Truth is a model

The most common misunderstanding about science is that scientists seek and find truth. They don’t. They make and test models…. Making sense of anything means making models that can predict outcomes and accommodate observations. Truth is a model. (Neil Gershenfeld, American physicist, 2011)



Why we cannot stand “conscious robots”

Robots start being everywhere and humans are afraid of losing their role (as well as their job). But the best human revenge over robots is to grant them no consciousness. They can do wonderful (or horrible) things but, whatever they are, they are (and possibly will) not (be) conscious. They mine big data for us, they clean for us and they will be driving for us but, no way,  they will do without consciousness. The rationale of this post is simple. Human have problems in defining consciousness but they feel extremely confident in denying it in other entities. So let’s try to define consciousness by focusing on why we deny it to others, in this case robots. It is easy to deny consciousness to rocks and simple organisms, on the basis of their non existing or irrelevant behavior, but why are we so sure in denying consciousness to robots? Since when we are talking of consciousness we refer to human consciousness, why robots do not have it (yet)?

My answer is simple: we deny consciousness to all behavior or functionality that can be described as automatic. So being conscious should mean first of all “not being automatic”. In Dennet’s words we could define automatic as “something that has competence without comprehension“. The behavior can be complex and astonishing (think about Google recognition of image features) but since is somewhat programmed is necessarily not conscious.

So the idea is the following: let us define better what is automatic and, by reverse, we will define better what is conscious.

I will list here below what I consider as peculiar of an automaton or an automatic agent. Note that I will not put the fact of being programmed in this list since the automatic behavior can be the result of a long evolutionary process and not necessarily the output of a programing effort. Also I will focus on agents, so entities which make actions and evolve in time. So, in my opinion, the most peculiar aspects of an automatic agent are:

  1. A well-defined and accessible cost function, which maps an internal state to a degree of cost (or gain)
  2. An accurate observation of the current state
  3. An accurate model of the impact of an action on the future state: note that accurate means that it is able to predict not only the effect of a single action but of an entire sequence of actions

Overall, an automatic agent is a dynamic agent who knows (or pretends to know) a lot about itself and the surrounding environment. This self-confidence allows him to proceed indeed in an automatic and rapid manner. This means that it does not need to remember or store much of its past or its present, it needs simply to act.

All this appears to humans as incredibly (or excessively) simple: humans do not have a simple and well-defined cost function, like they have no access to a perfect observation of their state or a clear idea of the impact of their actions on their future state. Think, for instance, to taking the decision of getting married: how many doubts, trade-offs, considerations, hesitations are crowding our minds. Think instead to a self-driving car which in a situation of inevitable accident (e.g. it has to take the gruesome decision of running over either a group of old walkers or a toddler)  takes anyway and rapidly a decision: it will be able to do that only because all the uncertainty has been somewhat removed. The robot can be fast and automatic because all the myriads of cost functions (ethical, practical, economical) that could pop up in a human mind in the same situation are condensed and summarized in a single and certain one.

So what? if all this is considered by human as not conscious, is not possible that consciousness is indeed the (human) functionality developed to manage all the configurations which cannot be addressed in an automatic manner? In other terms, it is well know that humans make myriads of things in an unconscious (i.e. automatic) manner. We could argue that the evolution led us to solve a lot of tasks in an automatic manner, or equivalently we have been able to address many functionalities in a robot-like manner. For many others we have not (yet) been able: this can be due to several reasons: limited sensory capabilities, limited intelligence, excessive complexity of the task to be solved (e.g. related to the fact that we live in multi-agent communities). So, though a big part of us acts automatically and unconsciously like a robot, all the rest still requires consciousness. Now the question is “why this is the case”? Why could consciousness help in addressing situations that we are not able to address in an automatic way?

Quoting Dennet “..the puzzle today is “what is consciousness for (if anything)?” if unconscious processes are fully competent to perform all the cognitive operations of perception and control”  .

My answer as data scientist, is: data collection. Consciousness could be a way to alert memory functionalities to store situations (and related actions) in which we are aware that an automatic procedure is not good enough (for us as individual or for our community). Since we are not able to solve this yet automatically, why not simply store the configuration waiting to learn (sooner or later) what to do? So conscious states could be (also) data collection phases, with the aim of training a learning capability that could be helpful in the future. Think about driving a car: the amount of consciousness during our first driving days is largely superior to the one we have after ten years of licence. The task has been learned and made automatic: consciousness is no more necessary.

