With the coronavirus growing more deadly in China, artificial intelligence researchers are applying machine-learning techniques to social media, web, and other data for subtle signs that the disease may be spreading elsewhere.
The new virus emerged in Wuhan, China, in December, triggering a global health emergency. It remains uncertain how deadly or contagious the virus is, and how widely it might have already spread. Infections and deaths continue to rise. More than 31,000 people have now contracted the disease in China, and 630 people have died, according to figures released by authorities there Friday.
John Brownstein, chief innovation officer at Harvard Medical School and an expert on mining social media information for health trends, is part of an international team using machine learning to comb through social media posts, news reports, data from official public health channels, and information supplied by doctors for warning signs the virus is taking hold in countries outside of China.
The program is looking for social media posts that mention specific symptoms, like respiratory problems and fever, from a geographic area where doctors have reported potential cases. Natural language processing is used to parse the text posted on social media, for example, to distinguish between someone discussing the news and someone complaining about how they feel. A company called BlueDot used a similar approach—minus the social media sources—to spot the coronavirus in late December, before Chinese authorities acknowledged the emergency.
“We are moving to surveillance efforts in the US,” Brownstein says. It is critical to determine where the virus may surface if the authorities are to allocate resources and block its spread effectively. “We’re trying to understand what’s happening in the population at large,” he says.
The rate of new infections has slowed slightly in recent days, from 3,900 new cases on Wednesday to 3,700 cases on Thursday to 3,200 cases on Friday, according to the World Health Organization. Yet it isn’t clear if the spread is really slowing or if new infections are simply becoming more difficult to track.
So far, other countries have reported far fewer cases of coronavirus. But there is still widespread concern about the virus spreading. The US has imposed a travel ban on China even though experts question the effectiveness and ethics of such a move. Researchers at Johns Hopkins University have created a visualization of the virus’s progress around the world based on official numbers and confirmed cases.
Health experts did not have access to such quantities of social, web, and mobile data when seeking to track previous outbreaks such as severe acute respiratory syndrome (SARS). But finding signs of the new virus in a vast soup of speculation, rumor, and posts about ordinary cold and flu symptoms is a formidable challenge. “The models have to be retrained to think about the terms people will use and the slightly different symptom set,” Brownstein says.
Even so, the approach has proven capable of spotting a coronavirus needle in a haystack of big data. Brownstein says colleagues tracking Chinese social media and news sources were alerted to a cluster of reports about a flu-like outbreak on December 30. This was shared with the WHO, but it took time to confirm the seriousness of the situation.
Beyond identifying new cases, Brownstein says the technique could help experts learn how the virus behaves. It may be possible to determine the age, gender, and location of those most at risk more quickly than using official medical sources.
Alessandro Vespignani, a professor at Northeastern University who specializes in modeling contagion in large populations, says it will be particularly challenging to identify new instances of the coronavirus from social media posts, even using the most advanced AI tools, because its characteristics still aren’t entirely clear. “It’s something new. We don’t have historical data,” Vespignani says. ”There are very few cases in the US, and most of the activity is driven by the media, by people’s curiosity.”
But Vespignani believes that if the disease spreads more widely in the US, it should become easier to monitor its spread by applying machine learning to social media, news reports, and medical information. Combining AI with other techniques “could be very powerful,” Vespignani says.
Crowdsourced information, collated by volunteers or via websites set up to offer information about the coronavirus, is also important to the effort. Brownstein is working with a Boston-based company, Buoy, that offers health advice to millions of people in the US online and through health provider portals. Buoy will offer advice for those who suspect they may have the coronavirus, feeding that to Brownstein and others as another data source.
An analysis of crowdsourced data from a Chinese physician community website, conducted by researchers at the National Institutes of Health, reveals a picture of delays in reporting new cases in Wuhan during the early stages of the pandemic. It also suggests that those younger than 15 years of age are more resilient.
Other signals may assist health officials in different countries prepare responses. Pings from mobile devices, along with flight and train itineraries, are helping epidemiologists build a picture of the spread of the virus and likely trajectory.
Andy Tatem, a professor at the UK’s University of Southampton, and colleagues recently used anonymized historical data from smartphones, supplied by the Chinese search company Baidu, to model how the virus may have moved out of Wuhan in the days after it appeared.
