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Were halfway through 2020 and its safe to say that the year will be forever associated with the COVID-19 pandemic. For most of the last six months travel restrictions, lockdowns, social distancing and mask wearing have been put in place in an attempt to curtail the spread. Now, as the world attempts to reopen, governments and businesses must deal with the dual problems of restarting after the shutdowns and, in the absence of a vaccine, trying to contain further outbreaks. Technology is playing a key role in the research trying to understand the virus and eventually developing that vaccine. Can it also play a part in containing the spread until it is deployed? Can it help life return to normal?

Addfor, an Italian company with almost two decades of experience in Artificial Intelligence Solutions development for engineering, has created an AI-enabled system called citySAFE that can help to monitor so-called hot spots as well as mitigation efforts in an attempt to limit the spread. citySAFE interfaces with already installed camera systems or mobile cams, to provide aggregate information for either outdoor or indoor public and private spaces. Lenovo is collaborating with Addfor by defining and providing the right system hardware configuration to support the immense raw processing power required by a high camera-count video streaming.

The citySAFE application relies on a simplified interface that displays critical situations in real-time, leveraging available data about infection and hospitalization rates, then color-coding areas accordingly. For example, if a hot spot springs up, citySAFE can, in real time, assess the extent to which mitigation efforts in that area are being followed. All indices, such as counting populations, population density, or percentage of mask usage are calculated in real-time and grouped in space and time to be explored both geographically and temporally with an advanced graphical interface. Health officials can then issue warnings and deploy personnel to remind people and businesses about masks social distancing, and other safe practices. This same scenario could play out for a private company with a large footprint campus, manufacturing or distribution complex.

citySAFE was recently tested on a large scale in Turin, Italy with the collaboration of the public administration and other local authorities. It is the only integrated system available at the moment that allows the timely and continuous monitoring of an entire city, either from city surveillance or aerial cameras, for the issues related to the control of the spread of the COVID-19 virus. By using existing city camera infrastructure, citizens are subjected only to their existing level of CCTV observation.

This type of continuous streaming of live video data requires tremendous processing power, both at the edge, where the video is captured, and in the data center where the results are compiled, run through the algorithms for inference, distributed and retrained. In this context, Lenovo is providing the unique rugged ThinkSystem SE350 edge server. By using the computational power of the high-performance NVIDIA T4 GPU, the ThinkSystem SE350 delivers video streaming wirelessly from cameras, and real-time inference at the edge. On the back end, the Lenovo GPU dense ThinkSystem SR670, designed to support up to eight high-performance NVIDIA V100 GPUs, is built for running large AI workloads such as citySAFE and scales linearly as requirements grow. Both the ThinkSystem SE350 and SR670 are built on Intel Xeon processors for optimal performance and security.

We know that the pandemic will not go on forever: A vaccine will be found and deployed. Life will return to normal. When it does, citySAFE and its associated infrastructure can be repurposed for developing new or enhancing existing services the city administration or private companies provide citizens or employees such in areas such as safety, parking management, waiting times, and advanced digital signage systems for traffic or missing persons alerts.

In 2100s, future historians will study the COVID-19 pandemic, the same way that weve studied the Spanish Flu outbreak of 1918. They will see the mistakes from 1918, some corrected, some repeated, in 2020. And they will see a global crisis fought on a local basis. Mitigating the spread of the virus in the interim until a vaccine is developed, while restarting the economies of the world, is the tightrope we find ourselves on now. How we execute those precarious steps may end up being the yardstick by which those future historians measure us. In the face of these challenges, how did we get life to return to normal?

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Using Artificial Intelligence in the Fight Against COVID-19 - HPCwire

Artificial intelligence once relegated to academic studies and sci-fi nerds has become one of the hottest technologies, infiltrating every industry.

AI adoption was already accelerating before the COVID-19 crisis, but experts say that demand for AI tools is now is poised to grow even faster.

"The entire industry of AI is getting a huge boost from this unfortunate crisis," David Blumberg, founder and managing partner at Blumberg Capital, told Business Insider in May. "There is a silver lining to this dark cloud that we've all been living in for over two and a half, three months."

Former Cisco CEO John Chambers, who now runs his own venture firm JC2 Ventures, told Business Insider in an interview in May that he expects one to five major AI players to emerge from the current crisis.

