Artificial intelligence is the simulation of human intelligence processes by machines, especially computer systems. Specific applications of AI include expert systems, natural language processing, speech recognition and machine vision
How does AI work?
AI systems work by combining large sets of data with intelligent, iterative processing algorithms to learn from patterns and features in the data that they analyze.
Each time an AI system runs a round of data processing, it tests and measures its own performance and develops additional expertise.
Because AI never needs a break, it can run through hundreds, thousands, or even millions of tasks extremely quickly, learning a great deal in very little time, and becoming extremely capable at whatever it’s being trained to accomplish.
But the trick to understanding how AI truly works is understanding the idea that AI isn’t just a single computer program or application, but an entire discipline, or a science.
The goal of AI science is to build a computer system that is capable of modeling human behavior so that it can use human-like thinking processes to solve complex problems.
To accomplish this objective, AI systems utilize a whole series of techniques and processes, as well as a vast array of different technologies.
By looking at these techniques and technologies, we can begin to really understand what AI actually does, and thus, how it works, so let’s take a look at those next.
No one programming language is synonymous with AI, but a few, including Python, R and Java, are popular.AI programming focuses on three cognitive skills: learning, reasoning and self-correction.
Learning processes. This aspect of AI programming focuses on acquiring data and creating rules for how to turn the data into actionable information. The rules, which are called algorithms, provide computing devices with step-by-step instructions for how to complete a specific task.
Reasoning processes. This aspect of AI programming focuses on choosing the right algorithm to reach a desired outcome.
Self-correction processes. This aspect of AI programming is designed to continually fine-tune algorithms and ensure they provide the most accurate results possible.
Why is artificial intelligence important?
AI gets the most out of data. When algorithms are self-learning, the data itself is an asset. The answers are in the data. You just have to apply AI to find them. Since the role of the data is now more important than ever, it can create a competitive advantage. If you have the best data in a competitive industry, even if everyone is applying similar techniques, the best data will win.
AI adds intelligence to existing products. Many products you already use will be improved with AI capabilities, much like Siri was added as a feature to a new generation of Apple products. Automation, conversational platforms, bots and smart machines can be combined with large amounts of data to improve many technologies. Upgrades at home and in the workplace, range from security intelligence and smart cams to investment analysis.
AI analyzes more and deeper data using neural networks that have many hidden layers. Building a fraud detection system with five hidden layers used to be impossible. All that has changed with incredible computer power and big data. You need lots of data to train deep learning models because they learn directly from the data.
AI adapts through progressive learning algorithms to let the data do the programming. AI finds structure and regularities in data so that algorithms can acquire skills. Just as an algorithm can teach itself to play chess, it can teach itself what product to recommend next online. And the models adapt when given new data.
AI achieves incredible accuracy through deep neural networks. For example, your interactions with Alexa and Google are all based on deep learning. And these products keep getting more accurate the more you use them. In the medical field, AI techniques from deep learning and object recognition can now be used to pinpoint cancer on medical images with improved accuracy.
What are the 4 types of artificial intelligence?
Arend Hintze, an assistant professor of integrative biology and computer science and engineering at Michigan State University, explained in a 2016 article that AI can be categorized into four types, beginning with the task-specific intelligent systems in wide use today and progressing to sentient systems, which do not yet exist. The categories are as follows:
Type 1: Reactive machines. These AI systems have no memory and are task specific. An example is Deep Blue, the IBM chess program that beat Garry Kasparov in the 1990s. Deep Blue can identify pieces on the chessboard and make predictions, but because it has no memory, it cannot use past experiences to inform future ones.
Type 2: Limited memory. These AI systems have memory, so they can use past experiences to inform future decisions. Some of the decision-making functions in self-driving cars are designed this way.
Type 3: Theory of mind. Theory of mind is a psychology term. When applied to AI, it means that the system would have the social intelligence to understand emotions. This type of AI will be able to infer human intentions and predict behavior, a necessary skill for AI systems to become integral members of human teams.
Type 4: Self-awareness. In this category, AI systems have a sense of self, which gives them consciousness. Machines with self-awareness understand their own current state. This type of AI does not yet exist.
