From time to time I check out the latest on attempts to shrink computer chips. In my July 11, 2014 posting I noted IBM’s announcement about developing a 7nm computer chip and later in my July 15, 2015 posting I noted IBM’s announcement of a working 7nm chip (from a July 9, 2015 IBM news release , “The breakthrough, accomplished in partnership with GLOBALFOUNDRIES and Samsung at SUNY Polytechnic Institute’s Colleges of Nanoscale Science and Engineering (SUNY Poly CNSE), could result in the ability to place more than 20 billion tiny switches — transistors — on the fingernail-sized chips that power everything from smartphones to spacecraft.”
GLOBALFOUNDRIES [GF] today announced an important step in its transformation, continuing the trajectory launched with the appointment of Tom Caulfield as CEO earlier this year. In line with the strategic direction Caulfield has articulated, GF is reshaping its technology portfolio to intensify its focus on delivering truly differentiated offerings for clients in high-growth markets.
GF is realigning its leading-edge FinFET roadmap to serve the next wave of clients that will adopt the technology in the coming years. The company will shift development resources to make its 14/12nm FinFET platform more relevant to these clients, delivering a range of innovative IP and features including RF, embedded memory, low power and more. To support this transition, GF is putting its 7nm FinFET program on hold indefinitely [emphasis mine] and restructuring its research and development teams to support its enhanced portfolio initiatives. This will require a workforce reduction, however a significant number of top technologists will be redeployed on 14/12nm FinFET derivatives and other differentiated offerings.
I tried to find a definition for FinFet but the reference to a MOSFET and in-gate transistors was too much incomprehensible information packed into a tight space, see the FinFET Wikipedia entry for more, if you dare.
Getting back to the 7nm chip issue, Samuel K. Moore (I don’t think he’s related to the Moore of Moore’s law) wrote an Aug. 28, 2018 posting on the Nanoclast blog (on the IEEE [Institute of Electronics and Electrical Engineers] website) which provides some insight (Note: Links have been removed),
In a major shift in strategy, GlobalFoundries is halting its development of next-generation chipmaking processes. It had planned to move to the so-called 7-nm node, then begin to use extreme-ultraviolet lithography (EUV) to make that process cheaper. From there, it planned to develop even more advanced lithography that would allow for 5- and 3-nanometer nodes. Despite having installed at least one EUV machine at its Fab 8 facility in Malta, N.Y., all those plans are now on indefinite hold, the company announced Monday.
The move leaves only three companies reaching for the highest rungs of the Moore’s Law ladder: Intel, Samsung, and TSMC.
It’s a huge turnabout for GlobalFoundries. …
GlobalFoundries rationale for the move is that there are not enough customers that need bleeding-edge 7-nm processes to make it profitable. “While the leading edge gets most of the headlines, fewer customers can afford the transition to 7 nm and finer geometries,” said Samuel Wang, research vice president at Gartner, in a GlobalFoundries press release.
“The vast majority of today’s fabless [emphasis mine] customers are looking to get more value out of each technology generation to leverage the substantial investments required to design into each technology node,” explained GlobalFoundries CEO Tom Caulfield in a press release. “Essentially, these nodes are transitioning to design platforms serving multiple waves of applications, giving each node greater longevity. This industry dynamic has resulted in fewer fabless clients designing into the outer limits of Moore’s Law. We are shifting our resources and focus by doubling down on our investments in differentiated technologies across our entire portfolio that are most relevant to our clients in growing market segments.”
(The dynamic Caulfield describes is something the U.S. Defense Advanced Research Agency is working to disrupt with its $1.5-billion Electronics Resurgence Initiative. Darpa’s [DARPA] partners are trying to collapse the cost of design and allow older process nodes to keep improving by using 3D technology.)
Roland Moore-Colyer (I don’t think he’s related to Moore of Moore’s law either) has written August 28, 2018 article for theinquirer.net which also explores this latest news from Global Foundries (Note: Links have been removed),
EVER PREPPED A SPREAD for a party to then have less than half the people you were expecting show up? That’s probably how GlobalFoundries [sic] feels at the moment.
The chip manufacturer, which was once part of AMD, had a fabrication process geared up for 7-nanometre chips which its customers – including AMD and Qualcomm – were expected to adopt.
But AMD has confirmed that it’s decided to move its 7nm GPU production to TSMC, and Intel is still stuck trying to make chips based on 10nm fabrication.
Arguably, this could mark a stymieing of innovation and cutting-edge designs for chips in the near future. But with processors like AMD’s Threadripper 2990WX overclocked to run at 6GHz across all its 32 cores, in the real-world PC fans have no need to worry about consumer chips running out of puff anytime soon. µ
That’s all folks.
Maybe that’s not all
Steve Blank in a Sept. 10, 2018 posting on the Nanoclast blog (on the IEEE [Institute of Electrical and Electronics Engineers] website) provides some provocative commentary on the Global Foundries announcement (Note: A link has been removed),
For most of our lives, the idea that computers and technology would get better, faster, and cheaper every year was as assured as the sun rising every morning. The story “GlobalFoundries Halts 7-nm Chip Development” doesn’t sound like the end of that era, but for you and anyone who uses an electronic device, it most certainly is.
Technology innovation is going to take a different direction.
This story just goes on and on
There was a new development according to a Sept. 12, 2018 posting on the Nanoclast blog by, again, Samuel K. Moore (Note Links have been removed),
At an event today [sept. 12, 2018], Apple executives said that the new iPhone Xs and Xs Max will contain the first smartphone processor to be made using 7 nm manufacturing technology, the most advanced process node. Huawei made the same claim, to less fanfare, late last month and it’s unclear who really deserves the accolades. If anybody does, it’s TSMC, which manufactures both chips.
TSMC went into volume production with 7-nm tech in April, and rival Samsung is moving toward commercial 7-nm production later this year or in early 2019. GlobalFoundries recently abandoned its attempts to develop a 7 nm process, reasoning that the multibillion-dollar investment would never pay for itself. And Intel announced delays in its move to its next manufacturing technology, which it calls a 10-nm node but which may be equivalent to others’ 7-nm technology.
There’s a certain ‘soap opera’ quality to this with all the twists and turns.
Libraries, archives, records management, oral history, etc. there are many institutions and names for how we manage collective and personal memory. You might call it a peculiarly human obsession stretching back into antiquity. For example, there’s the Library of Alexandria (Wikipedia entry) founded in the third, or possibly 2nd, century BCE (before the common era) and reputed to store all the knowledge in the world. It was destroyed although accounts differ as to when and how but its loss remains a potent reminder of memory’s fragility.
These days, the technology community is terribly concerned with storing ever more bits of data on materials that are reaching their limits for storage.I have news of a possible solution, an interview of sorts with the researchers working on this new technology, and some very recent research into policies for cryptocurrency mining and development. That bit about cryptocurrency makes more sense when you read what the response to one of the interview questions.
It seems University of Alberta researchers may have found a way to increase memory exponentially, from a July 23, 2018 news item on ScienceDaily,
The most dense solid-state memory ever created could soon exceed the capabilities of current computer storage devices by 1,000 times, thanks to a new technique scientists at the University of Alberta have perfected.
“Essentially, you can take all 45 million songs on iTunes and store them on the surface of one quarter,” said Roshan Achal, PhD student in Department of Physics and lead author on the new research. “Five years ago, this wasn’t even something we thought possible.”
Previous discoveries were stable only at cryogenic conditions, meaning this new finding puts society light years closer to meeting the need for more storage for the current and continued deluge of data. One of the most exciting features of this memory is that it’s road-ready for real-world temperatures, as it can withstand normal use and transportation beyond the lab.
“What is often overlooked in the nanofabrication business is actual transportation to an end user, that simply was not possible until now given temperature restrictions,” continued Achal. “Our memory is stable well above room temperature and precise down to the atom.”
Achal explained that immediate applications will be data archival. Next steps will be increasing readout and writing speeds, meaning even more flexible applications.
“With this last piece of the puzzle now in-hand, atom-scale fabrication will become a commercial reality in the very near future,” said Wolkow. Wolkow’s Spin-off [sic] company, Quantum Silicon Inc., is hard at work on commercializing atom-scale fabrication for use in all areas of the technology sector.
To demonstrate the new discovery, Achal, Wolkow, and their fellow scientists not only fabricated the world’s smallest maple leaf, they also encoded the entire alphabet at a density of 138 terabytes, roughly equivalent to writing 350,000 letters across a grain of rice. For a playful twist, Achal also encoded music as an atom-sized song, the first 24 notes of which will make any video-game player of the 80s and 90s nostalgic for yesteryear but excited for the future of technology and society.
As noted in the news release, there is an atom-sized song, which is available in this video,
For interested parties, you can find Quantum Silicon (QSI) here. My Edmonton geography is all but nonexistent, still, it seems to me the company address on Saskatchewan Drive is a University of Alberta address. It’s also the address for the National Research Council of Canada. Perhaps this is a university/government spin-off company?
I sent some questions to the researchers at the University of Alberta who very kindly provided me with the following answers. Roshan Achal passed on one of the questions to his colleague Taleana Huff for her response. Both Achal and Huff are associated with QSI.
Unfortunately I could not find any pictures of all three researchers (Achal, Huff, and Wolkow) together.
Roshan Achal (left) used nanotechnology perfected by his PhD supervisor, Robert Wolkow (right) to create atomic-scale computer memory that could exceed the capacity of today’s solid-state storage drives by 1,000 times. (Photo: Faculty of Science)
(1) SHRINKING THE MANUFACTURING PROCESS TO THE ATOMIC SCALE HAS
ATTRACTED A LOT OF ATTENTION OVER THE YEARS STARTING WITH SCIENCE
FICTION OR RICHARD FEYNMAN OR K. ERIC DREXLER, ETC. IN ANY EVENT, THE
ORIGINS ARE CONTESTED SO I WON’T PUT YOU ON THE SPOT BY ASKING WHO
STARTED IT ALL INSTEAD ASKING HOW DID YOU GET STARTED?
I got started in this field about 6 years ago, when I undertook a MSc
with Dr. Wolkow here at the University of Alberta. Before that point, I
had only ever heard of a scanning tunneling microscope from what was
taught in my classes. I was aware of the famous IBM logo made up from
just a handful of atoms using this machine, but I didn’t know what
else could be done. Here, Dr. Wolkow introduced me to his line of
research, and I saw the immense potential for growth in this area and
decided to pursue it further. I had the chance to interact with and
learn from nanofabrication experts and gain the skills necessary to
begin playing around with my own techniques and ideas during my PhD.
(2) AS I UNDERSTAND IT, THESE ARE THE PIECES YOU’VE BEEN
WORKING ON: (1) THE TUNGSTEN MICROSCOPE TIP, WHICH MAKE[s] (2) THE SMALLEST
QUANTUM DOTS (SINGLE ATOMS OF SILICON), (3) THE AUTOMATION OF THE
QUANTUM DOT PRODUCTION PROCESS, AND (4) THE “MOST DENSE SOLID-STATE
MEMORY EVER CREATED.” WHAT’S MISSING FROM THE LIST AND IS THAT WHAT
YOU’RE WORKING ON NOW?
