Category Archives: Artificial Intelligence

This article is part of our latest Artificial Intelligence special report, which focuses on how the technology continues to evolve and affect our lives.

Steve Jobs once described personal computing as a bicycle for the mind.

His idea that computers can be used as intelligence amplifiers that offer an important boost for human creativity is now being given an immediate test in the face of the coronavirus.

In March, a group of artificial intelligence research groups and the National Library of Medicine announced that they had organized the worlds scientific research papers about the virus so the documents, more than 44,000 articles, could be explored in new ways using a machine-learning program designed to help scientists see patterns and find relationships to aid research.

This is a chance for artificial intelligence, said Oren Etzioni, the chief executive of the Allen Institute for Artificial Intelligence, a nonprofit research laboratory that was founded in 2014 by Paul Allen, the Microsoft co-founder.

There has long been a dream of using A.I. to help with scientific discovery, and now the question is, can we do that?

The new advances in software applications that process human language lie at the heart of a long-running debate over whether computer technologies such as artificial intelligence will enhance or even begin to substitute for human creativity.

The programs are in effect artificial intelligence Swiss Army knives that can be repurposed for a host of different practical applications, ranging from writing articles, books and poetry to composing music, language translation and scientific discovery.

In addition to raising questions about whether machines will be able to think creatively, the software has touched off a wave of experimentation and has also raised questions about new challenges to intellectual property laws and concerns about whether they might be misused for spam, disinformation and fraud.

The Allen Institute program, Semantic Scholar, began in 2015. It is an early example of this new class of software that uses machine-learning techniques to extract meaning from and identify connections between scientific papers, helping researchers more quickly gain in-depth understanding.

Since then, there has been a rapid set of advances based on new language process techniques leading a variety of technology firms and research groups to introduce competing programs known as language models, each more powerful than the next.

What has been in effect an A.I. arms race reached a high point in February, when Microsoft introduced Turing-NLG (natural language generation), named after the British mathematician and computing pioneer Alan Turing. The machine-learning behemoth consists of 17 billion parameters, or weights, which are numbers that are arrived at after the program was trained on an immense library of human-written texts, effectively more than all the written material available on the internet.

As a result, significant claims have been made for the capability of language models, including the ability to write plausible-sounding sentences and paragraphs, as well as draw and paint and hold a believable conversation with a human.

Where weve seen the most interesting applications has really been in the creative space, said Ashley Pilipiszyn, a technical director at OpenAI, an independent research group based in San Francisco that was founded as a nonprofit research organization to develop socially beneficial artificial intelligence-based technology and later established a for-profit corporation.

Early last year, the group announced a language model called GPT-2 (generative pretrained transformer), but initially did not release it publicly, saying it was concerned about potential misuse in creating disinformation. But near the end of the year, the program was made widely available.

Everyone has innate creative capabilities, she said, and this is a tool that helps push those boundaries even further.

Hector Postigo, an associate professor at the Klein College of Media and Communication at Temple University, began experimenting with GPT-2 shortly after it was released. His first idea was to train the program to automatically write a simple policy statement about ethics policies for A.I. systems.

After fine-tuning GPT-2 with a large collection of human-written articles, position papers, and laws collected in 2019 on A.I., big data and algorithms, he seeded the program with a single sentence: Algorithmic decision-making can pose dangers to human rights.

The program created a short essay that began, Decision systems that assume predictability about human behavior can be prone to error. These are the errors of a data-driven society. It concluded, Recognizing these issues will ensure that we are able to use the tools that humanity has entrusted to us to address the most pressing rights and security challenges of our time.

Mr. Postigo said the new generation of tools would transform the way people create as authors.

We already use autocomplete all the time, he said. The cat is already out of the bag.

Since his first experiment, he has trained GPT-2 to compose classical music and write poetry and rap lyrics.

That poses the question of whether the programs are genuinely creative. And if they are able to create works of art that are indistinguishable from human works, will they devalue those created by humans?

A.I. researchers who have worked in the field for decades said that it was important to realize that the programs were simply assistive and that they were not creating artistic works or making other intellectual achievements independently.

The early signs are that the new tools will be quickly embraced. The Semantic Scholar coronavirus webpage was viewed more than 100,000 times in the first three days it was available, Dr. Etzioni said. Researchers at Google Health, Johns Hopkins University, the Mayo Clinic, the University of Notre Dame, Hewlett Packard Labs and IBM Research are using the service, among others.

Jerry Kaplan, an artificial-intelligence researcher who was involved with two of Silicon Valleys first A.I. companies, Symantec and Teknowledge during the 1980s, pointed out that the new language modeling software was actually just a new type of database retrieval technology, rather than an advance toward any kind of thinking machine.

Creativity is still entirely on the human side, he said. All this particular tool is doing is making it possible to get insights that would otherwise take years of study.

Although that may be true, philosophers have begun to wonder whether these new tools will permanently change human creativity.

Brian Smith, a philosopher and a professor of artificial intelligence at the University of Toronto, noted that although students are still taught how to do long division by hand, calculators now are universally used for the task.

We once used rooms full of human computers to do these tasks manually, he said, noting that nobody would want to return to that era.

In the future, however, it is possible that these new tools will begin to take over much of what we consider creative tasks such as writing, composing and other artistic ventures.

What we have to decide is, what is at the heart of our humanity that is worth preserving, he said.

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You Cant Spell Creative Without A.I. - The New York Times

Artificial intelligence (AI) refers to software-based systems that use data inputs to make decisions on their own. Machine learning is an application of AI that gives computer systems the ability to learn and improve from data without being explicitly programmed.

2019 saw financial institutions explore a broad-range of possible AI use cases in both customer-facing and back-office processes, increasing budgets, headcounts, and partnerships. 2020 will see increased focus on breaking out the marketing story from actual business impact to place bigger bets in fewer areas. This will help banks scale proven AI across the enterprise to forge competitive advantage.

