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.

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.

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.

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.

Whether you choose a background created exclusively for your business or a pre-made template, the extra touch of personalization will have a great impact on your audience. With this feature, users can simply customize and personalize videos in any way, while enjoying the video creator's renown for simplicity and ease-of-use. Users can upload a personalized background to fit their CI, edit the background to match their brand colors, or choose from simpleshow's pre-made backdrops. In addition to all of the latest features and additions mentioned above, simpleshow continues to stand out from the crowd through the launch of their latest collection of diversity characters. With this latest one-click feature, content creators can now build out their own video content with characters to fit their exact demographic and target audience.

It's since been an exciting time for startups as entrepreneurs continue to discover use cases for computer vision in everything from retail and agriculture to construction. With lower computing costs, greater model accuracy and rapid proliferation of raw data, an increasing number of startups are turning to computer vision to find solutions to problems. However, before founders begin building AI systems, they should think carefully about their risk appetite, data management practices and strategies for future-proofing their AI stack. TechCrunch is having a Memorial Day sale. You can save 50% on annual subscriptions for a limited time.

There are many ways to get started with studying machine learning. I have previously written a lot about how to design your own curriculum and roadmap as an alternative to taking courses. This approach allows you to pick and choose free, or low-cost, resources from across the internet that suit both your learning style and budget. However, when you are just starting out on the beginning of your journey into machine learning it can often be useful to follow at least a short course that will guide you through the basic concepts first. This will give you a good foundational overview of the field and it will make it easier to design your own learning path and then continue on with deeper self-directed learning.

Coral reefs have a complex soundscape -- and even experts have to conduct painstaking analysis to measure reef health based on sound recordings. In the new study, University of Exeter scientists trained a computer algorithm using multiple recordings of healthy and degraded reefs, allowing the machine to learn the difference. The computer then analysed a host of new recordings, and successfully identified reef health 92% of the time. The team used this to track the progress of reef restoration projects. "Coral reefs are facing multiple threats including climate change, so monitoring their health and the success of conservation projects is vital," said lead author Ben Williams.

The bleak and all-too-common spectacle of roadkill was upsetting to Vedant Srinivas -- particularly when his uncle and cousin's beloved German Shepherd-Rottweiler mix was fatally hit by a car. More importantly, the losses made the high school student wonder if he could do something about it. What if Srinivas could stop the pet owners' broken hearts, save wildlife and deflect the economic impacts caused by the collisions? This month his efforts were rewarded. The sophomore from Eastlake High School in Sammamish, Wash., brought home a $5,000, first place grand award for the category of Environmental Engineering from the Regeneron International Science and Engineering Fair (ISEF).

Technical advancements make skill-based matchmaking techniques better every year, enticing average audiences to play more. But those same changes have also left a sour taste in some players' mouths who publishers have a vested interest in keeping happy -- their live streams help market games. Game companies have the seemingly impossible task of satisfying both sides; on one end, the massive player base of everyday gamers that define their bottom line and, on the other, the pros and content creators they use as for PR for those same audiences. But if these systems are indeed built to maximize players' enjoyment, it can sometimes seem like they're not working very well. Hate for skill-based matchmaking is hardly a phenomenon confined to top streamers or salty Call of Duty players. As awareness about these algorithms grows, communities in "Valorant," "Overwatch," "Apex Legends" and even more casual games like "FIFA" and "Dead by Daylight" have all, at one point or another, sharply criticized matchmaking for reducing their enjoyment of the game.