One of the things missing from the list, that we are currently working
on, is the ability to easily communicate (electrically) from the
macroscale (our world) to the nanoscale, without the use of a scanning
tunneling microscope. With this, we would be able to then construct
devices using the other pieces we’ve developed up to this point, and
then integrate them with more conventional electronics. This would bring
us yet another step closer to the realization of atomic-scale
(3) PERHAPS YOU COULD CLARIFY SOMETHING FOR ME. USUALLY WHEN SOLID STATE
MEMORY IS MENTIONED, THERE’S GREAT CONCERN ABOUT MOORE’S LAW. IS
THIS WORK GOING TO CREATE A NEW LAW? AND, WHAT IF ANYTHING DOES
;YOUR MEMORY DEVICE HAVE TO DO WITH QUANTUM COMPUTING?
That is an interesting question. With the density we’ve achieved,
there are not too many surfaces where atomic sites are more closely
spaced to allow for another factor of two improvement. In that sense, it
would be difficult to improve memory densities further using these
techniques alone. In order to continue Moore’s law, new techniques, or
storage methods would have to be developed to move beyond atomic-scale
The memory design itself does not have anything to do with quantum
computing, however, the lithographic techniques developed through our
work, may enable the development of certain quantum-dot-based quantum
(4) THIS MAY BE A LITTLE OUT OF LEFT FIELD (OR FURTHER OUT THAN THE
OTHERS), COULD;YOUR MEMORY DEVICE HAVE AN IMPACT ON THE
DEVELOPMENT OF CRYPTOCURRENCY AND BLOCKCHAIN? IF SO, WHAT MIGHT THAT
I am not very familiar with these topics, however, co-author Taleana
Huff has provided some thoughts:
Taleana Huff (downloaded from https://ca.linkedin.com/in/taleana-huff]
“The memory, as we’ve designed it, might not have too much of an
impact in and of itself. Cryptocurrencies fall into two categories.
Proof of Work and Proof of Stake. Proof of Work relies on raw
computational power to solve a difficult math problem. If you solve it,
you get rewarded with a small amount of that coin. The problem is that
it can take a lot of power and energy for your computer to crunch
through that problem. Faster access to memory alone could perhaps
streamline small parts of this slightly, but it would be very slight.
Proof of Stake is already quite power efficient and wouldn’t really
have a drastic advantage from better faster computers.
Now, atomic-scale circuitry built using these new lithographic
techniques that we’ve developed, which could perform computations at
significantly lower energy costs, would be huge for Proof of Work coins.
One of the things holding bitcoin back, for example, is that mining it
is now consuming power on the order of the annual energy consumption
required by small countries. A more efficient way to mine while still
taking the same amount of time to solve the problem would make bitcoin
much more attractive as a currency.”
Thank you to Roshan Achal and Taleana Huff for helping me to further explore the implications of their work with Dr. Wolkow.
As usual, after receiving the replies I have more questions but these people have other things to do so I’ll content myself with noting that there is something extraordinary in the fact that we can imagine a near future where atomic scale manufacturing is possible and where as Achal says, ” … storage methods would have to be developed to move beyond atomic-scale [emphasis mine] storage”. In decades past it was the stuff of science fiction or of theorists who didn’t have the tools to turn the idea into a reality. With Wolkow’s, Achal’s, Hauff’s, and their colleagues’ work, atomic scale manufacturing is attainable in the foreseeable future.
Hopefully we’ll be wiser than we have been in the past in how we deploy these new manufacturing techniques. Of course, before we need the wisdom, scientists, as Achal notes, need to find a new way to communicate between the macroscale and the nanoscale.
A study [behind a paywall] published in Energy Research & Social Science warns that failure to lower the energy use by Bitcoin and similar Blockchain designs may prevent nations from reaching their climate change mitigation obligations under the Paris Agreement.
The study, authored by Jon Truby, PhD, Assistant Professor, Director of the Centre for Law & Development, College of Law, Qatar University, Doha, Qatar, evaluates the financial and legal options available to lawmakers to moderate blockchain-related energy consumption and foster a sustainable and innovative technology sector. Based on this rigorous review and analysis of the technologies, ownership models, and jurisdictional case law and practices, the article recommends an approach that imposes new taxes, charges, or restrictions to reduce demand by users, miners, and miner manufacturers who employ polluting technologies, and offers incentives that encourage developers to create less energy-intensive/carbon-neutral Blockchain.
“Digital currency mining is the first major industry developed from Blockchain, because its transactions alone consume more electricity than entire nations,” said Dr. Truby. “It needs to be directed towards sustainability if it is to realize its potential advantages.
“Many developers have taken no account of the environmental impact of their designs, so we must encourage them to adopt consensus protocols that do not result in high emissions. Taking no action means we are subsidizing high energy-consuming technology and causing future Blockchain developers to follow the same harmful path. We need to de-socialize the environmental costs involved while continuing to encourage progress of this important technology to unlock its potential economic, environmental, and social benefits,” explained Dr. Truby.
As a digital ledger that is accessible to, and trusted by all participants, Blockchain technology decentralizes and transforms the exchange of assets through peer-to-peer verification and payments. Blockchain technology has been advocated as being capable of delivering environmental and social benefits under the UN’s Sustainable Development Goals. However, Bitcoin’s system has been built in a way that is reminiscent of physical mining of natural resources – costs and efforts rise as the system reaches the ultimate resource limit and the mining of new resources requires increasing hardware resources, which consume huge amounts of electricity.
Putting this into perspective, Dr. Truby said, “the processes involved in a single Bitcoin transaction could provide electricity to a British home for a month – with the environmental costs socialized for private benefit.
“Bitcoin is here to stay, and so, future models must be designed without reliance on energy consumption so disproportionate on their economic or social benefits.”
The study evaluates various Blockchain technologies by their carbon footprints and recommends how to tax or restrict Blockchain types at different phases of production and use to discourage polluting versions and encourage cleaner alternatives. It also analyzes the legal measures that can be introduced to encourage technology innovators to develop low-emissions Blockchain designs. The specific recommendations include imposing levies to prevent path-dependent inertia from constraining innovation:
Registration fees collected by brokers from digital coin buyers.
“Bitcoin Sin Tax” surcharge on digital currency ownership.
Green taxes and restrictions on machinery purchases/imports (e.g. Bitcoin mining machines).
Smart contract transaction charges.
According to Dr. Truby, these findings may lead to new taxes, charges or restrictions, but could also lead to financial rewards for innovators developing carbon-neutral Blockchain.
The press release doesn’t fully reflect Dr. Truby’s thoughtfulness or the incentives he has suggested. it’s not all surcharges, taxes, and fees constitute encouragement. Here’s a sample from the conclusion,
The possibilities of Blockchain are endless and incentivisation can help solve various climate change issues, such as through the development of digital currencies to fund climate finance programmes. This type of public-private finance initiative is envisioned in the Paris Agreement, and fiscal tools can incentivize innovators to design financially rewarding Blockchain technology that also achieves environmental goals. Bitcoin, for example, has various utilitarian intentions in its White Paper, which may or may not turn out to be as envisioned, but it would not have been such a success without investors seeking remarkable returns. Embracing such technology, and promoting a shift in behaviour with such fiscal tools, can turn the industry itself towards achieving innovative solutions for environmental goals.
I realize Wolkow, et. al, are not focused on cryptocurrency and blockchain technology per se but as Huff notes in her reply, “… new lithographic techniques that we’ve developed, which could perform computations at significantly lower energy costs, would be huge for Proof of Work coins.”
Whether or not there are implications for cryptocurrencies, energy needs, climate change, etc., it’s the kind of innovative work being done by scientists at the University of Alberta which may have implications in fields far beyond the researchers’ original intentions such as more efficient computation and data storage.
ETA Aug. 6, 2018: Dexter Johnson weighed in with an August 3, 2018 posting on his Nanoclast blog (on the IEEE [Institute of Electrical and Electronics Engineers] website),
Researchers at the University of Alberta in Canada have developed a new approach to rewritable data storage technology by using a scanning tunneling microscope (STM) to remove and replace hydrogen atoms from the surface of a silicon wafer. If this approach realizes its potential, it could lead to a data storage technology capable of storing 1,000 times more data than today’s hard drives, up to 138 terabytes per square inch.
As a bit of background, Gerd Binnig and Heinrich Rohrer developed the first STM in 1986 for which they later received the Nobel Prize in physics. In the over 30 years since an STM first imaged an atom by exploiting a phenomenon known as tunneling—which causes electrons to jump from the surface atoms of a material to the tip of an ultrasharp electrode suspended a few angstroms above—the technology has become the backbone of so-called nanotechnology.
In addition to imaging the world on the atomic scale for the last thirty years, STMs have been experimented with as a potential data storage device. Last year, we reported on how IBM (where Binnig and Rohrer first developed the STM) used an STM in combination with an iron atom to serve as an electron-spin resonance sensor to read the magnetic pole of holmium atoms. The north and south poles of the holmium atoms served as the 0 and 1 of digital logic.
The Canadian researchers have taken a somewhat different approach to making an STM into a data storage device by automating a known technique that uses the ultrasharp tip of the STM to apply a voltage pulse above an atom to remove individual hydrogen atoms from the surface of a silicon wafer. Once the atom has been removed, there is a vacancy on the surface. These vacancies can be patterned on the surface to create devices and memories.
If you have the time, I recommend reading Dexter’s posting as he provides clear explanations, additional insight into the work, and more historical detail.
Vyacheslav Polonski’s (University of Oxford researcher) January 10, 2018 piece (originally published Jan. 9, 2018 on The Conversation) on phys.org isn’t a gossip article although there are parts that could be read that way. Before getting to what I consider the juicy bits (Note: Links have been removed),
Artificial intelligence [AI] can already predict the future. Police forces are using it to map when and where crime is likely to occur [Note: See my Nov. 23, 2017 posting about predictive policing in Vancouver for details about the first Canadian municipality to introduce the technology]. Doctors can use it to predict when a patient is most likely to have a heart attack or stroke. Researchers are even trying to give AI imagination so it can plan for unexpected consequences.
Many decisions in our lives require a good forecast, and AI agents are almost always better at forecasting than their human counterparts. Yet for all these technological advances, we still seem to deeply lack confidence in AI predictions. Recent cases show that people don’t like relying on AI and prefer to trust human experts, even if these experts are wrong.
The part (juicy bits) that satisfied some of my long held curiosity was this section on Watson and its life as a medical adjunct (Note: Links have been removed),
IBM’s attempt to promote its supercomputer programme to cancer doctors (Watson for Onology) was a PR [public relations] disaster. The AI promised to deliver top-quality recommendations on the treatment of 12 cancers that accounted for 80% of the world’s cases. As of today, over 14,000 patients worldwide have received advice based on its calculations.
But when doctors first interacted with Watson they found themselves in a rather difficult situation. On the one hand, if Watson provided guidance about a treatment that coincided with their own opinions, physicians did not see much value in Watson’s recommendations. The supercomputer was simply telling them what they already know, and these recommendations did not change the actual treatment. This may have given doctors some peace of mind, providing them with more confidence in their own decisions. But IBM has yet to provide evidence that Watson actually improves cancer survival rates.
On the other hand, if Watson generated a recommendation that contradicted the experts’ opinion, doctors would typically conclude that Watson wasn’t competent. And the machine wouldn’t be able to explain why its treatment was plausible because its machine learning algorithms were simply too complex to be fully understood by humans. Consequently, this has caused even more mistrust and disbelief, leading many doctors to ignore the seemingly outlandish AI recommendations and stick to their own expertise.
