An Introduction to'Machine Learning' -- I came across this article and thought it was worth a share, the original article was surrounded in adverts and difficult to read, so I make no apologies for plagiarizing it! I have kept the original link at the bottom of the article, enjoy . . . The concept that a computer program can learn and adapt to new data without human interference. Machine learning is a field of artificial intelligence that keeps a computer's built-in algorithms current regardless of changes in the worldwide economy. Various sectors of the economy are dealing with huge amounts of data available in different formats from disparate sources.

For example, let's say you want to teach a computer to distinguish between a cat and a dog. You might say cats have four legs, a tail, and pointed ears. Dogs have four legs, a tail, and floppy ears. But if you show that machine a chihuahua or a corgi, will it say it's a cat? Between tail length, fur texture, and color, there's a lot of labels a developer would have to program manually to help a traditional computer spot the difference. But with machine learning, you feed the computer thousands of photos of cats and dogs so it can learn to spot the difference by experience -- or in the world of machine learning -- lots of data.

What can we actually expect in 2017? Here's what happens in an "Internet Minute": We leave data footprints in many other ways. Which means more of us are taming data for better insights that guide our decisions. Data helps us focus and make better choices. Yet some truths hide among a high number of variables. Others stay hidden because questions are deemed too hard to answer.

Machine learning is a buzzword in the technology world right now, and for good reason: It represents a major step forward in how computers can learn. Very basically, a machine learning algorithm is given a "teaching set" of data, then asked to use that data to answer a question. For example, you might provide a computer a teaching set of photographs, some of which say, "this is a cat" and some of which say, "this is not a cat." Then you could show the computer a series of new photos and it would begin to identify which photos were of cats. Machine learning then continues to add to its teaching set.

At first I liked tinkering with computers and learn computer programming languages, after graduating high school I started to develop the concept of work on data processing and I've completed it. More recently the IT world the term Deep Learning (DL) number of campuses or institutions have been developing this concept, and many experts of computer data or data processing experts began to talk about it. I do not know that it is actually a concept I have done resemblance to Deep Learning or part of Deep Learning but once I learned it was different, DL they mean is to show something of what they are looking for based on the data input as much as possible so that what they the purpose is to learn to structure the deepest and provide advisory or decision, but it relates to the search engine or internet network application using algorithms, meaning that when it is applied in the world of the stock market as Wall Street, the working concept Deep Learning will detect fraud there is. Deep Learning systems work similar to the concept of the brain where the objects are visible to the eye to be delivered to specific parts to be stored and studied by contrasting the existing data and the use of certain alogritma method to render a decision as well as a warning signal. Deep Learning tend to use super computers or computer large capacity for looking at the use of data (big data), big data here can mean pictures, numbers, files, chat, text, web pages, maps of the world, the code algoritmatik, core decision made deep learning is seen in a comparison of all the data held (such as scanned photos) means more data entry means more comparisons, and if more and more comparisons, the decision is getting better, so that a deficiency also that deep learning must wear a large-capacity computers.

With the rise of machine learning, concerns are being voiced about the future involvement of humans in jobs that computers can handle. But at the same time, alternative views are being offered on ways in which the human element will influence the direction of data science, with passion playing a large role in that survival. "I think data science is one area in which a woman … will have a huge opportunity to move the needle," said Stephanie Gottlib-Zeh (pictured), president of Agyleo Sport. In her view, insularity is something more likely to damage an enterprise than to sustain it, and drawing in a diversity of viewpoints is a big step toward protecting against that sort of stagnancy. Gottlib-Zeh (pictured), president of Agyleo Sport, shared her thoughts on the ways in which data is changing business and how tech workers should aim for jobs that excite them.

VocalIQ concentrated its deep-learning capabilities to better understand human speech. This could provide Siri with an upgrade. Perceptio can run neural-network algorithms and perform advanced calculations locally on a cellphone, without uploading user data to the cloud. This would help Apple protect consumer data by keeping it local to each phone. Emotient attempts to read facial expressions in order to determine a person's emotional state.

Artificial Intelligence is transforming the entire world of technology, but AI isn't new. It has been around for decades, but AI technologies are only making headway now due to the proliferation of data and the investments being made in storage, compute and analytics technologies. Much of this progress is due to the ability of learning algorithms to spot patterns in larger and larger amounts of data. In this insideBIGDATA Guide to Artificial Intelligence, we provide an in depth look at AI and deep learning in terms of how it's being used and what technological advances have made it possible. Artificial Intelligence is an amazing tool set that is helping people create exciting applications and creating new ways to service customers, cure diseases, prevent security threats, and much more.

Chess enthusiasts watch World Chess champion Garry Kasparov on a television monitor as he holds his head in his hands at the start of the sixth and final match 11 May 1997 against IBM's Deep Blue computer in New York. Kasparov lost this match in just 19 moves giving overall victory to Deep Blue with a score of 2.5-3.5 (STAN HONDA/AFP/Getty Images) This week's milestones in the history of technology include the invention of the integrated circuit, the first singing telegram, and the first widely-publicized triumph of the machines over humans. Jack Kilby of Texas Instruments (TI) files for a patent on the integrated circuit. For this invention he received the 2000 Nobel Prize for Physics. The notion of an integrated circuit was there.

The featured guest for episode 148 is Paul Roetzer, Founder & CEO of PR 20/20, a well-known marketing agency specializing in inbound marketing strategies. Paul is the author of two popular books: The Marketing Agency Blueprint and The Marketing Performance Blueprint. In November 2016, Paul launched the Marketing Artificial Intelligence Institute (MAII). On this episode, we dive deep into what MAII is, why it exists, and why you should care. If you're not familiar with Artificial Intelligence (AI), Paul describes it as "the umbrella of the tools and technologies that are designed to make machines smarter."