So, will the robots ever have consciousness? If we design robot in order to solve automatically a number of tasks, it means that we think that this task is sufficiently well defined not to require consciousness. This is how we conceive nowadays robotics. Using robots implies (implicitly or explicitly) that the task is well-defined. Think therefore which strong assumption is made when we accept the use of autonomous robotic warriors (drones,…)….

Once robot will be used to address ill-defined tasks (e.g. manage an interstellar mission of interaction with new species) consciousness functionalities will be needed.



Data science: an argument for contigentism?

I recently discovered a passionating debate in philosophy of science about the contingentism/inevitabilism issue (see this book). Quoting Lena Soler, one the authors of the book and major expert in this domain, the debate is roughly about that: “Could science have been otherwise? could have it been dramatically different from science as we now it today? Is there something inevitable in a sound scientific enterprise? Could we have developed an alternative successful science based on different notions, conceptions, results?” Note that the aim here is not to discuss the importance of an exotic pseudo-science but to reason about the fact whether the scientific notions, concepts, techniques, we are using today are really necessary or if a different scientific path (i.e. still scientific in Popper’s terminology) would have been possible.

In my opinion, it is quite natural to conceive, that being science a human enterprise, it could have been evolving  in a very different manner. How many choices, decisions, conclusions (or Nobel prizes) in the scientific world have been dictated by contingencies, social aspects, historical contexts, politics, economics or nationalistic considerations? Was all of that inevitable? For instance, think simply to the obscure fate that could await today a revolutionary article, unfortunately written in very bad English…

This debate evoked  in me some considerations about the role of data science in all that. The success of data science is  the living proof that, starting from the same (or very similar) premises, modeling can have multiple, heterogenous outcomes. Think for instance to address a scientific prediction problem in a data driven manner and consider the overabundance of techniques, methods, algorithms that you could use to solve this problem. From a data scientist perspective contingentism is a pure evidence.  The same problem could be tackled in many different manners but with roughly the same accuracy from an external prediction perspective. So the question raises spontaneously: what would have happened if Keplero or Newton would have had the same attitude (or better computational power)? Would the gravitational laws have the same form, the same aspect? Notions like mass, gravity would be the same? Would the consequent course of science be the same?

Also, is not data science a formidable manner of playing again the history of science? Which kind of scientific product would have been returned by a today data scientist once put in front to the same experimental evidence of renowned scientists of the past centuries?

Bias/variance interpretation of conscience

Every philosopher (e.g. scientist) is slave of some formalism and tends to apply it as much as possible to any reality aspects. This is indeed a form of bias and my bias, recently, is that I tend to interpret everything in terms of bias/variance… So why not pushing this to the extreme and applying it to nothing less than the hardest issue of science and philosophy?  conscience, as simple as that…

In particular I will aim here to address issues like: does conscience exist really, what is its function, may robots have one, and so on…

Let’s go straight to the end of my reasoning: we could use the bias/variance formalism, useful to describe any learning procedure, to  support the idea that conscience is not only an epiphenomenon but rather a necessary component  of every rational cognitive process. In particular conscience is required for interacting with a complex multivariate, multi agent and uncertain reality where the criterion of effectiveness/success of such interaction is complex, multivariate, uncertain and dependent on others too.

In other terms what I claim is that surviving in our reality requires any intelligent agent to roughly decompose its intelligent activities in two parts: a part (made of several submodules if needed) which can be addressed as a problem of optimization (according to an univariate cost function) and implemented similarly to a fast regulator or automatic controller: a second part which has to deal with exploration, exception, multi criteria, interaction, uncertainty and adaption.

As far as the first part is concerned,  think for instance to visuomotor control regulations which allow us everyday to survive in an hostile environment thanks to their fast and unconscious control loops able to monitor, control or optimize some tasks. This part corresponds to any bias component of a cognitive effort: a stable, situated and limited module aiming to exploit some previous or acquired task in a specific application domain . This module is rapid and effective when the application domain is respected and the addressed cost function is of interest.