As a result, IBM Watson’s premier medical partner, the MD Anderson Cancer Center, recently announced it was dropping the programme. Similarly, a Danish hospital reportedly abandoned the AI programme after discovering that its cancer doctors disagreed with Watson in over two thirds of cases.
The problem with Watson for Oncology was that doctors simply didn’t trust it. Human trust is often based on our understanding of how other people think and having experience of their reliability. …
It seems to me there might be a bit more to the doctors’ trust issues and I was surprised it didn’t seem to have occurred to Polonski. Then I did some digging (from Polonski’s webpage on the Oxford Internet Institute website),
Vyacheslav Polonski (@slavacm) is a DPhil [PhD] student at the Oxford Internet Institute. His research interests are located at the intersection of network science, media studies and social psychology. Vyacheslav’s doctoral research examines the adoption and use of social network sites, focusing on the effects of social influence, social cognition and identity construction.
Vyacheslav is a Visiting Fellow at Harvard University and a Global Shaper at the World Economic Forum. He was awarded the Master of Science degree with Distinction in the Social Science of the Internet from the University of Oxford in 2013. He also obtained the Bachelor of Science degree with First Class Honours in Management from the London School of Economics and Political Science (LSE) in 2012.
Vyacheslav was honoured at the British Council International Student of the Year 2011 awards, and was named UK’s Student of the Year 2012 and national winner of the Future Business Leader of the Year 2012 awards by TARGETjobs.
Previously, he has worked as a management consultant at Roland Berger Strategy Consultants and gained further work experience at the World Economic Forum, PwC, Mars, Bertelsmann and Amazon.com. Besides, he was involved in several start-ups as part of the 2012 cohort of Entrepreneur First and as part of the founding team of the London office of Rocket Internet. Vyacheslav was the junior editor of the bi-lingual book ‘Inspire a Nation‘ about Barack Obama’s first presidential election campaign. In 2013, he was invited to be a keynote speaker at the inaugural TEDx conference of IE University in Spain to discuss the role of a networked mindset in everyday life.
Vyacheslav is fluent in German, English and Russian, and is passionate about new technologies, social entrepreneurship, philanthropy, philosophy and modern art.
Network science, social network analysis, online communities, agency and structure, group dynamics, social interaction, big data, critical mass, network effects, knowledge networks, information diffusion, product adoption
Positions held at the OII
DPhil student, October 2013 –
MSc Student, October 2012 – August 2013
Polonski doesn’t seem to have any experience dealing with, participating in, or studying the medical community. Getting a doctor to admit that his or her approach to a particular patient’s condition was wrong or misguided runs counter to their training and, by extension, the institution of medicine. Also, one of the biggest problems in any field is getting people to change and it’s not always about trust. In this instance, you’re asking a doctor to back someone else’s opinion after he or she has rendered theirs. This is difficult even when the other party is another human doctor let alone a form of artificial intelligence.
If you want to get a sense of just how hard it is to get someone to back down after they’ve committed to a position, read this January 10, 2018 essay by Lara Bazelon, an associate professor at the University of San Francisco School of Law. This is just one of the cases (Note: Links have been removed),
Davontae Sanford was 14 years old when he confessed to murdering four people in a drug house on Detroit’s East Side. Left alone with detectives in a late-night interrogation, Sanford says he broke down after being told he could go home if he gave them “something.” On the advice of a lawyer whose license was later suspended for misconduct, Sanders pleaded guilty in the middle of his March 2008 trial and received a sentence of 39 to 92 years in prison.
Sixteen days after Sanford was sentenced, a hit man named Vincent Smothers told the police he had carried out 12 contract killings, including the four Sanford had pleaded guilty to committing. Smothers explained that he’d worked with an accomplice, Ernest Davis, and he provided a wealth of corroborating details to back up his account. Smothers told police where they could find one of the weapons used in the murders; the gun was recovered and ballistics matched it to the crime scene. He also told the police he had used a different gun in several of the other murders, which ballistics tests confirmed. Once Smothers’ confession was corroborated, it was clear Sanford was innocent. Smothers made this point explicitly in an 2015 affidavit, emphasizing that Sanford hadn’t been involved in the crimes “in any way.”
Guess what happened? (Note: Links have been removed),
But Smothers and Davis were never charged. Neither was Leroy Payne, the man Smothers alleged had paid him to commit the murders. …
Davontae Sanford, meanwhile, remained behind bars, locked up for crimes he very clearly didn’t commit.
Police failed to turn over all the relevant information in Smothers’ confession to Sanford’s legal team, as the law required them to do. When that information was leaked in 2009, Sanford’s attorneys sought to reverse his conviction on the basis of actual innocence. Wayne County Prosecutor Kym Worthy fought back, opposing the motion all the way to the Michigan Supreme Court. In 2014, the court sided with Worthy, ruling that actual innocence was not a valid reason to withdraw a guilty plea [emphasis mine]. Sanford would remain in prison for another two years.
Doctors are just as invested in their opinions and professional judgments as lawyers (just like the prosecutor and the judges on the Michigan Supreme Court) are.
There is one more problem. From the doctor’s (or anyone else’s perspective), if the AI is making the decisions, why do he/she need to be there? At best it’s as if AI were turning the doctor into its servant or, at worst, replacing the doctor. Polonski alludes to the problem in one of his solutions to the ‘trust’ issue (Note: A link has been removed),
Research suggests involving people more in the AI decision-making process could also improve trust and allow the AI to learn from human experience. For example,one study showed people were given the freedom to slightly modify an algorithm felt more satisfied with its decisions, more likely to believe it was superior and more likely to use it in the future.
Having input into the AI decision-making process somewhat addresses one of the problems but the commitment to one’s own judgment even when there is overwhelming evidence to the contrary is a perennially thorny problem. The legal case mentioned here earlier is clearly one where the contrarian is wrong but it’s not always that obvious. As well, sometimes, people who hold out against the majority are right.
Getting back to building trust, it turns out the US Army Research Laboratory is also interested in transparency where AI is concerned (from a January 11, 2018 US Army news release on EurekAlert),
U.S. Army Research Laboratory [ARL] scientists developed ways to improve collaboration between humans and artificially intelligent agents in two projects recently completed for the Autonomy Research Pilot Initiative supported by the Office of Secretary of Defense. They did so by enhancing the agent transparency [emphasis mine], which refers to a robot, unmanned vehicle, or software agent’s ability to convey to humans its intent, performance, future plans, and reasoning process.
“As machine agents become more sophisticated and independent, it is critical for their human counterparts to understand their intent, behaviors, reasoning process behind those behaviors, and expected outcomes so the humans can properly calibrate their trust [emphasis mine] in the systems and make appropriate decisions,” explained ARL’s Dr. Jessie Chen, senior research psychologist.
The U.S. Defense Science Board, in a 2016 report, identified six barriers to human trust in autonomous systems, with ‘low observability, predictability, directability and auditability’ as well as ‘low mutual understanding of common goals’ being among the key issues.
In order to address these issues, Chen and her colleagues developed the Situation awareness-based Agent Transparency, or SAT, model and measured its effectiveness on human-agent team performance in a series of human factors studies supported by the ARPI. The SAT model deals with the information requirements from an agent to its human collaborator in order for the human to obtain effective situation awareness of the agent in its tasking environment. At the first SAT level, the agent provides the operator with the basic information about its current state and goals, intentions, and plans. At the second level, the agent reveals its reasoning process as well as the constraints/affordances that the agent considers when planning its actions. At the third SAT level, the agent provides the operator with information regarding its projection of future states, predicted consequences, likelihood of success/failure, and any uncertainty associated with the aforementioned projections.
In one of the ARPI projects, IMPACT, a research program on human-agent teaming for management of multiple heterogeneous unmanned vehicles, ARL’s experimental effort focused on examining the effects of levels of agent transparency, based on the SAT model, on human operators’ decision making during military scenarios. The results of a series of human factors experiments collectively suggest that transparency on the part of the agent benefits the human’s decision making and thus the overall human-agent team performance. More specifically, researchers said the human’s trust in the agent was significantly better calibrated — accepting the agent’s plan when it is correct and rejecting it when it is incorrect– when the agent had a higher level of transparency.
The other project related to agent transparency that Chen and her colleagues performed under the ARPI was Autonomous Squad Member, on which ARL collaborated with Naval Research Laboratory scientists. The ASM is a small ground robot that interacts with and communicates with an infantry squad. As part of the overall ASM program, Chen’s group developed transparency visualization concepts, which they used to investigate the effects of agent transparency levels on operator performance. Informed by the SAT model, the ASM’s user interface features an at a glance transparency module where user-tested iconographic representations of the agent’s plans, motivator, and projected outcomes are used to promote transparent interaction with the agent. A series of human factors studies on the ASM’s user interface have investigated the effects of agent transparency on the human teammate’s situation awareness, trust in the ASM, and workload. The results, consistent with the IMPACT project’s findings, demonstrated the positive effects of agent transparency on the human’s task performance without increase of perceived workload. The research participants also reported that they felt the ASM as more trustworthy, intelligent, and human-like when it conveyed greater levels of transparency.
Chen and her colleagues are currently expanding the SAT model into bidirectional transparency between the human and the agent.
“Bidirectional transparency, although conceptually straightforward–human and agent being mutually transparent about their reasoning process–can be quite challenging to implement in real time. However, transparency on the part of the human should support the agent’s planning and performance–just as agent transparency can support the human’s situation awareness and task performance, which we have demonstrated in our studies,” Chen hypothesized.
The challenge is to design the user interfaces, which can include visual, auditory, and other modalities, that can support bidirectional transparency dynamically, in real time, while not overwhelming the human with too much information and burden.
Interesting, yes? Here’s a link and a citation for the paper,
I recently published [March 20, 2018] a piece on ‘smart cities’ both an art/science event in Toronto and a Canadian government initiative without mentioning the necessity of new technology to support all of the grand plans. On that note, it seems the Canadian federal government and two provincial (Québec and Ontario) governments are prepared to invest in one of the necessary ‘new’ technologies, 5G wireless. The Canadian Broadcasting Corporation’s (CBC) Shawn Benjamin reports about Canada’s 5G plans in suitably breathless (even in text only) tones of excitement in a March 19, 2018 article,
The federal, Ontario and Quebec governments say they will spend $200 million to help fund research into 5G wireless technology, the next-generation networks with download speeds 100 times faster than current ones can handle.
The so-called “5G corridor,” known as ENCQOR, will see tech companies such as Ericsson, Ciena Canada, Thales Canada, IBM and CGI kick in another $200 million to develop facilities to get the project up and running.
The idea is to set up a network of linked research facilities and laboratories that these companies — and as many as 1,000 more across Canada — will be able to use to test products and services that run on 5G networks.
Benjamin’s description of 5G is focused on what it will make possible in the future,
If you think things are moving too fast, buckle up, because a new 5G cellular network is just around the corner and it promises to transform our lives by connecting nearly everything to a new, much faster, reliable wireless network.
The first networks won’t be operational for at least a few years, but technology and telecom companies around the world are already planning to spend billions to make sure they aren’t left behind, says Lawrence Surtees, a communications analyst with the research firm IDC.
