How to Keep an Edge in the Era of Pervasive AI? – Via News Agency

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AI adopters should be proactive in implementing certain measures if they want to gain or maintain an advantage over their industry peers as the disruptive technology becomes mainstream, according to global accounting firm Deloitte.

In the 3rd edition of its State of AI in the Enterprise report, Deloitte says there are three things that both current and future adopters can do.

Deloittes analysiswhich is based on a survey of2,737 IT and line-of-business executives from around the worldfound that many AI adopters are focused more on improving what they have than on creating something new.

Making processes more efficientandenhancing existing products and serviceswere the top two benefits that the respondents were seeking from AI technologies.

Read more: COVID-19: A Pivotal Moment for Future of AI

We found that companies are still using AI technologies mostly in IT- and cybersecurity-related functions. Forty-seven percent of respondents indicated that IT was one of the top two functions for which AI was primarily used, the accounting firm wrote.

As major functions for AI applications, IT was followed by cybersecurity, production and manufacturing, and engineering and product development. This is while marketing, human resources, legal, and procurement ranked at the bottom of the list.

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According to the report, businesses will soon need to use AI to differentiate themselves by going beyond using the technology to just improve efficiency and automation.

They will need to use the technology to differentiate themselves and can do that by taking inspiration from inventive use cases to come up with solutions that are both usefuland novel.

Push boundaries.Businesses should try to pursue a more diverse set of projects that could help enhance multiple business functions across the enterprise.

Create the new. A great area of opportunity for businesses. is developing new AI-powered products and services that can solve problems that humans cant.

Expand the circle.In addition to the IT department, employees in other sections should become involved in AI efforts. New vendors, data sources, tools, techniques, and partnerships can be instrumental in achieving this goal.

The survey showed that AI adopters tend to buy their capabilities rather than build them.

About 50 percent are buying more than they build, and another 30 percent use an even blend of buying and building from scratch. Seasoned (53 percent) and Skilled (51 percent) adopters are more likely than Starters (44 percent) to buy the AI systems they need.

According to Deloitte, this suggests that many organizations prefer to experience a period of internal learning and experimentation before deciding on what is necessary.

AI adopters view being a smart consumer as critical to boosting competitive advantage. When asked to select the top initiative for increasing their competitive advantage from AI, adopters picked modernizing our data infrastructure for AI as their top choice, closely followed by gaining access to the newest and best AI technologies.

The findings of the survey show that less than half of adopters (47 percent) claim to possess a high level of skill for selecting AI technologies and technology suppliers.

As more partners and vendors are offering their services, Deloitte says organizations should become savvier and choose the best-equipped ones to gain access to the latest and greatest technologies.

Leverage a diverse team. Both technical and business experts should be included in selecting AI technologies and suppliers as it is important to have a broad perspective from developers, integrators, end-users, and business owners.

Take a centralized approach. Businesses are advised to coordinate experiments, implementations, selection of AI technologies, and vendors across the enterprise as it helps avoid duplication of effort, competing methods, and multiple vendors.

The use of working groups, dedicated leaders, or communities of practice should be considered.

Focus on integrating and scaling.It should be made sure that vendors and partners can help the business integrate AI solutions into its broader IT infrastructure. It should also be verified that solutions can grow with the needs of the enterprise.

Read more: AI Replacing Teachers Will Have Dire Consequences

Deloitte says adopters face reservations as well despite strong enthusiasm for their AI efforts.

In fact, they rank managing AI-related risks as the top challenge for their AI initiatives, tied with persistent difficulties of data management and integrating AI into their companys processes.

Additionally, the surveys findings show that a troubling preparedness gap exists for adopters across a wide range of these potential operational, strategic, and ethical risks.

More than half of adopters report major or extreme concerns about these potential risks for their AI initiatives, while only four in 10 adopters rate their organization as fully prepared to address them.

According to the survey, 56 percent agree that their organization is slowing the adoption of AI due to the emerging risks.

The same proportion believes that negative public perceptions will slow or stop the adoption of some AI technologies, reads the report.

Deloitte says businesses should develop a set of principles and processes to manage potential AI risks if they want to build trust within their business and with customers and partners.

Align risk-related efforts. As many of the risks associated with AI are not unique, it is important to integrate AI-related risk management with broader risk efforts. An AI specialist can help with training and coordination of efforts in this regard.

Challenge your vendors. Organizations should make sure that the AI solutions used are aligned with their ethical principles.

Monitor regulatory efforts. Businesses need to ensure that legal, risk, compliance, and IT leaders are informed of the latest laws and policies regarding AI technologies.

Deloitte states in its report that AIs early adopter phase is apparently ending and the market is now heading into the early majority chapter of this maturing set of technologies.

This is reflected in the forecasts of global market intelligence firm IDC, which predicts that spending on AI technologies will grow to $97.9 billion in 2023more than two and a half times the spending level of 2019.

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How to Keep an Edge in the Era of Pervasive AI? - Via News Agency

AI photo check exposes scale of diversity problem at top firms – New Scientist

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Men are on board

H. Armstrong Roberts/ClassicStock/Getty

By Timothy Revell

Bias in boardrooms is tricky to assess. Many companies dont publish diversity reports, making useful information difficult to come by and hampering efforts to tackle institutional biases. Now artificially intelligent algorithms have been used to dig down into the data, confirming that there is a lack of diversity at the top of the worlds corporate ladder.

To evaluate the situation, researchers from biotech firm Insilico Medicine compiled pictures of the top executives taken from the websites of nearly 500 of the largest companies in the world. The final dataset comprised over 7200 photographs from companies spanning 38 countries.

They trained image recognition algorithms to automatically detect the age, race and sex of the board members, and compared the results to the age, race and gender profile of each firms country to see if they reflected the general population. AI is far from perfect at interpreting images and Insilico Medicine doesnt specialise in this particular area, so the results should be taken with a pinch of salt. But, nonetheless, they do give an impression of the current state of play.

Evidence from other studies suggests that boardroom diversity is increasing year on year, but it is clear there is still a long way to go. Overall, the team found that only 21.2 per cent of the corporate executives in the study were female. And in every single company, the percentage of female board members was lower than the percentage of women capable of work in that country. Twenty-two companies had no women on their boards, with the majority of those firms being in Asia.

Nearly 80 per cent of the corporate executives in the study were white, with 3.6 per cent black and 16.7 per cent Asian. South Africa had the highest proportion of black executives, representative of the fact that 80 per cent of its population is black. However, the two South African companies included in the list still only reached 54 and 35 per cent in terms of the proportion of black board members.

In the US, many companies reflected the 12 per cent of the population that is black in their boardrooms, although there were also 30 companies without any black board members at all. The median age across all corporate executives in the study was 52.

These huge companies lead industries and influence our everyday lives. Using machine learning makes it possible to examine their diversity in a way that couldnt be done before, says Polina Mamoshina at Insilico Medicine. The data for the study was collected on 20 March.

This paper confirms that we live in a biased world, says Sandra Wachter at the Oxford Internet Institute, UK. However, acknowledging the problems this causes is only a crucial first step. Having a public discourse about these issues is vital. It is important to find out where the biases stem from and tackle the roots, she says.

Anti-discrimination laws should be used to achieve parity at the top of companies, and a shift in mentality is required to start viewing diversity as an advantage, says Wachter. The studys methods could be used in any situation where management profiles are available, but diversity data isnt, and could help examine the diversity within governments, universities or media outlets.

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AI photo check exposes scale of diversity problem at top firms - New Scientist

AI Plant and Animal Identification Helps Us All Be Citizen Scientists – Smithsonian

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Screenshots from the iNaturalist app, which uses "deep learning" to automatically identify what bugor fish, bird, or mammalyou might be looking at.

On a recent trip to the local botanical gardens, I noticed a tall, striking purple flower Id never noticed before. I tried to Google it, but I didnt know quite what to ask. Purple flower brought me pictures of narcissus and freesia, orchids and primrose, gladiolus and morning glory. None of them were the flower Id seen.

But thanks to artificial intelligence, curious amateur naturalists like me now have better ways to identify the nature around us. Several new sites and apps use AI technology to put names to photographs.

iNaturalist.orgis one of these sites. Founded in 2008, has until now been solely a crowdsourcing site. Users post a picture of a plant or animal and a community of scientists and naturalists will identify it. Its mission is to connect experts and amateur "citizen scientists," getting people excited about plants and wildlife while using the data gathered to potentially help professional scientists monitor changes in biodiversity or even discover new species.

The crowdsourced model generally works well, says Scott Loarie, iNaturalists co-director. But there are some limitations. First, it can be much harder to get an identification of your photograph depending on where you live. In California, where Loarie is based, he can get an identification within an hour. Thats because a large number of the experts that frequent iNaturalist are based on the West Coast. But someone in, say, rural Thailandmay have to wait much longer to receive an ID: The average amount of time it takes to get an identification is 18 days. Another issue:As the site has become more popular, the balance of observers (people posting pictures) to identifiers (people telling you what the pictures are) has become skewed, with far more observers than identifiers. This threatens to overwhelm the volunteer experts.

This month, iNaturalist plans to launch an app that uses AI to identify plants and animals down to the species level. The app takes advantage of so-called deep learning, using artificial neural networks that allow computers to learn as humans do, so their capabilities can advance over time.

Were hopeful this will engage a whole new group of citizen scientists, Loarie says.

