Freedom From The Flesh

The human body resonates at the same frequency as Mother Earth. So instead of only focusing on trying to save the earth, which operates in congruence to our vibrations, I think it is more important to be one with each other. If you really want to remedy the earth, we have to mend mankind. And to unite mankind, we heal the Earth. That is the only way. Mother Earth will exist with or without us. Yet if she is sick, it is because mankind is sick and separated. And if our vibrations are bad, she reacts to it, as do all living creatures.

Suzy Kassem, Rise Up and Salute the Sun: The Writings of Suzy Kassem

Image from: The Outer Limits (1963) S 2 E 15 “The Brain of Colonel Barham”

i have been considering writing a series of blogs on the coming age of Cybernetics. For those unaware Cybernetics was a term coined by one of my heroes Dr. Norbert Weiner. Dr. Weiner was a professor of mathematics at MIT who early on became interested in stochastic and noise processes and has an entire area dedicated to him in the areas of Weiner Estimation Theory. However, he also is credited as being one of the first to theorize that all intelligent behavior was the result of feedback mechanisms, that could possibly be simulated by machines thereby coining the phrase “Cybernetics”. He wrote two seminal books in this area: (1) “Cybernetics” (2) “The Human Use of Humans”. This brings us to the present issue at hand.

The catalyst for the blog came from a tweet:

More concerning Ted is how long before people start paying for upgrades. What effects will this have when you have achieved functional immortality?


This was in response to me RT’ing a tweet from @alisonBLowndes concerning the International Manifesto of the “2045” Strategic Social Initiative. An excerpt from said manifesto:

We believe that before 2045 an artificial body will be created that will not only surpass the existing body in terms of functionality but will achieve perfection of form and be no less attractive than the human body. 

2045 Manifesto

Now for more context. I am a proponent of using technology to allow for increased human performance as i am an early adopter if you will of the usage of titanium to repair the skeletal system. i have staples, pins, plates and complete joints of titanium from pushing “Ye Ole MeatBag” into areas where it did not fair so well.

There are some objectives of the movement of specific interest is Number Two:

To create an international research center for cybernetic immortality to advance practical implementations of the main technical project – the creation of the artificial body and the preparation for subsequent transfer of individual human consciousness to such a body.

2045 Manifesto

This is closely related to Transhumanism which is more of a philosophy than an execution. The way i frame it is Transhumanism sets the philosophical framework for cybernetics. The contemporary meaning of the term “transhumanism” was foreshadowed by one of the first professors of futurology, a man who changed his name to FM-2030. In the 1960s, he taught “new concepts of the human” at The New School when he began to identify people who adopt technologies, lifestyles and worldviews “transitional” to post-humanity as “transhuman“.

Coming from a software standpoint we could map the areas into pipelines and deploy as needed either material biological or conscious. We could map these areas into a CI/CD deployment pipeline. .

For a direct reference, i work with an amazing practicing nocturnist who is also a great coder as well as a medical 3D Printing expert! He prints body parts! It is pretty amazing to think that something your holding that was printed that morning is going to enable someone to use their respective limbs or walk again. So humbling. By the way, the good doctor is also a really nice person. Just truly a great human. Health practitioners are truly some of humanity’s rockstars.

He printed me a fully articulated purple octopus that doesn’t go in the body:

Building upon this edict and for those who have read William Gibson’s “Neuromancer,” or Rudy Ruckers The Ware Tetralogy: Software, Wetware, Realware, Freeware “it calls into question the very essence of the use for the human body? Of the flesh is the only aspect we truly do know and associate with this thing called life. We continually talk about the ‘Big C Word” – Consciousness. However, we only know the body. We have no idea of the mind. Carnality seems to rule the roost for the Humans.

In fact most of the acts that we perform on a daily basis trend toward physical pleasure. However what if we can upgrade the pleasure centers? What if the whole concept of dysphoria goes away and you can order you a net-new body? What *if* this allows us as the above ponders to upgrade ad nauseam and live forever? Would you? Would it get tiresome and boring?

i can unequivocally tell you i would if given the chance. Why? Because if there *is* intelligent life somewhere then they have a much longer evolutionary scale that we mere humans on earth do not and they have figured out some stuff let’s say that can possibly change the way we view life in longer time scales ( or time loops or even can we say Infinite_Human_Do_Loops? ).

i believe we are coming to an age where instead of the “50 is the new 30” we can choose our age – lets say at 100 i choose new core and legs and still run a 40-yard dash in under 5 seconds? i am all for it.

What if you choose a body that is younger than your progeny?

What if you choose a body that isnt a representation of a human as we know it?

All with immortality?

i would love to hear folks thoughts on the matter in the comments below.

For reference again here is the link -> 2045 Manifesto.

Until then,

Be Safe.



Muzak To Blog By: Maddalena by Ennio Morricone

Computing The Human Condition – Project Noumena (Part 2)

In the evolution of a society, continued investment in complexity as a problem-solving strategy yields a declining marginal return.

Joseph A. Tainter

Someone asked me if from now on my blog will only be about Project_Noumena – on the contrary.

