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The Evolutionary Perspective
Category Archives: Ai
Posted: March 24, 2020 at 5:34 am
Todays Daily AI Roundup covers the latest Artificial Intelligence announcements on AI capabilities, AI mobility products, Robotic Service, Technology from NCI Information Systems, Honeywell, SAP SE , CompTIA, HCL Technologies, BrainChipand Nokia.
NCI Information Systems, Inc.(NCI), a leading provider of advanced information technology solutions and professional services to U.S. federal government agencies, announced the launch of the NCIEmpowerplatform to accelerate artificial intelligence (AI) adoption in the public sector.
Honeywellannounced that it will immediately expand its manufacturing operations inSmithfield, Rhode Island, to produce N95 face masks in support of the U.S. governments response to the novel coronavirus (COVID-19). Honeywell is ramping up operations to produce millions of N95 disposable respirators to help support the need for critical safety equipment.
SAP SEandAccenturelaunched a co-developed solution for upstream oil and gas companies based onSAP S/4HANA Cloud. Using intelligent technologies such as artificial intelligence (AI), the SAP S/4HANA Cloud solution for upstream oil and gas helps customers to further increase visibility into operations and cash flow. Additionally, the solution includes contributions from leading global oil and gas companies such as ConocoPhillips and Shell.
CompTIA, the leading provider of vendor-neutral skills certifications and training for information technology (IT) professionals around the world, said today it will soon allow candidates to take their CompTIA certification exams at home, at any time, in a secure testing environment. Together with our partner Pearson VUE, CompTIA certification exams will soon be available via a remote testing option so candidates can take their exam whenever and from wherever they choose, saidTodd Thibodeaux, president and CEO of CompTIA.
HCL Technologies, a leading global technology company, announced version 12.0, to be generally available in April 2020. HCL offers its services and products through three business units IT and Business Services (ITBS), Engineering and R&D Services (ERS) and Products & Platforms (P&P). ITBS enables global enterprises to transform their businesses through offerings in areas of Applications, Infrastructure, Digital Process Operations and next generational digital transformation solutions.
BrainChip Holdings Ltd,a leading provider of ultra-low power high performance AI technology, announced thatSocionext Inc.,a leader in advanced SoC solutions for video and imaging systems, will offer customers an Artificial Intelligence Platform that includes the Akida SoC, an ultra-low power high performance AI technology.
Nokiaannounced that it has been selected as 5G RAN vendor by Chunghwa Telecom.As the leading mobile operator in Taiwan, Chunghwa Telecom is ready to address the 5G market with the best band combination for consumers as well as enterprise demand. Supporting Chunghwa Telecoms ambitious plans, Nokia is responsible for 5G radio network deployment in the Central and Southern Region of Taiwan.
FujitsuComputer Products of America, Inc., the established leader in document imaging, is pleased to announce a collaboration with Adobe Acrobat, the worlds best and most trusted PDF solution.Fujitsu Computer Products of America is launching theScanSnap iX1500 Deluxe Bundlewhich combines the easy to use ScanSnap iX1500 scanner with Acrobat Pro DC software to provide a fast, efficient and easy document management solution.
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Posted: at 5:34 am
When Darktrace launched in 2013, the world of cybersecurity was an entirely different landscape.
Today, we are used to hearing about artificial intelligence. Six years ago, the idea that you simply couldnt keep all the bad guys out and that companies needed an AI-powered digital immune system to defend against attacks was radical, Poppy Gustafsson, co-CEO of Darktrace, tells Growth Quarters.
Fast-forward several years and Darktrace has become of the leading players in the cybersecurity space, in part due toGustafsson going against the worst advice she ever received: Being told not to do something in a certain way because it went against convention.
[Read:Reinvest and obsess: How BlaBlaCar became a European tech unicorn]
Darktraces proprietary technology has of course played a part too. Its Enterprise Immune System technology detects novel attacks and insider threats at an early stage.
