Monday, November 24, 2014

The Curing Machine

VIRGINIA BEACH – Last year, the Nobel Prize in Medicine was awarded for a discovery that took 44 years to develop, and involved two different research teams. The breakthrough promises new kinds of diagnosis and therapy, but what if such insights could be developed by computers in minutes, rather than decades? The recent appearance of a new coronavirus, which has killed nine people in the United Kingdom and the Middle East, is a reminder that novel treatments are sometimes needed in a hurry.

With different modeling abstractions, it might be possible to build an artificial-intelligence system (AI) that could design new treatments. That system would suggest surprising, effective therapies, because it would understand disease in ways that are difficult for humans to imagine. The notion seems like science fiction: everyone knows that AI is not particularly clever.

In order to build a “curing machine” of this kind, at least one far-reaching innovation is needed: a better way of modeling entire systems, which would deliver new conceptual tools to both biology and computer science.

In both fields, the conventional approach has been reductionist, with problems modeled at the level of their most basic components. While this makes it possible to build “expert systems” that reason in narrow domains, or to design search engines that can find discrete facts, we are no closer to AI that reasons the way that we do – in and across multiple contexts, including time. AI cannot integrate information about seemingly distinct processes, such as chemical, physiological, and psychological events, or anticipate novel outcomes. We do these things over dinner.

Novelty is key. To build a curing machine, we would need a way for computers to assemble concepts so that unexpected arrangements could emerge.

In biology, there has been a similar reductionist approach, best exemplified by the Human Genome Project, which catalogued the molecular “recipe” for every aspect of the body, in order to discover how the most basic pieces interacted. Alas, the expected revolution in therapies is yet to occur.

Worldwide, total annual investment in biomedical research is about $110 billion. HIV alone attracts many billions per year. Yet, despite 25 years of intense investment in structural biology, we remain unable to move from understanding molecules to an understanding of whole systems. A model of the dynamics between multiple processes – chemical, physiological, and psychological – would lend new insight into how diseases operate.

Consider, for example, the sense of smell. The nasal cavity is unique, as the only place where brain cells (neurons) are directly exposed to the environment. If we model smell on local terms, we can trace how nasal neurons interact with scent particles, sending signals through a network of other neurons to the brain. We have good abstractions in structural biology to describe this.

But these neurons are also part of an adaptive, regenerative system. Sensory neurons die a programmed death; you literally have a new sense of smell every month. Modeling it requires the consideration of more than just local signaling. Neurons operate as a cooperative group: more arrive at the location than are needed for a simple replacement. In order to facilitate the replacement, the extras become part of a dialog with surrounding cells, and others deep in the brain, then sacrifice themselves. To explain why they voluntarily die, we would need a new system-level vocabulary.

It does not stop there. When your neurons are replaced, they change. If you are newly in love (or experience a trauma) and a scent is associated with it, you may develop a heightened sensitivity to that scent. Your physio-cognitive apparatus evolves.

This process interests researchers enormously, because it is one of only two contexts in which neural regeneration occurs. If it were understood, it could lead to cures for many crippling diseases, both neurological (like Parkinson’s and Alzheimer’s) and degenerative (including those, like cancer, associated with aging).

One way to understand how this higher-level coherence emerges is to study it in a more accessible form. As it happens, there is a naturally occurring information structure in which the needed behaviors are easier to observe and explain. That structure can be found in stories.

Stories are remarkable for the way that they allow us to comprehend new concepts, and continue to make sense of them, even when they involve numerous contexts and unexpected associations. We take the resulting narrative effects for granted: surprise endings, intriguing situations, and a compulsion to read or watch until the finish. If models of biology and AI included these dynamics, they would demonstrate how unexpected elements can emerge from an evolving coherence.

Indeed, incorporating this principle into both fields could result in new diagnostic capabilities and new forms of individualized treatment, with a different therapy designed for each person. An infection such as the coronavirus is currently countered with a vaccine tailored to block it. But what if there was a way to “tune” bodies to reject all infections? For example, there are a few cases of natural immunity to HIV, but we are unable to understand why.

The challenge in biomedical and computational research is to model the dynamics between diverse processes at the level of whole systems. Once we can do this, we are much more likely to develop AI tools that are capable of unexpected breakthroughs in understanding how the body supports and resists illness.