The app is trained by being fed labeled images from iNaturalists massive database of research grade observationsobservations that havebeen verified by the sites community of experts. Once the model has been trained on enough labeled images, it begins to be able to identify unlabeled images. Currently iNaturalist is able to add a new species to the model every 1.7 hours. The more images uploaded by users and identified by experts, the better.

The more stuff we get, the more trained up the model will be, Loarie says.

The iNaturalist team wants to the model to always be accurate, even if that means not being as precise as possible. Right now the model tries to give a confident response about the animal's genus, then a more cautious response about the species, offering the top 10 possibilities. It currently is correct about the genus 86 percent of the time, and gives the species in its top 10 results 77 percent of the time. These numbers should improve as the model continues to be trained.

Playing around with a demo version, I entered a picture of a puffin perched on a rock. We're pretty sure this is in thegenusPuffins, it said, giving the correct speciesAtlantic puffinas the top suggested result. Then I entered a picture of an African clawed frog. We're pretty sure this is in thegenusWestern spadefoot toads, it told me, offering African clawed frog as among its top 10 results.

The AI was not confident enough to make a recommendation about a picture of my son, but suggested he might be a northern leopard frog, a garden snail or a gopher snake, among other, non-human creatures. As all of these are spotted, I realized the computer vision was seeing the polka-dot background of my sons highchair and misidentifying it as part of the specimen. So I cropped the picture until only his face was visible and pressed classify. We're pretty sure this is in thesuborderLizards, the AI responded. Either my baby looks like a lizard orthe real answer, I presumethis shows that the model only recognizes what its been fed. And no one is feeding it pictures of humans, for obvious reasons.

iNaturalist hopes the app will take pressure off its community of experts, and allow for a larger community of observers to participate, such as groups of schoolchildren. It could also allow for camera trapping sending in streams of images from a camera trap, which takes a picture when its triggered by motion. iNaturalist has discouraged camera trapping, as it floods the site with huge amounts of images that may or may not actually need expert identification that (some images will be empty, while others would catch common animals like squirrels that the camera's owner could easily identify himself or herself). But with the AI that wouldnt be a problem. iNaturalist also hopes the new technology will engage a new community of users, including people who might have an interest in nature but wouldnt be willing to wait several days for an identification under the crowdsourced model.

Quick species identificationcould also be useful in other situations, such as law enforcement.

Lets say TSA workers open a suitcase and someones got geckos, says Loarie. They need to know whether to arrest someone or not.

In this case, the AI could tell the TSA agents what type of gecko they were looking at, which could aid in an investigation.

iNaturalist is not the only site taking advantage of computer vision to engage citizen scientists. The CornellsMerlin Bird IDapp uses AI to identify more than 750 North American birds. You just have to answer a few simple questions first, including the size and color of the bird you saw.Pl@ntNetdoes the same for plants, after you tell it what part of the plant its looking at (flower, fruit, etc.).

This is all part of a larger wave of interest in using AI to identify images. There are AIprograms that canidentify objects from drawings(even bad ones).AIs can look at paintingsand identify artists and genres. Many experts think computer vision will play ahuge role in healthcare, making it easier to identify, for example, skin cancers. Car manufacturersuse computer vision to teach carsto identify and avoid hitting pedestrians. A plot point of arecent episode of the comedy Silicon Valleydealt with a computer vision app for identifying food. But since its creator only trained it on hot dogssince training a neural network requires countless hours of human laborit could only distinguish between hot dogs and not hot dogs.

This question of humor labor is important. Massive databases of correctly labeled images are crucial to training AIs, and can be hard to come by. iNaturalist, as a longtime crowdsourced site, already has exactly this kind of database, which is why its model has been advancing so quickly, Loarie says. Other sites and apps have to find their data elsewhere, often from academic images.

Its still early days, but I guarantee in the next year youre going to see a proliferation of these kinds of apps, Loarie says.

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AI Plant and Animal Identification Helps Us All Be Citizen Scientists - Smithsonian

Artificial intelligence expert moves to Montreal because it’s an AI hub – Montreal Gazette

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Irina Rish, now a renowned expert in the field of artificial intelligence, first became drawn to the topic as a teenager in the former Soviet republic of Uzbekistan. At 14, she was fascinated by the notion that machines might have their own thought processes.

I was interested in math in school and I was looking at how you improve problem solving and how you come up with algorithms, Rish said in a phone interview Friday afternoon. I didnt know the word yet (algorithm) but thats essentially what it was. How do you solve tough problems?

She read a book introducing her to the world of artificial intelligence and that kick-started a lifelong passion.

First of all, they sounded like just mind-boggling ideas, that you could recreate in computers something as complex as intelligence, said Rish. Its really exciting to think about creating artificial intelligence in machines. It kind of sounds like sci-fi. But the other interesting part of that is that you hope that by doing so, you can also better understand the human mind and hopefully achieve better human intelligence. So you can say AI is not just about computer intelligence but also about our intelligence. Both goals are equally exciting.

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Artificial intelligence expert moves to Montreal because it's an AI hub - Montreal Gazette

An ever-changing room of Ikea furniture could help AI navigate the world – MIT Technology Review

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In a building across from its main office in Seattle, the Allen Institute for Artificial Intelligence (AI2) has enough Ikea furniture to configure 14 different apartments. The lab isnt going into interior designnot exactly. The resources are meant to train smarter algorithms for controlling robots.

Household robots like the Roomba function well only because their tasks are relatively simple. Meandering around, doubling back, and returning to the same spots over and over dont really matter when the objective is to relentlessly clean the same floor.

But anything that requires more efficient or complex navigation still trips up many state-of-the-art robots. The research needed to improve this status quo is also expensivelimiting most cutting-edge progress to well-funded commercial labs.

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Now AI2 wants to kill two birds with one stone. On Tuesday, it announced a new challenge called RoboTHOR (THOR for The House Of inteRactionsyes, really). It will double as a way to crowdsource better navigation algorithms and lower the financial barriers for researchers who may not have robotics resources of their own.

The ultimate goal is to more rapidly advance AI by getting more research groups involved. Different communities should bring different perspectives and use cases that will expand the repertoire of robot capabilities, driving the field closer to more generalizable intelligence.

AI2

The lab has designed an easily reconfigurable room, the size of a cramped studio, to be the staging ground for all 14 apartment variations. It has also re-created identical virtual replicas in Unity, a popular video-game engineas well as 75 other configurationsthat have all been open-sourced online. Together, these 89 total configurations will offer realistic simulation environments for teams around the world to train and test their navigation algorithms. The environments also come pre-loaded with models of AI2s robots and mirror real-world physics like gravity and light reflections as closely as possible.

The challenge specifically asks teams to develop algorithms that can get a robot from a random starting location within a room to an object in that room just by telling it the objects name. This will be more difficult than simple navigation because it will require the robot to understand the command and recognize the object in its visual field as well.

AI2

Teams will compete in three phases. In phase one, they will be given the 75 digital-only simulation environments to train and validate their algorithms. In phase two, the highest performers will then be given four new simulation environments with corresponding physical doppelgangers. The teams will be able to remotely refine their algorithms by loading them into AI2s real robots.

In the final phase, the highest performers will need to demonstrate the generalizability of their algorithms in the last 10 digital and corresponding physical apartments. Whichever teams perform the best in this final phase will win bragging rights and an invitation to demo their models at the Conference on Computer Vision and Pattern Recognition, a leading AI research conference for vision-based systems.

AI2

After the challenge is over, AI2 plans to keep the setup available, giving anyone access to the environment to continue conducting robotics research. Researchers who clear a certain threshold of accuracy in the simulated environmentsproving they wont crash the robotswill be allowed to remotely deploy their algorithms in the physical ones. The room will rotate between the different furniture configurations.

We are going to maintain this environment, and we are going to maintain these robots, says Ani Kembhavi, a research scientist at AI2 who is leading the project. His team plans to develop a time-sharing system to allow different researchers to take turns remotely testing their algorithms in the real world.

AI2 hopes the strategy will make robotics research more accessible by eliminating as much of the associated hardware costs as possible. It also hopes that the scheme will inspire other well-funded organizations to open up their resources in similar ways. Additionally, it purposely designed its reconfigurable room with low materials costs and globally available Ikea furniture; the setup cost roughly $10,000. Should other researchers want to build their own physical training spaces, they can easily replicate it locally and still match the virtual simulation environments.

Kembhavi, whose dad is an astronomer, likens the idea to the global sharing of telescopes. Communities like astronomy have figured out how to take expensive resources and make it available to researchers all around the world, he says.

That's our vision for this environment, he adds. Embodied AI for all.

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An ever-changing room of Ikea furniture could help AI navigate the world - MIT Technology Review

AI Experts Rank Deepfakes and 19 Other AI-Based Crimes By Danger Level – Unite.AI

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Sarah Tatsis, is the Vice President of Advanced Technology Development Labs at BlackBerry.

BlackBerry already secures more than 500M endpoints including 150M cars on the road. BlackBerry is leading the way with a single platform for securing, managing and optimizing how intelligent endpoints are deployed in the enterprise, enabling customers to stay ahead of the technology curve that will reshape every industry.

BlackBerry launched the Advanced Technology Development Lab (Blackberry Labs) in late 2019. What was the strategic importance of creating an entire new business division for BlackBerry?

