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#436774 AI Is an Energy-Guzzler. We Need to ...
There is a saying that has emerged among the tech set in recent years: AI is the new electricity. The platitude refers to the disruptive power of artificial intelligence for driving advances in everything from transportation to predicting the weather.
Of course, the computers and data centers that support AI’s complex algorithms are very much dependent on electricity. While that may seem pretty obvious, it may be surprising to learn that AI can be extremely power-hungry, especially when it comes to training the models that enable machines to recognize your face in a photo or for Alexa to understand a voice command.
The scale of the problem is difficult to measure, but there have been some attempts to put hard numbers on the environmental cost.
For instance, one paper published on the open-access repository arXiv claimed that the carbon emissions for training a basic natural language processing (NLP) model—algorithms that process and understand language-based data—are equal to the CO2 produced by the average American lifestyle over two years. A more robust model required the equivalent of about 17 years’ worth of emissions.
The authors noted that about a decade ago, NLP models could do the job on a regular commercial laptop. Today, much more sophisticated AI models use specialized hardware like graphics processing units, or GPUs, a chip technology popularized by Nvidia for gaming that also proved capable of supporting computing tasks for AI.
OpenAI, a nonprofit research organization co-founded by tech prophet and profiteer Elon Musk, said that the computing power “used in the largest AI training runs has been increasing exponentially with a 3.4-month doubling time” since 2012. That’s about the time that GPUs started making their way into AI computing systems.
Getting Smarter About AI Chip Design
While GPUs from Nvidia remain the gold standard in AI hardware today, a number of startups have emerged to challenge the company’s industry dominance. Many are building chipsets designed to work more like the human brain, an area that’s been dubbed neuromorphic computing.
One of the leading companies in this arena is Graphcore, a UK startup that has raised more than $450 million and boasts a valuation of $1.95 billion. The company’s version of the GPU is an IPU, which stands for intelligence processing unit.
To build a computer brain more akin to a human one, the big brains at Graphcore are bypassing the precise but time-consuming number-crunching typical of a conventional microprocessor with one that’s content to get by on less precise arithmetic.
The results are essentially the same, but IPUs get the job done much quicker. Graphcore claimed it was able to train the popular BERT NLP model in just 56 hours, while tripling throughput and reducing latency by 20 percent.
An article in Bloomberg compared the approach to the “human brain shifting from calculating the exact GPS coordinates of a restaurant to just remembering its name and neighborhood.”
Graphcore’s hardware architecture also features more built-in memory processing, boosting efficiency because there’s less need to send as much data back and forth between chips. That’s similar to an approach adopted by a team of researchers in Italy that recently published a paper about a new computing circuit.
The novel circuit uses a device called a memristor that can execute a mathematical function known as a regression in just one operation. The approach attempts to mimic the human brain by processing data directly within the memory.
Daniele Ielmini at Politecnico di Milano, co-author of the Science Advances paper, told Singularity Hub that the main advantage of in-memory computing is the lack of any data movement, which is the main bottleneck of conventional digital computers, as well as the parallel processing of data that enables the intimate interactions among various currents and voltages within the memory array.
Ielmini explained that in-memory computing can have a “tremendous impact on energy efficiency of AI, as it can accelerate very advanced tasks by physical computation within the memory circuit.” He added that such “radical ideas” in hardware design will be needed in order to make a quantum leap in energy efficiency and time.
It’s Not Just a Hardware Problem
The emphasis on designing more efficient chip architecture might suggest that AI’s power hunger is essentially a hardware problem. That’s not the case, Ielmini noted.
“We believe that significant progress could be made by similar breakthroughs at the algorithm and dataset levels,” he said.
He’s not the only one.
One of the key research areas at Qualcomm’s AI research lab is energy efficiency. Max Welling, vice president of Qualcomm Technology R&D division, has written about the need for more power-efficient algorithms. He has gone so far as to suggest that AI algorithms will be measured by the amount of intelligence they provide per joule.
One emerging area being studied, Welling wrote, is the use of Bayesian deep learning for deep neural networks.
It’s all pretty heady stuff and easily the subject of a PhD thesis. The main thing to understand in this context is that Bayesian deep learning is another attempt to mimic how the brain processes information by introducing random values into the neural network. A benefit of Bayesian deep learning is that it compresses and quantifies data in order to reduce the complexity of a neural network. In turn, that reduces the number of “steps” required to recognize a dog as a dog—and the energy required to get the right result.
A team at Oak Ridge National Laboratory has previously demonstrated another way to improve AI energy efficiency by converting deep learning neural networks into what’s called a spiking neural network. The researchers spiked their deep spiking neural network (DSNN) by introducing a stochastic process that adds random values like Bayesian deep learning.