AI has helped businesses take on big and small tasks, from making long-term sales growth projections to the automation of routine, time-consuming tasks.A 2019 Gartner survey found that major organizations planned to double their number of AI-related initiatives in the following year, from an average of 4 to 10. But as they pandemic has forced businesses to try to adapt to major changes, like the sudden pivot to remote work and tightening budgets, they're looking for ways to streamline and operate more efficiently.

"AI is best at solving all these really boring meat-and-potato problems," James Cham, a partner at Bloomberg Partners, told Business Insider. Many of the opportunities for using AI involve "straightforward process engineering," he said, where the technology can be used to shorten or streamline a process.

Industries like collaboration, shipping logistics, and manufacturing and warehousing will be particularly keen on using AI, said Sandeep Bhadra, a partner at Vertex Ventures.

Jake Saper at Emergence Capital had a similar prediction, that "vertical" specific AI software will be in more demand.

Here are 12 startups that analysts and investors say are well-positioned to grow thanks to a surge in demand for AI tools:

Originally posted here:
These 12 artificial intelligence startups are poised for success, particularly in a post-COVID world, according to experts - Business Insider

Researchers have figured out a way to transform a few dozen pixels into a high resolution image of a face using artificial intelligence.

A team from Duke University in the US created an algorithm capable of "imagining" realistic-looking faces from blurry, unrecognisable pictures of people, with eight-times more effectiveness than previous methods.

"Never have super-resolution images been created at this resolution before with this much detail," said Duke computer scientist Cynthia Rudin, who led the research.

Sharing the full story, not just the headlines

The images generated by the AI do not resemble real people, instead they are faces that look plausibly real. It therefore cannot be used to identify people from low resolution images captured by security cameras.

The PULSE (Photo Upsampling via Latent Space Exploration) system developed by Dr Rudin and her team creates images with 64-times the resolution than the original blurred picture.

The PULSE algorithm is able to achieve such high levels of resolution by reverse engineering the image from high resolution images that look similar to the low resolution image when down scaled.

The images generated by enhancing the pixels do not represent real people (Duke University)

Through this process, facial features like eyelashes, teeth and wrinkles that were impossible to see in the low resolution image become recognisable and detailed.

"Instead of starting with the low resolution image and slowly adding detail, PULSE traverses the high resolution natural image manifold, searching for images that downscale to the original low resolution image," states a paper detailing the research.

The AI algorithm is able to enhance a few dozen pixels into a high-resolution picture of a face (Duke University)

"Our method outperforms state-of-the-art methods in perceptual quality at higher resolutions and scale factors than previously possible."

The system could theoretically be used on low resolution images of almost anything, ranging from medicine and microscopy, to astronomy and satellite imagery.

This means noisy, poor-quality images of distant planets and solar systems could be imagined in high resolution.

The research will be presented at the 2020 Conference on Computer Vision and Pattern Recognition (CVPR) this week.

Continued here:
Artificial intelligence enhances blurry faces into 'super-resolution images' - The Independent

London, June 15, 2020 (GLOBE NEWSWIRE) -- The automotive artificial intelligence market is expected to grow at a CAGR of 39.8% from 2019 to reach $15.9 billion by 2027.

Several established automotive organizations across the globe are increasingly struggling with the rising cost of operations, dissatisfied customers, declining sales, and unidentified competition. Advanced capabilities of AI, coupled with rising consumer expectations, have pushed the automotive industry into adopting artificial intelligence. Several organizations are investing heavily in order to reap the profits in highly dynamic and competitive market environments. The global artificial intelligence in automotive market is expected to witness strong growth over the coming years due to the growing demand for autonomous vehicles, adoption of advanced automotive solutions, growing adoption of artificial intelligence for traffic management, and government initiatives and investments towards connected and autonomous vehicles.

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The increasing volume of data gathered through IoT devices, coupled with the widespread availability of high-speed broadband networks and the emergence of 5G technologies is driving the need for faster data processing. Apart from this, widening the implementation of computer vision technologies across vehicles and shifting consumer preferences for premium vehicles to improve the driving experience while enhancing the vehicle and pedestrian safety are some of the key factors anticipated to drive the growth of artificial intelligence in automotive market in the near future. However, lack of infrastructure coupled with the high procurement operating cost is expected to challenge the growth of the artificial intelligence in automotive market growth during the forecast period.