Artificial intelligence will re-invigorate digital money management, helping incumbents drip-feed highly personalised spending tips to build trust and engagement in the absence of in-person interaction. Features like predictive insights around cashflow shortfalls, alerts on upcoming bill payments, and various what if scenarios when trying on different financial products give customers transparency around their options and the risks they face. This service will render as an always-on, in-your-pocket, and predictive advisor.

AI-enhanced customer relationship management (CRM) will help digital banks optimise product recommendations to rival the conversion rates of best-in-class online retailers. These product suggestions wont render as sales, but rather valuable advice received, such as a pre-approved loan before a cash shortfall or an option to remortgage to fund home improvements. This will help incumbents build customer advocacy and trust as new entrants vie for attention.

AI-powered onboarding, when combined with voice and facial recognition technologies, will help incumbents make themselves much easier to do business with, especially at the initial point of conversion but also thereafter at each moment of authentication. AI will offer particular support through Know Your Customer (KYC) processes, helping incumbents keep pace with new entrants. Standard Bank in South Africa, for example, used WorkFusions AI capabilities to reduce the customer onboarding time from 20 days to just five minutes.

Banks heavy compliance burden will continue to drive AI. Last year, large global banks such as OCBC Bank, Commonwealth Bank, Wells Fargo, and HSBC made big investments in areas such as automated data management, reporting, anti-money laundering (AML), compliance, automated regulation interpretation, and mapping. Increasingly partnering with artificial intelligence-enabled regtech firms will help incumbents reduce operational risk and enhance reporting quality.

As artificial intelligence becomes more embedded into all areas of customers lives, concerns around the black box driving decisions will grow, with more demands for explainable AI. As it is, customers with little or no digital footprint are less visible to applications that rely on data to profile people and assess risk. Traditional banks credit risk algorithms often disproportionately exclude black and Hispanic groups in the US as well as women, because these groups have historically earned less over their lifetimes.

In 2020, senior management will be held directly accountable for the decisions of AI-enabled algorithms. This will drive increased focus on data quality to feed the algorithms and perhaps limits to the use of the most dynamic machine learning because of their regulatory opacity.

This is an edited extract from the Banking & Payments Predictions 2020 Thematic Research report produced by GlobalData Thematic Research.

GlobalData is this websites parent business intelligence company.

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Banking and payments predictions 2020: Artificial intelligence - Verdict

Leading provider of training data platforms for machine learning, Labelbox receives prestigious SBIR contract from AFWERX for U.S. Air Force

SAN FRANCISCO, April 09, 2020 (GLOBE NEWSWIRE) -- This release for Labelbox corrects and replaces the release issued today at 7:00 am ET with the headline Labelbox Awarded Artificial Intelligence Grant by Department of Defense. The word grant has been replaced in the headline, subheadline, and release body with the word contract. The corrected release follows.

Labelbox, the worlds leading training data platform, is among an elite selection of artificial intelligence companies to receive a contract from the Department of Defense to support national security as the U.S. scrambles to stay ahead of its rivals.

While some in Silicon Valley balk at working with the government, Labelboxs founders are vocal about their belief that technology companies have a responsibility to help the U.S. maintain its technological advantage in the face of competition from nation states.

I grew up in a poor family, with limited opportunities and little infrastructure said Manu Sharma, CEO and one of Labelboxs co-founders, who was raised in a village in India near the Himalayas. He said that opportunities afforded by the U.S. have helped him achieve more success in ten years than multiple generations of his family back home. Weve made a principled decision to work with the government and support the American system, he said.

Labelbox is a software platform that allows data science teams to manage the data used to train supervised-learning models. Supervised learning is a branch of artificial intelligence that uses labeled data to train algorithms to recognize patterns in images, audio, video or text. After being fed millions of labeled pictures of mobile missile launchers from satellite imagery, for example, a supervised-learning system will learn to pick out missile launchers in pictures it has never seen.

For data science teams to work better with each other and with labelers around the world, they need a platform and tools. Without those things, managing large sets of data quickly becomes overwhelming. Labelbox solves that problem by facilitating collaboration, rework, quality assurance, model evaluation, audit trails, and model-assisted labeling in one platform. The platform is tailored for computer vision systems but can handle all forms of data. The platform also helps with billing and time management.

Labelbox is an integrated solution for data science teams to not only create the training data but also to manage it in one place, said Sharma. Its the foundational infrastructure for customers to build their machine learning pipeline.

The company won an Air Force AEFWRX Phase 1 Small Business Innovation Research contract to conduct feasibility studies on how to integrate the Labelbox platform with various stakeholders in the Air Force. Labelbox recently hired a representative in Washington, D.C., to manage the process.

The Small Business Innovation Research (SBIR) program is a highly competitive program that encourages domestic small businesses to engage in Federal Research and Development. The United States Department of Defense is the largest of 11 federal agencies participating in the program. Air Force Innovation Hub Network (AFWERX) is a United States Air Force program intended to engage innovators and entrepreneurs in developing effective solutions to challenges faced by the service.

About LabelboxFounded in 2018 and based in San Francisco, Labelbox is a collaborative training data platform for machine learning applications. Instead of building their own expensive and incomplete homegrown tools, companies rely on Labelbox as the training data platform that acts as a central hub for data science teams to interface with dispersed labeling teams. Better ways to input and manage data translates into higher-quality training data and more accurate machine-learning models. Labelbox has raised $39 million in capital from leading VCs in Silicon Valley. For more information, visit: https://www.Labelbox.com/

Editorial ContactLonn Johnston for Labelbox+1 650.219.7764lonn@flak42.com

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CORRECTION - Labelbox Awarded Artificial Intelligence Contract by Department of Defense - Yahoo Finance

The "Artificial Intelligence in Supply Chain Market by Component (Platforms, Solutions) Technology (Machine Learning, Computer Vision, Natural Language Processing), Application (Warehouse, Fleet, Inventory Management), and by End User - Global Forecast to 2027" report has been added to ResearchAndMarkets.com's offering.

