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) …
Deep learning has found its way into all kinds of research areas in the present times and has also become an integral part of our lives. "Artificial Intelligence is the new electricity." However, with any great technical breakthroughs come a large number of challenges too. This article will help you solve one of these hurdles, which is optimization. We know that if we set the learning rate too small, the algorithm will take too much time to converge fully, and if it's too large, the algorithm will diverge instead of converging.
IEEE Intelligent Systems 24, 2 (2009) In the decade since then, the research community have done a lot with quantity, but quality has been left behind 16. http://lora-aroyo.org Data Quality is not only human error 20. Data Quality should consider context of use it is not easy to give Y/N answer for most of our AI tasks the answer typically depends on the context, on the task, on the usage, etc 21. http://lora-aroyo.org Data Quality should include real world diversity it is not easy to give Y/N answer for most of our AI tasks the answer typically depends on the context, on the task, on the usage, etc disagreement is signal for diversity and should be included in AI training 22. http://lora-aroyo.org Data Quality is difficult even with experts For prevention of malaria, use only in individuals traveling to malarious areas where CHLOROQUINE resistant P. falciparum MALARIA has not been reported.
We are a community of Machine Learning Researchers and Engineers, working to help Twitter leverage ML through a range of systems such as recommendations, safety, abuse, content understanding, ads and more. We operate at scale whilst ensuring fair and ethical use of our models and data. We work collaboratively, often embedding among product teams, looking to apply the expertise of the individuals to improve our products and unlock new capabilities. We encourage publishing papers, but they are not the end goal, rather a by-product of us doing interesting work - the aim is to make a real-world impact! The Learning Methods Research Team, part of Cortex Applied Research, enables ML applications across our platform (e.g.
This is a place to share machine learning research papers, journals, and articles that you're reading this week. If it relates to what you're researching, by all means elaborate and give us your insight, otherwise it could just be an interesting paper you've read. Please try to provide some insight from your understanding and please don't post things which are present in wiki. Preferably you should link the arxiv page (not the PDF, you can easily access the PDF from the summary page but not the other way around) or any other pertinent links. Besides that, there are no rules, have fun.
Alex Fly's article presents some key statistics related to the development of AI and the utilisation of AI-based technologies within enterprises. The included statistics and figures are sourced from reputable surveyors such as Deloitte, Gartner, McKinsey & Company etc. The statistics and figures presented in the article paint a picture of how AI is transforming the entirety of a typical enterprise day to day functions. AI impact is felt from hiring initiatives to profitable revenue sources. One major takeaway from Alex's article is that AI is here to stay and enterprises are adapting at a pace that will see entire companies, industries and nation transform within the next five years.
Researchers from MIT, Stanford University, and the University of Pennsylvania have devised a method for predicting failure rates of safety-critical machine learning systems and efficiently determining their rate of occurrence. Safety-critical machine learning systems make decisions for automated technology like self-driving cars, robotic surgery, pacemakers, and autonomous flight systems for helicopters and planes. Unlike AI that helps you write an email or recommends a song, safety-critical system failures can result in serious injury or death. Problems with such machine learning systems can also cause financially costly events like SpaceX missing its landing pad. Researchers say their neural bridge sampling method gives regulators, academics, and industry experts a common reference for discussing the risks associated with deploying complex machine learning systems in safety-critical environments. In a paper titled "Neural Bridge Sampling for Evaluating Safety-Critical Autonomous Systems," recently published on arXiv, the authors assert their approach can satisfy both the public's right to know that a system has been rigorously tested and an organization's desire to treat AI models like trade secrets.
After months of staying home, perhaps you've done enough sourdough baking to last a lifetime. As we head into winter, it's time to mix it up. These cool DIY kits, which are all an extra 20% off through September 21 with the coupon code VIPSALE20, will help you build cool gadgets and models like programmable cars, game consoles, and more. Take on the challenge of constructing a game console yourself with the MAKERbuino Educational DIY Game Console. Usually $79.99, this unique product is an extra 20% off with code VIPSALE20, bringing the price to about $64.
Many people imagine that data science is mostly machine learning and that data scientists mostly build and train and tweak machine-learning models all day long. In fact, data science is mostly turning business problems into data problems and collecting data and understanding data and cleaning data and formatting data, after which machine learning is almost an afterthought. Even so, it's an interesting and essential afterthought that you pretty much have to know about in order to do data science. Before we can talk about machine learning we need to talk about models. It's simply a specification of a mathematical (or probabilistic) relationship that exists between different variables.
Amazon's 2019 Climate Pledge calls for a commitment to net zero carbon across their businesses by 2040. Since then, the company has reduced the weight of their outbound packaging by 33%, eliminating 915,000 tons of packaging material worldwide, or the equivalent of over 1.5 billion shipping boxes. With less packaging used throughout the supply chain, volume per shipment is reduced and transportation becomes more efficient. The cumulative impact across Amazon's enormous network is a dramatic reduction in carbon emissions. To make this happen, the customer packaging experience team partnered with AWS to build a machine learning solution powered by Amazon SageMaker.
In this article, we'll be learning the following: Object detection can be defined as a branch of computer vision which deals with the localization and the identification of an object. Object localization and identification are two different tasks that are put together to achieve this singular goal of object detection. Object localization deals with specifying the location of an object in an image or a video stream, while object identification deals with assigning the object to a specific label, class, or description. With computer vision, developers can flexibly do things like embed surveillance tracking systems for security enhancement, real-time crop prediction, real-time disease identification/ tracking in the human cells, etc. The TensorFlow Model Zoo is a collection of pre-trained object detection architectures that have performed tremendously well on the COCO dataset. The model zoo can be found here.