Interestingly, the technology is modelled on the human immune system. It can, Darktrace claims, spot the subtle signals of an advanced attack without having to rely on rules, signatures, or prior assumptions.
The tech leans on unsupervised machine learning and AI to understand an organizations inner structure. It observes users and devices, cloud containers, and workflows essentially learning what normal looks like for everycompany.
Darktrace must be doing something right. Itreported earnings of 72 million ($78 million) in revenue for the year ending June 2018, up from 37 million ($40 million) the previous year.
Then, in September of that same year, the company joined the highly coveted tech unicorn club with a valuation of 1.5 billion ($1.65 billion.)
But as most entrepreneurs will know, Darktraces trajectory hasnt always smooth sailing.
Changing the perspective of the entire industry was a challenge, Gustafsson says.
We had to take our customers on a journey to get them to the point where they would trust algorithms to fight back against attacks on their behalf, she adds, noting how she had to hone her storytelling skills to communicate the technologys capabilities in a way that made sense to the outside world.
Creating trust around a product and humanizing it are crucial steps for entrepreneurs to take when theyre entering a new market.To overcome this, Darktrace built trust mechanisms into the technology allowing organizations to first only turn on the AI when they werent in the office and slowly build up to give the tech full autonomy.
Theres no denying AIs transformative potential and the ways in which its already safeguarding companies and their data. However, things arent always what they seem.
Many organizations claim to be using artificial intelligence. In reality, they are using basic automation to recreate legacy approaches: They are automating the writing rules and signatures based on historical attacks, Gustafsson adds.
This approach, the co-CEO notes, is inherently flawed because the cyber threat landscape is ever-evolving and growing in sophistication.
[Read:This AI system predicts air pollution before it happens]
Theres clear evidence that hackers are using AI to intensify their attacks, and this, Gustafsson says, will be the biggest challenge the technology will have to endure in the coming years.
However, this could also be the biggest opportunity for companies operating in this niche and entrepreneurs seeking an entry-point into the market.
If the threat vector rapidly shifts to adversaries using AI in their attacks, this could change the landscape of the entire marketplace.It will quickly become a war of algorithms, and we see ourselves on the front line of defense, she adds.
Gustafsson sensibly points out that we cant bring humans to a machine fight, and this, she adds, is a reality the industry has already come to terms with.
Although AI is already taking on more higher level human thought processes, for example, in threat investigation,Gustafsson says were only just scratching the surface.
Gustafsson envisions a future where red teaming the practice of rigorously challenging plans, policies, systems, and assumptions by adopting an adversarial approach and cyber risk analysis are both driven by AI.
There is so much more we are yet to achieve, she concludes.
Published March 23, 2020 10:49 UTC
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Posted: at 5:34 am
By Krishna KumarWhen it comes to infectious diseases, prevention, surveillance and rapid-response efforts can go a long way toward slowing or stalling outbreaks. When a pandemic such as the recent coronavirus outbreak happens, it can create huge challenges for the government and public health officials to gather information quickly and coordinate a response.
In such a situation, artificial intelligence (AI) can play a huge role in predicting an outbreak and also minimising or stalling its spread.
Detecting an epidemicAI algorithms can help mine through news reports and online content from around the world, helping experts recognise anomalies even before it reaches epidemic proportions. The corona outbreak itself is a great example where researchers applied AI to study flight traveller data to predict where the novel coronavirus could pop up next. A National Geographic report demonstrates how monitoring the internet or social media can help detect the early stages of a potential outbreak.
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Building intelligence and knowledgeAI and big data analytics have a big role to play in modern genome sequencing methods. High-resolution computer-generated simulation allows scientists to study and interpret large disease-related data sets to learn more about how they spread. A greater understanding of these phenomena can empower the community to respond far more rapidly to attacks.
Augmenting medical careRecently, weve all seen poignant images of healthcare professionals across the globe working tirelessly to treat COVID-19 patients, often putting their own lives at risk. AI could play a crucial role in lightening their load while ensuring that the quality of care does not suffer. For instance, the Tampa General Hospital in Florida is using AI to detect fever in visitors with a simple facial scan. AI is also helping doctors at the Sheba Medical Center in Israel to predict complications such as respiratory failure or sepsis in COVID-19 patients.
