Artificial intelligence (AI) is an area of computer science that emphasizes the creation of intelligent machines that work and react like humans. Some of the activities computers with artificial intelligence are designed for include:
Speech recognition
Learning
Planning
Problem solving
Artificial intelligence is a branch of computer science that aims to create intelligent machines. It has become an essential part of the technology industry.
Research associated with artificial intelligence is highly technical and specialized. The core problems of artificial intelligence include programming computers for certain traits such as:
Knowledge
Reasoning
Problem solving
Perception
Learning
Planning
Ability to manipulate and move objects
Knowledge engineering is a core part of AI research. Machines can often act and react like humans only if they have abundant information relating to the world. Artificial intelligence must have access to objects, categories, properties and relations between all of them to implement knowledge engineering. Initiating common sense, reasoning and problem-solving power in machines is a difficult and tedious approach.
Machine learning is another core part of AI. Learning without any kind of supervision requires an ability to identify patterns in streams of inputs, whereas learning with adequate supervision involves classification and numerical regressions. Classification determines the category an object belongs to and regression deals with obtaining a set of numerical input or output examples, thereby discovering functions enabling the generation of suitable outputs from respective inputs. Mathematical analysis of machine learning algorithms and their performance is a well-defined branch of theoretical computer science often referred to as computational learning theory.
Machine perception deals with the capability to use sensory inputs to deduce the different aspects of the world, while computer vision is the power to analyze visual inputs with a few sub-problems such as facial, object and gesture recognition.
Robotics is also a major field related to AI. Robots require intelligence to handle tasks such as object manipulation and navigation, along with sub-problems of localization, motion planning and mapping.
The truth is that, whether or not true A.I. is out there or is actually a threat to our existence, there’s no stopping its evolution and its rise. Humans have always fixated themselves on improving life across every spectrum, and the use of technology has become the vehicle for doing just that. And although the past 100 years have seen the most dramatic technological upheavals to life than in all of human history, the next 100 years is set to pave the way for a multi-generational leap forward.
This will be at the hands of artificial intelligence. A.I. will also become smarter, faster, more fluid and human-like thanks to the inevitable rise of quantum computing. Quantum computers will not only solve all of life’s most complex problems and mysteries regarding the environment, aging, disease, war, poverty, famine, the origins of the universe and deep-space exploration, just to name a few, it’ll soon power all of our A.I. systems, acting as the brains of these super-human machines.
However, quantum computers hold their own inherent risks. What happens after the first quantum computer goes online, making the rest of the world’s computing obsolete? How will existing architecture be protected from the threat that these quantum computers pose? Considering that the world lacks any formidable quantum resistant cryptography (QRC), how will a country like the United States or Russia protect its assets from rogue nations or bad actors that are hellbent on using quantum computers to hack the world’s most secretive and lucrative information?
Applications of Artificial Intelligence In Use Today
Beyond our quantum-computing conundrum, today’s so-called A.I. systems are merely advanced machine learning software with extensive behavioral algorithms that adapt themselves to our likes and dislikes. While extremely useful, these machines aren’t getting smarter in the existential sense, but they are improving their skill and usefulness based on a large dataset. These are some of the most popular examples of artificial intelligence that’s being used today.
Everyone is familiar with Apple’s personal assistant, Siri. She’s the friendly voice-activated computer that we interact with on a daily basis. She helps us find information, gives us directions, add events to our calendars, helps us send messages and so on. Siri is a pseudo-intelligent digital personal assistant. She uses machine-learning technology to get smarter and better able to predict and understand our natural-language questions and requests.
Alexa’s rise to become the smart home’s hub, has been somewhat meteoric. When Amazon first introduced Alexa, it took much of the world by storm. However, it’s usefulness and its uncanny ability to decipher speech from anywhere in the room has made it a revolutionary product that can help us scour the web for information, shop, schedule appointments, set alarms and a million other things, but also help power our smart homes and be a conduit for those that might have limited mobility.
If you don’t own a Tesla, you have no idea what you’re missing. This is quite possibly one of the best cars ever made. Not only for the fact that it’s received so many accolades, but because of its predictive capabilities, self-driving features and sheer technological “coolness.” Anyone that’s into technology and cars needs to own a Tesla, and these vehicles are only getting smarter and smarter thanks to their over-the-air updates.
Originally co-founded by CEO, Joshua Feast and, Dr. Sandy Pentland, Cogito is quite possibly one of the most powerful examples of behavioral adaptation to improve the emotional intelligence of customer support representatives that exists on the market today. The company is a fusion of machine learning and behavioral science to improve the customer interaction for phone professionals. This applies to millions upon millions of voice calls that are occurring on a daily basis.
Boxever, co-founded by CEO, Dave O’Flanagan, is a company that leans heavily on machine learning to improve the customer’s experience in the travel industry and deliver ‘micro-moments,’ or experiences that delight the customers along the way. It’s through machine learning and the usage of A.I. that the company has dominated the playing field, helping its customers to find new ways to engage their clients in their travel journeys.
John Paul, a highly-esteemed luxury travel concierge company helmed by its astute founder, David Amsellem, is another powerful example of potent A.I. in the predictive algorithms for existing-client interactions, able to understand and know their desires and needs on an acute level. The company powers the concierge services for millions of customers through the world’s largest companies such as VISA, Orange and Air France, and was recently acquired by Accor Hotels.
Amazon’s transactional A.I. is something that’s been in existence for quite some time, allowing it to make astronomical amounts of money online. With its algorithms refined more and more with each passing year, the company has gotten acutely smart at predicting just what we’re interested in purchasing based on our online behavior. While Amazon plans to ship products to us before we even know we need them, it hasn’t quite gotten there yet. But it’s most certainly on its horizons.
Netflix provides highly accurate predictive technology based on customer’s reactions to films. It analyzes billions of records to suggest films that you might like based on your previous reactions and choices of films. This tech is getting smarter and smarter by the year as the dataset grows. However, the tech’s only drawback is that most small-labeled movies go unnoticed while big-named movies grow and balloon on the platform.
Pandora’s A.I. is quite possibly one of the most revolutionary techs that exists out there today. They call it their musical DNA. Based on 400 musical characteristics, each song is first manually analyzed by a team of professional musicians based on this criteria, and the system has an incredible track record for recommending songs that would otherwise go unnoticed but that people inherently love.
Most everyone is familiar with Nest, the learning thermostat that was acquired by Google in January of 2014 for $3.2 billion. The Nest learning thermostat, which, by the way, can now be voice-controlled by Alexa, uses behavioral algorithms to predictively learn from your heating and cooling needs, thus anticipating and adjusting the temperature in your home or office based on your own personal needs, and also now includes a suite of other products such as the Nest cameras.
PS : The shout out for this article post goes to a sophomore computer science engineer “Shovit Raj Paudel”.
Be sure to follow him on instagram : shovitrajpaudel