Tag Archives: Transcendence

I found it at the movies: a commentary on/review of “Films from the Future”

Kudos to anyone who recognized the reference to Pauline Kael (she changed film criticism forever) and her book “I Lost it at the Movies.” Of course, her book title was a bit of sexual innuendo, quite risqué for an important film critic in 1965 but appropriate for a period (the 1960s) associated with a sexual revolution. (There’s more about the 1960’s sexual revolution in the US along with mention of a prior sexual revolution in the 1920s in this Wikipedia entry.)

The title for this commentary is based on an anecdote from Dr. Andrew Maynard’s (director of the Arizona State University [ASU] Risk Innovation Lab) popular science and technology book, “Films from the Future: The Technology and Morality of Sci-Fi Movies.”

The ‘title-inspiring’ anecdote concerns Maynard’s first viewing of ‘2001: A Space Odyssey, when as a rather “bratty” 16-year-old who preferred to read science fiction, he discovered new ways of seeing and imaging the world. Maynard isn’t explicit about when he became a ‘techno nerd’ or how movies gave him an experience books couldn’t but presumably at 16 he was already gearing up for a career in the sciences. That ‘movie’ revelation received in front of a black and white television on January 1,1982 eventually led him to write, “Films from the Future.” (He has a PhD in physics which he is now applying to the field of risk innovation. For a more detailed description of Dr. Maynard and his work, there’s his ASU profile webpage and, of course, the introduction to his book.)

The book is quite timely. I don’t know how many people have noticed but science and scientific innovation is being covered more frequently in the media than it has been in many years. Science fairs and festivals are being founded on what seems to be a daily basis and you can now find science in art galleries. (Not to mention the movies and television where science topics are covered in comic book adaptations, in comedy, and in standard science fiction style.) Much of this activity is centered on what’s called ’emerging technologies’. These technologies are why people argue for what’s known as ‘blue sky’ or ‘basic’ or ‘fundamental’ science for without that science there would be no emerging technology.

Films from the Future

Isn’t reading the Table of Contents (ToC) the best way to approach a book? (From Films from the Future; Note: The formatting has been altered),

Table of Contents
Chapter One
In the Beginning 14
Beginnings 14
Welcome to the Future 16
The Power of Convergence 18
Socially Responsible Innovation 21
A Common Point of Focus 25
Spoiler Alert 26
Chapter Two
Jurassic Park: The Rise of Resurrection Biology 27
When Dinosaurs Ruled the World 27
De-Extinction 31
Could We, Should We? 36
The Butterfly Effect 39
Visions of Power 43
Chapter Three
Never Let Me Go: A Cautionary Tale of Human Cloning 46
Sins of Futures Past 46
Cloning 51
Genuinely Human? 56
Too Valuable to Fail? 62
Chapter Four
Minority Report: Predicting Criminal Intent 64
Criminal Intent 64
The “Science” of Predicting Bad Behavior 69
Criminal Brain Scans 74
Machine Learning-Based Precognition 77
Big Brother, Meet Big Data 79
Chapter Five
Limitless: Pharmaceutically-enhanced Intelligence 86
A Pill for Everything 86
The Seduction of Self-Enhancement 89
Nootropics 91
If You Could, Would You? 97
Privileged Technology 101
Our Obsession with Intelligence 105
Chapter Six
Elysium: Social Inequity in an Age of Technological
Extremes 110
The Poor Shall Inherit the Earth 110
Bioprinting Our Future Bodies 115
The Disposable Workforce 119
Living in an Automated Future 124
Chapter Seven
Ghost in the Shell: Being Human in an
Augmented Future 129
Through a Glass Darkly 129
Body Hacking 135
More than “Human”? 137
Plugged In, Hacked Out 142
Your Corporate Body 147
Chapter Eight
Ex Machina: AI and the Art of Manipulation 154
Plato’s Cave 154
The Lure of Permissionless Innovation 160
Technologies of Hubris 164
Superintelligence 169
Defining Artificial Intelligence 172
Artificial Manipulation 175
Chapter Nine
Transcendence: Welcome to the Singularity 180
Visions of the Future 180
Technological Convergence 184
Enter the Neo-Luddites 190
Techno-Terrorism 194
Exponential Extrapolation 200
Make-Believe in the Age of the Singularity 203
Chapter Ten
The Man in the White Suit: Living in a Material World 208
There’s Plenty of Room at the Bottom 208
Mastering the Material World 213
Myopically Benevolent Science 220
Never Underestimate the Status Quo 224
It’s Good to Talk 227
Chapter Eleven
Inferno: Immoral Logic in an Age of
Genetic Manipulation 231
Decoding Make-Believe 231
Weaponizing the Genome 234
Immoral Logic? 238
The Honest Broker 242
Dictating the Future 248
Chapter Twelve
The Day After Tomorrow: Riding the Wave of
Climate Change 251
Our Changing Climate 251
Fragile States 255
A Planetary “Microbiome” 258
The Rise of the Anthropocene 260
Building Resiliency 262
Geoengineering the Future 266
Chapter Thirteen
Contact: Living by More than Science Alone 272
An Awful Waste of Space 272
More than Science Alone 277
Occam’s Razor 280
What If We’re Not Alone? 283
Chapter Fourteen
Looking to the Future 288
Acknowledgments 293

The ToC gives the reader a pretty clue as to where the author is going with their book and Maynard explains how he chose his movies in his introductory chapter (from Films from the Future),

