Information Technology

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


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 best smart home and kitchen sales we found for Memorial Day


If you've been waiting to upgrade your home with the latest gear, this weekend might be the time to do so. From robot vacuums to Instant Pots, there are a number of great sales for connected appliances and kitchen gadgets for Memorial Day this year. As you can imagine, there are quite a lot of them, so we've collected some of the best ones below. Anker's Eufy RoboVac 11S is one of our favorite budget robot vacuums thanks to its slim profile, smart features and affordable price. It doesn't have WiFi, but it does have a remote control.

Artificial Intelligence in App Creation: Beginners Edition - Coursemetry


Note: 4.1/5 (122 notes) 39,729 students Welcome to experience the course "Artificial Intelligence in App Creation: Beginners Edition". Today, Artificial Intelligence (AI), Machine Learning, and Deep Learning technologies are used in diverse fields as part of the daily life of large organizations across the globe. The rapid speed of AI growth demonstrates that it is a groundbreaking technology designed to transform the way people use devices and conduct business: achievements in unmanned aerial vehicles, the ability to beat people in chess and sporting games, automated customer service, and analytical systems – of course. Talking about the business, development, or marketing field, for instance, it is worth noting that Artificial Intelligence does not apply in a pure form to real self-aware intelligence machines in this sense. Instead, it can be considered a generic term for the number of software powered by automation that is being used by developers of websites and smartphone apps. They include the recognition of images and speech, cognitive computing, automated processing, and machine learning – for that matter.

Sci-Fi Fantasy writers convention boots author for 'racial slur'; target says he was not offended

FOX News

Fox Nation host Piers Morgan reacts to the attack on Dave Chappelle and talks free speech on'Hannity.' The Science Fiction and Fantasy Writers of America (SFWA) booted award-winning author Mercedes Lackey from a conference over her use of a "racial slur," even though the Black author to whom she had been referring later said he did not consider the term offensive. Lackey had allegedly referred to Samuel R. "Chip" Delany, 80, a celebrated author and literary critic (winner of multiple SFWA Nebula awards), as "colored" while praising his work in the "Romancing Sci-Fi & Fantasy" panel at the SFWA Nebula Conference on Saturday, May 21. The SWFA Board of Directors released a statement Sunday announcing that they had removed Lackey, 71, from the conference, had disabled access to the footage of the panel to "avoid any additional harm being caused," and had reached out to other panelists to determine how they would prefer to proceed. "We learned yesterday that while participating in the'Romancing Sci-Fi & Fantasy' panel, Mercedes Lackey used a racial slur," the board wrote in a statement.

The problem with self-driving cars


We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - 28. Join AI and data leaders for insightful talks and exciting networking opportunities. Aka: Why this burning money pit has failed to produce meaningful results for decades. The future is here, and it looks nothing like we expected. As we approach the 10-year anniversary of Alexnet, we have to critically examine the successes and failures of machine learning. We are looking out from a higher plateau.

How Palo Alto Networks modernized its security management with AI


The SIEM, or security information and event management console, has been a staple for security teams for more than a decade. It's the single pane of glass that shows events, alerts, logs, and other information that can be used to find a breach. Despite its near ubiquity, I've long been a SIEM critic and believe the tool is long past its prime. This is certainly not the consensus; I've been criticized in the past for taking this stance. While robust passwords help you secure your valuable online accounts, hardware-based two-factor authentication takes that security to the next level.

Ethical Principles of Facial Recognition Technology


The sheer potential of facial recognition technology in various fields is almost unimaginable. However, certain errors that commonly creep into its functionality and a few ethical considerations need to be addressed before its most elaborate applications can be realized. An accurate facial recognition system uses biometrics to map facial features from a photograph or video. It compares the information with a database of known faces to find a match. Facial recognition can help verify a person's identity, but it also raises privacy issues. A few decades back, we could not have predicted that facial recognition would go on to become a near-indispensable part of our lives in the future.

Machine Learning at the Edge


I'm really excited to talk about advances in federated learning at the edge with you. When I think about the edge, I often think about small embedded devices, IoT, other types of things that might have a small computer in them, and I might not even realize that. I recently learned that these little scooters that are all over my city in Berlin, Germany, and maybe even yours as well, that they are collecting quite a lot of data and sending it. When I think about the data they might be collecting, and when I put on my data science and machine learning hat, and I think about the problems that they might want to solve, they might want to know about maintenance. They might want to know about road and weather conditions. They might want to know about driver performance. Really, the ultimate question they're trying to answer is this last one, which is, is this going to result in some problem for the scooter, or for the human, or for the other things around the scooter and the human? These are the types of questions we ask when we think about data and machine learning. When we think about it on the edge, or with embedded small systems, this often becomes a problem because traditional machine learning needs quite a lot of extra information to answer these questions. Let's take a look at a traditional machine learning system and investigate how it might go about collecting this data and answering this question. First, all the data would have to be aggregated and collected into a data lake. It might need to be standardized, or munged, or cleaned, or something done with it beforehand. Then, eventually, that data is pulled usually by a data science team or by scripts written by data engineering, or data scientists on the team.

How Microsoft plans to improve the low-code landscape


Taking on the challenges head-on that stand in the way of their low-code platforms growing, Microsoft's series of new product announcements this week at Build 2022 gives organizations new options for achieving low-code development goals. Microsoft's series of low-code announcements made this week include Power Pages, the latest Microsoft Power Platform addition for creating integrated, scalable and secure websites. Lured by the promises of democratizing app development with visual, declarative, drag and drop interfaces often bundled with enterprise-wide platforms like Microsoft, Salesforce, ServiceNow and others, enterprises have been quick to jump in and experiment. They're learning that support for a low-code platform can get expensive fast once app development moves from small department coding projects to larger-scale, enterprise-wide apps. Low-code platforms' hidden costs include limited process workflow support that further adds to the challenge of scaling them enterprise-wide.