(credit: Comp. Cog. Neurosci Lab/ Olaf Sporns, Indiana Univ.)

A proposal alluded to by President Obama in his State of the Union address to construct a dynamic “functional connectome” Brain Activity Map (BAM) would leverage current progress in neuroscience, synthetic biology, and nanotechnology to develop a map of each firing of every neuron in the human brain—a hundred billion neurons sampled on millisecond time scales. Although not the intended goal of this effort, a project on this scale, if it is funded, should also indirectly advance efforts to develop artificial intelligence and atomically precise manufacturing. In his blog, Robert L. Blum provides an excellent overview and brief introduction. From “BAM: Brain Activity Map: Every Spike from Every Neuron“:

A recent research proposal called BAM for Brain Activity Map Project generated much excitement. (The BAM proposal, published in Neuron in June 2012 is online, and an earlier draft with far greater detail is also online.)

(Addendum: 18 Feb 2013: I started drafting this story in Nov, 2012. Today it was headline news when it was made public that THIS is the very proposal that President Obama alluded to in his recent State of the Union address. See John Markoff’s NY Times piece. NIH is drafting a 3 billion dollar, 10 year proposal to fund this project. Also see this 25 Feb 2013 NY Times follow-up by Markoff.) …

The essence of the BAM proposal is to create the technology over the coming decade to be able to record every spike from every neuron in the brain of a behaving organism. While this notion seems insanely ambitious, coming from a group of top investigators, the paper deserves scrutiny. At minimum it shows what might be achieved in the future by the combination of nanotechnology and neuroscience. …

The Neuron article cited by Blum argues that in addition to breakthroughs in basic science with large medical and economic benefits, the BAM project will advance technology in terms of important general capabilities.

Many technological breakthroughs are bound to arise from the BAM Project, as it is positioned at the convergence of biotechnology and nanotechnology. These new technologies could include optical techniques to image in 3D; sensitive, miniature, and intelligent nanosystems for fundamental investigations in the life sciences, medicine, engineering, and environmental applications; capabilities for storage and manipulation of massive data sets; and development of biologically inspired, computational devices.

I think the emphasis on nanosystems of nanodevices integrated to provide complex functions is very important, even if many or most of those devices will, in the beginning, not be atomically precise. The more detailed description of the BAM proposal cited by Blum above hints at how nanoparticle-based sensors could be developed to noninvasively provide micrometer-scale spatial resolution and millisecond-scale temporal resolution to groups of millions of neurons deep inside the brain of a living, active animal (or human). The mention combining semiconductor quantum dots and nanodiamonds with organic nanostructures to functionalize them, so that they may be directed to and embedded in neural membranes to monitor synapses. In addition, nanotubes or nanowires could be developed to deliver photons to specific locations, or collect or release specific chemicals. Further, they suggest developing graphene into membrane patches for detailed monitoring of neurons. Taken together, the requirements for this ambitious project entail the need to develop a variety of nanoparticles for specific applications, and then integrating multifunctional nanoprobes, nanoparticles, and nanodevices into large functional systems, and producing such nanosystems en masse.

In his NY Times report John Markoff notes the possible effect of this project on the development of artificial intelligence: “Moreover, the project holds the potential of paving the way for advances in artificial intelligence.” Indeed, the information to be provided by BAM about how circuits of thousands or millions of neurons work should advance Ray Kurzweil’s program of reverse engineering the human brain to develop artificial general intelligence, as described in his new book How to Create a Mind: The Secret of Human Thought Revealed.

The next best thing to large program to develop molecular manufacturing is a large program aimed at other worthy and useful goals that also makes heavy use of nanotechnology and may promote some of the same or similar enabling technologies that will lead toward productive nanosystems.
—James Lewis, PhD

Christine Peterson

Foresight Co-Founder and Past President Christine Peterson is interviewed on the Singularity Weblog in a 47-minute tour that covers nanotechnology, the founding of the Foresight Institute, her work on personal life extension through Health Activator, open source, and the Technological Singularity. “Christine Peterson on Singularity 1 on 1: Join Us to Push the Future in a Positive Direction“:

During my Singularity 1 on 1 interview with Christine Peterson we discuss a variety of topics such as: how she got interested in nanotechnology and the definition thereof; how, together with Eric Drexler, she started the Foresight Institute for Nanotechnology; her interest in life extension; Dr. Drexler’s seminal book Engines of Creation; cryonics and chemical brain preservation; 23andMe and other high- and low-tech tips for improved longevity; whether we should fear nanotechnology or not; the 3 most exciting promises of nanotech; women in technology; coining the term “open source” and using Apple computers; the technological singularity and her take on it…

