Machine learning has advanced from the age of science fiction to a major component of modern enterprises, especially as businesses across almost all sectors use various machine learning technologies. As an example, the healthcare industry is utilizing machine learning business applications to achieve more accurate diagnoses and provide better treatment to their patients. Retailers also use machine learning to send the right goods and products to the right stores before it is out of stock. Medical researchers are also not left out when it comes to using machine learning as many introduce newer and more effective medicines with the help of this technology. Many use cases are emerging from all sectors as machine learning is being implemented in logistics, manufacturing, hospitality, travel and tourism, energy, and utilities.
A January survey from online travel company trivago showed 38% of Americans would give up sex for a year to travel right now. The other 62% appear to be actively hunting for love online. On Tuesday online dating company Match Group showed the quest for chemistry was a very popular New Year's resolution after many months of solitary confinement. The first quarter looked good from all angles, with revenue and adjusted earnings before interest, taxes, depreciation and amortization both coming in above Wall Street's expectations. Match's revenue forecast for the second quarter was also better than analysts had expected, though the company did say it will lean into its recent momentum and increase marketing spending relative to the same period last year, weighing slightly on its bottom line.
One of the priorities announced in the 2021 Examination Priorities Report of the U.S. Securities and Exchange Commission's Division of Examinations ("EXAMS") is a review of robo-advisory firms that build client portfolios with exchange-traded funds ("ETF's") and mutual funds. EXAMS notes that these clients are almost entirely retail investors without investments large enough to support the costs of regular human investment advisers. EXAMS sees that the risks involved in these robo-advisor accounts pose particular issues, that retail clients may well not recognize. Accordingly, it may help to reflect on the Laws of Robotics invented by that science fiction author Isaac Asimov (for "I Robot," a short story in his 1950 collection), particularly the First Law: A robot may not injure a human being or, through inaction, allow a human being to come to harm. Investors may not understand the risks associated with specific investments; the risk profiles of mutual funds and of ETF's vary widely, from diversified to concentrated, from simple to complex strategies.
Google has launched a way for users to better improve how its Assistant pronounces names. Users will now be able to teach Google Assistant to enunciate and recognise names of contacts as they are supposed to be said. This can be found in Google Assistant's settings, under Basic Info, and then Nickname. "Assistant will listen to your pronunciation and remember it, without keeping a recording of your voice", Google says in a blog post. "The feature will be available in English and we hope to expand to more languages soon."
Not long ago, consumers shopped for financial services by visiting branches of multiple banks and credit unions, collecting an assortment of brochures from a rack, talking to branch personnel, and comparing various value propositions. The decision was based on the human connection with the people at the branch, combined with the alignment of the products offered and the financial needs of the consumer. For today's consumer, shopping for a new banking relationship is far different. The increase in online and mobile banking options has empowered the consumer with far more alternatives, while making the decision-making easier. Consumers can browse, compare and purchase virtually any financial product or service from their computer or mobile device 24/7, without ever stepping foot in a branch.
Nowadays, almost everybody is aware of the effect Artificial Intelligence (AI) has on our every day lives. AI is already a part of many people's lives and maybe already a part of your life too -- whether you realize it or not. Alexa), Google Home, and Apple's HomePod (with Siri) are perhaps the three most popular products in the thriving field of AI assistants. It's estimated that Amazon has sold about 25 million Echo devices up to now, and they expect that number to go double or more by 2020. These AI assistants products understand spoken commands and speak in humanlike voices using natural language.
What is Artificial Narrow Intelligence (ANI)? This is the most common form of AI that you'd find in the market now. These Artificial Intelligence systems are designed to solve one single problem and would be able to execute a single task really well. By definition, they have narrow capabilities, like recommending a product for an e-commerce user or predicting the weather. This is the only kind of Artificial Intelligence that exists today. They're able to come close to human functioning in very specific contexts, and even surpass them in many instances, but only excelling in very controlled environments with a limited set of parameters. What is Artificial General Intelligence (AGI)? AGI is still a theoretical concept. It's defined as AI which has a human-level of cognitive function, across a wide variety of domains such as language processing, image processing, computational functioning and reasoning and so on.
Machine learning is one of the most used technologies in this generation. It has varied capabilities that can transform businesses across industries for the better. From being considered as a niche technology, machine learning is now seeing an increased adoption within companies in all sectors. From a global perspective, brands are leveraging machine learning to accelerate innovation and better customer experience. For example, Nike uses machine learning for personalized product recommendations.
Fintech products are gaining popularity and pose real competition to traditional banking. According to the 2019 FIS PACE study, 73% of consumer banking interactions are digital. Fintech startups have already raised a record $100M in Q2 2020 and, apparently, have cracked the secret to success -- better CX and personalized customer service. Direct-to-consumer banks have the highest consumer satisfaction outpacing credit unions. Digital banks also have the lowest rate of customer churn.
Individual investors are now massively using online brokers to trade stocks with convenient interfaces and low fees, albeit losing the advice and personalization traditionally provided by full-service brokers. We frame the problem faced by online brokers of replicating this level of service in a low-cost and automated manner for a very large number of users. Because of the care required in recommending financial products, we focus on a risk-management approach tailored to each user's portfolio and risk profile. We show that our hybrid approach, based on Modern Portfolio Theory and Collaborative Filtering, provides a sound and effective solution. The method is applicable to stocks as well as other financial assets, and can be easily combined with various financial forecasting models. We validate our proposal by comparing it with several baselines in a domain expert-based study.