Entries Tagged as 'Future'

IBM’s Blue Brain and Simulated Level of Detail

Henry Markham calls IBM’s cat scale brain simulation a hoax. Markham claims that the simulation doesn’t have the 10,000+ differential equations needed to simulate the synapses with fidelity.  This argument is a version of the naturalistic fallacy – if Nature requires X to achieve a result, we will have to perform X when replicating the effect.

It is useful to think about the simulation’s level of detail (LOD) in terms of certain thresholds, from most detailed to least detailed:

  • Noise level: at this level (call it LODn) the lack of precision is on the order of the noise present in a biological brain.  It is not possible to distinguish the functioning of such a simulated brain from the functioning of a biological brain.
  • Functional level: at this level (call it LODf) the lack of precision is greater, but the result is functionally similar.  There may be some behavior changes, but the overall capabilities (e.g. “intelligence”) are similar.
  • Equivalence level: at this level (call it LODe) the precision is even lower, but this is compensated with  tweaks to the simulated physiology.  The result is equivalent in capabilities, although some characteristics may be very different.  For example, the retina can be replaced with a non-biological equivalent.

The computation power required for LODn > LODf > LODe.  There are likely order of magnitude differences between the levels.

If we consider a non-biological example – a digital computer, what does it take to simulate it?  It is obviously enough to simulate the logic function.  The Markham’s line of reasoning would seem to argue that we have to simulate the voltage gradients and charge movements in each transistor!

In the transistor case, LODn would involve simulating each transistor’s logic function.  LODf would involve an instruction set simulation (e.g. the QEMU emulator).  LODe would involve using the most convenient instruction set (e.g. x86) and recompiling any software.  Clearly, an LODe simulation is several orders of magnitude more efficient.

Markham fails to convince that his preferred level of simulation is required for LODn, never mind the other levels.

One way to find out what levels require is to actually run simulations and compare to physical neural matter.  The Blue Brain project aims to do that, although the results are not conclusive yet.  It would be good if more research was directed at comparing their simulation to a biological brain.  This would make the project more grounded.

The computation market becomes more liquid

The Register tells us that Amazon will auction their excess capacity.  We’re a couple of steps away from computation becoming a liquid commodity.  The next step is for a couple of additional providers to arise (Google?).  The step after that is for the APIs to be brought in sync by the providers or by a third party intermediary.

Cool advanced user-interface video

Very nice attention to detail on the user-interface widgets…


World Builder from Bruce Branit on Vimeo.

Simplified nanobot anti-aging solution

[ I've started this article 2 years ago - will post it now even though I feel it is incomplete. ]

I’d like to propose a somewhat simplified approach to eliminate aging, given early-stage molecular robots and following the SENS approach to aging.

Results in this approach depend on equal amounts of creation of new cells and destruction of old cells to exponentially reduce the amount of aging related defects in the body over time.

Aubrey de Grey proposes 7 mechanisms for aging, which are believed to be comprehensive: [Read more →]

Chris Phoenix on Nanotech Fast Takeoff

Chris Phoenix is writing an interesting series of articles over at CRN about the dynamics of the development of molecular engineering.

His thesis, as far as I understand it after the first three articles, is that we’re likely to see a fast takeoff, because it’s easy to achieve excellent results after you’ve achieved good enough.

One example is error rate – going past an adequate error rate to a superlative one just requires additional purification / error correcting steps. Other things that may improve very quickly once a workable solution is found are reaction rates – which are exponentially dependent on positional accuracy / stiffness.

Cool virus infection and assembly videos

Fun video with accurate structures and mechanism, although motions are not realistic (Brownian motion is not goal directed). The third video is the most involved.

H/T: Eric Drexler

Human Scale Memory Timeline Calculator

I have previously mentioned my estimator for when human scale computation power will be available. I have since realized that the bottleneck might be memory rather than computation. I’ve created a similar estimator for memory.

Although we may achieve human level compute power in 2014, it looks like memory capacity will lag by another 6 years, assuming low estimates. With high estimates, compute power is available in 2020 and memory capacity will lag by 5 years after that, to 2025.

However, if Flash memory or similar technology will do the trick, a factor of 4 in cost reduction will advanced the timeline by about 4 years.

Brian Wang’s things to watch for 2009

Brian Wang writes about technologies to watch for 2009, including Memristors, high speed networking, optical computing and quantum computing.

Bill Joy for CTO of the USA?

Apparently, John Doerr recommended Bill Joy yesterday as the USA CTO to Barak Obama.

Misguided relinquishment anyone?

Excerpt from Joy’s article “Why the future doesn’t need us”:

These possibilities are all thus either undesirable or unachievable or both. The only realistic alternative I see is relinquishment: to limit development of the technologies that are too dangerous, by limiting our pursuit of certain kinds of knowledge.

This could be pretty bad.

H/T: slashdot

Update: Slashdot reports that Bill Joy is not in the running anymore. Haven’t looked at the two that are, yet.

Whole Brain Emulation Roadmap

A very detailed roadmap written by Anders Sandberg and Nick Bostrom and published by the Future of Humanity Institute / University of Oxford. Lots of nice complexity estimates for different emulation detail levels. Seems like 2020 will be an interesting year.

H/T: Next Big Future

2008 Singularity Summit

The 2008 Singularity Summit is coming up on Saturday. I’ve been helping out on the web site and payment processing. Should be interesting.

Making choices

Brian Wang posts a very cogent article about how global forces exist which delay progress and cause global harm. This includes the use of coal instead of cleaner energy (including nuclear), causing hundreds of thousands of deaths per year. It also includes corruption and violence preventing distribution of food and medicine and preventing economic growth.