Top 8 Companies Using Machine Learning
Pinterest is a social media service that’s a bit off-target from the norm. On Pinterest, users share ‘pins’ to help other users discover recipes, style inspiration, DIY projects, and other lifestyle ideas.In 2015, Pinterest announced the acquisition of Kosei, a company that provides business-to-business (B2B) machine learning capabilities for its clients. Now, machine learning is a fundamental part of almost everything that happens on Pinterest. Spam moderation, content discovery, and even ad monetization take place with machine learning at the center
Google is a proud user of neural networks. In humans, neural networks allow the brain to create relationships between large datasets. Naturally, artificial intelligence can imitate this ability. Right now, Google still uses classic algorithms for natural language processing (NLP), speech translation, and search ranking, among other things. But they are deeply invested in researching and refining neural networks to enhance further development. Google dubs this project the DeepMin
Twitter is a social media service where users exchange information through concise, primarily text-based blurbs. That said, not every tweet is welcome. Depending on who you are, you might find some tweets more relevant and/or entertaining than others. Luckily, Twitter uses a machine learning algorithm to score tweets based on various metrics. Then, Twitter curates your feed, making an educated guess about what you would like to see most
Yelp hosts reviews from a large assortment of businesses all over the world. The website can give locals and tourists recommendations of restaurants, bars, salons, and even dentists in their residing area. With the use of machine learning, Yelp has fine-tuned image curation to provide users with more accurate photo captions and attributes. Surely, if you take to reviews to learn whether or not a new spot in town is worth the hype, pictures are an essential part of the decision-making process. And obviously, accuracy is appreciated.
Apple needs no introduction. But just in case, you should know that Apple is a multinational company that specializes in computer software and consumer electronics. This means that not only is Apple behind the iOS that runs on iPhones but the iPhone itself. You can credit Apple with the Mac and its various operating systems as well. Apple is also behind Siri. Siri is technically an AI machine in the form of a handy digital assistant. She can send text messages, check your email, and answer random questions, among other tasks.
Amazon is one of the largest retailers in the world. Most people love Amazon because of its 2-day shipping, which makes immediate gratification an unhealthy but realistic expectation for consumer desires. That aside, Amazon uses ML for many of its retail-oriented tasks, such as product recommendations, forecasting, data cleansing, and capacity planning.
Can you guess why Netflix is on the list of major companies using machine learning? Think suggestions.
Netflix is a highly-favored streaming service with more films and television shows than you can count readily available at its users’ fingertips. But if that’s not reason enough to be enthusiastic, you don’t even have to do the work of figuring out what to watch. That\’s because Netflix\’s primary use of ML technology is to give user suggestions, catered specifically to their unique interests.
IBM, or International Business Machines, is one of the oldest technology companies that’s still alive and kicking.But unlike other old companies, IBM continues to expand its technological resources. New and innovative technologies are regular addendums to the metaphorical IBM grocery list. And thus, they continue to grow as a business.
One of IBM’s newer technologies is Watson, a machine learning tool that several hospitals and medical centers use to get treatment recommendations for different types of cancers.
But Watson can do more than that. The retail sector also uses Watson to assist shoppers. The hospitality industry uses Watson too. Truly, AI can do it all!
What are examples of AI technology and how is it used today?
AI is incorporated into a variety of different types of technology. Here are six examples:
Automation. When paired with AI technologies, automation tools can expand the volume and types of tasks performed. An example is robotic process automation (RPA), a type of software that automates repetitive, rules-based data processing tasks traditionally done by humans. When combined with machine learning and emerging AI tools, RPA can automate bigger portions of enterprise jobs, enabling RPA\’s tactical bots to pass along intelligence from AI and respond to process changes.
Machine learning. This is the science of getting a computer to act without programming. Deep learning is a subset of machine learning that, in very simple terms, can be thought of as the automation of predictive analytics. There are three types of machine learning algorithms:
Supervised learning. Data sets are labeled so that patterns can be detected and used to label new data sets.
Unsupervised learning. Data sets aren\’t labeled and are sorted according to similarities or differences.
Reinforcement learning. Data sets aren\’t labeled but, after performing an action or several actions, the AI system is given feedback.
