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Here on the cusp of the new year, the catchphrase 2020 hindsight has a distinctly different feel. Good riddance, yes. But also proof of sciences power to mobilize and do good when called upon. Theres gratitude by those who came through less scathed, and, maybe more willingness to assist those who didnt.

Despite the unrelenting pandemic, high performance computing (HPC) proved itself an able member of the worldwide community of pandemic fighters. We should celebrate that, perhaps quietly since the work isnt done. HPC made a significant difference in speeding up and enabling vastly distributed research and funneling the results to those who could turn them into patient care, epidemiology guidance, and now vaccines. Remarkable really. Necessary, of course, but actually got done too. (Forget the quarreling; thats who we are.)

Across the Tabor family of publications, weve run more than 200 pandemic-related articles. I counted nearly 70 significant pieces in HPCwire. The early standing up of Fugaku at RIKEN, now comfortably astride the Top500 for a second time and by a significant margin, to participate in COVID-19 research is a good metaphor for HPCs mobilization. Many people and organizations contributed to the HPC v. pandemic effort and that continues.

Before spotlighting a few pandemic-related HPC activities and digging into a few other topics, lets do a speed-drive through the 2020 HPC/AI technology landscape.

Consolidation continued among chip players (Nvidia/Arm, AMD/Xilinx) while the AI chip newcomers (Cerebras, Habana (now Intel), SambaNova, Graphcore et. al.) were winning deals. Nvidias new A100 GPU is amazing and virtually everyone else is taking potshots for just that reason. Suddenly RISC-V looks very promising. Systems makers weathered 2020s storm with varying success while IBM seems to be winding down its HPC focus; it also plans to split/spin off its managed infrastructure services. Firing up Fugaku (notably a non-accelerated system) quickly was remarkable. The planned Frontier (ORNL) supercomputer now has the pole position in the U.S. exascale race ahead of the delayed Aurora (ANL).

The worldwide quantum computing frenzy is in full froth as the U.S. looks for constructive ways to spend its roughly $1.25 billion (U.S. Quantum Initiative) and, impressively, China just issued a demonstration of quantum supremacy. Theres a quiet revolution going on in storage and memory (just ask VAST Data). Nvidia/Mellanox introduced its line of 400 Gbs network devices while Ethernet launched its 800 Gbs spec. HPC-in-the-cloud is now a thing not a soon-to-be thing. AI is no longer an oddity but quickly infusing throughout HPC (That happened fast).

Last but not least, hyperscalers demonstrably rule the IT roost. Chipmakers used to, consistently punching above their weight (sales volume). Not so much now:

Ok then. Apologies for the many important topics omitted (e.g. exascale and leadership systems, neuromorphic tech, software tools (can oneAPI flourish?), newer fabrics, optical interconnect, etc.).

Lets start.

I want to highlight two HPC pandemic-related efforts, one current and one early on, and also single out the efforts of Oliver Peckham, HPCwires editor who leads our pandemic coverage which began in earnest with articles on March 6 (Summit Joins the Fight Against the Coronavirus) and March 13 (Global Supercomputing Is Mobilizing Against COVID-19). Actually, the very first piece Tech Conferences Are Being Canceled Due to Coronavirus, March 3 was more about interrupted technology events and we picked it up from our sister pub, Datanami which ran it on March 2. Weve since become a virtualized event world.

Heres an excerpt from the first Summit piece about modeling COVID-19s notorious spike:

Micholas Smith, a postdoctoral researcher at the University of Tennessee/ORNL Center for Molecular Biophysics (UT/ORNL CMB), used early studies and sequencing of the virus to build a virtual model of the spike protein.[A]fter being granted time on Summit through a discretionary allocation, Smith and his colleagues performed a series of molecular dynamics simulations on the protein, cycling through 8,000 compounds within a few days and analyzing how they bound to the spike protein, if at all.

Using Summit, we ranked these compounds based on a set of criteria related to how likely they were to bind to the S-protein spike, Smith said in aninterviewwith ORNL. In total, the team identified 77 candidate small-molecule compounds (such as medications) that they considered worthy of further experimentation, helping to narrow the field for medical researchers.

It took us a day or two whereas it would have taken months on a normal computer, said Jeremy Smith, director of UT/ORNL CMB and principal researcher for the study. Our results dont mean that we have found a cure or treatment for the Wuhan coronavirus. We are very hopeful, though, that our computational findings will both inform future studies and provide a framework that experimentalists will use to further investigate these compounds. Only then will we know whether any of them exhibit the characteristics needed to mitigate this virus.

The flood (and diversity) of efforts that followed was startling. Olivers advice on what to highlight catches the flavor of the challenge: You could go with something like the Fugaku vs. COVID-19 piece or the grocery store piece, maybe contrast them a bit, earliest vs. current simulations of viral particle spreador something like the LANL retrospective piece vs. the piece I just wrote up on their vaccine modeling. Think that might work for a how far weve come angle, either way.

Theres too much to cover.

Last week we ran Olivers article on LANL efforts to optimize vaccine distribution (At Los Alamos National Lab, Supercomputers Are Optimizing Vaccine Distribution). Heres brief excerpt:

The new vaccines from Pfizer and Moderna have been deemed highly effective by the FDA; unfortunately, doses are likely to be limited for some time. As a result, many state governments are struggling to weigh difficult choices should the most exposed, like frontline workers, be vaccinated first? Or perhaps the most vulnerable, like the elderly and immunocompromised? And after them, whos next?

LANL was no stranger to this kind of analysis: earlier in the year, the lab had used supercomputer-powered tools like EpiCast to simulate virtual cities populated by individuals with demographic characteristics to model how COVID-19 would spread under different conditions. The first thing we looked at was whether it made a difference to prioritize certain populations such as healthcare workers or to just distribute the vaccine randomly,saidSara Del Valle, the LANL computational epidemiologist who is leading the labs COVID-19 modeling efforts. We learned that prioritizing healthcare workers first was more effective in reducing the number of COVID cases and deaths.

