Artificial intelligence (AI) makes it possible for machines to learn from experience, adjust to new inputs and perform human-like tasks by processing large amounts of data and recognizing patterns in the data.
The term artificial intelligence was coined in 1956, but AI has become more popular today thanks to increased data volumes, advanced algorithms, and improvements in computing power and storage. As the years rolled by so has AI’s evolution:
- Early AI research in the 1950s explored topics like problem-solving and symbolic methods
- In the 1960s, the U.S. Department of Defense took interest in this type of work and began training computers to mimic basic human reasoning
- In the 1970s, the Defense Advanced Research Projects Agency (DARPA) completed street mapping projects
- In 2003, DARPA produced intelligent personal assistants long before Siri, Alexa or Cortana were household names.
Today, there are several trends underway in the AI sector – none bigger than AI-enabled chips. Artificial intelligence heavily banks on specialized processors completing the CPU. Even the highly-advanced CPU may not improve the speed of training an AI model. While inferencing, the model needs additional hardware to perform complex mathematical computations to speed up tasks such as object detection and facial recognition.
Chip manufactures like NVIDIA, Intel, ARM, AMD and Qualcomm are anticipated to ship specialized chips capable of making the execution of artificial intelligence application much faster. These chips will be optimized for specific use cases and scenarios related to computer vision, natural language processing and speech recognition.
Advances in quantum computing are also expected. Quantum computing is a new paradigm that will play a big role in accelerating tasks for AI. Quantum computing offers researchers and developers access to open source frameworks and computing power that can operate beyond classical capabilities.
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