I will be interspersing subject matter within Parts 1 to (N) of Project_Noumena. To be transparent at this juncture i am not sure where it will end or if there is even a logical MVP 1.0.  As with open-source systems and frameworks technically one never achieves V1.0 as the systems evolve. i tend to believe this will be the case with Project Noumena.  i  recently provided a book review on CaTB and have a blog on Recurrent Neural Networks with respect to Multiple Time Scale Prediction in the works so stuff is proceeding. 

To that end, i would love comments and suggestions as to anything you would like my opinion on or for me to write about in the comments section.  Also feel free to call me out on typos or anything else you see in error.

Further within Project Noumena there are snippets that could be shorter blogs as well.  Look at Project Noumena as a fractal-based system.

Now on to the matter at hand.

In the previous blog Computing The Human_Condition – Project Noumena (Part 1) i discussed the initial overview of the model from the book World Dynamics.  i will take a part of that model which is what i call, the main, Human_Do_Loop(); and the main attributes of the model: Birth and Death of Humans. One must ask if we didn’t have humans we would not have to be concerned with such matters as societal collapse?  i don’t believe animals are concerned with such existential crisis concerns so my answer is a resounding – NO. We will be discussing such existential issues in this blog although i will address such items in future writings. 

Over the years i have been asking myself is this a biological model by definition?  Meaning do we have cellular components involved only?  Is this biological modeling at the very essence?  If we took the cell-based organisms out of the equation what do we still have as far as models on Earth? 

While i told myself i wouldn’t get too extensional here and i do want to focus on the models and then codebases i continually check the initial conditions of these systems as they for most systems dictate the response for the rest of the future operations of said systems.  Thus for biological systems, are there physical parameters that govern the initial exponential growth rate?  Can we model with power laws and logistic curves for coarse-grained behavior?  Is Bayesian reasoning biologically plausible at a behavioral level or at a neuronal level? Given that what are the atomic units that govern these models?  

These are just a sampling of initial condition questions i ask myself as i evolve through this process. 

So with that long-winded introduction and i trust i didn’t lose you oh reader lets hope into some specifics. 

Birth and Death Rates

The picture from the book depicts basic birth and death loops in the population sector.  In the case of these loops, they are generating positive feedback which causes growth.  Thus an increase in population P causes an increase in birthrate BR.  This, in turn, causes population P to further increase.  The positive feedback loop would if left to its own devices would create an exponentially growing situation.  As i said in the first blog and will continue to say, we seem to have started using exponential growth as a net positive fashion over the years in the technology industry.  In the case of basic population dynamics with no constraints, exponential growth is not a net positive outcome. 

Once again why start with simple models?  The human mind is phenomenal at perceiving pressures, fears, greed, homeostasis, and other human aspects and characteristics and attempting at a structure that is given say the best fit to a situation and categorizing these as attributes thereof.  However, the human mind is rather poor at predicting dynamical systems behaviors which are where the models come into play especially with social interactions and what i attempting to define from a self-organizing theory standpoint.  

The next sets of loops that have the most effective behavior is a Pollution loop and a Crowding Loop.  If we note that pollution POL increases one can assume up to a point that one hopes that nature absorbs and fixes the pollution otherwise it is a completely positive feedback loop and this, in turn, creates over pollution which we are already seeing the effects of around the worlds. One can then couple this with the amount of crowding humans can tolerate. 

Population, Birth Rate, Pollution

We see this behavior in urban sprawl areas when we have extreme heat or extreme cold or let’s say extreme pandemics.  If the population rises crowding ratio increases the birth rate multiplier declines and birth rates reduce.  The increasing death rate and reducing the birth rate are power system dynamic stabilizers coupled with pollution. This in turn obviously has an effect on food supplies. One can easily deduce that these seemingly simple coefficients if you will within the relative feedback loops create oscillations, exponential growth, or exponential decay.  The systems while that seem large and rather stable are very sensitive to slight variations.  If you are familiar with NetLogo it is a great agent-based modeling language.  I picked a simple pollution model whereas we can select the number of people, birthrate, and tree planting rate. 

population dynamics with pollution

As you can see without delving into the specifics after 77 years it doesn’t look to promising.  i ‘ll either be using python or netlogo or a combination of both to extended these models as we add other references. 

Ok enough for now.

Until Then,



Computing The Human Condition – Project Noumena (Part 1)

“I am putting myself to the fullest possible use, which is all I think any conscious entity can ever hope to do.” ~ HAL 9000

“If you want to make the world a better place take a look at yourself and then make a change.” ~ MJ.

First and foremost with this blog i trust everyone is safe.  The world is in an interesting place, space, and time both physically and dare i say collectively – mentally.

A Laundry List


This past week we celebrated  Earth Day.  i believe i heard it was the 50th year of Earth Day.  While I applaud the efforts and longevity for a day we should have Earth Day every day.  Further just “thoughting” about or tweeting about Earth Day – while it may wake up your posterior lobe of the pituitary gland and secret some oxytocin – creating the warm fuzzies for you it really doesn’t create an action for furthering Earth Day.  (much like typing /giphy YAY! In Slack).

