Artificial intelligence (AI) instructs computers to perform intelligent tasks or make complex decisions. AI solves problems associated with natural intelligence, such as learning, problem-solving, or recognition. The fundamental ideas behind the field are not new. The modern era of AI started in the 1930s when Alan Turing proposed the Turing test for assessing if a machine is intelligent. According to Turing, a machine is intelligent if a human cannot establish that he is having a conversation with a machine.
Artificial intelligence is everywhere, from apps that perform language translation to GPS and complex navigation systems. Moreover, AI is evolving. Fiction authors continue to describe artificial intelligence as entities possessing human-like characteristics. In reality, AI can have any form from search algorithms up to autonomous weapons or large scale robotics systems.
AI is a way of thinking rather than a software application which makes it a strategic domain. It consists of machines that learn, sens, and make decisions. These machines improve business effectiveness, efficiency, and productivity. Some popular applications for AI are autonomous vehicles, speech processing, and object recognition. Under the hood, all AI systems process large quantities of information and make optimal decisions in ever-changing environments. In other words, AI enables computers to feel, understand, learn and adapt, and to interact with the situation. Thus, AI is not a SaaS that you buy; it represents a practice, a way of thinking, and an approach for solving real-world problems.
AI’s primary purpose is to make machines capable of learning, sensing, and making decisions at least as good as human capability. In this context, technologies like deep learning are becoming dependable and useful. Deep learning is a sub-branch of artificial intelligence that aims to mimic the brain architecture and functioning. A deep learning network consists of many nodes linked to each other at different strengths. Each node seeks to replicate the basic operation of a neuron. The network uses data to adjust the strength of the connections. Once trained, such a system can reason about specific inputs or predict outcomes. Using deep learning unlocked computational models with a wide range of application. These algorithms are great for processing images and videos, speech, and language. Also, deep learning is famous for solving problems where the systems are interacting with its environment in real-time. Some examples are the control system for a plant or automated trading algorithms [see here for more applications].
Advances in AI lead to new and innovative products and services. Nowadays, we have devices that read, listen, and even comprehend how people think and behave. They understand user behaviour and anticipate users’ needs. Also, consumer products or services powered by AI now know how to fill users’ needs in time, like a super charged virtual assistant. In 2017 more and more products had AI embedded into their design. Google Home is an excellent example of a smart device. It allows the user to transform their home into an intelligent home, one that they control via a voice interface. Consumer products are not the only ones where artificial intelligence plays a critical role, industrial or medical devices rely on artificial intelligence too. Autonomous driving, algorithms for flight control, or algorithms for detecting tumours from scans are all built with at least a pinch of AI. Regardless of the application, artificial intelligence is changing how consumers, operators, and makers interact with devices. Thus, AI is percolating into all the products and services we use.
Artificial intelligence releases new waves of innovation, fueling new applications and business models. Integrating AI with operational technology (OT) and enterprise systems will bring about another factor of digitisation: intelligent automation (IA). Intelligent automation streamlines decision making by using AI methods to understand the tasks and decisions made during a process. IA systems are fantastic for the enterprise because they enable people to utilise their time efficiently and solve complex tasks. McKinsey found that a financial institution employed intelligent automation to automate 60-70% of their tasks which lead to a 30% increase in their process efficiency. Thus, businesses are in a unique position to improve their efficiency and reinvent themselves with the use of AI systems.
In conclusion, AI is a collection of methods that initiated a new paradigm in business, one where we solve problems without knowing the steps to take in advance. Using AI, we develop systems that mimic natural intelligence and in some cases even surpass it. Thus, AI creates systems capable of reasoning, solve problems, acquire and use knowledge, make decisions, and communicate in natural language.
