Artificial intelligence (AI)
In 1955, John McCarthy invented the term “artificial intelligence”. In the following year, McCarthy and a few others initiated a conference called the Dartmouth Summer Research Project on AI, which contributed to the development of machine learning, deep learning and predictive analytics. It also gave rise to a new research area – data science.
AI’s definition today refers to the simulation of human intelligence in computer systems. The system is programmed to mimic a human mind and actions, such as visual perception, speech recognition, decision making and translation between languages.
The main purpose of AI is to streamline and perform actions that have the greatest chance of achieving a particular intent. With that said, AI is not here to replace humans, but rather, it amplifies our capabilities and allows us to improve at what we do.
The use of AI has been growing exponentially, and the global AI market size is anticipated to grow at a CAGR of 42.2% from 2020 to 2027, reaching a value of USD 733.7 billion.
How AI works
Fundamentally, AI is trained through the use of algorithms to process a large amount of data and recognize the patterns in the data. Here are the major subfields of AI.
1. Neural network
Geoffrey created the concept of neural network in the 1980s. A neural network is a collection of algorithms that mimic the organisation of neurons in the human brain.
The artificial neural network consists of three-node layers – input, hidden and output layers. Each node functions in the same way as a human brain neuron. As such, data is being transferred across the node layers to identify correlations and extract valuable insights.
2. Machine learning
The main objective of machine learning is to allow computers to self-learn automatically. Through the use of neural networks, research and statistics, the system will study the data and discover the hidden insights. As the model continues to learn for itself, the insights identified will improve its intelligence and adjust actions accordingly.
3. Deep learning
Deep learning is the neural networks with several layers that allow learning. It is a form of machine learning. In conventional machine learning, the algorithm uses a collection of relevant features for analysis. On the other hand, the algorithm in deep learning uses raw data and the model will determine what are the relevant features.
Also, a deep learning model will improve its intelligence as the input data increases over time. The model is adept in solving problems that require “thinking”. The common applications of deep learning are image recognition and speech recognition.
4. Internet of Things
Internet of Things, in short IoT, is a system of interconnected devices, machines, computers, objects via the internet connection. AI and the Internet of Things (IoT) are the perfect partners for growth. With IoT, the connected devices generate a massive volume of data where most of which have not been studied. Hence, more data will be available for AI models to study and improve their abilities.
As mentioned above, we are generating a huge amount of data every day by both humans and computers. It is beyond human capacity to consume, analyze and make nuanced decisions, which is why AI plays an important role in doing the learning and decision-making efficiently and effectively for humans.
1. Process automation
Firstly, many of the AI applications we see today are robotic process automation, in short RPA. With RPA, users can configure automated robots that can mimic what a human can do. In other words, robots are aimed at automating the business process, and this is ideal for work that has multiple backend systems.
Just like humans, RPA robots use the interface to collect data and control applications. They study the data, trigger responses and interact with other systems to execute repetitive tasks. What’s more, robots can run around the clock, work faster, perform tasks objectively and not make careless mistakes.
As a result, the business can function more efficiently. Moreover, this form of AI frees up a large amount of effort and time for human employees, allowing them to prioritize higher-value tasks.
2. Cognitive insight
Secondly, cognitive insight uses algorithms to study a large amount of data and interpret their meaning. It is an analytics tool powered by deep learning. Through studying the data, the model can identify patterns in a business’s data and predict results in particular scenarios. For instance, analyzing past claims data to help insurance companies to determine insurance quotes and forecast claim possibilities.
To further elaborate, cognitive insight provides businesses with the ability to create personalization for their customers. This allows companies to better cater to their customers’ needs and wants. This is done by studying the customers’ purchase histories, demographics, etc.
Furthermore, as the model gets trained progressively through inputting more data, the insights generated will also improve and get more accurate over time. Some great examples of getting higher accuracy with more data input are Google Photos, Google Search and Alexa.
3. Cognitive engagement
Cognitive engagement is an extension of cognitive insight. With the help of cognitive engagement, the model can create personalization strategies automatically when engaging with individuals. The personalization strategies are created based on the individuals’ demographics, behaviour and other relevant data points.
Therefore, businesses can deliver customized service that caters to the exact needs and wants of the customer. An example is that Target was able to classify which customers were expecting a baby by analyzing the products they purchased.
Chatbots are also part of cognitive engagement. This technology uses natural language processing (NLP) that allows the model to interpret and decipher human languages in a useful manner.
Many companies have been deploying chatbots to answer frequently asked questions (FAQ). These chatbots can provide accurate and relevant answers and stay available 24/7.
Applications of AI
AI can provide the intelligence we want to empower and liberate our workforce. Let us take a look at a few examples of what groundup.ai can do with AI.
In the marketing industry, AI can be used in many ways.
One of them is the recommendation engine where the AI system can personalize recommendations easily and quickly. By digesting data such as the browsing history and personal preferences, the engine can deliver the most relevant recommendations to a specific individual. As the AI brain starts to learn about your users’ behaviour, the recommendations will eventually become more accurate.
This is how you can make your business different from others. Through a recommendation engine, you can better target your customers and identify their needs before they know it themselves. As such, enhancing the shopping experience for your customers.
Another example is creating virtual influencers for your brand. We are no strangers to influencer marketing, but there has been a new wave of online personalities surfacing in the past several years.
The primary goal of using a virtual influencer is to create a character that can truly represent the brand’s values. This is because they are created using AI, applied psychology and tons of market research on the target audience. Therefore, the character will appeal to the right target audience, and fit into their likes and behaviour.
2. Predictive analytics
Predictive analytics is a useful tool in preventing machine breakdown unexpectedly. It is an aspect of data analytics that makes predictions about future outcomes. This is done by studying historical data and using machine learning to derive trends.
With predictive analytics, you can forecast future machinery breakdowns and problems which gives your workers the early opportunity to carry out maintenance work before damage is done. Therefore, predictive maintenance is the solution to reducing occurrences of unplanned downtime through a more efficient maintenance approach.
Predictive analytics can also forecast an estimate of customers’ demand based on historical sales data, and this is termed as demand forecasting. This strategy can gain valuable insights about any particular customer’s demand patterns. It helps you to prioritize customers and accurately predict their demands. As such, you can plan your stock inventories based on your customers’ foreseeable purchase.
3. Computer vision
Beyond predictive maintenance, AI in the form of computer vision is extremely beneficial for the construction industry. Computer vision can perform tasks in the same manner as human vision, including “seeing” and interpreting the images. In other words, computer vision can perform image recognition and analysis to provide valuable insights.
An example of how computer vision can come in useful is through the automation of tasks within the construction worksite without any physical human intervention. The technology provides you with the opportunity to perform remote and real-time monitoring of the workplace and workers, ensure safety compliance, improve productivity and achieve cost-efficiency.
Ultimately, implementing computer vision at the workplace will help you to achieve long-term sustainability in your businesses.
Towards the future
In summary, this article aims to help you have a better understanding of AI technology. The above-mentioned applications are only a few examples of what AI can do. There are still many ways where AI can improve operations and bring your business to the next level.
Here at groundup.ai, we have extensive expertise in the field of AI and technologies to help your organisation strive towards digitisation. Chat with us to find out more about how AI can help you.