Hear Christine discuss some challenges while presenting an essentially optimistic message—a wonderful future is coming from science and technology over the next few decades—a future that encourages everyone to get involved.
—James Lewis, PhD

Bartosz A. Grzybowski (credit: Northwestern University)

From the standpoint of the development of advanced nanotechnology, the most useful type of machine intelligence is AI that facilitates scientific and engineering design. A computer network described in this news release from Northwestern University could greatly facilitate chemical synthesis useful for the development of molecular manufacturing. From “Northwestern Scientists Create Chemical Brain“, by Megan Fellman:

Northwestern University scientists have connected 250 years of organic chemical knowledge into one giant computer network — a chemical Google on steroids. This “immortal chemist” will never retire and take away its knowledge but instead will continue to learn, grow and share.

A decade in the making, the software optimizes syntheses of drug molecules and other important compounds, combines long (and expensive) syntheses of compounds into shorter and more economical routes and identifies suspicious chemical recipes that could lead to chemical weapons.

“I realized that if we could link all the known chemical compounds and reactions between them into one giant network, we could create not only a new repository of chemical methods but an entirely new knowledge platform where each chemical reaction ever performed and each compound ever made would give rise to a collective ‘chemical brain,’” said Bartosz A. Grzybowski, who led the work. “The brain then could be searched and analyzed with algorithms akin to those used in Google or telecom networks.”

Called Chematica, the network comprises some seven million chemicals connected by a similar number of reactions. A family of algorithms that searches and analyzes the network allows the chemist at his or her computer to easily tap into this vast compendium of chemical knowledge. And the system learns from experience, as more data and algorithms are added to its knowledge base.

Details and demonstrations of the system are published in three back-to-back papers in the Aug. 6 issue of the journal Angewandte Chemie. …

In the Angewandte paper titled “Parallel Optimization of Synthetic Pathways Within the Network of Organic Chemistry,” the researchers have demonstrated algorithms that find optimal syntheses leading to drug molecules and other industrially important chemicals.

“The way we coded our algorithms allows us to search within a fraction of a second billions of chemical syntheses leading to a desired molecule,” Grzybowski said. “This is very important since within even a few synthetic steps from a desired target the number of possible syntheses is astronomical and clearly beyond the search capabilities of any human chemist.”

Chematica can test and evaluate every possible synthesis that exists, not only the few a particular chemist might have an interest in. In this way, the algorithms find truly optimal ways of making desired chemicals. …

Another important area of application is the shortening of synthetic pathways into the so-called “one-pot” reactions. One of the holy grails of organic chemistry has been to design methods in which all the starting materials could be combined at the very beginning and then the process would proceed in one pot — much like cooking a stew — all the way to the final product.

The Northwestern researchers detail how this can be done in the Angewandte paper titled “Rewiring Chemistry: Algorithmic Discovery and Experimental Validation of One-Pot Reactions in the Network of Organic Chemistry.”

The chemists have taught their network some 86,000 chemical rules that check — again, in a fraction of a second — whether a sequence of individual reactions can be combined into a one-pot procedure. Thirty predictions of one-pot syntheses were tested and fully validated. Each synthesis proceeded as predicted and had excellent yields. …

The third area of application is the use of the Chematica network approach for predicting and monitoring syntheses leading to chemical weapons. This is reported in the Angewandte paper titled “Chemical Network Algorithms for the Risk Assessment and Management of Chemical Threats.”

“Since we now have this unique ability to scrutinize all possible synthetic strategies, we also can identify the ones that a potential terrorist might use to make a nerve gas, an explosive or another toxic agent,” Grzybowski said.

Algorithms known from game theory first are applied to identify the strategies that are hardest to detect by the federal government — the use of substances, for example, such as kitchen salt, clarifiers, grain alcohol and a fertilizer, all freely available from a local convenience store. Characteristic combinations of seemingly innocuous chemicals, such as this example, are red flags. …

It will be really interesting to see not only how the system improves as it learns from experience, but for which other areas of science and technology this approach might prove useful.
—James Lewis, PhD

The Nanocentury: Bringing Digital Control to the Physical World. Throughout human history, our species has worked to control the matter surrounding us — building larger and larger, smaller and smaller, more and more precise. The payoffs from these efforts are starting to accelerate, as we move toward the ability to build physical objects with atomic precision, just as we program information with bit-level precision. What will this mean for our bodies, our minds, our families, our nations, our culture, our planet? There’s good news and bad news, but one thing’s clear — we are in for a wild ride!