“There are some quite wonderful science fiction movies that didn’t make the cut because they didn’t fit the overarching narrative (Blade Runner and its sequel Blade Runner 2049, for instance, and the first of the Matrix trilogy). There are also movies that bombed with the critics, but were included because they ably fill a gap in the bigger story around emerging and converging technologies. Ultimately, the movies that made the cut were chosen because, together, they create an overarching narrative around emerging trends in biotechnologies, cybertechnologies, and materials-based technologies, and they illuminate a broader landscape around our evolving relationship with science and technology. And, to be honest, they are all movies that I get a kick out of watching.” (p. 17)

Jurassic Park (Chapter Two)

Dinosaurs do not interest me—they never have. Despite my profound indifference I did see the movie, Jurassic Park, when it was first released (someone talked me into going). And, I am still profoundly indifferent. Thankfully, Dr. Maynard finds meaning and a connection to current trends in biotechnology,

Jurassic Park is unabashedly a movie about dinosaurs. But it’s also a movie about greed, ambition, genetic engineering, and human folly—all rich pickings for thinking about the future, and what could possibly go wrong. (p. 28)

What really stands out with Jurassic Park, over twenty-five years later, is how it reveals a very human side of science and technology. This comes out in questions around when we should tinker with technology and when we should leave well enough alone. But there is also a narrative here that appears time and time again with the movies in this book, and that is how we get our heads around the sometimes oversized roles mega-entrepreneurs play in dictating how new tech is used, and possibly abused. These are all issues that are just as relevant now as they were in 1993, and are front and center of ensuring that the technologyenabled future we’re building is one where we want to live, and not one where we’re constantly fighting for our lives.  (pp. 30-1)

He also describes a connection to current trends in biotechnology,

De-Extinction

In a far corner of Siberia, two Russians—Sergey Zimov and his son Nikita—are attempting to recreate the Ice Age. More precisely, their vision is to reconstruct the landscape and ecosystem of northern Siberia in the Pleistocene, a period in Earth’s history that stretches from around two and a half million years ago to eleven thousand years ago. This was a time when the environment was much colder than now, with huge glaciers and ice sheets flowing over much of the Earth’s northern hemisphere. It was also a time when humans
coexisted with animals that are long extinct, including saber-tooth cats, giant ground sloths, and woolly mammoths.

The Zimovs’ ambitions are an extreme example of “Pleistocene rewilding,” a movement to reintroduce relatively recently extinct large animals, or their close modern-day equivalents, to regions where they were once common. In the case of the Zimovs, the
father-and-son team believe that, by reconstructing the Pleistocene ecosystem in the Siberian steppes and elsewhere, they can slow down the impacts of climate change on these regions. These areas are dominated by permafrost, ground that never thaws through
the year. Permafrost ecosystems have developed and survived over millennia, but a warming global climate (a theme we’ll come back to in chapter twelve and the movie The Day After Tomorrow) threatens to catastrophically disrupt them, and as this happens, the impacts
on biodiversity could be devastating. But what gets climate scientists even more worried is potentially massive releases of trapped methane as the permafrost disappears.

Methane is a powerful greenhouse gas—some eighty times more effective at exacerbating global warming than carbon dioxide— and large-scale releases from warming permafrost could trigger catastrophic changes in climate. As a result, finding ways to keep it in the ground is important. And here the Zimovs came up with a rather unusual idea: maintaining the stability of the environment by reintroducing long-extinct species that could help prevent its destruction, even in a warmer world. It’s a wild idea, but one that has some merit.8 As a proof of concept, though, the Zimovs needed somewhere to start. And so they set out to create a park for deextinct Siberian animals: Pleistocene Park.9

Pleistocene Park is by no stretch of the imagination a modern-day Jurassic Park. The dinosaurs in Hammond’s park date back to the Mesozoic period, from around 250 million years ago to sixty-five million years ago. By comparison, the Pleistocene is relatively modern history, ending a mere eleven and a half thousand years ago. And the vision behind Pleistocene Park is not thrills, spills, and profit, but the serious use of science and technology to stabilize an increasingly unstable environment. Yet there is one thread that ties them together, and that’s using genetic engineering to reintroduce extinct species. In this case, the species in question is warm-blooded and furry: the woolly mammoth.

The idea of de-extinction, or bringing back species from extinction (it’s even called “resurrection biology” in some circles), has been around for a while. It’s a controversial idea, and it raises a lot of tough ethical questions. But proponents of de-extinction argue
that we’re losing species and ecosystems at such a rate that we can’t afford not to explore technological interventions to help stem the flow.

Early approaches to bringing species back from the dead have involved selective breeding. The idea was simple—if you have modern ancestors of a recently extinct species, selectively breeding specimens that have a higher genetic similarity to their forebears can potentially help reconstruct their genome in living animals. This approach is being used in attempts to bring back the aurochs, an ancestor of modern cattle.10 But it’s slow, and it depends on
the fragmented genome of the extinct species still surviving in its modern-day equivalents.

An alternative to selective breeding is cloning. This involves finding a viable cell, or cell nucleus, in an extinct but well-preserved animal and growing a new living clone from it. It’s definitely a more appealing route for impatient resurrection biologists, but it does mean getting your hands on intact cells from long-dead animals and devising ways to “resurrect” these, which is no mean feat. Cloning has potential when it comes to recently extinct species whose cells have been well preserved—for instance, where the whole animal has become frozen in ice. But it’s still a slow and extremely limited option.

Which is where advances in genetic engineering come in.

The technological premise of Jurassic Park is that scientists can reconstruct the genome of long-dead animals from preserved DNA fragments. It’s a compelling idea, if you think of DNA as a massively long and complex instruction set that tells a group of biological molecules how to build an animal. In principle, if we could reconstruct the genome of an extinct species, we would have the basic instruction set—the biological software—to reconstruct
individual members of it.

