If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Whether you're studying for an exam or revising for a presentation, a quiz on identifying different learning methods promises to help you maximise the amount of information you can retain. Formed of ten questions, the quick quiz by Tutor House asks participants to consider how they would respond in a series of scenarios. This technique reveals if they would benefit most from visual, auditory, read and write or kinesthetic (interactive) learning methods. Created by Tutor House in partnership with educational experts, the quiz considers the widely used VARK (Visual, Aural, Read/write, and Kinesthetic) learning styles developed by Fleming's in 1987. Visual learners are likely to respond to visual stimuli like photos and videos to remember things.
Amazon is offering up $25 gift cards in exchange for 3D scans of your body. The internet giant is currently conducting a study at its New York office as part of Amazon Body Labs that seeks to'learn about diversity among body shapes,' according to a listing, which was first spotted by Mashable. Participants who set up a 30-minute appointment will be asked to take a survey and agree to have 3D scans, photos and videos taken of them. The move comes as Amazon has faced privacy concerns around its collection of data from Echo devices, as well as the deployment of its controversial facial recognition software. Amazon is offering up $25 gift cards in exchange for 3D scans of your body.
A peculiar new console small enough to fit in your back pocket is shaking up the patterns of portable gaming. Software company Panic, best known for developing Mac apps and more recently as publisher of the hit indie game Firewatch, has made its foray into hardware with a $149 handheld gaming device dubbed Playdate. It comes equipped with 12 'surprise' games at no extra cost – but, unlike other consoles with pre-loaded packages, Playdate's will be delivered one at a time on a weekly basis over a span of 12 weeks. The small yellow box sports a crank on the side which serves as a rotational controller and its LCD screen only displays in black-and-white. Panic unveiled the device this week after four years of development, and says it will officially launch in 2020.
How they describe themselves: Actionable analytics is the backbone of NYSE-listed Enova International, a global online lending company. In the past 14 years, the analytics team has applied predictive and prescriptive analytics to fraud detection, credit risk management, and customer retention and built the Colossus Digital Decisioning Platform to automate and optimize many of Enova's operational decisions. As a result, Enova has extended over $20 billion in credit to over 5 million customers worldwide. Enova Decisions was launched in 2016 to help businesses in financial services, insurance, healthcare, telecommunications, and higher education achieve similar outcomes by leveraging the same analytics expertise and decisioning technology. How they describe their product/innovation: Enova Decisions Cloud is a complete decision management suite where clients can integrate 1st and 3rd party data, deploy machine learning models, manage business rules, monitor performance, and continuously optimize performance.
Sounds a lot like the road that led to an AI winter historically... I think we're well past the point where that's a genuine risk of AI interest globally cooling down at all (it's already very practical and profitable in many arenas just with what we have) but openAI themselves? If historical trends are any indication, that kind of talk will buy them at most 5 years of normal investor questions, 5 years of severe questions, then bankruptcy. They've very generously got a decade to figure something actually practical out, and realistically the clock might only have five years on it or less. Wonder if they'll invent a thing that'll teach them to make money before then, haha.
This model learns low dimensional vectors to represent vertices appearing in a graph and, unlike existing work, integrates global structural information of the graph into the learning process. We also formally analyze the connections between our work and several previous research efforts, including the DeepWalk model of Perozzi et al. as well as the skip-gram model with negative sampling of Mikolov et al. We conduct experiments on a language network, a social network as well as a citation network and show that our learned global representations can be effectively used as features in tasks such as clustering, classification and visualization. Empirical results demonstrate that our representation significantly outperforms other state-of-the-art methods in such tasks.
To learn how actual news editors write headlines, Primer's system read more than 1 million news articles and the headlines they were paired with -- but only those where the headline was made up entirely of words found in the story. Once trained, it can read a new article and string together the best possible series of words to turn into a headline, according to Primer. In what Primer director of science John Bohannon calls a "headline Turing Test," evaluators were asked to rate computer-generated headlines against the originals -- without knowing which is which. In its final form, Primer tied or beat out humans more than half the time, the company said. To learn how actual news editors write headlines, Primer's system read more than 1 million news articles and the headlines they were paired with -- but only those where the headline was made up entirely of words found in the story.
The deployment of machine learning models is the process for making your models available in production environments, where they can provide predictions to other software systems. It is only once models are deployed to production that they start adding value, making deployment a crucial step. However, there is complexity in the deployment of machine learning models. This post aims to at the very least make you aware of where this complexity comes from, and I'm also hoping it will provide you with useful tools and heuristics to combat this complexity. If it's code, step-by-step tutorials and example projects you are looking for, you might be interested in the Udemy Course "Deployment of Machine Learning Models".