Artificial intelligence (AI) is ever-increasingly being interwoven into the complex fabric of our technology-driven lives. Whether we realize it or not, AI is becoming an enabler for us to accomplish our day-to-day tasks more efficiently than we've ever done before. Personal assistants such as Siri, Cortana, and Alexa are some of the most visible AI tools that we come across frequently. Less obvious AI tools are ones such as those used by rideshare firms that suggest drivers move to a high-density area, and adjust prices dynamically based on demand.
Across the world, there are organizations at different stages of the AI journey. To some organizations, AI is the core of their business model. In other organizations, they see the potential of leveraging AI to compete and innovate their business. Successful organizations recognize that digital transformation through AI is key to their survival over the long term. Sometimes, this involves changing an organization's business model to incorporate AI through new technologies such as the Internet of Things (IoT). Across this spectrum of AI maturity, organizations face challenges implementing AI solutions. Challenges are typically related to scalability, algorithms, libraries, accuracy, retraining, pipelines, integration with other systems, and so on.
The field of AI has been around for several decades now, but it's growth and adoption over the last decade has been tremendous. This can be attributed to three main drivers: large data, large compute, and enhanced algorithms. The growth in data stems mostly from entities that generate data, or from human interactions with those entities. The growth in compute can be attributed to improved chip design, as well as innovative compute technologies. Algorithms have improved partly due to the open source community and partly due to the availability of larger data and compute.