The DSNN actually imitates the way neurons interact with synapses, which send signals between brain cells. Individual “spikes” in the network indicate where to perform computations, lowering energy consumption because it disregards unnecessary computations.
The system is being used by cancer researchers to scan millions of clinical reports to unearth insights on causes and treatments of the disease.
Helping battle cancer is only one of many rewards we may reap from artificial intelligence in the future, as long as the benefits of those algorithms outweigh the costs of using them.
“Making AI more energy-efficient is an overarching objective that spans the fields of algorithms, systems, architecture, circuits, and devices,” Ielmini said.
Image Credit: analogicus from Pixabay Continue reading
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#436578 AI Just Discovered a New Antibiotic to ...
Penicillin, one of the greatest discoveries in the history of medicine, was a product of chance.
After returning from summer vacation in September 1928, bacteriologist Alexander Fleming found a colony of bacteria he’d left in his London lab had sprouted a fungus. Curiously, wherever the bacteria contacted the fungus, their cell walls broke down and they died. Fleming guessed the fungus was secreting something lethal to the bacteria—and the rest is history.
Fleming’s discovery of penicillin and its later isolation, synthesis, and scaling in the 1940s released a flood of antibiotic discoveries in the next few decades. Bacteria and fungi had been waging an ancient war against each other, and the weapons they’d evolved over eons turned out to be humanity’s best defense against bacterial infection and disease.
In recent decades, however, the flood of new antibiotics has slowed to a trickle.
Their development is uneconomical for drug companies, and the low-hanging fruit has long been picked. We’re now facing the emergence of strains of super bacteria resistant to one or more antibiotics and an aging arsenal to fight them with. Gone unchallenged, an estimated 700,000 deaths worldwide due to drug resistance could rise to as many as 10 million in 2050.
Increasingly, scientists warn the tide is turning, and we need a new strategy to keep pace with the remarkably quick and boundlessly creative tactics of bacterial evolution.
But where the golden age of antibiotics was sparked by serendipity, human intelligence, and natural molecular weapons, its sequel may lean on the uncanny eye of artificial intelligence to screen millions of compounds—and even design new ones—in search of the next penicillin.
Hal Discovers a Powerful Antibiotic
In a paper published this week in the journal, Cell, MIT researchers took a step in this direction. The team says their machine learning algorithm discovered a powerful new antibiotic.
Named for the AI in 2001: A Space Odyssey, the antibiotic, halicin, successfully wiped out dozens of bacterial strains, including some of the most dangerous drug-resistant bacteria on the World Health Organization’s most wanted list. The bacteria also failed to develop resistance to E. coli during a month of observation, in stark contrast to existing antibiotic ciprofloxacin.
“In terms of antibiotic discovery, this is absolutely a first,” Regina Barzilay, a senior author on the study and computer science professor at MIT, told The Guardian.
The algorithm that discovered halicin was trained on the molecular features of 2,500 compounds. Nearly half were FDA-approved drugs, and another 800 naturally occurring. The researchers specifically tuned the algorithm to look for molecules with antibiotic properties but whose structures would differ from existing antibiotics (as halicin’s does). Using another machine learning program, they screened the results for those likely to be safe for humans.
Early study suggests halicin attacks the bacteria’s cell membranes, disrupting their ability to produce energy. Protecting the cell membrane from halicin might take more than one or two genetic mutations, which could account for its impressive ability to prevent resistance.
“I think this is one of the more powerful antibiotics that has been discovered to date,” James Collins, an MIT professor of bioengineering and senior author told The Guardian. “It has remarkable activity against a broad range of antibiotic-resistant pathogens.”
Beyond tests in petri-dish bacterial colonies, the team also tested halicin in mice. The antibiotic cleared up infections of a strain of bacteria resistant to all known antibiotics in a day. The team plans further study in partnership with a pharmaceutical company or nonprofit, and they hope to eventually prove it safe and effective for use in humans.
This last bit remains the trickiest step, given the cost of getting a new drug approved. But Collins hopes algorithms like theirs will help. “We could dramatically reduce the cost required to get through clinical trials,” he told the Financial Times.
A Universe of Drugs Awaits
The bigger story may be what happens next.
How many novel antibiotics await discovery, and how far can AI screening take us? The initial 6,000 compounds scanned by Barzilay and Collins’s team is a drop in the bucket.
They’ve already begun digging deeper by setting the algorithm loose on 100 million molecules from an online library of 1.5 billion compounds called the ZINC15 database. This first search took three days and turned up 23 more candidates that, like halicin, differ structurally from existing antibiotics and may be safe for humans. Two of these—which the team will study further—appear to be especially powerful.
