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Since the introduction of AI, many different implementations have popped up across the world. Using these applications, businesses and individuals are utilizing AI for their day-to-day activities. One of the core technologies that empower this use is AI engines.
Think of an AI application as a car taking you on a journey. At the heart of every car is an engine that propels the vehicle to wherever you want to travel. Just like that, an AI Engine empowers an application to utilize all the capabilities an artificial intelligence can provide.
So, let’s take a look at what an AI Engine is, how it works, the different types, benefits, examples, real-world applications, and more.
An AI Engine is a specialized system that facilitates tasks like learning, reasoning, decision-making, and more for an AI-powered application. AI applications utilize an engine to do most of the computational work needed to function properly for a wide range of use cases.
That means an AI Engine is the workhorse behind any application, allowing it to process data and perform tasks like pattern recognition, natural language processing, and the like.
An AI Engine is like any other AI technology like LLMs, where you provide the data and it provides an output. However, the depth of AI Engines is much deeper as its functionality is not just dependent on data analysis. You can analyze videos, images, data, and more to empower different types of applications.
Thus, while the workflow is similar, AI Engine enables all sorts of applications to learn, reason, and make predictions or decisions in real time. Here is a breakdown of how AI Engines work.
The first step for an AI Engine is to collect data through input or other data sources. These sources could be documents, images, videos, audio, sensor data, etc.
Next, the AI Engine will remove errors, turn unstructured data into structured versions, verify data, and more. This prepares the data to be fed into a model to train it.
The data processed earlier will be fed into different Machine Learning (ML) or Deep Learning (DL) models. The model used depends on the purpose for which it is being trained. The different types of learning utilized by models are:
Utilizing different training methods, models are trained in batches for a faster and more efficient process. This entire process is based on what type of model you want to use for your application. Lastly, using an optimization process helps to reduce errors in the dataset.
After training, models can now make decisions about new data or queries that are asked or given. An AI Engine will then utilize the models to make predictions or decisions in real time. This can apply to many use cases like Fraud Detection.
Hence, different kinds of models like Chatbots that use NLP, or Generative AI models that analyze videos can be trained with an AI Engine.
After providing more and more results, you can use several processes to further optimize AI Engines. This can be done through user interactions, fine-tuning certain topics, or retraining the model in its entirety.
This kind of process is observed in applications like ChatGPT or Alexa as they learn through user interaction and typically get updates to
After further optimizations, the AI Engine is deployed and integrated into different applications like ChatGPT, Chatbots, IoTs, etc.
The AI Engine should be constantly monitored in case of problems that may occur down the line. Also, through this, the Engine can be improved further in the future.
Now that we know how AI Engines work, let’s discuss the different types of AI Engines. While there are many, the key types of AI Engines can be divided into three categories.
There are five main types of technology-based AI Engines. Let’s start with the simplest one.
These types of AI Engines are systems that are pre-defined with certain rules or logical statements. While limited in terms of how versatile they can be, Rule-Based AI Engines can make workflow management easier and help with analyzing certain tasks like troubleshooting and medical diagnosis more streamlined.
These types of AI Engines are mainly used to learn data and use it to make predictions, classify information, and detect anomalies. ML Engines make use of different types of learning methods to train the AI to detect spam, classify data, find anomalies, improve using trial and error, etc.
Using neural networks, Deep Learning AI Engines can analyze complex patterns for a large amount of data to carry out tasks like image processing and speech recognition. This type of AI Engine can be further classified into the following types.
These types of AI Engines are designed to solve problems with logical understanding and reasoning. While these types of engines are not commonly used, the concept still remains and many ML or DL Engines make use of this idea.
These types of engines combined the rule-based system with neural networks to be more versatile while keeping the application streamlined. Hybrid AI Engines combine the benefits of a rule-based approach with addressing the limitations of data-driven models with more robustness.
These are AI Engines that are based on different tasks that have a lot of need for many businesses.
While the previous AI engines like Computer Vision, NLP, ML, or DL focus on data analysis, Generative Engines focus more on generation. These AI Engines can generate any text, images, music, or videos using GANs, VAEs, and Transformers.
Examples are platforms like ChatGPT, Midjourney, etc.
These AI Engines can analyze previous data and get an understanding of your previous usage to give recommendations tailored to your preferences. Some practical examples are product recommendations on Amazon or content recommendations on YouTube or Netflix.
These types of AI Engines can empower AI Agents to perform autonomously in environments that are constantly changing. These types of AI Engines are built to adapt and work independently, providing a lot of value for drones, robots, or self-driving cars.
One of the most common AI Engines is for search platforms like Google, Bing, etc. These AI analyze information and rank them based on user queries. Platforms like Perplexity, Google, and such make use of this to rank content for users to view.
Using the power of NLP, Conversation AI Engines can provide contextual and personalized responses. These engines understand human language and can emulate it at a high level. Some specific uses of these types of engines are for Chatbots or platforms like ChatGPT.
Also known as Computer Vision Engines, these engines analyze and dissect videos and images to find the meaning behind them. It is a crucial system for features like facial recognition.
These AI Engines are created specifically for the industries they are meant to be used for. That means the engines address specific use cases for industries like finance, retail, healthcare, etc.
AI Engines are deployed in different ways and here are the types:
These AI Engines make use of smaller models to be run on devices like Smartphones or IoTs locally. Thus, enabling the use of AI on smaller devices. Mobile Apps and IoT devices rely on these types of engines for AI capabilities.
