The New Yorker recently examined the life and work of Alan Turing. It’s worth a read. Notably, Paul Grimstad’s article declares, “It is fortifying to remember that the very idea of artificial intelligence was conceived by one of the more unquantifiably original minds of the twentieth century.” While remembering one of computing history’s greatest minds, it can also be helpful for organizational leaders to reflect on the future and history of artificial intelligence.

Semantic Networks

As we look to the future of artificial intelligence, we often think back to its early innovators. In addition to Turing, it is impossible to ignore the contributions of M. Ross Quillian’s SYNTHEX. This project was the first semantic web to be used in AI applications. The future of AI would be impossible to imagine without such semantic networks.

Check Out Another Supercharge Lab Insight: The Future of Money: Projecting Consumer Spending Habits

The future of the Semantic Web promises untold possibilities. Users will experience interrelated topics and ideas across systems and platforms. There are certainly obstacles—primarily ethical and human ones—that need to be addressed. Optimism, however, abounds in this field in ways that will allow organizations to work more effortlessly and with greater growth potential. Specific tools are being developed to allow semantic AI to analyze data conceptually, not simply through search terms. In other words, future capabilities of these technologies will enhance users’ ability to find information much more seamlessly. This will lead to greater reliability and trust and allow organizations to scale at much faster rates than in the past.

Expert Systems and Deep Learning

Computer scientist Edward Feigenbaum received the ACM Turing Award in 1994 and is a prominent AI pioneer. An important figure in the history of artificial intelligence, he introduced the world to expert systems. He first implemented these systems in 1965 with the Stanford Heuristic Programming Project. Expert systems are designed to emulate human decision-making processes and can solve complex problems through human-like reasoning. Today, we might think about the logic behind expert systems as the foundation for neural networks and deep learning.

Deep learning is especially in fashion right now, and business leaders will undoubtedly reap the benefits of the massive work being undertaken by today’s data scientists and AI developers. The future of deep learning means improved accuracy across systems, which will provide financial and operational benefits to corporations in every sector of the economy. Natural language processing and medicine are making the greatest headway in ways that demonstrate the large-scale benefits of deep learning AI.

Neural Networks

Marvin Minsky co-founded MIT’s AI laboratory in 1959 and wrote the book on a theory of natural intelligence known as the society of mind (a theory to which entire college and graduate school courses are dedicated today). Minksy famously declares in his book—The Society of Minds­—that “Minds are simply what brains do.” In other words, minds are the “processes that carry our brains from state to state.”

Why does this matter? Well, the future of neural networks still depends upon our understanding of this theory, and this is important for business. They can already solve a variety of complex, real-world problems. Because of this, they have the potential to provide infinite organizational solutions across all industries. They are extremely flexible and can be applied to (almost) any process. Scaling in the future of business will depend heavily on neural networks and other AI technologies. The future of neural networks means machines will be able to learn from larger datasets to look for more accurate patterns of information to make better decisions for users. A useful example of this is the rise of the autonomous automobile. Self-driving cars are already beginning to utilize neural networks to avoid obstacles and plan optimal driving routes. Future applications will allow machines to make similarly complex decisions that seem incredibly human.

The history of artificial intelligence has no shortage of pioneers. We should also take the time to recognize and celebrate today’s leaders in the field. People like Andrew Ng, Fei-Fei Li, and Andrej Karpathy (among so many others) are dedicating their life’s work to the systems and processes that will allow organizations to expand their capabilities in ways that we cannot even begin to imagine.

What contributions from AI’s past do you think best represent its future applications?