Category Archives: Ai

AI Helping to commercialize space

Even before modern computers became a reality, science fiction gave us a plethora of examples of artificial intelligence and smart robots in the context of outer space. From Hal in 2001: A Space Odyssey and the computer on Star Trek to C3PO and R2D2 in Star Wars and even the fantastic machines in Hitchhikers Guide to the Galaxy, it seems that AI and space go together. While those examples are fiction, we are indeed starting to see examples in the real world where we are using artificial intelligence to help commercialize space.

AI Assisting in the Manufacturing of Satellites and Spacecraft

Satellites and spacecraft are complex and expensive pieces of equipment to put together. Within the spacecraft manufacturing operations, there are repetitive and complex tasks that need to be done with exacting measures of precision and often must be done in clean rooms with little exposure to potential contamination. AI-enabled systems and robotics are being used to help the manufacturing process and take away some of the tasks that humans currently do so that humans can focus on the parts that computers cant assemble.

When working to assemble satellites, not only can AI help to physically speed up the process but it can analyze the process itself to see if there are ways the process can be improved. In addition, the AI is also able to look at the work that has been performed and ensure that everything is done properly. Furthermore, the use of collaborative robots (cobots) as part of the manufacturing process are helping to reduce the need for human workers in clean rooms, and make more reliable manufacturing steps that can be error-prone.

AI-enhanced imagery

Satellites are generating thousands, if not millions, of images every minute of the day. Satellites process about 150 terabytes of data everyday. These images capture everything from weather and environmental imagery and data to images down to just inches of every inch of the globe. Capturing images of Earth automatically introduces a number of challenges and opportunities where AI is helping. Without AI, humans are mostly responsible for interpreting, understanding, and analyzing imagery. By the time a human gets around to interpreting an image, you may have to wait for the satellite to move back around to the same position to further refine image analysis.

The power of deep learning and AI-enabled recognition provides significant power in analyzing images and providing ability to review the millions of images produced by spacecraft. Artificial intelligence on the other end can analyze the images as they are being taken and determine if there are any issues with the images. Unlike humans, AI does not need to sleep or take breaks so it can rapidly process a lot of data. Using AI to capture images of Earth also prevents the need for large amounts of communication to and from Earth to analyze photos and determine whether a new photo needs to be taken. By cutting back on communication, the AI is saving processing power, reducing battery usage, and speeding up the image gathering process.

Satellites are also being used to analyze natural disasters from space. Detailed imagery from a satellite can help those on the ground to see victims, determine the course of the disaster, and more. Artificial intelligence is being used to help speed up the response of satellites to natural disasters. With the help of the onboard AI, satellites are able to determine where a natural disaster is located and navigate to that location. They are also able to automate the image gathering process so that the computer does not have to wait for a human in order to have a quick response.

AI systems are even being used to help analyze data collected from probes heading into deep space to see if they are capable of supporting life. The AI looks at patterns in worlds to help determine if they are habitable or might have some form of life existing on them. Potential planets are then sent to humans for further review.

Monitor the Health of Satellites

Satellites are complex pieces of equipment to operate. There are many potential problems that could arise, from equipment malfunctions to collisions with other satellites. In order to help keep satellites functioning properly, AI is used to monitor the health of satellites. AI can keep constant watch on sensors and equipment, provide alerts, and in some cases, carry out corrective action. SpaceX for example, uses AI to keep its satellites from colliding with other objects in space.

AI is also used to control the navigation of satellites and other spacecraft. The AI is able to look at the patterns of other satellites, planets, and space debris. Once the AI has found the patterns, it is able to change the path of the craft to avoid any collisions. While this is proving powerful, some AI experts have concerns about the potential vulnerability or failure of these systems. Experts believe that with AI navigation installed on a spacecraft, that the craft becomes more vulnerable. Turning to AI for cybersecurity and craft health monitoring can help to counteract this though.

In addition to keeping spacecraft operational, communicating between Earth and space can be challenging. Depending on the state of the atmosphere, interference from other signals and the environment, there may be a lot of communications difficulties that a satellite needs to overcome. AI is now being used to help control satellite communication to overcome any transmission problems. These AI-enabled systems are able to determine the amount of power and frequencies that are needed to transmit data back to Earth or to other satellites. With an AI onboard, the satellite is constantly doing this so that signals can get through as the satellite continues in its orbit.