The bad news is that DNA-reconstruction-based de-extinction is far more complex than this. First you need intact fragments of DNA, which is not easy, as DNA degrades easily (and is pretty much impossible to obtain, as far as we know, for dinosaurs). Then you
need to be able to stitch all of your fragments together, which is akin to completing a billion-piece jigsaw puzzle without knowing what the final picture looks like. This is a Herculean task, although with breakthroughs in data manipulation and machine learning,
scientists are getting better at it. But even when you have your reconstructed genome, you need the biological “wetware”—all the stuff that’s needed to create, incubate, and nurture a new living thing, like eggs, nutrients, a safe space to grow and mature, and so on. Within all this complexity, it turns out that getting your DNA sequence right is just the beginning of translating that genetic code into a living, breathing entity. But in some cases, it might be possible.

In 2013, Sergey Zimov was introduced to the geneticist George Church at a conference on de-extinction. Church is an accomplished scientist in the field of DNA analysis and reconstruction, and a thought leader in the field of synthetic biology (which we’ll come
back to in chapter nine). It was a match made in resurrection biology heaven. Zimov wanted to populate his Pleistocene Park with mammoths, and Church thought he could see a way of
achieving this.

What resulted was an ambitious project to de-extinct the woolly mammoth. Church and others who are working on this have faced plenty of hurdles. But the technology has been advancing so fast that, as of 2017, scientists were predicting they would be able to reproduce the woolly mammoth within the next two years.

One of those hurdles was the lack of solid DNA sequences to work from. Frustratingly, although there are many instances of well preserved woolly mammoths, their DNA rarely survives being frozen for tens of thousands of years. To overcome this, Church and others
have taken a different tack: Take a modern, living relative of the mammoth, and engineer into it traits that would allow it to live on the Siberian tundra, just like its woolly ancestors.

Church’s team’s starting point has been the Asian elephant. This is their source of base DNA for their “woolly mammoth 2.0”—their starting source code, if you like. So far, they’ve identified fifty plus gene sequences they think they can play with to give their modern-day woolly mammoth the traits it would need to thrive in Pleistocene Park, including a coat of hair, smaller ears, and a constitution adapted to cold.

The next hurdle they face is how to translate the code embedded in their new woolly mammoth genome into a living, breathing animal. The most obvious route would be to impregnate a female Asian elephant with a fertilized egg containing the new code. But Asian elephants are endangered, and no one’s likely to allow such cutting edge experimentation on the precious few that are still around, so scientists are working on an artificial womb for their reinvented woolly mammoth. They’re making progress with mice and hope to crack the motherless mammoth challenge relatively soon.

It’s perhaps a stretch to call this creative approach to recreating a species (or “reanimation” as Church refers to it) “de-extinction,” as what is being formed is a new species. … (pp. 31-4)

This selection illustrates what Maynard does so very well throughout the book where he uses each film as a launching pad for a clear, readable description of relevant bits of science so you understand why the premise was likely, unlikely, or pure fantasy while linking it to contemporary practices, efforts, and issues. In the context of Jurassic Park, Maynard goes on to raise some fascinating questions such as: Should we revive animals rendered extinct (due to obsolescence or inability to adapt to new conditions) when we could develop new animals?

General thoughts

‘Films for the Future’ offers readable (to non-scientific types) science, lively writing, and the occasional ‘memorish’ anecdote. As well, Dr. Maynard raises the curtain on aspects of the scientific enterprise that most of us do not get to see.  For example, the meeting  between Sergey Zimov and George Church and how it led to new ‘de-extinction’ work’. He also describes the problems that the scientists encountered and are encountering. This is in direct contrast to how scientific work is usually presented in the news media as one glorious breakthrough after the next.

Maynard does discuss the issues of social inequality and power and ownership. For example, who owns your transplant or data? Puzzlingly, he doesn’t touch on the current environment where scientists in the US and elsewhere are encouraged/pressured to start up companies commercializing their work.

Nor is there any mention of how universities are participating in this grand business experiment often called ‘innovation’. (My March 15, 2017 posting describes an outcome for the CRISPR [gene editing system] patent fight taking place between Harvard University’s & MIT’s [Massachusetts Institute of Technology] Broad Institute vs the University of California at Berkeley and my Sept. 11, 2018 posting about an art/science exhibit in Vancouver [Canada] provides an update for round 2 of the Broad Institute vs. UC Berkeley patent fight [scroll down about 65% of the way.) *To read about how my ‘cultural blindness’ shows up here scroll down to the single asterisk at the end.*

There’s a foray through machine-learning and big data as applied to predictive policing in Maynard’s ‘Minority Report’ chapter (my November 23, 2017 posting describes Vancouver’s predictive policing initiative [no psychics involved], the first such in Canada). There’s no mention of surveillance technology, which if I recall properly was part of the future environment, both by the state and by corporations. (Mia Armstrong’s November 15, 2018 article for Slate on Chinese surveillance being exported to Venezuela provides interesting insight.)

The gaps are interesting and various. This of course points to a problem all science writers have when attempting an overview of science. (Carl Zimmer’s latest, ‘She Has Her Mother’s Laugh: The Powers, Perversions, and Potential of Heredity’] a doorstopping 574 pages, also has some gaps despite his focus on heredity,)

Maynard has worked hard to give an comprehensive overview in a remarkably compact 279 pages while developing his theme about science and the human element. In other words, science is not monolithic; it’s created by human beings and subject to all the flaws and benefits that humanity’s efforts are always subject to—scientists are people too.

The readership for ‘Films from the Future’ spans from the mildly interested science reader to someone like me who’s been writing/blogging about these topics (more or less) for about 10 years. I learned a lot reading this book.

