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AI is the simulation of human intelligence in machines to perform tasks that require human cognitive abilities.
As opposed to the intelligence of humans or other animals, artificial intelligence (AI) is the intelligence of machines or software. Founded as an academic discipline within computer science in 1956, AI has been a research subject and a source of technological innovation for decades.
While AI-based tools have been around for quite some time, the next generation of technologies is challenging our understanding of learning and cognition, according to researchers at Microsoft Research.
In 1950, British mathematician Alan Turing published a seminal paper considering artificial intelligence, posing the question: Can machines think?
According to Turing, his ‘machines’ would be capable of making decisions, observing the results of their behaviours, learning by rewards and punishments, obeying commands, thinking, writing, and remembering. The test proposed by Turing – the Imitation Game – measured an AI system’s ability to imitate human behaviour.
Turing predicted that by 2000, an interrogator would not have more than a 70% chance of making the correct identification after five minutes of questioning. Although more than two decades late on the prediction, OpenAI’s GPT-4 and Google’s LaMDA have beaten Turing’s imitation game. The two LLMs have since sparked debate on new tests to test AI intelligence.
Artificial intelligence (AI) encompasses all intelligent systems and machines that mimic human capabilities, including learning, problem-solving, and decision-making. On the other hand, generative AI (GenAI) and other AI-based tools are all subsets of AI.
There are also differences in terms of functionality and data requirements. While AI analyses and responds, GenAI creates new things. As such, AI needs extensive, clean and specific data for specific tasks, while GenAI thrives on vast, general datasets. By learning on large datasets, GenAI algorithms can ‘learn’ how to make connections between data points (words, for instance) and generate humanlike responses.
Other AI-based tools address particular use cases by using technologies that enable AI. For instance, a meeting assistant (Firefly) is an AI-based application specialising in natural language processing (NLP) and text-to-speech automation.
AI has rapidly evolved to capture several critical aspects of our day-to-day lives. AI is commonly used in:
While AI continues to penetrate deeper into our lives, there are several genuine concerns regarding the continued use of AI and its impact on the world.
One of the key pursuits of AI research is to develop an artificial general intelligence (AGI), an AI system capable of self-control without human intervention, a degree of self-understanding, and the ability to learn new skills. An AGI system would theoretically be able to solve problems in settings and contexts that were not taught to it while it was being created.
While AI focusses on training software for specific, challenging tasks at human-level performance, AGI envisions a broader intelligence. This theoretical AI wouldn’t need hand-holding for every domain. Just like a human, it could learn and solve problems. In essence, AGI aims to replicate the full breadth of human cognitive abilities, allowing machines to conquer complex tasks across various domains.
If an AGI comes into existence, it could be humanity’s ‘last’ invention. An AGI could help accelerate drug research and cures for diseases like cancer, anticipate and prevent disasters, and help humanity make use of potentially catastrophic technologies like climate engineering and nanotechnology.
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