Tag Archives: machine learning

A look at how the world’s leading tech companies are using AI

Artificial intelligence is the biggest thing to happen in the world of technology since the invention of computers. AI market spending has gone through the roof. This year it’s an $8 billion market. By 2020, the market will reach $47 billion. Exciting things are coming. Here are some of the ways the world’s top tech companies are going to introduce AI into our daily lives.

Google

Google is currently leading the self-driving vehicle market and is probably going to be the first to have a fully autonomous vehicle in the hands of consumers. They also own TensorFlow, an open source AI software library that anyone can use to experiment with creating machine learning programs.

Apple

Apple’s digital assistant, Siri, relies heavily on machine learning to accurately provide information and assistance for iPhone and iPad users.

Microsoft

Microsoft has its own version of Siri called “Cortana.” If Microsoft’s claims are to be believed, Cortana is about to get a major update that would make her the most accurate speech recognition machine in existence.

Intel

Intel recently purchased chipmaker, Movidius and Nervana Systems, a deep learning AI startup. Intel is trying to create the first line of CPUs that use neural computer architectures.

Don’t let big companies have all the fun

The year 2016 has been an unprecedented one for artificial intelligence startups which have raised a record amount of funding. Expect to see a number of exciting AI startups in the coming years that do big things.

Source: Tech Vibes

Will AI cause a rise in crime?

AI has many implications for society. Most are good, but some are bad. For instance, some are predicting that AI can soon be used to commit smarter crimes. Though using software to commit crimes is nothing new, it could become a larger problem when AI opens up new possibilities for the commission of crimes.

For example, imagine if a computer could create a synthesized voice that sounds just like a close relative? Imagine the potential for crime when a computer can pose as your mother or grandmother on the phone to request sensitive information such as banking login information or bank account numbers? Though it may sound like science fiction, it isn’t exactly far-fetched. AI companies are already working on AI programs that can mimic human speech that sounds surprisingly natural. In just a few years such technology may be readily available and affordable for anyone who wants to take advantage of it to commit crime.

This is just one example of how AI could be used to commit a crime. AI can also be used to create more sinister malware that could wreak havoc on our current antivirus programs.

In 2003, researchers at Carnegie Mellon University created Captcha—a kind of Turing Test that prevents computers from stealing online accounts. Now programs are being developed that are smart enough to fool Captcha and essentially pass for humans in order to gain access to sensitive account information.

If there’s any consolation, it’s that law enforcement and cyber-security companies will have the same technologies available to combat such crimes. Better cyber-security and improved monitoring systems will help law enforcement and cyber-security companies to keep up with smarter cyber-criminals.

The potential for increased crime exists as AI is developed. Artificial intelligence companies and especially machine learning startups, need to anticipate this and prepare accordingly, because there’s going to be a big market for such technologies.

Source: The New York Times

Thought Vectors – Words Reduced To Math

The idea of turning manmade constructs (made up stuff) into mathematical formulas has been the key ingredient in almost every advancement in technology throughout history. Recent advancements in the process of turning written language into mathematical formulas, has allowed computers to calculate human language and ideas in amazingly constructive ways.

Character Vectors

Character codes aka numerical representations of a single character. This is a little different than the 1s and 0s that make up the actual character, but the concepts are related because the numbers are made up from bits anyway.

Example:
a” = 97
“A” = 65

Word Vectors

Numerical representations of whole words.

Example:
animal” = 97, 110, 105, 109, 97, 108

Phrase Vectors

animals run wild” = 97, 110, 105, 109, 97, 108, 115, 32, 114, 117, 110, 32, 119, 105, 108, 100

Thought/Idea Vectors

The mathematical calculation of vectors applies to thoughts by simply calculating multiple sentences and the vectors of each word.

However, it goes further. You also have to calculate the vectors for all the words in the definitions of each of those words. You have to take into account how the words can have different meanings based on the way they are used.

Concept Vectors

Here is where you get into “cognitive computing” as talked about in the media. This is a far more thorough and advanced approach. It calculates the vectors of every single layer mentioned above, but it includes synonyms, antonyms, grammar and even the history and origin of the words.

When you calculate all of these together you can create a mathematical formula for representing an actual complex concept. This is where the real advancements in machine learning are coming from such as IBM Watson, Siri, Google Brain and others.