Let me quote Christof Koch from his book “Consciousness: Confessions of a Romantic Reductionist. ” :

« The mystery deepens with the realization that much of the ebb and flow of daily life does indeed take place beyond the pale of consciousness. This is patently true for most of the sensory-motor actions that compose our daily routine: tying shoelaces, typing on a computer keyboard, driving a car, returning a tennis serve, running on a rocky trail, dancing a waltz. These actions run on automatic pilot, with little or no conscious introspection. Indeed, the smooth execution of such tasks requires that you not concentrate too much on any one component.  »


Conscience boils down to all that cannot be dealt with in this manner, in other terms to all that escapes to the  domain of automatic, fast yet biased servomotor modules. No free lunch theorems have shown that there is no optimization working for settings or models optimal for all distributions. Any biased approach, though effective in his own application domain, is doomed to failure (or better to low performance) in a complex world which cannot be interpreted at the light of a single criterion, or a single cost function.

So the two facets of our cognitive process address different aspects: on one side bias, exploitation, unconsciousness, automation, regularity, single variate criterion, rapidity, optimized solution on the other variance, exploration, awareness, attention, exception, multi criteria, delay, assessment of alternatives.

So as Koch said, consciousness is useful because « life sometimes throws you a curveball!  »

According to this interpretation, consciousness is a necessary component of a high level cognitive functionality; in other terms I refute the possibility of having a zombie who could have the equivalent cognitive capabilities of a conscious being, ale I don’t believe that a too biased learning agent could be effective in the long run in a ever changing world. Not being conscious would reduce the functionalities to automatic, biased learning or control processes, making the resulting behavior constrained to limited objectives, settings and criteria. Though a zombie could emulate in a short time and specific contexts the activities of a conscious agent, I believe that it is relatively easy for a conscious being to recognize that the zombie is only simulating intelligence (or at least conscious intelligence) and unmask it.

Think for example how it is easy for a young child to expose the limitations of a highly expensive robot just after some minutes of free interaction.

Think now about the rising success of self driving cars and the fact that it often occurs to people driving their car a very long way and realizing that they were thinking of something else. The growing appearance of self-driving cars seems to confirm that conscience is not necessarily  needed for implementing driving functionality in conventional setting. Think now about the dramatic eventuality of a car deciding in a fraction of second between two potential victims during a car accident: no automatic algorithm would be considered adequate to deal  with such ethical issue and we would be uncomfortable in dictating to the robot some behavior rules to act in such context. We are indeed entering the domain of consciousness where the conventional mechanistic way of proceeding is no more relevant.

I consider all these examples as evidence about the impossibility of attaining high level of cognitive capability without conscience, like it is impossible to attain knowledge with a biased, constrained, precooked modeling approach.

Another related consideration is provided by Stephen J. Gould (1977): “Animals become too committed to the peculiarities of their environment by evolving a fine-tuned design for a highly specific mode of life. They sacrifice plasticity for future change. Neoteny (i.e. the humans property of starting postnatal life in a less mature state than animals) shifts the emphasis from instincts to learning process as the dominant factor in the acquisition of the organism’s survival and coping skills.”



Bias/variance gnosiology

We learn only when we create a regularity and all that remains from our learning efforts is some sort of confortable simplification. Now, reality escapes or diverges from our regular expectations every time we want to use or enforce  them to explain or predict the course of nature. In front of the inescapable gap between our regular eden and the  natural hell of observations, we can take two extreme attitudes: negate or discredit reality and reduce all divergences to some sort of noise (measurement error) or try to incorporate discording data and measures in our model. Of course there is a continuum of intermediate positions which are possible between these two extrema and it is conceivable that we change/adapt our strategy according to the context, the topic, our age or mood. However, this post supports the idea that a large part of our approach to the understanding of reality  can be simplified (again a regularity 🙂 by making explicit how we position ourselves in this range between ideological defense of our model and  acceptation of the confutation power of data. This trade off is well known in (frequentist) statistics where the process of estimating models from data is described in terms of the bias/variance trade-off. An estimator is a generic name for describing whatever function/algorithm bringing from data to an estimate: we could generalize here to any data/observation process returning a sort of model, regularization or belief.

A biased estimator is typically an estimator which is insensitive to data: his strength derives from the intrinsic robustness and coherence as well as his weaknesses might originate in the (in)sane attitude of disregarding data or incoming evidence. A variant estimator adapts rapidly and swiftly to data and observations but it can be easily criticized for its excessive instability.

