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) …
A new robot project has been published to the Instructables Circuits website which is equipped with machine learning technology allowing it to see the world using a generic camera to perform tasks depending on the detected object's position and orientation. Check out the video below to learn more about the Raspberry Pi powered robot which is equipped with a 3D printed claw. "This robot is truly special because it can use Machine Learning models to'see' the world via a generic camera and perform tasks depending on how the detected object's position is changing in the camera. This robot is built around the ever popular Raspberry pi, the incredibly powerful RoboClaw motor controller, and the common Rover 5 robot platform. Furthermore, all the additional physical parts are 3D printed.
Everyone's talking about the fast.ai Massive Open Online Course (MOOC) so I decided to have a go at their 2019 deep learning course Practical Deep Learning for Coders, v3. I've always known some deep learning concepts/ideas (I've been in this field for about a year now, dealing mostly with computer vision), but never really understood some intuitions or explanations. I also understand that Jeremy Howard, Rachel Thomas and Sylvain Gugger (follow them on Twitter!) are influential people in the deep learning sphere (Jeremy has a lot of experience with Kaggle competitions), so I hope to gain new insights and intuitions, and some tips and tricks for model training from them. I have so much to learn from these folks.
Machine Learning (ML) is a type of Artificial Intelligence (AI) in which the main principle is that computers can learn and make decisions without relying on human programming. The technology is comprised of data consumption, algorithmic study, analytical model training, and building & predicting outcomes. ML has transformed the way most of the world's industries function, including financial, agriculture, real estate, science, manufacturing, shipping, healthcare, business, transportation, and commerce sectors. Machine Learning technology can be applied for various functions that developers may want to provide, including facial recognition, fraud detection, language identification & translation, contextual awareness, financial services, video analysis, autonomous robot, machine & car operations, conversation and speech patterns, text analysis, semantics, and even drawing. Application Programming Interfaces, or APIs are driving the insurgence of Machine Learning.
Have you ever wondered whom to thank for some of the modern conveniences you might have started taking for granted, like Siri, Cortana or Alexa (assuming you agree these are conveniences)? The people at the Association for Computing Machinery (ACM) decided to thank Geoffrey Hinton, Yoshua Bengio and Yann LeCun in April of this year by honoring them with the Turing Award for their contributions to deep learning and neural networks. These contributions are put to use every time you log into your smartphone using fingerprint or facial recognition or when you use Google Photos or a voice assistant, and likely every time you use Amazon, Netflix, Facebook or Instagram. The advances in automatic language translation and autonomous cars in recent years arguably wouldn't have progressed as rapidly had it not been for the contributions of these three researchers. All of that is still an understatement of their contributions to artificial intelligence (AI).
We live in exponential times, and merely having a digital strategy focused on continuous innovation is no longer enough to thrive in a constantly changing world. To transform an organisation and contribute to building a secure and rewarding networked society, collaboration among employees, customers, business units and even things is increasingly becoming key. Especially with the availability of new technologies such as artificial intelligence, organisations now, more than ever before, need to focus on bringing together the different stakeholders to co-create the future. Big data empowers customers and employees, the Internet of Things will create vast amounts of data and connects all devices, while artificial intelligence creates new human-machine interactions. In today's world, every organisation is a data organisation, and AI is required to make sense of it all.
This video is part of an online course, End-to-End Machine Learning with Tensorflow from Google Cloud. About this course: In the first course of this specialization, we will recap what was covered in the Machine Learning with TensorFlow on Google Cloud Platform Specialization. One of the best ways to review something is to work with the concepts and technologies that you have learned.
Automation testing in Selenium using Python language is probably the easiest way of getting into automation testing. Python is an easy to understand language. If you are looking to get into Selenium, this video will be a good start for you. We provide IT certifications training for professionals. We specialize in the following areas: a) Automation Testing (Selenium, DevOps) b) Business Analyst Certifications (Beginner and Senior levels) c) Robotic Process Automation (RPA) d) Tableau 10 Training Website: http://techcanvass.com
Do you have data and wonder what it can tell you? Do you need a deeper understanding of the core ways in which machine learning can improve your business? Do you want to be able to converse with specialists about anything from regression and classification to deep learning and recommender systems? In this course, you will get hands-on experience with machine learning from a series of practical case-studies. At the end of the first course you will have studied how to predict house prices based on house-level features, analyze sentiment from user reviews, retrieve documents of interest, recommend products, and search for images.
Last month's announcement by Amazon that it plans to spend $700 million (£569 million) over six years to retrain a third of its US workforce was eye-catching for many reasons. One was the price tag: even for the world's second most valuable company, spending three-quarters of a billion dollars over half a decade to retrain 100,000 workers is a huge undertaking. Also noteworthy was the firm's reasoning. Amazon explicitly attributed its move to the rise of automation, machine learning and other technology: the so-called fourth industrial revolution. There was a sense that the pioneer of online retailing, famed for its use of automation, was merely an early accepter of an inescapable truth that all employers will soon have to face: that the skills of their existing workforces will no longer have any market value as their old roles are taken by machines and new roles are created. The company reportedly has 20,000 current vacancies.