What is Artificial Intelligence?
Artificial intelligence highly refers to any human-like behavior displayed by a machine or system. According to AI, computers are programmed to "mimic" human behavior using extensive data from past examples of similar behavior. This can range from recognizing differences between a cat and a bird to performing complex activities in a manufacturing facility.
Artificial intelligence is a constellation of "many" different technologies working together to enable machines to sense, comprehend, act, and learn with human-like levels of intelligence. Maybe that’s why it seems as though everyone’s definition of artificial intelligence is different: AI isn’t just one thing.
History of AI
Before 1949, computers could execute commands, but they could not remember what they did as they were not able to store these commands. In 1950, Alan Turing discussed how to build intelligent machines and test this intelligence in his paper “Computing Machinery and Intelligence.” Five years later, the first AI program was presented at the Dartmouth Summer Research Project on Artificial Intelligence (DSP RAI). This event catalyzed AI research for the next few decades.
Computers became faster, cheaper, and more accessible between 1957 and 1974. Machine learning algorithms improved and, in 1970, one of the hosts of DSP RAI told Life Magazine that there would be a machine with the general intelligence of an average human being in three to eight years. Despite their success, computers’ inability to efficiently store or quickly process information created obstacles in the pursuit of artificial intelligence for the next ten years.
AI was revived in the 1980s with the expansion of the algorithmic toolkit and more dedicated funds. John Hope field and David Rumelhart introduced “deep learning” techniques that allowed computers to learn through experience. Edward Feigenbaum introduced “expert systems” that mimicked human decision-making. Despite a lack of government funding and public hype, AI thrived, and many landmark goals were achieved in the next two decades. In 1997, reigning chess World Champion and Grand master Gary Kasparov was defeated by IBM’s Deep Blue, a chess-playing computer program. The same year, speech recognition software developed by Dragon Systems was implemented on Windows. Cynthia Breazeal also developed Kismet, a robot who could recognize and display.
Modern applications for AI
AI has the unique ability to extract meaning from data when you can define what the answer looks like, but not how to get there. AI can amplify human capabilities and turn exponentially growing data into insight, action, and value. Today, AI is used in a variety of applications across industries, including healthcare, manufacturing, and government. Here are a few specific use cases:
Prescriptive maintenance and quality control improves production, manufacturing, and retail through an open framework for IT/ OT. Integrated solutions prescribe the best maintenance decisions, automate actions, and enhance quality control processes by implementing enterprise AI-based computer vision techniques.
Speech and language processing transforms unstructured audio data into insight and intelligence. It automates the understanding of spoken and written language with machines using natural language processing, speech-to-text analytics, biometric search, or live call monitoring.
Video analytics and surveillance automatically analyzes video to detect events, uncover identity, environment, and people, and obtain operational insights. It uses edge-to-core video analytics systems for a wide variety of workload and operating conditions.
Highly autonomous driving is built on a scale-out data ingestion platform to enable developers to build the optimum highly-autonomous driving solution tuned for open source services, machine learning, and deep learning neural networks.
Benefits of AI
End-to-end efficiency: AI eliminates friction and improves analytics and resource utilization across your organization, resulting in significant cost reductions. It can also automate complex processes and minimize downtime by predicting maintenance needs.
Improved accuracy and decision-making: AI augments human intelligence with rich analytics and pattern prediction capabilities to improve the quality, effectiveness, and creativity of employee decisions.
Intelligent offerings: Because machines think differently from humans, they can uncover gaps and opportunities in the market more quickly, helping you introduce new products, services, channels and business models with a level of speed and quality that wasn’t possible before.
Empowered employees: AI can tackle mundane activities while employees spend time on more fulfilling, high-value tasks. By fundamentally changing the way work is done and reinforcing the role of people to drive growth, AI is projected to boost labor productivity. Using AI can also unlock the incredible potential of talent with disabilities, while helping all workers thrive.
Superior customer service: Continuous machine learning provides a steady flow of 360-degree customer insights for hyper personalization. From 24/7 chatbots to faster help desk routing, businesses can use AI to curate information in real time and provide high-touch experiences that drive growth, retention and overall satisfaction.
AI is used in many ways, but the prevailing truth is that your AI strategy is your business strategy. To maximize your return on AI investments, identify your business priorities and then determine how AI can help.
Identify your business priorities and then determine how AI can help.
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