As AI quickly becomes mainstream, healthcare is certainly an area where it will play a big role in keeping us safer and healthier.
(The author is CEO and Founder of Simplilearn, a global ed-tech company which provides skilling programs for tech professionals)
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Posted: at 5:34 am
If not the most deadly, the novel coronavirus (COVID-19) is one of the most contagious diseases to have hit our green planet in the past decades. In little over three months since the virus was first spotted in mainland China, it has spread to more than 90 countries, infected more than 185,000 people, and taken more than 3,500 lives.
As governments and health organizations scramble to contain the spread of coronavirus, they need all the help they can get, including from artificial intelligence. Though current AI technologies arefar from replicating human intelligence, they are proving to be very helpful in tracking the outbreak, diagnosing patients, disinfecting areas, and speeding up the process of finding a cure for COVID-19.
Data science and machine learning might be two of the most effective weapons we have in the fight against the coronavirus outbreak.
Just before the turn of the year, BlueDot, an artificial intelligence platform that tracks infectious diseases around the world, flagged a cluster of unusual pneumonia cases happening around a market in Wuhan, China. Nine days later, the World Health Organization (WHO)released a statementdeclaring the discovery of a novel coronavirus in a hospitalized person with pneumonia in Wuhan.
BlueDot usesnatural language processingandmachine learning algorithmsto peruse information from hundreds of sources for early signs of infectious epidemics. The AI looks at statements from health organizations, commercial flights, livestock health reports, climate data from satellites, and news reports. With so much data being generated on coronavirus every day, the AI algorithms can help home in on the bits that can provide pertinent information on the spread of the virus. It can also find important correlations between data points, such as the movement patterns of the people who are living in the areas most affected by the virus.
The company also employs dozens of experts who specialize in a range of disciplines including geographic information systems, spatial analytics, data visualization, computer sciences, as well as medical experts in clinical infectious diseases, travel and tropical medicine, and public health. The experts review the information that has been flagged by the AI and send out reports on their findings.
Combined with the assistance of human experts, BlueDots AI can not only predict the start of an epidemic, but also forecast how it will spread. In the case of COVID-19, the AI successfully identified the cities where the virus would be transferred to after it surfaced in Wuhan. Machine learning algorithms studying travel patterns were able to predict where the people who had contracted coronavirus were likely to travel.
Coronavirus (COVID-19) (Image source:NIAID)
You have probably seen the COVID-19 screenings at border crossings and airports. Health officers use thermometer guns and visually check travelers for signs of fever, coughing, and breathing difficulties.
Now,computer vision algorithmscan perform the same at large scale. An AI system developed by Chinese tech giant Baidu uses cameras equipped with computer vision and infrared sensors to predict peoples temperatures in public areas. The system can screen up to 200 people per minute and detect their temperature within a range of 0.5 degrees Celsius. The AI flags anyone who has a temperature above 37.3 degrees. The technology is now in use in Beijings Qinghe Railway Station.
Alibaba, another Chinese tech giant, has developed an AI system that candetect coronavirus in chest CT scans. According to the researchers who developed the system, the AI has a 96-percent accuracy. The AI was trained on data from 5,000 coronavirus cases and can perform the test in 20 seconds as opposed to the 15 minutes it takes a human expert to diagnose patients. It can also tell the difference between coronavirus and ordinary viral pneumonia. The algorithm can give a boost to the medical centers that are already under a lot of pressure to screen patients for COVID-19 infection. The system is reportedly being adopted in 100 hospitals in China.
A separate AI developed by researchers from Renmin Hospital of Wuhan University, Wuhan EndoAngel Medical Technology Company, and the China University of Geosciences purportedly shows 95-percent accuracy on detecting COVID-19 in chest CT scans. The system is adeep learning algorithmtrained on 45,000 anonymized CT scans. According to a preprint paperpublished on medRxiv, the AIs performance is comparable to expert radiologists.