This report carries out an impact analysis of the key industry drivers, restraints, challenges, and opportunities. Adoption of artificial intelligence in the supply chain allows industries to track their operations, enhance supply chain management productivity, augment business strategies, and engage with customers in the digital world.

The growth of artificial intelligence in supply chain market is driven by several factors such as raising awareness of artificial intelligence and big data & analytics and widening implementation of computer vision in both autonomous & semi-autonomous applications. Moreover, the factors such as consistent technological advancements in the supply chain industry, rising demand for AI-based business automation solutions, and evolving supply chain automation are also contributing to the market growth.

The overall AI in supply chain market is segmented by component (hardware, software, and services), by technology (machine learning, computer vision, natural language processing, cognitive computing, and context-aware computing), by application (supply chain planning, warehouse management, fleet management, virtual assistant, risk management, inventory management, and planning & logistics), and by end-user (manufacturing, food and beverages, healthcare, automotive, aerospace, retail, and consumer-packaged goods), and geography.

Companies Mentioned

Key Topics Covered:

1. Introduction

2. Research Methodology

3. Executive Summary

3.1. Overview

3.2. Market Analysis, by Component

3.3. Market Analysis, by Technology

3.4. Market Analysis, by Application

3.5. Market Analysis, by End User

3.6. Market Analysis, by Geography

3.7. Competitive Analysis

4. Market Insights

4.1. Introduction

4.2. Market Dynamics

4.2.1. Drivers

4.2.1.1. Rising Awareness of Artificial Intelligence and Big Data & Analytics

4.2.1.2. Widening Implementation of Computer Vision in both Autonomous & Semi-Autonomous Applications

4.2.2. Restraints

4.2.2.1. High Procurement and Operating Cost

4.2.2.2. Lack of Infrastructure

4.2.3. Opportunities

4.2.3.1. Growing Demand for AI -Based Business Automation Solutions

4.2.3.2. Evolving Supply Chain Automation

4.2.4. Challenges

4.2.4.1. Data Integration from Multiple Resources

4.2.4.2. Concerns Over Data Privacy

4.2.5. Trends

4.2.5.1. Rising Adoption of 5g Technology

4.2.5.2. Rising Demand for Cloud-Based Supply Chain Solutions

5. Artificial Intelligence in Supply Chain Market, by Component

5.1. Introduction

5.2. Software

5.2.1. AI Platforms

5.2.2. AI Solutions

5.3. Services

5.3.1. Deployment & Integration

5.3.2. Support & Maintenance

5.4. Hardware

5.4.1. Networking

5.4.2. Memory

5.4.3. Processors

6. Artificial Intelligence in Supply Chain Market, by Technology

6.1. Introduction

6.2. Machine Learning

6.3. Natural Language Processing (NLP)

6.4. Computer Vision

6.5. Context-Aware Computing

7. Artificial Intelligence in Supply Chain Market, by Application

7.1. Introduction

7.2. Supply Chain Planning

7.3. Virtual Assistant

7.4. Risk Management

7.5. Inventory Management

7.6. Warehouse Management

7.7. Fleet Management

7.8. Planning & Logistics

8. Artificial Intelligence in Supply Chain Market, by End User

8.1. Introduction

8.2. Retail Sector

8.3. Manufacturing Sector

8.4. Automotive Sector

8.5. Aerospace Sector

8.6. Food & Beverage Sector

8.7. Consumer Packaged Goods Sector

8.8. Healthcare Sector

9. Global Artificial Intelligence in Supply Chain Market, by Geography

9.1. Introduction

9.2. North America

9.2.1. U.S.

9.2.2. Canada

9.3. Europe

9.3.1. Germany

9.3.2. U.K.

9.3.3. France

9.3.4. Spain

9.3.5. Italy

9.3.6. Rest of Europe

9.4. Asia-Pacific

9.4.1. China

9.4.2. Japan

9.4.3. India

9.4.4. Rest of Asia-Pacific

9.5. Latin America

9.6. Middle East & Africa

10. Competitive Landscape

10.1. Key Growth Strategies

10.2. Competitive Developments

10.2.1. New Product Launches and Upgradations

10.2.2. Mergers and Acquisitions

10.2.3. Partnerships, Agreements, & Collaborations

10.2.4. Expansions

10.3. Market Share Analysis

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Global Artificial Intelligence in Supply Chain Market (2020 to 2027) - by Component Technology, Application and by End User - ResearchAndMarkets.com -...

April 9, 2020 - LONDON, UK: Prior to the COVID-19 pandemic, the IDC (International Data Corporation) Worldwide Artificial Intelligence Spending Guide had forecast European artificial intelligence (AI) spending of $10 billion for 2020, and a healthy growth at a 33% CAGR throughout 2023. With the COVID-19 outbreak, IDC expects a variety of changes in spending in 2020. AI solutions deployed in the cloud will experience a strong uptake, showing that companies are looking at deploying intelligence in the cloud to be more efficient and agile.

"Following the COVID-19 outbreak, many industries such as transportation and personal and consumer services will be forced to revise their technology investments downwards," said Andrea Minonne, senior research analyst at IDC Customer Insights & Analysis. "On the other hand, AI is a technology that can play a significant role in helping businesses and societies deal with and solve large scale disruption caused by quarantines and lockdowns. Of all industries, the public sector will experience an acceleration of AI investments. Hospitals are looking at AI to speed up COVID-19 diagnosis and testing and to provide automated remote consultations to patients in self-isolation through chatbots. At the same time, governments will use AI to assess social distancing compliance"

In the IDC report, What is the Impact of COVID-19 on the European IT Market? (IDC #EUR146175020, April 2020) we assessed the impact of COVID-19 across 181 European companies and found that, as of March 23, 16% of European companies believe automation through AI and other emerging technologies can help them minimize the impact of COVID-19. With large scale lockdowns in place, a shortage of workers and supply chain disruptions will drive automation needs across manufacturing.