The new 5G is no tentative baby step toward the future. Rather, as Surtees puts it, “the move from 4G to 5G is a quantum leap.”
In a downtown Toronto soundstage, Alan Smithson recently demonstrated a few virtual reality and augmented reality projects that his company MetaVRse is working on.
The potential for VR and AR technology is endless, he said, in large part for its potential to help hurdle some of the walls we are already seeing with current networks.
Virtual Reality technology on the market today is continually increasing things like frame rates and screen resolutions in a constant quest to make their devices even more lifelike.
… They [current 4G networks] can’t handle the load. But 5G can do so easily, Smithson said, so much so that the current era of bulky augmented reality headsets could be replaced buy a pair of normal looking glasses.
In a 5G world, those internet-connected glasses will automatically recognize everyone you meet, and possibly be able to overlay their name in your field of vision, along with a link to their online profile. …
Benjamin also mentions ‘smart cities’,
In a University of Toronto laboratory, Professor Alberto Leon-Garcia researches connected vehicles and smart power grids. “My passion right now is enabling smart cities — making smart cities a reality — and that means having much more immediate and detailed sense of the environment,” he said.
Faster 5G networks will assist his projects in many ways, by giving planners more, instant data on things like traffic patterns, energy consumption, variou carbon footprints and much more.
Leon-Garcia points to a brightly lit map of Toronto [image embedded in Benjamin’s article] in his office, and explains that every dot of light represents a sensor transmitting real time data.
Currently, the network is hooked up to things like city buses, traffic cameras and the city-owned fleet of shared bicycles. He currently has thousands of data points feeding him info on his map, but in a 5G world, the network will support about a million sensors per square kilometre.
Very exciting but where is all this data going? What computers will be processing the information? Where are these sensors located? Benjamin does not venture into those waters nor does The Economist in a February 13, 2018 article about 5G, the Olympic Games in Pyeonchang, South Korea, but the magazine does note another barrier to 5G implementation,
“FASTER, higher, stronger,” goes the Olympic motto. So it is only appropriate that the next generation of wireless technology, “5G” for short, should get its first showcase at the Winter Olympics under way in Pyeongchang, South Korea. Once fully developed, it is supposed to offer download speeds of at least 20 gigabits per second (4G manages about half that at best) and response times (“latency”) of below 1 millisecond. So the new networks will be able to transfer a high-resolution movie in two seconds and respond to requests in less than a hundredth of the time it takes to blink an eye. But 5G is not just about faster and swifter wireless connections.
The technology is meant to enable all sorts of new services. One such would offer virtual- or augmented-reality experiences. At the Olympics, for example, many contestants are being followed by 360-degree video cameras. At special venues sports fans can don virtual-reality goggles to put themselves right into the action. But 5G is also supposed to become the connective tissue for the internet of things, to link anything from smartphones to wireless sensors and industrial robots to self-driving cars. This will be made possible by a technique called “network slicing”, which allows operators quickly to create bespoke networks that give each set of devices exactly the connectivity they need.
Despite its versatility, it is not clear how quickly 5G will take off. The biggest brake will be economic. [emphasis mine] When the GSMA, an industry group, last year asked 750 telecoms bosses about the most salient impediment to delivering 5G, more than half cited the lack of a clear business case. People may want more bandwidth, but they are not willing to pay for it—an attitude even the lure of the fanciest virtual-reality applications may not change. …
That may not be the only brake, Dexter Johnson in a March 19, 2018 posting on his Nanoclast blog (on the IEEE [Institute of Electrical and Electronics Engineers] website), covers some of the others (Note: Links have been removed),
Graphene has been heralded as a “wonder material” for well over a decade now, and 5G has been marketed as the next big thing for at least the past five years. Analysts have suggested that 5G could be the golden ticket to virtual reality and artificial intelligence, and promised that graphene could improve technologies within electronics and optoelectronics.
But proponents of both graphene and 5G have also been accused of stirring up hype. There now seems to be a rising sense within industry circles that these glowing technological prospects will not come anytime soon.
At Mobile World Congress (MWC) in Barcelona last month [February 2018], some misgivings for these long promised technologies may have been put to rest, though, thanks in large part to each other.
In a meeting at MWC with Jari Kinaret, a professor at Chalmers University in Sweden and director of the Graphene Flagship, I took a guided tour around the Pavilion to see some of the technologies poised to have an impact on the development of 5G.
Being invited back to the MWC for three years is a pretty clear indication of how important graphene is to those who are trying to raise the fortunes of 5G. But just how important became more obvious to me in an interview with Frank Koppens, the leader of the quantum nano-optoelectronic group at Institute of Photonic Sciences (ICFO) just outside of Barcelona, last year.
He said: “5G cannot just scale. Some new technology is needed. And that’s why we have several companies in the Graphene Flagship that are putting a lot of pressure on us to address this issue.”
In a collaboration led by CNIT—a consortium of Italian universities and national laboratories focused on communication technologies—researchers from AMO GmbH, Ericsson, Nokia Bell Labs, and Imec have developed graphene-based photodetectors and modulators capable of receiving and transmitting optical data faster than ever before.
The aim of all this speed for transmitting data is to support the ultrafast data streams with extreme bandwidth that will be part of 5G. In fact, at another section during MWC, Ericsson was presenting the switching of a 100 Gigabits per second (Gbps) channel based on the technology.
“The fact that Ericsson is demonstrating another version of this technology demonstrates that from Ericsson’s point of view, this is no longer just research” said Kinaret.
It’s no mystery why the big mobile companies are jumping on this technology. Not only does it provide high-speed data transmission, but it also does it 10 times more efficiently than silicon or doped silicon devices, and will eventually do it more cheaply than those devices, according to Vito Sorianello, senior researcher at CNIT.
Interestingly, Ericsson is one of the tech companies mentioned with regard to Canada’s 5G project, ENCQOR and Sweden’s Chalmers University, as Dexter Johnson notes, is the lead institution for the Graphene Flagship.. One other fact to note, Canada’s resources include graphite mines with ‘premium’ flakes for producing graphene. Canada’s graphite mines are located (as far as I know) in only two Canadian provinces, Ontario and Québec, which also happen to be pitching money into ENCQOR. My March 21, 2018 posting describes the latest entry into the Canadian graphite mining stakes.
As for the questions I posed about processing power, etc. It seems the South Koreans have found answers of some kind but it’s hard to evaluate as I haven’t found any additional information about 5G and its implementation in South Korea. If anyone has answers, please feel free to leave them in the ‘comments’. Thank you.
It’s that time of year again. The entertainment conference such as South by South West (SXSW) is being held from March 9-18, 2018. The science portion of the conference can be found in the Intelligent Future sessions, from the description,
Imagine a new kind of computer that can quickly solve problems that would stump even the world’s most powerful supercomputers. Quantum computers are fundamentally different. They can store information as not only just ones and zeros, but in all the shades of gray in-between. Several companies and government agencies are investing billions of dollars in the field of quantum information. But what will quantum computers be used for?
Antia Lamas-Linares is a Research Associate in the High Performance Computing group at TACC. Her background is as an experimentalist with quantum computing systems, including work done with them at the Centre for Quantum Technologies in Singapore. She joins podcast host Jorge Salazar to talk about her South by Southwest panel and about some of her latest research on quantum information.
Lamas-Linares co-authored a study (doi: 10.1117/12.2290561) in the Proceedings of the SPIE, The International Society for Optical Engineering, that published in February of 2018. The study, “Secure Quantum Clock Synchronization,” proposed a protocol to verify and secure time synchronization of distant atomic clocks, such as those used for GPS signals in cell phone towers and other places. “It’s important work,” explained Lamas-Linares, “because people are worried about malicious parties messing with the channels of GPS. What James Troupe (Applied Research Laboratories, UT Austin) and I looked at was whether we can use techniques from quantum cryptography and quantum information to make something that is inherently unspoofable.”
Antia Lamas-Linares: The most important thing is that quantum technologies is a really exciting field. And it’s exciting in a fundamental sense. We don’t quite know what we’re going to get out of it. We know a few things, and that’s good enough to drive research. But the things we don’t know are much broader than the things we know, and it’s going to be really interesting. Keep your eyes open for this.
Texas Advanced Computing Center at University of Texas
Startups and established players have sold 2000 Qubit systems, made freely available cloud access to quantum computer processors, and created large scale open source initiatives, all taking quantum computing from science fiction to science fact. Government labs and others like IBM, Microsoft, Google are developing software for quantum computers. What problems will be solved with this quantum leap in computing power that cannot be solved today with the world’s most powerful supercomputers?
[Programming descriptions are generated by participants and do not necessarily reflect the opinions of SXSW.]
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Primary Entry: Platinum Badge, Interactive Badge
Secondary Entry: Music Badge, Film Badge
Event Type: Session
Track: Intelligent Future
I wonder what ‘level’ means? I was not able to find an answer (quickly).
It’s was a bit surprising to find someone from D-Wave Systems (a Vancouver-based quantum computing based enterprise) at an entertainment conference. Still, it shouldn’t have been. Two other examples immediately come to mind, the TED (technology, entertainment, and design) conferences have been melding technology, if not science, with creative activities of all kinds for many years (TED 2018: The Age of Amazement, April 10 -14, 2018 in Vancouver [Canada]) and Beakerhead (2018 dates: Sept. 19 – 23) has been melding art, science, and engineering in a festival held in Calgary (Canada) since 2013. One comment about TED, it was held for several years in California (1984, 1990 – 2013) and moved to Vancouver in 2014.
For anyone wanting to browse the 2018 SxSW Intelligent Future sessions online, go here. or wanting to hear Antia Lamas-Linares talk about quantum computing, there’s the interview with Jorge Salazar (mentioned in the news release),
In a recent (Tuesday, March 6, 2018) live stream ‘conversation’ (‘Science in Canada; Investing in Canadian Innovation’ now published on YouTube) between Canadian Prime Minister, Justin Trudeau, and US science communicator, Bill Nye, at the University of Ottawa, they discussed, amongst many other topics, what AI (artificial intelligence) can and can’t do. They seemed to agree that AI can’t be creative, i.e., write poetry, create works of art, make jokes, etc. A conclusion which is both (in my opinion) true and not true.
There are times when I think the joke may be on us (humans). Take for example this March 6, 2018 story by Alexis Madrigal for The Atlantic magazine (Note: Links have been removed),
SkyKnit: How an AI Took Over an Adult Knitting Community
Ribald knitters teamed up with a neural-network creator to generate new types of tentacled, cozy shapes.
Janelle Shane is a humorist [Note: She describes herself as a “Research Scientist in optics. Plays with neural networks. …” in her Twitter bio.] who creates and mines her material from neural networks, the form of machine learning that has come to dominate the field of artificial intelligence over the last half-decade.
Perhaps you’ve seen the candy-heart slogans she generated for Valentine’s Day: DEAR ME, MY MY, LOVE BOT, CUTE KISS, MY BEAR, and LOVE BUN.
Or her new paint-color names: Parp Green, Shy Bather, Farty Red, and Bull Cream.
Or her neural-net-generated Halloween costumes: Punk Tree, Disco Monster, Spartan Gandalf, Starfleet Shark, and A Masked Box.
