This Web Application can recognize which numbers were drawn by you. To do this it uses Machine Learning (actually Deep Learning) techniques for predicting (determining) which digits were drawn by you on the canvas. It was trained (we refer to training as the way our model learn to predict correctly the given inputs) using two python frameworks for Machine Learning Tensorflow, Keras and another JavaScript framework used for inference (something like using a pre-trained model to make predictions) the TensorflowJS. For more information about the model used for inference, click on the Model Card.

Natural Language Processing is quite simply the study and use of machines to intelligently use, and create, natural language. I purposely leave out the word'understand' for now, as it is a bit of a prickly subject when using probabilistic models. But the conundrum is the following: In terms of bit-rate, verbal communication is awful. A lecturer might utter 300 words per minute, which is the paltry rate of about 70 bytes/second. But switch to a two-way conversation, and that can drop even further to 100 words/minute, or a definitely anaemic 25 bytes/second.

A key challenge in building AI systems is creating a mathematical definition of the subject of interest. This causes a problem because no mathematical definition can capture the full scope of the natural world. This is further complicated by the fact that these definitions are created by teams of AI developers and will inevitably reflect the attitudes of these developers. This issue affects all AI systems. But that doesn't mean we shouldn't build any models for anything. A model can bring a benefit, even if it isn't perfect.

Sydney's Tech Central has welcomed Q-Ctrl, Sydney Quantum Academy, and Quantum Brilliance as the first tenants of its newest precinct known as the quantum terminal. According to the state government, the quantum terminal will be the city's "first centralised live collaboration space for researchers, developers, engineers and entrepreneurs -- all working to advance quantum technology, high performance computing, and artificial intelligence". "Quantum terminal along with the rest of Tech Central will form one of the most vibrant innovation corridors in Australia," Minister for Jobs, Investment, Tourism and Western Sydney and Minister for Trade and Industry Stuart Ayres said. Alongside the opening of the quantum terminal, the NSW government announced on Monday it will invest up AU$21 million to prioritise affordable accommodation for scaleups. "From December, businesses can apply for rebates on rental and fit-out costs of up to AU$600,000 a year through the Tech Central Scaleup Accommodation Rebate," Ayres said.

Whether you're planning your annual viewing of White Christmas or gearing up for college football bowl season, now is a great time to upgrade your home theater. This Cyber Monday has steep discounts on many of our favorite TVs, from massive OLED displays to simple and affordable options with built-in Roku. There are also a wide assortment of great soundbars available for great prices this shopping holiday, making it a perfect time to just upgrade your sound already. Be sure to check out our guides to the Best TVs and Best Soundbars for more information about our favorite models right now. If you buy something using links in our stories, we may earn a commission. This helps support our journalism.

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As AI-powered technologies proliferate in the enterprise, the term "explainable AI" (XAI) has entered mainstream vernacular. XAI is a set of tools, techniques, and frameworks intended to help users and designers of AI systems understand their predictions, including how and why the systems arrived at them. A June 2020 IDC report found that business decision-makers believe explainability is a "critical requirement" in AI. To this end, explainability has been referenced as a guiding principle for AI development at DARPA, the European Commission's High-level Expert Group on AI, and the National Institute of Standards and Technology. Startups are emerging to deliver "explainability as a service," like Truera, and tech giants such as IBM, Google, and Microsoft have open-sourced both XAI toolkits and methods.

Preface: Three decades ago while working at Air Force Research Laboratory, I developed the first interactive Augmented Reality system, enabling users to reach out and touch a mixed world of real and virtual objects. I was so inspired by the reactions people had when they tried those early prototypes, I founded one of the first VR companies in 1993, Immersion Corp, and later founded the early AR company, Outland Research. Yes, I've been a believer for a long time. Looking forward, I expect augmented reality to become the platform of our lives, replacing smartphones as our primary means of accessing digital content. I still believe in the magical potential, but also fear the negative consequences. To paint a balanced picture of what our augmented lives will be like ten years from now, I've written the short narrative below. Like any fictional forecast it will not play out exactly like this, but I'm confident that the convergence of augmented reality and artificial intelligence will make much of this portrayal come true. It was a tiny room no larger than a walk-in closet. A small woman in a crisp white lab coat stood beside a large optometry machine, its smooth black surface covered in silver dials and knobs and levers. Flipping between settings she asked, "Better or worse?" "Better," rang a voice from behind the contraption. The woman pulled the machine forward, revealing Gordon Pines, squinting as the overhead lights suddenly came on. Balding with gray stubble, he looked older than his 68 years would suggest. That's because he was tired -- exhausted from the simple act of leaving his small apartment and venturing out into the busy city. Chicago had been his home for three decades but somehow it just didn't feel familiar anymore.

Machine learning and artificial intelligence are often used interchangeably, but they are two very different things. Artificial Intelligence (AI) is a broad field that has been around for decades -- it's the technology behind Siri and Alexa. Machine Learning (ML), on the other hand, is a subset of AI that uses statistical techniques to allow computers to learn without being explicitly programmed with rules or instructions. Therefore, you can see that machine learning and artificial intelligence aren't interchangeable terms; rather they're related fields in computer science that both utilize mathematics and statistics to create intelligent behavior from machines. To better understand the difference between the two, think of it this way: AI is a toaster that can make toast; ML is an algorithm where you tell the toaster how dark you want your toast and when.

If you are a data Scientist or learning Data science then there is nothing better than following a blog that provides the latest information. Here is a list of blogs you must follow to know more about data science, machine learnings, and AI. Simply Statistics is run by Jeff Leek, Roger Peng, and Rafa Irizarry. Simple Statistics also offers data science specialization courses. Flowing Data offers tutorials and resources for effective data visualization.