Applying intelligence to automate processes is a crucial response to the COVID-19 crisis. Not only does automation allow European companies to digitally transform, but also to make prompt data-driven decisions and have a positive impact on business efficiency. IDC expects a surge in adoption of automated COVID-19 diagnosis in healthcare to speed up diagnosis and save time for both doctors and patients. As the virus spreads quickly, labor shortages in industries where product demand is surging can become a critical problem. For that reason, companies are renovating their hiring processes, applying a mix of intelligent automation and virtualization in their hiring processes. Companies will also aim to automate their supply chains, maintain their agility and avoid production bottlenecks, especially for industries with vast supplier networks. With customer service centers becoming severely restricted, automation will be a crucial part for remote customer engagement and chatbots will help customers in self-isolation get the support they need without having to wait a long time.

"As a short-term response to the COVID-19 crisis, AI can play a crucial part in automating processes and limiting human involvement to a necessary minimum," said Petr Vojtisek, research analyst at IDC Customer Insights & Analysis. "In the longer term, we might observe an increase in AI adoption for companies that otherwise wouldn't consider it, both for competitive and practical reasons."

IDC's Worldwide Semiannual Artificial Intelligence Spending Guide provides guidance on the expected technology opportunity around the AI market across nine regions. Segmented by 32 countries, 19 industries, 27 use cases, and 6 technologies, the guide provides IT vendors with insight into this rapidly growing market and how the market will develop over the coming years.

For IDCs European coverage of COVID-19, click here.

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Spending in Artificial Intelligence to accelerate across the public sector due to automation and social distancing compliance needs in response to...

Imagine being a recent graduate at a competitive job fair. You look to the suit-clad candidates on your left and right, and you start to wonder, Am I smart enough to compete with my peers?

But todays college graduates have a new question on their radar: Will I have to compete with artificial intelligence (AI), too?

In a word, no. I believe fears around AI wiping out jobs are largely overblown, but you cant fault new grads for worrying. A quick scan of tech news could lead anyone to believe that AI is the Grim Reaper of the job market. But attention-grabbing headlines dont paint the full picture; they overlook the historical impact of productivity-enhancing technology on a modern workforce.

Like every major technological advancement in the last 500 years, AI will change the way we live and work, but this isnt a bad thing. Todays nervous job seekers have no reason to panic; theres more room for AI to enhance their careers than to cause harm. To help new graduates ensure their value is fully appreciated, here are three best practices for them to keep in mind as they enter the workforce.

Collaborate; dont compete.

As humans, its natural to see other job candidates, or even co-workers, as potential competition. But when it comes to AI, job seekers should embrace the potential for AI to enhance their opportunities. The introduction of the calculator didnt eliminate the need for engineers; it made them more efficient, and AI will do the same.

In certain ways, humans will never stack up against AI; people cant crunch numbers and make data-driven predictions as fast as AI can, and it would be disheartening and beside the point to even try. But there are a number of uniquely human qualities creativity, innovation, compassion that employees can harness to add value where AI cant. The future of work isnt about trying to be better, faster or smarter than technology; its about working alongside it for better overall results.

I recently spoke with Tom Davenport, a leading expert on digital business, about the future of work for an episode of my companys podcast, The ConversAItion. As a longtime industry authority, hes had a front-row seat watching attitudes around AI and jobs evolve, moving from widespread alarm to a subsequent reality checkto todays relative calm. Despite shifting outlooks, hes never seen significant AI-driven layoffs.

Instead, modern businesses are committed to leveraging AI to augment existing jobs, rather than replace them. By combining smart humans and smart machines, both employees and technology can play to their strengths, giving businesses the best of both worlds.

Become a lifelong student.

Theres no reason for todays employees to panic, but its also no time to be complacent. With technologys rapid advancement, even a college education wont prepare them indefinitely. Its more important than ever for professionals to remain eager to learn long after the diplomas have been handed out.

New skill sets, even entire industries, are constantly popping up. Upskilling is paramount to collaborating with technology and keeping employees professional development on track. Its every employees responsibility to take control of their own learning and pursue training that will make them more valuable, but employers can be a major help here as well.

Shell, for example, just announced that it will enroll thousands of employees in online AI training to boost technical competency. As graduates begin the job hunt, its important to be on the lookout for companies with ongoing professional development programs to start a lifetime of learning on the right foot.

Embrace change; with upheaval comes new opportunity.

If you told someone 30 years ago that they could make a killing driving people around in their familys car or selling mattresses on the internet, they probably would have thought you were crazy. But with new technologies come new opportunities. In fact, the World Economic Forum estimated that AI could create 58 million jobs between 2018 and 2022 some of which we cant even begin to imagine yet.

New opportunities will arise within existing roles, too. As AI tackles the most monotonous and time-consuming tasks, employees will have more room to stretch. But whether theyre pursuing entirely new paths or shifting existing responsibilities, those who embrace change will be best positioned to reap the professional benefits of AI.

The bottom line is that AI is here to stay, and thats a good thing. While tomorrows workforce will certainly look different than it does today, new graduates will have the opportunity to work alongside AI from the very beginning. If they remain willing to collaborate with the technology and committed to ongoing learning, upcoming graduates can walk fearlessly into the job fair knowing they have what it takes to add value in the age of AI.

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Don't Be Afraid Of The New Kid At The Job Fair: Artificial Intelligence - Forbes

The Artificial Intelligence (AI) tool has been shown to accurately predict which patients that have been newly infected with the COVID-19 virus would go on to develop severe respiratory disease.

Named SARS-CoV-2, the new novel coronavirus, as of March 30, had infected 735,560 patients worldwide. According to the World Health Organization, the illness has caused more than 34,830 deaths to date, more often among older patients with underlying health conditions.

