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AI for business users: a glossary

#artificialintelligence

When you work with IT staff and data scientists, they're going to use acronyms that you might not be familiar with. It's important to know some of the basic terms and acronyms so you can communicate. Business users should make themselves familiar with these common AI terms to communicate well with the data teams. Artificial intelligence is a form of intelligence demonstrated by a computer. A computer can be programmed with logic and business rules that will enable it to "reason" through situations and come up with a conclusion.


How do beginners make AI movies in 30 sec?

#artificialintelligence

There are several reasons why some filmmakers prefer to create their work outside of the studio structure, including those who are new to the industry or have a small budget. This kind of film is known as "indie film" because it is more controlled by the filmmaker in terms of substance and voice. Indie filmmakers have more leeway to express the tale they want to convey since they have a smaller budget and fewer crew members to work with. We introduces a new model "indie AI film". Your creativity is enough; machine learning will take care of the rest.


Making The Most Of MLOps - AI Summary

#artificialintelligence

Today, MLOps offers a fairly robust framework for operationalizing AI, says Zuccarelli, who's now innovation data scientist at CVS Health. By way of example, Zuccarelli points to a project he worked on previously to create an app that would predict adverse outcomes, such as hospital readmission or disease progression. That meant creating a mobile app that was reliable, fast, and stable, with a machine learning system on the back end connected via API. As MLOps platforms mature, they accelerate the entire model development process because companies don't have to reinvent the wheel with every project, he says. And this means developing expertise in a wide range of activities, says Meagan Gentry, national practice manager for the AI team at Insight, a Tempe-based technology consulting company.


GitHub - lucidrains/imagen-pytorch: Implementation of Imagen, Google's Text-to-Image Neural Network, in Pytorch

#artificialintelligence

It is the new SOTA for text-to-image synthesis. Architecturally, it is actually much simpler than DALL-E2. It consists of a cascading DDPM conditioned on text embeddings from a large pretrained T5 model (attention network). It also contains dynamic clipping for improved classifier free guidance, noise level conditioning, and a memory efficient unet design. It appears neither CLIP nor prior network is needed after all.


Manage ML Automation Workflow with DagsHub, GitHub Action, and CML

#artificialintelligence

Originally published on Towards AI the World's Leading AI and Technology News and Media Company. If you are building an AI-related product or service, we invite you to consider becoming an AI sponsor. At Towards AI, we help scale AI and technology startups. Let us help you unleash your technology to the masses. It's free, we don't spam, and we never share your email address.


But is it art, Ma'am? Robot's platinum jubilee Queen portrait unveiled

#artificialintelligence

At first glance, the Queen could be wearing a tin hat with camouflage netting set against a thunderous sky. A commentary on the inevitable conflicts and turbulence that took place during her 70-year reign, perhaps. But no, it seems that Ai-Da, the robot artist who painted the Queen's portrait to mark her platinum jubilee, was simply paying tribute to "an amazing human being". The monarch's trademark pearls and bold colours, along with a stoic facial expression, are the standout features of Algorithm Queen, which was unveiled on Friday. Ai-Da, billed by her creators as "the world's first ultra-realistic humanoid robot artist", said: "I'd like to thank Her Majesty the Queen for her dedication, and for the service she gives to so many people. She is an outstanding, courageous woman who is utterly committed to public service."


Moving from Red AI to Green AI: A Practitioner's Guide to Efficient Machine Learning

#artificialintelligence

In our previous post, we talked about how red AI means adding computational power to "buy" more accurate models in machine learning, and especially in deep learning. We also talked about the increased interest in green AI, in which we not only measure the quality of a model based on accuracy but also how big and complex it is. We covered different ways of measuring model efficiency and showed ways to visualize this and select models based on it. Maybe you also attended the webinar? If not, take a look at the recording where we also cover a few of the points we'll describe in this blog post.


GitHub - anshkumar/yolact: Tensorflow 2.x implementation YOLACT

#artificialintelligence

This is a Tensorflow 2.3 implementation of the paper YOLACT: Real-time Instance Segmentation and YOLACT: Better Real-time Instance Segmentation. The paper presents a fully-convolutional model for real- time instance segmentation that achieves 29.8 mAP on MS COCO at 33.5 fps evaluated on a single Titan Xp, which is significantly faster than any previous competitive approach. Unlike original implemetation of YOLACT/YOLACT in which image is resized to 550x550, this repo can handle image of size MxN. For detailed steps to install Tensorflow, follow the Tensorflow installation instructions. The remaining libraries can be installed on Ubuntu 16.04 using via apt-get: The default metrics are based on those used in Pascal VOC evaluation.


The Promise of AI Maturity - Theory vs. Practice - insideBIGDATA

#artificialintelligence

Phil Hall is Chief Growth Officer at LXT, an emerging leader in global AI training data that powers intelligent technology. Earlier this year, we introduced our first executive survey, The Path to AI Maturity. The report highlights that investment in artificial intelligence is strong at mid-to-large US organizations, and 40% rate themselves at the three highest levels of AI maturity, having achieved operational to transformative implementations. The new survey by research firm Reputation Leaders included 200 senior executives (two-thirds C-suite) with AI experience at companies with annual revenue of over $100 million and more than 500 employees – and details the impact that AI investment is having across organizations of varying revenue levels and industries. As part of the survey, executives placed their companies on the Gartner AI Maturity Model.


Top 50 emerging technologies

#artificialintelligence

Frost & Sullivan has released its annual Top 50 emerging technologies that are poised to generate multi-billion-dollar markets and set new growth opportunities worldwide. The emerging technologies are distributed across nine key clusters and represent the bulk of the R&D and innovation activity happening today, Frost & Sullivan said. Some of the emerging technologies noted by the market research company include: Flash lidar, graphene sensors, 5G materials, smart object security, carbon upcycling, battery recycling, grid-scale energy storage, autonomous mobile robots, robotic exoskeletons, cognitive manufacturing and behavioral biometrics. Other emerging tech listed include digital biomarkers, hyperspectral imaging, solid-state batteries, multi-cloud automation, sub-millimeter wave sensing, adaptive computing and accelerated storage. Frost & Sullivan will be hosting a webinar called "The 2021 Top 50 Technologies Transforming the Future," on April 27 at 11 a.m. EDT, discussing these converging technologies and how companies will be able to take advantage of the opportunities for growth.