AI is not always ‘artificial intelligence’; You need to know these things!

In August 1955, a group of scientists made a funding request for USD 13,500 to host a summer workshop at Dartmouth College. The field they proposed to explore was artificial intelligence (AI) Since these humble beginnings, movies and media have romanticised AI or cast it as a villain. Yet for most people, AI has remained as a point of discussion and not part of conscious lived experience.

AI has arrived in our lives

ChatGPT is the most dramatic entrant in a year of generative AI success. A cleverly worded prompt can produce an essay or put together a recipe and shopping list, or create a poem in the style of Elvis Presley. Generative AI has also produced worries about plagiarism, exploitation of original content used to create models, and abuse of trust. At the centre of all this is the question that has been growing in urgency since the Dartmouth summer workshop: does AI differ from human intelligence?

What does ‘AI’ actually mean?

AI is broadly defined in two categories: Artificial Narrow Intelligence (ANI) and Artificial General Intelligence (AGI). To date, AGI does not exist. The key challenge for creating a general AI is to adequately model the world. For now, the most notable example of trying to achieve this is the use of neural networks and ‘deep learning’.

The breakthroughs in AI are being driven by advances in the way we can train large neural networks, readjusting vast numbers of parameters in each run. As more data is fed through the network, the parameters stabilise; the final outcome is the ‘trained’ neural network, which can then produce the desired output on new data.

What does AI need to work?

Artificial intelligence (AI) needs high-quality, unbiased data, and lots of it. Researchers building neural networks use the large data sets that have come about as society has digitised. AI models will continue to evolve in sophistication and impact as we digitise more of our lives. As computers become more powerful, models that now require intensive efforts and large-scale computing may in the near future be handled locally.

Another prominent camp in AI research is symbolic AI, which relies on rules and knowledge similar to human thought. The balance of power has heavily tilted toward data-driven approaches over the last decade. Data, computation and algorithms form the foundation of the future of AI.