The study, published in the journalComputers, Materials & Continua, was led by NYU Grossman School of Medicine and the Courant Institute of Mathematical Sciences at New York University, in partnership with Wenzhou Central Hospital and Cangnan Peoples Hospital, both in Wenzhou, China.

The study has revealed the best indicators of future severity and found that they were not as expected.

Corresponding author Megan Coffee, clinical assistant professor in the Division of Infectious Disease & Immunology at NYU Grossman School of Medicine, said: While work remains to further validate our model, it holds promise as another tool to predict the patients most vulnerable to the virus, but only in support of physicians hard-won clinical experience in treating viral infections.

Our goal was to design and deploy a decision-support tool using AI capabilities mostly predictive analytics to flag future clinical coronavirus severity, says co-author Anasse Bari, PhD, a clinical assistant professor in Computer Science at the Courant institute. We hope that the tool, when fully developed, will be useful to physicians as they assess which moderately ill patients really need beds, and who can safely go home, with hospital resources stretched thin.

For the study, demographic, laboratory, and radiological findings were collected from 53 patients as each tested positive in January 2020 for COVID-19 at the two Chinese hospitals. In a minority of patients, severe symptoms developed with a week, including pneumonia.

The researchers wanted to find out whether AI techniques could help to accurately predict which patients with the virus would go on to develop Acute Respiratory Distress Syndrome or ARDS, the fluid build-up in the lungs that can be fatal in the elderly.

To do this they designed computer models that make decisions based on the data fed into them, with programmes getting smarter the more data they consider. Specifically, the current study used decision trees that track series of decisions between options, and that model the potential consequences of choices at each step in a pathway.

The AI tool found that changes in three features levels of the liver enzyme alanine aminotransferase (ALT), reported myalgia, and haemoglobin levels were most accurately predictive of subsequent, severe disease. Together with other factors, the team reported being able to predict risk of ARDS with up to 80% accuracy.

ALT levels, which rise dramatically as diseases like hepatitis damage the liver, were only a bit higher in patients with COVID-19, but still featured prominently in prediction of severity. In addition, deep muscle aches (myalgia) were also more commonplace and have been linked by past research to higher general inflammation in the body.

Lastly, higher levels of haemoglobin, the iron-containing protein that enables blood cells to carry oxygen to bodily tissues, were also linked to later respiratory distress. Could this be explained by other factors, like unreported smoking of tobacco, which has long been linked to increased haemoglobin levels?

Of the 33 patients at Wenzhou Central Hospital interviewed on smoking status, the two who reported having smoked, also reported that they had quit.

Limitations of the study, say the authors, included the relatively small data set and the limited clinical severity of disease in the population studied.

I will be paying more attention in my clinical practice to our data points, watching patients closer if they for instance complain of severe myalgia, adds Coffee. Its exciting to be able to share data with the field in real time when it can be useful. In all past epidemics, journal papers only published well after the infections had waned.

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How Artificial Intelligence is helping the fight against COVID-19 - Health Europa

When Machines Design: Artificial Intelligence and the Future of Aesthetics

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Are machines capable of design? Though a persistent question, it is one that increasingly accompanies discussions on architecture and the future of artificial intelligence. But what exactly is AI today? As we discover more about machine learning and generative design, we begin to see that these forms of "intelligence" extend beyond repetitive tasks and simulated operations. They've come to encompass cultural production, and in turn, design itself.

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When artificial intelligence was envisioned during thethe 1950s-60s, thegoal was to teach a computer to perform a range of cognitive tasks and operations, similar to a human mind. Fast forward half a century, andAIis shaping our aesthetic choices, with automated algorithms suggesting what we should see, read, and listen to. It helps us make aesthetic decisions when we create media, from movie trailers and music albums to product and web designs. We have already felt some of the cultural effects of AI adoption, even if we aren't aware of it.

As educator and theorist Lev Manovich has explained, computers perform endless intelligent operations. "Your smartphones keyboard gradually adapts to your typing style. Your phone may also monitor your usage of apps and adjust their work in the background to save battery. Your map app automatically calculates the fastest route, taking into account traffic conditions. There are thousands of intelligent, but not very glamorous, operations at work in phones, computers, web servers, and other parts of the IT universe."More broadly, it's useful to turn the discussion towards aesthetics and how these advancements relate to art, beauty and taste.

Usually defined as a set of "principles concerned with the nature and appreciation of beauty, aesthetics depend on who you are talking to. In 2018, Marcus Endicott described how, from the perspective of engineering, the traditional definition of aesthetics in computing could be termed "structural, such as an elegant proof, or beautiful diagram." A broader definition may include more abstract qualities of form and symmetry that "enhance pleasure and creative expression." In turn, as machine learning is gradually becoming more widely adopted, it is leading to what Marcus Endicott termed a neural aesthetic. This can be seen in recent artistic hacks, such as Deepdream, NeuralTalk, and Stylenet.

Beyond these adaptive processes, there are other ways AI shapes cultural creation. Artificial intelligence hasrecently made rapid advances in the computation of art, music, poetry, and lifestyle. Manovich explains that AIhas given us the option to automate our aesthetic choices (via recommendation engines), as well as assist in certain areas of aesthetic production such as consumer photography and automate experiences like the ads we see online. "Its use of helping to design fashion items, logos, music, TV commercials, and works in other areas of culture is already growing." But, as he concludes, human experts usually make the final decisions based on ideas and media generated by AI. And yes, the human vs. robot debate rages on.

According to The Economist, 47% of the work done by humans will have been replaced by robots by 2037, even those traditionally associated with university education. The World Economic Forum estimated that between 2015 and 2020, 7.1 million jobs will be lost around the world, as "artificial intelligence, robotics, nanotechnology and other socio-economic factors replace the need for human employees." Artificial intelligence is already changing the way architecture is practiced, whether or not we believe it may replace us. As AI is augmenting design, architects are working to explore the future of aesthetics and how we can improve the design process.