Her latest project, still ongoing, pushes the joke into a new, physical realm. Prodded by a knitter on the knitting forum Ravelry, Shane trained a type of neural network on a series of over 500 sets of knitting instructions. Then, she generated new instructions, which members of the Ravelry community have actually attempted to knit.
“The knitting project has been a particularly fun one so far just because it ended up being a dialogue between this computer program and these knitters that went over my head in a lot of ways,” Shane told me. “The computer would spit out a whole bunch of instructions that I couldn’t read and the knitters would say, this is the funniest thing I’ve ever read.”
It appears that the project evolved,
The human-machine collaboration created configurations of yarn that you probably wouldn’t give to your in-laws for Christmas, but they were interesting. The user citikas was the first to post a try at one of the earliest patterns, “reverss shawl.” It was strange, but it did have some charisma.
Shane nicknamed the whole effort “Project Hilarious Disaster.” The community called it SkyKnit.
I’m not sure what’s meant by “community” as mentioned in the previous excerpt. Are we talking about humans only, AI only, or both humans and AI?
Here’s some of what underlies Skyknit (Note: Links have been removed),
The different networks all attempt to model the data they’ve been fed by tuning a vast, funky flowchart. After you’ve created a statistical model that describes your real data, you can also roll the dice and generate new, never-before-seen data of the same kind.
How this works—like, the math behind it—is very hard to visualize because values inside the model can have hundreds of dimensions and we are humble three-dimensional creatures moving through time. But as the neural-network enthusiast Robin Sloan puts it, “So what? It turns out imaginary spaces are useful even if you can’t, in fact, imagine them.”
Out of that ferment, a new kind of art has emerged. Its practitioners use neural networks not to attain practical results, but to see what’s lurking in the these vast, opaque systems. What did the machines learn about the world as they attempted to understand the data they’d been fed? Famously, Google released DeepDream, which produced trippy visualizations that also demonstrated how that type of neural network processed the textures and objects in its source imagery.
Madrigal’s article is well worth reading if you have the time. You can also supplement Madrigal’s piece with an August 9, 2017 article about Janelle Shane’s algorithmic experiments by Jacob Brogan for slate.com.
SkyKnit fancy addite rifopshent
Fingering (14 wpi) ?
24 stitches and 30 rows = 4 inches
in stockinette stitch
US 4 – 3.5 mm
This pattern is available as a free Ravelry download
SkyKnit is a type of machine learning algorithm called an artificial neural network. Its creator, Janelle Shane of AIweirdness.com, gave it 88,000 lines of knitting instructions from Stitch-Maps.com and Ravelry, and it taught itself how to make new patterns. Join the discussion!
SkyKnit seems to have created something that has paralell columns, and is reversible. Perhaps a scarf?
Test-knitting & image courtesy of Chatelaine
Patterns may include notes from testknitters; yarn, needles, and gauge are totally at your discretion.
About the designer
SkyKnit’s favorites include lace, tentacles, and totally not the elimination of the human race.
For more information, see: http://aiweirdness.com/
If you’ve been on the internet today, you’ve probably interacted with a neural network. They’re a type of machine learning algorithm that’s used for everything from language translation to finance modeling. One of their specialties is image recognition. Several companies – including Google, Microsoft, IBM, and Facebook – have their own algorithms for labeling photos. But image recognition algorithms can make really bizarre mistakes.
Microsoft Azure’s computer vision API [application programming interface] added the above caption and tags. But there are no sheep in the image of above. None. I zoomed all the way in and inspected every speck.
I have become quite interested in Shane’s self descriptions such as this one from the aiweirdness.com website,
I train neural networks, a type of machine learning algorithm, to write unintentional humor as they struggle to imitate human datasets. Well, I intend the humor. The neural networks are just doing their best to understand what’s going on. Currently located on the occupied land of the Arapahoe Nation.
As for the joke being on us, I can’t help remembering the Facebook bots that developed their own language (Facebotlish), and were featured in my June 30, 2017 posting, There’s a certain eerieness to it all, which seems an appropriate response in a year celebrating the 200th anniversary of Mary Shelley’s 1818 book, Frankenstein; or, the Modern Prometheus. I’m closing with a video clip from the 1931 movie,
A November 21, 2017 news item on Nanowerk describes a rather extraordinary (to me, anyway) approach to using CRRISP ( Clustered Regularly Interspaced Short Palindromic Repeats)-CAS9 (Note: A link has been removed),
A team of scientists led by Virginia Commonwealth University physicist Jason Reed, Ph.D., have developed new nanomapping technology that could transform the way disease-causing genetic mutations are diagnosed and discovered. Described in a study published today [November 21, 2017] in the journal Nature Communications (“DNA nanomapping using CRISPR-Cas9 as a programmable nanoparticle”), this novel approach uses high-speed atomic force microscopy (AFM) combined with a CRISPR-based chemical barcoding technique to map DNA nearly as accurately as DNA sequencing while processing large sections of the genome at a much faster rate. What’s more–the technology can be powered by parts found in your run-of-the-mill DVD player.
The human genome is made up of billions of DNA base pairs. Unraveled, it stretches to a length of nearly six feet long. When cells divide, they must make a copy of their DNA for the new cell. However, sometimes various sections of the DNA are copied incorrectly or pasted together at the wrong location, leading to genetic mutations that cause diseases such as cancer. DNA sequencing is so precise that it can analyze individual base pairs of DNA. But in order to analyze large sections of the genome to find genetic mutations, technicians must determine millions of tiny sequences and then piece them together with computer software. In contrast, biomedical imaging techniques such as fluorescence in situ hybridization, known as FISH, can only analyze DNA at a resolution of several hundred thousand base pairs.
Reed’s new high-speed AFM method can map DNA to a resolution of tens of base pairs while creating images up to a million base pairs in size. And it does it using a fraction of the amount of specimen required for DNA sequencing.
“DNA sequencing is a powerful tool, but it is still quite expensive and has several technological and functional limitations that make it difficult to map large areas of the genome efficiently and accurately,” said Reed, principal investigator on the study. Reed is a member of the Cancer Molecular Genetics research program at VCU Massey Cancer Center and an associate professor in the Department of Physics in the College of Humanities and Sciences.
“Our approach bridges the gap between DNA sequencing and other physical mapping techniques that lack resolution,” he said. “It can be used as a stand-alone method or it can complement DNA sequencing by reducing complexity and error when piecing together the small bits of genome analyzed during the sequencing process.”
IBM scientists made headlines in 1989 when they developed AFM technology and used a related technique to rearrange molecules at the atomic level to spell out “IBM.” AFM achieves this level of detail by using a microscopic stylus — similar to a needle on a record player — that barely makes contact with the surface of the material being studied. The interaction between the stylus and the molecules creates the image. However, traditional AFM is too slow for medical applications and so it is primarily used by engineers in materials science.
“Our device works in the same fashion as AFM but we move the sample past the stylus at a much greater velocity and use optical instruments to detect the interaction between the stylus and the molecules. We can achieve the same level of detail as traditional AFM but can process material more than a thousand times faster,” said Reed, whose team proved the technology can be mainstreamed by using optical equipment found in DVD players. “High-speed AFM is ideally suited for some medical applications as it can process materials quickly and provide hundreds of times more resolution than comparable imaging methods.”
Increasing the speed of AFM was just one hurdle Reed and his colleagues had to overcome. In order to actually identify genetic mutations in DNA, they had to develop a way to place markers or labels on the surface of the DNA molecules so they could recognize patterns and irregularities. An ingenious chemical barcoding solution was developed using a form of CRISPR technology.
CRISPR has made a lot of headlines recently in regard to gene editing. CRISPR is an enzyme that scientists have been able to “program” using targeting RNA in order to cut DNA at precise locations that the cell then repairs on its own. Reed’s team altered the chemical reaction conditions of the CRISPR enzyme so that it only sticks to the DNA and does not actually cut it.
“Because the CRISPR enzyme is a protein that’s physically bigger than the DNA molecule, it’s perfect for this barcoding application,” Reed said. “We were amazed to discover this method is nearly 90 percent efficient at bonding to the DNA molecules. And because it’s easy to see the CRISPR proteins, you can spot genetic mutations among the patterns in DNA.”
To demonstrate the technique’s effectiveness, the researchers mapped genetic translocations present in lymph node biopsies of lymphoma patients. Translocations occur when one section of the DNA gets copied and pasted to the wrong place in the genome. They are especially prevalent in blood cancers such as lymphoma but occur in other cancers as well.
While there are many potential uses for this technology, Reed and his team are focusing on medical applications. They are currently developing software based on existing algorithms that can analyze patterns in sections of DNA up to and over a million base pairs in size. Once completed, it would not be hard to imagine this shoebox-sized instrument in pathology labs assisting in the diagnosis and treatment of diseases linked to genetic mutations.
Here’s a link to and a citation for the paper,
DNA nanomapping using CRISPR-Cas9 as a programmable nanoparticle by Andrey Mikheikin, Anita Olsen, Kevin Leslie, Freddie Russell-Pavier, Andrew Yacoot, Loren Picco, Oliver Payton, Amir Toor, Alden Chesney, James K. Gimzewski, Bud Mishra, & Jason Reed. Nature Communications 8, Article number: 1665 (2017) doi:10.1038/s41467-017-01891-9 Published online: 21 November 2017
One of the winners in Canada’s 2017 federal budget announcement of the Pan-Canadian Artificial Intelligence Strategy was Edmonton, Alberta. It’s a fact which sometimes goes unnoticed while Canadians marvel at the wonderfulness found in Toronto and Montréal where it seems new initiatives and monies are being announced on a weekly basis (I exaggerate) for their AI (artificial intelligence) efforts.
Intriguingly, it seems that Edmonton has higher aims than (an almost unnoticed) leadership in AI. Physicists at the University of Alberta have announced hopes to be just as successful as their AI brethren in a Nov. 27, 2017 article by Juris Graney for the Edmonton Journal,
Physicists at the University of Alberta [U of A] are hoping to emulate the success of their artificial intelligence studying counterparts in establishing the city and the province as the nucleus of quantum nanotechnology research in Canada and North America.
Google’s artificial intelligence research division DeepMind announced in July  it had chosen Edmonton as its first international AI research lab, based on a long-running partnership with the U of A’s 10-person AI lab.
Retaining the brightest minds in the AI and machine-learning fields while enticing a global tech leader to Alberta was heralded as a coup for the province and the university.
It is something U of A physics professor John Davis believes the university’s new graduate program, Quanta, can help achieve in the world of quantum nanotechnology.
The field of quantum mechanics had long been a realm of theoretical science based on the theory that atomic and subatomic material like photons or electrons behave both as particles and waves.
“When you get right down to it, everything has both behaviours (particle and wave) and we can pick and choose certain scenarios which one of those properties we want to use,” he said.
But, Davis said, physicists and scientists are “now at the point where we understand quantum physics and are developing quantum technology to take to the marketplace.”
“Quantum computing used to be realm of science fiction, but now we’ve figured it out, it’s now a matter of engineering,” he said.
Quantum computing labs are being bought by large tech companies such as Google, IBM and Microsoft because they realize they are only a few years away from having this power, he said.
Those making the groundbreaking developments may want to commercialize their finds and take the technology to market and that is where Quanta comes in.
East vs. West—Again?