As an innovation accelerator, BlackBerry Advanced Technology Development Labs is an intentional investment of 120 team members into the future of the company. The rise of the Internet of Things (IoT) alongside a dynamic threat landscape has fostered a climate where organizations have to guard against new threats and breaches at all times. Weve handpicked the team to include experts in the embedded IoT space with diverse capabilities, including strong data science expertise, whose innovation funnel investigates, incubates and develops technologies to keep BlackBerry at the forefront of security innovation. ATD Labs works in strong partnership with the other BlackBerry business units, such as QNX, to further the companys commitment to safety, security and data privacy for its customers. BlackBerry Labs is also partnering with universities on active research and development. Were quite proud of these initiatives and think they will greatly benefit our future roadmap.

Last year, BlackBerry Labs successfully integrated Cylances machine learning technology into BlackBerrys product pipeline. BlackBerry Labs is currently focused on incubating and developing new concepts to accelerate the innovation roadmaps for our Spark and IoT business units. My role is primarily helping to drive the innovation funnel and partner with our business units to deliver valuable solutions for our customers.

What type of products are being developed at BlackBerry Labs?

BlackBerry Labs is facilitating applied research and using insights gained to innovate in the lines of business where were already developing market-leading solutions. For instance, were applying machine learning and data science to our existing areas of application, including automotive, mobile security, etc. This is possible in large part due to the influx of BlackBerry Cylance technology and expertise, which allows us to combine our ML pipeline and market knowledge to create solutions that are securing information and devices in a really comprehensive way. As new technologies and threats emerge, BlackBerry Labs will allow us to take a proactive approach to cybersecurity, not only updating our existing solutions, but evaluating how we can branch out and provide a more comprehensive, data-based, and diverse portfolio to secure the Internet of Things.

At CES, for instance, we unveiled an AI-based transportation solution geared towards OEMs and commercial fleets. This solution provides a holistic view of the security and health of a vehicle and provides control over that security for a manufacturer or fleet manager. It also uses machine learning based continuous authentication to identify a driver of a vehicle based on past driving behavior. Born in BlackBerry Labs, this concept marked the first time BlackBerry Cylances AI and ML technologies have been integrated with BlackBerry QNX solutions, which are currently powering upwards of 150 million vehicles on the road today.

For additional insights into how we envision AI and ML shaping the world of mobility in the years to come, I would encourage you to read Security Confidence Through Artificial Intelligence and Machine Learning for Smart Mobility from our recently released Road to Mobility guide. Also released at this years CES, The Road to Mobility: The 2020 Guide to Trends and Technology for Smart Cities and Transportation, is a comprehensive resource that government regulators, automotive executives and technology innovators can turn to for forward-thinking considerations for making safe and secure autonomous and connected vehicles a reality, delivering a transportation future that drivers, passengers and pedestrians alike can trust.

Featuring a mix of insights from both our own internal experts and recognized voices from across the transportation industry, the guide provides practical strategies for anyone whos interested in playing a vital role in shaping what the vehicles and infrastructure of our shared autonomous future will look like.

How important is artificial intelligence to the future of BlackBerry?

As both IoT and cybersecurity risk explodes, traditional methods of keeping organizations, things, and people safe and secure are becoming unscalable and ineffective. Preventing, detecting, and responding to potential threats needs to account for large amounts of data and intelligent automation of appropriate responses. AI and data science include tools that address these challenges and are therefore critical to the roadmap of BlackBerry. These tools allow BlackBerry to provide even greater value to our customers by reducing risk in efficient ways. BlackBerry leverages AI to deliver innovative solutions in the areas of cybersecurity, safety and data privacy as part of our strategy to connect, secure, and manage every endpoint in the Internet of Things.

For instance, BlackBerry trains our end point protection AI model against billions of files, good and bad, so that it learns to autonomously convict, or not convict files, pre-execution. The result of this massive, ongoing training effort is a proven track record of blocking payloads attempting to exploit zero-days for up to two years into the future.

The ability to protect organizations from zero-day payloads, well before they are developed and deployed, means that when other IT teams are scrambling to recover from the next major outbreak, it will be business as usual for BlackBerry customers. For example, WannaCry, which rendered millions of computers across the globe useless, was prevented by a BlackBerry (Cylance) machine learning model developed, trained, and deployed 24 months before the malware was first reported.

BlackBerrys QNX software is embedded in more than 150 million cars. Can you discuss what this software does?

Our software provides the safe and secure software foundation for many of the systems within the vehicle. We have a broad portfolio of functional safety-certified software including our QNX operating system, development tools and middleware for autonomous and connected vehicles. In the automotive segment, the companys software is deployed across the vehicle in systems such as ADAS and Safety Systems, Digital Cockpits, Digital Instrument Clusters, Infotainment, Telematics, Gateways, V2X and increasingly is being selected for chassis control and battery management systems that are advancing in complexity.

QNX software includes cybersecurity which protects autonomous vehicles from various cyber-attacks. Can you discuss some of the potential vulnerabilities that autonomous vehicles have to cyberattacks?

I think there is still a misconception out there that when you get into your car to drive home from work later today you might fall prey to a massive and coordinated vehicle cyberattack in which a rogue state threatens to hold you and your vehicle ransom unless you meet their demands. Hollywood movies are good at exaggerating what is possible, for example, instant and entire compromise of fleets that undermines all safety systems in cars. Whilst there are and always will be vulnerabilities within any system, to exploit a vulnerability and on scale with unprecedented reliability presents all kinds of hurdles that must be overcome, and would also require a significant investment of time, energy and resources. I think the general public needs to be reminded of this and the fact that hacking, if and when they do occur, are undesirable but not as movies would have you believe.

With a modern connected vehicle now containing well over 100 million lines of code and some of the most complex software ever deployed by automakers, the need for robust security has never been more important. As the software in a car grows so does the attack surface, which makes it more vulnerable to cyberattacks. Each poorly constructed piece of software represents a potential vulnerability that can be exploited by attackers.

BlackBerry is perfectly positioned to address these challenges as we have the solutions, the expertise and pedigree to be the safety certified and secure foundational software for autonomous and connected vehicles.

How does QNX software protect vehicles from these potential cyberattacks?

BlackBerry has a broad portfolio of products and services to protect vehicles against cybersecurity attacks. Our software has been deployed in critical embedded systems for over three decades and its worth pointing out, has also been certified to the highest level of automotive certification for functional safety with ISO 26262 ASIL D. As a company, we are investing significantly to broaden our safety and security product and services portfolio. Simply put, this is what our customers demand and rely on from us a safe, secure and reliable software platform.

As it pertains to security, we firmly believe that security cannot be an afterthought. For automakers and the entire automotive supply chain, security should be inherent in the entire product lifecycle. As part of our ongoing commitment to security, we published a 7-Pillar Cybersecurity Recommendation to share our insight and expertise on this topic. In addition to our safety-certified and secure operating system and hypervisor, BlackBerry provides a host of security products such as managed PKI, FIPS 140-2 certified toolkits, key inject tools, binary code static analysis tools, security credential management systems (SCMS), and secure Over-The-Air (OTA) software update technology. The worlds leading automakers, tier ones, and chip manufacturers continue to seek out BlackBerrys safety-certified and highly-secure software for their next-generation vehicles. Together with our customers we will help to ensure that the future of mobility is safe, secure and built on trust.

Can you elaborate on what is the QNX Hypervisor?

The QNX Hypervisor enables developers to partition, separate, and isolate safety-critical environments from non-safety critical environments reliably and securely; and to do so with the precision needed in an embedded production system. The QNX Hypervisor is also the worlds first ASIL D safety-certified commercial hypervisor.

What are some of the auto manufacturers using QNX software?

BlackBerrys pedigree in safety, security, and continued innovation has led to its QNX technology being embedded in more than 150 million vehicles on the road today. It is used by the top seven automotive Tier 1s, and by 45+ OEMs including Audi, BMW, Ford, GM, Honda, Hyundai, Jaguar Land Rover, KIA, Maserati, Mercedes-Benz, Porsche, Toyota, and Volkswagen.

Is there anything else that you would like to share about Blackberry Labs?

BlackBerry is committed to constant and consistent innovation its at the forefront of everything we do but we also have a unique legacy of being one of the pioneers of mobile based security, and further the idea of a truly secure devices, endpoints, and communications. The lessons we learned over the past decades, as well as the technology we developed, will be instrumental for helping us to create a new standard for privacy and security as the tsunami of connected devices enter the IoT. Much of what BlackBerry has done in the past is re-emerging in front of us, and were one of the only companies prioritizing a fundamental belief that all users deserve solutions that allow them to own their data and secure communications its baked into our entire development pipeline and is one of our key differentiators. BlackBerry Labs is combining this history with new technology innovations to address the rapidly expanding landscape of mobile and connected endpoints, including vehicles, and increased security threats. Through our strong partnerships with BlackBerry business units we are creating new features, products, and services to deliver value to both new and existing customers.

Thank you for the wonderful interview and for your extensive responses. Its clear to me that Blackberry is at the forefront of technology and its best days are still ahead. Readers who wish to learn more should visit the Blackberry website.

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AI Experts Rank Deepfakes and 19 Other AI-Based Crimes By Danger Level - Unite.AI

AI Tool Created to Study the Universe, Unlock the Mysteries of Dark Energy – Newsweek

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An artificial intelligence tool has been developed to help predict the structure of the universe and aid research into the mysteries of dark energy and dark matter.