One of the main ways to prevent the spread of the novel coronavirus is to reduce contact between infected patients and people who have not contracted the virus. To this end, several companies and organizations have engaged in efforts to automate some of the procedures that previously required health workers and medical staff to interact with patients.
Chinese firms are using drones and robots to perform contactless delivery and to spray disinfectants in public areas to minimize the risk of cross-infection. Other robots are checking people for fever and other COVID-19 symptoms and dispensing free hand sanitizer foam and gel.
Inside hospitals, robots are delivering food and medicine to patients and disinfecting their rooms to obviate the need for the presence of nurses. Other robots are busy cooking rice without human supervision, reducing the number of staff required to run the facility.
In Seattle, doctors used a robot to communicate with and treat patients remotely to minimize exposure of medical staff to infected people.
At the end of the day, the war on the novel coronavirus is not over until we develop a vaccine that can immunize everyone against the virus. But developing new drugs and medicine is a very lengthy and costly process. It can cost more than a billion dollars and take up to 12 years. Thats the kind of timeframe we dont have as the virus continues to spread at an accelerating pace.
Fortunately, AI can help speed up the process. DeepMind, the AI research lab acquired by Google in 2014, recently declared that it has used deep learning to find new information about the structure of proteins associated with COVID-19. This is a process that could have taken many more months.
Understanding protein structures can provide important clues to the coronavirus vaccine formula. DeepMind is one of several organizations who are engaged in the race to unlock the coronavirus vaccine. It has leveraged the result of decades of machine learning progress as well as research on protein folding.
Its important to note that our structure prediction system is still in development and we cant be certain of the accuracy of the structures we are providing, although we are confident that the system is more accurate than our earlier CASP13 system, DeepMinds researchers wroteon the AI labs website. We confirmed that our system provided an accurate prediction for the experimentally determined SARS-CoV-2 spike protein structure shared in the Protein Data Bank, and this gave us confidence that our model predictions on other proteins may be useful.
Although its too early to tell whether were headed in the right direction, the efforts are commendable. Every day saved in finding the coronavirus vaccine can save hundredsor thousandsof lives.
This story is republished fromTechTalks, the blog that explores how technology is solving problems and creating new ones. Like them onFacebookhere and follow them down here:
Published March 21, 2020 17:00 UTC
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Posted: at 5:34 am
In the months since the novel coronavirus emerged in Wuhan, China, last December, almost 2,000 research papers have been published on the health effects of the new virus, possible treatments, and the dynamics of the resulting pandemic.
This outpouring of research is a testament to the speed with which science can tackle big problems. But it also presents a headache for anyone wanting to stay up to date with the literature, or hoping to mine it for insight about the virus, its behavior, or possible treatments.
Naturally, some believe that artificial intelligence may help. Monday, the White House announced a project in collaboration with tech companies and academics to make a huge amount of coronavirus research accessible to AI researchers and their algorithms for the first time.
The effort will ask AI to mine through the avalanche of research to answer questions that could help medical and public health experts. By cross-referencing papers and searching for patterns, AI algorithms might help discover new possible treatments or factors that make the virus worse for some patients.
Machine learning has huge potential to help wrangle and draw insights from scientific research. But some experts say the approach is at an early stage and is unlikely to help address the current crisis, where the US suffers from more basic needs, like a shortage of test kits.
Microsoft Research, the National Library of Medicine, and the Allen Institute for AI (AI2), gathered and prepared over 29,000 papers related to the new virus and the wider coronavirus family, 13,000 of them processed so that computers can read the underlying data, plus information about the authors and their affiliations. Kaggle, a platform that runs data science competitions, is creating challenges around 10 key questions related to the coronavirus. These range from questions about risk factors and treatments that do not involve drugs, to the genetic properties of the virus and efforts to develop vaccines. The project also involves the Chan Zuckerberg Initiative and the Center for Security and Emerging Technology at Georgetown University.