You get the idea. The well of HPC efforts to tackle and stymie COVID-19 is extremely deep. Turning unproven mRNA technology into a vaccine in record time was awe-inspiring and required many disciplines. For those unfamiliar with mRNA mechanism heres a brief CDC explanation as it relates to the new vaccines. Below are links to a few HPCwirearticles on the worldwide effort to bring HPC computational power to bear. (The last is a link to the HPCwire COVID-19 Archive which has links to all our major pandemic coverage):

COVID COVERAGE LINKS

Global Supercomputing Is Mobilizing Against COVID-19 (March 12, 2020)

Gordon Bell Special Prize Goes to Massive SARS-CoV-2 Simulations (November 19, 2020)

Supercomputer Research Leads to Human Trial of Potential COVID-19 Therapeutic Raloxifene (October 29, 2020)

AMDs Massive COVID-19 HPC Fund Adds 18 Institutions, 5 Petaflops of Power (September 14, 2020)

Supercomputer-Powered Research Uncovers Signs of Bradykinin Storm That May Explain COVID-19 Symptoms (July 28, 2020)

Researchers Use Frontera to Investigate COVID-19s Insidious Sugar Coating (June 16, 2020)

COVID-19 HPC Consortium Expands to Europe, Reports on Research Projects (May 28, 2020)

At SC20, an Expert Panel Braces for the Next Pandemic (December, 17, 2020)

Whats New in Computing vs. COVID-19: Cerebras, Nvidia, OpenMP & More (May 18, 2020)

Billion Molecules Against COVID-19 Challenge to Launch with Massive Supercomputing Support (April 22, 2020)

Pandemic Wipes Out 2020 HPC Market Growth, Flat to 12% Drop Expected (March 31, 2020)

[emailprotected]Turns Its Massive Crowdsourced Computer Network Against COVID-19 (March 16, 2020)

2020 HPCwire Awards Honor a Year of Remarkable COVID-19 Research (December, 23, 2020)

HPCWIRE COVID-19 COVERAGE ARCHIVE

Making sense of the processor world is challenging. Microprocessors are still the workhorses in mainstream computing with Intel retaining its giant market share despite AMDs encroachment. That said, the rise of heterogeneous computing and blended AI/HPC requirements has shifted focus to accelerators. Nvidias A100 GPU (54 billion transistors on 826mm2of silicon, worlds largest seven-nanometer chip) was launched this spring. Then at SC20 Nvidia announced an enhanced version of the A100, doubling its memory to 80GB; it now delivers 2TB/s of bandwidth. The A100 is an impressive piece of work.

The A100s most significant advantage, says Rick Stevens, associate lab director, Argonne National Laboratory, is its multi-instance GPU capability.

For many people the problem is achieving high occupancy, that is, being able to fill the GPU up because that depends on how much work you have to do. [By] introducing this MIG, this multi instance stuff that they have, theyre able to virtualize it. Most of the real-world performance wins are actually kind of throughput wins by using the virtualization. What weve seen isour big performance improvement is not that individual programs run much faster its that we can run up to seven parallel things on each GPU. When you add up the aggregate performance, you get these factors of three to five improvement over the V100, said Stevens.

Meanwhile, Intels XE GPU line is slowly trickling to market, mostly in card form. At SC20 Intel announced plans to make its high performance discrete GPUs available to early access developers. Notably, the new chips have been deployed at ANL and will serve as a transitional development vehicle for the future (2022) Aurora supercomputer, subbing in for the delayed IntelXE-HPC (Ponte Vecchio) GPUs that are the computational backbone of the system.

AMD, also at SC20, launched its latest GPU the MI100. AMD says it delivers 11.5 teraflops peak double-precision (FP64), 46.1 teraflops peak single-precision matrix (FP32), 23.1 teraflops peak single-precision (FP32), 184.6 teraflops peak half-precision (FP16) floating-point performance, and 92.3 peak teraflops of bfloat16 performance. HPCwire reported, AMDs MI100GPU presents a competitive alternative to Nvidias A100 GPU, rated at 9.7 teraflops of peak theoretical performance. However, the A100 is returning even higher performance than that on its FP64 Linpack runs. It will be interesting to see the specs of the GPU AMD eventually fields for use in its exascale system wins.

The stakes are high in what could become a GPU war. Today, Nvidia is the market leader in HPC.

Turning back to CPUs, which many in HPC/AI have begun to regard as the lesser of CPU/GPU pairings. Perhaps that will change with the spectacular showing of Fujitsus A64FX at the heart of Fugaku. Nvidias proposed acquisition of Arm, not a done deal yet (regulatory concerns), would likely inject fresh energy in what was already a surging Arm push into the datacenter. Of course, Nvidia has jumped into the systems business with its DGX line and presumably wants a home-grown CPU. The big mover of the last couple of years, AMDs Epyc microprocessor line, continues its steady incursion into Intel x86 territory.

Theres not been much discussion around Power10 beyond IBMs summer announcement that Power10 would offer a ~3x performance gain and ~2.6x core efficiency gain over Power9. The new executive director of OpenPOWER Foundation, James Kulina, says attracting more chipmakers to build Power devices is a top goal. Well see. RISC-V is definitely drawing interest but exactly how it fits into the processor puzzle is unclear. Esperanto unveiled a RISC-V based chip aimed at machine learning with 1,100 low-power cores based on the open-source RISC-V. Esperanto reported a goal of 4,000 cores on a single device. Europe is betting on RISC-V. However, at least near-term, RISC-V variants are seen as specialized chips.

The CPU waters are murkier than ever.

Sort of off in a land of their own are AI chip/system players. Their proliferation continues with the early movers winning important deployments. Some observers think 2021 will start sifting winners from the losers. Lets not forget that last year Intel stopped development of its newly-acquired Nervana line in favor of its even more newly-acquired Habana products. Its a high-risk, high-reward arena still.

PROCESSOR COVERAGE LINKS

Intel Xe-HP GPU Deployed for Aurora Exascale Development

Is the Nvidia A100 GPU Performance Worth a Hardware Upgrade?