Hope to see you there!  (Sadly, I don’t think they are webcasting or video recording.) —Christine Peterson

This schematic shows how the various elements assemble themselves into mechanically interlocked molecules (Credit: University of Windsor).

Canadian chemists have induced a metal-organic framework to self-assemble and function as a molecular wheel on an axle in a solid state material. From a University of Windsor news article “Chemists break new ground in molecular machine research“:

A graduate student and his team of researchers have turned the chemistry world on its ear by becoming the first ever to prove that tiny interlocked molecules can function inside solid materials, laying the important groundwork for the future creation of molecular machines.

“Until now, this has only ever been done in solution,” explained Chemistry & Biochemistry PhD student Nick Vukotic, lead author on a front page article recently published in the June issue of the journal Nature Chemistry [abstract]. “We’re the first ones to put this into a solid state material.”

The material Vukotic is referring to is UWDM-1, or University of Windsor Dynamic Material, a powdery substance that the team made which contains rotaxane molecules and binuclear copper centers. The rotaxane molecules, which resemble a wheel around the outside of an axle, were synthesised in their lab. The group found that heating of these rotaxane molecules with a copper source resulted in the formation of a crystalline material which contained structured arrangement of the rotaxane molecules, spaced out by the binuclear copper centers.

“Basically, they self-assemble in to this arrangement,” said Vukotic, who works under the tutelage of chemistry professor Steve Loeb. Other team members include professor Rob Schurko, and post-doctoral fellows Kristopher Harris and Kelong Zhu.

Heating the material causes the wheels to rapidly rotate around the axles, while cooling the material causes the wheels to stop, he said. The entire process can’t be viewed with a microscope, so the motion was confirmed in Dr. Schurko’s lab using a process called nuclear magnetic resonance spectroscopy.

“You can actually measure the motion and you can do it unambiguously by placing an isotopic tag on the ring,” explained Dr. Harris, who helped oversee that verification process.

Although the team admits their findings are still very much a proof of principle, they insist that molecules in solid materials can be manipulated to form switches and machines. This could be extremely significant and could find future applications in the fields of computer storage, data transfer or controlling the electronic properties of materials at the molecular level. …

A key component of exploratory engineering studies for molecular manufacturing or productive nanosystems is the ability to model molecular systems reliably. Modeling motions of molecules in solution is very difficult. A method to produce molecular machines in a solid state environment is a huge step forward. It will be interesting to see just how complex a system of machines can be built in the solid state using these methods, and what can be done with them.
—James Lewis, PhD

Christine Peterson, Foresight Co-Founder & Past President

August 8, 2012 Stanford University, Stanford, CA USA

Exploring the frontiers of knowledge and imagination, fostering interdisciplinary networking

Foresight Institute co-founder and Past President Christine Peterson will speak at the Leonardo Art/Science Evening Rendezvous of August 2012, chaired by Piero Scaruffi. Her talk is scheduled from 8:30-8:55pm and is titled “The Nanocentury: Bringing Digital Control to the Physical World”.

Image courtesy of The University of Sheffield, project leader Professor John Rodenburg, of the University of Sheffield´s Department of Electronic and Electrical Engineering

Although not visible at this magnification, the high resolution image in the open access research publication clearly shows the gold atoms, with a spacing of 0.236 nm between atomic planes.

In his famous visionary 1959 talk in which he described molecular machines building with atomic precision, Feynman suggested that if physicists wanted to help biologists, they should improve the electron microscope by a hundred times to see individual atoms. Many improvements have been made over the years, such as aberration-correcting electron lenses, but a recent contribution from the University of Sheffield has succeeded in showing for the first time that it is possible to recover the complex exit wave from a diffraction image at atomic resolution, over a wide field of view, and using low-energy electrons. A hat tip to ScienceDaily for reprinting this University of Sheffield news release “Scientists revolutionise electron microscope“:

For over 70 years, transmission electron microscopy (TEM), which `looks through´ an object to see atomic features within it, has been constrained by the relatively poor lenses which are used to form the image.

The new method, called electron ptychography, dispenses with the lens and instead forms the image by reconstructing the scattered electron-waves after they have passed through the sample using computers.