Another group of researchers used data from Tencent, the Chinese company behind the popular Chinese app WeChat, to model the contagion. This suggests that the travel restrictions imposed by the Chinese authorities may have slowed the spread of the disease by a few days, providing critical time for countermeasures. Similar techniques could predict the spread through other countries should the contagion spread.
While it might be possible for the authorities to track individuals using the movement of their smartphones, Tatem says this is less useful than understanding broader trends and dynamics. And although it is unclear how widely the virus might yet travel, he says the biggest concern is that it could appear in countries with fewer health care resources to combat it. “Whether it can be contained in China, that’s the question for the world right now,” Tatem says.
Is It Worth Investing in a Website Builder?
There are many different ways to build a website these days. There’s the timeless method of building your site code in Adobe Dreamweaver and exporting it to the web.
You can build a site in WordPress with a bit of CSS knowledge, or you can just outsource everything to a website design agency. Then there’s also the option of using a website builder, which is perhaps the easiest solution of all.
“Website builders are a popular way for people to easily and quickly set up a website with as little hassle as possible.”
They’re great for small retail businesses, whether you’re selling handmade crafts or drop shipping products from Amazon, but larger companies can effectively use website builders as well. They certainly aren’t for everyone, but let’s take a look at whether or not investing in a website builder is the right choice for you.
How much do website builders actually cost?
Website builders are always going to be cheaper than custom website design, and to an extent WordPress, but there are some variables. The thing is that some people (design agencies) like to point out that website builders cost a little more in things like domain hosting, SSL certificates, and other little monthly fees, compared to DIY hosting or a WordPress domain host.
So it becomes a question of upfront costs versus long-term costs in monthly fees, but there are several catches people don’t like to mention. Let me try to explain it succinctly.
Cost of a website builder
If you use a website builder to create, for example, a small eCommerce website. You’re probably going to pay around $200 ~ $500 upfront. This will include your domain name, any premium themes and add-ons (like a shopping cart module), and monthly hosting (which you’ll probably pay as an annual subscription upfront). It’s kind of like an “all-inclusive” vacation package, where everything is included in the total upfront cost.
So you’ll pay a small upfront fee which is mostly the annual hosting subscription, followed by monthly fees for the additional customizations you add to your website. Hosting plans on website builder platforms average around $9 to $75 per month, depending on your plan.
Again, it really depends on your plan, as website builders aren’t just for eCommerce websites. For example, there are a number of platforms which are built for specific industries, such as real estate, as this guide describes. Ultimately if you are going to use a website builder, it’s best to find one that is best suited to the industry you are operating in.
Cost of a WordPress website
If you use WordPress, you can expect to pay around $500 – $1,000 upfront for a similar small eCommerce website, with lower monthly fees. This is because you can shop around for a domain name and domain hosting from sites like HostGator, BlueHost, etc. to get the best subscription-based pricing available, but you’ll also be paying additionally for WordPress themes, mobile design plug-ins, shopping cart plug-ins, etc.
Using the vacation package analogy again, WordPress is like you’re paying for your own drinks, meals, and WiFi access at the resort.
This means that you’ll be spending a bit more upfront on piecing together the different elements of your website, but you’ll pay on average around $11 – $40 per month for domain hosting. Of course, you could also pay monthly for plug-in subscriptions, website maintenance, etc.
Cost of custom website design
So in the vacation package analogy, custom website design is like flying first-class to a resort, and you own the resort. Custom website design is going to cost a minimum of around $5,000 and could go much higher, depending on your web project.
“Website designers are paid around $50 – $100 per hour, and custom website design takes around 14 weeks on average, from beginning to launch.”
Now some website design agencies are going to be mad at me for saying this, but when they like to point out the “higher monthly costs” of a website builder, take a look at their fine print. Many website design agencies can lock you into monthly maintenance contracts, which can range from an additional $500 up to $3,000 per month or more, depending on the size of your site.
It’s kind of like if you have a contract with a car mechanic to inflate your tires and change your oil every month, except they keep billing you for a clutch assembly replacement. I’m not saying that website design agencies are dishonest, but you do need to be aware of what kind of monthly maintenance your website actually needs.