LLNL, ANL and GSK Provide Early Glimpse into Cerebras AI System Performance

David Patterson Kicks Off AI Hardware Summit Championing Domain Specific Chips

Graphcores IPU Tackles Particle Physics, Showcasing Its Potential for Early Adopters

Intel Debuts Cooper Lake Xeons for 4- and 8-Socket Platforms

Intel Launches Stratix 10 NX FPGAs Targeting AI Workloads

Nvidias Ampere A100 GPU: Up to 2.5X the HPC, 20X the AI

AMD Launches Three New High-Frequency Epyc SKUs Aimed at Commercial HPC

IBM Debuts Power10; Touts New Memory Scheme, Security, and Inferencing

AMDs Road Ahead: 5nm Epyc, CPU-GPU Coupling, 20% CAGR

AI Newcomer SambaNova GAs Product Lineup and Offers New Service

Japans AIST Benchmarks Intel Optane; Cites Benefit for HPC and AI

Storage and memory dont get the attention they deserve. 3D XPoint memory (Intel and Micron), declining flash costs, and innovative software are transforming this technology segment. Hard disk drives and tape arent going away, but traditional storage management approaches such as tiering based on media type (speed/capacity/cost) are under attack. Newcomers WekaIO, VAST Data, and MemVerge are all-in on solid state, and a few leading-edge adopters (NERSC/Perlmutter) are taking the plunge. Data-intensive computing driven by the data flood and AI compute requirements (gotta keep those GPUs busy!) are big drivers.

Our storage systems typically see over an exabyte of I/O annually. Balancing this I/O intensive workload with the economics of storage means that at NERSC, we live and breathe tiering. And this is a snapshot of the storage hierarchy we have on the floor today at NERSC. Although it makes for a pretty picture, we dont have storage tiering because we want to, and in fact, Id go so far as to say its the opposite of what we and our users really want. Moving data between tiers has nothing to do with scientific discovery, said NERSC storage architect Glenn Lockwood during an SC20 panel.

To put some numbers behind this, last year we did a study that found that between 15% and 30% of that exabyte of I/O is not coming from our users jobs, but instead coming from data movement between storage tiers. That is to say that 15% to 30% of the I/O at NERSC is a complete waste of time in terms of advancing science. But even before that study, we knew that both the changing landscape of storage technology and the emerging large-scale data analysis and AI workloads arriving at NERSC required us to completely rethink our approach to tiered storage, said Lockwood.

Not surprisingly Intel and Micron (Optane/3D XPoint) are trying to accelerate the evolution. Micron released what it calls a heterogeneous-memory storage engine (HSE) designed for solid-state drives, memory-based storage and, ultimately, applications requiring persistent memory. Legacy storage engines born in the era of hard disk drives have historically failed to architecturally provide for the increased performance and reduced latency of next-generation nonvolatile media, said the company. Again, well see.

Software defined storage leveraging newer media has all the momentum at the moment with all of the established players IBM, DDN, Panasas, etc., mixing those capabilities into their product sets. WekaIO and Intel have battled it out for the top IO500 spot the last couple of years and Intels DAOS (distributed asynchronous object store) is slated for use in Aurora.

The concept of asynchronous IO is very interesting, noted Ari Berman, CEO, BioTeam research consultancy. Its essentially a queue mechanism at the system write level so system waits in the processors dont have to happen while a confirmed write back comes from the disks. So asynchronous IO allows jobs can keep running while youre waiting on storage to happen, to a limit of course. That would really improve the data input-output pipelines in those systems. Its a very interesting idea. I like asynchronous data writes and asynchronous storage access. I can see there very easily being corruption that creeps into those types of things and data without very careful sequencing. It will be interesting to watch. If it works it will be a big innovation.

Change is afoot and the storage technology community is adapting. Memory technology is also advancing.

Micron introduced a 176-layer 3D NAND flash memory at SC230 that it says increases read and write densities by more than 35 percent.JEDEC published the DDR5 SDRAM spec, the next-generation standard for random access memory (RAM) in the summer. Compared to DDR4, the DDR5 spec will deliver twice the performance and improved power efficiency, addressing ever-growing demand from datacenter and cloud environments, as well as artificial intelligence and HPC applications. At launch, DDR5 modules will reach 4.8 Gbps, providing a 50 percent improvement versus the previous generation. Density goes up four-fold with maximum density increasing from 16 Gigabits per die to 64 Gigabits per die in the new spec. JEDEC representatives indicated there will be 8 Gb and 16 Gb DDR5 products at launch.

There are always the wildcards. IBMs memristive technology is moving closer to practical use. One outlier is DNA-based storage. Dave Turek, longtime IBMer, joined DNA storage start-up Catalog this year and, says Catalog is working on proof of concepts with government agencies and a number of Fortune 500 companies. Some of these are whos-who HPC players, but some are non-HPC players many names you would recognizeWere at what I would say is the beginning of the commercial beginning. Again, well see.

STORAGE & MEMORY LINKS

SC20 Panel OK, You Hate Storage Tiering. Whats Next Then?

Intels Optane/DAOS Solution Tops Latest IO500

Startup MemVerge on Memory-centric Mission

HPC Strategist Dave Turek Joins DNA Storage (and Computing) Company Catalog

DDN-Tintri Showcases Technology Integration with Two New Products

Intel Refreshes Optane Persistent Memory, Adds New NAND SSDs

Micron Boosts Flash Density with 176-Layer 3D NAND

DDR5 Memory Spec Doubles Data Rate, Quadruples Density

IBM Touts STT MRAM Technology at IDEM 2020

The Distributed File Systems and Object Storage Landscape: Whos Leading?

Its tempting to omit quantum computing this year. Too much happened to summarize easily and the overall feel is of steady carry-on progress from 2019. There was, perhaps, a stronger pivot at least by press release count towards seeking early applications for near-term noisy intermediate scale quantum (NISQ) computers. Ion trap qubit technology got another important player in Honeywell which formally rolled out its effort and first system. Intel also stepped out from the shadows a bit in terms of showcasing its efforts. D-Wave launched a giant 5000-qubit machine (Advantage), again using a quantum annealing approach thats different from universal gate-based quantum system. IBM announced a stretch goal of achieving one million qubits!