Read more from our "The Innovation Revolution" series.

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    1. CommentedSteven Calascione

      The perfect cure is a recipe recovered in the kitchen not in the labs of Big Pharma, which, with its annual trillion dollar R&D budget simply can't keep up with even the mind of the smallest protein molecule which can rearrange itself in 20 googol different ways (that's 20 followed by 100 zeros; figures provided by Bernt-Olaf Kuppers, 1990, p.11). Of course, a pharmacological cure can lead to a good thing; but it is not that good thing in itself.

    2. CommentedZsolt Hermann

      The last paragraph is worth repeating:
      "...The challenge in biomedical and computational research is to model the dynamics between diverse processes at the level of whole systems. Once we can do this, we are much more likely to develop AI tools that are capable of unexpected breakthroughs in understanding how the body supports and resists illness..."

      Moreover this paragraph could be extended way beyond medicine, into the realm of all the processes, events of our lives.
      We all exist in the same interconnected system, where all elements are fully dependent on each other.
      We are all fully integrated in each other within human society, and human society is fully integrated within all the still, vegetative, and animate levels of nature encompassing the whole Universe.
      But even if we simply zoom back into the present global crisis the reason nobody finds a solution for it is that we only look at symptoms, local, national problems without connecting them back to the system.
      And it is the same with medicine.
      Even this article is talking about curing already existing diseases, when we all know that the best cure is prevention.
      And prevention can only work if we know the whole system, we know what the role of each and every element is within the system, how they are supposed to interact, complement each other, and so on.
      Vast majority of the illnesses presently burdening, killing us are simply the result of our unnatural attitude, lifestyle, the way we go against the system we exist in.
      If humanity knew the vast natural system around and would adapt to its template, its laws, staying within the framework of the general balance and homeostasis, almost all of the current diseases would disappear completely.
      And the same would happen to the other problems, all the elements of the global crisis threatening to bury all of us underneath.
      Prevention starts with education, teaching each and every one of us the system, its laws and ways of adapting to it.
      This is where humanity should put all its efforts into, and then we do not even need a sci-fi AI, since we all would live life naturally, adapted to the system, in an effortless, healthy lifestyle.
      And this is not science fiction, this is not a utopia, but it is very much reality within our grasp.

        CommentedEdward Ponderer

        Good points Mr. Hermann -- only you don't go far enough. Forgive me for extending the "Sci-Fi" into what you are saying -- because I believe it can become the most powerful science that we've ever known.

        What I propose is that the very network you talk of -- an d on the human level, lets add some "white matter" to it with the internet and the intertwining of globalization (the rest of the system you mention is already quite well connected naturally). This whole need only become a full-fledged neural network by a low-level sense of mutual responsibility.

        The system can undergo something akin to Hebbean learning (Donald Hebb, 1949 introduced a learning algorithm for the unsupervised neural network. Per Hebb, "When an axom in cell A is near enough to excite cell B and repeatedly and persistently takes part in firing it, some growth process or metabolic change takes place in one or both cells such that A's efficiency in firing B is increased".

        Hebb's rule is that two cells with similar activations, the strength ("weight") of their interconnect should be increased, thus causing reinforcement of each other that promotes learning.

        This interconnect weighting system is mimic in general in nature in swarming/schooling/flocking behaviour (in AI -- "particle swarm optimization"), and would do so most equivalently within Humanity via a system of "round table" interfaces -- whether in physical proximity or remotely (via Internet or wireless).

        The needed jump start at the human-level to complete the system, would again be a hardware of a mutual-society valuing society that could be initiated by self-help "selling" of these environmental values by the media, and a global course of integral education.

        Admittedly the above is an oversimplification -- an idealized framework. However, so is basic Hebbean learning theory without introducing the "interface layer," etc. Similarly, the hopeless limitations of the early one-stage perceptrons as pointed out by Minsky and Papert (**Perceptrons: An Introduction to Computational Geometry**), were later bypassed by the unforeseen two-stage perceptron.

        I do believe that if we begin the focus on just integral education alone, we can start the ball rolling to a much brighter future. The presently insurmountable problems of today's humans could well become "child's play" adaption to the "artifical" (maybe not...) intelligence of a newborn holistic Humanity.