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 (AI) is a computer or electronic device performing actions as if it were a human – it would apply some sort of intelligence factor or representation to accomplish the task. Some of these human services these electronic devices are performing include different planning methods and actions that include learning, problem solving, motion, thought manipulation, social response and intelligence, creativity, knowledge representation, and imitation. These electronic manipulations happening occur simultaneously with our daily lives, most of the time without us even realizing. Different examples of frequently used AI programming include virtual assistants (like Amazon's Alexa or Apple's Siri) photo recognition (like on social platforms and personal devices), and spam and credit card fraud testing; as well as more in-depth projects, like self-driving cars, check-out kiosks, and recommendation engines that frequent your past purchases to create their own ads. As consumers and participants in a fast-paced electronically changing world, we have not only let these AI infiltrations become a part of our daily lives, but we also have not educated ourselves on their pros and cons.
Gazing up at the night sky in a rural area, you'll probably see the shining moon surrounded by stars. If you're lucky, you might spot the furthest thing visible with the naked eye--the Andromeda galaxy. When the Department of Energy's (DOE) Legacy Survey of Space and Time (LSST) Camera at the National Science Foundation's Vera Rubin Observatory turns on in 2022, it will take photos of 37 billion galaxies and stars over the course of a decade. The output from this huge telescope will swamp researchers with data. In those 10 years, the LSST Camera will take 2,000 photos for each patch of the Southern Sky it covers.
Machine Learning might be a department of computer science pointed at empowering computers to memorize unused behaviors based on experimental data. The objective is to plan the algorithms that allow a computer to show the behavior learned from past encounters, instead human interaction. Now we will examine applications of machine learning in cybersecurity and see how the machine learning algorithms offering assistance to us for battle with cyber-attacks. Machine learning (without human interaction) can collect analyze and prepare data. In cybersecurity, this innovation makes a big difference to analyze past cyber-attacks and create individual defense reactions.
Artificial Intelligence is a paragliding term for technologies such as RPA and describes the ability of a computer to imitate human thinking. RPA is a rule-based, non-intelligence program that automates repetitive tasks. Artificial Intelligence, the buzzword that has spread in the tech world, has given rise to hundreds of discussions about the developments that surround it, and how it is reaching the industries. All the hype around AI and its technologies -Robotic Process Automation, Machine Learning (ML), and Natural Learning Process (NLP)- has created a lot of confusion. One of the many myths is that they are synonymous with Artificial Intelligence and Robotic Process Automation (RPA).
Garry Kasparov is perhaps the greatest chess player in history. For almost two decades after becoming world champion in 1985, he dominated the game with a ferocious style of play and an equally ferocious swagger. Outside the chess world, however, Kasparov is best known for losing to a machine. In 1997, at the height of his powers, Kasparov was crushed and cowed by an IBM supercomputer called Deep Blue. The loss sent shock waves across the world, and seemed to herald a new era of machine mastery over man.
Today, artificial Intelligence (AI) helps you shop, provides suggestions on what music to listen to and what shows to watch, connects you with friends on social media and even drives your car. As more companies focus their efforts on AI-based solutions, 2020 is shaping up to be a turning point as we begin to witness the third wave of AI -- when AI systems not only not learn and reason as they encounter new tasks and situations, but have the ability to explain their decision making. The first wave of AI focused on enabling reasoning over narrowly defined problems, but lacked any learning capability and poorly handled uncertainty. Financial products like Turbotax and Quickbooks, for example, are able to take information from a situation where rules have previously been defined and work through it to achieve a desired outcome. However, they are unable to operate beyond the previously defined rules.
"We're trying to change how people run agronomic research. Instead of establishing a small field plot, running statistics, and publishing the means, what we're trying to do involves the farmer far more directly. We are running experiments with farmers' machinery in their own fields. We can detect site-specific responses to different inputs. And we can see whether there's a response in different parts of the field," says Nicolas Martin, assistant professor in the Department of Crop Sciences at Illinois and co-author of the study.
Every linguist has probably at some time had a conversation similar to the following. Somebody asks them: "so, what do you do for a living?" and they answer "I'm a linguist" without an extended explanation. Then a somewhat puzzled look appears in the face of the person who asked. Right, linguists deal with languages, so the next question goes something like this: "so do you speak a lot of languages?". While this comes in handy and is many times the case, it's not always true (ever heard of Chomsky?) because the focus of linguists is language as a system and as a human ability, rather than specific languages such as Japanese, French or Xhosa.
Humans are error-prone and biased, but that doesn't mean that algorithms are necessarily better. Still, the tech is already making important decisions about your life and potentially ruling over which political advertisements you see, how your application to your dream job is screened, how police officers are deployed in your neighborhood, and even predicting your home's risk of fire. But these systems can be biased based on who builds them, how they're developed, and how they're ultimately used. This is commonly known as algorithmic bias. It's tough to figure out exactly how systems might be susceptible to algorithmic bias, especially since this technology often operates in a corporate black box.