Next time, I’m hopeful there’ll be a next time, Maynard might want to describe the parameters he’s set for his book in more detail that is possible in his chapter headings. He could have mentioned that he’s not a cinéaste so his descriptions of the movies are very much focused on the story as conveyed through words. He doesn’t mention colour palates, camera angles, or, even, cultural lenses.

Take for example, his chapter on ‘Ghost in the Shell’. Focused on the Japanese animation film and not the live action Hollywood version he talks about human enhancement and cyborgs. The Japanese have a different take on robots, inanimate objects, and, I assume, cyborgs than is found in Canada or the US or Great Britain, for that matter (according to a colleague of mine, an Englishwoman who lived in Japan for ten or more years). There’s also the chapter on the Ealing comedy, The Man in The White Suit, an English film from the 1950’s. That too has a cultural (as well as, historical) flavour but since Maynard is from England, he may take that cultural flavour for granted. ‘Never let me go’ in Chapter Two was also a UK production, albeit far more recent than the Ealing comedy and it’s interesting to consider how a UK production about cloning might differ from a US or Chinese or … production on the topic. I am hearkening back to Maynard’s anecdote about movies giving him new ways of seeing and imagining the world.

There’s a corrective. A couple of sentences in Maynard’s introductory chapter cautioning that in depth exploration of ‘cultural lenses’ was not possible without expanding the book to an unreadable size followed by a sentence in each of the two chapters that there are cultural differences.

One area where I had a significant problem was with regard to being “programmed” and having  “instinctual” behaviour,

As a species, we are embarrassingly programmed to see “different” as “threatening,” and to take instinctive action against it. It’s a trait that’s exploited in many science fiction novels and movies, including those in this book. If we want to see the rise of increasingly augmented individuals, we need to be prepared for some social strife. (p. 136)

These concepts are much debated in the social sciences and there are arguments for and against ‘instincts regarding strangers and their possible differences’. I gather Dr. Maynard hies to the ‘instinct to defend/attack’ school of thought.

One final quandary, there was no sex and I was expecting it in the Ex Machina chapter, especially now that sexbots are about to take over the world (I exaggerate). Certainly, if you’re talking about “social strife,” then sexbots would seem to be fruitful line of inquiry, especially when there’s talk of how they could benefit families (my August 29, 2018 posting). Again, there could have been a sentence explaining why Maynard focused almost exclusively in this chapter on the discussions about artificial intelligence and superintelligence.

Taken in the context of the book, these are trifling issues and shouldn’t stop you from reading Films from the Future. What Maynard has accomplished here is impressive and I hope it’s just the beginning.

Final note

Bravo Andrew! (Note: We’ve been ‘internet acquaintances/friends since the first year I started blogging. When I’m referring to him in his professional capacity, he’s Dr. Maynard and when it’s not strictly in his professional capacity, it’s Andrew. For this commentary/review I wanted to emphasize his professional status.)

If you need to see a few more samples of Andrew’s writing, there’s a Nov. 15, 2018 essay on The Conversation, Sci-fi movies are the secret weapon that could help Silicon Valley grow up and a Nov. 21, 2018 article on slate.com, The True Cost of Stain-Resistant Pants; The 1951 British comedy The Man in the White Suit anticipated our fears about nanotechnology. Enjoy.

****Added at 1700 hours on Nov. 22, 2018: You can purchase Films from the Future here.

*Nov. 23, 2018: I should have been more specific and said ‘academic scientists’. In Canada, the great percentage of scientists are academic. It’s to the point where the OECD (Organization for Economic Cooperation and Development) has noted that amongst industrialized countries, Canada has very few industrial scientists in comparison to the others.

Roadmap to neuromorphic engineering digital and analog) for the creation of artificial brains *from the Georgia (US) Institute of Technology

While I didn’t mention neuromorphic engineering in my April 16, 2014 posting which focused on the more general aspect of nanotechnology in Transcendence, a movie starring Johnny Depp and opening on April 18, that specialty (neuromorphic engineering) is what makes the events in the movie ‘possible’ (assuming very large stretches of imagination bringing us into the realm implausibility and beyond). From the IMDB.com plot synopsis for Transcendence,

Dr. Will Caster (Johnny Depp) is the foremost researcher in the field of Artificial Intelligence, working to create a sentient machine that combines the collective intelligence of everything ever known with the full range of human emotions. His highly controversial experiments have made him famous, but they have also made him the prime target of anti-technology extremists who will do whatever it takes to stop him. However, in their attempt to destroy Will, they inadvertently become the catalyst for him to succeed to be a participant in his own transcendence. For his wife Evelyn (Rebecca Hall) and best friend Max Waters (Paul Bettany), both fellow researchers, the question is not if they canbut [sic] if they should. Their worst fears are realized as Will’s thirst for knowledge evolves into a seemingly omnipresent quest for power, to what end is unknown. The only thing that is becoming terrifyingly clear is there may be no way to stop him.

In the film, Carter’s intelligence/consciousness is uploaded to the computer, which suggests the computer has human brainlike qualities and abilities. The effort to make computer or artificial intelligence more humanlike is called neuromorphic engineering and according to an April 17, 2014 news item on phys.org, researchers at the Georgia Institute of Technology (Georgia Tech) have published a roadmap for this pursuit,

In the field of neuromorphic engineering, researchers study computing techniques that could someday mimic human cognition. Electrical engineers at the Georgia Institute of Technology recently published a “roadmap” that details innovative analog-based techniques that could make it possible to build a practical neuromorphic computer.

