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) …
Machine learning (ML) extracts meaningful insights from raw data to quickly solve complex, data-rich business problems. ML algorithms learn from the data iteratively and allow computers to find different types of hidden insights without being explicitly programmed to do so. ML is evolving at such a rapid rate and is mainly being driven by new computing technologies. Machine learning in business helps in enhancing business scalability and improving business operations for companies across the globe. Artificial intelligence tools and numerous ML algorithms have gained tremendous popularity in the business analytics community.
What do you want to find out or discover using your data? Do you have the appropriate data to analyze? Data is key to any data science and machine learning task. Data comes in different flavors such as numerical data, categorical data, text data, image data, sound data, and video data. The predictive power of a model depends on the quality of data used in building the model.
We conduct a large-scale benchmark experiment aiming to advance the use of machine-learning quantile regression algorithms for probabilistic hydrological post-processing "at scale" within operational contexts. The experiment is set up using 34-year-long daily time series of precipitation, temperature, evapotranspiration and streamflow for 511 catchments over the contiguous United States. Point hydrological predictions are obtained using the Génie Rural à 4 paramètres Journalier (GR4J) hydrological model and exploited as predictor variables within quantile regression settings. Six machine-learning quantile regression algorithms and their equal-weight combiner are applied to predict conditional quantiles of the hydrological model errors. The individual algorithms are quantile regression, generalized random forests for quantile regression, generalized random forests for quantile regression emulating quantile regression forests, gradient boosting machine, model-based boosting with linear models as base learners and quantile regression neural networks.
In this week's episode of Growth Interviews, we invite you to join our podcast conversation with Miroslav Varga, search engine advertising expert, professor and MENSA member, experienced and specialized in Google Ads account optimization and statistical analysis (data mining). Our mission is to provide insights and ideas from world-class professionals on the topic of growth and to cut through the noise of so-called marketing tips and tricks, revealing the money-making strategies behind e-commerce. Welcome to Growth Interviews, the fun, stimulating and engaging series of conversations driven by digital business growth. Each episode is an intriguing challenge involving an insightful expert who reveals some of their best-kept secrets, which you can use right away to boost your business. Miroslav is a Google AdWords Certified Trainer – GCT and online marketing lecturer at several schools and institutions and probably the only Google Ads certified Trainer and GAIQ triple grandfather in the world.
I have read a book or some posts on machine learning. I have watched some of the Coursera machine learning course. I still don't know how to get started… How do you get started in machine learning? The most common question I'm asked by developers on my newsletter is: How do I get started in machine learning? I honestly cannot remember how many times I have answered it. In this post, I lay out all of my very best thinking on this topic. You are a developer and you're interested in getting into machine learning. You read some blog posts.
All of these machine learning capabilities center around the user, and help to create business value for organizations. Another facet of this is personalization, and making sure each customer's Workday experience is catered to their needs. As my colleague Stuart Bowness explains in more detail, machine learning powers our new Workday People Experience, which predicts what people want and gives them quick access to it, eliminating needless navigation with a simpler, more engaging digital experience, We believe that saving users' time while making them more effective is one of the most valuable things we can do.
It's a bright April day in Boston, and Gabi Zijderveld, a pioneer in the field of emotional artificial intelligence, is trying to explain why teaching robots to feel is as important as teaching them to think. "We live in a world surrounded by all these super-advanced technologies, hyper-connected devices, AI systems with super cognitive abilities -- or, as I like to say, lots of IQ but absolutely no EQ," says Zijderveld, chief marketing officer of Affectiva, the startup that spun out of the MIT Media Lab 10 years ago to build emotionally intelligent machines. "Just like humans that are successful in business and in life -- they have high emotional intelligence and social skills -- we should expect the same with technology, especially for these technologies that are designed to interact with humans." Giving machines a soul has been a dream of scientists, and sci-fi writers, for decades. But until recently, the idea of robots with heart was the stuff of moviemaking.
The graph represents a network of 3,752 Twitter users whose tweets in the requested range contained "futureofwork ", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Thursday, 17 October 2019 at 01:35 UTC. The requested start date was Monday, 14 October 2019 at 00:01 UTC and the maximum number of days (going backward) was 14. The maximum number of tweets collected was 5,000. The tweets in the network were tweeted over the 2-day, 16-hour, 29-minute period from Friday, 11 October 2019 at 07:31 UTC to Monday, 14 October 2019 at 00:00 UTC.