Even spacecraft on other planets or deep in space are using AI in their operation, such as the Mars rovers currently operating on the red planet. On a recent AI Today podcast, NASA Jet Propulsion Laboratory (JPL) chief Tom Soderstrom shared insights into how AI is being used for the Mars rovers, spacecraft, and operations at facilities across the world.

AI on the mars rover is used to help it navigate the planet. The computer is able to make multiple changes to the rovers course every minute. Technology behind the Mars rovers are very similar to that used by self-driving cars. The major difference is that the rover has to navigate more complicated terrain and does not have other vehicular or pedestrian traffic to take into account. That complicated terrain is analyzed by the computer vision systems in the rover as it moves. If a terrain problem is encountered, the autonomous system makes a change to the course of the rover to avoid it or adjust navigation.

AI and Space: Made for Each Other

Over the last few years we have continued to see a large effort to commercialize space. Several companies are even looking to start tourist trips into space. Artificial intelligence is working to make space commercialization a possibility and to make space a safe environment in which to operate. The various benefits of AI in space all work together to enable further venturing into the unknown.

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How Is AI Helping To Commercialize Space? - Forbes

Artificial Intelligence (AI) is having a transformative effect on a huge range of industries, and the world of media and entertainment is no exception. Creators and machines arecontinuing to become more intertwined, with creative workflows taking on new shapes as AI-assistance gathers momentum.

At a broad level, people are recognising that technology and creativity go hand in hand. Creative professionals are expressing an interest in how AI and machine learning can aid the creative process. And although the discussion about machines replacing humans remains prevalent, the reality is much less dystopian. Rather than being worried about losing their jobs to technology, they are recognising the potential for AI-powered tools to make processes more intuitive and reduce the time spent on tedious, uncreative tasks.

When talking about audio specifically, its no secret that AI is quickly becoming a vital cog in the machine and the truth is were only just scratching the surface. So, what role is AI currently playing within audio workflows and how is this growing trend likely to develop in the future?

Transforming workflows

When it comes to audio workflows, there are three main areas where AI is starting to have an impact: assisted mastering, assisted mixing and assisted composition. All three are at slightly different points on the adoption scale.

For example, AI is already well established in the mastering process despite this arguably being the most specialised area of music production. The goal of mastering is to make the listening experience consistent across all formats. The process varies across different formats (Spotify, CDs, movies, etc.) as each has different loudness constraints, making mastering extremely technical and potentially costly.

There are very few skilled mastering engineers around, but AIis proving to be a viable and democratising alternative for many musicians. By analysing data and learning from previous tracks, AI-powered tools enable less experienced engineers to quickly and easily achieve professional results, albeit without the finesse of a human expert.

Next, we come to assisted mixing which, although currently slightly behind mastering in terms of adoption, is developing fast. With so much content being created for OTT services such as Netflix and Amazon Prime, the volume of audio work happening in post is increasing dramatically. Facilities are therefore looking for ways to work faster and more cost-efficiently.

AI tools can help engineers and audio teams make basicdecisions and complete the more routine tasks, thereby saving valuable pre-mixing time and enabling humans to focus on the more complex and creative elements.

For example, some mastering plugins contain built-in intelligence that analyses source material (such as guitars or vocals) and puts it in the context of the rest of the mix to suggest mixing decisions. By taking on much of the initial heavy lifting, tools such as this can be hugely beneficial for less experienced users.

Finally, theres audio composition, another area of music production that is quickly realising the value of AI. More and more tools are using deep learning algorithms to identify patterns in huge amounts of source material and then utilising the insights generated to compose basic tunes and melodies.

Theyre by no means perfect. But intuitive, user-friendly AI systems are having a transformative effect on audio workflows.

Preparing for an AI future

The prevalence of AI in audio workflows is only going to gather momentum in the months and years to come. AI could be well suited to up-and-coming artists who dont rely on music as their primary income and have limited time and resources to dedicate to song writing.

But the real opportunity is in post-production, due to the time- to-market pressures involved. Sound engineers can use AI to speed up and simplify baseline tasks, enabling them to focus on the high- value aspects that require more creativity.

In the long term, AI could be used to manage complex installations and systems. With audio-over-IP, teams manage routing from central software so they can pool resources to support projects. AI could be used to manage these complex networks of computers and software.

Ultimately, were at the tip of the iceberg. For beginner and intermediate-level creative professionals, AI tools can act as an assistant that can learn their mixing habits over time and help audio sound the best it possibly can. For more experienced professionals, it can help increase efficiency by removing many of the tedious, time-consuming tasks.