Plus: How can I avoid catching it? Is Covid-19 more deadly than the flu? Our in-house Know-It-Alls answer your questions.
I think the initiative is definitely worthwhile, says Giovanni Colavizza, an assistant professor at the University of Amsterdam and a visiting researcher at the Alan Turing Institute. Whether interesting findings will come from these initiatives remains to be seen, but this initiative highlights the importance of structured, open, and programmatic access to the scientific literature.
Mining scientific papers has sometimes proven useful, finding, for example, connections that suggested magnesium might treat migraines. The hope is that AI will accelerate insights into the novel coronavirus by finding more subtle connections across more data.
Despite an occasionally frosty relationship with big tech, the White House has been meeting with tech executives in an effort to find solutions to the coronavirus crisis. High tech in general has gotten something of a bad rap, but something like this crisis shows how AI can potentially do a world of good, says Oren Etzioni, CEO of AI2. The scientific literature on the coronavirus is growing exponentially.
John Brownstein, an expert on health bioinformatics at Harvard Medical School, says the effort is worthwhile, and it is good to see so many people trying to help. At the same time, he notes that worthwhile data projects such as Predict, which is designed to predict pandemics, have been starved of funding in recent years. He also says the government should have been prepared in advance for pandemics, citing a lack of testing kits as a big problem. Weve had a severe lack of funding and resources, Brownstein says. We want to think about the bigger picture.
After the US and other governments last week called for scientific publishers to open up research on the coronavirus, a number of big publishers said they would offer free access to relevant papers and data. Many scientists support the idea of making research more open and accessible generally.
Anything that will expedite a systematic review of the literature surrounding Covid is useful, says Suzanne Fricke, a librarian at Washington State University who has studied data mining of scientific literature. Rapid review with AI is needed to develop guidelines for practitioners and to identify gaps in knowledge, she says. Fricke adds that there are significant delays with peer-reviewed research papers. She adds that mining raw data from doctors on the front line could conceivably provide even more insights. Thats not immediately part of the new initiative.
For some AI researchers, the new project is an opportunity to feel useful. Kristian Lum, an assistant research professor at the University of Pennsylvania, recently posted on Twitter offering to help apply her statistical modelling skills to projects related to the virus. I'll definitely have a look and see if my skills are useful here, she says.
WIRED is providing unlimited free access to stories about the coronavirus pandemic. Sign up for our Coronavirus Update to get the latest in your inbox.
More From WIRED on Covid-19
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Posted: at 5:34 am
In this new whitepaper from our friends over at Panasas, we take a look at whether your storage infrastructure is ready for the robust requirements in support of AI workloads. AI promises to not only create entirely new industries, but it will also fundamentally change the way organizations large and small conduct business. IT planners need to start revising their storage infrastructure now to prepare the organization for the coming AI wave.
This guide includes 4 important chapters that focus on the new levels of storage infrastructure needed for the demands of AI:
Chapter 1 Is Your Storage Architecture Ready for the Coming AI Wave?
Chapter 2 Understanding the Challenges that AI at Scale Creates
Chapter 3 Can Current Storage Infrastructure Meet the AI at Scale Demand?
Chapter 4 The Requirements of AI at Scales Storage Infrastructures
AI/ML workloads are fundamentally different from any other workload the organization may have run in the past. Early AI/ML projects have counted on DAS for data storage. The problem is that DAS doesnt distribute the load evenly, something that is critical as the number of GPUs per AI workload increases. Also, DAS is highly inefficient, and the waste in capacity and time spent copying or moving data eliminates the price advantage of cheap internal drives.
Panasas data storage provides the extreme performance, enterprise-grade reliability and manageability required to process the large and complex datasets associated with mixed workload HPC environments as well as emerging applications like AI, AR, VR, precision medicine, and autonomous driving.
Download the new whitepaper courtesy of Panasas, Is Your Storage Infrastructure Ready for the Coming AI Wave? to understand how IT planners need to start revising their storage infrastructure now to prepare the organization for the coming AI wave.