 As such, i decided to embark on a multipart blog that i have been “thinking” about what i call an Ecological Computing System.  Then the more i thought about it why stop at Ecology?   We are able to model and connect essentially anything, we now have models for the brain that while are coarse-grained can account for gross behaviors, we have tons of data on buying habits and advertisement data and everything is highly mobile and distributed.  Machine learning which can optimize, classify and predict with extremely high dimensionality is no longer an academic exercise.  

Thus, i suppose taking it one step further from ecology and what would differentiate it from other efforts is that <IT>  would actually attempt to provide a compute framework that would compute The Human Condition.  I am going to call this effort Project Noumena.  Kant the eminent thinker of 18th century Germany defined Noumena as a thing as it is in itself, as distinct from a thing as it is knowable by the senses through phenomenal attributes and proposed that the experience was a product of the mind.

My impetus for this are manifold:

  • i love the air, water, trees, and animals,
  • i am an active water person,
  • i want my children’s children’s children to know the wonder of staring at the azure skies, azure oceans and purple mountains,
  • Maybe technology will assist us in saving us from The Human Condition.


i have waited probably 15+ years to write about this ideation of such a system mainly due to the technological considerations were nowhere near where they needed to be and to be extremely transparent no one seemed to really think it was an issue until recently.  The pandemic seems to have been a global wakeup call that in fact, Humanity is fragile.  There are shortages of resources in the most advanced societies.  Further due to the recent awareness that the pollution levels appear (reported) to be subsiding as a function in the reduction of humans’ daily involvement within the environment. To that point over the past two years, there appears to be an uptake of awareness in how plastics are destroying our oceans.  This has a coupling effect that with the pandemic and other environmental concerns there could potentially be a food shortage due to these highly nonlinear effects.   This uptake in awareness has mainly been due to the usage of technology of mobile computing and social media which in and of itself probably couldn’t have existed without plastics and massive natural resource consumption.  So i trust the irony is not lost there.   

From a technical perspective, Open source and Open Source Systems have become the way that software is developed.  For those that have not read The Cathedral and The Bazaar and In The Beginning Was The Command Line i urge you to do so it will change your perspective.

We are no longer hampered by the concept of scale in computing. We can also create a system that behaves at scale with only but a few human resources.  You can do a lot with few humans now which has been the promise of computing.

Distributed computing methods are now coming to fruition. We no longer think in terms of a monolithic operating system or in place machine learning. Edge computing and fiber networks are accelerating this at an astonishing rate.  Transactions now dictate trust. While we will revisit this during the design chapters of the blog I’ll go out on a limb here and say these three features are cogent to distributed system processing (and possibly the future of computing at scale).

  • Incentive models
  • Consensus models
  • Protocol models

We will definitely be going into the deeper psychological, mathematical, and technical aspects of these items.

Some additional points of interest and on timing.  Microsoft recently released press about a Planetary Computer and announced the position of Chief Ecology Officer.  While i do not consider Project Nuomena to be of the same system type there could be similarities on the ecological aspects which just like in open source creates a more resilient base to work.

The top market cap companies are all information theoretic-based corporations.  Humans that know the science, technology, mathematics and liberal arts are key to their success.  All of these companies are woven and interwoven into the very fabric of our physical and psychological lives.

Thus it is with the confluence of these items i believe the time is now to embark on this design journey.  We must address the Environment, Societal factors and the model of governance.

A mentor once told me one time in a land far away: “Timing is everything as long as you can execute.”  Ergo Timing and Execution Is Everything.


It is my goal that i can create a design and hopefully, an implementation that is utilizing computational means to truly assist in building models and sampling the world where we can adhere to goals in making small but meaningful changes that can be used within what i am calling the 3R’s:  recycle, redact, reuse.  Further, i hope with the proper incentive models in place that are dynamic it has a mentality positive feedback effect.  Just as in complexity theory a small change – a butterfly wings – can create hurricanes – in this case positive effect. 

Here is my overall plan. i’m not big on the process or gant charts.  I’ll be putting all of this in a as well.  I may ensconce the feature sets etc into a trello or some other tracking mechanism to keep me focused – WebSphere feel free to make recommendations in the comments section:

Action Items:

  • Create Comparative Models
  • Create Coarse-Grained Attributes
  • Identify underlying technical attributes
  • Attempt to coalesce into an architecture
  • Start writing code for the above.


Humanity has come to expect growth as a material extension of human behavior.  We equate growth with progress.  In fact, we use the term exponential growth as it is indefinitely positive.  In most cases for a fixed time interval, this means a doubling of the relevant system variable or variables.  We speak of growth as a function of gross national production.  In most cases, exponential growth is treacherous where there are no known or perceived limits.  It appears that humanity has only recently become aware that we do not have infinite resources.  Psychologically there is a clash between the exponential growth and the psychological or physical limit.  The only significance is the relevant (usually local) limit.  How does it affect me, us, and them?  This can be seen throughput most game theory practices – dominant choice.  The pattern of growth is not the surprise it is the collision of the awareness of the limit to the ever-increasing growth function is the surprise.

