Point Roberts, Washington--(Newsfile Corp. - September 26, 2019) - Investorideas.com (www.investorideas.com), a global investor news source covering Artificial Intelligence (AI) brings you today's special edition of The AI Eye- watching stock news, deal tracker and advancements in artificial intelligence.
Today's edition features recent interviews with two experts in C Level management positions discussing recent developments within their companies and the overall sector: Dr. Danny Rittman, CTO of GBT Technologies Inc. (OTC Pink: GTCH), and Mr. Louis DiNardo, President and CEO of BrainChip Holdings Limited (ASX: BRN).
Listen to the podcast interview with Dr. Danny Rittman, CTO of GBT Technologies Inc. (OTC Pink: GTCH) discussing how the company leverages AI in the IoT space and recent develops with analog computing.
GBT Technologies' employment of internet-of-things (IoT) technology predates the development of the company's AI core, according to CTO Danny Rittman. In fact, it was the company's development of IoT technologies that led to a growing need to create an artificial intelligence core.
"We went further into the data science … [and] figured out that we needed to be more on the secure side, we need to handle vast amounts of data, especially as we went into autonomous machines domain etc…" he said. "So, we realized we would need a mechanism that involved AI. For the past year and-a-half, GBT developed a core AI that is based on machine learning."
"IoT will become more and more permanent in our daily domain, and of course GBT is progressing forward with this trend because our technology is natively IoT," he said. "It started as a radio technology, and since IoT is basically radio-activated … we were naturally embedded in this domain a few years ago."
A report from Zion Market Research echoes GBT's leveraging of AI in IoT in its finding that globally, the AI in IoT market is projected to grow to $19.9 billion USD by 2025. This represents a compound annual growth rate (CAGR) of 25.89 percent from 2018-2025.
Recently, the company has announced R&D in analog computing, which Rittman points out will complement and enhance GBT's tech:
"Analog computing for artificial intelligence, and for machine learning, will enable much more further capabilities for the IoT domain," he said.
In a recent press release, Rittman expanded on the role of analog computing with regard to replicating human intelligence:
"With this new research activity we are laying the foundation of a true human-like learning system. Analog computing for machine learning enables robots to interact with humans, not only based on visual cues, but also according to tone of speech and body language. Our AI technology plans to incorporate both high and low-level computing, which we believe is a more accurate model of human intelligence."
Listen to the podcast interview with Mr. Louis DiNardo, President and CEO of BrainChip Holdings Limited (ASX: BRN) discussing the company's Akida neural processing core (NPC) and the Edge AI market.
BrainChip Holdings Limited, a neuromorphic computing firm, combines neural science with digital technology to produce its Akida neuromorphic system-on-a-chip for edge application. The company's President and CEO, Mr. Louis DiNardo, explained how the technology takes cues from the human brain:
"We've developed, and began to market, what we call an event-based neural network processor," he said. "The technology is inspired by the spiking nature of the human brain. Whenever you, I or anyone sees, hears, thinks or touches anything, our nervous system generates small bioelectrical spikes that create memory. By mimicking brain processing, BrainChip has pioneered a hardware-spiking neural network called Akida."
DiNardo explained the application of the BrainChip's technology on edge computing.
"Akida is exceptionally well-suited to applications at the Edge, where sensor inputs are analyzed at the point of acquisition, rather than transmissions to the cloud or a data centre," he said. "The device is designed to provide a complete, ultra low-power AI edge network for vision, audio and other smart transducer applications. This creates a reduction in system latency, when the time it takes to get from a sample to a decision. Reducing system latency provides a faster response, it's a more power-efficient system, and it can reduce the carbon footprint of data centres."
Expanding on the idea of carbon footprint reduction, DiNardo highlights the advantages of edge computing over alternatives such as the cloud.
"Data centres are basically power hogs," he said. "They suck up a lot of power, and when you can do analysis at the edge and only go back to the central processing unit, the data centre or the cloud to make decisions on metadata, you can reduce the overall carbon footprint of the data centre."
"Our belief is that smart vision systems, including consumer, industrial, [and] the internet-of-things present a target-rich environment. Acoustic analysis and smart sensors in a wide array of end use-cases, including very good opportunities in the automotive industry and cybersecurity also offer great promise.
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