Artificial Intelligence (AI) is by far one of the most praised trends in Silicon Valley. Yet, in the "real world", opinions tend to be split into two camps: those who desire AI-driven personal assistants on the one hand, and those who fear that such AI solutions will steal their jobs in the future, on the other. Nevermind that Hollywood-like scenario though – the truth is that AI is already here, reading our emails, listening to our conversations, recognizing our faces, and even smelling our breath! AI is not a shiny addition. It is a must-have for any business innovations, and e-commerce seems to be in the avant-garde.

A mere four years ago AI was not even able to pass a Grade eight science test. Seven hundred computer scientists competed in a contest with a significant amount of money as prize. They had to build artificial intelligence that could pass a Grade eight science test. The computer scientists did their best, but not even the most advanced AI system could score better than 60percent in the test. It seems that the AI was just not advanced enough to fully reach the language and logic skills expected of students in the eighth grade.

The Electronic Frontier Foundation (EFF) has published an extensive study into the hidden techniques and methods used by online service providers to collect and track our personal information and activities. On Monday, as shoppers plundered e-commerce websites for Cyber Monday bargains, the civil and privacy rights outfit released "Behind the One-Way Mirror," outlining corporate surveillance methods with a focus on behind-the-scenes tracking. The paper covers a variety of different tracking methods including browser fingerprinting, invisible pixel images, social widgets, mobile tracking, and facial recognition employed by tech giants including Amazon, Facebook, Google, Twitter, as well as countless data brokers, to "collect information about who we are, what we like, where we go, and who our friends are." Third-party tracking is usually invisible to the naked eye. Code, images, and plugins can all contain functions that track browsing, activities, purchases, the duration of visits, ad engagement, and clicks, and may link up different data sources to create a comprehensive shadow profile of your digital self. According to the EFF, for example, Facebook uses invisible "conversion pixels" to collect data on third-party websites and to track ad engagement; Google uses location information to track user visits to physical stores and makes use of transparent pixel images for tracking, and smart home devices -- including Amazon Echo and Google Home -- can harvest audio data and may be used by human employees to improve voice recognition technologies.

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.