In a tech report on artificial intelligence, Building Design + Construction explored how Arup had applied a neural network to a light rail design and reduced the number of utility clashes by over 90%, saving nearly 800 hours of engineering. In the same vein, the areas of site and social research that utilize artificial intelligence have been extensively covered, and examples are generated almost daily. We know that machine-driven procedures can dramatically improve the efficiency of construction and operations, like by increasing energy performance and decreasing fabrication time and costs. The neural network application from Arup extends to this design decision-making. But the central question comes back to aesthetics and style.

Designer and Fulbright fellow Stanislas Chaillou recently created a project at Harvard utilizing machine learning to explore the future of generative design, bias and architectural style. While studying AI and its potential integration into architectural practice, Chaillou built an entire generation methodology using Generative Adversarial Neural Networks (GANs). Chaillou's project investigates the future of AI through architectural style learning, and his work illustrates the profound impact of style on the composition of floor plans.

As Chaillou summarizes, architectural styles carry implicit mechanics of space, and there are spatial consequences to choosing a given style over another. In his words, style is not an ancillary, superficial or decorative addendum; it is at the core of the composition.

Artificial intelligence and machine learningare becomingincreasingly more important as they shape our future. If machines can begin to understand and affect our perceptions of beauty, we should work to find better ways to implement these tools and processes in the design process.

Architect and researcher Valentin Soana once stated that the digital in architectural design enables new systems where architectural processes can emerge through "close collaboration between humans and machines; where technologies are used to extend capabilities and augment design and construction processes." As machines learn to design, we should work with AI to enrich our practices through aesthetic and creative ideation.More than productivity gains, we can rethink the way we live, and in turn, how to shape the built environment.

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When Machines Design: Artificial Intelligence and the Future of Aesthetics - ArchDaily

London, April 07, 2020 (GLOBE NEWSWIRE) -- According to a new market research report Automotive Artificial Intelligence (AI) Market by Component (Hardware, Software), Technology (Machine Learning, Computer Vision), Process (Signal Recognition, Image Recognition) and Application (Semi-Autonomous Driving) - Global Forecast to 2027, published by Meticulous Research, the automotive artificial intelligence market is expected to grow at a CAGR of 39.8% from 2019 to 2027 to reach $15.9 billion by 2027.

Various well-established automotive organizations across the globe are increasingly struggling with rising cost of operations, dissatisfied customers, declining sales, and unidentified competition. Therefore, the adoption of artificial intelligence technologies in the automotive sector is on the rise in order to create new opportunities and enhance operational capabilities by leveraging new possibilities, speeding up processes, and making organizations flexible to changes in the future.

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Realizing the fact, various organizations are investing heavily in order to reap profits in highly dynamic and competitive market environment. The organizations are aggressively adopting AI-based solutions and services to reshape their business processes and increase profitability. The rapid adoption of advanced automotive solutions, increasing demand for autonomous vehicles, and widening implementation of computer vision technologies across vehicles are the key factors driving steady growth in the global automotive artificial intelligence market.

Impact of COVID-19 on Global AI in Automotive Market

The global spread of COVID-19 has resulted in stalled automotive production across Europe and America. The scenario in Asia appears to be improving but the production capacities of leading automobile manufacturers have taken a hit due to lower demand and reduced consumer spending. With the current situation expected to persist until the global numbers recede, the global automobile industry may be looking at a recovery period of at least 3-6 months due to disrupted supply chain, economic loss, and reduced consumer confidence in the market.

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However, if the reports by health experts are to be believed and the epidemic can be controlled by June 2020, the automotive industry can bounce back in terms of revenue growth during the latter half of 2020. This would enable AI technology suppliers to capitalize on market consolidation during Q2, 2020. It is also predicted that with better control of the COVID-19 situation and normalcy established, the global automakers could witness an upsurge in demand for advanced technology enabled vehicles and thereby drive the automotive AI market revenue 2021 onwards.

In recent years, the funding for development and implementation of artificial intelligence solutions for autonomous vehicles has increased significantly. For instance, in 2019, the U.K., government and industry launched an AI Sector Deal to boost technology potential, with 250 million ($324.1 million) budget for the development of connected and autonomous vehicles. By the end of 2020, the South Korean government has planned to expand the countrys production of automation systems and industrial robotics with investments worth $6.1 billion. In addition, the government of Korea will launch an Intelligent Robot Industry Development Strategy led by the Ministry of Trade, Industry, and Energy. Such initiatives are enabling the automotive sector, to develop intelligent solutions for autonomous vehicles through the incorporation of robotics and artificial intelligence technologies.

The automotive artificial intelligence market study presents historical market data in terms of value (2017 and 2018), current data (2019), and forecasts for 2027 by component, technology, process, applications, and geography.

Based on component, the software segment is estimated to account for the largest share of the overall automotive artificial intelligence market in 2019. The large share of this segment is attributed to the decrease in the cost of cloud-enabling technology deployment, growing usage of learning analytics and awareness towards cloud computing, and increasing demand for automated solutions to meet changing business needs in the automotive sector. On the other hand, the services segment is slated to grow at the fastest CAGR during the forecast period, due to the demand for AI-capable services in autonomous vehicles and a rise in the adoption of cloud-based solutions.

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Based on technology, the machine learning segment is estimated to account for the largest share of the overall automotive artificial intelligence market, in 2019. This is primarily due to the growing demand for AI-based intelligent solutions, increasing government initiatives, and the ability of AI solutions to efficiently handle and analyze big data and quickly scan, analyze, and react to anomalies. On the other hand, computer vision technology is slated to grow at the fastest CAGR during the forecast period, due to a widespread implementation of computer vision in autonomous vehicles for signal recognition, image recognition, driver monitoring, spotting suspicious objects, and avoid vehicle collision.

