Even though the origins of artificial intelligence theory trace back to the 1940s, AI has never captured the imagination of the general public (and business and government) to the degree we have seen in the past 12 months. It’s suddenly common to find AI breakthroughs covered in national news broadcasts. And according to Google Trends, AI as a topic (which has been on a relatively slow and steady burn for decades) is currently boiling over with interest.
It’s not all talk, either: it’s about money to be made, too. PwC’s Global Artificial Intelligence Study predicts a potential $15.7T contribution to the global economy by 2030 driven by AI technology. What is driving all of this sudden commotion?
Undoubtedly, the arrival of generative AI applications, enabled by rapidly improving and expanding large language models, has opened the eyes of many more people to creative and intuitive experiences using deep learning. Per Exploding Topics, the popularity of the term “Generative AI” has grown 4,400% in the last year.
Four key AI enablers: Connectivity, computation, algorithms, and data
Since the beginning of the field of AI, the curiosity to understand what artificial intelligence could be capable of (as Alan Turing wrote in 1948: “whether it is possible for machinery to show intelligent behavior”) has commonly led researchers and futurists to dream of the day when AI could perform astounding feats regularly, reliably, and efficiently. Suddenly, it seems like that day is here.
Broadly speaking, AI is hotter than ever right now after roughly 80 years of relative obscurity, AI winters, and the grunt work of overcoming limitations for stepping-stone achievements. It’s evolving so fast that leading tech figures are calling for a six-month moratorium on new AI experiments so the world can recalibrate itself to a new vision of its future, with AI playing a much more significant role.
The exponential advancements we are seeing in AI are driving intense excitement today, but in reality, they have been a long time coming, with very logical reasons behind them. It’s the perfect storm of opportunity meeting preparation, as SparkCognition’s Chief Science Officer, Dr. Bruce Porter, explained in one of our recent webinars: Visual AI 101: How Computer Vision Works and Why It Matters to Your Business.
Professor Porter details the path AI has traveled to get where it is today and identifies four critical enablers behind the current AI revolution: Connectivity, computation, algorithms, and data.
Connectivity
The ability of devices to connect to each other and to the internet has led to an explosion in the number of devices that can collect and share data. This data can be used to train AI systems, enabling them to make better decisions and predictions.
Computation
Computers can perform more complex calculations and process larger amounts of data faster than ever. Advances in computer hardware, such as the development of graphics processing units (GPUs), have made it possible to train AI models more quickly. Today’s powerful machines can handle the massive amounts of data required to train deep learning algorithms.
Algorithms
Algorithms are mathematical models that enable AI systems to learn from data. These include supervised learning, unsupervised learning, and reinforcement learning. Advances in algorithms have made it possible to solve problems previously considered unsolvable.
Data
Data is the lifeblood of AI. Without data, AI systems cannot learn and improve. Advances in data storage and processing have made it possible to collect and store massive amounts of data. Work has been done to improve our ability to collect more diverse and representative data to train AI models and improve their accuracy.
Dr. Porter explained: “There are four enabling technologies making artificial intelligence and, more specifically, computer vision a reality. Two of them are kind of raising all the boats: connectivity and computation. They are improving computer science applications across the board. We have connectivity like, for example, the connection of CCTV cameras to powerful GPU boxes. We have computation which, thanks to Moore’s Law, continues to deliver significant advances and computational horsepower.
But the two enablers that are really specific to my field of AI and computer vision are these latter two: algorithms and data. The algorithms are in this class called machine learning, and I’m going to drill in on that—and as you learn more about machine learning, you’ll find it is the heart and soul of machine learning. It all depends on having lots of data (and knowing exactly where it is coming from). What we’re seeing right now is this sweet spot where these four things are coming together nicely.”
In the webinar, Professor Porter illuminates the scientific theory and practice behind machine learning and connects it to a very important (and rapidly evolving) artificial intelligence subfield, computer vision. Dr. Porter and Stephen Gold, Chief Marketing Officer, provide examples of how SparkCognition Visual AI Advisor operationalizes enterprise computer vision technology, enabling organizations across industries to realize efficiency gains through real-time visual analytics and alerts with over 125 end-to-end computer vision use cases for safety, security, visual inspection, productivity, and situational awareness. Visual AI Advisor was recently named a 2023 product winner in the computer vision category by The Business Intelligence Group.
Keeping perspective: AI is constantly advancing
Understanding these four key enablers of AI helps us keep the current moment of heightened AI awareness in perspective. Did the AI engineers and data scientists suddenly discover the holy grail of machine learning? No. Were they making some critical mistake all these years, holding progress back? Not that, either. Did someone build a super-computer that showed all the other computers how to be better computers? Again, no.
Instead, what we are witnessing is a continuation of a logical path for a phenomenally-high impact technology being scaled like never before by the convergence of big data, robust connectivity, more sophisticated algorithms, and powerful computers. These four enablers have always been prerequisites for AI progress, but they are becoming less scarce in general and abundant in some cases. You might even say the field of AI is advancing steadily as ever—the outcomes are just getting more exciting. This is because AI is unlike any other technology due to the exponential power of machine learning.
Connectivity, computation, algorithms, and data are vital accelerants in the progress of AI: “raising all the boats,” as Dr. Porter put it. As AI advances, we must keep these drivers in mind as we responsibly build an AI-enabled future.
Watch our webinar to learn more about visual AI, machine learning, deep learning, and high-value use cases for business:
Visual AI 101: How Computer Vision Works and Why It Matters to Your Business