Calling quantum computing a market is probably premature but monies are being spent. The Quantum Economic Development Consortium (QED-C) and Hyperion Research issued a forecast (see slide) that projects the global quantum computing (QC) market worth an estimated $320 million in 2020 to grow 27% CAGR between 2020 and 2024. That would reach approximately $830 million by 2024. Chump change? Perhaps but real activity.

IBMs proposed Quantum Volume metric has drawn support as a broad benchmark of quantum computer performance. Honeywell promoted the 128QV score of its launch system. In December IBM reported it too had achieved a 128QV. The first QV reported by IBM was 16 in 2019 at the APS March meeting. Just what a QV of 128 means in determining practical usefulness is unclear but it is steady progress and even Intel agrees that QV is as good as any measure at the moment. DoE is also working on benchmarks, focusing a bit more on performance on given workloads.

[One] major component of benchmarking is asking what kind of resources does it take to run this or that interesting problem. Again, these are problems of interest to DoE, so basic science problems in chemistry and nuclear physics and things like that. What well do is take applications in chemistry and nuclear physics and convert them into what we consider a benchmark. We consider it a benchmark when we can distill a metric from it. So the metric could be the accuracy, the quality of the solution, or the resources required to get a given level of quality, said Raphael Pooser, PI for DoEs Quantum Testbed Pathfinder project at ORNL, during an HPCwire interview.

Next year seems likely to bring more benchmarking activity around system quality, qubit technology, and performance on specific problem sets. Several qubit technologies still vie for sway superconducting, trapped ion, optical, quantum dots, cold atoms, et al. The need to operate at near-zero (K) temps complicates everything. Google claimed achieving Quantum Supremacy last year. This year a group of China researchers also did so. The groups used different qubit technologies (superconducting v. optical) and Chinas effort tried to skirt criticisms that were lobbed at Googles effort. Frankly, both efforts were impressive. Russia reported early last year it would invest $790 million in quantum with achieving quantum supremacy as one goal.

Whats happening now is a kind of pell-mell rush among a larger and increasingly diverse quantum ecosystem (hardware, software, consultants, governments, academia). Fault tolerant quantum computing still seems distant but clever algorithms and error mitigation strategies to make product use of NISQ systems, likely on narrow applications, look more and more promising.

Here are a few snapshots:

The persistent question is when will all of these efforts pay off and will they be as game-changing as many believe. With new money flowing into quantum, one has the sense there will be few abrupt changes in the next couple years barring untoward economic turns.

QUANTUM COVERAGE LINKS

IBMs Quantum Race to One Million Qubits

Googles Quantum Chemistry Simulation Suggests Promising Path Forward

Intel Connects the (Quantum) Dots in Accelerating Quantum Computing Effort

D-Wave Delivers 5000-qubit System; Targets Quantum Advantage

Honeywell Debuts Quantum System, Subscription Business Model, and Glimpse of Roadmap

Global QC Market Projected to Grow to More Than $800 million by 2024

ORNLs Raphael Pooser on DoEs Quantum Testbed Project

RigettiComputing Wins $8.6M DARPA Grant to Demonstrate Practical Quantum Computing

Braket: Amazons Cloud-First Quantum Environment Is Generally Available

IBM-led Webinar Tackles Quantum Developer Community Needs

Microsofts Azure Quantum Platform Now Offers Toshibas Simulated Bifurcation Machine

As always theres personnel shuffling. Lately hyperscalers have been taking HPC folks. Two long-time Intel executives, Debra Goldfarb and Bill Magro, recently left for the cloud Goldfarb to AWS as director for HPC products and strategy, and Magro to Google as CTO for HPC. Going in the other direction, John Martinis left Googles quantum development team and recently joined Australian start-up Silicon Quantum Computing. Ginny Rometty, of course, stepped down as CEO and chairman at IBM. IBMs long-time HPC exec Dave Turek left to take position with DNA storage start-up, Catalog, and last January, IBMer Brad McCredie joined AMD as corporate VP, GPU platforms.

See the original post:
Farewell 2020: Bleak, Yes. But a Lot of Good Happened Too - HPCwire

This time last year we were commemorating the end of a decade and looking ahead to the next one. Enter the year that felt like a decade all by itself: 2020. News written in January, the before-times, feels hopelessly out of touch with all that came after. Stories published in the early days of the pandemic are, for the most part, similarly naive.

The years news cycle was swift and brutal, ping-ponging from pandemic to extreme social and political tension, whipsawing economies, and natural disasters. Hope. Despair. Loneliness. Grief. Grit. More hope. Another lockdown. Its been a hell of a year.

Though 2020 was dominated by big, hairy societal change, science and technology took significant steps forward. Researchers singularly focused on the pandemic and collaborated on solutions to a degree never before seen. New technologies converged to deliver vaccines in record time. The dark side of tech, from biased algorithms to the threat of omnipresent surveillance and corporate control of artificial intelligence, continued to rear its head.

Meanwhile, AI showed uncanny command of language, joined Reddit threads, and made inroads into some of sciences grandest challenges. Mars rockets flew for the first time, and a private company delivered astronauts to the International Space Station. Deprived of night life, concerts, and festivals, millions traveled to virtual worlds instead. Anonymous jet packs flew over LA. Mysterious monoliths appeared and disappeared worldwide.

It was all, you know, very 2020. For this years (in-no-way-all-encompassing) list of fascinating stories in tech and science, we tried to select those that werent totally dated by the news, but rose above it in some way. So, without further ado: This years picks.

How Science Beat the VirusEd Yong | The AtlanticMuch like famous initiatives such as the Manhattan Project and the Apollo program, epidemics focus the energies of large groups of scientists. But nothing in history was even close to the level of pivoting thats happening right now, Madhukar Pai of McGill University told me. No other disease has been scrutinized so intensely, by so much combined intellect, in so brief a time.

It Will Change Everything: DeepMinds AI Makes Gigantic Leap in Solving Protein StructuresEwen Callaway | NatureIn some cases, AlphaFolds structure predictions were indistinguishable from those determined using gold standard experimental methods such as X-ray crystallography and, in recent years, cryo-electron microscopy (cryo-EM). AlphaFold might not obviate the need for these laborious and expensive methodsyetsay scientists, but the AI will make it possible to study living things in new ways.

