Recently, practical applications for passenger flow prediction have brought many benefits to urban transportation development. With the development of urbanization, a real-world demand from transportation managers is to construct a new metro station in one city area that never planned before. Authorities are interested in the picture of the future volume of commuters before constructing a new station, and estimate how would it affect other areas. In this paper, this specific problem is termed as potential passenger flow (PPF) prediction, which is a novel and important study connected with urban computing and intelligent transportation systems. For example, an accurate PPF predictor can provide invaluable knowledge to designers, such as the advice of station scales and influences on other areas, etc. To address this problem, we propose a multi-view localized correlation learning method. The core idea of our strategy is to learn the passenger flow correlations between the target areas and their localized areas with adaptive-weight. To improve the prediction accuracy, other domain knowledge is involved via a multi-view learning process. We conduct intensive experiments to evaluate the effectiveness of our method with real-world official transportation datasets. The results demonstrate that our method can achieve excellent performance compared with other available baselines. Besides, our method can provide an effective solution to the cold-start problem in the recommender system as well, which proved by its outperformed experimental results.

Most of us want digital privacy, and most of us also want autocorrection that works, speech to text that is accurate, and smart systems that find all our selfies with Serena, or surface the most important emails we need right now. But are those two imperatives in direct opposition? According to some tech analysts and AI experts, they are. Especially those who are experiencing huge issues with iPhone's autocorrection capability in Apple's latest mobile operating system upgrade, iOS 13. "It's way worse on my iPhone," says veteran industry observer Robert Scoble, chief strategy officer at Infinite Retina. "And I've tried several things to fix it, including deleting all the settings and deleting all the history and trying to reboot everything ... I'm seeing a lot of bugs in the spellchecker where it's putting capitalization where it doesn't need to go, where it's switching words a lot more often than it used to. Apple's iOS 13's spellcheck was so bad Scoble ran a Twitter poll, asking his 400,000 followers whether they had similar issues. Twitter polls are hardly scientific, of course. But there's a broad range of people who are claiming that Apple's recent software release has been a big backward step in terms of autocorrection. "iOS 13 got significantly worse for me," says mobile entrepreneur Albert Renshaw, CEO at Apps4Life. "I've been learning French with Duolingo and have been typing in French every day for a little over a year in that app, but have never had an issue with it affecting my autocorrect.

Cheetah uses supervised learning techniques to catch its prey. That's a bizarre, random out-of-the-blue statement you may say. A cheetah has adapted a very refined approach to hunting by honing its skills through practice, observation, experience, and computation. Much like training datasets to create a spectacular AI model. They're trained and taught continuously until they're able to operate on their own.

This epic list article has been updated to include 20 more digital marketing trends to help you get ready for next year! At one time, artificial intelligence, data-driven marketing and voice search engine optimization (VSEO) were ambitious concepts bordering on the ridiculous. Today, these innovative digital marketing trends are among the top priorities for most business owners in 2020. And why wouldn't they be? After all, if your business has any intention of remaining competitive in today's online landscape, you must adapt to the rapidly evolving changes in digital marketing. "Each business is a victim of Digital Darwinism, the evolution of consumer behavior when society and technology evolve faster than the ability to exploit it. Digital Darwinism does not discriminate. Make no mistake: We live in a time when marketing technology moves fast and consumer interests and behaviors are hard to predict. Marketers can no longer stick their heads in the sand and hope that educated guesses ...

Are you a vegan who likes kombucha? Are you real, lit, or looking for a real lit match? Do you even know what these words mean? If not, you probably need to lower your expectations on Tinder. Yesterday, the dating platform – which has an estimated 50 million users worldwide – released its Year in Swipe roundup: an analysis of user data and activity in the last year, that tells us how the world dated on the app in 2019.

AI models invariably encounter ambiguous situations that they struggle to respond to with instructions alone. That's problematic for autonomous agents tasked with, say, navigating an apartment, because they run the risk of becoming stuck when presented with several paths. To solve this, researchers at Amazon's Alexa AI division developed a framework that endows agents with the ability to ask for help in certain situations. Using what's called a model-confusion-based method, the agents ask questions based on their level of confusion as determined by a predefined confidence threshold, which the researchers claim boosts the agents' success by at least 15%. "Consider the situation in which you want a robot assistant to get your wallet on the bed … with two doors in the scene and an instruction that only tells it to walk through the doorway," wrote the team in a preprint paper describing their work.

Here's how we see it at Ladder -- Historically, marketing has been predominantly driven by guesswork and luck based on what marketers think or have heard/read works best. Over the years since we started Ladder, we've been working to remove that guesswork from growth, bit by bit. In 2016, we built a proprietary test management platform to empower our growth strategists to build and execute marketing experiments – a drag-n-drop sprint builder that allowed for full transparency into what marketing tests are live, paused, off, or yet to launch… as well as the hypothesis, performance notes, and assets in play (creative, messaging, audience). This was the foundation needed for our eventual recommendation engine. In 2016 we also built and released the world's largest database of growth marketing tactics.

No, the robots are not coming for your job as they ready to take over the world ... yet. But the future of the world's workforce will mark a significant shift and work will be heavily reliant on the teamwork of human and machine, noted the just-released IDC white paper, Content Intelligence for the Future of Work. And we're not quite in sci-fi film territory either, said Holly Muscolino, research vice president of content and process strategies and the future of work at IDC. "A software robot (or'digital worker') is essentially a software program that automates a task that has previously been accomplished by a human worker," Muscolino explained. "The term'robot' is used to signify the role that these software solutions play in automation, however, beyond that, there is no relationship between a software robot and the physical robots that we may see on the manufacturing line, patrolling supermarket aisles on starring in'Star Wars'' movies." Muscolino added, "A variety of software technologies are classified as'digital workers.' The technology gaining the most airtime today is robotic process automation (RPA), but other automation technologies, and AI-enabled technologies, like digital assistants and chatbots, are also classified as'digital workers'."

In the epoch of cutting edge technologies, several devices and technological tools are available in the market. Smartphones namely, Google's Android and Apple's iPhone are the most widely used smartphones and are profoundly popular nowadays. With a combined mobile app development market share close to 99%, both Google and Apple work to bring more innovations and new features into their smartphones as billions of people all over the globe are utilizing them daily. In the limelight comes the significance and role of Artificial Intelligence in mobile application development, which gives more fuel to Google & Apple to bring advanced changes to their respective platforms. AI is beneficial for developers and even for the general users.

In her popular book, Weapons of Math Destruction, data scientist Cathy O'Neil elegantly describes to the general population the danger of the data science revolution in decision making. She describes how the US News ranking of universities, which orders universities based on 15 measured properties, created new dynamics in university behavior, as they adapted to these measures, ultimately resulting in decreased social welfare. Unfortunately, the idea that data science-related algorithms, such as ranking, cause changes in behavior, and that this dynamic may lead to socially inferior outcomes, is dominant in our new online economy. Ranking also plays a crucial role in search engines and recommendation systems--two prominent data science applications that we focus on in this article. Recommendation systems endorse items by ranking them using information induced from some context--for example, the Web page a user is currently browsing, a specific application the user is running on her mobile phone, or the time of day.