When we compare all three options (website builder, WordPress, and custom website design), it’s quite clear that website builders are the most affordable option. However, you’ll also be limited in customization options with a website builder, as you’re really piecing together templates and blocks, so you won’t get the exclusive customization and brand appeal you would with custom web design or a WordPress website. So you’ll have to consider what’s best for your long-term business plan.
Also, Read Tips to Automate Your Ecommerce
Amazon EC2 Inf1 instances featuring AWS Inferentia chips now available in five new Regions and with improved performance
Following strong customer demand, AWS has expanded the availability of Amazon EC2 Inf1 instances to five new Regions: US East (Ohio), Asia Pacific (Sydney, Tokyo), and Europe (Frankfurt, Ireland). Inf1 instances are powered by AWS Inferentia chips, which Amazon custom-designed to provide you with the lowest cost per inference in the cloud and lower barriers for everyday developers to use machine learning (ML) at scale.
As you scale your use of deep learning across new applications, you may be bound by the high cost of running trained ML models in production. In many cases, up to 90% of the infrastructure spent on developing and running an ML application is on inference, making the need for high-performance, cost-effective ML inference infrastructure critical. Inf1 instances are built from the ground up to support ML inference applications and deliver up to 30% higher throughput and up to 45% lower cost per inference than comparable GPU-based instances. This gives you the performance and cost structure you need to confidently deploy your deep learning models across a broad set of applications.
Customers and Amazon services adopting Inf1 instances
Since the launch of Inf1 instances, a broad spectrum of customers, such as large enterprises and startups, as well as Amazon services, have begun using them to run production workloads. Amazon’s Alexa team is in the process of migrating their Text-To-Speech workload from running on GPUs to Inf1 instances. INGA Technology, a startup focused on advanced text summarization, got started with Inf1 instances quickly and saw immediate gains.
“We quickly ramped up on AWS Inferentia-based Amazon EC2 Inf1 instances and integrated them in our development pipeline,” says Yaroslav Shakula, Chief Business Development Officer at INGA Technologies. “The impact was immediate and significant. The Inf1 instances provide high performance, which enables us to improve the efficiency and effectiveness of our inference model pipelines. Out of the box, we have experienced four times higher throughput, and 30% lower overall pipeline costs compared to our previous GPU-based pipeline.”
SkyWatch provides you with the tools you need to cost-effectively add Earth observation data into your applications. They use deep learning to process hundreds of trillions of pixels of Earth observation data captured from space every day.
“Adopting the new AWS Inferentia-based Inf1 instances using Amazon SageMaker for real-time cloud detection and image quality scoring was quick and easy,” says Adler Santos, Engineering Manager at SkyWatch. “It was all a matter of switching the instance type in our deployment configuration. By switching instance types to AWS Inferentia-based Inf1, we improved performance by 40% and decreased overall costs by 23%. This is a big win. It has enabled us to lower our overall operational costs while continuing to deliver high-quality satellite imagery to our customers, with minimal engineering overhead.”
AWS Neuron SDK performance and support for new ML models
You can deploy your ML models to Inf1 instances using the AWS Neuron SDK, which is integrated with popular ML frameworks such as TensorFlow, PyTorch, and MXNet. Because Neuron is integrated with ML frameworks, you can deploy your existing models to Amazon EC2 Inf1 instances with minimal code changes. This gives you the freedom to maintain hardware portability and take advantage of the latest technologies without being tied to vendor-specific software libraries.
Since its launch, the Neuron SDK has seen dramatic improvement in performance, delivering throughput up to two times higher for image classification models and up to 60% improvement for natural language processing models. The most recent launch of Neuron added support for OpenPose, a model for multi-person keypoint detection, providing 72% lower cost per inference than GPU instances.
The easiest and quickest way to get started with Inf1 instances is via Amazon SageMaker, a fully managed service for building, training, and deploying ML models. If you prefer to manage your own ML application development platforms, you can get started by either launching Inf1 instances with AWS Deep Learning AMIs, which include the Neuron SDK, or use Inf1 instances via Amazon Elastic Kubernetes Service (Amazon EKS) or Amazon Elastic Container Service (Amazon ECS) for containerized ML applications.
For more information, see Amazon EC2 Inf1 Instances.