One must stop and ask: 

Q: Are progress (and capacity) and the ever-increasing function a positive and how does it relate to 2nd law of thermodynamics aka Entropy?  Must it always expand?

We are starting to see that our world can exert dormant forces that within our life can greatly affect our well being. When we approach the actual or perceived limit the forces which are usually negative begin to gain strength.

So given these aspects of why i’ll turn now to start the discussion.  If we do not understand history we cannot predict the future by inventing it or in most cases re-inventing it as it where.

I want to start off the history by referencing several books that i have been reading and re-reading on subjects of modeling the world, complexity, and models for collapse throughout this multipart blog.  We will be addressing issues concerning complex dynamics as are manifested with respect to attributes model types, economics, equality, and mental concerns.  

These core references are located at the end of the blog under references.  They are all hot-linked.  Please go scroll and check them out.  i’ll still be here.  i’ll wait.

Checked them out?  i know a long list. 

As you can see the core is rather extensive due to the nature of the subject matter.  The top three books are the main ones that have been the prime movers and guides of my thinking.  These three books i will refer to as The Core Trilogy:

World Dynamics

The Collapse of Complex Societies 

Six Sources of Collapse 

 As i mentioned i have been deeply thinking about all aspects of this system for quite some time. I will be mentioning several other texts and references along the continuum of creation of this design.

We will start by referencing the first book: World Dynamics by J.W. Forrestor.  World Dynamics came out of several meetings of the Rome Club a 75 person invite-only club founded by the President of Fiat.  The club set forth the following attributes for a dynamic model that would attempt to predict the future of the world:

  • Population Growth
  • Capital Investment
  • Geographical Space
  • Natural Resources
  • Pollution
  • Food Production

The output of this design was codified in a computer program called World3.  It has been running since the 1970s what was then termed a golden age of society in many cases.  All of these variables have been growing at an exponential rate. Here we see the model with the various attributes in action. There have been several criticisms of the models and also analysis which i will go into in further blogs. However, in some cases, the variants have been eerily accurate. The following plot is an output of the World3 model:

2060 does not look good

Issues Raised By World3 and World Dynamics

The issues raised by World3 and within the book World Dynamics are the following:

  • There is a strong undercurrent that technology might not be the savior of humankind
  • Industrialism (including medicine and public health) may be a more disturbing force than the population.  
  • We may face extreme psychological stress and pressures from a four-pronged dilemma via suppression of the modern industrial world.
  • We may be living in a “golden age” despite a widely acknowledged feeling of malaise.  
  • Exhtortions and programs directed at population control may be self-defeating.  Population control, if it works, would yield excesses thereby allowing further procreation.
  • Pollution and Population seem to oscillate whereas the high standard of living increases the production of food and material goods which outrun the population.  Agriculture as it hits a space limit and as natural resources reach a pollution limit then the quality of life falls in equalizing population.
  • There may be no realistic hope of underdeveloped countries reaching the same standard and quality of life as developed countries.  However, with the decline in developed countries, the underdeveloped countries may be equalized by that decline.
  • A society with a high level of industrialization may be unsustainable.  
  • From a long term 100 years hence it may be unwise for underdeveloped countries to seek the same levels of industrialization.  The present underdeveloped nations may be in better conditions for surviving the forthcoming pressures.  These underdeveloped countries would suffer far less in a world collapse.  

Fuzzy Human – Fuzzy Model

The human mind is amazing at identifying structures of complex situations. However, our experiences train us poorly for estimating the dynamic consequences of said complexities.  Our mind is also not very accurate at estimating ad hoc parts of the complexities and the variational outcomes.  

One of the problems with models is well it is just a model  The subject-observer reference could shift and the context shifts thereof.  This dynamic aspect needs to be built into the models.

Also while we would like to think that our mental model is accurate it is really quite fuzzy and even irrational in most cases.  Also attempting to generalize everything into a singular model parameter is exceedingly difficult.  It is very difficult to transfer one industry model onto another.  

In general parameterization of most of these systems is based on some perceptual model we have rationally or irrationally invented.  

When these models were created there was the consideration of modeling social mechanics of good-evil, greed – altruism, fears, goals, habits, prejudice, homeostasis, and other so-called human characteristics.  We are now at a level of science where we can actually model the synaptic impulse and other aspects that come with these perceptions and emotions.

There is a common cross-cutting construct in most complex models within this text that consists of and mainly concerned with the concept of feedback and how the non-linear relationships of these modeled systems feedback into one another.  System-wide thinking permeates the text itself.  On a related note from the 1940’s of which Dr Norbert Weiner and others such as Claude Shannon worked on ballistic tracking systems and coupled feedback both in a cybernetic and information-theoretic fashion of which he attributed the concept of feedback as one of the most fundamental operations in information theory.  This led to the extremely famous Weiner Estimation Filters.  Also, side note: Dr Weiner was a self-styled pacifist proving you can hold two very opposing views in the same instance whilst being successful at executing both ideals.   