A core technological hurdle in this field involves the electrical power requirements of computing hardware. Although a human brain functions on a mere 20 watts of electrical energy, a digital computer that could approximate human cognitive abilities would require tens of thousands of integrated circuits (chips) and a hundred thousand watts of electricity or more – levels that exceed practical limits.

The Georgia Tech roadmap proposes a solution based on analog computing techniques, which require far less electrical power than traditional digital computing. The more efficient analog approach would help solve the daunting cooling and cost problems that presently make digital neuromorphic hardware systems impractical.

“To simulate the human brain, the eventual goal would be large-scale neuromorphic systems that could offer a great deal of computational power, robustness and performance,” said Jennifer Hasler, a professor in the Georgia Tech School of Electrical and Computer Engineering (ECE), who is a pioneer in using analog techniques for neuromorphic computing. “A configurable analog-digital system can be expected to have a power efficiency improvement of up to 10,000 times compared to an all-digital system.”

An April 16, 2014 Georgia Tech news release by Rick Robinson, which originated the news item, describes why Hasler wants to combine analog (based on biological principles) and digital computing approaches to the creation of artificial brains,

Unlike digital computing, in which computers can address many different applications by processing different software programs, analog circuits have traditionally been hard-wired to address a single application. For example, cell phones use energy-efficient analog circuits for a number of specific functions, including capturing the user’s voice, amplifying incoming voice signals, and controlling battery power.

Because analog devices do not have to process binary codes as digital computers do, their performance can be both faster and much less power hungry. Yet traditional analog circuits are limited because they’re built for a specific application, such as processing signals or controlling power. They don’t have the flexibility of digital devices that can process software, and they’re vulnerable to signal disturbance issues, or noise.

In recent years, Hasler has developed a new approach to analog computing, in which silicon-based analog integrated circuits take over many of the functions now performed by familiar digital integrated circuits. These analog chips can be quickly reconfigured to provide a range of processing capabilities, in a manner that resembles conventional digital techniques in some ways.

Over the last several years, Hasler and her research group have developed devices called field programmable analog arrays (FPAA). Like field programmable gate arrays (FPGA), which are digital integrated circuits that are ubiquitous in modern computing, the FPAA can be reconfigured after it’s manufactured – hence the phrase “field-programmable.”

Hasler and Marr’s 29-page paper traces a development process that could lead to the goal of reproducing human-brain complexity. The researchers investigate in detail a number of intermediate steps that would build on one another, helping researchers advance the technology sequentially.

For example, the researchers discuss ways to scale energy efficiency, performance and size in order to eventually achieve large-scale neuromorphic systems. The authors also address how the implementation and the application space of neuromorphic systems can be expected to evolve over time.

“A major concept here is that we have to first build smaller systems capable of a simple representation of one layer of human brain cortex,” Hasler said. “When that system has been successfully demonstrated, we can then replicate it in ways that increase its complexity and performance.”

Among neuromorphic computing’s major hurdles are the communication issues involved in networking integrated circuits in ways that could replicate human cognition. In their paper, Hasler and Marr emphasize local interconnectivity to reduce complexity. Moreover, they argue it’s possible to achieve these capabilities via purely silicon-based techniques, without relying on novel devices that are based on other approaches.

Commenting on the recent publication, Alice C. Parker, a professor of electrical engineering at the University of Southern California, said, “Professor Hasler’s technology roadmap is the first deep analysis of the prospects for large scale neuromorphic intelligent systems, clearly providing practical guidance for such systems, with a nearer-term perspective than our whole-brain emulation predictions. Her expertise in analog circuits, technology and device models positions her to provide this unique perspective on neuromorphic circuits.”

Eugenio Culurciello, an associate professor of biomedical engineering at Purdue University, commented, “I find this paper to be a very accurate description of the field of neuromorphic data processing systems. Hasler’s devices provide some of the best performance per unit power I have ever seen and are surely on the roadmap for one of the major technologies of the future.”

Said Hasler: “In this study, we conclude that useful neural computation machines based on biological principles – and potentially at the size of the human brain — seems technically within our grasp. We think that it’s more a question of gathering the right research teams and finding the funding for research and development than of any insurmountable technical barriers.”

Here’s a link to and a citation for the roadmap,

Finding a roadmap to achieve large neuromorphic hardware systems by Jennifer Hasler and Bo Marr.  Front. Neurosci. (Frontiers in Neuroscience), 10 September 2013 | doi: 10.3389/fnins.2013.00118

This is an open access article (at least, the HTML version is).

I have looked at Hasler’s roadmap and it provides a good and readable overview (even for an amateur like me; Note: you do have to need some tolerance for ‘not knowing’) of the state of neuromorphic engineering’s problems, and suggestions for overcoming them. Here’s a description of a human brain and its power requirements as compared to a computer’s (from the roadmap),

One of the amazing thing about the human brain is its ability to perform tasks beyond current supercomputers using roughly 20 W of average power, a level smaller than most individual computer microprocessor chips. A single neuron emulation can tax a high performance processor; given there is 1012 neurons operating at 20 W, each neuron consumes 20 pW average power. Assuming a neuron is conservatively performing the wordspotting computation (1000 synapses), 100,000 PMAC (PMAC = “Peta” MAC = 1015 MAC/s) would be required to duplicate the neural structure. A higher computational efficiency due to active dendritic line channels is expected as well as additional computation due to learning. The efficiency of a single neuron would be 5000 PMAC/W (or 5 TMAC/μW). A similar efficiency for 1011 neurons and 10,000 synapses is expected.

Building neuromorphic hardware requires that technology must scale from current levels given constraints of power, area, and cost: all issues typical in industrial and defense applications; if hardware technology does not scale as other available technologies, as well as takes advantage of the capabilities of IC technology that are currently visible, it will not be successful.

