Utilising artificial intelligence could aid economic stimulation
HUMANS CAN do cognitive tasks, that is, humans can think. Humans can think to learn, think to teach, think to do disease diagnosis as doctors, think to translate language, etc.
When we think, electrical impulses bounce around in the brain to generate thought. These electrical impulses can be seen as a type of ‘software’ running on our brains, the ‘hardware’.
In a similar way, we give intelligence to computers/ hardware by running on those computers brain-inspired software applications. An example of braininspired application is called an artificial neural network. artificial neural networks power a large majority of smart applications today, and they’re better at doing individual cognitive tasks than humans, such as disease diagnosis, language translation, and they help to do things like detect planets, etc.
As artificial-intelligence researchers replicate more and more brain function in the form of brain-inspired software/hardware applications, we approach a form of artificial intelligence called artificial general intelligence (AGI). Instead of being good at individual cognitive tasks or small groups of individual cognitive tasks as smart machines are today, AGI is a model that will be able to do the entire cognitive landscape of cognitive/ thinking tasks – that is, AGI will be equal to human-level intelligence overall.
These general learning models will likely help us to solve cancer, ageing, etc. Google chief AI engineer Ray Kurzweil (who predicted the emergence of the Internet before it came along) also predicts that AGI will likely arrive in 2029! Kurzweil had largely correctly predicted the emergence of future hardware/software applications by graphing how price/performance of technology scales with time.
Nowadays, each large firm (Google, Microsoft ... ) seeks to achieve AGI, hiring the smartest machine-learning researchers. These large firms (or otherwise expert machine-learning researchers) produce free-to-use machine learning APIs (or easy-touse apps) to the public, such as TensorFlow, or MXNet.
For now though, these large firms mostly build machinelearning models that can solve individual cognitive tasks or small task groups (aka thinking tasks like disease diagnosis or language translation).