OpenAIs Latest Breakthrough Is Astonishingly Powerful, But Still Fighting Its FlawsJames Vincent | The VergeWhat makes GPT-3 amazing, they say, is not that it can tell you that the capital of Paraguay is Asuncin (it is) or that 466 times 23.5 is 10,987 (its not), but that its capable of answering both questions and many more beside simply because it was trained on more data for longer than other programs. If theres one thing we know that the world is creating more and more of, its data and computing power, which means GPT-3s descendants are only going to get more clever.

Artificial General Intelligence: Are We Close, and Does It Even Make Sense to Try?Will Douglas Heaven | MIT Technology ReviewA machine that could think like a person has been the guiding vision of AI research since the earliest daysand remains its most divisive idea. So why is AGI controversial? Why does it matter? And is it a reckless, misleading dreamor the ultimate goal?

The Dark Side of Big Techs Funding for AI ResearchTom Simonite | WiredTimnit Gebrus exit from Google is a powerful reminder of how thoroughly companies dominate the field, with the biggest computers and the most resources. [Meredith] Whittaker of AI Now says properly probing the societal effects of AI is fundamentally incompatible with corporate labs. That kind of research that looks at the power and politics of AI is and must be inherently adversarial to the firms that are profiting from this technology.i

Were Not Prepared for the End of Moores LawDavid Rotman | MIT Technology ReviewQuantum computing, carbon nanotube transistors, even spintronics, are enticing possibilitiesbut none are obvious replacements for the promise that Gordon Moore first saw in a simple integrated circuit. We need the research investments now to find out, though. Because one prediction is pretty much certain to come true: were always going to want more computing power.

Inside the Race to Build the Best Quantum Computer on EarthGideon Lichfield | MIT Technology ReviewRegardless of whether you agree with Googles position [on quantum supremacy] or IBMs, the next goal is clear, Oliver says: to build a quantum computer that can do something useful. The trouble is that its nearly impossible to predict what the first useful task will be, or how big a computer will be needed to perform it.

The Secretive Company That Might End Privacy as We Know ItKashmir Hill | The New York TimesSearching someone by face could become as easy as Googling a name. Strangers would be able to listen in on sensitive conversations, take photos of the participants and know personal secrets. Someone walking down the street would be immediately identifiableand his or her home address would be only a few clicks away. It would herald the end of public anonymity.

Wrongfully Accused by an AlgorithmKashmir Hill | The New York TimesMr. Williams knew that he had not committed the crime in question. What he could not have known, as he sat in the interrogation room, is that his case may be the first known account of an American being wrongfully arrested based on a flawed match from a facial recognition algorithm, according to experts on technology and the law.

Predictive Policing Algorithms Are Racist. They Need to Be Dismantled.Will Douglas Heaven | MIT Technology ReviewA number of studies have shown that these tools perpetuate systemic racism, and yet we still know very little about how they work, who is using them, and for what purpose. All of this needs to change before a proper reckoning can take pace. Luckily, the tide may be turning.

The Panopticon Is Already HereRoss Andersen | The AtlanticArtificial intelligence has applications in nearly every human domain, from the instant translation of spoken language to early viral-outbreak detection. But Xi [Jinping] also wants to use AIs awesome analytical powers to push China to the cutting edge of surveillance. He wants to build an all-seeing digital system of social control, patrolled by precog algorithms that identify potential dissenters in real time.

The Case For Cities That Arent Dystopian Surveillance StatesCory Doctorow | The GuardianImagine a human-centered smart city that knows everything it can about things. It knows how many seats are free on every bus, it knows how busy every road is, it knows where there are short-hire bikes available and where there are potholes. What it doesnt know isanything about individuals in the city.

The Modern World Has Finally Become Too Complex for Any of Us to UnderstandTim Maughan | OneZeroOne of the dominant themes of the last few years is that nothing makes sense. I am here to tell you that the reason so much of the world seems incomprehensible is that itisincomprehensible. From social media to the global economy to supply chains, our lives rest precariously on systems that have become so complex, and we have yielded so much of it to technologies and autonomous actors that no one totally comprehends it all.

The Conscience of Silicon ValleyZach Baron | GQWhat I really hoped to do, I said, was to talk about the future and how to live in it. This year feels like a crossroads; I do not need to explain what I mean by this. I want to destroy my computer, through which I now work and have drinks and stare at blurry simulations of my parents sometimes; I want to kneel down and pray to it like a god. I want someoneI want Jaron Lanierto tell me where were going, and whether its going to be okay when we get there. Lanier just nodded. All right, then.

Yes to Tech Optimism. And Pessimism.Shira Ovide | The New York TimesTechnology is not something that exists in a bubble; it is a phenomenon that changes how we live or how our world works in ways that help and hurt. That calls for more humility and bridges across the optimism-pessimism divide from people who make technology, those of us who write about it, government officials and the public. We need to think on the bright side. And we need to consider the horribles.

How Afrofuturism Can Help the World MendC. Brandon Ogbunu | Wired[W. E. B. DuBois] The Comet helped lay the foundation for a paradigm known as Afrofuturism. A century later, as a comet carrying disease and social unrest has upended the world, Afrofuturism may be more relevant than ever. Its vision can help guide us out of the rubble, and help us to consider universes of better alternatives.

Wikipedia Is the Last Best Place on the InternetRichard Cooke | WiredMore than an encyclopedia, Wikipedia has become a community, a library, a constitution, an experiment, a political manifestothe closest thing there is to an online public square. It is one of the few remaining places that retains the faintly utopian glow of the early World Wide Web.

Can Genetic Engineering Bring Back the American Chestnut?Gabriel Popkin | The New York Times MagazineThe geneticists research forces conservationists to confront, in a new and sometimes discomfiting way, the prospect that repairing the natural world does not necessarily mean returning to an unblemished Eden. It may instead mean embracing a role that weve already assumed: engineers of everything, including nature.

At the Limits of ThoughtDavid C. Krakauer | AeonA schism is emerging in the scientific enterprise. On the one side is the human mind, the source of every story, theory, and explanation that our species holds dear. On the other stand the machines, whose algorithms possess astonishing predictive power but whose inner workings remain radically opaque to human observers.

