The Octet System: A Way to Think About AI

You see countless headlines about AI these days, littered with references to “deep learning”, “neural networks”, “bots”, “Q&A systems”, “virtual assistant”, and all manner of other proxy terms. What’s missing from this entire discussion is a way to gauge what each system is really capable of.

In the spirit of the Kardashev Scale, I’ve put together my own ranking system for AIs, which we’ll be using at Machine Colony.

(Note: I’ll try to provide as much background and example information as I can without this post reading like a cog-sci textbook. For those savvy in AI these examples will no doubt seem pedestrian, however I do try to illustrate concepts as much as possible, in a perhaps ill-fated effort to refrain from being too esoteric.)

Introducing the Octet System

I’m not much for fancy names, but in this case it was fitting: list the qualitative capabilities of an information system, and break it down into eight distinct ranks, or “classes”. They are as follows:

Class Null

The zeroth class is something which does not qualify as an intelligent system whatsoever. While this can cover any manner of programs – be they in software or manifested in processes emerging from hardware – I choose to focus on software for this example.

Programs that fall into class null have the following characteristics:

  • They are only able to follow explicit, predetermined / deterministic logic.
  • They follow simple rules – “if this then that” – with no capacity to ever learn anything.
  • They have no capacity to make nuanced decisions, i.e. based on probability and/or data.
  • They have no internal model of the world (this is related to learning).
  • They do not have their own agency.

This is by far the broadest class, as it covers the vast majority of our software systems today. Most of the world’s software is programmed for a specific task and does not really need to be a learning, decision-making system.

Class I

Programs of this class have the following characteristics that are different from Class Null:

  • They have the ability to make rudimentary decisions based on data, based on some trained model.
  • They have the ability to learn from the outcomes of their decisions, and thus to update their core model.
  • As such, their behavior may vary over time, as the data changes and their model changes.
  • They are trained for a small number of narrow tasks, and do not have the capability to go outside those tasks.

This covers things like fraud detection agents, decent spam detection, basic crawling bots (assuming they’re at least using decision trees or something similar). The decision could be a classification action – marking something as spam, for instance – or it could be deciding how well a website ranks in the universe of websites.

Class II

Classes I and II are the most similar on this scale, because the distinction is subtle: a Class II program has all of the capabilities of a Class I, but may have more than one core model and more than one domain of action. For instance, the new Google Translate app has natural language and vision capabilities, with different models for each. These models are linked and ‘cooperate’ to translate the words in the field of view of your smartphone’s camera.

Class Is, by contrast, only have one area that they’re focused on, and make decisions only based on the model relevant to that domain.

Class III

Programs of Class III have a two main distinctions from Class IIs:

  • They have a basic memory mechanism, and the ability to learn from their history in those memories. This is more advanced than simply referring to data; these programs are actually building up heuristics from their own behavioral patterns.
  • They persist some form of internal model of the world. This assists in creation of memories and new heuristics in their repertoire.

Class IV

This class starts to loosely resemble the intelligence level of insects. Class IVs not only have some kind of internal model of the world, but they gain an ability that was essential to the evolution of all complex life on earth: collaboration.

Thus, their characteristics are:

  • They have the ability to collaborate with other agents/programs. That is, they have mechanisms with which to become aware of other agents, and a medium through which to communicate signals. (Think of ants and bees leaving chemical traces, for instance.)
  • Like Class IIIs, they have an internal model of the world. However, a Class IV’s model is more closely linked to its goal structure, and not merely ad hoc / bound in one model. Its internal model may be distributed across several subsystems / mathematical models; representations of complex phenomena or experiences are encoded across various components in its cognitive architecture (vision systems, memory components, tactile systems, etc).
  • They have the ability to perform rudimentary planning, driven by fairly rigid heuristics but with a little flexibility for learning.
  • They have the ability to form basic concepts, schema, and prototypes.

While it is not a prerequisite that Class IVs have multiple distinct sensory modalities – optic, auditory, tactile, olfactory systems – that serves as a good example of the complexity level these programs start to achieve. In an AI setting, a program could have hundreds of different types of inputs, each with their own data type and respective subsystem for processing the input. The key distinction is that in Class IV programs, these subsystems have a high degree of connectivity, and thus generate more complex behavior.

Many robotics software systems could also be placed in Class IV.

Class V

The capabilities of Class Vs begin to resemble more complex animal behavior, such as rats (but not as intelligent as many apes). The primary characteristics of note are:

  • They have the ability to reflect on their own ‘thoughts’. In an AI program setting, this would mean it has the ability to optimize its own metaheuristics. In rats, for example, this is manifested as a basic form of metacognition.
  • They have the ability to perform complex planning, especially in which they are able to simulate the world and themselves in it. Which leads to:
  • They have the ability, to some degree, to simulate the world in their minds. That is, they can perturb their internal model of the world without actually taking action, and play out the results of hypothetical actions. They can imagine scenarios based on their knowledge of the world, which is intimately related to their memories (recall the memory capability from Class III).
  • Related to their planning and internal simulation capabilities, they have the ability to set their own goals and take steps to achieve them. For instance, a rat may see two different pieces of food, decide that it likes the looks of one of them better than the other, set its goal to acquire the better-looking morsel, and subsequently plan a path to get it. The planning part relies on actions it knows it can do – how fast it can or run, how far can it jump – and the terrain ahead of it, as well as memories of how it may have conquered that type of terrain before. Thus goal-setting and planning rely heavily on memory and the internal model.
  • They have a rudimentary awareness of their own agency in the environment. That is, when they are planning, they treat themselves as a factor in the environment they are simulating.