Researchers in Japan used two of the world's fastest astrophysical simulation supercomputers, known as ATERUI and ATERUI II, to create an aptly-named "Dark Emulator" tool, which is able to ingest vast quantities of data and produce analysis of the universe in seconds.

The AI could play a role in studying the nature of dark energy, which seems to make up a large amount of the universe but remains an enigma.

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When observed from a distance, the team noted how the universe appears to consist of clusters of galaxies and massive voids that appear to be empty.

But as noted by NASA, leading models of the universe indicate it is made of entities that cannot be seen. Dark matter is suspected of helping to hold galaxy clusters in place gravitationally, while dark energy is believed to play a role in how the universe is expanding.

According to the researchers responsible for Dark Emulator, the AI tool is able to study possibilities about the "origin of cosmic structures" and how dark matter distribution may have changed over time, using data from some of the top observational surveys conducted about space.

"We built an extraordinarily large database using a supercomputer, which took us three years to finish, but now we can recreate it on a laptop in a matter of seconds," said Associate Prof. Takahiro Nishimichi, of the Yukawa Institute for Theoretical Physics.

"Using this result, I hope we can work our way towards uncovering the greatest mystery of modern physics, which is to uncover what dark energy is. I also think this method we've developed will be useful in other fields such as natural sciences or social sciences."

Nishimichi added: "I feel like there is great potential in data science."

The teams, which included experts from the Kavli Institute for the Physics and Mathematics of the Universe and the National Astronomical Observatory of Japan, said in a media release this week that Dark Emulator had already shown promising results during extensive tests.

In seconds, the tool predicted some of effects and patterns found in previous research projects, including the Hyper Suprime-Cam Survey and Sloan Digital Sky Survey. The emulator "learns" from huge quantities of data and "guesses outcomes for new sets of characteristics."

As with all AI tools, data is key. The scientists said the supercomputers have essentially created "hundreds of virtual universes" to play with, and Dark Emulator predicts the outcome of new characteristics based on data, without having to start new simulations every time.

Running simulations through a supercomputer without the AI would take days, researchers noted. Details of the initial study were published in The Astrophysical Journal last October. The team said they hope to input data from upcoming space surveys throughout the next decade.

While work on this one study remains ongoing, there is little argument within the scientific community that understanding dark energy remains a key objective.

"Determining the nature of dark energy [and] its possible history over cosmic time is perhaps the most important quest of astronomy for the next decade and lies at the intersection of cosmology, astrophysics, and fundamental physics," NASA says in a fact-sheet on its website.

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AI Tool Created to Study the Universe, Unlock the Mysteries of Dark Energy - Newsweek

How Google And Amazon Are Torpedoing The Retail Industry With Data, AI And Advertising – Forbes

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How Google And Amazon Are Torpedoing The Retail Industry With Data, AI And Advertising
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(Note: After an award-winning career in the media business covering the tech industry, Bob Evans was VP of Strategic Communications at SAP in 2011, and Chief Communications Officer at Oracle from 2012 to 2016. He now runs his own firm, Evans Strategic ...
Street Sees Dollar Signs as Microsoft Invests in Cloud, Artificial IntelligenceWindows IT Pro

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How Google And Amazon Are Torpedoing The Retail Industry With Data, AI And Advertising - Forbes

Dynatrace Drives Digital Innovation With AI Virtual Assistant – Forbes

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Dynatrace Drives Digital Innovation With AI Virtual Assistant
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Innovation in the white-hot digital performance management (DPM) market continues to accelerate, and it was clear from this week's Perform conference in Las Vegas that Dynatrace is setting the pace. In fact, Dynatrace's innovations are so cutting-edge ...

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Dynatrace Drives Digital Innovation With AI Virtual Assistant - Forbes

RSIP Vision CEO: AI in medical devices is reducing dependence on human skills and improving surgical procedures and outcome – PRNewswire

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By now, we all know that more and more AI driven applications are introduced to the Radiology market, changing the diagnostic field and improving the diagnostic process. Not far behind, a substantial shift is happening in another critical medical segment the operating room.

More and more medical robotic companies are taking advantage of the new capabilities driven from AI and innovative computer vision algorithms, to provide solutions that add accuracy and stability, save time and improve supervision on many daily surgery procedures. Besides improving the surgery process, this AI revolution also shortens recovery time and reduces infection risks, by favoring minimally invasive methods, which expose only the minimal needed surface for the robotic arms operation.

"Our teams are developing state-of-the-art algorithmics solutions that make this revolution so real, giving surgical systems the capability to understand the medical scene, detect and monitor surgical tools during the procedure, offer a crystal clear real-time view of the treated organ, while allowing precise depth measurements and video supervision of the whole procedure indicating irregularities, missing tools or disposables and much more," explained Ron Soferman in a recent interview.

RSIP Vision, is a global leader in artificial intelligence (AI) and computer vision technology, is developing and providing these advanced modules to Medical Surgery Vendors allowing them to provide specific solutions for a variety of surgical operations, in the Orthopedics, Aesthetics and General Surgery fields.

RSIP Vision is headquartered in Jerusalem Israel, and has a U.S. office in San Jose, CA.

More information is available on the company website: http://www.rsipvision.com.

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RSIP Vision CEO: AI in medical devices is reducing dependence on human skills and improving surgical procedures and outcome - PRNewswire

Brand-New Graphcore Partner Program Built With AI In Mind – CRN

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As solution providers up their game to meet customer demand for the hottest technology, the right vendor partner program is key to their success.

CRNtv welcomes vendors to The Partner Program Pitch to share with the channel what makes their channel program unique, starting with an elevator pitch on why solution providers should join their partner program.

In this episode of The PPP, CRNtvs Jennifer Zarate talks with Victoria Rege, director of alliances and strategic partnerships at Graphcore, about how the UK-based chipmaker is bringing the excitement back to hardware.

For quite sometime its been all about software, and now the VARs (value added resellers) and resellers, and our channel get the opportunity to learn about our unique hardware and help customers do amazing things with it, said Rege.

Company At A Glance

Location: Bristol, United Kingdom

Number of partners: 15 launch partners

Percentage of sales that go through the channel: Too early to disclose

Product and services in which Graphcore specializes in:

- Software Polar Programming Stack

- AI Hardware for Data Centers

The field of AI is shifting so quickly and growing, and changing that the opportunity for [partners] to learn on the ground with us as we put [our second generation IPU-M2000 packs] into production is really a great learning opportunity and a great sales opportunity, Rege added.

Partner Program Details

Program Tiers: Elite and Gold

Partner program requirements:

Commitment to joint GTM activities, quarterly business and marketing planning, and review.

Eligible partner-types:

- Resellers

- Original equipment manufacturers (OEMs)

- Software partners

- Storage companies

- VARs.

Head over to CRNtv to learn more on why you should partner with Graphcore.

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Brand-New Graphcore Partner Program Built With AI In Mind - CRN

Global AI in Manufacturing Market 2020-2026: COVID-19’s Impact on the Industry and Future Projections – PRNewswire

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DUBLIN, Aug. 7, 2020 /PRNewswire/ -- The "Artificial Intelligence in Manufacturing Market by Offering (Hardware, Software, and Services), Technology (Machine Learning, Computer Vision, Context-Aware Computing, and NLP), Application, End-user Industry and Region - Global Forecast to 2026" report has been added to ResearchAndMarkets.com's offering.

The AI in the manufacturing market is expected to be valued at USD 1.1 billion in 2020 and is likely to reach USD 16.7 billion by 2026; it is expected to grow at a CAGR of 57.2% during the forecast period.

The major drivers for the market are the increasing number of large and complex datasets (often known as big data), evolving Industrial IoT and automation, improving computing power, and increasing venture capital investments. The major restraint for the market is the reluctance among manufacturers to adopt AI-based technologies. The critical challenges facing the AI in the manufacturing market include limited skilled workforce, concerns regarding data privacy, and significant financial and operational impact of the COVID-19 outbreak on manufacturing.

The machine learning technology is expected to account for the largest size of the AI in manufacturing market during the forecast period.

Machine learning's ability to collect and handle big data and its applications in real-time speech translation, robotics, and facial analysis is fuelling its growth in the manufacturing market. AI constitutes various technologies that play a vital role in developing its ecosystem. As AI enables machines to perform activities similar to those performed by human beings, enormous market opportunities have opened.

The predictive maintenance and machinery inspection application of the AI in manufacturing market is projected to hold the largest share during the forecast period.

The predictive maintenance and machinery inspection application held the largest share of the AI in the manufacturing market in 2019. Extensive use of computer vision cameras in machinery inspection, adoption of the Industrial Internet of Things (IIoT), and use of big data in the manufacturing industry are the factors driving the growth of the AI in the manufacturing market for predictive maintenance and machinery inspection application. The increasing demand for reducing the operational costs and machine downtime is also supplementing the growth of predictive maintenance and machinery inspection application in industries.

The automobile industry held the largest size of the AI in manufacturing market in 2019.

The extensive use of computer vision cameras in machinery inspection and adoption industrial IoT are the factors driving the growth of the AI in the manufacturing market for the automobile industry. The application of AI to boost employee productivity, improve quality control, and gain better control over business support functions is supporting the growth of AI in the automobile industry.

