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) …
Online dating as the standard way to meet someone isn't even news anymore. Nowadays, "We met on Hinge" is far more plausible than "We met at a bar." Still, looking for love online comes with nervousness, catfish paranoia, and doubtful looks from nosy family members. To that, we ask: Is waiting around to stumble upon your soulmate in public really more promising? We love Love Island as much as the next reality TV Trekkie, but we can't all put our lives on hold to find a fiancé.
The never-ending fight with bias and AI systems that learn by watching YouTube. EU mobilizes to rein in tech giants. Facebook's AI has migrated all their AI systems to PyTorch. Within a year, there are more than 1,700 PyTorch-based inference models in full production at Facebook, and 93 percent of their new training models are on PyTorch. The times are hardly perfect for self-driving car companies.
The Self-Driving car might still be having difficulties understanding the difference between humans and garbage can, but that does not take anything away from the amazing progress state-of-the-art object detection models have made in the last decade. Combine that with the image processing abilities of libraries like OpenCV, it is much easier today to build a real-time object detection system prototype in hours. In this guide, I will try to show you how to develop sub-systems that go into a simple object detection application and how to put all of that together. I know some of you might be thinking why I am using Python, isn't it too slow for a real-time application, and you are right; to some extent. The most compute-heavy operations, like predictions or image processing, are being performed by PyTorch and OpenCV both of which use c behind the scene to implement these operations, therefore it won't make much difference if we use c or python for our use case here.
If you're one of the many people who feel like you've been watching a little too much Netflix and eating far too many processed foods out of a box, can, or bag throughout the pandemic, you're not alone. But it's never too late to reset if you've lost your routine and are struggling to find your new normal. Perhaps a home gym is the solution you need. Those of you who want to figure out ways to maintain a healthy work-life balance should create a dedicated space in your house that serves as an at-home gym. But, rather than continually rotating between couches and screens all day long, you can break up the hours at home by squeezing in some much-needed time to sweat.
Voice continue to the most widely-utilized customer service channel by consumers, with 73% of consumers calling into the call center for customer service needs, according to Forrester. Other channels are gaining ground, however, with digital channels, such as chat and email, and web-based self-service becoming increasingly utilized by consumers. New technologies are providing consumers with more options for connecting with the companies they do business with, but technology advancements are also reshaping the way companies are meeting those needs. Once a pipe dream believed to be far off in the future, artificial intelligence (AI) is one innovation that's transforming the customer service landscape. We've put together this guide to provide a comprehensive history of AI in the call center, from the advent of artificial intelligence as a whole to its first use in the call center and the potential for future disruption.
Here's a great lineup of gift ideas and resources to get you started. All the signs were there. If my parents knew then what parents know now, they would have been prepared. But back in the 1960s and 1970s, the maker movement was still far in the future. Robots were something you only saw in movies and awesome TV shows (or as my Mom would often put it, "What in the world are you watching?"). Telling her that Lost in Space wasn't "in the world" tended to get me the All Powerful Glare of Motherly Annoyance.
In a bid to standardise programs designed to increase female participation in science, technology, engineering, and mathematics (STEM), the Office of Women in STEM Ambassador has published a national guide to help those running these gender equity programs to evaluate the effectiveness of their initiatives. The Evaluating STEM Gender Equity Programs guide is an online resource that provides advice, planning tools, and other guidance, and has been organised into five steps: Define, plan, design, execute, and share. "We're trying to attract and keep women and girls in STEM … and that end goal is to have a diverse gender balance in the STEM workforce … and if we're trying to create that change, then we need to know what's working, and we need to know what's not working and how to improve that," guide author and research associate at the Office of the Women in STEM Ambassador Isabelle Kingsley told ZDNet. "We're really hoping that anyone running a [STEM] program will be using this guide. One of the main things that is part of this guide is that evaluation is embedded from the beginning and not tacked on at the end."
Building your own computer vision model from scratch can be fun and fulfilling. You get to decide your preferred choice of machine learning framework and platform for training and deployment, design your data pipeline and neural network architecture, write custom training and inference scripts, and fine-tune your model algorithm's hyperparameters to get the optimal model performance. On the other hand, this can also be a daunting task for someone who has no or little computer vision and machine learning expertise. This post shows a step-by-step guide on how to build a natural flower classifier using Amazon Rekognition Custom Labels with AWS best practices. Amazon Rekognition Custom Labels is a feature of Amazon Rekognition, one of the AWS AI services for automated image and video analysis with machine learning. It provides Automated Machine Learning (AutoML) capability for custom computer vision end-to-end machine learning workflows.
So, you've decided you want to purchase a machine dedicated to training machine learning models. Or, rather, you work in an organization where the buzzwords of this guide are constantly thrown around and you simply want to know a bit more about what they mean. This isn't a terribly simple topic, so I've decided to write this guide. You can discuss those terms from various angles, and this guide will tackle one of them. I'm Nir Ben-Zvi, a Deep Learning researcher and a hardware enthusiast from early middle school days, where I would tear computers apart while friends were playing basketball (tried that too, went back to hardware pretty fast). In the past few years I got to consult some friends on building deep learning machines for companies of various sizes, and ultimately decided to put that knowledge into this guide. Today I work for trigo, doing some Deep Learning and Data. A lot of the knowledge for this guide came from the decisions made towards building our first deep learning machines. Some parts of this guide are kept despite being way out of date.