Given that basic function of feedback, lets look at the principle structures.  Essentially the model states there will be levels and rates.  Rates are flows that cause levels to change.  Levels can accumulate the net level. Either addition or subtraction to that level.  The various system levels can in aggregate describe the system state at any given time \((t)\).  Levels existing in all subsystems of existence.  These subsystems as you will see include but are not limited to financial, psychological, biological, and economic.   The reason that i say not limited to because i also believe there are some yet to be identified subsystems at the quantum level.  The differential or rate of flow is controlled by one or more systems.  All systems that have some Spatio-temporal manifestation can be represented by using the two variables levels and rates.  Thus with respect to the spatial or temporal variables, we can have a dynamic model.  

The below picture is the model that grew out of interest from the initial meetings of the Club of Rome.  The inaugural meeting which was the impetus for the model was held in Bern, Switzerland on June 29, 1970.  Each of the levels presents a variable in the previously mentioned major structures. System levels appear as right triangles.  Each level is increased or decreased by the respective flow.  As previously mentioned on feedback any closed path through the diagram is a feedback loop.  Some of the closed loops given certain information-theoretic attributes be positive feedback loops that generate growth and others that seek equilibrium will be negative feedback loops.  If you notice something about the diagram it essentially is a birth and death loop. The population loop if you will.  For the benefit of modeling, there are really only two major variables that affect the population.  Birth Rate (BR) and Death Rate (DR).  They represent the total aggregate rate at which the population is being increased or decreased.  The system has coefficients that can initialize them to normal rates.  For example, in 1970 BRN is taken as 0.0885 (88.5 per thousand) which is then multiplied by population to determine BR.  DRN by the same measure is the outflow or reduction.  In 1970 it was 9.5% or 0.095.  The difference is the net and called normal rates.  The normale rates correspond to a physical normal world.  When there are normal levels of food, material standard of living, crowding, and pollution.  The influencers are then multipliers that increase or decrease the normal rates.

Feedback and isomorphisms abound

As a caveat, there have been some detractors of this model. To be sure it is very coarse-grained however while i haven’t seen the latest runs or outputs it is my understanding as i said the current outputs are close. The criticisms come in the shape of “Well its just modeling everything as a \(y=x*e^{{rt}}\). I will be using this concept and map if you will as the basis for Noumena.  The concepts and values as i evolve the system will vary greatly from the World3 model but i believe starting with a minimum viable product is essential here as i said humans are not very good at predicting all of the various outcomes in high dimensional space. We can asses situations very quickly but probably outcomes no so much. Next up we will be delving into the loops deeper and getting loopier.

So this is the first draft if you will as everything nowadays can be considered an evolutionary draft.  

Then again isn’t really all of this just  The_Inifinite_Human_Do_Loop?

until then,




(Note: They are all hotlinked)

World Dynamics

The Collapse of Complex Societies 

Six Sources of Collapse 

Beyond The Limits 

The Limits To Growth 

Thinking In Systems Donella Meadows

Designing Distributed Systems Brendan Burns

Introduction to Distributed Algorithms 

A Pragmatic Introduction to Secure Multi-Party Computation 

Reliable Secure Distributed Programming 

Distributed Algorithms 

Dynamic General Equilibrium Modeling 

Advanced Information Systems Engineering 

Introduction to Dynamic Systems Modeling 

Nonlinear Dynamics and Chaos 

Technological Revolutions and Financial Capital 

Marginalism and Discontinuity 

How Nature Works 

Complexity and The Economy 

Complexity a Guided Tour

Future Shock 


Nudge Theory In Action

The Structure of Scientific Revolutions

Agent-Based Modelling In Economics


Human Use Of Human Beings

The Technological Society

The Origins Of Order

The Lorax

Blog Muzak: Brain and Roger Eno: Mixing Colors

NuerIPS 2019

And they asked me how I did it, and I gave ’em the Scripture text,
“You keep your light so shining a little in front o’ the next!”
They copied all they could follow, but they couldn’t copy my mind,
And I left ’em sweating and stealing a year and a half behind.

~ “The Mary Gloster”, Rudyard Kipling, 1896

My Badge – I exist.

Well, your humble narrator finally made it to NuerIPS2019. There were several starts and stops to my travel itinerary but I finally persevered!

Bienvenue – Vancouver, British Columbia

First and foremost while the location at least for me required multiple hops Vancouver, BC is a beautiful city. The Vancouver conference center is spacious and an exemplary venue. Also for those that have the time Whistler / Blackcomb is one of the best mountains in North America for snow sports this time of the year. While I didn’t get to go I am being hopeful that I will win the registration the lottery system next year for 2020 and will plan accordingly.

Vancouver Conference Center – Oh Canada!