Posted: at 5:34 am
An unmanned convenience store opened Monday at Takanawa Gateway Station, making use of artificial intelligence not just to speed up checkouts but also to prevent shoplifting.
The store is a key feature at the station, which opened on March 14 as the first addition to the Yamanote loop line in nearly 50 years.
About 50 cameras in the roughly 60-sq.-meter store identify every item thats picked up. The stores exit gates open after the customer pays.
The AI has been trained to recognize customer behavior, including how items are carried, and it almost fully prevents shoplifting by accurately recognizing when merchandise is taken from the shelves, according to developer Touch To Go Co.
Attempts in a demonstration to hide merchandise under clothes or to avoid the cameras while stashing them in a bag were all detected.
Our AI learned by capturing images from different angles. It is not completely fail-proof, but it is almost impossible to shoplift, said Touch To Go President Tomoki Akutsu.
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Posted: at 5:34 am
I hate auto shows. Cars are meant to move, make noise and be felt. Seeing a Bugatti caged on an auto-show stand is worse than watching a lion at a particularly cruel zoo. At least you get some sense of the lions power from the languid roll of its shoulders as it pads miserably around its pen. At an auto show? Nothing.
The luxury marques know this. Dragging you out to a cold, soulless exhibition hall in a grim part of town isntIm guessinggoing to get you in a spendy frame of mind. Ferrari or Aston would far rather show you its latest model on some sunlit lawn at Pebble Beach or Goodwood, with a glass of good Champagne in your hand, or better yet, let you drive it on a track or along the scenic, scented roads of Carmel or the Mediterranean coast.
And so it seems strange that luxury carmakers make a show of going to CES, the dauntingly vast tech extravaganza in Vegas. Pressure on floor space means their booths at CES are typically far smaller and less showy than those at an auto show. But in truth, the manufacturers dont actually want their customers to visit them. They just want you to know theyre there. They are often unfairly maligned as industrial dinosaurs whose grip on how we get around will soon be broken by Uber, Waymo and whichever of the raft of new mobility start-ups actually hit pay dirt. The automakers hope that, by appearing at CES alongside tech giants and start-ups, theyll appropriate a little of their mojo.
The other reason for carmakers to attend CES is to learn stuff. I bumped into Daimler CEO Ola Kallenius three times as he stalked the Las Vegas Convention Center. Despite having an R&D budget in the billions, he told me, his people might spot an idea or a possible collaboration among the myriad tiny booths of exhibitors that would not have presented itself otherwise.
Experiencing the Audi AI:MEs VR technology from Holoride.Photo: Courtesy of Audi AG.
For me, two trends stood out: screens and AI. The full-width displays in Sonys shock Vision-S concept car and in the production version of automotive-tech newcomer Bytons M-Byte electric SUV made Teslas hallmark iPad-style displays look kind of puny, and Id bet on others following Sonys and Bytons big-screen lead. Endlessly configurable, the screens allow you to display a bunch of your in-car apps at once.
Artificial intelligence is making huge strides, but some applications of it might still be fictional at this point. Audis AI:ME concept car appeared at CES with a working interior for the first time and added eye control for use with VR headsets, so youll need your Audi to be autonomous before you can free your eyes from the road for long enough to scroll through Spotify with a glance.
The show also saw the launch of affordable LiDAR laser sensors that can build a 3-D image of your cars surroundings and will make systems for driver assistance and crash avoidance almost supernatural in their abilities. Its just a pity that, despite the braininess of AI, the data it generates may never be used to drive our cars for us: At the show, in private, car-industry leaders were saying that full autonomy seems to be driving off into the distance rather than getting closer, with the emphasis switching to self-driving trucks, unmanned delivery vehicles and driverless ride-hailing services.
CES is enlightening, but its scale kills the fun. As with most auto shows, unless you have a professional interest I cant recommend you go. Its better to read about than to attend. You enjoy the Champagne at that Porsche private party on a manicured lawn, and let me rack up the steps and peer into the ever-bigger screens of the future in Vegas for you.