So, nothing really new, but I feel sometimes delighted in  mapping attitudes, beliefs, ideologies to this trade-off (definitely another illusion of almighty regularity) or to characterize/explain differences in terms of this classification.

Bias/variance tradeoffs
On the biased side of the world On the variance side of the world
Right-wing Left-wing
Old Young
Parent Son
Idealism Empiricism
Self-confident Doubtful
Optimist Pessimist
Reformist Revolutionary
Woytila Bergoglio
German football team Italian football team
Classical art Modern art
Academia Université du peuple
Official press Social networks
European institutions Populism
Mainstream science Scientific breakthrough
Mathematics Statistics
Parametric statistics Nonparametric statistics
Expert driven Data driven
Faithful Playboy
Boring Charming
Bill Gates Steve Jobs
Long-term Short-term
Conventional Breakthrough
Official medicine Homeopathy
Apple Start-up
Book Webpage
Raiuno Raitre
Classic music Rock
Rock Rap
Risk-averse Risk-taker
Orthodox Unconventional
Dogma Unconventional
Aristotle Galileo
Formal informal
Descartes Popper
Manzoni Leopardi
Idealism Relativism
Truth Opinion
Linearity Nonlinearity
Simplicity Complexity
Certainty Doubt
Exploitation Exploration
Communist Populist
Automatic Conscious (?)
Heuristics Unbounded rationality

And now up to you…

PS. OK, but after all, is there a better side to stay? Hum, if you thing there is, welcome on the biased side ;-). If you think it depends, welcome on the variant side of the world.

From open data to open decisions

Open data is the next (or already current) big thing. Is it enough?

We should be doing data science, not (only) for the sake of having good models or nice predictions, but for providing quantified, data driven and assessed evidence to decision makers.

Is a good data science process enough? I would say no. Whatever is the evidence data scientists will be able to provide, such evidence will be affected (or better annotated) by uncertainty, risk, confidence intervals, variance. The role of the decision maker is not to take blindly the outcome of the data science process but to weight properly the risk and the costs.

Let us take an academic example: the doctor deciding whether to prescribe or not a treatment (e.g. a chemotherapy) to a patient. It is not only about the potential success (and risk) of the treatment. It is also about the cost of a false positive (prescribe a treatment and suffer only side effects) and false negative (avoid to prescribe it and deteriorate the patient state).

Eventually, it is the doctor who decides on the basis of

  1. a model (implicit in his knowledge or made explicit for instance in a statistical model)
  2. a measure of utility or cost (associated to false positives and false negatives)

whether it is more  beneficial for the patient to deliver or not a drug.

If the data that led to the statistical model are (or will presumably be) open and then available in the future for the sake of reproducibility and scientific validation, what about the final choice of the doctor (or more generally of the decision maker)?

Decision making is either irrational or rational. In the first case let us just cross our fingers. In the second case it would deserve a description, a documentation and (why not) an open sharing. I advocate that, like for open data, a comparable (or greater) effort should be deserved to provide tools, repositories and dashboards to edit, store and disseminate open decision models describing

  1. the decision making setting (date, author, target, expected impact)
  2. the evidence it relied on (informal knowledge, literature, statistical models)
  3. in case of statistical evidence, the (open) data  that were used for inferring it
  4. the utility (or cost) function used for the decision
  5. the decision making process, specifically how the material in points 2., 3. and 4. was used to deliver the final decision

And the confidentiality? The decision model (once formalized) could be kept confidential or have a restricted access if needed. The issue here is not really about the  disclosure of sensitive information but more about the degree of reproducibility of a decision. We can only learn from our (or other) errors. Think about political decision makers, democratically required to  document and safely store their decisions, and the possibility for a citizen of rerunning their decisions (once disclosed) in a near (or far) future.

The regularity gamble

All human knowledge relies on a gamble: “regularity exists“. Equivalently, only what is  regular (e.g. a pattern), or what seems to be regular, has the right of entering our knowledge and scientific heritage.

Note that regular does not mean necessarily something boring (a constant) or shallow or deterministic. We could find regularity in the behavior of a spring, as well as in the volatility of the stock market or in the way a complex dynamics evolves itself with time.

Nevertheless, humans start to consider that they know something only when they put that something within a pattern, a model, a map. All the rest is unknown (or not yet known) and deserves labels like noise, error, uncertainty.