The global market for artificial intelligence in automotive industry is expected to grow at a CAGR of 39.8% from 2019 to reach $15.9 billion by 2027. The market is witnessing consistent growth owing to the increasing demand for smart IoT devices in automotive, surging demand for connected vehicles, and adoption of advanced driver assistance systems. Apart from this, surging adoption of AI-based solutions and services among the automotive industry is also contributing to the overall growth of artificial intelligence in automotive market. While developed economies offer technological growth opportunities through the proliferation of advanced technologies, the ongoing digital transformation initiatives across emerging economies such as Asia-Pacific and Latin America are likely to offer high growth opportunities for vendors operating in the market.

The global artificial intelligence in automotive market is mainly segmented by components (hardware, software, services), by technology (machine learning, computer vision, natural language processing, context-aware computing), by process (signal recognition, image recognition, voice recognition, data mining), by application (semi-autonomous driving, human-machine interface), and region.

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Based on components, the artificial intelligence in automotive market is segmented into hardware, software, and services. The software segment dominated the artificial intelligence in automotive market in 2019 in terms of market share. This is mainly attributed to the growing usage of learning analytics, growing acceptance of in-car assistants driven by machine learning techniques and an increase in demand for autonomous platforms for automotive industry. However, the services segment is slated to grow at the fastest CAGR during the forecast period and will emerge as the major segment in terms of market share by 2027. This growth is mainly driven by the surging demand for AI-based cloud services for autonomous vehicles, over-the-air (OTA) software services, traffic and mapping services, shared mobility services, remote maintenance services, technical support & training services, maintenance & support services, integration services, performance measurement services, and consulting services.

Based on technology, the artificial intelligence in automotive market is segmented into machine learning, computer vision, natural language processing, and context-aware computing. The machine learning technology segment held the largest share of the overall automotive artificial intelligence market in 2019, owing to the demand for signal diagnosing, image recognition, speech recognition, data mining, and an increase in unstructured data generated by the automotive industry. However, the computer vision technology is slated to grow at the fastest CAGR during the forecast period, due to a widening implementation of computer vision in semi-autonomous vehicles to tackle distracted/ drowsy driving and surging use of LIDAR sensors and cameras to avoid vehicle collisions.

Based on process, the overall artificial intelligence in automotive market is segmented into signal recognition, image recognition, voice recognition, and data mining. The signal recognition segment dominated the artificial intelligence in the automotive market in 2019 and is also estimated to continue its dominance over the forecast period. The growth in this market segment is attributed to the increasing growth of automotive safety systems, rising consumer preference for signal recognition in autonomous vehicles, and government regulations pertaining to the safety rating of a vehicle to reduce road collisions. However, the image recognition process is slated to grow at the fastest CAGR during the forecast period, due to growing demand for advanced driver assistance systems (ADAS) such as road signs detection and pedestrian protection systems.

Based on application, the artificial intelligence in automotive market is majorly segmented into semi-autonomous driving and human-machine interface. The human-machine interface segment dominated the artificial intelligence in automotive market in 2019. This is attributed to the increasing demand for interactive technologies in vehicles, connected systems, and smart convenient features.

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Geographically, the global artificial intelligence in automotive market is segmented into five regions, namely, North America, Europe, Asia-Pacific, Latin America, and the Middle East & Africa with a further analysis of major countries in these regions. North America accounted for the largest share of global artificial intelligence in automotive market in 2019, followed by Europe and the Asia-Pacific region. The largest share of this region is mainly attributed to the presence of developed economies, such as the United States and Canada, focusing on enhancing the existing solutions in the automotive industry, and the existence of major players in this market along with a high willingness to adopt advanced technologies. Apart from this, the growing demand for enhanced user experience, rising living standards, growing adoption of autonomous vehicles and availability of high-end infrastructure, increasing R&D expenditure, and various government initiatives supporting AI research are contributing to the growth in this region.

On the other hand, Asia-Pacific region is projected to grow at the highest CAGR during the forecast period. This growth is attributed to an increase in demand for premium vehicles, growing investments in AI technology for improved productivity, and increasing adoption of AI-based solutions and services in the automotive industry. Apart from this, developing internet & connectivity infrastructure, growing adoption of intelligent solutions and increasing digitalization, and increasing investments by the major players in this region are contributing to the growth in the Asia Pacific AI in automotive market.