On the flip side of Edge Engines, Cloud-Based ones provide AI models for businesses. These generally offer APIs, pre-trained models, and the like-to business for different services. Also, Cloud-Based AI Engines are designed to be deployed at a larger scale.
This type of engine combines the best of both worlds to provide the best type of AI-driven solutions. It combines the capabilities of real-time and large-scale and utilizes both cases whenever needed. Platforms like NVIDIA Metropolis and Microsoft Azure Percept make use of this deployment technique.
While all AI systems have the same benefits, like cost-efficiency and automation, there are some unique benefits to utilizing AI engines.
While AI Engines have a wide range of capabilities and use cases, it comes with some limitations and challenges that you have to overcome. Here are some key ones.
Given the benefits and limitations of AI Engines, here are some of the real-life applications of the system.
Utilizing Conversational AI Engines, the customer service industry can configure smart chatbots and text-related platforms to provide contextual responses, adding a touch of personalization. Conversational AI Engines can empower companies to create or use excellent chatbots to completely automate queries and make customer support more efficient.
An example of AI engine implementations in Customer Service is REVE Chat, an all-in-one omnichannel customer engagement platform. Utilizing AI Engines, our chatbots are smarter and more contextually aware, changing customer support for businesses in any industry.
While in different sectors, the use cases for AI Engines for both banking and finance mostly overlap. Situations like detecting fraud, qualifying loans, analyzing credit risk and such are applicable for both industries.
Thus, using a combination of ML and DL AI Engines, banking and finance businesses can detect fraudulent activity, provide the best solutions for loans, and execute efficient risk assessment mechanisms. Also, for the finance industry, you can also use AI engines for stock trading.
Since there are many use cases in this category, here are some applications that make use of AI Engines for banking and finance:
Using many different AI Engines, can a lot of value to retail and e-commerce companies. Whether it be for supply chain management, personalized recommendations, or dynamic pricing, AI Engines can streamline a lot of workflows for these businesses.
Examples of different implementations of AI Engines in retail and e-commerce are:
Telecom companies can make use of AI Engines to gain a lot of benefits. These use cases can range from network optimization to predictive management and managing cybersecurity threats. This reduces the load on networks and creates a more efficient system for telecom industries.
Some examples of applications of AI Engines in telecom are:
Google Cloud AI: Using many different kinds of AI Engines, Google Cloud optimized networks and prevented network failures.
Cisco AI: Monitors network traffic and identifies any security issues in real-time.
Amdocs AI: A solution that provides network management as well as improving service through analytics.
One of the major industries for AI use, healthcare can make use of AI Engines in a variety of ways. Using Vision Engines, medical imaging can be analyzed more efficiently and swiftly. Furthermore, AI Engines can assist in drug research and provide personalized healthcare solutions.
Examples of applications that use AI Engines for different use cases of healthcare are:
With technology advancing, businesses can use AI Agents to autonomously complete objectives. At the core, these agents are implementing AI Engines to provide solutions to the automobile and robotics industries.
Here is how AI Engines empower apps to provide autonomous solutions.
It can be very hard to pick the right AI Engine for you. Thus, here are some tips that can guide you to the right solution for your business.
Understanding the industry you are in and then factoring in what you need out of an application can help you decide on an AI Engine. For example, if you need automated customer service, a solution like REVE Chat.
So it is important to identify the use cases you need to fulfill for your business before you choose one or multiple solutions.
If possible, try to choose one application to fulfill your needs as opposed to getting multiple for different segments of one use case. For example, you do not want to take one solution for chatbots and another solution for live chat. Having one platform to do both is not only better but also more economical and efficient for you.
As AI Engines can be expensive, you should do an analysis on whether the cost is worth the improvement in output. That largely depends on the type of industry you are in, how much a solution would cost and how much improvement would you see in your business.
AI Engines can improve even further in the future. Here is how.
Quantum Computing is the next big advance when it comes to computation. With different types of hardware, Quantum Computing will take to a whole new technological era. Now imagine how it would be if you could combine this and AI Engines.
Many companies like Google are already testing the waters and in the future, this concept could revolutionize the world as we know it.
Currently, there are not that many AI Engines that provide multi-modal capabilities and that could change quite quickly. Many AI platforms are already improving and implementing multi-modal capabilities across the board. This would reduce the necessity of using different solutions for generating text, images, videos, and more.
As AI becomes more affordable and is developed more efficiently, AI Engines will become more accessible, and different frameworks will allow you to deploy them more easily. Over time, this process to become easier and cheaper for the masses to use.
Ethically, AI Engines are also improving with many platforms like DeepSeek and ChatGPT providing a reasoning option to see how their AI models think. This means AI will become more accountable and more understandable over time and both situations will make AI more accurate and ethical in the long run.
In short, AI Engines are the core of all artificial intelligence systems across the world. Because of their specialized nature, many different use cases can be fulfilled for businesses across all industries.
As there are many types of AI Engines, the use cases for them are vast. Thus, AI Engines are a pivotal part of artificial intelligence and will continue to be so as it improves in the future due to the advancement of technology.
AI Engines are already making a huge mark in the world, and they will continue to grow its influence over time.
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Nur-Nabi Siddique is the CTO at REVE Chat. He is renowned for his deep proficiency in the Spring Framework, NLP, and Chatbot Development. He brings a wealth of knowledge and a forward-thinking approach to technological innovation.
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