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By Associated Press

SEOUL: In a cramped office in eastern Seoul, Hwang Seungwon points a remote control toward a huge NASA-like overhead screen stretching across one of the walls.

With each flick of the control, a colourful array of pie charts, graphs and maps reveals the search habits of thousands of South Korean senior citizens being monitored by voice-enabled "smart"speakers, an experimental remote care service the company says is increasingly needed during the coronavirus crisis.

"We closely monitor for signs of danger, whether they are more frequently using search words that indicate rising states of loneliness or insecurity,"said Hwang, director of a social enterprise established by SK Telecom to handle the service.

Trigger words lead to a recommendation for a visit by local public health officials.

As South Korea's government pushes to allow businesses to access vast amounts of personal information and to ease restrictions holding back telemedicine, tech firms could potentially find much bigger markets for their artificial intelligence and other emerging technologies.

The drive, resisted for years by civil liberty advocates and medical professionals, has been reinvigorated by a technology-driven fight against COVID-19.

It has so far allowed South Korea to emerge as something of a coronavirus success story but also raised broader worries that privacy is being sacrificed for epidemiological gains.

Armed with an infectious disease law that was strengthened after a 2015 outbreak of a different coronavirus, MERS, health authorities have aggressively used credit-card records, surveillance videos and cellphone data to find and isolate potential virus carriers.

Locations where patients went before they were diagnosed are published on websites and released through cellphone alerts.

Smartphone tracking apps are used to monitor around 30,000 individuals quarantined at home.

Starting in June, entertainment venues will be required to register customers with smartphone QR codes so they could be easily located if needed.

But there's a dark side.

People here have often managed to trace back the online information to the unnamed virus carriers, exposing embarrassing personal details and making them targets of public contempt.

A low point came earlier this month when local media described some Seoul nightclubs linked to dozens of infections as catering to sexual minorities, triggering homophobic responses.

Officials reacted by expanding "anonymous testing,"which allowed people to provide only their phone numbers and not their names during tests.

There was a subsequent increase in tests.

The past months have exposed a stark division about the best ways to make important decisions when privacy concerns collide with public health needs, said Haksoo Ko, a Seoul National University law professor and co-director of the school's Artificial Intelligence Policy Initiative.

Around 3,200 people across the country, mostly older than 70 and living alone, have so far allowed the SK Telecom speakers to listen to them 24 hours a day since the service launched in April 2019.

The company expects users to at least double by the end of the year, judging by local government interest.

The technology has reduced human contact in welfare services while still providing governments with a tool to prevent elderly residents from dying alone.

That's especially needed in a country grappling with an aging population and high poverty rates among retirees.

The speakers are built with an artificial intelligence and a lamp that turns blue when processing voice commands for news, music and internet searches.

The devices can also use quizzes to monitor the memory and cognitive functions of their elderly users, which would be potentially useful for advising treatments.

But it's difficult for SK Telecom's clients to use the information without clear legal guidelines for handling health data on private networks.

Similar reasons may also impede domestic use of health technologies developed by Samsung Electronics, which recently received approval for a smartwatch application that monitors blood pressure.

Officials are preparing regulations for revised data laws that lawmakers passed in January after months of wrangling.

They aim to allow businesses greater freedom in collecting and analyzing anonymous personal data without seeking individual consent.

But activist Oh Byoung-il said the changes could bring excessive privacy infringements unless robust safeguards are installed.

Doctors' groups have also resisted government calls for legalising telemedicine, raising concerns related to data security and a negative impact on smaller hospitals.

Industrial benefits will be limited if officials can't find the right combination of techniques to process personal information so that it can't be used to identify individuals.

Health and government authorities have failed to do this during the pandemic.

In Seoul's Yangcheon district, officials are using SK Telecom's tech to monitor some 200 seniors who live alone.

"It's nice to have something to talk to,"said Lee Chang-geun, an 89-year-old who has lived alone in his small apartment since his wife died three years ago.

"But I wish they developed an Aria function for opening doors. What good is a distress signal if I die while emergency workers try to force open my door?"

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In coronavirus-hit South Korea, Artificial Intelligence takes over to monitor lonely elders - The New Indian Express

Business continuity amid the COVID-19 lockdown is a big issue for all companies. Firms are not just at risk of facing outages, but also face continuous data security vulnerabilities and cyber threats. As per a study by PwC, the volume of cyberattacks on Indian companies has gone exponential as cybercriminals utilise the new work paradigm brought about by the COVID-19 outbreak to infiltrate corporate networks and steal data.