Some of the key players operating in the global artificial intelligence in automotive market are Google LLC (U.S.), IBM Corporation (U.S.), Intel Corporation (U.S.), Microsoft Corporation (U.S.), Nvidia Corporation (U.S.), Tesla, Inc. (U.S.), Xilinx, Inc. (U.S.), Micron Technology, Inc. (U.S.), Ford Motor Company (U.S.), General Motors Company (U.S.), Harman International Industries Inc. (South Korea), Honda Motor Co., Ltd. (Japan), Audi AG (Germany), and Qualcomm Technologies, Inc. (U.S.), among others.

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Scope of the Report:

Automotive AI Market, by Component

Automotive AI Market, by Technology

Automotive AI Market, by Process

Automotive AI Market, by Application

Automotive AI Market, by Geography

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Automotive Artificial Intelligence Market Worth $15.9 Billion by 2027, Growing at a CAGR of 39.8% from 2019- Global Market Opportunity Analysis and...

Jun 11th 2020

IT WILL BE as if the world had created a second China, made not of billions of people and millions of factories, but of algorithms and humming computers. PwC, a professional-services firm, predicts that artificial intelligence (AI) will add $16trn to the global economy by 2030. The total of all activityfrom banks and biotech to shops and constructionin the worlds second-largest economy was just $13trn in 2018.

PwCs claim is no outlier. Rival prognosticators at McKinsey put the figure at $13trn. Others go for qualitative drama, rather than quantitative. Sundar Pichai, Googles boss, has described developments in AI as more profound than fire or electricity. Other forecasts see similarly large changes, but less happy ones. Clever computers capable of doing the jobs of radiologists, lorry drivers or warehouse workers might cause a wave of unemployment.

Yet lately doubts have been creeping in about whether todays AI technology is really as world-changing as it seems. It is running up against limits of one kind or another, and has failed to deliver on some of its proponents more grandiose promises.

There is no question that AIor, to be precise, machine learning, one of its sub-fieldshas made much progress. Computers have become dramatically better at many things they previously struggled with. The excitement began to build in academia in the early 2010s, when new machine-learning techniques led to rapid improvements in tasks such as recognising pictures and manipulating language. From there it spread to business, starting with the internet giants. With vast computing resources and oceans of data, they were well placed to adopt the technology. Modern AI techniques now power search engines and voice assistants, suggest email replies, power the facial-recognition systems that unlock smartphones and police national borders, and underpin the algorithms that try to identify unwelcome posts on social media.

Perhaps the highest-profile display of the technologys potential came in 2016, when a system built by DeepMind, a London-based AI firm owned by Alphabet, Googles corporate parent, beat one of the worlds best players at Go, an ancient Asian board game. The match was watched by tens of millions; the breakthrough came years, even decades, earlier than AI gurus had expected.

As Mr Pichais comparison with electricity and fire suggests, machine learning is a general-purpose technologyone capable of affecting entire economies. It excels at recognising patterns in data, and that is useful everywhere. Ornithologists use it to classify birdsong; astronomers to hunt for planets in glimmers of starlight; banks to assess credit risk and prevent fraud. In the Netherlands, the authorities use it to monitor social-welfare payments. In China AI-powered facial recognition lets customers buy groceriesand helps run the repressive mass-surveillance system the country has built in Xinjiang, a Muslim-majority region.

AIs heralds say further transformations are still to come, for better and for worse. In 2016 Geoffrey Hinton, a computer scientist who has made fundamental contributions to modern AI, remarked that its quite obvious that we should stop training radiologists, on the grounds that computers will soon be able to do everything they do, only cheaper and faster. Developers of self-driving cars, meanwhile, predict that robotaxis will revolutionise transport. Eric Schmidt, a former chairman of Google (and a former board member of The Economists parent company) hopes that AI could accelerate research, helping human scientists keep up with a deluge of papers and data.

In January a group of researchers published a paper in Cell describing an AI system that had predicted antibacterial function from molecular structure. Of 100 candidate molecules selected by the system for further analysis, one proved to be a potent new antibiotic. The covid-19 pandemic has thrust such medical applications firmly into the spotlight. An AI firm called BlueDot claims it spotted signs of a novel virus in reports from Chinese hospitals as early as December. Researchers have been scrambling to try to apply AI to everything from drug discovery to interpreting medical scans and predicting how the virus might evolve.

