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Revolution Of Artificial Intelligence
Artificial is the technology which makes the machine to understand the real environment
The best example of AI is a smartphone camera the AI support camera provide us huge types
Of effects and AI tells the system that what thing was front of the camera its was a dog, cat or human
By understanding this. camera adjust their quality and we capture a nice image. Now in current period this technology develops too much like it comes in every machine whatever it was like smartphone,
Bulbs , television etc.
Advances in artificial intelligence (AI)—and the millions of data points created by the Internet of Things—are starting to change the nature of this tradeoff, particularly where trust is part of the product or service. As AI systems learn more, they can be trained to suggest next best actions, automate some repetitive tasks and minimize the greatest risk: human error. With the growing intelligence of edge devices, capable of making real-time and near-real-time determinations, security can be built into every transaction. Here’s how AI is revolutionizing security in three industries.
Gig Economy:
Building trust is a particular challenge in a business model that relies on doing business with strangers. Uber, the ride-hailing company that matches drivers with passengers, is constantly developing and testing new ways to prevent fraud and reduce risk.
“Our philosophy is no strangers,” explains Kate Parker, head of trust and safety initiatives at Uber. “So we designed our platform to introduce drivers and riders right away, to promote comfort on both sides.” Because Uber’s business model is built on speed as well as safety, the company needed a tool that didn’t make drivers and riders feel like they were waiting in the airport security line. So, to safeguard against fraud and enhance both driver and rider peace of mind, Uber uses an intelligent facial recognition technology to help ensure the driver using the Uber app matches the account on file. Cognitive capabilities ensure that the extra verification step is fast, works on all smartphones and in dim light, and scales to more than 1 million drivers.
Uber is not the only firm using facial recognition. “This is becoming a standard sign-in method in many industries, such as financial services,” says Dima Kovalev, product manager at Uber. “Your face is your new password.”
Financial Services
Mobile and online access has created amazing opportunities for banks, insurance companies and wealth managers to engage with customers anywhere, anytime. But these same capabilities have also sparked incredible innovation on the part of bad actors. New AI capabilities promise to help refine the ways financial firms confirm identity and build risk models as well as detect fraud and money laundering. These capabilities can also help solve age-old problems, such as privacy protection, regulatory compliance and credit-risk assessment.
HDFC Bank in India, for example, deploys machine learning to build score cards for loan applications where credit history may be thin. By using demographic, geographic and other data to augment loan applications, HDFC Bank analysts are able to do faster, more accurate credit analyses. This helps the bank identify the best loan applicants and manage its own risk better.
“We can process a lot more unstructured data, build sharper risk models and make better decisions faster,” says Bharath Shasthri, HDFC’s head of advanced analytics for the risk analytics unit. “Ultimately, that helps us acquire more new customers—and make sure they are the right customers.”
Cybercrime, meanwhile, costs organizations, governments and individuals an estimated $600 billion annually, primarily by targeting banks, according to a joint report by the Center for Strategic and International Studies (CSIS), a, nonprofit policy research organization, and McAfee, a cybersecurity firm. Deep learning algorithms are helping detect anomalies in real time by analyzing millions of payment transactions, and where it makes sense, behavioral and sentiment analysis. Money laundering—the leading source of compliance fines for North American and European institutions, according to Corporate Compliance Insights, a trade publication—is another area that has long frustrated financial institutions and authorities.
Current money-laundering detection techniques yield an overwhelming number of false positives, leading to alert fatigue. By deploying more sophisticated AI capabilities, financial institutions can reduce the number of false positives that must be followed with due diligence. There is more than paperwork and fines at stake. Only 1% of the estimated $3 trillion in illicit global financial flows are detected and seized by authorities.
Health care:
Nowhere is the dilemma of security versus access more apparent than with digital medical records. For practitioners and patients trying to navigate digital records, access can often feel elusive. Too many different systems require multiple logins, and too much information must be entered manually. Yet healthcare systems are notoriously insecure—especially through unprotected equipment and devices. Telemetry and AI can help create immediate accessibility where it’s needed, put relevant information into healthcare providers hands and reduce medical errors.
Imagine a hospital room where a doctor’s device can communicate with a patient’s identity bracelet and allow her to unlock the patient’s medical file on the spot. A system infused with cognitive capabilities can also bring up a selective array of relevant research, available treatments and accepted protocols based on the patient’s unique situation. When the doctor leaves the room, the visit is logged and the file is locked and saved until an authorized user calls it up again. When nursing staff make their rounds, sensors in the room provide real-time information about the patient’s condition and activity. Metadata from all interactions as well as institutional and sensor data, combined with patient outcomes, can then be used to improve hospital procedures and, in turn, train cognitive systems to make more intelligent and relevant suggestions to caregivers.
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