Impact of COVID-19 on the AI in the manufacturing market

The market is likely to witness a slight plunge in terms of year-on-year growth in 2020. This is largely attributed to the affected supply chains and limited adoption of AI in manufacturing in 2020 due to the lockdowns and shifting priorities of different industries. The ongoing COVID-19 pandemic has caused disruptions in economies. It is likely to cause supply chain mayhem and eventually force companies and entire industries to rethink and adapt to the global supply chain model.

Many manufacturing companies have halted their production, which has collaterally damaged the supply chain and the industry. This disruption has caused a delay in the adoption of AI-based software and hardware products in the manufacturing sector. The industries have started to restructure their business model for 2020, and many SMEs and large manufacturing plants have halted/postponed any new technology upgrade in their factories in order to recover from the losses caused by the lockdown and economic slowdown.

Research Coverage

The AI in the manufacturing market has been segmented based on offering, technology, application, industry and region. It also provides a detailed view of the market across 4 main regions: North America, Europe, APAC, and RoW.

Key Topics Covered1 Introduction

2 Research Methodology

3 Executive Summary3.1 COVID-19 Impact Analysis: AI in Manufacturing Market3.1.1 Pre-COVID-19 Scenario3.1.2 Post-COVID-19 Scenario

4 Premium Insights4.1 Attractive Opportunities in AI in Manufacturing Market4.2 AI in Manufacturing Market, by Offering4.3 AI in Manufacturing Market, by Technology4.4 APAC: AI in Manufacturing Market, by Industry and Country4.5 AI in Manufacturing Market, by Country

5 Market Overview5.1 Introduction5.2 Market Dynamics5.2.1 Drivers5.2.1.1 Increasingly Large and Complex Dataset5.2.1.2 Evolving Industrial IoT and Automation5.2.1.3 Improving Computing Power5.2.1.4 Increasing Venture Capital Investments5.2.2 Restraints5.2.2.1 Reluctance Among Manufacturers to Adopt AI-Based Technologies5.2.3 Opportunities5.2.3.1 Growth in Operational Efficiency of Manufacturing Plants5.2.3.2 Application of AI for Intelligent Business Process5.2.3.3 Adoption of Automation Technologies to Curb Effects of COVID-195.2.4 Challenges5.2.4.1 Limited Skilled Workforce5.2.4.2 Concerns Regarding Data Privacy5.2.4.3 Significant Financial and Operational Impact of COVID-19 Outbreak on Manufacturing5.3 Value Chain Analysis5.4 Case Studies5.4.1 Siemens Gamesa Uses Fujitsu's AI Solution to Accelerate Inspection of Turbine Blades5.4.2 Volvo Uses Machine Learning-Driven Data Analytics for Predicting Breakdown and Failures5.4.3 Rolls-Royce Using Microsoft Cortana Intelligence for Predictive Maintenance5.4.4 Paper Packaging Firm Used Sight Machine's Enterprise Manufacturing Analytics to Improve Production5.5 Adjacent and Related Markets

6 Artificial Intelligence in Manufacturing Market, by Offering6.1 Introduction6.2 Hardware6.3 Software6.4 Services6.5 Impact of COVID-19 on Various Offering of AI Technology for Manufacturing

7 Artificial Intelligence in Manufacturing Market, by Technology7.1 Introduction7.2 Machine Learning7.3 Natural Language Processing7.4 Context-Aware Computing7.5 Computer Vision7.6 Impact of COVID-19 on Various Technologies of AI in Manufacturing

8 Artificial Intelligence in Manufacturing Market, by Application8.1 Introduction8.2 Predictive Maintenance and Machinery Inspection8.3 Material Movement8.4 Production Planning8.5 Field Services8.6 Quality Control8.7 Cybersecurity8.8 Industrial Robots8.9 Reclamation

9 Artificial Intelligence in Manufacturing Market, by Industry9.1 Introduction9.2 Automobile9.3 Energy and Power9.4 Pharmaceuticals9.5 Heavy Metals and Machine Manufacturing9.6 Semiconductors and Electronics9.7 Food & Beverages9.8 Others

10 Artificial Intelligence in Manufacturing Market, by Region10.1 Introduction10.2 North America10.3 Europe10.4 APAC10.5 RoW

11 Competitive Landscape11.1 Overview11.2 Ranking of Players, 201911.3 Competitive Leadership Mapping11.3.1 Visionary Leaders11.3.2 Dynamic Differentiators11.3.3 Innovators11.3.4 Emerging Companies11.4 Competitive Scenario11.4.1 Product Launches and Developments11.4.2 Collaborations, Partnerships, and Agreements11.4.3 Acquisitions & Joint Ventures

12 Company Profiles12.1 Key Players12.1.1 Nvidia12.1.2 Intel12.1.3 IBM12.1.4 Siemens12.1.5 General Electric (GE) Company12.1.6 Google12.1.7 Microsoft12.1.8 Micron Technology12.1.9 Amazon Web Services (AWS)12.1.10 Sight Machine12.2 Other Companies12.2.1 Progress Software Corporation (DataRPM)12.2.2 AIbrain12.2.3 General Vision12.2.4 Rockwell Automation12.2.5 Cisco Systems12.2.6 Mitsubishi Electric12.2.7 Oracle12.2.8 SAP12.2.9 Vicarious12.2.10 Ubtech Robotics12.2.11 Aquant12.2.12 Bright Machines12.2.13 Rethink Robotics GmbH12.2.14 Sparkcognition12.2.15 Flutura

For more information about this report visit https://www.researchandmarkets.com/r/erpy1o

Research and Markets also offers Custom Research services providing focused, comprehensive and tailored research.

Media Contact:

Research and Markets Laura Wood, Senior Manager [emailprotected]

For E.S.T Office Hours Call +1-917-300-0470 For U.S./CAN Toll Free Call +1-800-526-8630 For GMT Office Hours Call +353-1-416-8900

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Top 10 AI-Powered Telecom Companies in World – AiThority

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Telecommunications, one of the fastest-growing industries, uses Artificial Intelligence and in several business operations to enhance customer experience, improve network reliability and predictive maintenance. Also, telecom companies implement AI-powered solutions to extract relevant business insights from massive amounts of data collected from multiple data sources. These insights enable them to offer better customer experience, scale up operations and impact overall revenue health of the organization.

Gartner predicts the use of approximately 20.4 billion connected devices by 2020. Hence, communication service providers (CSPs) across the globe are realizing and exploring opportunities to harness the power of Artificial Intelligence.

Telecom companies leverage AI, Machine Learning and Predictive Analytics to collect and analyze huge data sets. Insights from collected data automatically detect failures in transmission resulting in quick corrective action. Automated support services help build transparency and deliver customer delight. AI applications complement Cloud operations such as IoT, email and database storage.

Many telecom companies across the globe are experimenting with AI and harnessing its capabilities. In this blog, we have listed the top 10 telecom companies leveraging AI.

The company is transforming customer experiences using AI and Machine Learning applications. These applications have enabled the company to improve forecasting and capacity planning with field staff to deliver efficient customer assistance. Optimized schedules help technicians complete more tasks during the day and minimizes commute time thereby maximizing customer satisfaction. AT&T recorded a 7% reduction in miles traveled per dispatch and a 5% increase in productivity.

Additionally, a Machine Learning program has enhanced their end-to-end incident management process. The application detects network issues in real-time even before the customers get a hint of the problem. In this way, the company can manage 15 million alarms per day. AT&T is exploring the scope of AI and ML to deliver effective and efficient 5G network experience to its customers.

Sentio, COLTs on-demand AI platform provides automated service optimization and network restoration. Leveraging the existing IQ Network, the platform supports dynamic real-time quoting, ordering and provisioning of high bandwidth connectivity between various customer locations data centers, Cloud service providers and enterprise buildings. Customers gain full control and can flex bandwidth needs in real-time. Pricing options are flexible in this model. Customers choose plans on an hourly basis or for a fixed-term contract.

The company has developed a chatbot, Tinka, which is similar to a search engine. Continuous updates to search results help the company to provide round-the-clock assistance to customers in Austria. The icon of a young woman with long hair appears on the users screen, with a box below for typing the search query. Tinka processes about 80% of the queries. The unanswered queries are forwarded to a human customer support representative.

Vanda, another chatbot, focuses on NLP including semantics, customer support, and appropriate behavior. Hub:raum is another digital assistant developed by Deutsche Telekom. This chatbot answers questions about job offers facilitating personnel recruitment. It is fast, well-informed and available 24/7.

Globe Telecom combines ML with Cloudera to enhance Omnichannel customer experience, boost product optimization, and comply with the latest industry standards. Leveraging AI and Predictive Analytics the company uses insights to make informed business decisions quickly and design target-specific Marketing campaigns.

Aura, an AI-powered platform enables the company to develop a new customer relationship model with the help of personal data and other cognitive services. This platform helps business users to reimagine customer interaction, data transparency, personalized and contextualized customer support services, round-the-clock assistance and technical support.

TOBi, their Machine Learning chatbot, has launched in 11 markets and is quite popular. The company plans to launch in 5 more markets. In Italy, the chatbot has a huge market reach. It has automated two-third of the companys customer interaction thus, enabling human support agents to focus on strategic tasks resulting in higher productivity and growth across the entire organization.

ZBrain an AI platform developed by ZeroStack analyzes private cloud telemetry storage. It is used for improving capacity planning, upgrades, and general management tasks.