With the lockdown around the world, employees are expected to continue working remotely, which is undoubtedly a threat to most companies as the network perimeter has expanded radically. In the new work setting, fraudsters are using fake emails, websites, and VPAs (Virtual Payment Address) for fraud and social engineering.

To understand the situation better, Analytics India Magazine connected with Bharat Panchal, Chief Risk Officer India, Middle-East & Africa, Fidelity National Information Services (FIS) a Fortune 500 company and a leading provider of technology solutions for merchants, banks and capital markets firms globally.

Bharat comes with extensive leadership experience in managing cyber threats. Prior to his current role at FIS, he served as the SVP & Head Risk Management & Compliance at National Payments Corporation Of India (NPCI), and previously was also the Vice President and Group InfoSec Audit Head at Kotak Mahindra Bank.

According to Bharat, to mitigate cyber threats and protect data, FIS is taking a comprehensive and multi-layered approach. We make use of advanced tools that include artificial intelligence to monitor and detect fraudulent transactions on a real-time basis, he said. The system continuously monitors various threat vectors and advises our customers to remain vigilant against such cyberattacks.

Here are the edited excerpts from the interaction:

With India under lockdown, organisations are increasingly allowing employees to work from home. However, as greater numbers of staff access sensitive data and process remotely, the possibility of a data breach, accidental data loss, virus or malware attack is a major risk for businesses across the country. The biggest risk is around accidental or unintentional leaks of sensitive information given the potential for reputation loss, customer claims and regulatory actions.

Cloud-based platforms are a key component to enabling business continuity during remote-working. The best line of defence for organisations looking to protect against platform vulnerabilities is ensuring employees are only using licensed platforms, a security-aware employee base, and the automatic deployment of all available security patches in a timely fashion.

The fraudsters are smart and try to find opportunity in every situation. In the current environment, fake emails for donations, emergency medical support, a charity for migrant labours, feeding to daily wagers etc. are rampant; people could easily be tricked into giving donations on those fake accounts possessed by fraudsters. The moratorium by RBI of EMI of any loan is a good attempt to ease the situation for the middle class. But, fraudsters have started making fake calls/messages to gullible customers asking for OTP to delay their EMIs and make use of pre-collected information about a customer to steal money from their account.

Fraudsters are using fake emails, websites, and VPAs (Virtual Payment Address) to solicit donations for a range of fraudulent matters ranging from emergency medical support, charity for migrant labourers, food for daily wagers, to fake hospitals, medicine, and people infected during the pandemic. Businesses can reduce these incidents by monitoring network traffic, transaction patterns, and user access habits. Companies can also reduce data security risks by restricting access to systems and emails for non-critical staff.

FIS takes a comprehensive and multi-layered approach to risk and security. We also make use of advanced tools including artificial intelligence to monitor and detect fraudulent transactions on a real-time basis. Our risk engine with Artificial intelligence is capable of predicting a probability of fraudulent transactions which helps our customers. We continuously monitor various threat vectors and our advice to our customers is to remain vigilant against such cyber-attacks.

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How FIS Is Using Artificial Intelligence To Monitor And Prevent Cyber Fraud - Analytics India Magazine

In September 1955, John McCarthy, a young assistant professor of mathematics at Dartmouth College, boldly proposed that "every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it."

McCarthy called this new field of study "artificial intelligence," and suggested that a two-month effort by a group of 10 scientists could make significant advances in developing machines that could "use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves."

At the time, scientists optimistically believed we would soon have thinking machines doing any work a human could do. Now, more than six decades later, advances in computer science and robotics have helped us automate many of the tasks that previously required the physical and cognitive labor of humans.

But true artificial intelligence, as McCarthy conceived it, continues to elude us.

A great challenge with artificial intelligence is that it's a broad term, and there's no clear agreement on its definition.

As mentioned, McCarthy proposed AI would solve problems the way humans do: "The ultimate effort is to make computer programs that can solve problems and achieve goals in the world as well as humans," McCarthy said.

Andrew Moore, Dean of Computer Science at Carnegie Mellon University, provided a more modern definition of the term in a 2017 interview with Forbes: "Artificial intelligence is the science and engineering of making computers behave in ways that, until recently, we thought required human intelligence."

But our understanding of "human intelligence" and our expectations of technology are constantly evolving. Zachary Lipton, the editor of Approximately Correct, describes the term AI as "aspirational, a moving target based on those capabilities that humans possess but which machines do not." In other words, the things we ask of AI change over time.

