As global technology has evolved over the years, we have moved from television to the internet, and today we are smoothly and gradually adapting Artificial Intelligence. The term AI was first coined by John McCarthy in 1956. It involves a lot of the main things ranging from process automation of robotics to the actual process of robotics. It has become highly popular among large enterprises today owing to the amount of data these companies are dealing in. Increase in the demand for understanding the data patterns has led to the growth in demand of AI.
Artificial intelligence technology is about collecting vast amounts of data from devices around the world and making sense out of it. Data is only useful, though, if you can do something with it. Artificial intelligence is how companies can analyze and learn from massive amounts of data to make their companies operate with greater efficiency and to offer their customers a better user experience. Machine learning in insurance and artificial intelligence in manufacturing and banking is already proving its worth as financial institutions are using this technology for fraud detection. There is no industry that can't benefit from implementing artificial intelligence; the three industries we discuss in our video, "How Can Companies Better Use AI Technology," include the financial, manufacturing and insurance sectors.
Bottom Line: Barclays' and Kount's co-developed new product, Barclays Transact reflects the future of how companies will innovate together to apply AI-based fraud prevention to the many payment challenges merchants face today. Merchant payment providers have seen the severity, scope, and speed of fraud attacks increase exponentially this year. Account takeovers, card-not-present fraud, SMS spoofing, and phishing are just a few of the many techniques cybercriminals are using to defraud merchants out of millions of dollars. But it doesn't have to be a choice between security and a frictionless transaction. Frustrated by the limitations of existing fraud prevention systems, many payment providers are working as fast as they can to pilot AI- and machine-learning-based applications and platforms.
When will artificial intelligence really have'arrived'? For a long time, this was a question for philosophers and computer scientists, pondering over whether passing the Turing test truly indicates intelligence, or debating about how broad our definition of artificial intelligence should be. Over the last several years, however, this question has changed considerably: with the advent of consumer AI tools such as virtual assistants and the increasing availability of off-the-shelf solutions offering to bring the power of AI to business operations, the issue has become less philosophical, and much more pragmatic. Now, for business leaders, it is often a matter not of whether to respond to the arrival of AI, but of how to respond to the arrival of AI. The promise is, of course, huge.
Finance is one of the most conservative sectors and one of the latest technology adopters for a good reason: it requires stability, predictability, and risk hedging. Even if there are significant opportunities presented by technology, financial companies will be lagging behind because they should wait for a technology to enter its maturity stage as well as for somebody else to test it first and absorb associated risks. However, once a technology is stable enough and has gained mass adoption among businesses, it becomes appealing for the finance sector. This is the case of machine learning (ML), which has ceased to be just a buzz word now. SEE ALSO: Meet Manifold: Uber's machine learning model debugging tool goes open source Machine learning uses statistics to recognize patterns and associate them with entities or cases.
Early this year, I detailed that Intel (INTC) was poised to lead the AI revolution over the coming decade. The widespread adoption of AI will contribute significantly to demand for (Intel) compute silicon, and hence, will be a growth driver for the company. Intel forecasts it will be about a $25 billion opportunity by 2025, compared to $3.8 billion revenue in 2019. On June 18, Intel launched its third-generation Xeon Scalable platform, codenamed Cooper Lake. This follows a bit over a year after the company's April 2019 data-centric portfolio launch, which included second-generation Cascade Lake, the 10nm Agilex FPGA and 800 series of 100G Ethernet adapters.
AI is making strides at many levels in the world of investment management. Investors may already be riding the wave of artificial intelligence, unaware of the many ways they've been integrated. There are three main levels where AI is making a mark, says Amit Gupta, a managing director in Accenture's capital market industry group. At the first level, firms are using AI in back-office administrative tasks like net asset value calculations, reconciliation, settlement operations. At the second level, they use it in front-office tasks like client targeting and management, profiling of clients, personalization of service.
Data is growing by leaps and bounds, the convergence of extremely large data sets both structured and unstructured define Big Data. The increasing awareness of the Internet of Things (IoT) devices among organizations and volume, variety, velocity and veracity at which data is generated have caught the attention of the enterprise in a bid to enhance digital technologies and guide digital transformation. Analytics Insights eliminates that the big data market size will grow at a CAGR of 10.9%, globally from US$ 193.5 billion in 2020 to US$ 301.5 billion by 2023. This region is witnessing significant developments in the big data market gaining remarkable traction in the BFSI industry vertical. Numerai is the world's first hedge fund, to predict the stock market.
"Coworking and flexible Office is one of the most dynamic asset classes in commercial real estate right now, and we think Cherre is the right partner to help institutional owners, investors, and other stakeholders see the past, present and future of this asset class," said Ben Wright, Founder and CEO of Upsuite. Cherre seamlessly connects disparate real estate data into a single-source of truth, empowering companies to instantly explore all their connected data. Cherre has the largest real estate knowledge graph in the world and enables customers to uncover granular insights, automate workflows, and build models and visualizations. "Property owners need comprehensive data to make more informed investment and business decisions," said L.D. Salmanson, CEO and Co-Founder of Cherre. "Analyzing coworking and flex space data alongside other connected data sources will enable better trend and market analysis for decision making."