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The 20 technologies that defined the first 20 years of the 21st Century

The Independent - Tech

The early 2000s were not a good time for technology. After entering the new millennium amid the impotent panic of the Y2K bug, it wasn't long before the Dotcom Bubble was bursting all the hopes of a new internet-based era. Fortunately the recovery was swift and within a few years brand new technologies were emerging that would transform culture, politics and the economy. They have brought with them new ways of connecting, consuming and getting around, while also raising fresh Doomsday concerns. As we enter a new decade of the 21st Century, we've rounded up the best and worst of the technologies that have taken us here, while offering some clue of where we might be going. There was nothing much really new about the iPhone: there had been phones before, there had been computers before, there had been phones combined into computers before. There was also a lot that wasn't good about it: it was slow, its internet connection barely functioned, and it would be two years before it could even take a video.


Podcast: What's AI doing in your wallet?

MIT Technology Review

Our entire financial system is built on trust. We can exchange otherwise worthless paper bills for fresh groceries, or swipe a piece of plastic for new clothes. But this trust--typically in a central government-backed bank--is changing. As our financial lives are rapidly digitized, the resulting data turns into fodder for AI. Companies like Apple, Facebook and Google see it as an opportunity to disrupt the entire experience of how people think about and engage with their money. But will we as consumers really get more control over our finances? In this first of a series on automation and our wallets, we explore a digital revolution in how we pay for things. This episode was produced by Anthony Green, with help from Jennifer Strong, Karen Hao, Will Douglas Heaven and Emma Cillekens.


Here's What AI Will Never Be Able to Do

#artificialintelligence

At a Fintech conference in New York put on by Fordham University in the spring of 2017, an AI expert made a bold prediction: Someday there would be a company with a market cap of one trillion dollars. He predicted that this valuation, which at the time seemed incredible, would be based on that firm's extensive use of AI. He was correct in at least one regard: Apple became the world's first trillion-dollar company a little over a year later. Was Apple's staggering valuation due to the power of AI? Are AI and, more broadly, data analytics, the key drivers of business growth? Apple uses data analytics and AI extensively.


What Comes After Smartphones?

#artificialintelligence

For as long as most people can remember, the tech industry has had a new centre roughly every fifteen years. A model of computing sets the agenda, and the company or companies that win that model dominate the industry, and everyone is scared of them, and then a new model comes along, forms a new centre, and the old model stops mattering. Mainframes were followed by PCs, and then the web, and then smartphones. Each of these new models started out looking limited and insignificant, but each of them unlocked a new market that was so much bigger that it pulled in all of the investment, innovation and company creation and so grew to overtake the old one. Meanwhile, the old models didn't go away, and neither, mostly, did the companies that had been created by them. Mainframes are still a big business and so is IBM; PCs are still a big business and so is Microsoft (NASDAQ:MSFT).


Mobile App Development: Latest Trends Of 2020

#artificialintelligence

Over the past few years, mobile app use has exploded. More and more customers are using apps to order their favorite food, book tickets, conduct business transactions, listen to favorite music on the go, etc., with the ever-growing adoption of new smartphones. Today, our world is a digital sphere, where it is no longer a challenge to stay in contact with friends across continents. As the number of mobile apps continues to grow, so does our capacity to perform previously tricky tasks. This paper looks at some developments in mobile app development to watch out for in 2020.


Why People Drive Artificial Intelligence Today and Tomorrow

#artificialintelligence

Like it or not, artificial intelligence (AI) is already part of our daily lives. From the smartphones in our pockets to the Alexa virtual assistants on our kitchen counters, AI and its applications are accepted norms today. While we appreciate that AI can automate repetitive workplace tasks or even drive a car, the reality is that its implications are much further reaching. Luminaries like Elon Musk and Bill Gates have spoken out about the potential downsides of AI. At times, they have even issued outright warnings.


Top 5 Mobile App Development Trends to Watch Out for in 2020

#artificialintelligence

Mobile Development has become one of the most critical aspects of many companies. This is due to the acquisition of a more organic base of clients. Without a mobile-optimized solution, a company could face the question of lagging behind its competitors. Given that revenue from mobile apps' development has reached a new peak, people are following the trend in app development. Both the users and the developers follow the path of making life more comfortable.