Based on the process, signal recognition is estimated to hold the largest share of the overall automotive artificial intelligence market, in 2019. This is driven by the growing demand for autonomous vehicles, and safety controls implemented to enable driver assistance systems in the automotive sector. On the other hand, the demand for AI solutions and services for the image recognition process is slated to grow at the fastest CAGR during the forecast period, owing to its ability to enhance and automate operations, enrich user experience, and growing need for security solutions.

Based on the application, the human-machine interface is estimated to hold the largest share of the overall automotive artificial intelligence market, in 2019. Increasing demand for enhancing the in-car user experience, growth in terms of smart IoT devices, and proliferation of internet infrastructure, are some key factors contributing to growth of human-machine interface segment. On the other hand, semi-autonomous driving application is slated to grow at the fastest CAGR during the forecast period, mainly due to rising preference of consumers to adopt semi-autonomous mode of driving.

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Geographically, North America commanded the largest share of the the global automotive AI market in 2019. The large share of this region is mainly attributed to factors such as presence of several developed economies, such as the United States and Canada; the presence of leading automotive players; demand for enhanced user experience; growing adoption of autonomous vehicles; and availability of high-end infrastructure. However, Asia Pacific region is predicted to challenge the dominance of North America in the overall automotive artificial intelligence market in the near future.

The report also includes an extensive assessment of the key strategic developments adopted by leading market participants in the industry over the past 4 years (2016-2019). The automotive artificial intelligence market has witnessed a number of partnerships & agreements in recent years. For instance, in 2019, Intel Corporation collaborated with Mobileye and Great Wall Motors to explore the development of highly autonomous systems. This collaboration will further push computing, safety and mapping to develop future L2+ semi-autonomous capabilities. Also, in 2018, Microsoft Corporation partnered with Volkswagen to create a new automotive cloud. This will enable Volkswagen to deliver new Azure-based connected vehicle services.

The global automotive artificial intelligence market is highly fragmented with the presence of key players, such as 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.), Uber Technologies, 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.) along with several local and regional players.

Browse In-depth Summary of This Research Report:https://www.meticulousresearch.com/product/automotive-artificial-intelligence-market-4996/

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 (AI) Market Worth $15.9 Billion by 2027 - Exclusive Report by Meticulous Research - Yahoo Finance

By publishing the White Paper[1] in February, the European Union opened a public consultation (due to expire on May 19, 2020) about the Artificial Intelligence in order to assess the possibility of adopting a common regulatory approach to overcome nationalism and partial visions of the issue. According to the Commission, the development of common legislation on Artificial Intelligence would enable businesses and citizens of the Union to face more consciously the most current social challenges such as the fight against climate change, challenges related to sustainability and demographic change, the protection of democracies and the fight against crime. However, will these issues still be perceived in the same manner to be topical following the advent of the health emergency resulting from the spread of the COVID-19 virus?

Artificial Intelligence: definition and status of the EU legislation

As part of the definition of a common strategy for the digital single market, the European Union began to take a more active interest in Artificial Intelligence (AI) in 2017, when the European Council recognized the issue as urgent and rapidly evolving, inviting the European Commission to lay the foundations for a common legislation. According to the Council, it was necessary to approach AI in a uniform manner throughout the Union "while ensuring a high level of data protection, digital rights and ethical standards[2]".

Hence, the Commission responded to the Council's invitation and on April 25, 2018[3] published a Communication to the other European institutions concerned in which it made public the IA strategy that would lead to an exponential increase in the Union's resources allocated to the development of IA projects (almost 2.5 billion euro between 2018 and 2020, in addition to private investment and an economic support plan for the decade 2020-2030), also with a view to making IA-based technologies as accessible as possible to citizens and businesses. This initiative is clearly aimed at competing with other powers on the globe (USA, China, Japan and Canada) in the technology race to exploit the opportunities offered by the "home" development of AI.

In short, the European industry cannot miss the train, borrowing the Commission's words. This was reiterated by the Commission itself in its subsequent Communication of December 7, 2018 in which it set out its coordinated plan on IA[4].

Setting this apart, the Commissions Communication of April 25, 2018 contains a (although imprecise) definition of AI, which was absent until then in the legislative texts of the Union. Scholars and technicians have provided partially different definitions over the years, but no agreement was reached on one specific definition[5]. In the Communication, the Commission defined IA as "systems that show intelligent behaviour by analysing their environment and taking actions, with a certain degree of autonomy, to achieve specific objectives[6]". This definition was then further elaborated by the so-called Independent High Level Expert Group on Artificial Intelligence[7] which, in its report "Ethical Guidelines for Reliable AI", specified the notion of artificial intelligence as follows: "Software systems (and possibly hardware) designed by humans that, given a complex objective, act in the physical or digital dimension by perceiving their environment through data acquisition, interpreting the structured or unstructured data collected, reasoning on knowledge or processing the information derived from this data and deciding the best actions to be taken to achieve the given objective". To this definition it was added that "AI systems can use symbolic rules or learn a numerical model, and can also adapt their behavior by analyzing the effects that their previous actions have had on the environment".

White Paper on IA, pillars to promote common regulation

It is clear that the adoption of a common regulation desired by the Council in 2017 on AI has been abruptly accelerated by the publication of the White Paper on AI by the Commission on last February 19th. The public consultation launched by the Commission will expire on May 19, 2020, date in which proposals and comments on how to further boost AI research and development, improve the development of AI knowledge by European small and medium-sized enterprises and provide the essential elements for a legislative framework on AI can be received by the Commission itself. In the Commission's view, AI systems can help the Union to address current social challenges such as the fight against climate change, challenges related to sustainability and demographic change, the protection of democracies and the fight against crime. All this without neglecting respect for fundamental human rights, such as human dignity and the protection of individuals' privacy.

The Commission therefore intends to approach the AI issue through common legislation based on the development of the European economy by exploiting the large amount of data available through trustworthy AI systems that can guarantee fundamental human rights. The economic system of excellence that the Commission intends to develop is based on rationalization of research, on the promotion of collaboration between Member States (as well as between the public and private sectors), increasing investment in the development and dissemination of AI.