Machine vision. This technology gives a machine the ability to see. Machine vision captures and analyzes visual information using a camera, analog-to-digital conversion and digital signal processing. It is often compared to human eyesight, but machine vision isn\’t bound by biology and can be programmed to see through walls, for example. It is used in a range of applications from signature identification to medical image analysis. Computer vision, which is focused on machine-based image processing, is often conflated with machine vision.
Natural language processing (NLP). This is the processing of human language by a computer program. One of the older and best-known examples of NLP is spam detection, which looks at the subject line and text of an email and decides if it\’s junk. Current approaches to NLP are based on machine learning. NLP tasks include text translation, sentiment analysis and speech recognition.
Robotics. This field of engineering focuses on the design and manufacturing of robots. Robots are often used to perform tasks that are difficult for humans to perform or perform consistently. For example, robots are used in assembly lines for car production or by NASA to move large objects in space. Researchers are also using machine learning to build robots that can interact in social settings.
Self-driving cars. Autonomous vehicles use a combination of computer vision, image recognition and deep learning to build automated skill at piloting a vehicle while staying in a given lane and avoiding unexpected obstructions, such as pedestrians.
What are the applications of AI?
Artificial intelligence has made its way into a wide variety of markets. Here are nine examples.
AI in healthcare. The biggest bets are on improving patient outcomes and reducing costs. Companies are applying machine learning to make better and faster diagnoses than humans. One of the best-known healthcare technologies is IBM Watson. It understands natural language and can respond to questions asked of it. The system mines patient data and other available data sources to form a hypothesis, which it then presents with a confidence scoring schema. Other AI applications include using online virtual health assistants and chatbots to help patients and healthcare customers find medical information, schedule appointments, understand the billing process and complete other administrative processes. An array of AI technologies is also being used to predict, fight and understand pandemics such as COVID-19.
AI in business. Machine learning algorithms are being integrated into analytics and customer relationship management (CRM) platforms to uncover information on how to better serve customers. Chatbots have been incorporated into websites to provide immediate service to customers. Automation of job positions has also become a talking point among academics and IT analysts.
AI in education. AI can automate grading, giving educators more time. It can assess students and adapt to their needs, helping them work at their own pace. AI tutors can provide additional support to students, ensuring they stay on track. And it could change where and how students learn, perhaps even replacing some teachers.
AI in finance. AI in personal finance applications, such as Intuit Mint or TurboTax, is disrupting financial institutions. Applications such as these collect personal data and provide financial advice. Other programs, such as IBM Watson, have been applied to the process of buying a home. Today, artificial intelligence software performs much of the trading on Wall Street.
AI in law. The discovery process — sifting through documents — in law is often overwhelming for humans. Using AI to help automate the legal industry\’s labor-intensive processes is saving time and improving client service. Law firms are using machine learning to describe data and predict outcomes, computer vision to classify and extract information from documents and natural language processing to interpret requests for information.
AI in manufacturing. Manufacturing has been at the forefront of incorporating robots into the workflow. For example, the industrial robots that were at one time programmed to perform single tasks and separated from human workers, increasingly function as cobots: Smaller, multitasking robots that collaborate with humans and take on responsibility for more parts of the job in warehouses, factory floors and other workspaces.
AI in banking. Banks are successfully employing chatbots to make their customers aware of services and offerings and to handle transactions that don\’t require human intervention. AI virtual assistants are being used to improve and cut the costs of compliance with banking regulations. Banking organizations are also using AI to improve their decision-making for loans, and to set credit limits and identify investment opportunities.
AI in transportation. In addition to AI\’s fundamental role in operating autonomous vehicles, AI technologies are used in transportation to manage traffic, predict flight delays, and make ocean shipping safer and more efficient.
Security. AI and machine learning are at the top of the buzzword list security vendors use today to differentiate their offerings. Those terms also represent truly viable technologies. Organizations use machine learning in security information and event management (SIEM) software and related areas to detect anomalies and identify suspicious activities that indicate threats. By analyzing data and using logic to identify similarities to known malicious code, AI can provide alerts to new and emerging attacks much sooner than human employees and previous technology iterations. The maturing technology is playing a big role in helping organizations fight off cyber-attacks.