Even more ambitiously, Barzilay hopes the approach can find or even design novel antibiotics that kill bad bacteria with alacrity while sparing the good guys. In this way, a round of antibiotics would cure whatever ails you without taking out your whole gut microbiome in the process.
All this is part of a larger movement to use machine learning algorithms in the long, expensive process of drug discovery. Other players in the area are also training AI on the vast possibility space of drug-like compounds. Last fall, one of the leaders in the area, Insilico, was challenged by a partner to see just how fast their method could do the job. The company turned out a new a proof-of-concept drug candidate in only 46 days.
The field is still developing, however, and it has yet to be seen exactly how valuable these approaches will be in practice. Barzilay is optimistic though.
“There is still a question of whether machine-learning tools are really doing something intelligent in healthcare, and how we can develop them to be workhorses in the pharmaceuticals industry,” she said. “This shows how far you can adapt this tool.”
Image Credit: Halicin (top row) prevented the development of antibiotic resistance in E. coli, while ciprofloxacin (bottom row) did not. Collins Lab at MIT Continue reading
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#436550 Work in the Age of Web 3.0
What is the future of work? Is our future one of ‘technological socialism’ (where technology is taking care of our needs)? Or will tomorrow’s workplace be completely virtualized, allowing us to hang out at home in our PJs while “walking” about our virtual corporate headquarters?
This blog will look at the future of work during the age of Web 3.0, examining scenarios in which artificial intelligence, virtual reality, and the spatial web converge to transform every element of our careers, from training, to execution, to free time.
To offer a quick recap on what the Spatial Web is and how it works, let’s cover some brief history.
A Quick Recap on Web 3.0
While Web 1.0 consisted of static documents and read-only data (static web pages), Web 2.0 introduced multimedia content, interactive web applications, and participatory social media, all of these mediated by two-dimensional screens.
But over the next two to five years, the convergence of 5G, artificial intelligence, VR/AR, and a trillion-sensor economy will enable us to both map our physical world into virtual space and superimpose a digital data layer onto our physical environments. Suddenly, all our information will be manipulated, stored, understood and experienced in spatial ways.
In this blog, I’ll be discussing the Spatial Web’s vast implications for:
Professional Training
Delocalized Business & the Virtual Workplace
Smart Permissions & Data Security
Let’s dive in.
Virtual Training, Real-World Results
Virtual and augmented reality have already begun disrupting the professional training market. As projected by ABI Research, the enterprise VR training market is on track to exceed $6.3 billion in value by 2022.
Leading the charge, Walmart has already implemented VR across 200 Academy training centers, running over 45 modules and simulating everything from unusual customer requests to a Black Friday shopping rush.
Then in September 2018, Walmart committed to a 17,000-headset order of the Oculus Go to equip every US Supercenter, neighborhood market, and discount store with VR-based employee training. By mid-2019, Walmart had tracked a 10-15 percent boost in employee confidence as a result of newly implemented VR training.
In the engineering world, Bell Helicopter is using VR to massively expedite development and testing of its latest aircraft, FCX-001. Partnering with Sector 5 Digital and HTC VIVE, Bell found it could concentrate a typical 6-year aircraft design process into the course of 6 months, turning physical mock-ups into CAD-designed virtual replicas.
But beyond the design process itself, Bell is now one of a slew of companies pioneering VR pilot tests and simulations with real-world accuracy. Seated in a true-to-life virtual cockpit, pilots have now tested countless iterations of the FCX-001 in virtual flight, drawing directly onto the 3D model and enacting aircraft modifications in real-time.
And in an expansion of our virtual senses, several key players are already working on haptic feedback. In the case of VR flight, French company Go Touch VR is now partnering with software developer FlyInside on fingertip-mounted haptic tech for aviation.
Dramatically reducing time and trouble required for VR-testing pilots, they aim to give touch-based confirmation of every switch and dial activated on virtual flights, just as one would experience in a full-sized cockpit mockup. Replicating texture, stiffness, and even the sensation of holding an object, these piloted devices contain a suite of actuators to simulate everything from a light touch to higher-pressured contact, all controlled by gaze and finger movements.
When it comes to other high-risk simulations, virtual and augmented reality have barely scratched the surface.
Firefighters can now combat virtual wildfires with new platforms like FLAIM Trainer or TargetSolutions. And thanks to the expansion of medical AR/VR services like 3D4Medical or Echopixel, surgeons might soon perform operations on annotated organs and magnified incision sites, speeding up reaction times and vastly improving precision.