For instance, In the 1950s, scientists viewed chess and checkers as great challenges for artificial intelligence. But today, very few would consider chess-playing machines to be AI. Computers are already tackling much more complicated problems, including detecting cancer, driving cars, and processing voice commands.

The first generation of AI scientists and visionaries believed we would eventually be able to create human-level intelligence.

But several decades of AI research have shown that replicating the complex problem-solving and abstract thinking of the human brain is supremely difficult. For one thing, we humans are very good at generalizing knowledge and applying concepts we learn in one field to another. We can also make relatively reliable decisions based on intuition and with little information. Over the years, human-level AI has become known as artificial general intelligence (AGI) or strong AI.

The initial hype and excitement surrounding AI drew interest and funding from government agencies and large companies. But it soon became evident that contrary to early perceptions, human-level intelligence was not right around the corner, and scientists were hard-pressed to reproduce the most basic functionalities of the human mind. In the 1970s, unfulfilled promises and expectations eventually led to the "AI winter," a long period during which public interest and funding in AI dampened.

It took many years of innovation and a revolution in deep-learning technology to revive interest in AI. But even now, despite enormous advances in artificial intelligence, none of the current approaches to AI can solve problems in the same way the human mind does, and most experts believe AGI is at least decades away.

The flipside, narrow or weak AI doesn't aim to reproduce the functionality of the human brain, and instead focuses on optimizing a single task. Narrow AI has already found many real-world applications, such as recognizing faces, transforming audio to text, recommending videos on YouTube, and displaying personalized content in the Facebook News Feed.

Many scientists believe that we will eventually create AGI, but some have a dystopian vision of the age of thinking machines. In 2014, renowned English physicist Stephen Hawking described AI as an existential threat to mankind, warning that "full artificial intelligence could spell the end of the human race."

In 2015, Y Combinator President Sam Altman and Tesla CEO Elon Musk, two other believers in AGI, co-founded OpenAI, a nonprofit research lab that aims to create artificial general intelligence in a manner that benefits all of humankind. (Musk has since departed.)

Others believe that artificial general intelligence is a pointless goal. "We don't need to duplicate humans. That's why I focus on having tools to help us rather than duplicate what we already know how to do. We want humans and machines to partner and do something that they cannot do on their own," says Peter Norvig, Director of Research at Google.

Scientists such as Norvig believe that narrow AI can help automate repetitive and laborious tasks and help humans become more productive. For instance, doctors can use AI algorithms to examine X-ray scans at high speeds, allowing them to see more patients. Another example of narrow AI is fighting cyberthreats: Security analysts can use AI to find signals of data breaches in the gigabytes of data being transferred through their companies' networks.

Early AI-creation efforts were focused on transforming human knowledge and intelligence into static rules. Programmers had to meticulously write code (if-then statements) for every rule that defined the behavior of the AI. The advantage of rule-based AI, which later became known as "good old-fashioned artificial intelligence" (GOFAI), is that humans have full control over the design and behavior of the system they develop.

Rule-based AI is still very popular in fields where the rules are clearcut. One example is video games, in which developers want AI to deliver a predictable user experience.

The problem with GOFAI is that contrary to McCarthy's initial premise, we can't precisely describe every aspect of learning and behavior in ways that can be transformed into computer rules. For instance, defining logical rules for recognizing voices and imagesa complex feat that humans accomplish instinctivelyis one area where classic AI has historically struggled.

An alternative approach to creating artificial intelligence is machine learning. Instead of developing rules for AI manually, machine-learning engineers "train" their models by providing them with a massive amount of samples. The machine-learning algorithm analyzes and finds patterns in the training data, then develops its own behavior. For instance, a machine-learning model can train on large volumes of historical sales data for a company and then make sales forecasts.

Deep learning, a subset of machine learning, has become very popular in the past few years. It's especially good at processing unstructured data such as images, video, audio, and text documents. For instance, you can create a deep-learning image classifier and train it on millions of available labeled photos, such as the ImageNet dataset. The trained AI model will be able to recognize objects in images with accuracy that often surpasses humans. Advances in deep learning have pushed AI into many complicated and critical domains, such as medicine, self-driving cars, and education.

One of the challenges with deep-learning models is that they develop their own behavior based on training data, which makes them complex and opaque. Often, even deep-learning experts have a hard time explaining the decisions and inner workings of the AI models they create.

Here are some of the ways AI is bringing tremendous changes to different domains.

Self-driving cars: Advances in artificial intelligence have brought us very close to making the decades-long dream of autonomous driving a reality. AI algorithms are one of the main components that enable self-driving cars to make sense of their surroundings, taking in feeds from cameras installed around the vehicle and detecting objects such as roads, traffic signs, other cars, and people.