This is not the first wave of AI-related excitement (see timeline in next article). The field began in the mid-1950s when researchers hoped that building human-level intelligence would take a few yearsa couple of decades at most. That early optimism had fizzled by the 1970s. A second wave began in the 1980s. Once again the fields grandest promises went unmet. As reality replaced the hype, the booms gave way to painful busts known as AI winters. Research funding dried up, and the fields reputation suffered.

Many of the grandest claims made about AI have once again failed to become reality

Modern AI technology has been far more successful. Billions of people use it every day, mostly without noticing, inside their smartphones and internet services. Yet despite this success, the fact remains that many of the grandest claims made about AI have once again failed to become reality, and confidence is wavering as researchers start to wonder whether the technology has hit a wall. Self-driving cars have become more capable, but remain perpetually on the cusp of being safe enough to deploy on everyday streets. Efforts to incorporate AI into medical diagnosis are, similarly, taking longer than expected: despite Dr Hintons prediction, there remains a global shortage of human radiologists.

Surveying the field of medical AI in 2019, Eric Topol, a cardiologist and AI enthusiast, wrote that the state of AI hype has far exceeded the state of AI science, especially when it pertains to validation and readiness for implementation in patient care. Despite a plethora of ideas, covid-19 is mostly being fought with old weapons that are already to hand. Contacttracing has been done with shoe leather and telephone calls. Clinical trials focus on existing drugs. Plastic screens and paint on the pavement enforce low-tech distancing advice.

The same consultants who predict that AI will have a world-altering impact also report that real managers in real companies are finding AI hard to implement, and that enthusiasm for it is cooling. Svetlana Sicular of Gartner, a research firm, says that 2020 could be the year AI falls onto the downslope of her firms well-publicised hype cycle. Investors are beginning to wake up to bandwagon-jumping: a survey of European AI startups by MMC, a venture-capital fund, found that 40% did not seem to be using any AI at all. I think theres definitely a strong element of investor marketing, says one analyst delicately.

This Technology Quarterly will investigate why enthusiasm is stalling. It will argue that although modern AI techniques are powerful, they are also limited, and they can be troublesome and difficult to deploy. Those hoping to make use of AIs potential must confront two sets of problems.

The first is practical. The machine-learning revolution has been built on three things: improved algorithms, more powerful computers on which to run them, andthanks to the gradual digitisation of societymore data from which they can learn. Yet data are not always readily available. It is hard to use AI to monitor covid-19 transmission without a comprehensive database of everyones movements, for instance. Even when data do exist, they can contain hidden assumptions that can trip the unwary. The newest AI systems demand for computing power can be expensive. Large organisations always take time to integrate new technologies: think of electricity in the 20th century or the cloud in the 21st. None of this necessarily reduces AIs potential, but it has the effect of slowing its adoption.

The second set of problems runs deeper, and concerns the algorithms themselves. Machine learning uses thousands or millions of examples to train a software model (the structure of which is loosely based on the neural architecture of the brain). The resulting systems can do some tasks, such as recognising images or speech, far more reliably than those programmed the traditional way with hand-crafted rules, but they are not intelligent in the way that most people understand the term. They are powerful pattern-recognition tools, but lack many cognitive abilities that biological brains take for granted. They struggle with reasoning, generalising from the rules they discover, and with the general-purpose savoir faire that researchers, for want of a more precise description, dub common sense. The result is an artificial idiot savant that can excel at well-bounded tasks, but can get things very wrong if faced with unexpected input.

Without another breakthrough, these drawbacks put fundamental limits on what AI can and cannot do. Self-driving cars, which must navigate an ever-changing world, are already delayed, and may never arrive at all. Systems that deal with language, like chatbots and personal assistants, are built on statistical approaches that generate a shallow appearance of understanding, without the reality. That will limit how useful they can become. Existential worries about clever computers making radiologists or lorry drivers obsoletelet alone, as some doom-mongers suggest, posing a threat to humanitys survivalseem overblown. Predictions of a Chinese-economy-worth of extra GDP look implausible.

Todays AI summer is different from previous ones. It is brighter and warmer, because the technology has been so widely deployed. Another full-blown winter is unlikely. But an autumnal breeze is picking up.

This article appeared in the Technology Quarterly section of the print edition under the headline "Reality check"

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An understanding of AIs limitations is starting to sink in - The Economist