In order to make IA systems reliable, the Commission has confirmed the Group of Experts view that any development of reliable IA cannot be without ensuring the following seven key pillars:

(i) human contribution and supervision; (ii) technical soundness and security; (iii) privacy and data governance; (iv) transparency; (v) diversity, non-discrimination and fairness; (vi) social and environmental well-being; (vii) accountability.

This is obviously to prevent the development of AI-based systems from having distorting effects. Indeed, the Commission is well aware that AI systems, through the automation of activities that were previously the exclusive competence of humans, may threaten the privacy of citizens, infringe their right of expression, the principle of non-discrimination and the self-realization of individuals (just to mention some of the risks).

However, the fact that AI systems are able to process an immense amount of data through automated algorithmic processes (predetermined by humans) can be a valuable help to counter the spread of the COVID-19 virus in the current health emergency situation, leading individual Member States to "run alone" on the issue of the medical emergency.

Medical AI systems COVID 19 case

It is no surprise that, in the medical field, the most advanced diagnostic systems are able to process by themselves and in real time a myriad of data from the Internet, public administrations and other networked health facilities on their own. This has been happening for a long time in this area, so much so that it is common to talk about the Internet of Medical Things[8]. Obviously there are many ethical problems underlying the use of AI in the medical field both as reliability of the data processed (acquiring patient data from the Internet or other databases if not sufficiently validated could lead to incorrect diagnoses) and as objectives pursued (AI could pursue unethical objectives such as driving towards medical practices that meet administrative objectives but not the real quality of care)[9].

The White Paper sets the whole health care sector as an example for a high-risk area that should be taken into account in any future regulation.

In the situation now described, the health emergency resulting from the COVID 19 infection, which - with a shocking effect - brought to light two crucial issues: the first concerns the new balance between the medical field and fundamental rights in the emergency health context, while the second concerns the real possibility of continuing, and how, with collaboration between Member States in the European context.

In clear contrast to the indications of principle set out in the White Paper, in fact, in the current health emergency to combat COVID-19 there is a progressive complication of the ways of balancing the pillars of ethics and the protection of fundamental human rights with the objectives of protecting the right to health, precisely in that high-risk field identified in the White Paper. This could mean, on the one hand, that the path of regulation based on ethical principles indicated by the Commission will be subject to harsh criticism for failing to take account of what was already happening in the medical field in relation to AI. In particular, the Commission makes no mention of the necessary balance between fundamental human rights (such as privacy) and the necessary applicability of AI systems, which necessarily have to sacrifice citizens' privacy in order to protect public health. On the other hand, we are already seeing clear positions taken by individual Member States in favor of the applicability of AI in the medical field, with the consequent sacrifice of fundamental human rights as set out in the White Paper, such as privacy.

To confirm this, see the discussion underway in Italy where, among other mandatory measures to be taken, the possibility of using a smartphone application capable of tracking citizens' movements to stop the COVID-19 epidemic is being considered. Obviously, the privacys breach connected to the use of the aforementioned application is clear. Even more clear is the use of AI capable of processing the data received by providing a diagnosis in a short time to contain and fight the pandemic without citizens being able (perhaps) to oppose the tracking of their personal and medical data. In addition to the tracking via smartphone application, the Ministry of Technological Innovation and Digitization, the Ministry of Economic Development and the Ministry of University and Research have published an invitation to research centers and innovative companies to provide a contribution in the field of devices for prevention, diagnostics and monitoring to contain and combat the spread of the COVID-19 virus (including the possible use of a monitoring smartphone application) [10]. On this point, the approach of Antonello Soro, President of the Guarantor for the protection of personal data, appears wavering. On the one hand, in the first days of the emergency, he was in favor of using the app in question if such a system of data collection and processing, even if invasive, is in any case aimed at the general interest of health protection[11]. On the other hand, recently, the Guarantor has expressed an opinion that would appear to be different, arguing that the use of apps of this type can only take place on a voluntary basis. Now, it would seem appropriate to approach the issue from a holistic perspective, asking whether or not the health emergency justifies the compression of fundamental rights. If this is the case (and it seems that Italian constitutionalists have expressed themselves in this sense), the compression of the right to privacy is no different from that of the right to freedom of movement, of association, and of work that citizens have already been suffering for several weeks, for the - at least allegedly - superior good of public health. It is obvious that the compression can only be justified to the extent that the health emergency actually exists, and that the two opposing demands must be constantly balanced, also from an evolutionary point of view. When the threat is greatest, more invasive measures can be justified; when the threat is least, these measures can become illegitimate. It is equally obvious that it will be necessary to ensure adequate control of the choices that in these days are made by governments, regional governors, other administrative authorities, with little or no involvement of Parliament and in the paralysis of the judicial system. Both these aspects must be rapidly restored, on pain of upsetting the fundamental characteristics of our society. It will be interesting to see what decisions will be taken by Italy and the other EU Member States. In fact, it is to be expected that the latter will undertake their own paths to implement AI systems in the medical field to counter the spread of COVID-19.

And here comes to light the second issue mentioned above, namely the disruptive effect that the COVID - 19 emergency is having on all the structures and principles of the European Union. Member States soon suspended some of these principles, first of all that of free movement of people and goods, blocking borders, deliveries of goods considered essential, and sometimes seizing such goods in transit to supply their own facilities. Also in the field of IA and apps for health control it is foreseeable that each Member State will move autonomously, with good peace of mind of the initiatives undertaken by the Community bodies, and highlighting how the whole system must now be rethought.

A fundamental question therefore remains to be asked: will the fundamental pillars - also in terms of AI - developed by the European Union stand up to the challenge of the health emergency that has upset Europe?

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Artificial intelligence, European Union in balance between common approach and nationalism - Lexology