Artificial intelligence (AI) is pretty mainstream. It's about everything---to Netflix movie suggestions to digital pre-employment assessment.
But the common question is: how does it work?
Mainly, AI is used to save time. From adding people to email automation and allowing AI to do other tasks, like AI in recruiting. Even governments will receive the benefits of AI-based technology. Concerning the government workforce, it could free up significant amounts of labor hours by automating certain tasks and allowing managers to shift employees to tasks requiring human judgment.
These new applications could save hundreds of millions of staff hours and billions of dollars annually. But the shift’s size and impact will depend on many factors, some political and some financial. With adequate investment and support, we believe, AI could free up 30 percent of the government workforce’s time within five to seven years.
What is AI?
AI means “artificial intelligence” and we use it to describe any time a computer does something that would require the intelligence of a human — or anything that mimics human intelligence, whichever way you want to think of it.
AI in marketing is already prevalent, and you probably interact with AI on a daily basis. Here are some ways you interact with artificial intelligence:
-Search engines like Google use AI (algorithms like Rankbrain) to determine the most appropriate result for a search.
-Automated marketing emails use AI to figure out what emails to send based on how you’ve interacted with a business or website.
-Various types of online ads use AI to determine who should see a specific ad, based on past behavior, interests and search queries.
-Chatbots are becoming more common in online messengers so that larger brands can assist customers immediately and efficiently.
-Voice searches on smart speakers or even smartphones use AI to determine the best result for those long-tail keywords and conversational queries.
How does AI work?
Artificial intelligence uses a field of computer science called machine learning to mimic human intelligence. The computer learns how to respond to certain actions, fed with algorithms and historical data to create a propensity model.
A propensity model makes predictions on the likelihood of something. It’s creating mathematical models to predict whether someone will take a particular action, like continuing with their job application, signing up for an interview, or making a purchase.
AI works by combining large amounts of data with multiple fast and intelligent algorithms. These algorithms enable the software to learn quickly patterns and features within the dataset. The field of AI is a broad field. In fact the term “artificial intelligence” is a broad umbrella term for the many theories, methods, and technologies within the field, including the major subfields:
Machine learning automates analytical model building. It pulls from many fields, like operations research physics, and neural networks to find the hidden patterns within data but it does not need to be explicitly programmed to where to look or what to conclude.
It automatically learns and improves from experience without being explicitly programmed. ML focuses on creating algorithms that analyze data and make predictions. Machine learning’s applications are in the thousands. It can not only predict what you’d like on Netflix or what’s the shortest route home on Google Maps, it’s useful to other areas too. It’s used to diagnose cancer and optimize medical image interpretation.
A neural network learns by training. It uses machine learning to create a web of interconnected units (similar to neurons) to process and relay information. The whole idea is that these computer systems are modeled after the neural connections that are made in the human brain. Much how the mind uses different data points to make connections and create meaning, neural network comput derives meaning from undefined data.
Since they learn from undefined data, the data sets must be incredibly large and varied. By processing as many inputs as possible, the machine is able to produce a single output. Over a period of time, it will get better and better at giving the correct output.
Deep learning consists of multiple layers of neural networks processing data, creating a single output from many inputs. It takes a great amount of computing power and improves training techniques to create complex patterns of data. Image and speech recognition are deep learning specialities. It uses images to learn and becomes better at identifying words or an image. In deep learning, the machine learns both through positive and negative reinforcement of the tasks they complete. This requires expert AI scientists to constantly monitor its progress, inputs, and outputs.
Cognitive computing is a subfield of AI that strives for a natural, human-like interaction with machines. It makes inferences from context. Using AI and cognitive computing, the ultimate goal is for a machine to simulate human processes through the ability to interpret images and speech – and then speak coherently in response.
Cognitive computing is another essential component of AI. Its purpose is to imitate and improve interaction between humans and machines. Cognitive computing seeks to recreate the human thought process in a computer model, in this case, by understanding human language and the meaning of images. Together, cognitive computing and artificial intelligence strive to endow machines with human-like behaviors and information processing abilities.
Computer vision relies on pattern recognition and deep learning to recognize what’s in a picture or video. When machines can process, analyze and understand images, they can capture images or videos in real time and interpret their surroundings.
Computer vision is a technique that implements deep learning and pattern identification to interpret the content of an image; including the graphs, tables, and pictures within PDF documents, as well as, other text and video. Computer vision is an integral field of AI, enabling computers to identify, process and interpret visual data.
Applications of this technology have already begun to revolutionize industries like research & development and healthcare. Computer Vision is being used to diagnose patients faster by using Computer Vision and machine learning to evaluate patients’ x-ray scans.
Natural language processing
Natural language processing (NLP) is the ability of computers to analyze, understand and generate human language, including speech. The next stage of NLP is natural language interaction, which allows humans to communicate with computers using normal, everyday language to perform tasks.
An illustration of a gearwheel combined with a human head. Natural Language Processing or NLP, allows computers to interpret, recognize, and produce human language and speech. The ultimate goal of NLP is to enable seamless interaction with the machines we use every day by teaching systems to understand human language in context and produce logical responses. Real-world examples of NLP include Skype Translator, which interprets the speech of multiple languages in real-time to facilitate communication.
Learning about how AI works AI in recruiting is only one use of AI-powered tools. It’s incredibly useful for a wide-range of applications and industries.
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