But perhaps most urgent, Web 3.0 and its VR interface will offer an immediate solution for today’s constant industry turnover and large-scale re-education demands. VR educational facilities with exact replicas of anything from large industrial equipment to minute circuitry will soon give anyone a second chance at the 21st-century job market.
Want to be an electric, autonomous vehicle mechanic at age 15? Throw on a demonetized VR module and learn by doing, testing your prototype iterations at almost zero cost and with no risk of harming others.
Want to be a plasma physicist and play around with a virtual nuclear fusion reactor? Now you’ll be able to simulate results and test out different tweaks, logging Smart Educational Record credits in the process.
As tomorrow’s career model shifts from a “one-and-done graduate degree” to continuous lifelong education, professional VR-based re-education will allow for a continuous education loop, reducing the barrier to entry for anyone wanting to enter a new industry.
But beyond professional training and virtually enriched, real-world work scenarios, Web 3.0 promises entirely virtual workplaces and blockchain-secured authorization systems.
Rise of the Virtual Workplace & Digital Data Integrity
In addition to enabling a virtual goods marketplace, the Spatial Web is also giving way to “virtual company headquarters” and completely virtualized companies, where employees can work from home or any place on the planet.
Too good to be true? Check out an incredible publicly listed company called eXp Realty.
Launched on the heels of the 2008 financial crisis, eXp Realty beat the odds, going public this past May and surpassing a $1B market cap on day one of trading. But how? Opting for a demonetized virtual model, eXp’s founder Glenn Sanford decided to ditch brick and mortar from the get-go, instead building out an online virtual campus for employees, contractors, and thousands of agents.
And after years of hosting team meetings, training seminars, and even agent discussions with potential buyers through 2D digital interfaces, eXp’s virtual headquarters went spatial. What is eXp’s primary corporate value? FUN! And Glenn Sanford’s employees love their jobs.
In a bid to transition from 2D interfaces to immersive, 3D work experiences, virtual platform VirBELA built out the company’s office space in VR, unlocking indefinite scaling potential and an extraordinary new precedent. Foregoing any physical locations for a centralized VR campus, eXp Realty has essentially thrown out all overhead and entered a lucrative market with barely any upfront costs.
Delocalize with VR, and you can now hire anyone with Internet access (right next door or on the other side of the planet), redesign your corporate office every month, throw in an ocean-view office or impromptu conference room for client meetings, and forget about guzzled-up hours in traffic.
Throw in the Spatial Web’s fundamental blockchain-based data layer, and now cryptographically secured virtual IDs will let you validate colleagues’ identities or any of the virtual avatars we will soon inhabit.
This becomes critically important for spatial information logs—keeping incorruptible records of who’s present at a meeting, which data each person has access to, and AI-translated reports of everything discussed and contracts agreed to.
But as I discussed in a previous Spatial Web blog, not only will Web 3.0 and VR advancements allow us to build out virtual worlds, but we’ll soon be able to digitally map our real-world physical offices or entire commercial high rises too.
As data gets added and linked to any given employee’s office, conference room, or security system, we might then access online-merge-offline environments and information through augmented reality.
Imagine showing up at your building’s concierge and your AR glasses automatically check you into the building, authenticating your identity and pulling up any reminders you’ve linked to that specific location.
You stop by a friend’s office, and his smart security system lets you know he’ll arrive in an hour. Need to book a public conference room that’s already been scheduled by another firm’s marketing team? Offer to pay them a fee and, once accepted, a smart transaction will automatically deliver a payment to their company account.
With blockchain-verified digital identities, spatially logged data, and virtually manifest information, business logistics take a fraction of the time, operations grow seamless, and corporate data will be safer than ever.
Final Thoughts
While converging technologies slash the lifespan of Fortune 500 companies, bring on the rise of vast new industries, and transform the job market, Web 3.0 is changing the way we work, where we work, and who we work with.
Life-like virtual modules are already unlocking countless professional training camps, modifiable in real time and easily updated. Virtual programming and blockchain-based authentication are enabling smart data logging, identity protection, and on-demand smart asset trading. And VR/AR-accessible worlds (and corporate campuses) not only demonetize, dematerialize, and delocalize our everyday workplaces, but enrich our physical worlds with AI-driven, context-specific data.
Welcome to the Spatial Web workplace.
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(1) A360 Executive Mastermind: If you’re an exponentially and abundance-minded entrepreneur who would like coaching directly from me, consider joining my Abundance 360 Mastermind, a highly selective community of 360 CEOs and entrepreneurs who I coach for 3 days every January in Beverly Hills, Ca. Through A360, I provide my members with context and clarity about how converging exponential technologies will transform every industry. I’m committed to running A360 for the course of an ongoing 25-year journey as a “countdown to the Singularity.”
