Jul 19 · 9 min read Artificial Intelligence (AI) is such a buzz word these days and one thing about buzz words is… 'They often get lost in translation'. But I think it's time we all take a deep breath, exhale, pause… And realize that AI is a well-founded discipline in its own right. Machine Learning and Deep Learning do not define Artificial Intelligence. AI is a much broader field than ML, which is a Statistical subset of AI and DL, which is a specialized subset of ML involving Neural networks computation… ML and DL are Subsets of a much broader field called AI… So what exactly is Artificial Intelligence? To answer this question we must consider the four historical approaches to AI.

Artificial Intelligence (AI) is such a buzz word these days and one thing about buzz words is… 'They often get lost in translation'. Ask any Data Scientist (including yours truly) about AI, and you're likely to hear Machine Learning (ML) algorithms or Deep learning (DL) and its fantastic applications, such as in AlphaGo… Where the Neural network learned through reinforcement learning, defeated the Go world champion, making AlphaGo arguably the strongest Go player in history… These are all applicable responses. But I think it's time we all take a deep breath, exhale, pause… And realize that AI is a well-founded discipline in its own right. Machine Learning and Deep Learning do not define Artificial Intelligence. To answer this question we must consider the four historical approaches to AI.

Artificial Intelligence (AI) is such a buzz word these days and one thing about buzz words is… 'They often get lost in translation'. Ask any Data Scientist (including yours truly) about AI, and you're likely to hear Machine Learning (ML) algorithms or Deep learning (DL) and its fantastic applications, such as in AlphaGo… Where the Neural network learned through reinforcement learning, defeated the Go world champion, making AlphaGo arguably the strongest Go player in history… These are all applicable responses. But I think it's time we all take a deep breath, exhale, pause… And realize that AI is a well-founded discipline in its own right. Machine Learning and Deep Learning do not define Artificial Intelligence. To answer this question we must consider the four historical approaches to AI.

A University of Arizona information scientist wants to make computers behave more like natural human partners. UA researcher Clay Morrison focuses on machine learning. He is looking at ways to get artificial intelligence to work alongside people. "We're not trying to say the computer has to behave exactly like a human," he said. "Instead it's how the computer is natural enough to interact with, so when we team up with them and they collaborate with us on a project, the strengths the computer brings to the table and the strengths the human brings to the table are really much more effectively combined."

A group of researchers has developed a computer model that's capable of cracking text-based CAPTCHA keys. Given that the purpose of CAPTCHA is to test whether the entity attempting to access a service is human, this is a considerable step forward for developing computers that think like humans. Text-based CAPTCHAs–the name coming from the phrase Completely Automated Turing Test to Tell Computers and Humans Apart–are often used by web services that might suffer an attack based around an influx of automated users. Account sign-up services and ticketing sites often use them to confirm that visitors are human. CAPTCHA work by displaying letters that are relatively easy for a human read, but hard for a computer that's trained on the standard shapes of the alphabet to decipher.

UC Berkeley researchers are tapping the cognitive smarts of babies, toddlers and preschoolers to program computers to think more like humans. "Children are the greatest learning machines in the universe. Imagine if computers could learn as much and as quickly as they do," said Alison Gopnik a developmental psychologist at UC Berkeley and author of "The Scientist in the Crib" and "The Philosophical Baby." In a wide range of experiments involving lollipops, flashing and spinning toys, and music makers, among other props, UC Berkeley researchers are finding that children -- at younger and younger ages -- are testing hypotheses, detecting statistical patterns and drawing conclusions while constantly adapting to changes. "Young children are capable of solving problems that still pose a challenge for computers, such as learning languages and figuring out causal relationships," said Tom Griffiths, director of UC Berkeley's Computational Cognitive Science Lab. "We are hoping to make computers smarter by making them a little more like children."

The Pentagon's blue-sky research agency is readying a nearly four-year project to boost artificial intelligence systems by building machines that can teach themselves -- while making it easier for ordinary schlubs like us to build them, too. That path fell out of favor among computer scientists years ago as a means of creating artificial intelligence; we'd have to understand our own brains first before building a working artificial version of one. But the agency thinks we can build machines that learn and evolve, using algorithms -- "probabilistic programming" -- to parse through vast amounts of data and select the best of it. After that, the machine learns to repeat the process and do it better. But building such machines remains really, really hard: The agency calls it "Herculean."

Larry Page wants patients to hand over their data to researchers in order to save "100,000 lives". It's just one of the ideas expressed in a wide-ranging interview at the Ted (Technology, Entertainment and Design) conference in Vancouver. But he added that consumers need to accept that a new era of open data is inevitable. Interviewed on the Ted stage by US television host Charlie Rose, Mr Page was asked why Google bought the UK machine learning firm DeepMind. "I was looking at search and trying to understand how to make computers less clunky and also thinking about how speech recognition is not very good," said Mr Page.

Larger data sets and faster computers have enabled a recent flurry of progress--and investment--in artificial intelligence. David Cox of Harvard thinks the next big jump will depend on understanding what happens inside the head of a rat when it plays video games. Cox leads a 28 million project called Ariadne, funded by the U.S. Office of the Director of National Intelligence, that is looking for clues in mammalian brains to make software smarter. "This is a huge, moonshot-like effort to go into the brain and see what clues and tricks are hiding there for us to find," he said today at EmTech MIT 2016. Recent progress in tasks such as image recognition and translation sprang from putting more computing power behind a technique known as deep learning, which is loosely inspired by neuroscience.

Google is planning to put the art in artificial intelligence. The announcement was made at a session in Moogfest, a four-day music and technology festival in Durham, North Carolina. The company's artificial researcher Douglas Eck had developed a new group that would focus on figuring out what computers can truly create. The group is named Magenta, and they will launch more publicly at the start of June. However, the Moogfest attendees got an introduction on what it will be working on.