Digital assistants and smart speakers: Siri, Alexa, Cortana, and Google Assistant use artificial intelligence to transform spoken words to text and map the text to specific commands. AI helps digital assistants make sense of different nuances in spoken language and synthesize human-like voices.

Translation: For many decades, translating text between different languages was a pain point for computers. But deep learning has helped create a revolution in services such as Google Translate. To be clear, AI still has a long way to go before it masters human language, but so far, advances are spectacular.

Facial recognition: Facial recognition is one of the most popular applications of artificial intelligence. It has many uses, including unlocking your phone, paying with your face, and detecting intruders in your home. But the increasing availability of facial-recognition technology has also given rise to concerns regarding privacy, security, and civil liberties.

Medicine: From detecting skin cancer and analyzing X-rays and MRI scans to providing personalized health tips and managing entire healthcare systems, artificial intelligence is becoming a key enabler in healthcare and medicine. AI won't replace your doctor, but it could help to bring about better health services, especially in underprivileged areas, where AI-powered health assistants can take some of the load off the shoulders of the few general practitioners who have to serve large populations.

In our quest to crack the code of AI and create thinking machines, we've learned a lot about the meaning of intelligence and reasoning. And thanks to advances in AI, we are accomplishing tasks alongside our computers that were once considered the exclusive domain of the human brain.

Some of the emerging fields where AI is making inroads include music and arts, where AI algorithms are manifesting their own unique kind of creativity. There's also hope AI will help fight climate change, care for the elderly, and eventually create a utopian future where humans don't need to work at all.

There's also fear that AI will cause mass unemployment, disrupt the economic balance, trigger another world war, and eventually drive humans into slavery.

We still don't know which direction AI will take. But as the science and technology of artificial intelligence continues to improve at a steady pace, our expectations and definition of AI will shift, and what we consider AI today might become the mundane functions of tomorrow's computers.

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What Is Artificial Intelligence (AI)? | PCMag

Credit: N. Hanacek/NIST

Artificial Intelligence (AI) is rapidly transforming our world. Remarkable surges in AI capabilities have led to a number of innovations including autonomous vehicles and connected Internet of Things devices in our homes. AI is even contributing to the development of a brain-controlled robotic arm that can help a paralyzed person feel again through complex direct human-brain interfaces. These new AI-enabled systems are revolutionizing everything from commerce and healthcare to transportation and cybersecurity.

AI has the potential to impact nearly all aspects of our society, including our economy, but the development and use of the new technologies it brings are not without technical challenges and risks. AI must be developed in a trustworthy manner to ensure reliability, safety and accuracy.

NIST has a long-standing reputation for cultivating trust in technology by participating in the development of standards and metrics that strengthen measurement science and make technology more secure, usable, interoperable and reliable. This work is critical in the AI space to ensure public trust of rapidly evolving technologies, so that we can benefit from all that this field has to promise.

AI systems typically make decisions based on data-driven models created by machine learning, or the systems ability to detect and derive patterns. As the technology advances, we will need to develop rigorous scientific testing that ensures secure, trustworthy and safe AI. We also need to develop a broad spectrum of standards for AI data, performance, interoperability, usability, security and privacy.

NIST participates in interagency efforts to further innovation in AI. NIST Director and Undersecretary of Commerce for Standards and Technology Walter Copan serves on the White House Select Committee on Artificial Intelligence. Charles Romine, Director of NISTs Information Technology Laboratory, serves on the Machine Learning and AI Subcommittee.

A February 11, 2019,Executive Order on Maintaining American Leadership in Artificial Intelligence tasks NIST with developing a plan for Federal engagement in the development of technical standards and related tools in support of reliable, robust, and trustworthy systems that use AI technologies. For more information, see: https://www.nist.gov/topics/artificial-intelligence/ai-standards.

NIST research in AI is focused on how to measure and enhance the security and trustworthiness of AI systems. This includes participation in the development of international standards that ensure innovation, public trust and confidence in systems that use AI technologies. In addition, NIST is applying AI to measurement problems to gain deeper insight into the research itself as well as to better understand AIs capabilities and limitations.

The NIST AI program has two major goals:

The recently launched AI Visiting Fellowprogram brings nationally recognized leaders in AI and machine learning to NIST to share their knowledge and experience and to provide technical support.

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Artificial intelligence | NIST

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Opening Weekend USA: $29,352,630,1 July 2001

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Runtime: 146 min

Aspect Ratio: 1.85 : 1

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A.I. Artificial Intelligence (2001) - IMDb