If you’d like to learn more and consider joining our 2021 membership, apply here.
(2) Abundance-Digital Online Community: I’ve also created a Digital/Online community of bold, abundance-minded entrepreneurs called Abundance-Digital. Abundance-Digital is Singularity University’s ‘onramp’ for exponential entrepreneurs—those who want to get involved and play at a higher level. Click here to learn more.
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This article originally appeared on diamandis.com. Read the original article here.
Image Credit: Image by Gerd Altmann from Pixabay Continue reading
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#436546 How AI Helped Predict the Coronavirus ...
Coronavirus has been all over the news for the last couple weeks. A dedicated hospital sprang up in just eight days, the stock market took a hit, Chinese New Year celebrations were spoiled, and travel restrictions are in effect.
But let’s rewind a bit; some crucial events took place before we got to this point.
A little under two weeks before the World Health Organization (WHO) alerted the public of the coronavirus outbreak, a Canadian artificial intelligence company was already sounding the alarm. BlueDot uses AI-powered algorithms to analyze information from a multitude of sources to identify disease outbreaks and forecast how they may spread. On December 31st 2019, the company sent out a warning to its customers to avoid Wuhan, where the virus originated. The WHO didn’t send out a similar public notice until January 9th, 2020.
The story of BlueDot’s early warning is the latest example of how AI can improve our identification of and response to new virus outbreaks.
Predictions Are Bad News
Global pandemic or relatively minor scare? The jury is still out on the coronavirus. However, the math points to signs that the worst is yet to come.
Scientists are still working to determine how infectious the virus is. Initial analysis suggests it may be somewhere between influenza and polio on the virus reproduction number scale, which indicates how many new cases one case leads to.
UK and US-based researchers have published a preliminary paper estimating that the confirmed infected people in Wuhan only represent five percent of those who are actually infected. If the models are correct, 190,000 people in Wuhan will be infected by now, major Chinese cities are on the cusp of large-scale outbreaks, and the virus will continue to spread to other countries.
Finding the Start
The spread of a given virus is partly linked to how long it remains undetected. Identifying a new virus is the first step towards mobilizing a response and, in time, creating a vaccine. Warning at-risk populations as quickly as possible also helps with limiting the spread.
These are among the reasons why BlueDot’s achievement is important in and of itself. Furthermore, it illustrates how AIs can sift through vast troves of data to identify ongoing virus outbreaks.
BlueDot uses natural language processing and machine learning to scour a variety of information sources, including chomping through 100,000 news reports in 65 languages a day. Data is compared with flight records to help predict virus outbreak patterns. Once the automated data sifting is completed, epidemiologists check that the findings make sense from a scientific standpoint, and reports are sent to BlueDot’s customers, which include governments, businesses, and public health organizations.
AI for Virus Detection and Prevention
Other companies, such as Metabiota, are also using data-driven approaches to track the spread of the likes of the coronavirus.
Researchers have trained neural networks to predict the spread of infectious diseases in real time. Others are using AI algorithms to identify how preventive measures can have the greatest effect. AI is also being used to create new drugs, which we may well see repeated for the coronavirus.
If the work of scientists Barbara Han and David Redding comes to fruition, AI and machine learning may even help us predict where virus outbreaks are likely to strike—before they do.
The Uncertainty Factor
One of AI’s core strengths when working on identifying and limiting the effects of virus outbreaks is its incredibly insistent nature. AIs never tire, can sift through enormous amounts of data, and identify possible correlations and causations that humans can’t.
However, there are limits to AI’s ability to both identify virus outbreaks and predict how they will spread. Perhaps the best-known example comes from the neighboring field of big data analytics. At its launch, Google Flu Trends was heralded as a great leap forward in relation to identifying and estimating the spread of the flu—until it underestimated the 2013 flu season by a whopping 140 percent and was quietly put to rest.
Poor data quality was identified as one of the main reasons Google Flu Trends failed. Unreliable or faulty data can wreak havoc on the prediction power of AIs.
In our increasingly interconnected world, tracking the movements of potentially infected individuals (by car, trains, buses, or planes) is just one vector surrounded by a lot of uncertainty.
The fact that BlueDot was able to correctly identify the coronavirus, in part due to its AI technology, illustrates that smart computer systems can be incredibly useful in helping us navigate these uncertainties.
Importantly, though, this isn’t the same as AI being at a point where it unerringly does so on its own—which is why BlueDot employs human experts to validate the AI’s findings.
Image Credit: Coronavirus molecular illustration, Gianluca Tomasello/Wikimedia Commons Continue reading
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