Scientists involved in the scheme consider their findings to be a `first step´ in a `completely new epoch of electron imaging´. The process has no fundamental experimental boundaries and it is thought it will transform sub-atomic scale transmission imaging.

Project leader Professor John Rodenburg, of the University of Sheffield´s Department of Electronic and Electrical Engineering, said: “To understand how material behaves, we need to know exactly where the atoms are. This approach will enable us to look at how atoms sit next to one another in a solid object as if we´re holding them in our hands.

“We´ve shown we can improve upon the resolution limit of an electron lens by a factor of five. An extension of the same method should reach the highest resolution transmission image ever obtained; about one tenth of an atomic diameter. No longer does TEM have to be bound by the paradigm of the lens, its Achilles´ heel since its invention in 1933.” …

Professor Rodenburg added: “We measure diffraction patterns rather than images. What we record is equivalent to the strength of the electron, X-ray or light waves which have been scattered by the object – this is called their intensity. However, to make an image, we need to know when the peaks and troughs of the waves arrive at the detector – this is called their phase.

“The key breakthrough has been to develop a way to calculate the phase of the waves from their intensity alone. Once we have this, we can work out backwards what the waves were scattered from: that is, we can form an aberration-free image of the object, which is much better than can be achieved with a normal lens.

“A typical electron or X-ray microscope image is about one hundred times more blurred than the theoretical limit defined by the wavelength. In this project, the eventual aim is to get the best-ever pictures of individual atoms in any structure seen within a three-dimensional object.” …

The research was published in Nature Communications as an open access article “Ptychographic electron microscopy using high-angle dark-field scattering for sub-nanometre resolution imaging“. Although the image in the press release (above) of the gold particles is too low a magnification to see the rows of atoms, the high resolution version of the image in the research paper clearly shows the gold atoms, with a spacing of 0.236 nm between atomic planes. Additional information is available on the authors’ project web site “Welcome to the ΠΦ project“.
—James Lewis

Hi folks — Join fellow Foresight members, self-trackers, self-experimenters, and health geeks to explore the latest ways to optimize your body and brain & slow aging.

Foresight is a partner on the conference, so we can use discount code NANODOT for $100 off.

If you plan to register but haven’t quite gotten around to it, now is the time. Tomorrow, March 14, is the cutoff for early rate registration, and also the deadline for the hotel room discount (see Logistics page).

Not sure? Check out the program, look through the blog posts, and see the list of participants on the registration page. Lots of Foresight names on that list.

Based on feedback from last time, I can say for certain that you’ll be healthier if you join us for this meeting. Hope to meet you there! –Christine Peterson, Conference Chairman & Foresight co-founder

A SUITE of artificial intelligence algorithms may become the ultimate chemistry set. Software can now quickly predict a property of molecules from their theoretical structure. Similar advances should allow chemists to design new molecules on computers instead of by lengthy trial-and-error.

Our physical understanding of the macroscopic world is so good that everything from bridges to aircraft can be designed and tested on a computer. There’s no need to make every possible design to figure out which ones work. Microscopic molecules are a different story. “Basically, we are still doing chemistry like Thomas Edison,” says Anatole von Lilienfeld of Argonne National Laboratory in Lemont, Illinois.

The chief enemy of computer-aided chemical design is the Schrödinger equation. In theory, this mathematical beast can be solved to give the probability that electrons in an atom or molecule will be in certain positions, giving rise to chemical and physical properties.

But because the equation increases in complexity as more electrons and protons are introduced, exact solutions only exist for the simplest systems: the hydrogen atom, composed of one electron and one proton, and the hydrogen molecule, which has two electrons and two protons. …

The researchers developed a machine learning model to calculate the atomisation energy—the energy of all the bonds holding a molecule together and applied it to a database of 7165 small organic molecules of known structure and atomization energy and containing up to seven atoms of carbon, nitrogen, oxygen, or sulfur, plus the number of hydrogen atoms necessary to saturate the bonds. These molecules had atomization energies ranging from 800 to 2000 kcal/mol. The model was trained on a subset of 1000 compounds and then used to calculate the energies of the remaining molecules in the database. The results showed a mean error of only 9.9 kcal/mol, comparable to the accuracy of methods based upon the Schrödinger equation, but the computations were done in milliseconds rather than hours. The authors suggest that extensions of their approach might permit rational molecule design or molecular dynamics calculations of systems of atoms undergoing chemical reactions.

The research was published in Physical Review Letters [abstract]. A free full text preprint is available.
—James Lewis