If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Over the past several years, commercial use of biometric data has become increasingly prevalent. In response, several states have adopted biometric data privacy legislation. Consequently, companies that rely on biometric data face new regulatory risks, in addition to increased legal exposure to individual and class action lawsuits. In fact, the Ninth Circuit Court of Appeals recently affirmed certification of a class action alleging Facebook's face-scanning practices violate Illinois' biometric privacy law, finding that the class alleged sufficiently concrete injuries based on Facebook's alleged use of facial recognition technology without users' consent to establish standing. Insurance policies currently available on the market, including cyber insurance policies, may not adequately cover these risks.
SANTA CLARA, CA – Micro Focus (LSE: MCRO; NYSE: MFGP) today announced the general availability of Service Management Automation X (SMAX) 2019.05. SMAX is the first application suite for Enterprise Service Management and IT Service Management built on machine learning and analytics, powered by an embedded CMDB and Discovery to help drive down costs and speed up time to resolution. Built-in best practices are quickly and easily configured and extended in an entirely codeless way with the SMAX Studio enabling customers to achieve faster time to value. The scalable, multi-tenant cloud-native solution delivers significantly lower cost of ownership and enables customers to deploy on their choice of public or private cloud. SMAX is also available as-a-service by Micro Focus partners worldwide.
Difficulties in explaining machine learning (ML) models is causing concern as banks look to the technology for default risk analysis, according to market participants. "Many different types of'black-box' models have been developed out there even by banks claiming that they can accurately predict mortgage defaults. This is only partially true," said Panos Skliamis, chief executive officer at SPIN Analytics in an email. "[These models] usually target a relatively short-term horizon and their validation windows of testing remain actually in an environment too similar to that of the development samples. However, mortgage loans are almost always long-term and their lives extend to multiple economic cycles, while the entire world changes over time and several features of ML models severely influenced by these changes of the environment," he said.
Most financial institutions know it's critical to manage the ever-increasing amounts of accessible data, but many miss the potential in using that data in innovative ways. Financial institutions have a plethora of data they can access, either through their own systems or through public sources. However, many can't -- or won't -- exploit the large volumes of data, particularly the "owned" data that an organization holds about customers. This kind of data is typically called customer relationship management data, such as the purchase history tied to app installs, email addresses and postal addresses. Though financial institutions maintain and collect massive volumes of data, many firms are restricted from fully using that data because they are required to comply with stringent regulations around what can and cannot be done with customer data.
It's time for city administrations and local employers to close AI-related skills gaps. This article is part of an MIT SMR initiative exploring how technology is reshaping the practice of management. While there is much discussion of how artificial intelligence will continue to transform industries and organizations, a key driver of AI's role in the global economy will be cities. How cities deal with coming changes will determine which ones will thrive in the future. Many cities have plans to become "smart cities" armed with AI-driven processes and services, like AI-based traffic control systems, to improve residents' lives.
Should Elon Musk's robot-surgeon start inserting electrodes into human brains to connect humans and computers via a high-bandwidth brain-machine? What exactly are the implications for medical insurance? Should a self-driving flying taxi crash and kill civilians? These are the questions our CEO, Lizé Lambrechts, is asking. The insurance industry is developing new ways to assess and underwrite risk as artificial intelligence (AI) and automation advance.
Specialty re/insurer Canopius has partnered with technology start-up Arturo on artificial intelligence (AI) and deep-learning property analytics. The integration of Arturo's AI-powered technology will enable Canopius to gain access to the physical property characteristic and predictive analytics using the latest satellite, aerial, and ground-level data. As a result, Canopius will be able to make more informed and differentiated pricing decisions at the point of underwriting. Canopius chief digital officer Marek Shafer said: "Arturo's, AI-powered image analytics capability is hugely impressive. Canopius is excited to be harnessing this pioneering technology, which will help to fine-tune our risk selection process and improve point-of-sale underwriting."
As technology, including robots, artificial intelligence, machine learning, and other forces change the nature of work, employees will need new skills to adapt to shifting roles. Research firm Gartner predicts that employees who regularly update their skill sets and invest in new training will be more valued than those with experience or tenure. But it's not going to be easy. The World Economic Forum's "Future of Jobs 2018" report estimates that, by 2022, more than half (54%) of employees will require significant skills updating or retraining. More than one-third (35%) will need about six months to get up to speed, while nearly one in five will require a year or more of additional training.
What I saw didn't look very much like the future -- or at least the automated one you might imagine. The offices could have been call centers or payment processing centers. One was a timeworn former apartment building in the middle of a low-income residential neighborhood in western Kolkata that teemed with pedestrians, auto rickshaws and street vendors. In facilities like the one I visited in Bhubaneswar and in other cities in India, China, Nepal, the Philippines, East Africa and the United States, tens of thousands of office workers are punching a clock while they teach the machines. Tens of thousands more workers, independent contractors usually working in their homes, also annotate data through crowdsourcing services like Amazon Mechanical Turk, which lets anyone distribute digital tasks to independent workers in the United States and other countries.