NexOptic (TSXV:NXO

#artificialintelligence

NexOptic's ALIIS solution powered by NVIDIA (Nasdaq:NVDA, $250 billion market cap) Jetson Edge AI platform to unlock new application paths in robotics, smart cities, industrial automation, and healthcare NexOptic Technology Corp. (OTCQB:NXOPF, TSXV:NXO) is led by a blue chip team, including turnaround specialist and former CEO of Lexmark International, Rich Geruson. Their remarkable A.I. imaging technology has been noticed by numerous multinationals. NexOptic's collaborations with Qualcomm (Nasdaq:QCOM, $103 billion) and now Ndiva's "Preferred Partner" network puts them into an elite group of AI firms gaining integrated access to an A-list customer base. NexOptic further announced on July 20th the introduction of a revolutionary AI for neural image signal processors (ISP's) – a fusion of leading edge and traditional imaging technologies, further expanding their defensive intellectual property portfolio strategy. NextGen machines, cars, cities and platforms need to "see" efficiently and process instantly.


Alphabet's Next Billion-Dollar Business: 10 Industries To Watch - CB Insights Research

#artificialintelligence

Alphabet is using its dominance in the search and advertising spaces -- and its massive size -- to find its next billion-dollar business. From healthcare to smart cities to banking, here are 10 industries the tech giant is targeting. With growing threats from its big tech peers Microsoft, Apple, and Amazon, Alphabet's drive to disrupt has become more urgent than ever before. The conglomerate is leveraging the power of its first moats -- search and advertising -- and its massive scale to find its next billion-dollar businesses. To protect its current profits and grow more broadly, Alphabet is edging its way into industries adjacent to the ones where it has already found success and entering new spaces entirely to find opportunities for disruption. Evidence of Alphabet's efforts is showing up in several major industries. For example, the company is using artificial intelligence to understand the causes of diseases like diabetes and cancer and how to treat them. Those learnings feed into community health projects that serve the public, and also help Alphabet's effort to build smart cities. Elsewhere, Alphabet is using its scale to build a better virtual assistant and own the consumer electronics software layer. It's also leveraging that scale to build a new kind of Google Pay-operated checking account. In this report, we examine how Alphabet and its subsidiaries are currently working to disrupt 10 major industries -- from electronics to healthcare to transportation to banking -- and what else might be on the horizon. Within the world of consumer electronics, Alphabet has already found dominance with one product: Android. Mobile operating system market share globally is controlled by the Linux-based OS that Google acquired in 2005 to fend off Microsoft and Windows Mobile. Today, however, Alphabet's consumer electronics strategy is being driven by its work in artificial intelligence. Google is building some of its own hardware under the Made by Google line -- including the Pixel smartphone, the Chromebook, and the Google Home -- but the company is doing more important work on hardware-agnostic software products like Google Assistant (which is even available on iOS).


The Value of Big Data for Credit Scoring: Enhancing Financial Inclusion using Mobile Phone Data and Social Network Analytics

arXiv.org Machine Learning

Credit scoring is without a doubt one of the oldest applications of analytics. In recent years, a multitude of sophisticated classification techniques have been developed to improve the statistical performance of credit scoring models. Instead of focusing on the techniques themselves, this paper leverages alternative data sources to enhance both statistical and economic model performance. The study demonstrates how including call networks, in the context of positive credit information, as a new Big Data source has added value in terms of profit by applying a profit measure and profit-based feature selection. A unique combination of datasets, including call-detail records, credit and debit account information of customers is used to create scorecards for credit card applicants. Call-detail records are used to build call networks and advanced social network analytics techniques are applied to propagate influence from prior defaulters throughout the network to produce influence scores. The results show that combining call-detail records with traditional data in credit scoring models significantly increases their performance when measured in AUC. In terms of profit, the best model is the one built with only calling behavior features. In addition, the calling behavior features are the most predictive in other models, both in terms of statistical and economic performance. The results have an impact in terms of ethical use of call-detail records, regulatory implications, financial inclusion, as well as data sharing and privacy.