One of my main areas of interest is the memristor (a nanoscale ‘device/circuit element’ which emulates synaptic plasticity), which was mentioned in a way that allows me to understand how the device fits (or doesn’t fit) into the overall conceptual framework (from the roadmap),

The density for a 10 nm EEPROM device acting as a synapse begs the question of whether other nanotechnologies can improve on the resulting Si [silicon] synapse density. One transistor per synapse is hard to beat by any approach, particularly in scaled down Si (like 10 nm), when the synapse memory, computation, and update is contained within the EEPROM device. Most nano device technologies [i.e., memristors (Snider et al., 2011)] show considerable difficulties to get to two-dimensional arrays at a similar density level. Recently, a team from U. of Michigan announced the first functioning memristor two-dimensional (30 × 30) array built on a CMOS chip in 2012 (Kim et al., 2012), claiming applications in neuromorphic engineering, the same group has published innovative devices for digital (Jo and Lu, 2009) and analog applications (Jo et al., 2011).

I notice that the reference to the University’s of Michigan is relatively neutral in tone and the memristor does not figure substantively in Hasler’s roadmap.

Intriguingly, there is a section on commercialization; I didn’t think the research was at that stage yet (from the roadmap),

Although one can discuss how to build a cortical computer on the size of mammals and humans, the question is how will the technology developed for these large systems impact commercial development. The cost for ICs [integrated circuits or chips] alone for cortex would be approximately $20 M in current prices, which although possible for large users, would not be common to be found in individual households. Throughout the digital processor approach, commercial market opportunities have driven the progress in the field. Getting neuromorphic technology integrated into commercial environment allows us to ride this powerful economic “engine” rather than pull.

In most applications, the important commercial issues include minimization of cost, time to market, just sufficient performance for the application, power consumed, size and weight. The cost of a system built from ICs is, at a macro-level, a function of the area of those ICs, which then affects the number of ICs needed system wide, the number of components used, and the board space used. Efficiency of design tools, testing time and programming time also considerably affect system costs. Time to get an application to market is affected by the ability to reuse or quickly modify existing designs, and is reduced for a new application if existing hardware can be reconfigured, adapting to changing specifications, and a designer can utilize tools that allow rapid modifications to the design. Performance is key for any algorithm, but for a particular product, one only needs a solution to that particular problem; spending time to make the solution elegant is often a losing strategy.

The neuromorphic community has seen some early entries into commercial spaces, but we are just at the very beginning of the process. As the knowledge of neuromorphic engineering has progressed, which have included knowledge of sensor interfaces and analog signal processing, there have been those who have risen to the opportunities to commercialize these technologies. Neuromorphic research led to better understanding of sensory processing, particularly sensory systems interacting with other humans, enabling companies like Synaptics (touch pads), Foveon (CMOS color imagers), and Sonic Innovation (analog–digital hearing aids); Gilder provides a useful history of these two companies elsewhere (Gilder, 2005). From the early progress in analog signal processing we see companies like GTronix (acquired by National Semiconductor, then acquired by Texas Instruments) applying the impact of custom analog signal processing techniques and programmability toward auditory signal processing that improved sound quality requiring ultra-low power levels. Further, we see in companies like Audience there is some success from mapping the computational flow of the early stage auditory system, and implementing part of the event based auditory front-end to achieve useful results for improved voice quality. But the opportunities for the neuromorphic community are just beginning, and directly related to understanding the computational capabilities of these items. The availability of ICs that have these capabilities, whether or not one mentions they have any neuromorphic material, will further drive applications.

One expects that part of a cortex processing system would have significant computational possibilities, as well as cortex structures from smaller animals, and still be able to reach price points for commercial applications. In the following discussion, we will consider the potential of cortical structures at different levels of commercial applications. Figure 24 shows one typical block diagram, algorithms at each stage, resulting power efficiency (say based on current technology), as well as potential applications of the approach. In all cases, we will be considering a single die solution, typical for a commercial product, and will minimize the resulting communication power to I/O off the chip (no power consumed due to external memories or digital processing devices). We will assume a net computational efficiency of 10 TMAC/mW, corresponding to a lower power supply (i.e., mostly 500 mV, but not 180 mV) and slightly larger load capacitances; we make these assumptions as conservative pull back from possible applications, although we expect the more aggressive targets would be reachable. We assume the external power consumed is set by 1 event/second/neuron average event-rate off chip to a nearby IC. Given the input event rate is hard to predict, we don’t include that power requirement but assume it is handled by the input system. In all of these cases, getting the required computation using only digital techniques in a competitive size, weight, and especially power is hard to foresee.

We expect progress in these neuromorphic systems and that should find applications in traditional signal processing and graphics handling approaches. We will continue to have needs in computing that outpace our available computing resources, particularly at a power consumption required for a particular application. For example, the recent emphasis on cloud computing for academic/research problems shows the incredible need for larger computing resources than those directly available, or even projected to be available, for a portable computing platform (i.e., robotics). Of course a server per computing device is not a computing model that scales well. Given scaling limits on computing, both in power, area, and communication, one can expect to see more and more of these issues going forward.

We expect that a range of different ICs and systems will be built, all at different targets in the market. There are options for even larger networks, or integrating these systems with other processing elements on a chip/board. When moving to larger systems, particularly ones with 10–300 chips (3 × 107 to 109 neurons) or more, one can see utilization of stacking of dies, both decreasing the communication capacitance as well as board complexity. Stacking dies should roughly increase the final chip cost by the number of dies stacked.

