Point Roberts, Washington and Vancouver, British Columbia--(Newsfile Corp. - October 16, 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.
Today's podcast features recent interviews with [two] experts in top AI 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. Babak Hodjat, VP of Evolutionary AI, Cognizant Technology Solutions Corporation (NASDAQ: CTSH).
Listen to the podcast interview with Dr. Danny Rittman, CTO of GBT Technologies Inc. (OTC Pink: GTCH) discussing the company's recently announced implementation and development of recurrent relational reasoning (RRN) in its AI, and its applications in the medical field.
In a recently published press release, GBT Technologies CTO, Dr. Danny Rittman explained the company's rationale for incorporating recurrent relational reasoning (RRN) into its Avant! AI:
"Our goal is to implement a fundamental part of human intelligence called relational reasoning, which is planned to enable Avant! to acquire expertise on its own by understanding object's relations. Avant! will include an advanced artificial neural network (ANN) capable of pattern recognition and reasoning about those patterns which is very similar to the human brain."
In an interview with Investorideas.com, Dr. Rittman identified the shortcomings of ANN on its own, and how integrating RRN improved the AI performance:
"This type of technology is perfect to recognize the actual object, but it doesn't do well when it comes to interaction between objects," he said.
"That's why we decided to implement and start development for the RRN (Recurrent Relational Network), which [is] very good for the interaction between objects. So, the major advantage of the Recurrent Relational Network is that they can actually perform what we call 'reasoning between objects'."
A major application of this object relational reasoning is in the field of medical imaging, according to Rittman. A recently-published report from Technavio finds that the global Medical Imaging market is projected to grow $9.55 billion USD from 2019 to 2023, with a compound annual growth rate (CAGR) of 5 percent in the forecast period. An excerpt from that report outlines the role of AI in the market's growth:
With the advent of advanced technologies such as artificial intelligence and machine learning, vendors have been integrating AI into medical imaging devices. This is one of the key medical imaging market trends that will gain traction over the forecast period. AI-enabled medical imaging allows users to automate and standardize complex diagnostic procedures, improve patient experience outcomes, and reduce the costs. Vendors are also developing AI platforms for end-users such as hospitals to manage medical imaging data, which help clinicians in making quick diagnosis decisions. The factors will increase the adoption of AI-enabled medical imaging among hospitals and clinics, subsequently driving market growth at a CAGR of over 5% during 2019-2023.
Dr. Rittman echoes this by picking out specific applications of AI, particularly with RRN integration:
"If you want to identify suspicious objects - let's say within an MRI scan or a CT scan image or an X-ray image - our RRN will actually scan the data, make sure it actually knows and understands relations between objects … and then will be able to alert us," he said.
"It is a process of evolution. It will only get better with time."
Listen to the podcast interview with Mr. Babak Hodjat, VP of Evolutionary AI, Cognizant Technology Solutions Corporation (NASDAQ: CTSH) discussing the role of AI and ML in the banking and finance space.
Professional services firm Cognizant Technology Solutions Corporation's report, Leveraging AI to Shape the Digital Future of Banking (Part 2), analyzes the already vast application of AI in the banking and finance sector. Investorideas.com spoke to the company's VP of Evolutionary AI, Babak Hodjat, who explained where the $3.3 billion USD spent on AI applications by banks in 2018 was going:
"According to IDC, much of the $3.3 billion is being spent around automated threat intelligence and prevention systems," he said. "fraud analysis, investigations, program advisors, recommendation systems. And that's what we see as well in the market is, generally speaking, people who already have incumbent models - basically statistics-based primarily - and lots of data that they've collected around them are looking to augment and improve these models using AI."
Hodjat outlined the use of machine learning technology in back office application:
"Primarily these are improved data-driven decisions using machine learning," he said. "They have been used in areas such as fraud detection. We see a lot of contract digitization and standardization that people are using bits and pieces of AI models for, ranging from the actual OCR (Optical Character Recognition) and scanning… all the way to slot-filling and natural language-based approaches to take legacy contracts and map them to structured databases for further and future use…"
"We also see some use in risk management. There are other use cases where it can be used, and as Cognizant we are promoting that, but it's still in its infancy. But those are areas such as compliance, task assignment, product hyper-personalization - even HR is an area where throughout the HR process AI can be used in the decision-making, and the list goes on."
The growth of this market is attested to in a report from Prescient & Strategic Intelligence, which finds that the global AI in BFSI (banking, financial services and insurance) will exhibit a compound annual growth rate (CAGR) of 37.2 percent from 2019 to 2024.
According to Hodjat, AI adoption is well under way, whether it is applied in improving existing models or actually being an active ingredient in enterprise decision-making.
"Companies are on an AI journey," he said. "It starts with the data and leads to improvements in models. Incumbent models can be improved. We can add measures of certainty to existing models, all the way to improving models and replacing them with advanced AI. But, it shouldn't stop there and I think it should continue on to the point where we actually take the insights that we expect from the models and also augment the decision-making itself. I think we have to take it all the way to the point where we impact our enterprises decision-making using AI."
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