Is the Internet Conscious? If It Were, How Would We Know?Meghan OGieblyn | WiredDoes the internetbehavelike a creature with an internal life? Does it manifest the fruits of consciousness? There are certainly moments when it seems to. Google can anticipate what youre going to type before you fully articulate it to yourself. Facebook ads can intuit that a woman is pregnant before she tells her family and friends. It is easy, in such moments, to conclude that youre in the presence of another mindthough given the human tendency to anthropomorphize, we should be wary of quick conclusions.

The Internet Is an Amnesia MachineSimon Pitt | OneZeroThere was a time when I didnt knowwhat a Baby Yoda was. Then there was a time I couldnt go online without reading about Baby Yoda. And now, Baby Yoda is a distant, shrugging memory. Soon there will be a generation of people who missed the whole thing and for whom Baby Yoda is as meaningless as it was for me a year ago.

Digital Pregnancy Tests Are Almost as Powerful as the Original IBM PCTom Warren | The VergeEach test, which costs less than $5, includes a processor, RAM, a button cell battery, and a tiny LCD screen to display the result. Foone speculates that this device is probably faster at number crunching and basic I/O than the CPU used in the original IBM PC. IBMs original PC was based on Intels 8088 microprocessor, an 8-bit chip that operated at 5Mhz. The difference here is that this is a pregnancy test you pee on and then throw away.

The Party Goes on in Massive Online WorldsCecilia DAnastasio | WiredWere more stand-outside types than the types to cast a flashy glamour spell and chat up the nearest cat girl. But, hey, itsFinal Fantasy XIVonline, and where my body sat in New York, the epicenter ofAmericas Covid-19 outbreak, there certainly werent any parties.

The Facebook Groups Where People Pretend the Pandemic Isnt HappeningKaitlyn Tiffany | The AtlanticLosing track of a friend in a packed bar or screaming to be heard over a live band is not something thats happening much in the real world at the moment, but it happens all the time in the 2,100-person Facebook group a group where we all pretend were in the same venue. So does losing shoes and Juul pods, and shouting matches over which bands are the saddest, and therefore the greatest.

Did You Fly a Jetpack Over Los Angeles This Weekend? Because the FBI Is Looking for YouTom McKay | GizmodoDid you fly a jetpack over Los Angeles at approximately 3,000 feet on Sunday? Some kind of tiny helicopter? Maybe a lawn chair with balloons tied to it? If the answer to any of the above questions is yes, you should probably lay low for a while (by which I mean cool it on the single-occupant flying machine). Thats because passing airline pilots spotted you, and now its this whole thing with the FBI and the Federal Aviation Administration, both of which are investigating.

Image Credit: Thomas Kinto / Unsplash

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These Were Our Favorite Tech Stories From Around the Web in 2020 - Singularity Hub

Quantum computers, once fully scaled, could lead to breakthroughs on many fronts medicine, finance, architecture, logistics.

First, its important to understand why quantum computers are superior to the conventional ones weve been using for years:

In conventional electronic devices, memory consists of bits with only one value, either 0 or 1. In quantum computing, a quantum bit (qubit) exhibits both values in varying degrees at the same time. This is called quantum superposition. These ubiquitous states of each qubit are then used in complex calculations, which read like regular bits: 0 and 1.

Since qubits can store more information than regular bits, this also means quantum computers are capable of processing greater quantities of information. Having four bits enables 16 possibilities, but only one at a time. Four qubits in quantum superposition, however, let you calculate all 16 states at once. This means that four qubits equal 65,500 regular bits. Each qubit added to the quantum computing system increases its power exponentially.

To put things in perspective, a top supercomputer can currently accomplish as much as a five- to 20-qubit computer, but its estimated that a 50-qubit quantum computer will be able to solve computational problems no other conventional device can in any feasible amount of time.

This quantum supremacy has been achieved many times so far. Its important to mention that this doesnt mean the quantum computer can beat a traditional one in every task rather, it shines only in a limited set of tasks specially tailored to outline its strengths. Also, a quantum computer still needs to overcome many obstacles before it can become a mainstream device.

But once it does, its computational power will boost science and industries that profit from it.

Large companies working on quantum computing in their respective industries include AT&T T, +0.75%, Google holding company Alphabet GOOG, +1.33% GOOGL, +1.22%, IBM IBM, +1.49% and Microsoft MSFT, +0.57%.

Here are a few industries that could benefit the most:

Quantum chemistry, also called molecular quantum mechanics, is a branch of chemistry focused on the application of quantum mechanics to chemical systems. Here, quantum computers help in molecule modeling, taking into account all of their possible quantum states a feat that is beyond the power of conventional computing.

That, in turn, helps us understand their properties, which is invaluable for new material and medicine research.

Quantum cryptography, also known as quantum encryption, employs principles of quantum mechanics to facilitate encryption and protection of encrypted data from tampering. Using the peculiar behavior of subatomic particles, it enables the reliable detection of tampering or eavesdropping (via the Quantum Key Distribution method).

Quantum encryption is also used for secure encryption key transfer, which is based on the entanglement principle. Both methods are currently available, but due to their complexity and price, only governments and institutions handling delicate data (most notably in China and the U.S.) can afford them for the time being.

Quantum financeis an interdisciplinary research field that applies theories and methods developed by quantum physicists and economists to solve problems in finance.This especially includes complex calculations, such as the pricing of various financial instruments and other computational finance problems.

Some scientists argue that quantum pricing models will provide more accuracy than classical ones because theyre able to take into account market inefficiency, which is something classical models disregard.

Quantum computing will also enhance analysis of large and unstructured data sets, which will improve decision making across different areas from better-timed offers to risk assessment. Many of these calculations will require a quantum computer with thousands of qubits to resolve, but the way things have been progressing recently, its not unrealistic to see quantum computers reach this processing potential in a matter of years, rather than decades.

Although still in the domain of conceptual research, principles of quantum mechanics will help quantum computers achieve a markedly greater speed and efficiency than what is currently possible on classical computers when executing AI algorithms this goes especially for machine learning.