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Posted: March 5, 2020 at 6:24 pm
AI can be used for the early detection of virus outbreaks that might result in a pandemic. (Photo by ... [+] Emanuele Cremaschi/Getty Images)
AI detected the coronavirus long before the worlds population really knew what it was. On December 31st, a Toronto-based startup called BlueDot identified the outbreak in Wuhan, several hours after the first cases were diagnosed by local authorities. The BlueDot team confirmed the info its system had relayed and informed their clients that very day, nearly a week before Chinese and international health organisations made official announcements.
Thanks to the speed and scale of AI, BlueDot was able to get a head start over everyone else. If nothing else, this reveals that AI will be key in forestalling the next coronavirus-like outbreak.
BlueDot isn't the only startup harnessing AI and machine learning to combat the spread of contagious viruses. One Israel-based medtech company, Nanox, has developed a mobile digital X-ray system that uses AI cloud-based software to diagnose infections and help prevent epidemic outbreaks. Dubbed the Nanox System, it incorporates a vast image database, radiologist matching, diagnostic reviews and annotations, and also assistive artificial intelligence systems, which combine all of the above to arrive at an early diagnosis.
Nanox is currently building on this technology to develop a new standing X-ray machine that will supply tomographic images of the lungs. The company plans to market the machine so that it can be installed in public places, such as airports, train stations, seaports, or anywhere else where large groups of people rub shoulders.
Given that the new system, as well as the existing Nanox System, are lower cost mobile imaging devices, it's unsurprising to hear that Nanox has attracted investment from funds looking to capitalise on AI's potential for thwarting epidemics. This month, the company announced a $26 million strategic investment from Foxconn. It also signed an agreement this week to supply 1,000 of its Nanox Systems to medical imaging services across Australia, New Zealand and Norway. Coronavirus be warned.
Its CEO and co-founder Ran Poliakine, explains that such deals are a testament to how the future of epidemic prevention lies with AI-based diagnostic tools. "Nanox has achieved a technological breakthrough by digitizing traditional X-rays, and now we are ready to take a giant leap forward in making it possible to provide one scan per person, per year, for preventative measures," he tells me.
Importantly, the key feature of AI in terms of preventing epidemics is its speed and scale. As Poliakine explains, "AI can detect conditions instantly which makes it a great source of power when trying to prevent epidemics. If we talk about 1,000 systems scanning 60 people a day on average, this translates to 60,000 scans that need to be processed daily by the professional teams."
Poliakine also affirms that no human force available today that can support this volume with the necessary speed and efficiency. Time and again, this is a point made forcefully by other individuals and companies working in this burgeoning sector.
"When it comes to detecting outbreaks, machines can be trained to process vast amounts of data in the same way that a human expert would," explains Dr Kamran Khan, the founder and CEO of BlueDot, as well as a professor at the University of Toronto. "But a machine can do this around the clock, tirelessly, and with incredible speed, making the process vastly more scalable, timely, and efficient. This complements human intelligence to interpret the data, assess its relevance, and consider how best to apply it with decision-making."
Basically, AI is set to become a giant firewall against infectious diseases and pandemics. And it won't only be because of AI-assisted screening and diagnostic techniques. Because as Sergey Young, a longevity expert and founder of the Longevity Vision Fund, tells me, artificial intelligence will also be pivotal in identifying potential vaccines and treatments against the next coronavirus, as well as COVID-19 itself.
"AI has the capacity to quickly search enormous databases for an existing drug that can fight coronavirus or develop a new one in literally months," he says. "For example, Longevity Vision Funds portfolio company Insilico Medicine, which specializes in AI in the area of drug discovery and development, used its AI-based system to identify thousands of new molecules that could serve as potential medications for coronavirus in just four days. The speed and scalability of AI is essential to fast-tracking drug trials and the development of vaccines."