I find it amazing that the human race spends on the order of a million dollars per year on molecular manufacturing when the potential impacts are measured in trillions. In other words, the race is spending less than one ten millionth of its efforts on this technology. The shortfall is mostly due to political issues, such as a lack of interdisciplinary thinking.

VCs at the Singularity Summit

Steven Jurveston and Peter Thiel gave presentations today. I found these to be very interesting perspectives.

Thiel’s theorized
that the increase in frequency and magnitude of boom and bust cycles are a prelude to the Singularity, which would be a sustained boom driven by radical increases in productivity. Something to watch.

Jurveston’s main thesis was that AI created by evolutionary algorithms would have a strong competitive advantage over attempts to use traditional design. Some of the advantages include lack of brittleness and speed of implementation. Once you go down the evolution path, you diverge from the design path, because reverse engineering an evolved system is either very difficult or impossible. One idea I had while listening to this is that you can evolve subsystems with well defined I/O and connect the subsystems into a designed overall architecture.

Presentation by Artificial Development

Artificial Development is giving a presentation at the Singularity Summit about their CCortex and CorticalDB products. Seems like a full featured product to create biologically plausible neural networks/brain maps (CorticalDB) and simulate the resulting network (CCortex). They claim biological high fidelity simulation, 8 bit action potentials, etc. .

The claim of up to 100 million neurons seems pretty aggressive. Not sure why they would be so much ahead of the IBM effort using x86 CPUs, even if they have thousands of them.

CCortex will be open-source.

They claim multiple levels of simulation: 1. detailed equations 2. “estimation of action potentials” 3. a proprietary method.

At the Singularity Summit

The most memorable morning session at the Singularity Summit 2007 was the inimitable Eliezer Yudkowsky’s.

He talked about three different schools of thought about the Singularity:

Vingean – where prediction becomes impossible
Accelerationist – exponential technological change
Greater than Human Inteligence – in a positive feedback loop

His thesis was that the three schools reinforce but also contradict each-other.

Another good point Eliezer makes is that advances in scientific knowledge and algorithms reduce the threshold for the Singularity.

Many-worlds Immortality and the Simulation Argument

An alternative to the simulation argument:

Nick Bostrom’s Simulation Argument argues that at least one of the following must be true:

  • the human species is very likely to go extinct before reaching a “posthuman” stage
  • any posthuman civilization is extremely unlikely to run a significant number of simulations of their evolutionary history
  • or we are almost certainly living in a computer simulation

However, I see other possibilities. Assumptions:

  • The strong many-worlds theory is correct (i.e. all consistent mathematical systems exist as universes, a.k.a “everything exists”)
  • The many-worlds immortality theory is correct (i.e. for every conscious state there is at least one smooth continuation of that state in the many-worlds)

Given these assumptions, it doesn’t matter if we are in a simulation because our conscious state exists in many simulations and many non-simulated worlds that look identical to us (but are different in imperceptible ways). Even if all the simulations stopped, there would still be a continuation of our conscious state in a non-simulated world consistent with our observations to date.

Further, it seems that there are more non-simulated worlds than simulated worlds. This is because there are many ways a mathematical model can exist so that it cannot be formulated in a finite way, and therefore not simulatable by an intelligent entity. It might even be that simulatable world are of measure zero in the many-worlds.

Further out ideas:

A fascinating related idea is the Egan Jump as described in the book Permutation City. The idea is to jump to another world in the many-worlds by simulating the genesis of a new universe. In this universe you code yourself into the initial conditions, and design the rules so that you end up as an upload in the substrate of the new universe. Because that universe will continue as it’s own mathematical model, your conscious state will continue in that universe, branching off your original self.

Yet another, more distantly related idea is that the peculiarities of our universe (quantum physics, large amounts of empty space) are in a sense an error correcting mechanism. Because any perturbation of a world is also a world, the result is quite chaotic and inhospitable to meaningful life. The structure we see around us with large aggregates “average out” the chaos. This leads to a stable environment as required for conscious observers to arise.

An insufficient present

I strongly identify with this concept.

Mouse brain simulated at 1/10 of real-time

Update: the BlueGene/L instance used here is only 1/32 of the size of the one deployed at LLNL, so we are still within the high bound after all. On the other hand, it remains to be seen how accurate the model is compared to a functional neuron.


Dharmendra S Modha posts an article about a recent result presented at CoSyNe 2007.

We deployed the simulator on a 4096-processor BlueGene/L supercomputer with 256 MB per CPU. We were able to represent 8,000,000 neurons (80% excitatory) and 6,300 synapses per neuron in the 1 TB main memory of the system. Using a synthetic pattern of neuronal interconnections, at a 1 ms resolution and an average firing rate of 1 Hz, we were able to run 1s of model time in 10s of real time!

This is excellent news, since it will now be possible to figure out what biological modeling aspects are important to functionality.

Since the human brain has 100 billion neurons, this represents 1/10,000 of a human brain. The computer was a $100 million BlueGene/L. So an improvement of 10,000,000 is required in order to model a human brain for $1M in real time.

However, the BlueGene/L is two years old, and it is about 20 times less efficient compared to commodity hardware (based on a quoted 360 teraflops). So the real improvement required is only around 100,000.

Based on this data, the human brain requires 10 Exa CPS, one order of magnitude above the high estimate use in my calculator. Human equivalent for $1M would be available around the year 2023.

Hardware specifically suitable for this application may bring this back to 1 Exa CPS and pull this back to the year 2020.

Nano and lightyears in context

Check out this interactive “powers-of-ten” flash presentation from Nikon. Good for some perspective…

Make sure your browser is full screen or you may miss the controls at the bottom.

Hat tip to Nanodot.

Advances in top-down nanotech

Brian Wang has a good review article about recent advances in top-down nanotech and some projections. Maybe top-down will meet bottom-up in 10 years?