Sep 8, 2022
In 1960, artificial intelligence first entered public consciousness through the scientist Alan Turing. In an article, Turing underpinned what would quickly become known as the Turing Test: for a machine to be considered intelligent, a person must first be able to converse with it for five minutes. If by that time, they did not realize that he or she was not interacting with another human being, the AI would have passed the test.
Key definition: ″Artificial intelligence (AI) is defined as the capabilities of a machine or a computer program to think and learn.″
Ever since, AI has captured the public’s imagination like few other sci-fi tropes. 2001: Space Odyssey’s chilling rendition of HAL, a free-thinking computer, was more of a doomsday prophetic than a love letter to the future of AI. Blade Runner, The Matrix, and countless other films sustained this legacy, conjuring dystopian visions of robots with a sinister kind of intelligence.
But by 2022, technology is a far cry from the complexity imagined in cinema. The full intricacy of the human brain still manages to elude scientists, with many considering the full mapping out of its functions an insurmountable goal.
Yet AI is progressing. Investments in this area are inching technology closer to the kind of lucidity imagined in pop culture. But currently, the revolutionization of industry is taking place without the humanlike intelligence of cinematic robots. The automation of routine processes and machines that can "think" - which is, perform low-level point-of-action decision-making - are becoming increasingly occurring fixtures. ‘Smart’ technology as applied to business is seeing mass investments. The value of AI has never been more apparent – or more of a reality.
A recent report from BCC Research predicts that the global market for AI will reach a staggering $294.8 billion by 2026, up from $55.3 billion in 2021. The compound annual growth rate (CAGR) will fall at around 39.7%. This much is clear: the industry has clear potential to transform the global economy altogether.
The human brain is the model for all AI technology. Naturally, the technology is complex, though machines can be programmed with isolated functions to perform specific tasks.
Scientists categorize AI into four specific types.
The reactive machine is the most basic kind of AI. It doesn’t store memories or past experiences, so it cannot use them to learn. It produces output through a relatively limited set or combination of human-programmed inputs. The IBM Deep Blue system, for example, has the ability to play chess with humans automatically.
As the name suggests, limited memory machines can learn from experience and store data to make decisions. However, limited storage space means data is cleared after a certain amount of time. This technology is already finding usage in self-driving cars and AI chatbots.
While the previous two AIs are commercial realities, numbers three and four are levels of artificial intelligence that are still in development. Theory of mind machines will have decision-making abilities like a human. They will be able to understand human thoughts, emotions, and beliefs, enabling them to interact with humans socially. This will be a highly advanced form of voice assistant.
This will be the final stage of AI technology development. Self-aware machines will have independent consciousness. They will have sentiments and self-awareness just like humans. Ultimately, this technology will be smarter and higher functioning than the human brain. They will think by themselves and reflect on their internal states. As they are still in research and development, it is unlikely self-aware machines will be perfected for centuries.
AI is being steadily adopted by businesses all over the world. While individuals interact with AI in the form of smart gadgets (hello, Alexa), in business, AI is taking the form of customer relationship management (CRM) systems and data aggregation. This kind of technology is referred to as narrow AI. It is not designed to replicate human intelligence, as is the aim of self-aware technology. Narrow AI performs one task and is a current commercial reality. Humanlike AIs are referred to as artificial general intelligence (AGI) and are not yet on the market.
In other words, specific forms of AI are being used to solve real-world problems. Within both public and private sectors, managers are witnessing higher productivity levels and cost reductions thanks to this technology. Here are the four uses of narrow AI in businesses today:
This technology learns from experience independently. It’s making waves across the healthcare, finance, retail, automotive, and government industries. By making use of various statistical, algorithmic, and mathematical models, solutions can learn from data interactively, providing companies with technology that can solve numerous business problems. For example, banks use ML technology to track fraudulent transactions. If new transactions do not mirror the learned algorithmic model, then an alert for potentially fraudulent activity ensues. Machine learning technology is set to dominate the global market for AI in the coming years.
The primary goal of computer vision systems is to understand the visual world. It’s used heavily in the automotive industry in driver-assistance systems, notably by Tesla in their autopilot function. This technology uses deep learning, image analysis, facial recognition, and neural networks to acquire, process, and ultimately understand an image.
3. Natural language processing (NLP)
NLP has gained traction over recent years. The technology can represent and analyze human language. This finds applications in numerous areas, including email spam detection, information extraction, and summarization. Rather than mechanically inputting machine-specific languages, this technology gives humans the ability to communicate freely with computers in natural languages.
4. Context-aware computing
One of the most prominent AI technologies, context-aware computing analyses and gathers information about the user environment. This information is then used to make problem-solving decisions. The technology provides information about people, places, time, and the weather. Most Internet of Things (IoT) devices already use context-aware computing; for example, when cell phone screens adjust their brightness automatically, depending on the intensity of natural light. This technology could one day function as a cost-effective alternative to human experts – particularly in narrow, specialized fields.
Artificial Intelligence is receiving huge investments thanks to its capacity to positively transform society. With businesses and individuals absorbing the benefits of various kinds of commercially available AI, the high growth rate for this industry is naturally unsurprising.
BCC Research’s report on global artificial intelligence markets aims to provide a comprehensive study on the various types and applications of AI across numerous end-user industries. It provides an in-depth review of AI tech, as well as analyzing how these factors influence contemporary and future markets. If you’d like more information, consider downloading a free report overview.
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