About the Author
Michal Skiba is a Senior Product Manager at AWS and passionate about enabling developers to leverage innovative hardware. Over the past ten years he has managed various cloud computing infrastructure products at Silicon Valley companies, large and small.
Argonne National Labs Using AI To Predict Battery Cycles
By Allison Proffitt, Editorial Director, AI Trends
Thanks to the cost reductions that have come from global electric vehicle adoption, lithium ion batteries now have an important role to play in grid storage, Susan Babinec, Argonne National Laboratory, told audiences last week at the International Battery Virtual Seminar and Exhibit. But making full use of them is going to require a bit of help from artificial intelligence.
While EVs prize high energy density, and only need to last about eight years, grid applications require more cycles, more calendar life—20 to 30 years—and more safety at a lower cost.
“Grid economics requires precise life data, which is very time and resource intensive to generate,” Babinec said. “We are using approximations that create risk, limit our design creativity, and increase cost.” The solution? Of course, in today’s day and age the solution is always artificial intelligence, Babinec quipped. “In this case, we’re going to use AI to massively reduced time to cycle life prediction.”
Babinec’s team categorized the variables impacting lithium ion batteries for grid applications—acknowledging that adjusting any one variable will always mean changes in others. “For grid storage, first and foremost, low cost is always the most important,” Babinec said. But others include state-of-charge swing, C-rate, average state-of-charge, and temperature.
“Today we handle this variability by estimating the cycle life, but those estimates do not really allow us to push these cells to the limits of what they can really do,” Babinec said. “We just simply don’t have enough information on the cycle life and we are limited by the information that is provided by the cell manufacturer, which is really all about them making sure they can live up to their warranty.”
Babinec is prioritizing overall cost per cycle (levelized cost of storage, LCOS). This is a better metric than capital cost because grid storage batteries are durable goods, she explained. The Department of Energy’s target for LCOS is $0.02/kWh, a target for which we currently fall far short.
“No matter how you look at it, we are not there today with any combination of capital and cycles,” Babinec said. “We need to bring the capital down, but right here and now we need to bring the number of cycles up.”
Looking to AI to Decrease Testing Time from Two Years to Two Weeks
Argonne is applying artificial intelligence to the problem. Babinec’s group is developing rapid cycle life evaluations using AI to decrease testing from the current two years to a goal of two weeks. Argonne is the right spot for this research, Babinec argues. As the DOE’s battery hub, Argonne has plenty of data, a team of AI experts, and a new supercomputer up to the task. Aurora, created in partnership with Argonne, Cray and the DOE, will be the first exascale computer in the U.S.
The scope of the project is broad. They are using several AI approaches—from physics-based tools to deep neural nets. “We want to see which AI approach is the best for this problem,” Babinec said. All of the Li-ion chemistries will be tested deliberately and sequentially, and the current, voltage, and time will be recorded for every second, of every cycle, for every cell.
Babinec describes the basic AI process as encoding data from one cell running one cycle. Each cell cycle generates 150 features. Narrowing in on one feature from many cells, you determine correlations and relationships and decode for one behavior: cycles to failure.
To test their plan, the group used public data published last year in Nature Energy (DOI: 10.1038/s41560-019-0356-8). They compared the capacity at a certain voltage in cycle one to the capacity at the same voltage in cycle 20 and generated correlations and relationships then predictions from there. The results: the experimental cycles to failure and the predicted cycles to failure aligned.
Her presentation at Florida Battery was the first presentation of Argonne’s experimental results, and Babinec shared that the approach seems to be working. When testing many chemistries, like cells self-organize by chemistry and cycles to failure. When run on real cells, predictions match observed. So far, Babinec says it looks like it will take as few as 40-60 cycles to predict cycle life—more for high cycle life, less for low cycle life.
The key to a high-quality prediction, she emphasized, is using training data from cells with a cycle life that is similar to your goal cycle life. For example, cells that failed at 150 cycles will not accurately train an algorithm to predict 2,000 cycles.
While work on the cycle life predictions continues, Babinec says Argonne is also focused on cleaning up more than 20 years’ worth of spreadsheets, databases, and machine files containing battery data. “The data is wonderful, but it has to be cleaned up. It’s a major effort, which we are working on,” she said. The team is working toward machine learning-ready training data including, for example, capacity vs. cycle comparisons and discharge curves. Some data are available on Github: https://github.com/materials-data-facility/battery-data-toolkit
“There is promise for this,” Babinec said. Testing timelines will decrease, which she says may open up assessments of complex and changing use scenarios, eventually enhancing deployment flexibility while minimizing risk.