In the following subsections, we overview general guidelines to consider when considering using neuromorphic ICs in the commercial market, first for low-cost consumer electronics, and second for a larger neuromorphic processor IC.

I have a casual observation to make. while the authors of the roadmap came to this conclusion “This study concludes that useful neural computation machines based on biological principles at the size of the human brain seems technically within our grasp.,” they’re also leaving themselves some wiggle room because the truth is no one knows if copying a human brain with circuits and various devices will lead to ‘thinking’ as we understand the concept.

For anyone who’s interested, you can search this blog for neuromorphic engineering, artificial brains, and/or memristors as I have many postings on these topics. One of my most recent on the topic of artificial brains is an April 7, 2014 piece titled: Brain-on-a-chip 2014 survey/overview.

One last observation about the movie ‘Transcendence’, has no one else noticed that it’s the ‘Easter’ story with a resurrected and digitized ‘Jesus’?

* Space inserted between ‘brains’ and ‘from’ in head on April 21, 2014.

Nanotechnology at the movies: Transcendence opens April 18, 2014 in the US & Canada

Screenwriter Jack Paglen has an intriguing interpretation of nanotechnology, one he (along with the director) shares in an April 13, 2014 article by Larry Getlen for the NY Post and in his movie, Transcendence. which is opening in the US and Canada on April 18, 2014. First, here are a few of the more general ideas underlying his screenplay,

In “Transcendence” — out Friday [April 18, 2014] and directed by Oscar-winning cinematographer Wally Pfister (“Inception,” “The Dark Knight”) — Johnny Depp plays Dr. Will Caster, an artificial-intelligence researcher who has spent his career trying to design a sentient computer that can hold, and even exceed, the world’s collective intelligence.

After he’s shot by antitechnology activists, his consciousness is uploaded to a computer network just before his body dies.

“The theories associated with the film say that when a strong artificial intelligence wakes up, it will quickly become more intelligent than a human being,” screenwriter Jack Paglen says, referring to a concept known as “the singularity.”

It should be noted that there are anti-technology terrorists. I don’t think I’ve covered that topic in a while so an Aug. 31, 2012 posting is the most recent and, despite the title, “In depth and one year later—the nanotechnology bombings in Mexico” provides an overview of sorts. For a more up-to-date view, you can read Eric Markowitz’s April 9, 2014 article for Vocative.com. I do have one observation about the article where Markowitz has linked some recent protests in San Francisco to the bombings in Mexico. Those protests in San Francisco seem more like a ‘poor vs. the rich’ situation where the rich happen to come from the technology sector.

Getting back to “Transcendence” and singularity, there’s a good Wikipedia entry describing the ideas and some of the thinkers behind the notion of a singularity or technological singularity, as it’s sometimes called (Note: Links have been removed),

The technological singularity, or simply the singularity, is a hypothetical moment in time when artificial intelligence will have progressed to the point of a greater-than-human intelligence, radically changing civilization, and perhaps human nature.[1] Because the capabilities of such an intelligence may be difficult for a human to comprehend, the technological singularity is often seen as an occurrence (akin to a gravitational singularity) beyond which the future course of human history is unpredictable or even unfathomable.

The first use of the term “singularity” in this context was by mathematician John von Neumann. In 1958, regarding a summary of a conversation with von Neumann, Stanislaw Ulam described “ever accelerating progress of technology and changes in the mode of human life, which gives the appearance of approaching some essential singularity in the history of the race beyond which human affairs, as we know them, could not continue”.[2] The term was popularized by science fiction writer Vernor Vinge, who argues that artificial intelligence, human biological enhancement, or brain-computer interfaces could be possible causes of the singularity.[3] Futurist Ray Kurzweil cited von Neumann’s use of the term in a foreword to von Neumann’s classic The Computer and the Brain.

Proponents of the singularity typically postulate an “intelligence explosion”,[4][5] where superintelligences design successive generations of increasingly powerful minds, that might occur very quickly and might not stop until the agent’s cognitive abilities greatly surpass that of any human.

Kurzweil predicts the singularity to occur around 2045[6] whereas Vinge predicts some time before 2030.[7] At the 2012 Singularity Summit, Stuart Armstrong did a study of artificial generalized intelligence (AGI) predictions by experts and found a wide range of predicted dates, with a median value of 2040. His own prediction on reviewing the data is that there is an 80% probability that the singularity will occur between 2017 and 2112.[8]

The ‘technological singularity’ is controversial and contested (from the Wikipedia entry).

In addition to general criticisms of the singularity concept, several critics have raised issues with Kurzweil’s iconic chart. One line of criticism is that a log-log chart of this nature is inherently biased toward a straight-line result. Others identify selection bias in the points that Kurzweil chooses to use. For example, biologist PZ Myers points out that many of the early evolutionary “events” were picked arbitrarily.[104] Kurzweil has rebutted this by charting evolutionary events from 15 neutral sources, and showing that they fit a straight line on a log-log chart. The Economist mocked the concept with a graph extrapolating that the number of blades on a razor, which has increased over the years from one to as many as five, will increase ever-faster to infinity.[105]

By the way, this movie is mentioned briefly in the pop culture portion of the Wikipedia entry.

Getting back to Paglen and his screenplay, here’s more from Getlen’s article,

… as Will’s powers grow, he begins to pull off fantastic achievements, including giving a blind man sight, regenerating his own body and spreading his power to the water and the air.

This conjecture was influenced by nanotechnology, the field of manipulating matter at the scale of a nanometer, or one-billionth of a meter. (By comparison, a human hair is around 70,000-100,000 nanometers wide.)

“In some circles, nanotechnology is the holy grail,” says Paglen, “where we could have microscopic, networked machines [emphasis mine] that would be capable of miracles.”

