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) …
Elon Musk is CEO of Tesla and SpaceX, has plans to colonize Mars, and thinks AI may turn humans into its pets. But beyond the hype and his enormous net worth and Twitter presence, here's how Musk's companies are actually taking on ... virtually every industry. Elon Musk thinks and acts on a larger, more cosmic scale than we're accustomed to from entrepreneurs. Elon Musk has become a household name synonymous with the future. Whether he's working on electric vehicles (Tesla) or sending rockets into space (SpaceX), his larger-than-life reputation attracts its fair share of hero-worship.
Newswire) indico, a provider of enterprise machine learning solutions for unstructured content, today announced $4 million in new equity seed funding led by Osage Venture Partners. Ventures, Boston Seed, and Hyperplane also participated. The funding follows a $1.5 million round of convertible debt financing announced in October, and the addition of veteran CEO Tom Wilde in September. Artificial intelligence and machine learning offer big opportunities for businesses, but many organizations struggle to find practical business applications for the technology due to limited access to required skillsets, insufficient data and infrastructure, and poorly designed pre-trained offerings. Philadelphia-based Osage Venture Partners focuses on early stage, business-to-business technology companies including analytics and artificial intelligence-based startups such as Automated Insights, BAInsight, SevOne, and Sidecar.
Hoy traemos a este espacio esta infografía de ZDnet, que nos presentan así: Infographic: 50 percent of companies plan to use AI soon, but haven't worked out the details yet Despite lacking experience and skills, many respondents to a recent Tech Pro Research survey said they'd find a way to pull off the implementation in-house. In a recent survey by Tech Pro Research, only 28 percent of respondents, most of whom were in IT leadership positions, said they have firsthand experience with AI or machine learning. However, if the survey results hold true, the majority of respondents will be using the technologies at work in the next few years. Another interesting findings from this survey was that while 42 percent of respondents said their technical staff lack the skills to implement and support AI and machine learning, 41 percent said that all the work in this area would be done in-house. Thirty-nine percent of respondents said their companies were also still working on selecting AI and machine learning vendors.
With new neural network architectures popping up every now and then, it's hard to keep track of them all. Knowing all the abbreviations being thrown around (DCIGN, BiLSTM, DCGAN, anyone?) can be a bit overwhelming at first. So I decided to compose a cheat sheet containing many of those architectures. Most of these are neural networks, some are completely different beasts. Though all of these architectures are presented as novel and unique, when I drew the node structures… their underlying relations started to make more sense.
What happens when you take a perfectly good neural network and, figuratively, stick a screwdriver in its brain? You get melancholy glitch-art music videos that turn talking heads into digital puppets. A machine learning developer named Jeff Zito made a series of music videos using a deep learning network based on Face2Face. Originally developed to generate stunningly realistic image transfers, like controlling a digital Obama in real-time using your own facial movements, this project takes it in a different direction. Sometimes the best AI isn't good enough.
Much of the strategic focus in the digital economy thus far has revolved around getting better insights into consumers. B2C firms have been the leaders in customer analytics initiatives. E-commerce, mobile commerce, and social media platforms have enabled businesses to better sculpt marketing and customer support initiatives and customer services. Extensive data and advanced analytics for B2C have enabled strategists to better understand consumer behavior and corresponding propensities as visitors and purchasers conduct daily activities through online systems. But there is also an emerging capability to gain insights on business customers.
It's easy to take water for granted. Turn on the tap, and you'll receive clean, life-giving water (with some very notable exceptions). But for a myriad of reasons, ranging from our changing climate to aging infrastructure to growing demands for water, all aspects of the water cycle -- how it is collected, cleaned, distributed (and repeat) -- are overdue for a technological makeover. For one thing, the workforce behind our waterworks is aging, at least within the public water utility sector, which is composed of an astounding 50,000 individual systems. "Lots of senior engineers are 30 years into their job and are reaching retirement," says Will Maize, a water industry analyst with market research firm Bluefield Research.
A robot'artist' transforms can transform your imagination into beautiful sketches. The creative Microsoft software composes colourful drawings based on simple text descriptions. It adds details to its creations not specified in its instructions, showing the bot has an'artificial imagination', according to Microsoft. The technology could one day generate entire animated movies based on a script, the researchers claim. A robot'artist' transforms your imagination into beautiful sketches.
Spark lets you apply machine learning techniques to data in real time, giving users immediate machine-learning based insights based on what's happening right now. Using Spark, we can create machine learning models and programs that are distributed and much faster compared to standard machine learning toolkits such as R or Python. In this course, you'll learn how to use the Spark MLlib. You'll find out about the supervised and unsupervised ML algorithms. You'll build classifications models, extracting proper futures from text using Word2Vect to achieve this.
This course was funded by a wildly successful Kickstarter. Let's learn how to perform automated image recognition! In this course, you learn how to code in Python, calculate linear regression with TensorFlow, and perform CIFAR 10 image data and recognition. We interweave theory with practical examples so that you learn by doing. AI is code that mimics certain tasks.