7 Patterns of AI

From autonomous vehicles to predictive analytics facial recognition, virtual assistants and chatbots cognitive automation, and fraud detection, the applications for AI are numerous. But regardless of the use of AI there is an underlying pattern that is common to all of these applications. People who have worked on hundreds, or perhaps thousands of AI projects have realized that despite all the variety in use, AI use cases fall into one or more of the seven common patterns. The seven patterns are hyperpersonalization, autonomous systems, predictive analytics and decision support, conversational/human interactions, patterns and anomalies, recognition systems, and goal-driven systems. Any approach that is customized to AI requires its own pattern and programming however, regardless of the combination these patterns are employed to create, they will all adhere to their own set of guidelines. The seven patterns can then be utilized individually or in different combinations, depending on the method to which AI is being applied.

The Hyperpersonalization Pattern: Treat every client like an individual

The hyperpersonalization model is described as the use of machine learning to create an individual profile and then letting the profile evolve and change as time passes for an array of uses, such as displaying relevant content, recommending appropriate products, offering individualized recommendations, and more. The aim of this model is to treat every person like an individual.

The hyperpersonalization-specific pattern can result in personal recommendations based on search patterns and browsing patterns. One company currently using a tech that is similar to this could be Netflix. They are using AI to suggest movies and shows to viewers based on their personal preferences. Another instance is Starbucks which is using hyperpersonalization to interact with its customer base.

The use of hyperpersonalization isn’t limited to just the marketing sector. It’s popping up in various industries, including healthcare, finance, or personalized fitness and wellness programs. One common use where hyperpersonalization is able to be a major factor is in loans and finance. For instance, in the US FICO is the most widely used credit score in America. FICO credit score can be utilized to classify people who may have different levels of creditworthiness and penalizes certain groups of people with no credit history. If we shift away from the standard FICO score to one that considers each person as an individual, we could have more accurate images of individuals and determine how likely they will be able to repay loans.

Autonomous systems Pattern Eliminating the requirement of manual work

Autonomous systems are physical and digital hardware and software systems that can complete a task, achieve a goal, interact with their environment, and accomplish their goal without human intervention. The autonomous system requires machine learning that is able to independently see the world around us, anticipate the future behavior of external elements and devise a strategy to adapt to those changes.

Its obvious applications include autonomous vehicles and machines of all kinds, including trains, cars, boat airplanes, trains, and other. This pattern can also include autonomous systems, such as autonomous knowledge generation and documentation and autonomous business processes, and cognitive autonomic systems. These are systems that be operated in close proximity to humans, with the possibility of the ability to make decisions with preference.

AI engineered predictive analytics

Another type of AI includes predictive analytics as well as decision-support. It is the process of using machine learning as well as other methods of cognitive analysis to discover the ways in which past or current patterns can be used to determine future outcomes or assist humans to make choices about future outcomes by analyzing these patterns. The goal of this type of pattern is to help humans make better choices.

The patterns’ uses include assisted retrieval and search, forecasting some value in the future for data and behavior, forecasting failure, assisted resolution of problems by identifying and choosing the most appropriate fit, identifying matches between data, optimization actions offering guidance, and intelligent navigation. It assists in making better decisions by enhancing intelligence capabilities. Machine learning is can help make the choice, and is evolving over time to deliver more effective outcomes.

The Conversational Pattern is a pattern of machines that converse as human beings do

Another form of AI is human/conversational interactions. It is defined as humans and machines interacting with each other via conversations and content in various methods, including text, voice, as well as images. This can include human-to-machine and human-machine interaction as well as back and forth between human and machine interaction. The goal of this pattern is to enable machines to be able to interact with by interacting with humans by another.

The most evident examples of this include the chatbots and voice assistants and sentiment, mood, and intent analysis. The idea is that it’s trying to comprehend the motivation of human interactions. It is also a method to aid human-to-human interactions via translation. One thing to be aware of is that this pattern can be utilized to provide a more efficient method for humans to communicate with machines and each other using methods that are natural and comfortable for humans.

Detecting Patterns and Anomalies using AI

Machine learning is particularly adept in identifying patterns, and also identifying outliers or anomalies. This “pattern-matching pattern” is among the methods that repeat in AI projects that have seen an increase in adoption and widespread use. The aim of this Patterns and Anomalies pattern of AI is to make use of machine learning and other methods of cognitive analysis to identify patterns in data and to learn the higher-order connections between the data to determine whether it matches an existing pattern or the possibility of an outlier or an anomaly. The purpose of this particular pattern is to determine the data that matches and what isn’t.

Examples of this type of pattern include the detection of risk and fraud to determine if something is unusual or if something is not as expected. Another aspect is the ability to identify patterns in data and helping to reduce or eliminate human errors. This is also a pattern that includes predictive text, in which it is able to look for patterns in grammar and speech to identify words to use to improve the speed of writing.

Machines that recognize the world The Pattern of Recognition Pattern

One of the major advances that machine learning has made is the application of deep learning in order to enhance the accuracy of tasks related to recognition like audio, video, and image as well as object recognition, classification, and recognition. Recognition pattern is described as the use of machine learning as well as other cognitive techniques to detect and identify objects or other objects that are to be identified in images or audio or text or other unstructured data in a concise manner. The aim of this type of pattern is to help machines recognize and comprehend things.

Examples include object recognition and image recognition, facial recognition of sound and audio handwriting, and recognition of text as well as gesture recognition. This is a well-developed technique that computers are adept at and is extensively utilized. There are many businesses that invest heavily in recognition technology. One of the largest and well-funded AI firms, Sense time, is focused on facial recognition software as well as The Chinese authorities are investing heavily into the development and acceptance of this pattern.

Finding the Answer The Goal-Driven Systems Model

Machines have proved especially adept at learning game rules and even beating humans at playing their games. The past has seen machines easily defeat checkers, chess, or solving mazes. With the help of reinforcement learning and other advanced computing capabilities, machines can now win in Go or multi-player games, like DoTA and other complex games. Alpha Go and Alpha Zero were invented through Google’s DeepMind division with the belief that computers, through the use of goals can learn anything from games. These games are only the start of possible solutions that could be the catalyst for breakthroughs in the long-awaited goals of Artificial General Intelligence (AGI).

There is more than one way to create goal-driven systems. Through the power of reinforcement learning as well as other techniques for machine learning organizations can use machine learning as well as other cognitive methods to provide their systems with the capability to learn by trial and trial. This is beneficial in any scenario where you wish to help the system discover the best solution to a challenge. The primary method of learning for this pattern is to use reinforcement learning. Examples of the pattern include games resource optimization and iterative problem-solving, and auctions that are bidding or real-time. Although the goal-driven system's design isn’t yet so widely used as different patterns it’s gaining rapid acceptance.

Combining Patterns to AI Project Success

While they might appear as distinct patterns that are used separately in common AI projects, in actual we have seen companies blend any or all of these patterns in order to accomplish their goals. If companies consider AI projects using these patterns, it will aid them in their efforts to design, and execute AI projects. Indeed, the newest techniques are focusing on using these seven patterns in order to speed up AI plan-of-action. When you’ve identified that you’re using an identification pattern for instance it’s possible to gain insight into a myriad of options that have been used to solve the problem, and insight into the data required to support the pattern, examples of use, and examples of the use of the pattern as well as algorithm and model development techniques, as well as other knowledge that will speed up the process of delivering top-quality AI projects.

Although AI is in the beginning of the majority stage of its adoption, it’s evident that the identification and implementation of these patterns can assist organizations in reaching their AI goals faster and with less needing to reinvent this wheel and higher chances of achievement.

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