This kind of treatment-discovery will prove vitally important in the future. And in conjunction with screening, it suggests that artificial intelligence will become one of the primary ingredients in ensuring that another coronavirus won't have an outsized impact on the global economy. Already, the COVID-19 coronavirus is likely to cut global GDP growth by $1.1 trillion this year, in addition to having already wiped around $5 trillion off the value of global stock markets. Clearly, avoiding such financial destruction in the future would be more than welcome, and artificial intelligence will prove indispensable in this respect. Especially as the scale of potential pandemics increases with an increasingly populated and globalised world.
Sergey Young also explains that AI could play a substantial role in the area of impact management and treatment, at least if we accept their increasing encroachment into society. He notes that, in China, robots are being used in hospitals to alleviate the stresses currently being piled on medical staff, while ambulances in the city of Hangzhou are assisted by navigational AI to help them reach patients faster. Robots have even been dispatched to a public plaza in Guangzhou in order to warn passersby who aren't wearing face-masks. Even more dystopian, China is also allegedly using drones to ensure residents are staying at home and reducing the risk of the coronavirus spreading further.
Even if we don't reach that strange point in human history where AI and robots police our behaviour during possible health crises, artificial intelligence will still become massively important in detecting outbreaks before they spread and in identifying possible treatments. Companies such as BlueDot, Nanox, and Insilico Medicine will prove increasingly essential in warding off future coronavirus-style pandemics, and with it they'll provide one very strong example of AI being a force for good.
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Posted: at 6:24 pm
Artificial Intelligence has progressed immensely in the past few years. From being just a fiction context to penetrating into the regular lives of people, AI has brought transformation in several ways. Such advancements are an output of various factors that include the application of new statistical approaches and enhanced computing powers. However, according to 2017 report by DeepMind,a Perspective in the journal Neuron, argues that people often discount the contribution and use of ideas from experimental and theoretical neuroscience.
TheDeepMind reportsresearchers believe that drawing inspiration from neuroscience in AI research is important for two reasons. First, neuroscience can help validate AI techniques that already exist. They said, Put simply if we discover one of our artificial algorithms mimics a function within the brain, it suggests our approach may be on the right track. Second, neuroscience can provide a rich source of inspiration for new types of algorithms and architectures to employ when building artificial brains. Traditional approaches to AI have historically been dominated by logic-based methods and theoretical mathematical models.
Moreover,in a recent blog post, DeepMind suggests that the human brain and AI learning methods are closely linked when it comes to learning through reward.
Computer scientists have developed algorithms for reinforcement learning in artificial systems. These algorithms enable AI systems to learn complex strategies without external instruction, guided instead by reward predictions.
As noted by the post, a recent development in computer science which yields significant improvements in performance on reinforcement learning problems may provide a deep, parsimonious explanation for several previously unexplained features of reward learning in the brain, and opens up new avenues of research into the brains dopamine system, with potential implications for learning and motivation disorders.
DeepMind found that dopamine neurons in the brain were each tuned to different levels of pessimism or optimism. If they were a choir, they wouldnt all be singing the same note, but harmonizing each with a consistent vocal register, like bass and soprano singers. In artificial reinforcement learning systems, this diverse tuning creates a richer training signal that greatly speeds learning in neural networks, and researchers speculate that the brain might use it for the same reason.
The existence of distributional reinforcement learning in the brain has interesting implications both for AI and neuroscience. Firstly, this discovery validates distributional reinforcement learning it gives researchers increased confidence that AI research is on the right track since this algorithm is already being used in the most intelligent entity they are aware of: the brain.
Therefore, a shared framework for intelligence in context to artificial intelligence and neuroscience will allow scientists to build smarter machines, and enable them to understand humankind better. This collaborative drive to propel both could possibly expand human cognitive capabilities while bridging the gap between humans and machines.
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Smriti is a Content Analyst at Analytics Insight. She writes Tech/Business articles for Analytics Insight. Her creative work can be confirmed @analyticsinsight.net. She adores crushing over books, crafts, creative works and people, movies and music from eternity!!
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