Current computational models used in weather forecasting employ dynamic variables, from air temperature, pressure and density to historic data and other factors that go into creating climate prediction models. Due to limited available processing power, classical computers and even conventional supercomputers are the bottlenecks that limit the speed and efficacy of forecasting calculations.

To predict extreme weather events and limit the loss of life and property, we need faster and more robust forecasting models. By harnessing the power of qubits, quantum computing is capable of providing necessary the raw processing power to make that happen. Furthermore, machine learning provided by the quantum AI can additionally improve these forecasting models.

Despite its rapid progress, quantum computing is still in its infancy, but its clearly a game changer, capable of solving problems previously deemed insurmountable for classical computers.

This power will provide most benefits not only to science and medicine, but also to businesses and industries where fast processing of large datasets is paramount.

As a marketing specialist, I can see a huge advantage for my industry, but others, especially finance and cryptography, will undoubtedly find the quantum boost to their decision-making processes and quality of their final product hugely beneficial.

The real question is who will be the first to harness this power and use quantum computing as a part of their unique value proposition and competitive advantage? The race is on.

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Quantum computers' power will remake competition in industries from technology to finance - MarketWatch

Safe Deposit. Symbol of cryptocurrency safety. The man puts a physical bitcoin in small Residential ... [+] Vault. Toned soft focus picture.

Theres a lurking fear in cryptocurrency communities about quantum computing. Could it break cryptocurrencies and the encryption that protects them? How close might that be? Do the headlines around quantum supremacy mean that my private keys are at risk?

The simple answer: no. But lets dive deeper into this phenomenon and really try to understand why this is the case and how quantum computing will interact with cryptocurrencies.

To start off with, lets define quantum computing and the classical computing were all used to, and seeing where the terms compare and contrast with one another. Quantum computing can be roughly placed in the same paradigm as classical pre-1900s physics and modern physics which comprises Einsteins insights on relativity and quantum physics.

Classical computing is the kind of computers weve grown used to, the extensions of Turings theories on computation, the laptops or mobile phones that you carry around with you. Classical computing relies heavily on the manipulation of physical bits the famous 0s and 1s.

Quantum computing relies on qubits, bits that are held in superposition and use quantum principles to complete calculations. The information captured or generated by a quantum system benefits from the ability of qubits to be in more than one physical state at a time (superposition), but there is information decay in capturing the state of the system.

One point that will be immediately relevant to the discussion is that quantum computers are not universally better than classical computers as a result. When people speak about quantum supremacy, including reports from Google GOOG and/or China, they really mean that a quantum computer can do a certain task better than classical computers, perhaps one that is impossible to do in any reasonable timeframe with classical computers.

We can think of this in terms of time scales from a computing perspective there are some, but not all functions, that go from being impossible to accomplish in any meaningful human-level time period to ones that become slow but manageable with a large enough quantum computer.

In a way, you can think of Turing tests and quantum supremacy tests in much the same way. Designed at first to demonstrate the superiority of one system over another (in the case of Turing tests, artificial language generation vs. human language comprehension, in the case of quantum supremacy tests, quantum computing systems vs classical computers), theyve become more gimmick than substance.

A quantum computer has to perform better at some minute and trivial task that might seem impressive but completely useless in much the same way a Turing test of machine-generated English might fool a Ukrainian child with no fluency in the language.

This means that we have to narrow down to a function that quantum computers can be better on that would materially affect cryptocurrencies or the encryption theyre built on in order for quantum supremacy to matter.

One area of specific focus is Shors Algorithm, which can factor large prime numbers down into two smaller ones. This is a very useful property for breaking encryption, since the RSA family of encryption depends on factoring large prime numbers in exactly this manner. Shors Algorithm works in theory with a large enough quantum computer and so its a practical concern that eventually, Shors Algorithm might come into play and among other things, RSA encryption might be broken.

On this front, the US National Institute of Standards and Technology (NIST) has already started gathering proposals for post-quantum cryptography, encryption that would operate and not be broken even with much larger quantum computers than the ones were currently able to build. They estimate that large enough quantum computers to disrupt classical encryption will potentially arrive in the next twenty years.

For cryptocurrencies, a fork in the future that might affect large parts of the chain, but it will be somewhat predictable there is a lot of thought being placed on post-quantum encryption technology. Bitcoin would not be one of the first planks to fall if classical encryption were suddenly broken for a number of reasons. Yet, a soft fork (as opposed to a hard one) might be enough to help move crypto-assets from suddenly insecure keys to secure post-quantum encryption.

Even an efficient implementation of Shors Algorithm may not break some of the cryptography standards used in bitcoin. SHA-256 is theorized to be quantum-resistant.

The most efficient theoretical implementation of a quantum computer to detect a SHA-256 collision is actually less efficient than the theorized classical implementation for breaking the standard. The wallet file in the original Bitcoin client is using SHA-512 (a more secure version than SHA-256) to help encrypt private keys.

Most of the encryption in modern cryptocurrencies are built on elliptic curve cryptography rather than RSA especially in the generation of signatures in bitcoin which requires ECDSA. This is largely due to the fact that elliptic curves are correspondingly harder to crack than RSA (sometimes exponentially so) from classical computers.

Thanks to Moores law and better classical computing, secure RSA key sizes have grown so large so as to be impractical compared to elliptic curve cryptography so most people will opt for elliptic curve cryptography for performance reasons for their systems, which is the case with bitcoin.

However, quantum computers seem to flip this logic on its head: given a large enough quantum computer with enough qubits, you can break elliptic curve cryptography easier than you might break RSA.

Both elliptic curve cryptography are widely used in a bunch of other industries and use cases as well RSA-2048 and higher are standards in the conventional banking system to send encrypted information, for example.

Yet, even with a large enough quantum computer, you would still have to reveal or find somebodys public keys so they could be subject to attack. With cryptocurrency wallet reuse being frowned upon, and a general encouragement of good privacy practices, the likelihood of this attack is already being reduced.

Another area of attack could be Grovers algorithm, which can exponentially speed up mining with a large enough quantum computer though its probable that ASICs, the specialized classical computers mostly used to mine bitcoin now, would be faster compared to the earliest versions of more complete quantum computers.