Ivan Semeniuk in his article, Quantum Supremacy, ignores any quantum research effort not located in either Waterloo, Ontario or metro Vancouver, British Columbia to describe a struggle between the East and the West (a standard Canadian trope). From Semeniuk’s Oct. 17, 2017 quantum article [link follows the excerpts] for the Globe and Mail’s October 2017 issue of the Report on Business (ROB),
Lazaridis [Mike], of course, has experienced lost advantage first-hand. As co-founder and former co-CEO of Research in Motion (RIM, now called Blackberry), he made the smartphone an indispensable feature of the modern world, only to watch rivals such as Apple and Samsung wrest away Blackberry’s dominance. Now, at 56, he is engaged in a high-stakes race that will determine who will lead the next technology revolution. In the rolling heartland of southwestern Ontario, he is laying the foundation for what he envisions as a new Silicon Valley—a commercial hub based on the promise of quantum technology.
Semeniuk skips over the story of how Blackberry lost its advantage. I came onto that story late in the game when Blackberry was already in serious trouble due to a failure to recognize that the field they helped to create was moving in a new direction. If memory serves, they were trying to keep their technology wholly proprietary which meant that developers couldn’t easily create apps to extend the phone’s features. Blackberry also fought a legal battle in the US with a patent troll draining company resources and energy in proved to be a futile effort.
Since then Lazaridis has invested heavily in quantum research. He gave the University of Waterloo a serious chunk of money as they named their Quantum Nano Centre (QNC) after him and his wife, Ophelia (you can read all about it in my Sept. 25, 2012 posting about the then new centre). The best details for Lazaridis’ investments in Canada’s quantum technology are to be found on the Quantum Valley Investments, About QVI, History webpage,
History has repeatedly demonstrated the power of research in physics to transform society. As a student of history and a believer in the power of physics, Mike Lazaridis set out in 2000 to make real his bold vision to establish the Region of Waterloo as a world leading centre for physics research. That is, a place where the best researchers in the world would come to do cutting-edge research and to collaborate with each other and in so doing, achieve transformative discoveries that would lead to the commercialization of breakthrough technologies.
Establishing a World Class Centre in Quantum Research:
The first step in this regard was the establishment of the Perimeter Institute for Theoretical Physics. Perimeter was established in 2000 as an independent theoretical physics research institute. Mike started Perimeter with an initial pledge of $100 million (which at the time was approximately one third of his net worth). Since that time, Mike and his family have donated a total of more than $170 million to the Perimeter Institute. In addition to this unprecedented monetary support, Mike also devotes his time and influence to help lead and support the organization in everything from the raising of funds with government and private donors to helping to attract the top researchers from around the globe to it. Mike’s efforts helped Perimeter achieve and grow its position as one of a handful of leading centres globally for theoretical research in fundamental physics.
Perimeter is located in a Governor-General award winning designed building in Waterloo. Success in recruiting and resulting space requirements led to an expansion of the Perimeter facility. A uniquely designed addition, which has been described as space-ship-like, was opened in 2011 as the Stephen Hawking Centre in recognition of one of the most famous physicists alive today who holds the position of Distinguished Visiting Research Chair at Perimeter and is a strong friend and supporter of the organization.
Recognizing the need for collaboration between theorists and experimentalists, in 2002, Mike applied his passion and his financial resources toward the establishment of The Institute for Quantum Computing at the University of Waterloo. IQC was established as an experimental research institute focusing on quantum information. Mike established IQC with an initial donation of $33.3 million. Since that time, Mike and his family have donated a total of more than $120 million to the University of Waterloo for IQC and other related science initiatives. As in the case of the Perimeter Institute, Mike devotes considerable time and influence to help lead and support IQC in fundraising and recruiting efforts. Mike’s efforts have helped IQC become one of the top experimental physics research institutes in the world.
Mike and Doug Fregin have been close friends since grade 5. They are also co-founders of BlackBerry (formerly Research In Motion Limited). Doug shares Mike’s passion for physics and supported Mike’s efforts at the Perimeter Institute with an initial gift of $10 million. Since that time Doug has donated a total of $30 million to Perimeter Institute. Separately, Doug helped establish the Waterloo Institute for Nanotechnology at the University of Waterloo with total gifts for $29 million. As suggested by its name, WIN is devoted to research in the area of nanotechnology. It has established as an area of primary focus the intersection of nanotechnology and quantum physics.
With a donation of $50 million from Mike which was matched by both the Government of Canada and the province of Ontario as well as a donation of $10 million from Doug, the University of Waterloo built the Mike & Ophelia Lazaridis Quantum-Nano Centre, a state of the art laboratory located on the main campus of the University of Waterloo that rivals the best facilities in the world. QNC was opened in September 2012 and houses researchers from both IQC and WIN.
Leading the Establishment of Commercialization Culture for Quantum Technologies in Canada:
For many years, theorists have been able to demonstrate the transformative powers of quantum mechanics on paper. That said, converting these theories to experimentally demonstrable discoveries has, putting it mildly, been a challenge. Many naysayers have suggested that achieving these discoveries was not possible and even the believers suggested that it could likely take decades to achieve these discoveries. Recently, a buzz has been developing globally as experimentalists have been able to achieve demonstrable success with respect to Quantum Information based discoveries. Local experimentalists are very much playing a leading role in this regard. It is believed by many that breakthrough discoveries that will lead to commercialization opportunities may be achieved in the next few years and certainly within the next decade.
Recognizing the unique challenges for the commercialization of quantum technologies (including risk associated with uncertainty of success, complexity of the underlying science and high capital / equipment costs) Mike and Doug have chosen to once again lead by example. The Quantum Valley Investment Fund will provide commercialization funding, expertise and support for researchers that develop breakthroughs in Quantum Information Science that can reasonably lead to new commercializable technologies and applications. Their goal in establishing this Fund is to lead in the development of a commercialization infrastructure and culture for Quantum discoveries in Canada and thereby enable such discoveries to remain here.
Semeniuk goes on to set the stage for Waterloo/Lazaridis vs. Vancouver (from Semeniuk’s 2017 ROB article),
… as happened with Blackberry, the world is once again catching up. While Canada’s funding of quantum technology ranks among the top five in the world, the European Union, China, and the US are all accelerating their investments in the field. Tech giants such as Google [also known as Alphabet], Microsoft and IBM are ramping up programs to develop companies and other technologies based on quantum principles. Meanwhile, even as Lazaridis works to establish Waterloo as the country’s quantum hub, a Vancouver-area company has emerged to challenge that claim. The two camps—one methodically focused on the long game, the other keen to stake an early commercial lead—have sparked an East-West rivalry that many observers of the Canadian quantum scene are at a loss to explain.
Is it possible that some of the rivalry might be due to an influential individual who has invested heavily in a ‘quantum valley’ and has a history of trying to ‘own’ a technology?
Getting back to D-Wave Systems, the Vancouver company, I have written about them a number of times (particularly in 2015; for the full list: input D-Wave into the blog search engine). This June 26, 2015 posting includes a reference to an article in The Economist magazine about D-Wave’s commercial opportunities while the bulk of the posting is focused on a technical breakthrough.
Semeniuk offers an overview of the D-Wave Systems story,
D-Wave was born in 1999, the same year Lazaridis began to fund quantum science in Waterloo. From the start, D-Wave had a more immediate goal: to develop a new computer technology to bring to market. “We didn’t have money or facilities,” says Geordie Rose, a physics PhD who co0founded the company and served in various executive roles. …
The group soon concluded that the kind of machine most scientists were pursing based on so-called gate-model architecture was decades away from being realized—if ever. …
Instead, D-Wave pursued another idea, based on a principle dubbed “quantum annealing.” This approach seemed more likely to produce a working system, even if the application that would run on it were more limited. “The only thing we cared about was building the machine,” says Rose. “Nobody else was trying to solve the same problem.”
D-Wave debuted its first prototype at an event in California in February 2007 running it through a few basic problems such as solving a Sudoku puzzle and finding the optimal seating plan for a wedding reception. … “They just assumed we were hucksters,” says Hilton [Jeremy Hilton, D.Wave senior vice-president of systems]. Federico Spedalieri, a computer scientist at the University of Southern California’s [USC} Information Sciences Institute who has worked with D-Wave’s system, says the limited information the company provided about the machine’s operation provoked outright hostility. “I think that played against them a lot in the following years,” he says.
It seems Lazaridis is not the only one who likes to hold company information tightly.
Back to Semeniuk and D-Wave,
Today [October 2017], the Los Alamos National Laboratory owns a D-Wave machine, which costs about $15million. Others pay to access D-Wave systems remotely. This year , for example, Volkswagen fed data from thousands of Beijing taxis into a machine located in Burnaby [one of the municipalities that make up metro Vancouver] to study ways to optimize traffic flow.
But the application for which D-Wave has the hights hope is artificial intelligence. Any AI program hings on the on the “training” through which a computer acquires automated competence, and the 2000Q [a D-Wave computer] appears well suited to this task. …
Yet, for all the buzz D-Wave has generated, with several research teams outside Canada investigating its quantum annealing approach, the company has elicited little interest from the Waterloo hub. As a result, what might seem like a natural development—the Institute for Quantum Computing acquiring access to a D-Wave machine to explore and potentially improve its value—has not occurred. …
I am particularly interested in this comment as it concerns public funding (from Semeniuk’s article),
Vern Brownell, a former Goldman Sachs executive who became CEO of D-Wave in 2009, calls the lack of collaboration with Waterloo’s research community “ridiculous,” adding that his company’s efforts to establish closer ties have proven futile, “I’ll be blunt: I don’t think our relationship is good enough,” he says. Brownell also point out that, while hundreds of millions in public funds have flowed into Waterloo’s ecosystem, little funding is available for Canadian scientists wishing to make the most of D-Wave’s hardware—despite the fact that it remains unclear which core quantum technology will prove the most profitable.
There’s a lot more to Semeniuk’s article but this is the last excerpt,
The world isn’t waiting for Canada’s quantum rivals to forge a united front. Google, Microsoft, IBM, and Intel are racing to develop a gate-model quantum computer—the sector’s ultimate goal. (Google’s researchers have said they will unveil a significant development early next year.) With the U.K., Australia and Japan pouring money into quantum, Canada, an early leader, is under pressure to keep up. The federal government is currently developing a strategy for supporting the country’s evolving quantum sector and, ultimately, getting a return on its approximately $1-billion investment over the past decade [emphasis mine].
I wonder where the “approximately $1-billion … ” figure came from. I ask because some years ago MP Peter Julian asked the government for information about how much Canadian federal money had been invested in nanotechnology. The government replied with sheets of paper (a pile approximately 2 inches high) that had funding disbursements from various ministries. Each ministry had its own method with different categories for listing disbursements and the titles for the research projects were not necessarily informative for anyone outside a narrow specialty. (Peter Julian’s assistant had kindly sent me a copy of the response they had received.) The bottom line is that it would have been close to impossible to determine the amount of federal funding devoted to nanotechnology using that data. So, where did the $1-billion figure come from?
In any event, it will be interesting to see how the Council of Canadian Academies assesses the ‘quantum’ situation in its more academically inclined, “The State of Science and Technology and Industrial Research and Development in Canada,” when it’s released later this year (2018).