By this time, you have a program which is able to reflect, plan, collaborate with other agents, set goals, learn new behaviors and strategies for achieving its goals, and simulate hypothetical scenarios.

Class VI

You’re a class VI. So am I. Almost every human being is a Class VI – ‘almost’ because, well, this designation is questionable when applied to some politicians.

Programs of this class will start to resemble human-level intelligence and capability, though not necessarily human-like in nature. While in humans a major difference is more complex emotions, this scale does not consider emotions directly.

Artificial intelligence has not yet reached this level, and there are varying predictions as to when it will. The good news is that the expert consensus is clear on the idea that it will happen, it’s just that no one knows exactly when.

Key components of Class VI agents/AIs are:

  • Full self-awareness. The agent is fully aware of itself, its history, where the environment ends and it begins, and so on. This is related to consciousness, though Class VIs need not necessarily be conscious in a strict sense.
  • They have the ability to plan in the extremely long term, thinking ahead in ways that more basic systems cannot. Specific timescales are relative to its natural domain: for a person, decades; for an AI program, perhaps, seconds or hours.
  • Class VIs are able to invent new behaviors, processes, and even create other ‘programs’. In the case of a human, this is obviously an inventor creating a new way of solving a problem, or a software developer programming AIs somewhere in Brooklyn…

Class VII

This is what might well be referred to as ‘superintelligence‘. While some AI experts are skeptical of whether or not this can be achieved, there does seem to be broad agreement that it is imminent. Nick Bostrom writes elegantly about the subject in his book of the same name.

While nobody knows exactly what this may look like, there are two major distinguishing factors which would almost certainly be present:

  • They have the ability to systematically control their own evolution.
  • They have the ability to recursively improve themselves, perhaps even at alarmingly minuscule timescales.

Their first ability is perhaps their most profound. While humans do in some sense control our own fate, we do not yet have fine-grained control over the evolution of our brains and hence our cognitive abilities (though CRISPR may soon change that). With an artificial superintelligence, many limitations are removed. They can arbitrarily copy-paste themselves, ad finitum, and perform risk-free simulations of their new versions. They also will be essentially immortal, so long as their hardware persists and has a supply of energy.

With respect to the second ability, one might imagine an ASI (artificial superintelligence) making multiple clones of itself, each clone independently applying a self-improving strategy, and then each one in turn performing a set of benchmark tests to determine which one improved the most from the original copy. Whichever agent performed the best would become the new master copy, while the others would be taken out of the running.

This is a supercharged evolutionary algorithm, essentially. The tests would be agreed upon in advance, and even perhaps written as a cryptographically secure contract (blockchain-based or otherwise) to prevent cheating. In doing so, the agent would keep improving up to hardware limits or some theoretical asymptote.

The kind of scenario above is not at all unlikely in the near future.



AI capabilities currently exist somewhere between the Class IV and Class V marks, but are quickly marching toward Class VI. DeepMind and Facebook are leading the way in this direction, though other notable players are making important contributions. Certainly the brand-new OpenAI will have some interesting insights as well.

My hope is that this type of classification system, and others like it, will help bring some structure to the conversation around fast-emerging AI. With deeper clarity in our common language, we can have more meaningful and productive conversations about how we wish for this technology to advance and how it ought to be used. We owe it to ourselves to have the linguistic tools to accurately describe our progress.


A World Inside the Mind

Short post today, but a few things occurred to me as I was reading the paper on Bayesian Program Learning:

  • This form of recursive program induction starts to look suspiciously like simulation – something we do in our minds all the time.
  • Simulation may be a better framing for concept formation than via the classification route.
  • Mapping the ‘inner world’ to the ‘outer world’ seems a more sensible approach to understanding what’s going on. If you look at the paper, you also see some thought-provoking examples of new concept generation, such as the single-wheel motorbike example (in the images). This is the most exciting point of all.

A final design?

Combine all the elements together, along with ideas from my last post, and you get something that:

  1. Simulates an internal version of the world
  2. Is able to synthesize concepts and simulate the results, or literally ‘imagine’ the results – much like we do
  3. Is able to learn concepts from few examples
  4. Has memories of events in its lifetime / runtime, and can reference those events to recall the specific context of what else was happening at that time. That is, memories have deep linkage to one another.
  5. Is able to act of its own volition, i.e. in the absence of external stimulus. It may choose to kick off imagination routines – ‘dreaming’, if you will – optimize its internal connections, or do some other maintenance work in its downtime. Again, similar to how our brains do it while we sleep.

