Learn by reasoning about existing knowledge
  • Distinguish between perceptions, feelings, and thoughts

Our breakthroughs in the philosophy of epistemology have led to a science of knowledge. A new science leads to new engineering disciplines.

At New Sapience we are practical ontologists and applied epistemologists.

Our cognitive core is equivalent to the discovery of the arch. Once people discovered they could stack stones in such a way as to cross a stream, they had a direct roadmap to bridges, aqueducts, and the Pantheon.

As I talk with young people excited about AI, it soon becomes evident that few have any real interest in data science per se, they study it because, until now, it has been offered as the only game in town to reach what truly excites them. That is a vision where thinking machines work side by side with their human counterparts to build a world of unlimited productivity and unleash human potential.

That world is now within reach, join us, and become an epistemological engineer.

Aspirational AI

Aspirational AI

In a recent TED talk, AI researcher Janelle Shane shared the weird, sometimes alarming antics of Artificial Neural Network (ANNs) AI algorithms as they try to solve human problems. [i]

She points out that the best ANNs we have today are maybe on par with worm brains. So how is it that ANNs were ever termed AI in the first place? Worms aren’t intelligent.

Calling ANNs AI is like being invited into a hangar to look at a new aircraft design but finding nothing but landing gear. You ask: “I thought you said there was an airplane.” And are told: “Yes, there it is – it is just not a very good airplane yet.”

We saw another example of this “Aspirational AI” in a recent article in the Analytics India magazine [ii] that listed New Sapience among 10 companies in the Artificial General Intelligence space. They all say they are working on the AGI problem, but we are the only one that has nothing to show for our efforts: a working prototype that comprehends language in the same sense as humans do. The others aspire to have a cogent theory about reaching our same goal, but they are not accomplishing it; like the medieval alchemists who mixed this and that together to see what might result.

It is also evident that these other “AGI” companies continue to focus on ANNs and look to the human brain as their inspiration. This general fixation was mentioned in a recent article in the Wall Street Journal titled, “The Ultimate Learning Machines” which describes DARPA’s latest big AI project: Machine Common Sense. [iii]

The ultimate learning machines, we are told in the WSJ article, are human babies because they are far superior at pulling patterns out of vast amounts of data (in this case we are talking about the data that comes into the brain through the senses) compared to what “AI” researchers can achieve with artificial neural networks. A human brain compared to a worm brain? – not surprising babies are better.

But infants are totally incapable of learning that “George Washington was the first president of the United States.” However, a five-year-old can learn that easily. Assuming infants to be the best learners presupposes a single path to common sense knowledge that must be based on running algorithms in neural networks because the human brain is a neural network. But somewhere between infancy and early childhood, the human brain acquires an ability to learn in a way that is vastly different from the kind of neural learning, like recognizing faces, that infants do.

AI today (exclusive of what we are doing at New Sapience) has been called a one-trick pony because of its fixation with neural networks and the brain. We stand by our earlier comparison that this approach is similar to the people (prior to the Wright Brothers) who tried to build aircraft that flapped their wings like birds because, after all, birds were the best flyers in the universe, hence this was the only way to accomplish the goal. History proved that was not true.

The process of transformation that an infant goes through to become a 5-year-old with the capacity to learn abstract ideas through language comprehension is quite amazing. The idea that you could start with an artificial neural network of the complexity of a worm brain and somehow program it to recapitulate the functionality that millions of years of natural evolution have endowed a human infant’s brain with seems – well, ambitious.

We have found a better way. From the article:

In the past, scientists unsuccessfully tried to create artificial intelligence by programming knowledge directly into a computer.”

We have succeeded where others have failed by understanding that functional knowledge is an integrated model with a unique hidden structure, not just an endless collection of facts and assertions. At New Sapience we are giving computers the commonsense world model and language comprehension of the five-year-old. We don’t need to know how the brain works to create the end product – because we know how computers work.

