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) …
Highlights: Prior to Yolo majority of approaches for object detection tried to adapt the classifiers for the purpose of detection. In YOLO, an object detection has been framed as a regression problem to spatially separated bounding boxes and associated class probabilities. In this post we will learn about the YOLO Object Detection system, and how to implement such a system in TensorFlow 2.0. About Yolo: Our unified architecture is extremely fast. Our base YOLO model processes images in real-time at 45 frames per second.
Our clients often ask us how our language technology is able to detect questions that are formulated differently, contain errors, or combine multiple questions in one text. In order to answer these questions, we have written this article to provide a general overview of our technology. The descriptions are intentionally kept simple to ensure easy understanding. This article is primarily intended for service managers and business managers who want to automate their customer service. We have been working in the field of NLU (Natural Language Understanding) / NLP (Natural Language Processing) for several years.
How is it that we're able to look at a tree and see beauty, hear a song and feel moved, or take comfort in the smell of rain or the taste of coffee? How do we know we still exist when we close our eyes and lie in silence? To date, science doesn't have an answer to those questions. Forget phrase books or even Google Translate. New translation devices are getting closer to replicating the fantasy of the Babel fish, which in the "Hitchhiker's Guide to the Galaxy" sits in one's ear and instantly translates any foreign language into the user's own.
This webinar will help educators at all levels learn more about artificial intelligence (AI) and how AI concepts can be woven into many subject areas. Participants will find out how to help students understand the capabilities and potential for AI in education, business and industry, as well as how AI will impact the future of education and work. We'll also explain the everyday uses of AI and share ways for students to experiment with it using simple chatbots, machine learning and recommender systems. Webinar participants will be given multiple resources to discover more about AI and teaching it in schools, and will meet members of the AI4K12 working group who are creating guidelines for teaching it in schools.
John Carmack, CTO of Oculus and recent recipient of a Lifetime Achievement Award for his work in VR, is scaling back his duties at Oculus. In a Facebook post, he states that he will become a'Consultant CTO' for Oculus while pursuing a much more ambitious goal: Artificial General Intelligence. In other words, technical genius John Carmack, who revolutionised video games and then VR, is now going to help bring about human-like, or even human-surpassing, AI. While a radical shift in focus like this may seem out of the blue, there have been hints recently that Carmack isn't as engaged by VR as he once was. In his recipients' speech for the Lifetime Achievement Award at this year's VR Awards, he talked about how he felt VR wasn't advancing quickly enough for him to feel satisfied.
Generating clean data has become an important step for AI in logistics companies as many simply do not have usable figures to implement. Efficiency gains are difficult to measure as some companies generate their data from multiple points and multiple people. Such figures cannot be easily improved at the source, so algorithms are being used to analyze historical data, identify issues and improve data quality to the level where significant transparency on the business is gained. A good example of data cleansing in action is when companies have incomplete shipment data, AI can systematically go through past shipments to create precise deductions on the unknown quantity. As written previously, these AI algorithms only require 5 to 10 percent of correct data in order to create a training dataset which can be used as a basis for data cleansing and enrichment.
Argo AI was founded to tackle one of the most challenging applications in computer science, robotics and artificial intelligence with self-driving vehicles. Argo AI is developing and deploying the latest advancements in artificial intelligence, machine learning and computer vision to help build safe and efficient self-driving vehicles that enable these transformations and more. The challenges are significant, but we are a team that believes in tackling hard, meaningful problems to improve the world. We are building a high-performance team that is excited by complex engineering challenges and is passionate about making transportation safer, more affordable and accessible for all. This team solves problems that involve geometric computer vision: processing sensor data from Lidar or imagery and geometrically reasoning about that data.
As we progress further into Industry 4.0, finance needs to further leverage new technologies to add real value to a business's bottom-line, yet it remains in its infancy stages. Industry 4.0 has impacted a range of industries, and with the digitisation of industrial value chains, many forget about finance, which has only touched the tip of the iceberg when it comes to leveraging new technologies. Disruptive technologies such automation, artificial intelligence (AI), the Internet of Things (IoT), Bots, blockchain and machine learning are thrusting the global economy into a new digital era. Instead of seeing all these new developments in isolation, finance must focus rather on connection points, finding ways to optimise them to provide greater value to the organisation. Companies risk losing ground if they do not understand the changes and opportunities Industry 4.0 brings.
Artificial intelligence has been the talk of the town these days. Frequent news updates and developments are being reported and a lot of organizations are trying to develop their own AIs and integrate them into their business structure. But is this a reality or just an availability bias? Well, reality states that only a few firms are successfully using AIs as compared to those struggling to reap its benefits. AI-based chatbots are very common in most organization's websites and customer care services.
Although deep learning architectures have shown remarkable results in scene understanding problems, they exhibit a critical drop of overall performance due to catastrophic forgetting when they are required to incrementally learn to recognize new classes without forgetting the old ones. This phenomenon impacts on the deployment of artificial intelligence in real world scenarios where systems need to learn new and different representations over time. Current approaches for incremental learning deal only with the image classification and object detection tasks. In this work we formally introduce the incremental learning problem for semantic segmentation. To avoid catastrophic forgetting we propose to distill the knowledge of the previous model to retain the information about previously learned classes, whilst updating the current model to learn the new ones.