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) …
Artificial intelligence is one of the most significant breakthroughs of the 21st century. Experts from different industries study its capabilities and discover new ways of its application. We call AI an emerging technology, however, scientists have been working in this direction since the 1950s. At first, AI was far from smart robots we see in sci-fi movies. Nevertheless, thanks to such technologies as machine learning and deep learning, AI became one of the most promising areas of the IT industry.
AI has developed from the early years of pure research to something we will take for granted as part of our daily business and home lives. Three big trends drive the adoption of AI. Let's take a brief look at each of them and what they might mean for your organization today, tomorrow and in the future. Each of these alone would not be enough to bring about "the life of AI." It is a chain of technology that together brings about the perfect ecosystem to deliver the AI storm.
Cybersecurity suffers from a skills shortage in the market. As a result, the opportunities for artificial intelligence (AI) automation are vast. In many cases, AI is used to enhance and improve certain defensive aspects of cybersecurity. Prime examples are combating spam and detecting malware. From the attacker point of view, there are many incentives to using AI when trying to penetrate others' vulnerable systems.
In the previous article in this series, "Diving into Machine Learning" we looked at some common approaches to machine learning, which is a subset of AI that provides systems with the ability to learn from data and improve over time without being explicitly programmed. In this latest article in our Enterprise AI series, we provide an overview of deep learning, which is a specific approach to the more general category of machine learning. As with other machine learning techniques, deep learning is an important building block for artificial intelligence in the enterprise. First, let's quickly review what machine learning is. Machine learning refers to the process of training a model, which is nothing more than a function that maps inputs (e.g., house size, customer preferences) to outputs (e.g., house value, new product recommendations).
AI refers to a category comprised of a whole host of technologies that mimic cognitive functions, traditionally ascribed to the human mind, from neutral networks, natural language processing (NLP), robotics, expert systems, to intelligent systems. In the commercial context today, the term is most often used to refer to speech and vision recognition systems, machine learning, and deep learning. Machine learning is a branch of AI where systems can "learn" from data, identify patterns, and make decisions with minimal human assistance. As Adeel Najmi, Senior Vice President, Products at One Network Enterprises puts it, "learning occurs when a machine takes the output, observes the accuracy of the output, and updates its own model so that better outputs will occur." Deep learning, a specialized form of machine learning, uses many layers of neural networks to classify images without extracting features from images.
You can't fool all the people all the time, but a new dataset of untouched nature photos seems to confuse state-of-the-art computer vision models all but two-percent of the time. AI just isn't very good at understanding what it sees, unlike humans who can use contextual clues. The new dataset is a small subset of ImageNet, an industry-standard database containing more than 14 million hand-labeled images in over 20,000 categories. The purpose of ImageNet is to teach AI what an object is. If you want to train a model to understand cats, for example, you'd feed it hundreds or thousands of images from the "cats" category.
When Alan Turing invented the first intelligent machine, few could have predicted that the advanced technology would become as widespread and ubiquitous as it is today. Since then, companies have adopted AI for pretty much everything, from self-driving cars to medical technology to banking. We live in the age of big data, an age in which we use machines to collect and analyze massive amounts of data in a way that humans couldn't do on their own. In many respects, the cognition of machines is already surpassing that of humans. With the explosion of the internet, AI has also become a critical element of web design.
Your purchase helps support NPR programming. We've been talking to robots for a while now. In the decade or so since Siri and her compatriots first appeared, we've all gotten pretty used to having conversations with computers in various forms. While your Alexa doesn't look much like a Cylon (the scary metal kind or hotty flesh kind) now, it seems like it's just a matter of time of time before we'll be talking with all kinds of robots -- including those that look just like us. Time, robots and conversations are at the heart of David Ewing Duncan's new book Talking to Robots: Tales from Our Human-Robot Futures.
The lunar space station, Gateway, will orbit the moon in an ellipse -- with a path that will resemble a halo -- when it is assembled in the next decade, NASA and the European Space Agency have announced. The station will act as a half-way house between the Earth and the Moon, acting as a place of shelter, making trips to the moon more efficient and providing a launch pad for missions heading further out into the solar system. Much like the International Space Station, the Gateway will be a permanent base on which astronauts will live for extended periods, conducting research on-board and making regular excursions down to the moon's surface. The halo-like orbit of the lunar gateway will see it trace a halo-like path around the moon (pictured). A stepping stone to allow astronauts to more easily travel to the Moon as well as a forward outpost for crewed excursions further into the solar system, the Lunar Orbital Platform is due for construction within the next decade.
Meanwhile, companies are installing more and more sensors hoping to improve efficiency and cut costs. However, machine learning consultants from InData Labs say that without proper data management and analysis strategy, these technologies are just creating more noise and filling up more servers without actually being used to their potential. Is there a way to convert simple sensor recordings into actionable industrial insights? The simple answer is yes, and it lies in machine learning (ML). The scope of ML is to mimic the way the human brain processes inputs to generate logical responses.