This starts to look like a pretty solid recipe for a complete cognitive architecture. Every requirement has been covered in some way or another, though in different models and in different situations. To really put the pieces together into a robust architecture will require many years of work, but it is worth exploring multi-model cognitive approaches.

If it results in a useful AI, then I’m all in.


What Computers Think About

It’s anybody’s guess, really.

That said, we do have some clues about what kinds of things go on inside the ‘minds’ of these little silicon monsters – under one specific condition.

There’s nothing more thrilling than seeing the spark of intelligence pop up in an AI system. You truly feel as though there’s something peeking out at you from behind all those numbers – a prototype of some bigger life form, curious and eager to grow as its learning algorithms wander the deep inner space of its mind. Yet all of this magic only takes place when there is active input, that is, when data is flowing into the system. This could mean it’s being fed images, text, audio, or raw numbers such as time series.

What happens when there’s no data flowing in?

Nothing. Nada. Zilch. Nichts.

The unexciting reality is that as soon as the flow of data is turned off, most of these things just go to sleep, so to speak. They stop. Nothing is happening in there, save for maybe a handful of residual calculations.

Spoiler alert: This is the condition mentioned at the top of the post. Computers ‘think’ about nothing when there is no data actively being fed to them.

I’ve written a bit about this before, but I reiterate that this stands in stark contrast to humans and other animals. Even in our sleep, our brains display a massive symphony of activity, repairing, adjusting, and moving memories around. Our most complex organ has a remarkable ability to reorganize itself, with or without the presence of sensory inputs. This should be a hint to us as to how we might build true AIs in the future.

So…what do computers think about, exactly?

As far as anybody can tell, nothing. Not yet anyway. For me, this is the most exciting part: creating things that persist in thinking even when there is no immediate sensory information flowing in.

Imagine if your laptop kept doing work even when you were away, and would notify you via your smartphone or smartwatch whenever it did/learned something particularly interesting or important.

Imagine if AIs of the future were trained as scientists, absorbing knowledge from human experts, and even when they ‘stalled’ would simply try new thought experiments. This is completely different from just finding patterns in data, and stopping when the data stops flowing.

Imagine if your house had a persistent AI that could think about cost-effective ways to improve itself, its property value on the market, or even how to organize a party within its halls. This requires a persistence that is not seen in today’s machine learning systems.

Who else is thinking about this?

I’ve referenced him before: Dr. Stephen Thaler has done some interesting work on this subject. Some others have mentioned ‘persistent AI’ in passing, but few seem to be focusing on it as a qualitative shift from passive machine learning systems like we use now. Even Siri is passive: it doesn’t do anything until you ask it a question or give it a command.

DeepDream and all of the related work got many people thinking about what AIs ‘see’ when they see the world, which is a similar idea to inner thought and persistence. This work shows some of what goes on on the inside, under the condition that the network is being actively stimulated from external sources.

To explore these ideas, I’ve been toying with simple AI models that that ‘think’ about their past experiences. They have a long-term memory bank, and a way of referencing past experiences through various measures of context. The choice of context metric is extremely important, which I’ll expand upon in a later post. This was partially inspired by Facebook’s Memory Network architecture, which showed a big shift in how we think about cognitive AI systems.

A past experience could be as simple as when it read a certain segment of text in an essay, or when it learned a new type of melody from a song. Our memories tend to be quite long, often entailing many sequences of intertwining sounds, sights, smells, and recollection of emotional states: “I was at the beach this last weekend, it felt amazing to just lie under the sun and relax.” In this example you’re recalling the time (or perception of it), the place, the feeling of the warm sun on your skin, and the emotions you had at the time – repose, tranquility.

AIs are different, especially the toy models I’m working on now. They don’t have high-level concepts yet, and it will be many years before they truly do. However, they can be enabled to have simple memories that are much smaller: a flashback to its particular state when it learned something new or performed some action, such as a prediction that it got correct. They also have a unique feature that we humans do not: their memories can be made essentially perfect. They can recall a scene with arbitrary accuracy, provided sufficient space in storage and/or memory.

The memory component is a necessary ingredient in useful persistence, as you would not want persistent AIs that forgot every interaction, everything they ever learned or did.

Imagine if Siri started remembering conversations it had had with you in the past? Admittedly, this could be dangerous for some.

What comes next?

My new AI startup, Machine Colony, will be taking up most of my time these days. However, as part of my work with Machine Colony and more generally, I’ll continue to investigate these working memory and long-term memory components in AI architectures. If I get really ambitious I may even attempt to publish something on it, be it a white paper or a full-on academic paper. At the very least, you can expect more blog posts and the occasional code snippet, likely in Python. I remain noncommittal because of time constraints, of course.

My sincere hope is that you finish reading this not necessarily with an immediate answer to a question in hand, but more that it provokes thought in the direction of “what if our devices and systems were truly intelligent and persistent?” It is worth thinking about how this may affect your life, because one thing is certain: it’s not a matter of if persistent AIs will emerge, but when.