Artificial Intelligence (AI) has been around for a while. The ability for machines – to perform complicated actions based off specific inputs with minimal human involvement – has helped us increase our standards of living and is the predominant reason why we live so differently now than 200 years ago. Technology has been a godsend and a challenge for humanity. There’s been a lot of great science fiction written about the technology we create taking over significantly more complicated tasks – running our cities, predicting the future and planning our lives – this is something we’re seriously, today, taking steps towards doing.
These changes are all going to be equal parts opportunity and complication for everyone involved. We are going to have to adapt quickly, more quickly than ever before. A key idea involved with technological change is the idea of singularity – when technological change and growth goes beyond our ability to control or direct it. Many people predict that this will happen when machines have the ability to evolve and improve themselves without our involvement – we’re not there yet. Within the world of AI, there is an area called Deep Learning. This area of study focuses on applying neural networks – silicon facsimiles based on how our own grey matter processes information – to unorganized, raw data data so information can be processed with very little human ‘teaching.’ Machine Learning is a broader plain that allows for more human involvement in preparing or processing the data and adjusting the algorithms directly.
It can be difficult to reconcile the possibilities of AI from how it appears in popular culture with the current reality. Being able to anticipate the future, to make sensible investments of time and money, is something any job seeker or investor should try their best to do. Knowing what is possible today should inform every business leader to have first-mover advantage. This piece will go into a specific area of AI called Machine Learning and also briefly address Deep Learning, placing them both in relation to the broader technological changes currently in play. This article will help product leaders understand how and when to apply Machine Learning to their challenges and help investors have a view for the right mix of ingredients in early stage teams using Machine Learning technologies.
| This article will endeavour to explore: | |
|---|---|
| 2 | Our language for AI advancements |
| 3 | Understanding Machine and Deep Learning |
| 4 | Successful applications of the technology |
| 5 | Technological and social undercurrents |
Ryan Guichon 