Artificial intelligence (AI) makes it possible for machines to learn from experience, adjust to new inputs and perform human-like tasks. Most AI examples that you hear about today – from chess-playing computers to self-driving cars – rely heavily on deep learning and natural language processing. Using these technologies, computers can be trained to accomplish specific tasks by processing large amounts of data and recognizing patterns in the data. AI’s recent resurgence can be attributed to increased data volumes, advanced algorithms, and improvements in computing power and storage, but AI is not new. The term artificial intelligence was coined in 1956 by John McCarthy.
Early AI research in the 1950s explored topics like problem-solving and symbolic methods. In the 1960s, the US Department of Defense took interest in this type of work and began training computers to mimic basic human reasoning. For example, the Defense Advanced Research Projects Agency (DARPA) completed street mapping projects in the 1970s. And DARPA produced intelligent personal assistants in 2003, long before Siri, Alexa or Cortana were household names. These efforts paved the way for the automation and formal reasoning that we see in computers today, including decision support systems and smart search systems that can be designed to complement and augment human abilities.
Even as artificial intelligence has become the most disruptive class of technologies driving digital business forward, there is confusion about what it is, and what it can and cannot do—even among otherwise tech-savvy professionals. If you search the web, you’ll find as many definitions of AI as there are people who write them. So let’s take a different approach and identify what AI can do in an applied environment.
The 6 Pillars of AI
- AI automates repetitive learning and discovery through data. But AI is different from hardware-driven, robotic automation. Instead of automating manual tasks, AI performs frequent, high-volume, computerized tasks reliably and without fatigue. For this type of automation, human inquiry is still essential to set up the system and ask the right questions.
- AI adds intelligence to existing products. In most cases, AI will not be sold as an individual application. Instead, products you already use will be improved with AI capabilities, much like Alexa was added as a feature to Amazon. Automation, conversational platforms, bots and smart machines can be combined with large amounts of data to improve many technologies.
- AI adapts through progressive learning algorithms to let the data do the programming. AI finds structure and regularities in data so that the algorithm acquires a skill: The algorithm becomes a classifier or a predictor. Just as the algorithm can teach itself how to play chess, it can also teach itself what product to recommend online, and adapt when given new data.
- AI analyzes more and deeper data using neural networks that have multiple hidden layers. Building