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) …
Facial recognition software could be used to detect hail storms - and their severity. That's according to scientists at the US National Center for Atmospheric Research, who've tested the software's effectiveness on meteorological data. Specifically, they found that a deep learning model called a convolutional neural network can spot the early signs as they happen - better than current methods. The promising results, published in the American Meteorological Society's Monthly Weather Review, could be a game-changer for providing accurate weather warnings. AI: The promising results, published in the American Meteorological Society's Monthly Weather Review, could be a game-changer for providing accurate weather warning Whether or not a storm produces hail hinges on myriad meteorological factors.
High-throughput phenotypic screening, based on high content imaging, is increasingly often used as a tool in the context of drug discovery. Compound screens are used to find hits that produce the desired phenotypes in relevant cellular assays. Genomic screens are used to elucidate the underlying molecular pathways and identify suitable drug targets. Since a wealth of data is produced in the process of high- content screening, data science approaches such as statistics, machine learning and neural networks can play an important role in making the most of the collected data. Much like virtual screening can be performed in more classical chemoinformatic settings by, e.g., learning predictive models for QSAR (quantitative structure-activity relations) from data obtained through compound screens, similar approaches can be taken in the context of high-throughput phenotypic screening.
I am a Mechanical engineer by education. And I started my career with a core job in the steel industry. But I didn't like it and so I left that. I made it my goal to move into the analytics and data science space somewhere around in 2013. From then on, it has taken me a lot of failures and a lot of efforts to shift.
HOSTKEY deploys a well-established environment for machine learning applications such as neural networks with high-performance GPUs and dedicated servers with NVIDIA GTX 1080/1080Ti and RTX 2080Ti graphics cards. Just start your TensorFlow experience in a straightforward and user-friendly environment making it easy to build, train and deploy machine learning models at scale. TensorFlow runs up to 50% faster on our high-performance GPUs and scales easily. Now your machines learn in hours, not days. Deep Learning is a buzzword that will be familiar to most people.
From all indicators, robots as a service (RaaS) is growing rapidly. ABI Research predicts there will be 1.3 million installations of RaaS by 2026 generating $34 billion in revenue. Let's look at what robots as a service entails, the reasons for its growth and some companies that offer RaaS solutions and the tasks it can support. Many are now familiar with the concept of software as a service (SaaS) or big data as a service (BDaaS), the pay-as-you-go or subscription-based service model. In a similar set-up, those who sign up for robots as a service get the benefits of robotic process automation by leasing robotic devices and accessing a cloud-based subscription service rather than purchasing the equipment outright.
The SMART Forecasting team at Walmart Labs is tasked with providing demand forecasts for over 70 million store-item combinations every week! For example, just how much of every type of ginger needs to go to every Walmart store in the U.S., every week for the next 52 weeks, with the goal of improving in stocks and reducing food waste. Our algorithm strategy was to build a suite of machine learning models and deploy them at scale to generate bespoke solutions for (oh so many!) store-item-week combinations. Random Forests would be part of this suite. We went through the traditional model development workflow of data discovery, identifying demand drivers, feature engineering, training, cross validation and testing.
SANTA CLARA, CA – Micro Focus (LSE: MCRO; NYSE: MFGP) today announced the general availability of Service Management Automation X (SMAX) 2019.05. SMAX is the first application suite for Enterprise Service Management and IT Service Management built on machine learning and analytics, powered by an embedded CMDB and Discovery to help drive down costs and speed up time to resolution. Built-in best practices are quickly and easily configured and extended in an entirely codeless way with the SMAX Studio enabling customers to achieve faster time to value. The scalable, multi-tenant cloud-native solution delivers significantly lower cost of ownership and enables customers to deploy on their choice of public or private cloud. SMAX is also available as-a-service by Micro Focus partners worldwide.
Machine learning is a powerful tool in the AI toolbox, but its limitations must be understood to use effectively. Machine learning has become the latest darling of the IT marketing space, a secret sauce that is supposed to turbo-charge computers and bring us closer to the nirvana of artificial intelligence dominance ... or something like that. Like so much of what comes out of IT marketing, most of it is hype, and deceptive hype at that. While there is a lot of power in what machine learning can do, by assigning it magical capabilities the mavens of marketing may actually be setting the whole field up for yet another artificial intelligence winter, as expectations continue to fall short of reality. It's worth understanding what Machine Learning is, and isn't.