Tier 1 telecom companies are implementing Aria Networks, an AI-based solution to automate and optimize supply chain operations. The solution leverages prescriptive analytics and automates design processes for telecom and OTT service providers.

NetFusion an AI-powered platform by Sedona Systems, optimizes traffic routing and quick delivery of 5G-enabled services like AR/VR

Nokia launched AVA, a Machine Learning platform and Cloud network management solution. It improves capacity planning and predicts service degradations on cell sites a week in advance.

AI will be an integral part of the future digital marketplace. Increased adoption of AI in the telecommunication industry enabling CSPs manage, maintain and optimize infrastructure and support operations. The use cases mentioned in this blog exhibit the impact of AI on the telecom industry. It has enabled enterprises to deliver enhanced customer experience and boost business value.

As emerging technologies become more sophisticated and accessible, industry experts expect AI to accelerate growth in the business world. Are you ready to take the plunge?

Read more: LegalTech: DocuSigns Seal Software Acquisition Will Boost AIs Role in Contract Automation

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Top 10 AI-Powered Telecom Companies in World - AiThority

In the red corner: Malware-breeding AI. And in the blue corner: The AI trying to stop it – The Register

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Script kid-ai ... What the malware-writing bot doesn't look like

Feature The magic AI wand has been waved over language translation, and voice and image recognition, and now: computer security.

Antivirus makers want you to believe they are adding artificial intelligence to their products: software that has learned how to catch malware on a device. There are two potential problems with that. Either it's marketing hype and not really AI or it's true, in which case don't forget that such systems can still be hoodwinked.

It's relatively easy to trick machine-learning models especially in image recognition. Change a few pixels here and there, and an image of a bus can be warped so that the machine thinks its an ostrich. Now take that thought and extend it to so-called next-gen antivirus.

Enter Endgame, a cyber-security biz based in Virginia, USA, which you may recall popped up at DEF CON this year. It has effectively pitted two machine-learning systems against each other: one trained to detect malware in downloaded files, and the other is trained to customize malware so it slips past the aforementioned detector. The aim is to craft software that can manipulate malware into potentially undetectable samples, and then use those variants to improve machine-learning-based scanners, creating a constantly improving antivirus system.

The key thing is recognizing that software classifiers from image recognition to antivirus can suck, and that you have to do something about it.

Machine learning is not a one-stop shop solution for security, said Hyrum Anderson, principal data scientist and researcher at Endgame. He and his colleagues have teamed up with researchers from the University of Virginia to create this aforementioned cat and mouse game that breeds better and better malware and learns from it.

When I tell people what Im trying to do, it raises eyebrows, Anderson told TheRegister. People ask me, Youre trying to do what now? But let me explain.

A lot of data is required to train machine learning models. It took ImageNet which contains tens of millions of pictures split into thousands of categories to boost image recognition models to the performance possible today.

The goal of the antivirus game is to generate adversarial samples to harden future machine learning models against increasingly stealthy malware.

To understand how this works, imagine a software agent learning to play the game Breakout, Hyrum says. The classic arcade game is simple. An agent controls a paddle, moving it left or right to hit a ball bouncing back and forth from a brick wall. Every time the ball strikes a brick, it disappears and the agent scores a point. To win the game, the brick wall has to be cleared and the agent has to continuously bat the ball and prevent it from falling to the bottom of the screen.

Endgames malware game is somewhat similar, but instead of a ball the bot is dealing with malicious Windows executables. The aim of the game is to fudge the file, changing bytes here and there, in a way so that it hoodwinks an antivirus engine into thinking the harmful file is safe. The poisonous file slips through like the ball carving a path through the brick wall in Breakout and the bot gets a point.

It does this by manipulating the contents, and changing the bytes in the malware, but the resulting data must still be executable and fulfill its purpose after it passes through the AV engine. In other words, the malware-generating agent can't output a corrupted executable that slips past the scanner but, due to deformities introduced in the binary to evade detection, it crashes or doesn't work properly when run.

The virus-cooking bot is rewarded for getting working malicious files past the antivirus engine, so over time it learns the best sequence of moves for changing a malicious files in a way that it still functions and yet tricks the AV engine into thinking the file is friendly.

Its a much more difficult challenge than tricking image recognition models. The file still has to be able to perform the same function and have the same format. Were trying to mimic what a real adversary could do if they didnt have the source code, says Hyrum.

Its a method of brute force. The agent and the AV engine are trained on 100,000 input malware seeds after training, 200 malware files are given to the agent to tamper with. These samples were then fed into the AV engine and about 16per cent of evil files dodged the scanner, we're told. That seems low, but imagine crafting a strain of spyware that is downloaded and run a million times: that turns into 160,000 potentially infected systems to your control. Not bad.

After the antivirus engine model was updated and retrained using those 200 computer-customized files, and it was given another fresh 200 samples churned from the virus-tweaking agent, the evasion rate dropped to half as the scanner got wise to the agent's tricks.

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AI used to predict Covid-19 patients’ decline before proven to work – STAT

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Dozens of hospitals across the country are using an artificial intelligence system created by Epic, the big electronic health record vendor, to predict which Covid-19 patients will become critically ill, even as many are struggling to validate the tools effectiveness on those with the new disease.

The rapid uptake of Epics deterioration index is a sign of the challenges imposed by the pandemic: Normally hospitals would take time to test the tool on hundreds of patients, refine the algorithm underlying it, and then adjust care practices to implement it in their clinics.

Covid-19 is not giving them that luxury. They need to be able to intervene to prevent patients from going downhill, or at least make sure a ventilator is available when they do. Because it is a new illness, doctors dont have enough experience to determine who is at highest risk, so they are turning to AI for help and in some cases cramming a validation process that often takes months or years into a couple weeks.

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Nobody has amassed the numbers to do a statistically valid test of the AI, said Mark Pierce, a physician and chief medical informatics officer at Parkview Health, a nine-hospital health system in Indiana and Ohio that is using Epics tool. But in times like this that are unprecedented in U.S. health care, you really do the best you can with the numbers you have, and err on the side of patient care.

Epics index uses machine learning, a type of artificial intelligence, to give clinicians a snapshot of the risks facing each patient. But hospitals are reaching different conclusions about how to apply the tool, which crunches data on patients vital signs, lab results, and nursing assessments to assign a 0 to 100 score, with a higher score indicating an elevated risk of deterioration. It was already used by hundreds of hospitals before the outbreak to monitor hospitalized patients, and is now being applied to those with Covid-19.

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At Parkview, doctors analyzed data on nearly 100 cases and found that 75% of hospitalized patients who received a score in a middle zone between 38 and 55 were eventually transferred to the intensive care unit. In the absence of a more precise measure, clinicians are using that zone to help determine who needs closer monitoring and whether a patient in an outlying facility needs to be transferred to a larger hospital with an ICU.

Meanwhile, the University of Michigan, which has seen a larger volume of patients due to a cluster of cases in that state, found in an evaluation of 200 patients that the deterioration index is most helpful for those who scored on the margins of the scale.

For about 9% of patients whose scores remained on the low end during the first 48 hours of hospitalization, the health system determined they were unlikely to experience a life-threatening event and that physicians could consider moving them to a field hospital for lower-risk patients. On the opposite end of the spectrum, it found 10% to 12% of patients who scored on the higher end of the scale were much more likely to need ICU care and should be closely monitored. More precise data on the results will be published in coming days, although they have not yet been peer-reviewed.

Clinicians in the Michigan health system have been using the score thresholds established by the research to monitor the condition of patients during rounds and in a command center designed to help manage their care. But clinicians are also considering other factors, such as physical exams, to determine how they should be treated.

This is not going to replace clinical judgement, said Karandeep Singh, a physician and health informaticist at the University of Michigan who participated in the evaluation of Epics AI tool. But its the best thing weve got right now to help make decisions.

Stanford University has also been testing the deterioration index on Covid-19 patients, but a physician in charge of the work said the health system has not seen enough patients to fully evaluate its performance. If we do experience a future surge, we hope that the foundation we have built with this work can be quickly adapted, said Ron Li, a clinical informaticist at Stanford.

Executives at Epic said the AI tool, which has been rolled out to monitor hospitalized patients over the past two years, is already being used to support care of Covid-19 patients in dozens of hospitals across the United States. They include Parkview, Confluence Health in Washington state, and ProMedica, a health system that operates in Ohio and Michigan.

Our approach as Covid was ramping up over the last eight weeks has been to evaluate does it look very similar to (other respiratory illnesses) from a machine learning perspective and can we pick up that rapid deterioration? said Seth Hain, a data scientist and senior vice president of research and development at Epic. What we found is yes, and the result has been that organizations are rapidly using this model in that context.

Some hospitals that had already adopted the index are simply applying it to Covid-19 patients, while others are seeking to validate its ability to accurately assess patients with the new disease. It remains unclear how the use of the tool is affecting patient outcomes, or whether its scores accurately predict how Covid-19 patients are faring in hospitals. The AI system was initially designed to predict deterioration of hospitalized patients facing a wide array of illnesses. Epic trained and tested the index on more than 100,000 patient encounters at three hospital systems between 2012 and 2016, and found that it could accurately characterize the risks facing patients.