Despite any doubts one might have about Lazaridis’ approach to research and technology, his tremendous investment and support cannot be denied. Without him, Canada’s quantum research efforts would be substantially less significant. As for the ‘cowboys’ in Vancouver, it takes a certain temperament to found a start-up company and it seems the D-Wave folks have more in common with Lazaridis than they might like to admit. As for the Quanta graduate programme, it’s early days yet and no one should ever count out Alberta.
Meanwhile, one can continue to hope that a more thoughtful approach to regional collaboration will be adopted so Canada can continue to blaze trails in the field of quantum research.
The days when you went cruising to ‘get away from it all’ seem to have passed (if they ever really existed) with the introduction of wearable technology that will register your every preference and make life easier according to Cliff Kuang’s Oct. 19, 2017 article for Fast Company,
This month [October 2017], the 141,000-ton Regal Princess will push out to sea after a nine-figure revamp of mind-boggling scale. Passengers won’t be greeted by new restaurants, swimming pools, or onboard activities, but will instead step into a future augured by the likes of Netflix and Uber, where nearly everything is on demand and personally tailored. An ambitious new customization platform has been woven into the ship’s 19 passenger decks: some 7,000 onboard sensors and 4,000 “guest portals” (door-access panels and touch-screen TVs), all of them connected by 75 miles of internal cabling. As the Carnival-owned ship cruises to Nassau, Bahamas, and Grand Turk, its 3,500 passengers will have the option of carrying a quarter-size device, called the Ocean Medallion, which can be slipped into a pocket or worn on the wrist and is synced with a companion app.
The platform will provide a new level of service for passengers; the onboard sensors record their tastes and respond to their movements, and the app guides them around the ship and toward activities aligned with their preferences. Carnival plans to roll out the platform to another seven ships by January 2019. Eventually, the Ocean Medallion could be opening doors, ordering drinks, and scheduling activities for passengers on all 102 of Carnival’s vessels across 10 cruise lines, from the mass-market Princess ships to the legendary ocean liners of Cunard.
Kuang goes on to explain the reasoning behind this innovation,
The Ocean Medallion is Carnival’s attempt to address a problem that’s become increasingly vexing to the $35.5 billion cruise industry. Driven by economics, ships have exploded in size: In 1996, Carnival Destiny was the world’s largest cruise ship, carrying 2,600 passengers. Today, Royal Caribbean’s MS Harmony of the Seas carries up to 6,780 passengers and 2,300 crew. Larger ships expend less fuel per passenger; the money saved can then go to adding more amenities—which, in turn, are geared to attracting as many types of people as possible. Today on a typical ship you can do practically anything—from attending violin concertos to bungee jumping. And that’s just onboard. Most of a cruise is spent in port, where each day there are dozens of experiences available. This avalanche of choice can bury a passenger. It has also made personalized service harder to deliver. …
Kuang also wrote this brief description of how the technology works from the passenger’s perspective in an Oct. 19, 2017 item for Fast Company,
On the web or on the app, you can book experiences, log your tastes and interests, and line up your days. That data powers the recommendations you’ll see. The Ocean Medallion arrives by mail and becomes the key to ship access.
When you draw near, your cabin-room door unlocks without swiping. The room’s unique 43-inch TV, which doubles as a touch screen, offers a range of Carnival’s bespoke travel shows. Whatever you watch is fed into your excursion suggestions.
When you order something, sensors detect where you are, allowing your server to find you. Your allergies and preferences are also tracked, and shape the choices you’re offered. In all, the back-end data has 45,000 allergens tagged and manages 250,000 drink combinations.
The right algorithms can go beyond suggesting wines based on previous orders. Carnival is creating a massive semantic database, so if you like pricey reds, you’re more apt to be guided to a violin concerto than a limbo competition. Your onboard choices—the casino, the gym, the pool—inform your excursion recommendations.
In Kuang’s Oct. 19, 2017 article he notes that the cruise ship line is putting a lot of effort into retraining their staff and emphasizing the ‘soft’ skills that aren’t going to be found in this iteration of the technology. No mention is made of whether or not there will be reductions in the number of staff members on this cruise ship nor is the possibility that ‘soft’ skills may in the future be incorporated into this technological marvel.
Personalization/customization is increasingly everywhere
How do you feel about customized news feeds? As it turns out, this is not a rhetorical question as Adrienne LaFrance notes in her Oct. 19, 2017 article for The Atlantic (Note: Links have been removed),
Today, a Google search for news runs through the same algorithmic filtration system as any other Google search: A person’s individual search history, geographic location, and other demographic information affects what Google shows you. Exactly how your search results differ from any other person’s is a mystery, however. Not even the computer scientists who developed the algorithm could precisely reverse engineer it, given the fact that the same result can be achieved through numerous paths, and that ranking factors—deciding which results show up first—are constantly changing, as are the algorithms themselves.
We now get our news in real time, on demand, tailored to our interests, across multiple platforms, without knowing just how much is actually personalized. It was technology companies like Google and Facebook, not traditional newsrooms, that made it so. But news organizations are increasingly betting that offering personalized content can help them draw audiences to their sites—and keep them coming back.
Personalization extends beyond how and where news organizations meet their readers. Already, smartphone users can subscribe to push notifications for the specific coverage areas that interest them. On Facebook, users can decide—to some extent—which organizations’ stories they would like to appear in their news feeds. At the same time, devices and platforms that use machine learning to get to know their users will increasingly play a role in shaping ultra-personalized news products. Meanwhile, voice-activated artificially intelligent devices, such as Google Home and Amazon Echo, are poised to redefine the relationship between news consumers and the news [emphasis mine].
While news personalization can help people manage information overload by making individuals’ news diets unique, it also threatens to incite filter bubbles and, in turn, bias [emphasis mine]. This “creates a bit of an echo chamber,” says Judith Donath, author of The Social Machine: Designs for Living Online and a researcher affiliated with Harvard University ’s Berkman Klein Center for Internet and Society. “You get news that is designed to be palatable to you. It feeds into people’s appetite of expecting the news to be entertaining … [and] the desire to have news that’s reinforcing your beliefs, as opposed to teaching you about what’s happening in the world and helping you predict the future better.”
Still, algorithms have a place in responsible journalism. “An algorithm actually is the modern editorial tool,” says Tamar Charney, the managing editor of NPR One, the organization’s customizable mobile-listening app. A handcrafted hub for audio content from both local and national programs as well as podcasts from sources other than NPR, NPR One employs an algorithm to help populate users’ streams with content that is likely to interest them. But Charney assures there’s still a human hand involved: “The whole editorial vision of NPR One was to take the best of what humans do and take the best of what algorithms do and marry them together.” [emphasis mine]
The skimming and diving Charney describes sounds almost exactly like how Apple and Google approach their distributed-content platforms. With Apple News, users can decide which outlets and topics they are most interested in seeing, with Siri offering suggestions as the algorithm gets better at understanding your preferences. Siri now has have help from Safari. The personal assistant can now detect browser history and suggest news items based on what someone’s been looking at—for example, if someone is searching Safari for Reykjavík-related travel information, they will then see Iceland-related news on Apple News. But the For You view of Apple News isn’t 100 percent customizable, as it still spotlights top stories of the day, and trending stories that are popular with other users, alongside those curated just for you.
Similarly, with Google’s latest update to Google News, readers can scan fixed headlines, customize sidebars on the page to their core interests and location—and, of course, search. The latest redesign of Google News makes it look newsier than ever, and adds to many of the personalization features Google first introduced in 2010. There’s also a place where you can preprogram your own interests into the algorithm.
Google says this isn’t an attempt to supplant news organizations, nor is it inspired by them. The design is rather an embodiment of Google’s original ethos, the product manager for Google News Anand Paka says: “Just due to the deluge of information, users do want ways to control information overload. In other words, why should I read the news that I don’t care about?” [emphasis mine]
Meanwhile, in May [2017?], Google briefly tested a personalized search filter that would dip into its trove of data about users with personal Google and Gmail accounts and include results exclusively from their emails, photos, calendar items, and other personal data related to their query. [emphasis mine] The “personal” tab was supposedly “just an experiment,” a Google spokesperson said, and the option was temporarily removed, but seems to have rolled back out for many users as of August [2017?].
Now, Google, in seeking to settle a class-action lawsuit alleging that scanning emails to offer targeted ads amounts to illegal wiretapping, is promising that for the next three years it won’t use the content of its users’ emails to serve up targeted ads in Gmail. The move, which will go into effect at an unspecified date, doesn’t mean users won’t see ads, however. Google will continue to collect data from users’ search histories, YouTube, and Chrome browsing habits, and other activity.
The fear that personalization will encourage filter bubbles by narrowing the selection of stories is a valid one, especially considering that the average internet user or news consumer might not even be aware of such efforts. Elia Powers, an assistant professor of journalism and news media at Towson University in Maryland, studied the awareness of news personalization among students after he noticed those in his own classes didn’t seem to realize the extent to which Facebook and Google customized users’ results. “My sense is that they didn’t really understand … the role that people that were curating the algorithms [had], how influential that was. And they also didn’t understand that they could play a pretty active role on Facebook in telling Facebook what kinds of news they want them to show and how to prioritize [content] on Google,” he says.
The results of Powers’s study, which was published in Digital Journalism in February , showed that the majority of students had no idea that algorithms were filtering the news content they saw on Facebook and Google. When asked if Facebook shows every news item, posted by organizations or people, in a users’ newsfeed, only 24 percent of those surveyed were aware that Facebook prioritizes certain posts and hides others. Similarly, only a quarter of respondents said Google search results would be different for two different people entering the same search terms at the same time. [emphasis mine; Note: Respondents in this study were students.]
This, of course, has implications beyond the classroom, says Powers: “People as news consumers need to be aware of what decisions are being made [for them], before they even open their news sites, by algorithms and the people behind them, and also be able to understand how they can counter the effects or maybe even turn off personalization or make tweaks to their feeds or their news sites so they take a more active role in actually seeing what they want to see in their feeds.”
On Google and Facebook, the algorithm that determines what you see is invisible. With voice-activated assistants, the algorithm suddenly has a persona. “We are being trained to have a relationship with the AI,” says Amy Webb, founder of the Future Today Institute and an adjunct professor at New York University Stern School of Business. “This is so much more catastrophically horrible for news organizations than the internet. At least with the internet, I have options. The voice ecosystem is not built that way. It’s being built so I just get the information I need in a pleasing way.”
LaFrance’s article is thoughtful and well worth reading in its entirety. Now, onto some commentary.
Loss of personal agency
I have been concerned for some time about the increasingly dull results I get from a Google search and while I realize the company has been gathering information about me via my searches , supposedly in service of giving me better searches, I had no idea how deeply the company can mine for personal data. It makes me wonder what would happen if Google and Facebook attempted a merger.
More cogently, I rather resent the search engines and artificial intelligence agents (e.g. Facebook bots) which have usurped my role as the arbiter of what interests me, in short, my increasing loss of personal agency.
I’m also deeply suspicious of what these companies are going to do with my data. Will it be used to manipulate me in some way? Presumably, the data will be sold and used for some purpose. In the US, they have married electoral data with consumer data as Brent Bambury notes in an Oct. 13, 2017 article for his CBC (Canadian Broadcasting Corporation) Radio show,
How much of your personal information circulates in the free-market ether of metadata? It could be more than you imagine, and it might be enough to let others change the way you vote.