This year the conference was veritable who’s who of information-theoretic companies. Most of the top market cap companies are now information theoretic-based technology companies and as such have representation here at the conference. To wit IBM Research AI was a diamond sponsor:

While it is nearly impossible to quantify the breadth and depth of the subject matter presented here at the conference I have attempted to classify some overall themes:

  • Agent-Based Modelling and Behaviors
  • Imitation, Meta, Transfer, Policy Learning and Behavioral Cloning
  • Morphological Systems based on Evolutionary Biology
  • Optimization methods for non-convex models
  • Hybrid Bayesian and MCMC methods
  • Ordinary Differential Equation (ODE) direct Modelling and Systems
  • Neuroscience models that couple computational agents and hypotheses of consciousness

Side Note: I think it is amazing that 10 years ago you could not say “I’m using a Neural Network for …” without being laughed out the room. Now there is an entire set of tracks dedicated to said technology and algorithms.

The one major difference in this conference compared to what I have read and heard albeit second hand or through reports or blogs is the focus on ‘Where is your github?” and the question of how fast can we get to production? There was a very focused and volitional undertone to the questions

One aspect that has not changed and appears to have been amplified is the recruiter/job marketplace and (ahem) situation at the conference. To say that it was transparent and out in the open would be an understatement.

New To NeurIPS:

For those that have never been to neurips I’ll provide some recommendations:

  • Download the conference app and fill out your profile
  • Plan your agenda
  • Get to the poster sessions – early
  • Network as much as possible
  • Wear comfortable shoes – it is in the same venue next year, lots of walking.
  • Attempt to get a close hotel as possible due to \(P(Rain | Conference Timing) > 0.5\)

Trends and Catagories:

Agent-Based Modelling and Behaviors

This area is finally coming to fruition in the production market at scale. We are seeing both ABB (agent based modeling) and ABM (agent-based modeling aka self emergent / self organizing behaviors). There were many presentations on multi-agent behaviors in the context of both policy and environment responses using reinforcement learning and q-learning.

Imitation, Meta, Transfer, Policy Learning and Behavioral Cloning

I grouped all of these together while technically they are different in application and scope. However, they can and are mixed together for applied systems. For instance in imitation learning (IL). IL instead of trying to learn from the sparse rewards or manually specifying a reward function, an expert (typically a human) provides us with a set of demonstrations. The agent then tries to learn the optimal policy by following, imitating the expert’s decisions. Historically this was called Expert Systems Engineering. However, note the policy learning implicit in this area as well. Furthermore Behavioral cloning is a method by which human subcognitive skills can be captured and reproduced in a computer program. As the human subject performs the skill, his or her actions are recorded along with the situation that gave rise to the action. So as one can see all of these areas are closely related to a so-called expert reference. Algorithms of consensus among multi-agents will play a crucial role here.

Morphological Systems based on Evolutionary Biology

Morphology is a branch of biology dealing with the study of the form and structure of organisms and their specific structural features. Morphology is a branch of life science dealing with the study of a structure of an organism and its component parts. Turing wrote a paper on Morphology and S. Kaufman wrote “The Origins of Order: Self-Organization and Selection in Evolution” just to name a few. We are headed into areas where physics, chemistry, and biology are being brought into play with computing, once again at scale. This multi-modality computing will also benefit from access to the developments in accessible quantum computing.

Optimization methods for non-convex models

Gradient descent in all of its flavors has been our friend for decades. Are the local minima our friend or foe? The algorithms are now starting to ask “Where Am I”?

Hybrid Bayesian and MCMC methods

In 2007 I founded a machine learning and NLP as a service company called “BeliefNetworks”. This self-referencing name should illustrate where I stand on inference methods. Due to access to cycles and throughput, we are finally starting to see these methods integrated system-wide.

Ordinary Differential Equation (ODE) direct Modelling and Systems

Having worked for years in the areas of numerical optimization this is another area that is near and dear. I saw several papers mapping ODE’s to geometric representations. Analog computing could very well be in our return to the future. Naiver-Stokes equation anyone? I see the industry moving into flow models with truly modeling foundational Cauchy momentum equations depending on the application area. We are going to see both software and hardware development in this area.

Neuroscience models that couple computational agents and hypotheses of consciousness

Given all of the above computer scientist are pulling in physicists, biologists, chemists and finally neuroscientists-finally. Possibly the “C” word is no longer anathema? I promise I will not insert a terminator picture here. However, given the developments in cognition and understanding quantum biology, we are now starting to be able to model at least initially what we “think” we are thinking about in some cases. Yoshua Bengio gave a great talk on volitional causal and “conscious” tasks easily accomplished by humans. We also see this with the developments in the areas of spiking algorithms.

Papers, Posters, Demos – Oh My!

As part of this blog, I wanted to review a couple of my favorite presentations, posters, and papers. While this is not a ranked list nor is it a temporal chronological review it is a list of papers that resonated with me for various reasons. While I will be listing papers I will also be posting pictures of poster papers and some meetups that I attended.

Blind Super-Resolution Kernel Estimation using an Internal-GAN

This paper was interesting to me on several fronts. The basic premise for super-resolution kernels are thus: $$ILR = (I{_H}{_R}∗ks)↓_S$$ The paper introduced “KernelGAN” – an image-specific internal-GAN, which estimates the SR kernel that best preserves the distribution of patches across scales of the LR image. This is what I would consider significant progress over previous methods by estimating an image-specific SR-kernel based on the LR image alone. This allows a one-shot mode for training based on the LR image. Network training is done during test time. There is no actual inference step since the training implicitly contains the resulting SR-kernel. They give results in the paper as well a metrics of performance based on NTIRE 2018 dataset although given the first application of a deep linear network I would imagine this doesn’t really do it justice. Very impressive and I can see several applications of this method and algorithm.