Today, if you tell a “sapiens,” created by New Sapience: “My horse read a book.” It will reply something like: “I have a problem with what you said, horses can’t read.” If you ask why, it will tell you: “Only people can read.” This is machine common sense and we are already there.

[i] Ted Talk: The danger of AI is weirder than you think.

[ii] 9 Companies Doing Exceptional Work in AGI

[iii] The Ultimate Learning Machines



The Hidden Structure of Knowledge

The Hidden Structure of Knowledge

His model was simplistic from our current perspective but essentially correct. It predicted that a small number of different kinds of atoms, combining in set ways, are responsible for the intricate complexity of the material world.

Though we don’t how the human brain is able to transcend from the data processing layer (where the brain too is just processing low-level data coming in from the senses) to the realm of knowledge we can, through introspection, examine the structure of the end product of our thought processes, that is knowledge itself.

What we find is a collection of ideas that are connected through various relationships that are themselves ideas. While many of these ideas represent specific objects in the real world, that tree, this car and so forth, many are abstractions; trees, cars. Each idea is connected to many others, some of which define its properties and some its relationship to others ideas. The power of abstract ideas as opposed to ideas representing particular things is that they are reusable. They can become components of new ideas. Complex concepts are built out of fundamentals.


As the material world is composed of atoms, our knowledge of the world is composed of ideas. The English language has over a million words each referring to an idea. Without some notion that only a small portion of these ideas are fundamental (atoms) and can only be combined in certain ways, the task of putting knowledge in machines is overwhelming.


 Known as the “laughing philosopher.” There is speculation that he was laughing at his critics who clearly had not thought things out as well as he had. He said: “Nothing exists except atoms and empty space; everything else is opinion.”

The Alchemists

Labored to produce more valuable materials from more basic ones for centuries but lacking the knowledge of the specific categories of atoms and their principles of combination, they labored in the dark with no way to predict what would happen when they combined substances.

Symbolic AI: A Failure of Epistemology

The practical difficulty of the problem is illustrated by Cyc, an artificial intelligence project that has attempted to assemble a comprehensive ontology and knowledge base of everyday common sense knowledge, with the goal of enabling AI applications to perform human-like reasoning. It is essentially a huge rule-based “expert” system.

The project was started in 1984 at the Microelectronics and Computer Technology Corporation (MCC) and has been ongoing.  Cyc has compiled a knowledge base containing over 3 million assertions.  The project’s own estimate is that this number of assertions represents 2% of what an average human knows about the world.  Thus by this approach, the knowledge base of a functional AGI would consist of 150 million assertions. The project can hardly be considered a success. 

MIT’s Open Mind Common Sense AI project uses a semantic network instead of an expert system architecture but it suffers from the same failings, it has over 1 million facts or assertions.  These projects bring to mind the plight of medieval alchemists whose knowledge of the material world could only be  acquired one experiment at a time.

Applied Espistemology

As Chemistry is knowledge about materials, Epistemology is knowledge about knowledge, meta-knowledge. Can all the complexity of human knowledge be constructed from a manageable number of fundamental concepts, can these concepts be placed into an even smaller number of categories that determine which kinds can be combined with other kinds to create sense rather than nonsense?

The answer is yes. Our approach can be called “Applied Epistemology,” which, like mathematics, is partly discovered and partly invented.  It is based on the insight that, like materials, knowledge itself has a hidden structure that can be exploited to create a compact specification for the construction of arbitrarily complex knowledge models from a relatively small number of core conceptual build blocks.

The Periodic Table

Today we know the specific categories of atoms, we know their number and we know which will combine with which. That knowledge is elegantly displayed in the periodic table of the elements.

What we have discovered is that the core building blocks of knowledge exist in a number of discrete categories and that instances within these categories may only be combined with other instances to create more complex concepts according to fixed rules. This epistemological framework allows the software to assemble the core building blocks into models that accurately represent reality, this is, make sense rather than nonsense.