Artificial intelligence (AI) is now very much part of our collective consciousness, which is increasingly validated in the world around us. Yet there are still many ill-defined notions of AI and what it consists of – ranging from “smart computers” to “artificial life”.
As we search for a deeper application and understanding of everything that AI can do for a business, it’s worth taking time to fully consider exactly what AI is.
Defining AI: Reasoning
“Artificial intelligence” was originally defined by the pioneering computer scientist John McCarthy; and refers to “the science and engineering of making intelligent machines”. But that still leaves us asking what is meant by “intelligent”?
Usually, intelligence is defined and measured by the capacity to acquire and process knowledge, using it to “understand” concepts and make correct “judgments”. The twin abilities to learn and to make decisions are two of the key attributes that make AI useful to us.
An AI system learns through the processing of data, which becomes the basis for its decisions. That data evolves into a store of lessons that it draws on in the future.
When a machine learns, it can make decisions. It analyses data, categorises it, and makes recommendations based on what it already knows. The more an AI system knows, the better decisions it will make.
It follows that when a machine makes decisions autonomously with a good enough degree of ever-improving judgment, the tasks it performs will be accomplished quickly, precisely, and tirelessly. AI systems are excellent at relieving human workers of the strain of repetitive tasks that most would much rather avoid.
A helpful worker
Popular culture sometimes portrays an AI system as a person or a creature. In reality, AI is more akin to a tool, or perhaps an obedient robot. It is, nevertheless, an extremely sophisticated tool that can help with many of our manual and cognitive tasks.
Give an AI system information and it can judge insurance claims in bulk and determine the appropriate payouts, or alternatively, detect potential frauds. It can process these claims quickly and accurately, taking a large workload of painstaking, time-consuming tasks off the insurance underwriter’s desk.
AI systems can also monitor industrial machinery, analyse performance, and spot a glitch or impending failure. It can even predict when a breakdown will occur based on historic equipment lifecycle data. That means maintenance personnel have to undertake fewer routine checks and can dedicate more time to more strategic work such as finding new ways to boost production output.
Far from futuristic, these are just two readily-implemented examples of how AI systems are capable of assisting in a task, increasing efficiency, and optimising organisations’ processes.
Interacting digitally with the physical world
As AI systems progress, they’re able to interact with our world in new, more useful ways than ever before. Machines now have the processing power, code complexity, and sensory sophistication to examine video, audio and other sensor data in real time, quickly and deeply.
Just as an aircraft’s autopilot makes the pilot’s life easier, AI systems help drivers on their journeys. Autonomous vehicles process visual data to analyse their surroundings and navigate, using GPS and proximity sensor data. They do so with precision. The beauty of an AI system is that is can perpetually learn from its experiences on the road and implement improvements, making each journey with greater efficiency and safety.
More data makes for more potential outcomes
The internet of things (IoT), an ever-expanding network of smart devices throughout the world, offers today’s AI systems access to a larger amount of data than ever before—enabling them to be even more useful to mankind.
These exciting developments give us the opportunity to analyse, understand and change our world in ways never possible before. Climate sensors thousands of miles apart send back data for comparison. Carbon emissions from Beijing to Montreal to Addis Ababa are recorded and simultaneously subjected to analysis.
We’re now in a world where valuable data is created each time a transaction is completed, when goods are transported, or a product is manufactured. With the IoT there to collect it, the data is instantly added to the huge mass of information AI systems can learn from.
This wider, deeper view of our world will enable ever-smarter AI systems to generate remarkable new insights. The usefulness of our digital helpers will simply carry on growing.
Verdict deals analysis methodology
This analysis considers only announced and completed cloud-deals deals from the GlobalData financial deals database and excludes all terminated and rumoured deals. Country and industry are defined according to the headquarters and dominant industry of the target firm. The term ‘acquisition’ refers to both completed deals and those in the bidding stage.
GlobalData tracks real-time data concerning all merger and acquisition, private equity/venture capital and asset transaction activity around the world from thousands of company websites and other reliable sources.
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