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AI will never replace humans entirely, but its clear that the technology is set to play a key role in the years to come as it continues to get more advanced. Audio professionals have to be prepared to embrace the AI revolution.

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'Embrace the AI revolution': The growing role of AI in audio workflows - PSNEurope

With well over 6 million views since its mid-February release, YouTuber EZRyderX47s Back to the Future deepfake video, with Robert Downey Jr. and Tom Holland seamlessly replacing Christopher Lloyd and Michael J. Fox, has become quite the viral sensation. The video is brilliantly done, from the lip-sync to the anything but uncanny eyes; the choice of films, and clip, was inspired as well, a welcome window into a new riff on a Hollywood classic. Produced using two readily available pieces of free software HitFilm Express, from FXhome, and Deepfacelab the startingly believable piece instantly conjures up all sorts of notions, both wonderful and sinister, regarding the seemingly unlimited horizons of AI-enhanced digital technology. If todays visual magicians can create any image with stunning photoreal clarity, what, dare we ask, can propogandists, criminals and other bad actors do with the same digital tools? Ah, so nice to find a new target, if for only a few minutes, for our coronavirus-stoked paranoia.

If you watch AWN's exclusive interview with AI expert Andrew Glassner at FMX 2019, not only will you get a great overview of AI, neural networks,and machine learning fundamentals, but... you'll come away afraid... very afraid.

For the films director, Franois Brousseau (aka EZRyderX47), the underlying technology points to a limitless creative future. With these tools, I can create an almost infinite number of parallel universes, he gushes. I can revive great actors from the past. I can put actors-of-now into movies of the past. It is almost a limitless magical universe. For Josh Davies, CEO of HitFilm Express creator FXhome, the technology helps level the creative playing field, enhancing competition by enabling smaller studios to produce more impactful work. It will enable more of the things that take time and effort, so that smaller teams can achieve the level of quality that larger teams have, he notes. Larger teams will then also be able to use these tools to produce even more amazing imagery and benefit from a better workflow. In short, whats good will be made better.

So, whats a deepfake video? How are they made? How can they be detected?

The use of digital technology to replace someone in an image or video has been around for some time, from simple Photoshop morphs to elaborately crafted films like Forrest Gump. More recently, weve seen a slew of digital characters, both replaced and de-aged, from Carrie Fisher as Princess Leia to Samuel Jackson as Nick Fury. But, with the rapidly expanding integration of AI in VFX methodology and production, coupled with fast, AI-enabled GPUs, todays replacement technology has taken a significant leap in sophistication. Case in point, Martin Scorseses recent gem for Netflix, The Irishman, made use of cutting-edge AI-backed digital tools developed by ILM. Their software, ILM Facefinder, used AI to sift through thousands of images from years worth of performances by actors Robert DeNiro, Al Pacino, and Joe Pesci, matching the camera angles, framing, lighting and expressions of the scene being rendered. This gave ILM artists a relevant reference to compare against every frame in the live-action shot. These visual references were used to refine digital doubles created for each actor, so they could be transformed into the target age for each specific scene in the film. The results were dramatic, allowing the actors, all in their 70s, to be transformed back into their 20s, something not possible using even the best makeup techniques.

The term deepfake derives from the notion of deep learning, a branch of machine learning, which is itself part of the AI world, and the notion of fake, which is to say, a counterfeit or forged version of something. With new methods of channeling enormous computer processing power to analyze massive amounts of data, AI is being harnessed more and more to visualize that data; by analyzing lots of images of a persons face, for example, software can us AI and machine learning to get really good at understanding what that face really looks like, down to the pixel level, and how it can then be manipulated and recreated in new ways with a high degree of accuracy. With a deepfake video, the software can get good not only at analyzing and learning about the face you want to recreate, but it can also get good at understanding the image or video you want to transpose that face onto. Given the time to properly learn both faces, AI-enabled software can digitally create a face that overlays the two sets of learned data, placing the new face onto the old. Tom Holland becomes Michael J. Fox!

Brousseau has been releasing deepfake videos for some time; earlier efforts included replacing Nicolas Cage with Keanu Reeves in Ghost Rider, and Jim Parsons Sheldon Cooper suddenly sporting Jack Nicholsons smiling Joker face in an episode of The Big Bang Theory. The Back to the Future video is his best yet.