When the coronavirus began spreading in the United States, health systems raced to repurpose existing AI models to help keep tabs on patients and manage the supply of beds, ventilators and other equipment in their hospitals. Researchers have tried to develop AI models from scratch to focus on the unique effects of Covid-19, but many of those tools have struggled with bias and accuracy issues, according to a review published in the BMJ.

The biggest question hospitals face in implementing predictive AI tools, whether to help manage Covid-19 or advanced kidney disease, is how to act on the risk score it provides. Can clinicians take actions that will prevent the deterioration from happening? If not, does it give them enough warning to respond effectively?

In the case of Covid-19, the latter question is the most relevant, because researchers have not yet identified any effective treatments to counteract the effects of the illness. Instead, they are left to deliver supportive care, including mechanical ventilation if patients are no longer able to breathe on their own.

Knowing ahead of time whether mechanical ventilation might be necessary is helpful, because doctors can ensure that an ICU bed and a ventilator or other breathing assistance is available.

Singh, the informaticist at the University of Michigan, said the most difficult part about making predictions based on Epics system, which calculates a score every 15 minutes, is that patients ratings tend to bounce up and down in a sawtooth pattern. A change in heart rate could cause the score to suddenly rise or fall. He said his research team found that it was often difficult to detect, or act on, trends in the data.

Because the score fluctuates from 70 to 30 to 40, we felt like its hard to use it that way, he said. A patient whos high risk right now might be low risk in 15 minutes.

In some cases, he said, patients bounced around in the middle zone for days but then suddenly needed to go to the ICU. In others, a patient with a similar trajectory of scores could be managed effectively without need for intensive care.

But Singh said that in about 20% of patients it was possible to identify threshold scores that could indicate whether a patient was likely to decline or recover. In the case of patients likely to decline, the researchers found that the system could give them up to 40 hours of warning before a life-threatening event would occur.

Thats significant lead time to help intervene for a very small percentage of patients, he said. As to whether the system is saving lives, or improving care in comparison to standard nursing practices, Singh said the answers will have to wait for another day. You would need a trial to validate that question, he said. The question of whether this is saving lives is unanswerable right now.

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Now More Than Ever We Should Take Advantage of the Transformational Benefits of AI and ML in Healthcare – Managed Healthcare Executive

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As healthcare businesses transform for a post-COVID-19 era, they are embracing digital technologies as essential for outmaneuvering the uncertainty faced by businesses and as building blocks for driving more innovation. Maturing digital technologies such as social, mobile, analytics and cloud (SMAC); emerging technologies such as distributed ledger, artificial intelligence, extended reality and quantum computing (DARQ);and scientific advancements (e.g., CRISPR, materials science) are helping to make innovative breakthroughs a reality.

These technologies are also proving essential in supporting COVID-19 triage efforts. For example, hospitals in China are using artificial intelligence (AI) to scan lungs, which is reducing the burden on healthcare providers and enabling earlier intervention. Hospitals in the United States are also using AI to intercept individuals with COVID-19 symptoms from visiting patients in the hospital.

Because AI and machine learning (ML) definitions can often be confused, it may be best to start by defining our terms.

AI can be defined as a collection of different technologies that can be brought together to enable machines to act with what appears to be human-like levels of intelligence. AI provides the ability for technology to sense, comprehend, act and learn in a way that mimics human intelligence.

ML can be viewed as a subset of AI that provides software, machines and robots the ability to learn without static program instructions.

ML is currently being used across the health industry to generate personalized product recommendations to consumers, identify the root cause of quality problems and fix them, detect healthcare claims fraud, and discover and recommend treatment options to physicians. ML-enabled processes rely on software, systems, robots or other machines which use ML algorithms.

For the healthcare industry, AI and ML represent a set of inter-related technologiesthat allow machines to perform and help with both administrative and clinical healthcare functions. Unlike legacy technologies that are algorithm-based tools that complement a human, health-focused AI and ML today can truly augment human activity.

The full potential of AI is moving beyond mere automation of simple tasks into a powerful tool enabling collaboration between humans and machines. AI is presenting an opportunity to revolutionize healthcare jobs for the better.

Recent research indicates that in order to maximize the potential of AI and to be digital leaders, healthcare organizations must re-imagine and re-invent their processes and create self-adapting, self-optimizing living processes that use ML algorithms and real-time data to continuously improve.

In fact, theres consensus among healthcare organizations hat ML-enabled processes help achieve previously hidden or unobtainable value, and that these processes are finding solutions to previously unsolved business problems.

Despite these key findings, additional research surprisingly finds that only 39% of healthcare organizations report that they have inclusive design or human-centric design principles in place to support human-machine collaboration. Machines themselves will become agents of process change, unlocking new roles and new ways for humans and machines to work together.

In order to tap into the unique strengths of AI, healthcare businesses will need to rely on their peoples talent and ability to steward, direct, and refine the technology. Advances in natural language processing and computer vision can help machines and people collaborate and understand one another and their surroundings more effectively. It will be vital to prioritize explainability to help organizations ensure that people understand AI.

Powerful AI capabilities are already delivering profound results across other industries such as retail and automotive. Healthcare organizations now have an opportunity to integrate the new skills needed to enable fluid interactions between human and machines and adapt to the workforce models needed to support these new forms of collaboration.

By embracing the growing adoption of AI, healthcare organizations will soon see the potential benefits and value of AI such as organizational and workflow improvements that can unleash improvements in cost, quality and access. Growth in the AI health market is expected to reach $6.6 billion by 2021 thats a compound annual growth rate of 40%. In just the next couple of years,the health AI market will grow more than 10 times.

AI generally, and ML specifically, gives us technology that can finally perform specialized nonroutine tasks as it learns for itself without explicit human programing shifting nonclinical judgment tasks away from healthcare enterprise workers.

What will be key to the success of healthcare organizations leveraging AI and ML across every process, piece of data and worker? When AI and ML are effectively added to the operational picture, we will see healthcare systems where machines will take on simple, repetitive tasks so that humans can collaborate on a larger scale and work at a higher cognitive level. AI and ML can foster a powerful combination of strategy, technology and the future of work that will improve both labor productivity and patient care.

Brian Kalis is a managing director of digital health and innovation for Accenture's health business.

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Now More Than Ever We Should Take Advantage of the Transformational Benefits of AI and ML in Healthcare - Managed Healthcare Executive

The Father of Siri Has Grown Wary of the Artificial Intelligence He Helped Create – Willamette Week

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As a psychologist, Tom Gruber is in awe of Facebook. As a computer scientist and citizen of the earth, it scares the crap out of him.

Facebook runs experiments on human behavior that psychologists can only dream about, Gruber says. The trials are done on millions of people, a sample size that's impossible in academia. Dozens of times a day, Mark Zuckerberg tweaks his artificial intelligence to see what will keep his 2.5 billion subscribers scrolling through Facebook, and to make them confuse advertising with news so they click on the ads, Gruber says.

"They have the world's largest psychology experiment at their disposal every single day," Gruber says. "They can do experiments that science can't do, at scale."

Gruber, who speaks at TechfestNW this April, is hardly a bomb-thrower. He is a pioneer in artificial intelligence and the co-inventor of Siri, the digital assistant on the iPhone that uses AI and speech recognition to answer billions of questions each year.

Since selling Siri to Apple in 2010, though, Gruber has become one of a small group of technologists who have grown wary of the AI they helped create. He plans to talk about the dangerand promiseof artificial intelligence at TechfestNW.

Facebook and YouTube have more than 2 billion users each, making them as big as the world's two biggest religions, Christianity and Islam, Gruber says.

"And I would add that even the people who pray to Mecca five times a day, only do it five times a day," Gruber says. "Our millennials check their phones 150 times a day."

Gruber has deep roots in techdom. He earned a bachelor's degree in computer science and psychology from Loyola University in New Orleans, got his Ph.D. in computer and information science from the University of Massachusetts, then did research at Stanford University for five years.

Siri grew out of a Stanford spinoff called SRI International. Gruber consulted at SRI in 2007, and, soon after, he and two others, Dag Kittlaus and Adam Cheyer, spun off newer digital-assistant technology that went beyond the DARPA work. They named the new company Siri, which means "beautiful woman who leads you to victory."

Siri is actually a collection of powerful neural networks: mathematical formulas running on computers that analyze huge amounts of data and learn the patterns within them. Turn a neural net loose on a million samples of spoken language, and it will start to recognize words and their meaning. No longer do programmers have to tell computers what to do, logic step by logic step.

Steve Jobs persuaded Gruber and his partners to sell to Apple in 2010 for some $200 million, according to Wired magazine.

Gruber retired from Apple in 2018 and founded Humanistic AI, a firm that helps companies use machine intelligence to collaborate with humans, not replaceor terrorizethem.

Unlike some AI doomsayers, including Tesla inventor Elon Musk and podcasting neuroscientist Sam Harris, Gruber thinks AI can be tamed. Right now, it's a science experiment gone wrong. Frankenstein never meant for his monster to become a killer, and Zuckerberg, he says, never intended Facebook to set us at each other's throats, over politics or anything else.

"My argument is that this is an unintended consequence," Gruber says. "We'll give them a pass on being evil geniuses. Maybe some of them are. But let's assume good intentions."

When it comes to Zuckerberg, assuming good intentions is controversial. In July, Facebook agreed to pay a record $5 billion fine to settle charges by the Federal Trade Commission that it abused users' personal information.

So call Gruber an optimist. He thinks the same algorithms that prey on our bad habits can be used to encourage good ones.