Learn more at Nature Energy (DOI: 10.1038/s41560-019-0356-8).
Gnomes & Goblins to be Wevr’s Biggest Production, 10x Larger Than the Preview
Is It Worth Investing in a Website Builder?
How to Create a Cloud-connect AR Experience in 15 Minutes or Less
Mortal Blitz: Combat Arena’s PlayStation VR Open Beta Begins Next Week
AvidXchange Announces New “Tech Rising” Initiative to Remove Barriers to Technology Education
Swipe Is the Latest Project to Integrate Chainlink’s Price Oracles
Craig Wright Won’t Need to Pay Hodlnaut $60K Until Appeal Is Over, Says Counsel
Bitcoin a Hedge Against Elon Musk Mining Asteroid Gold, Say Winklevoss Twins
Solaris Offworld Combat has Been Delayed to September
Mastercard Announces Global Commercial Partnership With Pollinate
Oculus Social VR App ‘Venues’ to Get Overhaul in Preparation for ‘Facebook Horizon’
Thailand’s Central Bank Eyes DeFi Use Cases for Its Digital Baht
Bitcoin Proceeds of COVID-19 Business Support Scheme Fraud Seized
VR Giants’ Co-op Kickstarter Achieves Funding Success
Huntington Bancshares picks BillGo for faster payments
Banco Ripley goes live on Temenos Transact
OakNorth’s UK bank has approved £600m in loans since March
How a “Chad” minted Curve tokens early and briefly surpassed BTC’s market cap
Diplomatic ties Between Israel and UAE :Donald Trump
As the pandemic persists, New Zealand considers negative interest rates
Stock futures rise slightly after S&P 500 struggles to reach February record high
ABN Amro to slash size of investment bank after losses
Weed memes, explained
The $150 billion video game industry grapples with a murky track record on diversity
Cas & Chary Present: Top 10 ‘Half-Life: Alyx’ Mods So Far
J.B. Hunt’s 1st Delivery With Fully Electric Freightliner eCascadia
Sabesp anuncia resultados do 2T20
CarParts.com Announces Pricing of Public Offering of Common Stock
Four of the Top Five South Korean Banks to Offer Crypto Services
SABESP Announces 2Q20 Results
Alt Lending – week ending 14th August
Brussels Airport Company has selected Ecolog to perform COVID-19 Tests at the Brussels Airport
Coronavirus live updates: Congress leaves without passing relief bill; Fauci concerned with U.S. outbreak
Is Chainlink Poised for a Sell Off After Reaching New ATH?
China may never catch up with its commitments to the U.S. in ‘phase one’ deal, expert says
Danke Partners with Leading Chinese Media to Release 2020 College Graduate Housing Blue Book
$12K Bitcoin Price in Sight as Retail, Institutional Traders Turn ‘Greedy’
$99 Gas Fees on Ethereum Are Crippling DeFi’s Growth
UK’s Federation of Small Businesses Says Next Budget Must be “Most Pro-Business Ever” to Combat Negative Effects of First Recession in 11 Years
Former New York Times reporter Alex Berenson: I’m increasingly convinced that COVID-19 is a creation of the media/technology complex. (NO – I do not mean it’s not real or was bioengineered)
Gaming1 week ago
Server status – Is Fall Guys down?
Esports1 week ago
The best loadouts for the ISO in Call of Duty: Warzone and Modern Warfare
Esports1 week ago
Stuck on loading screen error in Fall Guys explained
AI1 week ago
AI Machine Learning Efforts Encounter A Carbon Footprint Blemish
Esports1 week ago
The best Standard Hearthstone decks to try for Scholomance Academy
Mobility1 week ago
Photos: A first look at the Samsung Galaxy Note 20 and Galaxy Note 20 Ultra
Esports1 week ago
The best loadouts for the AN-94 in Call of Duty: Warzone and Modern Warfare
Cannabis4 days ago
An in-depth look at the study that discovered THCP, a cannabinoid more potent than THC