The potential uses of, and implications for, nanotechnology are vast and widely debated, but many believe the effects could be life-changing.

“When I visited MIT,” says Pfister, “I visited a cancer research institute. They’re talking about the ability of nanotechnology to be injected inside a human body, travel immediately to a cancer cell, and deliver a payload of medicine directly to that cell, eliminating [the need to] poison the whole body with chemo.”

“Nanotechnology could help us live longer, move faster and be stronger. It can possibly cure cancer, and help with all human ailments.”

I find the ‘golly gee wizness’ of Paglen’s and Pfister’s take on nanotechnology disconcerting but they can’t be dismissed. There are projects where people are testing retinal implants which allow them to see again. There is a lot of work in the field of medicine designed to make therapeutic procedures that are gentler on the body by making their actions specific to diseased tissue while ignoring healthy tissue (sadly, this is still not possible). As for human enhancement, I have so many pieces that it has its own category on this blog. I first wrote about it in a four-part series starting with this one: Nanotechnology enables robots and human enhancement: part 1, (You can read the series by scrolling past the end of the posting and clicking on the next part or search the category and pick through the more recent pieces.)

I’m not sure if this error is Paglen’s or Getlen’s but nanotechnology is not “microscopic, networked machines” as Paglen’s quote strongly suggests. Some nanoscale devices could be described as machines (often called nanobots) but there are also nanoparticles, nanotubes, nanowires, and more that cannot be described as machines or devices, for that matter. More importantly, it seems Paglen’s main concern is this,

“One of [science-fiction author] Arthur C. Clarke’s laws is that any sufficiently advanced technology is indistinguishable from magic. That very quickly would become the case if this happened, because this artificial intelligence would be evolving technologies that we do not understand, and it would be capable of miracles by that definition,” says Paglen. [emphasis mine]

This notion of “evolving technologies that we do not understand” brings to mind a  project that was announced at the University of Cambridge (from my Nov. 26, 2012 posting),

The idea that robots of one kind or another (e.g. nanobots eating up the world and leaving grey goo, Cylons in both versions of Battlestar Galactica trying to exterminate humans, etc.) will take over the world and find humans unnecessary  isn’t especially new in works of fiction. It’s not always mentioned directly but the underlying anxiety often has to do with intelligence and concerns over an ‘explosion of intelligence’. The question it raises,’ what if our machines/creations become more intelligent than humans?’ has been described as existential risk. According to a Nov. 25, 2012 article by Sylvia Hui for Huffington Post, a group of eminent philosophers and scientists at the University of Cambridge are proposing to found a Centre for the Study of Existential Risk,

While I do have some reservations about how Paglen and Pfister describe the science, I appreciate their interest in communicating the scientific ideas, particularly those underlying Paglen’s screenplay.

For anyone who may be concerned about the likelihood of emulating  a human brain and uploading it to a computer, there’s an April 13, 2014 article by Luke Muehlhauser and Stuart Armstrong for Slate discussing that very possibility (Note 1: Links have been removed; Note 2: Armstrong is mentioned in this posting’s excerpt from the Wikipedia entry on Technological Singularity),

Today scientists can’t even emulate the brain of a tiny worm called C. elegans, which has 302 neurons, compared with the human brain’s 86 billion neurons. Using models of expected technological progress on the three key problems, we’d estimate that we wouldn’t be able to emulate human brains until at least 2070 (though this estimate is very uncertain).

But would an emulation of your brain be you, and would it be conscious? Such questions quickly get us into thorny philosophical territory, so we’ll sidestep them for now. For many purposes—estimating the economic impact of brain emulations, for instance—it suffices to know that the brain emulations would have humanlike functionality, regardless of whether the brain emulation would also be conscious.

Paglen/Pfister seem to be equating intelligence (brain power) with consciousness while Muehlhauser/Armstrong simply sidestep the issue. As they (Muehlhauser/Armstrong) note, it’s “thorny.”

If you consider thinkers like David Chalmers who suggest everything has consciousness, then it follows that computers/robots/etc. may not appreciate having a human brain emulation which takes us back into Battlestar Galactica territory. From my March 19, 2014 posting (one of the postings where I recounted various TED 2014 talks in Vancouver), here’s more about David Chalmers,

Finally, I wasn’t expecting to write about David Chalmers so my notes aren’t very good. A philosopher, here’s an excerpt from Chalmers’ TED biography,

In his work, David Chalmers explores the “hard problem of consciousness” — the idea that science can’t ever explain our subjective experience.

David Chalmers is a philosopher at the Australian National University and New York University. He works in philosophy of mind and in related areas of philosophy and cognitive science. While he’s especially known for his theories on consciousness, he’s also interested (and has extensively published) in all sorts of other issues in the foundations of cognitive science, the philosophy of language, metaphysics and epistemology.

Chalmers provided an interesting bookend to a session started with a brain researcher (Nancy Kanwisher) who breaks the brain down into various processing regions (vastly oversimplified but the easiest way to summarize her work in this context). Chalmers reviewed the ‘science of consciousness’ and noted that current work in science tends to be reductionist, i.e., examining parts of things such as brains and that same reductionism has been brought to the question of consciousness.

Rather than trying to prove consciousness, Chalmers proposes that we consider it a fundamental in the same way that we consider time, space, and mass to be fundamental. He noted that there’s precedence for additions and gave the example of James Clerk Maxwell and his proposal to consider electricity and magnetism as fundamental.

Chalmers next suggestion is a little more outré and based on some thinking (sorry I didn’t catch the theorist’s name) that suggests everything, including photons, has a type of consciousness (but not intelligence).

Have a great time at the movie!