Making Artificial Intelligence Smarter Like Human Brain – Analytics Insight

Original article can be found here (source): artificial intelligence

The latest advancements in Artificial Intelligence have been much tremendous and inspiring. It has become a part of everyday life for almost all consumers. In a large range of domains, the technology has transformed the way humans work and live. From smart home devices like Alexa, Siri, among others to large scale data security and fraud detection, all are inspired by and relied on AI. Despite this, there is still a large gap between current AI systems and human-like intelligence.

Over time, the human brain has developed and advanced in order to respond to survival instincts, harness intellectual curiosity, and achieve demands of nature. While the human brain finds innovative ways to exceed its physical capabilities, human scientific pursuit amplified by the amalgamation of mathematics, algorithms, computational methods, and statistical models.

After Alan Turing developed a mathematical model for biological morphogenesis and authored a seminal paper on computing intelligence, the use of AI garnered rapid momentum. And today, the technology has grown from data models for problem-solving to artificial neural networks, a computational model based on the structure and functions of human biological neural networks.

 

Training Machines to Think

There are a wide range of tasks that humans may naturally perform, even at a subconscious level, which the current generation of AI struggle with. Human brains are even flexible enough to see, hear, smell, feel, walk, talk, empathize, reason, and create memories all at once while in a deep conversation with others. Thus, to consider what AI can currently do and the next level of artificial intelligence, it needs effort to make this technology mimic the human brain.

AI nowadays is able to make industrial machinery precise, reliable, and self-healing, paving ways for calibrated performance similar to human action. The first generation of AI developed machine learning systems, which focuses on the creation of computer programs that can alter, or learn, when exposed to new data.

However, with advancements, it incorporates with robotic controls, vision-based sensing, and geospatial systems to automate advanced systems. Even, the technology these days is providing just close to human customer care, enhancing disease prevention and treatment, bolstering engineering systems, and automating supply chains. Though these require an enormous amount of computing power, with large electricity bills and big carbon footprint. Thus, systems that could – like the human brain – be rewired to optimize the computer circuitry for each task would provide significantly enhanced energy efficiency.

Recently, scientists at Osaka University developed a new computing device from field-programmable gate arrays (FPGA) that can be customized by the user for maximum efficiency in AI applications. Compared with currently used rewireable hardware, the system enhanced circuit density by a factor of 12. It is also expected to reduce energy usage by 80 percent. This advance may lead to flexible AI solutions that provide enhanced performance while consuming much less electricity.

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