This poses more of a stronger threat when it comes to the state of cryptocurrencies: the ability to mine quickly in a sudden quantum speedup could lead to destabilization of prices and more importantly control of the chain itself an unexpected quantum speedup could, if hidden, lead to vast centralization of mining and possible 51% attacks. Yet the most likely case is that larger systems of quantum computing will be treated like any kind of hardware, similar to the transition for miners between GPUs, FGPAs and ASICs a slow economic transition to better tooling.

Its conceivable that these avenues of attack and perhaps other more unpredictable ones might emerge, yet post-quantum encryption planning is already in process and through the mechanism of forks, cryptocurrencies can be updated to use post-quantum encryption standards and defend against these weaknesses.

Bitcoin and even other cryptocurrencies and their history are filled with examples of hardware and software changes that had to be made to make the network more secure and performant and good security practices in the present (avoiding wallet reuse) can help prepare for a more uncertain future.

So quantum computers being added to the mix wont suddenly render classical modes of encryption useless or mining trivial quantum supremacy now doesnt mean that your encryption or the security of bitcoin is at risk right at this moment.

The real threat is when quantum computers become many scales larger than they currently are by which point planning for post-quantum encryption, which is already well on the way would come to the fore, and at which point bitcoin and other cryptocurrencies can soft fork and use both decentralized governance and dynamism when needed in the face of new existential threats to defeat the threat of quantum supremacy.

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Here's Why Quantum Computing Will Not Break Cryptocurrencies - Forbes

New York, USA, Dec. 22, 2020 (GLOBE NEWSWIRE) -- A latest report published by Research Dive on the globalquantum computing market sheds light on the current outlook and future growth of the market. As per the report, the global quantum computing market is expected to garner $667.3 million by growing at a CAGR of 30.0% from 2020 to 2027. This report is drafted by market experts by evaluating all the important aspects of the market. It is a perfect source of information and statistics for new entrants, market players, shareholders, stakeholders, investors, etc.

Check out How COVID-19 impacts the Global Quantum Computing Market. Click here to Connect with our Analyst to get more Market Insight: https://www.researchdive.com/connect-to-analyst/8332

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Download Sample Report of the Global Quantum Computing Market and Reveal the Market Overview, Opportunity, Expansion, Growth and More: https://www.researchdive.com/download-sample/8332

The report includes:

A summary of the market with its definition, advantages, and application areas. Detailed insights on market position, dynamics, statistics, growth rate, revenues, market shares, and future predictions. Key market segments, boomers, restraints, and investment opportunities. Present situation of the global as well as regional market from the viewpoint of companies, countries, and end industries. Information on leading companies, current market trends and developments, Porter Five Analysis, and top winning business strategies.

Factors Impacting the Market Growth:

As per the report, the growing cyber-attacks across the world is hugely contributing to the growth of the global quantum computing market. Moreover, the rising implementation of quantum computing technologies in agriculture for helping farmers to improve the efficiency and yield of crops is likely to unlock rewarding opportunities for the market growth. However, absence of highly experienced employees, having knowledge regarding quantum computing is likely to hinder the market growth.

Access Varied Market Reports Bearing Extensive Analysis of the Market Situation, Updated With The Impact of COVID-19: https://www.researchdive.com/covid-19-insights

COVID-19 Impact Analysis:

The sudden outbreak of COVID-19 pandemic has made a significant impact on the global quantum computing market. During this crisis period, quantum computing technology can be used for medical research and other activities related to COVID-19 pandemic. Moreover, the technology can be beneficial for developing advanced drugs at an accelerated speed and for analyzing different types of interactions between biomolecules and fight infectious like viruses. In addition, businesses are greatly investing in the development of quantum computers for drug discovery amidst the crisis period. All these factors are expected to unlock novel investment opportunities for the market growth in the upcoming years.

Check out all Information and communication technology & media Industry Reports: https://www.researchdive.com/information-and-communication-technology-and-media

Segment Analysis:

The report segments the quantum computing market into offerings type, end user, and application.

By offerings type, the report further categorizes the market into: Consulting solutions Systems

Among these, the systems segment is expected to dominate the market by garnering a revenue of $313.3 million by 2027. This is mainly due to growing use of quantum computing in AI, radar making, machine learning technologies, and many others.

Based on application, the report further classifies the market into: Optimization Machine Learning Material Simulation

Among these, themachine learning segment is expected to observe accelerated growth and garner $236.9 million by 2027. This is mainly due to significant role of quantum computing in enhancing runtime, capacity, and learning efficiency. Moreover, quantum machine learning has the potential to speed-up various machine learning processes such as optimization, linear algebra, deep learning, and Kernel evaluation, which is likely to boost the market growth during the forecast period.

Regional Analysis:

The report explains the lookout of the global quantum computing market across several regions, including: Europe Asia Pacific LAMEA North America

Among these, the Asia-Pacific region is estimated to lead the market growth by growing at a striking growth rate of 31.60% during the forecast period. This is mainly because of the growing adoption of quantum computing technologies in numerous sectors including chemicals, healthcare, utilities & pharmaceuticals, and others in this region.

Market Players and Business Strategies:

The report offers a list of global key players in the quantum computing market and discloses some of their strategies and developments. The key players listed in the report are:

QC Ware, Corp. Cambridge Quantum Computing Limited D-Wave Systems Inc., International Business Machines Corporation Rigetti Computing 1QB Information Technologies River Lane Research StationQ Microsoft Anyon Google Inc.

These players are massively contributing to the growth of the market by performing activities such as mergers and acquisitions, novel developments, geographical expansions, and many more.

Our market experts have made use of several tools, methodologies, and research methods to get in-depth insights of the global quantum computing sector. Moreover, we strive to deliver a customized report to fulfill special requirements of our clients, on demand.Click Here to Get Absolute Top Companies Development Strategies Summary Report.

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Global Quantum Computing Market Predicted to Garner $667.3 Million by 2027, Growing at 30.0% CAGR from 2020 to 2027 - [193 pages] Informative Report...