Tech companies make excuses for why they can't police their networks, and most involve money. So far, humans are better at sorting lies from truth, and hate from news. That means you have to hire a lot of humans, which is anathema to the tech monopolies. Gruber says they need to suck it up.

"It's like when the auto industry said, 'Air bags are going to put us out of business, so don't impose this onerous thing on us,'" Gruber says. "It's all bullshit."

And there's more. Why not run all these vast experiments on human behavior to improve human life, instead of wrecking it? Why not use AI to change the habits that lead to type 2 diabetes, heart disease, hypertension, and suicide?

"We have weak theories about what makes people tick, and what to do to help them do better things," Gruber says. "But AI has shown that if you want to get 2 billion people addicted to something that's not good for them, you can do it."

AI doesn't know if it's operating for good or evil, Gruber says. Someday it may, but for now, it's up to humans to direct it.

So far, we've been crappy shepherds.

GO: TechfestNW is at Portland State University's Viking Pavilion, 930 SW Hall St., techfestnw.com. Thursday-Friday, April 2-3. Visit the website for tickets.

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The Father of Siri Has Grown Wary of the Artificial Intelligence He Helped Create - Willamette Week

Japanese Scientists Develop AI to Show What The Universe is Made Of – Tech Times

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A multi-university team of researchers from Japan creates the world's fastest astrophysics simulator using anartificial intelligence(AI) system to predict the shape of the universe itself. The scientists hope that in doing so, they'll liberate the mysteries surrounding dark matter and dark energy.

Dubbed "Dark Emulator," the AI device parses massive troves of astrophysics data. The device makes use of the facts to build simulations of our universe. It taps into a big database complete of records gleaned from special telescopes that compare current data with what scientists anticipate based on theories surrounding the universe's origin.

The simulation basically attempts to demonstrate what theuniversemay look like, such as its edges, based on the big bang concept and the subsequent rapid growth that keeps taking place.

The lead author on the team's research paper, Takahiro Nishimichi, toldPhys.Orgthey built an "extraordinarily" big database using asupercomputer, which took them three years to finish.

"Using this result, I hope we could work toward uncovering the greatest mystery of modern physics, which is to uncover what dark energy is," Nishimichi said.

Scientists would be able to form better theories on how dark matter works by understanding the overall cosmology of the entire universe. However, nobody could still prove that dark matter exists through scientific rigor, observation, and measurement. And that leaves astrophysicists struggling to provide a unified concept of the universe that encompasses all of the different thoughts in play.

ALSO READ:Different Versions Of Reality Can Exist In The Quantum World, Study Confirms

Nishimichi said the method they've conceptualized would be useful in other fields such as natural sciences or social sciences.

The group from Japan hopes to reconcile theories with the information we're capable of glean from Dark Emulator. The AI gadget doesn't merely analyze information for free ends; it learns from every simulation it creates and uses the output to tell the subsequent iteration.

It does this by studying the invisible tendrils between galaxies and performing astronomical (literally) feats of mathematics to create more specific simulations. According to apaperthe group posted in Astrophysical Journal, it's extraordinarily accurate.

"The emulator predicts the halo-matter cross-correlation, relevant for galaxy-galaxy lensing, with an accuracy better than 2% and the halo autocorrelation, applicable for galaxy clustering correlation, with an accuracy higher than 4%."

ALSO READ:Theory Explains Dark Matter By Finding A Link Between Quantum Mechanics And General Relativity

Eventually, this technology could help flesh out our know-how of the universe and permit scientists to determine exactly what dark matter is and how darkish energy works. For now, the move would mean filling in some big blanks we have in our know-how of what the universe honestly looks like.

But in the future, having clear information of darkish energy could result in myriad far-off technology fiction technology along with warp drives, time-travel, and teleportation. That is, of course, if dark matter even exists.

ALSO READ:Is Artificial Intelligence Really A Threat To Humanity?

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Japanese Scientists Develop AI to Show What The Universe is Made Of - Tech Times

Emerson and QRI to Deliver AI-Based Analytics to the Global Oil and Gas Industry – Yahoo Finance

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Technology agreement will offer solutions to optimize reservoir management

Emerson (NYSE: EMR) and Quantum Reservoir Impact (QRI) announced today they have teamed up to develop and market next-generation applications for artificial intelligence (AI)-based analytics and decision-making tools customized for oil and gas exploration and production (E&P). Together, the two E&P software industry leaders will help oil and gas customers embrace digital transformation technologies and harness vast amounts of data to optimize their reservoir management strategies.

The collaboration combines the power of Emersons global reach and the worlds largest independent E&P software portfolio with QRIs leading industry expertise in applying augmented AI, machine learning and advanced analytics for asset and reservoir management.

"The combination of our technologies and deep E&P expertise in offshore, unconventional and mature fields results in a robust offering that can give customers a significant advantage in the marketplace," said Steve Santy, president for E&P software at Emerson. "Collaborating with QRI enhances our capabilities to give customers meaningful analytics to maximize production and capital efficiency and for better reserve assessment."

As part of the ongoing collaboration, the companies will apply advanced computational technologies to help geoscientists and engineers make actionable and reliable field development decisions quickly, mitigating risks and leading to higher productivity and better performance.

"People, process and data are as important as technology to the success of the solution. Our partnership with Emerson makes for a very powerful team to ensure that our offerings will become a prominent choice in the market," said Dr. Nansen Saleri, QRIs chairman, CEO and co-founder. "As our industry continues to transform, we share Emersons vision of applying state-of-the-art deep learning tools to automate next-generation workflows and offer our customers a rapid means of generating value."

For more information, visit http://www.Emerson.com/EPSoftware.

About Emerson

Emerson (NYSE: EMR), headquartered in St. Louis, Missouri (USA), is a global technology and engineering company providing innovative solutions for customers in industrial, commercial and residential markets. Our Automation Solutions business helps process, hybrid and discrete manufacturers maximize production, protect personnel and the environment while optimizing their energy and operating costs. Our Commercial & Residential Solutions business helps ensure human comfort and health, protect food quality and safety, advance energy efficiency and create sustainable infrastructure. For more information visit Emerson.com.

About QRI

Quantum Reservoir Impact (QRI) was founded in 2007 with a goal to help clients increase production, reserves and capital efficiency using a metrics-based approach. Today, QRI is leading the industry as a value creation advisory company and an artificial intelligence (AI) solutions provider reinventing the way asset teams manage their oil and gas portfolios. Applying Augmented AI and Advanced Analytics to automate complex workflows, SpeedWise technologies give clients the ability to harness vast amounts of data and optimize reservoir management and CapEx/OpEx strategies. For more information visit QRIGroup.com.

View source version on businesswire.com: https://www.businesswire.com/news/home/20200304005025/en/

Contacts

For EmersonDenise ClarkePhone: 512-587-5879Email: denise.clarke@fleishman.com

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Emerson and QRI to Deliver AI-Based Analytics to the Global Oil and Gas Industry - Yahoo Finance

Dave Copps’ stealthy AI startup emerges with $10 million in backing – The Dallas Morning News

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Dallas entrepreneur Dave Copps new venture is coming out of stealth mode with $10 million in backing from the venture funds of two energy giants and an Austin private equity firm.

Copps company, Worlds, is developing an artificial intelligence platform with applications for the energy industry. Its being spun out of Hypergiant Sensory Sciences, a division of Hypergiant Industries founded by Dallas serial entrepreneur Ben Lamm. Hypergiant designs AI tools to help companies decipher big data.

The funding round led by Austin-based Align Capital brought in the investing arms of oil giants Chevron and Petronas. Hypergiant Industries is also an investor.

Worlds technology combines artificial intelligence and internet of things capabilities in a 4D environment, giving companies what it describes as active physical analytics. The company is led by Copps and Chris Rohde, who launched and sold two previous machine learning and AI companies. Brainspace, one of their prior startups, sold in 2016 as part of $2.8 billion deal to turn a large footprint of data centers and several companies into a global cybersecurity firm.

Barbara Burger, president of Chevron Technology Ventures, said its investment in Worlds reflects a belief that digital innovation plays a critical role in accelerating business value at Chevron.

Its also the first disclosed investment by San Francisco-based Piva, a recently launched venture capital firm and Petronas subsidiary that operates independently from the Malaysian company, according to tech web site Axios. Piva is looking to invest its first $250 million fund in disruptive energy and industrial startups.

Piva CEO Ricardo Angel wrote in a blog post that hes long been impressed with Copps and Rohde.

Weve seen them build great teams, great companies and great technologies before, and weve been highly looking forward to seeing what they do next, he said.

AI and automation companies like Worlds will play a critical part in industrys future, Angel said.

Were seeing many corporations in verticals such as oil and gas, manufacturing and logistics, investing in hardware solutions often generating too much data without getting valuable insights, he wrote. As the number of IoT devices continues to grow, so will the need for AI and machine learning solutions to help manage the massive influx of data these devices will create.

Worlds funding round continues the fast start this year for Dallas-based startups in attracting growth capital. At least $86 million in funding has flowed into the region in the first five weeks of the year.

North Texas startups and early stage companies attracted more than $753 million in growth capital last year, up nearly 35% from $560 million in 2018, according to data compiled by the National Venture Capital Association and PitchBook.

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Dave Copps' stealthy AI startup emerges with $10 million in backing - The Dallas Morning News