We live in the midst of the invisible revolution: artificial intelligence, simply defined as intelligence in machines, is all around us, embedded in our everyday apps and devices. AI technology is not only amplifying human ingenuity, but opening more and different kinds of opportunities in the tech industry, and according to a study by the job-matching service The Ladders, science, technology, engineering and math jobs are growing the fastest. To make AI available for all and encourage more Latinx to pursue a tech-related job, Microsoft is offering several training courses on AI designed for a variety of expertise levels. Here are three free training in AI courses that you can take advantage of.

Microsoft Professional Program for AI: This program provides job-ready skills and real-world experiences to aspiring AI engineers and those looking to improve their skills in AI. The series of online courses feature hands-on labs and expert instructors, and takes students from basic introduction of AI to mastery of the skills needed to build models for AI solutions that exhibit human-like behavior and intelligence. Each course runs for three months and starts at the beginning of each quarter. Microsoft AI Residency Program: This program offers students the opportunity to work alongside prominent Microsoft researchers and engineers to gain real-world, hands-on experience industry.

Participants can learn for a year about deploying machine learning solutions across a range of areas such as healthcare, entertainment and productivity. AI School: This training provides online videos and other assets to help developers build AI skills, including general educational tools to expand AI capabilities and specific guidance on the use of Microsoft's tools and services. With the available resources, you can learn more about Bot Framework, Azure Machine Learning, Cognitive Toolkit, and much more. Whether your solutions are existing or new, this is the intelligence platform to build on. The future is now, and at Microsoft we believe everyone should have the resources to learn more and become an expert in AI. Tags: Accessibility, Artificial Intelligence, cloud, data, Education, Machine Learning, Microsoft Latinx, The Future is now.

There can be no two opinions as to what a highbrow is. He is the man or woman of thoroughbred intelligence who rides his mind at a gallop across country in pursuit of an idea.
author: Virginia Woolf

 

Why AI Is The Most Dangerous Thing You Can Imagine

In the world of business, science and technology, is much talk of these three concepts, but often do not have a clear idea of their meaning, their relationships and their limits. In this post we will define the concept of Machine Learning and its differences with respect to the concepts of Artificial Intelligence and Deep Learning. For example, a chess machine detects the movements of the chips on the Board, and applying the rules of chess to the data they have collected, decide the best move.

The digital assistants like Siri and Cortana are also examples of this type of IA. You can give us the weather forecast, or recommend an alternative route to go to work, but not to read our messages and delete that are not important to us. One of his best-known areas of application is the robotic, but it has important applications in the fields of medicine, education, entertainment, information management, mathematics, military applications, urban design, architecture etc. Machine Learning or machine learning is a branch of Artificial intelligence that began to gain importance from the 80s. It is a form of AI that no longer depends on rules and a programmer, but that computer can establish their own rules and learning itself.

One of the algorithms of ML that most arouses expectation, are neural networks, a technique inspired by the functioning of neurons in our brain. There is no single definition of what is Deep Learning. In general, when we talk about Deep Learning we are talking about a class of Machine Learning algorithms based on neural networks which, as we have seen, characterized by cascading data processing. The input signal spreads by the different layers, and each of them is subject to a non-linear transformation that is extracting and transforming the variables according to certain parameters. There is a limit to the number of layers that must have a neural network to be considered Deep Learning.