A data firm that specializes in creating psychological profiles of voters claims to have up to 5,000 data points on 220 million Americans. Cambridge Analytica has deep ties to the American right and was hired by the campaigns of Ben Carson, Ted Cruz and Donald Trump.
During the U.S. election, CNN called them “Donald Trump’s mind readers” and his secret weapon.
David Carroll is a Professor at the Parsons School of Design in New York City. He is one of the millions of Americans profiled by Cambridge Analytica and he’s taking legal action to find out where the company gets its masses of data and how they use it to create their vaunted psychographic profiles of voters.
On Day 6 [Banbury’s CBC radio programme], he explained why that’s important.
“They claim to have figured out how to project our voting behavior based on our consumer behavior. So it’s important for citizens to be able to understand this because it would affect our ability to understand how we’re being targeted by campaigns and how the messages that we’re seeing on Facebook and television are being directed at us to manipulate us.” [emphasis mine]
The parent company of Cambridge Analytica, SCL Group, is a U.K.-based data operation with global ties to military and political activities. David Carroll says the potential for sharing personal data internationally is a cause for concern.
“It’s the first time that this kind of data is being collected and transferred across geographic boundaries,” he says.
But that also gives Carroll an opening for legal action. An individual has more rights to access their personal information in the U.K., so that’s where he’s launching his lawsuit.
Reports link Michael Flynn, briefly Trump’s National Security Adviser, to SCL Group and indicate that former White House strategist Steve Bannon is a board member of Cambridge Analytica. Billionaire Robert Mercer, who has underwritten Bannon’s Breitbart operations and is a major Trump donor, also has a significant stake in Cambridge Analytica.
In the world of data, Mercer’s credentials are impeccable.
“He is an important contributor to the field of artificial intelligence,” says David Carroll.
“His work at IBM is seminal and really important in terms of the foundational ideas that go into big data analytics, so the relationship between AI and big data analytics. …
Banbury’s piece offers a lot more, including embedded videos, than I’ve not included in that excerpt but I also wanted to include some material from Carole Cadwalladr’s Oct. 1, 2017 Guardian article about Carroll and his legal fight in the UK,
But, he [Carroll] argues, “it’s not just a US company and it’s not just a civilian company”. Instead, he says, it has ties with the military through SCL – “and it doesn’t just do voter targeting”. Carroll has provided information to the Senate intelligence committee and believes that the disclosures mandated by a British court could provide evidence helpful to investigators.
Frank Pasquale, a law professor at the University of Maryland, author of The Black Box Society and a leading expert on big data and the law, called the case a “watershed moment”.
“It really is a David and Goliath fight and I think it will be the model for other citizens’ actions against other big corporations. I think we will look back and see it as a really significant case in terms of the future of algorithmic accountability and data protection. …
Nobody is discussing personal agency directly but if you’re only being exposed to certain kinds of messages then your personal agency has been taken from you. Admittedly we don’t have complete personal agency in our lives but AI along with the data gathering done online and increasingly with wearable and smart technology means that another layer of control has been added to your life and it is largely invisible. After all, the students in Elia Powers’ study didn’t realize their news feeds were being pre-curated.
If this continues, they’re going to have change the scale from nano to pico. IBM has announced work on a 5 nanometre (5nm) chip in a June 5, 2017 news item on Nanotechnology Now,
IBM (NYSE: IBM), its Research Alliance partners GLOBALFOUNDRIES and Samsung, and equipment suppliers have developed an industry-first process to build silicon nanosheet transistors that will enable 5 nanometer (nm) chips. The details of the process will be presented at the 2017 Symposia on VLSI Technology and Circuits conference in Kyoto, Japan. In less than two years since developing a 7nm test node chip with 20 billion transistors, scientists have paved the way for 30 billion switches on a fingernail-sized chip.
The resulting increase in performance will help accelerate cognitive computing [emphasis mine], the Internet of Things (IoT), and other data-intensive applications delivered in the cloud. The power savings could also mean that the batteries in smartphones and other mobile products could last two to three times longer than today’s devices, before needing to be charged.
Scientists working as part of the IBM-led Research Alliance at the SUNY Polytechnic Institute Colleges of Nanoscale Science and Engineering’s NanoTech Complex in Albany, NY achieved the breakthrough by using stacks of silicon nanosheets as the device structure of the transistor, instead of the standard FinFET architecture, which is the blueprint for the semiconductor industry up through 7nm node technology.
“For business and society to meet the demands of cognitive and cloud computing in the coming years, advancement in semiconductor technology is essential,” said Arvind Krishna, senior vice president, Hybrid Cloud, and director, IBM Research. “That’s why IBM aggressively pursues new and different architectures and materials that push the limits of this industry, and brings them to market in technologies like mainframes and our cognitive systems.”
The silicon nanosheet transistor demonstration, as detailed in the Research Alliance paper Stacked Nanosheet Gate-All-Around Transistor to Enable Scaling Beyond FinFET, and published by VLSI, proves that 5nm chips are possible, more powerful, and not too far off in the future.
Compared to the leading edge 10nm technology available in the market, a nanosheet-based 5nm technology can deliver 40 percent performance enhancement at fixed power, or 75 percent power savings at matched performance. This improvement enables a significant boost to meeting the future demands of artificial intelligence (AI) systems, virtual reality and mobile devices.
Building a New Switch
“This announcement is the latest example of the world-class research that continues to emerge from our groundbreaking public-private partnership in New York,” said Gary Patton, CTO and Head of Worldwide R&D at GLOBALFOUNDRIES. “As we make progress toward commercializing 7nm in 2018 at our Fab 8 manufacturing facility, we are actively pursuing next-generation technologies at 5nm and beyond to maintain technology leadership and enable our customers to produce a smaller, faster, and more cost efficient generation of semiconductors.”
IBM Research has explored nanosheet semiconductor technology for more than 10 years. This work is the first in the industry to demonstrate the feasibility to design and fabricate stacked nanosheet devices with electrical properties superior to FinFET architecture.
This same Extreme Ultraviolet (EUV) lithography approach used to produce the 7nm test node and its 20 billion transistors was applied to the nanosheet transistor architecture. Using EUV lithography, the width of the nanosheets can be adjusted continuously, all within a single manufacturing process or chip design. This adjustability permits the fine-tuning of performance and power for specific circuits – something not possible with today’s FinFET transistor architecture production, which is limited by its current-carrying fin height. Therefore, while FinFET chips can scale to 5nm, simply reducing the amount of space between fins does not provide increased current flow for additional performance.
“Today’s announcement continues the public-private model collaboration with IBM that is energizing SUNY-Polytechnic’s, Albany’s, and New York State’s leadership and innovation in developing next generation technologies,” said Dr. Bahgat Sammakia, Interim President, SUNY Polytechnic Institute. “We believe that enabling the first 5nm transistor is a significant milestone for the entire semiconductor industry as we continue to push beyond the limitations of our current capabilities. SUNY Poly’s partnership with IBM and Empire State Development is a perfect example of how Industry, Government and Academia can successfully collaborate and have a broad and positive impact on society.”
Part of IBM’s $3 billion, five-year investment in chip R&D (announced in 2014), the proof of nanosheet architecture scaling to a 5nm node continues IBM’s legacy of historic contributions to silicon and semiconductor innovation. They include the invention or first implementation of the single cell DRAM, the Dennard Scaling Laws, chemically amplified photoresists, copper interconnect wiring, Silicon on Insulator, strained engineering, multi core microprocessors, immersion lithography, high speed SiGe, High-k gate dielectrics, embedded DRAM, 3D chip stacking and Air gap insulators.
I last wrote about IBM and computer chips in a July 15, 2015 posting regarding their 7nm chip. You may want to scroll down approximately 55% of the way where I note research from MIT (Massachusetts Institute of Technology) about metal nanoparticles with unexpected properties possibly having an impact on nanoelectronics.
Getting back to IBM, they have produced a slick video about their 5nm chip breakthrough,
Meanwhile, Katherine Bourzac provides technical detail in a June 5, 2017 posting on the Nanoclast blog (on the IEEE [Institute of Electrical and Electronics Engineers] website), Note: A link has been removed,
Researchers at IBM believe the future of the transistor is in stacked nanosheets. …
Today’s state-of-the-art transistor is the finFET, named for the fin-like ridges of current-carrying silicon that project from the chip’s surface. The silicon fins are surrounded on their three exposed sides by a structure called the gate. The gate switches the flow of current on, and prevents electrons from leaking out when the transistor is off. This design is expected to last from this year’s bleeding-edge process technology, the “10-nanometer” node, through the next node, 7 nanometers. But any smaller, and these transistors will become difficult to switch off: electrons will leak out, even with the three-sided gates.
So the semiconductor industry has been working on alternatives for the upcoming 5 nanometer node. One popular idea is to use lateral silicon nanowires that are completely surrounded by the gate, preventing electron leaks and saving power. This design is called “gate all around.” IBM’s new design is a variation on this. In their test chips, each transistor is made up of three stacked horizontal sheets of silicon, each only a few nanometers thick and completely surrounded by a gate.
Why a sheet instead of a wire? Huiming Bu, director of silicon integration and devices at IBM, says nanosheets can bring back one of the benefits of pre-finFET, planar designs. Designers used to be able to vary the width of a transistor to prioritize fast operations or energy efficiency. Varying the amount of silicon in a finFET transistor is not practicable because it would mean making some fins taller and other shorter. Fins must all be the same height due to manufacturing constraints, says Bu.
IBM’s nanosheets can range from 8 to 50 nanometers in width. “Wider gives you better performance but takes more power, smaller width relaxes performance but reduces power use,” says Bu. This will allow circuit designers to pick and choose what they need, whether they are making a power efficient mobile chip processor or designing a bank of SRAM memory. “We are bringing flexibility back to the designers,” he says.
The test chips have 30 billion transistors. …
It was a struggle trying to edit Bourzac’s posting with its good detail and clear writing. I encourage you to read it (June 5, 2017 posting) in its entirety.
As for where this drive downwards to the ‘ever smaller’ is going, there’s Dexter’s Johnson’s June 29, 2017 posting about another IBM team’s research on his Nanoclast blog on the IEEE website (Note: Links have been removed),
There have been increasing signs coming from the research community that carbon nanotubes are beginning to step up to the challenge of offering a real alternative to silicon-based complementary metal-oxide semiconductor (CMOS) transistors.
Now, researchers at IBM Thomas J. Watson Research Center have advanced carbon nanotube-based transistors another step toward meeting the demands of the International Technology Roadmap for Semiconductors (ITRS) for the next decade. The IBM researchers have fabricated a p-channel transistor based on carbon nanotubes that takes up less than half the space of leading silicon technologies while operating at a lower voltage.
In research described in the journal Science, the IBM scientists used a carbon nanotube p-channel to reduce the transistor footprint; their transistor contains all components to 40 square nanometers [emphasis mine], an ITRS roadmap benchmark for ten years out.
One of the keys to being able to reduce the transistor to such a small size is the use of the carbon nanotube as the channel in place of silicon. The nanotube is only 1 nanometer thick. Such thinness offers a significant advantage in electrostatics, so that it’s possible to reduce the device gate length to 10 nanometers without seeing the device performance adversely affected by short-channel effects. An additional benefit of the nanotubes is that the electrons travel much faster, which contributes to a higher level of device performance.