Project website:∼vision/kernelgan

q-means: A Quantum Algorithm for Unsupervised Machine Learning

The cogent aspect of this paper was the efficiency of storing the vectors in First, classical data expressed in the form of N-dimensional complex vectors can be mapped onto quantum states over \(log2Nqubits\): when the data is stored in a quantum random access memory (qRAM). Specifically, the distance estimation becomes very efficient when having quantum access to the vectors and the centroids via qRAM. The optimization yields a k-means optimization $$T=O(log(d))$$further the paper showed that you can also query the norm of the vectors within the state preparation.

Making AI Forget You: Data Deletion in Machine Learning

One of the issues with GDPR legislation and the right to be forgotten comes up when you must re-train the entire data set. This paper addresses methodologies that enable partial re-training. The paper goes over past methods of cryptography and differential privacy of which do not delete data but attempt to make data private or non-identifiable. From the paper: “Algorithms that support efficient deletion do not have to be private, and algorithms that are private do not have to support efficient deletion. To see the difference between privacy and data deletion, note that every learning algorithm supports the naive data deletion operation of retraining from scratch. The algorithm is not required to satisfy any privacy guarantees. Even an operation that outputs the entire dataset in the clear could support data deletion, whereas such an operation is certainly not private.” The paper goes on to define four areas of metric performance for DDIML: Linearity, Laziness, Modularity, and Quantization. They do state that e also assumed that user-based deletion requests correspond to only a single datapoint and this needs to be extended. However, for the unsupervised k-means they describe they have deletion efficiency with substantial algorithm speedup.

paper here:

Casual Confusion in Imitation Learning

From Wikipedia: “Behavioral cloning is a method by which human sub-cognitive skills can be captured and reproduced in a computer program. As the human subject performs the skill, his or her actions are recorded along with the situation that gave rise to the action.” The fundamental premise was comparing expert versus computational policy and minimizing a graph-based approach: $$\mathbb{E}_G[ \mathcal {l}(fφ([X_i \bigodot\ G,G]),Ai)]$$ where \(G_i\) is drawn uniformly at random overall \(2^{n}\) graphs and optimize for the mean squared error loss for the continuous action environments and a cross-entropy loss for the discrete action environments. Something very interesting happens during this process of imitation learning with experts. In particular, it leads to a counter-intuitive “causal misidentification” phenomenon: access to more information can yield worse performance ergo more is not better! The paper discusses with demonstrations of an autonomous vehicle scenario of phases with targeted intervention to predict the graph behavior. They did state the solutions are not production-ready. I really appreciated the honesty.


Learning To Control Self Assembling Morphologies: A Study of Generalized via Modularity

The idea of modular and self-assembling agents goes back at least to Von Neumman’s Theory of Self-Reproducing Automata. In robotics, such systems have been termed “self-reconfiguring modular robots”. E. Schrödinger posed this same question in “What is Life?”. This was one of my favorite demonstrations and presentations. I have been extremely “pro” using agent base self-organizing algorithms for quite some time. This paper and presentation utilizes zero-shot generalization and trains policies and generalizes to changes in the number of limbs of the entity as well as the environment. They then pick the best model from training and evaluate it without any fine-tuning at test-time.


Quantum Wassertain GANs

The poster and paper dealt with supposedly the first design of quantum Wasserstein Generative Adversarial Networks (WGANs), which has been shown to improve the robustness and the scalability of the adversarial training of quantum generative on noisy quantum hardware. Parameterized quantum circuits These circuits can be used as a parameterized representation of functions as called quantum neural networks, which can be applied to classical supervised learning models, or to construct generative models. The paper also showed how to turn the quantum Wasserstein semimetrics into a concrete design of quantum WGANs that can be efficiently implemented on quantum machines. FWIW in functional analysis, pseudometrics often come from seminorms on vector spaces, and so it is natural to call them “semimetrics”. The paper used WGANs to generate a 3-qubit quantum circuit of 50 gates that approximated a 3-qubit simulation circuit that requires over 10k gates using off the shelf standard techniques. The QWGAN then can was used to approximate complex quantum circuits with smaller circuits. A smaller circuit was then trained to approximate the Choi–Jamiolkowski isomorphism or Choi state which encodes the action of a quantum circuit.

