In a rapidly evolving world, technology is at the forefront of innovation, and artificial intelligence (AI) is at the centre of attention of global tech pioneers. AI refers to a computer’s ability to exhibit signs of intelligence. This intelligence manifests itself in part in a machine’s capacity to make decisions as if it were human, using what data it has collected before, to provide the most optimal solution to a command—a process known as machine learning.
Given that the practical applications of AI technologies are not widely understood yet, some media outlets project an exaggeratedly negative representation of the uncertainty that comes with a rapidly-developing technological future. Shows like Black Mirror (2011) and films like Ex-Machina (2014) make it is even easier to imagine a world where humanity’s well-being is threatened by the existence of sentient machines. In reality, however, AI has many practical applications that are a lot less scary.
In today’s digital age, leading tech companies such as Apple, Google, and Microsoft are all investing in AI in an effort to enhance machine learning technologies. Currently, these technologies focus on data collection. In 2014, Google acquired the company DeepMind—a world leader in AI research and its applications. DeepMind’s team of researchers and engineers focus on the development of neural networks—a computational model that partially imitates the structure and functions of biological neural networks. DeepMind uses these artificial neural networks (ANNs) to expand a machine learning method based on learning data representations known as deep learning.
An ANN is built around a collection of nodes or “artificial neurons,” which transmit signals among one another, in the same way that neurons in a human brain do. When an ANN receives an input of information, the network also takes into consideration the many other inputs it has received in the past. Using this catalogued information, the software formulates a solution and forms a pattern so that if a similar situation arises again, the program can work out an answer faster. If the AI program were to play a game of Space Invaders for example, it would play round after round learning through trial and error until it found the optimal strategy to go about blasting all of that space scum.