For the director, the process begins with the faces. To start, I had to build the 2 facesets, he explains. I had to find all the angles of the actors' faces. I found images from interviews and films. I used HitFilm to cut the scenes where the faces were at their best. I then extracted and cleaned the faces using Deepfacelab tools. I deleted the problematic images -- blurred images, obstructed faces, bad angles, etc. This part took me around two or three days. This step is not very difficult, but it takes a long time.

You need to collate a high-quality image database, using images of the person that youre trying to deepfake, Davies adds. They will also need to have some knowledge - you cant simply rip every single photo from all kinds of footage. There are a number of things that will compromise a deepfake - for example, if an actor has a beard in one set of images and not in the other, if some images are lower resolution, are blurrier, etc. Currently, there is still a degree of manual process needed to find the best images. Of course, in the future we hope this will be AI enhanced, and they will be able to automatically identify images of the same people, and also the best kinds of images to use for deepfakes.

Finding a deepfakable scene with both actors side by side is a critical, and difficult step. I tested several scenes before finding the right one, and it took me about a week of trial and error to get it right, Rousseau reveals.

The face detection phase of the scene was also challenging. Over several frames, Deepfacelab had difficulty detecting the correct angles of faces and obstructed faces, he goes on to describe. I had to do the work manually. I also had to add a mask on some frames where the faces were obstructed. This part can be tricky, and it took me one or two days of trial and error.

With the scene and face data in hand, Rousseau brought on the AI tool. At that point, I started training artificial intelligence, he states. I tried two architecture models: the DF and the LIAE. The DF was problematic, but the LIAE was doing a pretty good job. This part took me around four or five days per face; it was time consuming but pretty easy.

Once the AI learning was over, he used Deepfacelab to convert the images to an MP4 video, performing several tests with different parameters. It took him one day to process both faces.

Then, he editing the video using HitFilm Express. I used the video transition effect Fade to color, as well as the audio transition Fade, he shares At 0:28 of the video, there is a guy who passes in front of Doc for 2 frames and Deepfacelab wasn't able to correctly render it. I had to use a mask of RDJ's face provided by the Deepfacelab software. I took the mask from a frame before the guy passes and I put it on the 2 problematic frames. Then I used the Blend Darken effect on the mask for the hairs of the guy to be visible. It took me about a day and the masking part was pretty tricky.

After watching the Back to the Future deepfake a couple times and marveling at its sophisticated visual trickery, you may say to yourself, Of course its fake. Ive seen the original. But what if I hadnt? How would I ever know which is which? According to Davies, there are ways to spot a deepfake. At the moment, the main places you can see telltale signs of manipulation are on the edges of what its replacing, he says. Generally, you can see this in the central two-thirds of the face, including shadowing around the chin area and where the forehead meets the hairline. You can also find issues caused by a limited set of facial perspectives in the sampled facial datasets. Deepfake generally works better on front angles of the face, Davies continues. A way around this of course is to ensure that your actor doesnt move much, or turn his face too far from the camera. But again, AI technology will do a far better job of looking at this - it is likely they will be able to see the discrepancy in a single pixel, which far surpasses what the human eye can detect.

When asked about the arms race already begun between those creating, and those trying to uncover, deepfake videos, Davies is optimistic good will triumph over evil. It has been often assumed in the past that advancing technology will spell the end of humanity, he muses. This has never really been evidenced but it continues to be in the forefront of many peoples minds when presented with something new. Quite simply put, more money and resources will be put into working out what has been created by AI, rather than the creators making it in the first place. This is because those wanting to distinguish between real and fake life, will be supported and backed by governments, by insurance companies and industry, who want to identify anyone using this for nefarious reasons. Even now, we can see that deepfakes are being uncovered, and those fighting the manipulation of imagery will always be a step ahead of the latest deepfake tech.

Dan Sarto is Publisher and Editor-in-Chief of Animation World Network.

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The Latest and Greatest AI-Enabled Deepfake Takes us 'Back to the Future' - Animation World Network

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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Daily AI Roundup: The Coolest Things on Earth Today - AiThority

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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Darktraces co-CEO on trusting AI to fight cyberattacks on our behalf - The Next Web

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.

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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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The world wasn't prepared for COVID-19: In future, AI will curb the next pandemic - Economic Times

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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Why AI might be the most effective weapon we have to fight COVID-19 - The Next Web

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.

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Researchers Will Deploy AI to Better Understand Coronavirus - WIRED

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

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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.

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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.

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Is Your Storage Infrastructure Ready for the Coming AI Wave? - insideHPC

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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Unmanned convenience store at new Tokyo train station boasts AI to thwart thieves - The Japan Times