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) …
The field of machine learning is becoming more and more mainstream every year. With this growth come many libraries and tools to abstract away some of the most difficult concepts to implement for people starting out. Most people will say you need a higher level degree in ML to work in the industry. If you love working with data and practical math, then I would say this is not true. I did not graduate college with a Machine Learning or data degree yet I am working with ML right now at a startup.
How the features and benefits of data virtualization can make working with data easier and more efficient. Data lakes have become the principal data management architecture for data science. A data lake's primary role is to store raw structured and unstructured data in one central location, making it easy for data scientists and other investigative and exploratory users to analyze data. The data lake can store vast amounts of data affordably. It can potentially store all data of interest to data scientists in a single physical repository, making discovery easier.
There seems to be much confusion among the ranks of the untrained when it comes to an understanding of some basic IT concepts. This is especially true when looking at Artificial Intelligence (AI) and the misuse of nomenclature such as Machine Learning (ML), and Deep Learning (DL). In this article, I will try to brush away the mists of confusion, and present the case for each specific data related technologies. First of all, you need to understand that all three are related and sit within a specific hierarchy. To properly understand the differences between these three technologies, let's first look at how they stack together in terms of hierarchy.
Advancements in robotics are continually taking place in the fields of space exploration, health care, public safety, entertainment, defense, and more. These machines--some fully autonomous, some requiring human input--extend our grasp, enhance our capabilities, and travel as our surrogates to places too dangerous or difficult for us to go. Gathered here are recent images of robotic technology, including a Japanese probe reaching a distant asteroid, bipedal-robot fighting matches in Japan, a cuddly cat-substitute robotic pillow, an automated milking machine, delivery bots, telepresence robots, technology on the fashion runway, robotic prosthetic limbs and exoskeletons, and much more.
GitHub is used by more than 30 million developers around the world and hosts repositories for some of the biggest ML-driven open source projects on the planet, but is perhaps less well known for the creation of AI-driven tools to help them do their jobs. VentureBeat sat down with GitHub senior data scientist Omoju Miller to talk about how one of the biggest homes for developers online is performing applied machine learning research to create more AI-driven services. At the GitHub Universe conference Tuesday, a number of major upgrades were made to GitHub and GitHub Enterprise services for businesses. Miller also spoke during the keynote address about Experiments, a new GitHub initiative to explore the use of AI and machine learning meant for developers. The first Experiments prototype named Semantic Code Search launched last month.
Google is eager to invest in other insurance technology companies well beyond its newly announced minority stake in Applied Systems, a principal investor with the global search engine giant said on Oct. 17. "We really like the market," said Jesse Wedler, a principal with CapitalG, the growth equity investment fund of Google's parent Alphabet. "We will definitely be looking for additional investments in the insurance technology space." Wedler, speaking during a second conference call held to discuss CapitalG's new investment in Applied, said he didn't want to define the scope of CapitalG's insurance technology investment search "too narrowly." However, he said the search would be for other businesses like Applied, ones "that add insurance technology to the market."
As companies increasingly turn to AI and machine learning, a clearer picture of what it takes to succeed with real-world AI is beginning to take shape. Beyond the small circle of tech giants and early adopters, a different set of skills and approaches is emerging as must-haves for enterprise AI teams. Not every organization can compete with the likes of Google and Facebook for top AI talent. And it's not just data science PhDs that companies are looking for. To meet their business needs, CIOs assembling AI teams are looking for subject matter expertise, software engineering skills, and the ability to translate learning algorithms into actual business value.
CES 2017 was set to be the biggest so far, with more exhibitors, products, and visitors than ever before in its 50 year history. But one signal sparked my interest above everything else: increasing focus on how technology brings value over technology per se. It was hard to find anything not labeled "smart" or "intelligent" this year. While just a year ago AI seemed futuristic, people now expect it … and expect it to work straight out of the box. This is because we've reached sufficient maturity in what I call the 4Cs of useful AI: computing, connectivity, cognition, and convergence.