Deep Signature Transforms

Signatures refer to a set of statistics given a stream of data. The other type of signature is for the transform. Sometimes this is also called the transform kernel. In the case of a signal kernel or transform to model a curve as a linear combination. Signatures provide a basis for functions on the space of curves. These functions can then be used as operative building blocks. The stream can then be defined as: $$S(V) ={x= (x1,…,xn) :xi∈V,n∈N}$$ This also has interesting ramifications as a feature mapping/engineering processes as well as embedding the signatures within algorithms, in this case, a layer within a Neural Networks. This is akin to some fingerprinting techniques in the past for media and the paper does mention: “in order to preserve the stream-like nature is to sweep a one-dimensional convolution along the stream.” The embedding techniques as part of the path and preserving nature made this an extremely enjoyable discussion.

code here:

paper here:

Metamers Of Neural Networks

This paper was near and dear to me due to some of my past lives working in the areas of psychological and perceptual media models. Metamers are a psychophysical color match between two patches of light that have different sets of wavelengths. This means that metamers are two patches of color that look identical to us in color but are made up of different physical combinations of wavelengths. In the case of this paper for metamers they “model metamers” to test the similarity between human and artificial neural network representations. The group generated model metamers for natural stimuli by performing gradient descent on noise signal, matching the responses of individual layers of image and audio networks to a natural image or speech signal. The resulting signals reflect the invariances instantiated in the network up to the matched layer. As with most things in machine learning the team sought whether the nature of the invariances would be similar to those of humans, in which case the model metamers should remain human-recognizable regardless of the stage from which they are generated. In this case, the humans were divergent from the neural networks. We need more of this type of work and how perceptions affect machine learning outcomes or possibly priors?

paper here:

Weight Agnostic Neural Networks

I particularly enjoyed this poster and the commentary “Animals have innate abilities…” I also believe most of the animal kingdom is sentiment as well as operating on literally different wavelengths (spectrum etc). The paper was to demonstrate a method that can find minimal neural network architectures that can perform several reinforcement learning tasks without weight training. Ergo the title Weight Agnostic. In place of optimizing weights of a fixed network, they sought to optimize instead for architectures that perform well over a wide range of weights. When I walked up to the poster I immediately thought of Algorithmic Information Theory (AIT) and how soft weights have been used for neural networks. AIT which based using Kolmogorov complexity of a computable object is the minimum length of the program that can compute it. The paper goes into detail concerning The Minimal Description Length (MDL) of a program and the recent dusting off of these processes applied to larger deep learning nets. The poster did not reflect the transparency of the paper in that the research was very focused on creating generalized network architectures in which IMHO is a step toward AGI and stated the WANN is not approaching the performance of engineered CNNs. I also appreciated the overall frankness of the paper. Quote from the paper: “This paper is strongly motivated towards these goals of blending innate behavior and learning, and we believe it is a step towards addressing the challenge posed by Zador. We hope this work will help bring neuroscience and machine learning communities closer together to tackle these challenges.”

Interactive version of the paper here:

Regular paper here:

Inducing Brain Relevant Bias in Natural Language Processing Models

This poster was part of a general theme that I saw throughout the conference. Utilizing medical imaging devices to create better canonical models for machine learning. The paper shows the relationship between language and brain activity learned by BERT (Bidirectional Encoder Representations from Transformers) during fine-tuning transfers across multiple participants. The paper goes on to show that, for some participants, the fine-tuned representations learned from both magnetoencephalography (MEG) and functional magnetic resonance imaging(fMRI) are better for predicting fMRI than the representations learned from fMRI alone, indicating that the learned representations capture brain-activity-relevant information that is not simply an artifact of the modality. The model predicts the fMRI activity associated with reading arbitrary text passages, well enough to distinguish which of two-story segments is being read with 74% accuracy. That is impressive and I believe we need more multi-modality papers of this nature and research.

Full site with paper data etc:

A Robust Non-Clairvoyant Dynamic Mechanism for Contextual Auctions

This paper caught my eye as I spend a great deal of time researching agents in game-theoretic of mechanism design based situations. What really caught my eye was the terminology non-clairvoyant. I suppose if there was a method that was truly calirvoynet we wouldn’t be concerned with the robustness of said algorithms. Actually, it is a real definition – a dynamic mechanism is non-clairvoyant if the allocation and pricing rule at each period does not depend on the type distributions in the future periods. In many types of auctions, especially ad networks the seller must rely on approximate or asymmetric models of the buyer’s preferences to effectively set auction parameters such as a reserve price. In mechanism design, you essentially have three vectors of input: [1] collective decision problem, [2] measure of quality to evaluate any candidate solution, [3] description of the resources – information – held by the participants. The paper presented a learned policy model and framework that could be applied in phases and possibly extrapolated to other types of applications. I personally think dynamic mechanism design has great applicability in the areas of distributed computing and distributed ledger platforms.

I also attended the NASA Frontier Design Labs that was sponsored by Google, Intel and Nvidia. I was part of the NASA FDL AI Astronaut Health research project over the summer of 2019. The efforts, technology and most importantly the people are astounding. The event was standing room only and several amazing conversations on the various projects with NASA FDL were had at the event.

Machine Learning For Space

I do hope you will continue to visit my site. If you continue to visit you will notice I have a type of “disease” called Biblomaniac-ism. As such I bought a book at the conference:

The future is distributed

So there you have it. While this probably was tl;dr I hope you gave it a good scan while you were doing a pull request or two. I hope this has at least provided some insight into the conference.

\(\forall\) papers:

Until Then,