How to Build Artificial Intelligence to Make Things Smarter

Document Type:Article

Subject Area:Artificial Intelligence

Document 1

Before one can achieve AI, he must build specialized knowledge such as: smart decisions to be created, current context and interest, perception of the environment as well as the product to be marked smarter. This article tries to give some answers as to why we need this knowledge. The Essence of Enhancing Our Perception About Data Streams It is evidence that existing products are able to produce more streams of data than we can fathom. For instance, smartphones have the capability of producing up to one gigabyte per second, with car generators producing up to 4 times the data produced by smartphones. Besides, humans with their natural senses and nerves can create approximately 100 times more data than some of the best cars. Considering the above statistics, one comes to appreciate the fact that man needs extra data volume than can be handled by current technologies. The necessity of AI can be envisaged by the need to create autonomous driving cars. Research shows that creating such cars demand that more than 100 exabytes(EB) of data is needed to drive more that 25 million kilometers. Evidently such data is way above what has been complied and published by Randall Munroe about Google’s data center in 2013. Besides, based on IDC’s research, the world will be able to build much data within a short time by 2020. An analysis of the existing big data reveals that with the current technology, it is now quite possible to build some megabytes per second, albeit with some acceptable costs.

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However, there is a need to learn and evolve so as to create capabilities, such as object recognition on human levels. In order to achieve such milestones, there is need to create up to 4GB per second that can generate enormous petabytes(PB) of data. Conversely, such data would need enormous time in years for training and inference. This point out the existing complexity that is associated with AI. One of the greatest challenges associated with big data technology is how to address the issue of cost limit and complexity. As quick win and more naive approach is to address cost limit is usage of effective data formats and compression. Unfortunately, it is quite difficult to maneuver around the issue due to hard constraints and power consumptions of current products. Moreover, innovators who will need to make AI available to all and sundry would require to overcome storage challenges and of course deal with data sampling rates and move the new created local boundaries.

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Even then, the current capabilities are insufficient to handle the above challenges, specifically in creating innovative solutions for smart product and environment. Most often, it is advisable to define the perception of data balance, its relevance and sampling. The most important thing is, the professionals would need to consider the solution, possible requirements and the extensions that are needed so as to solve the hardest problem within its context. Creating the Contest of Interest in Order to Focus on Smart Decisions Most often, individuals make the mistake of focusing more on perception, storage and connectivity, and this makes them to lose focus on the essentials which include data wrangling, training, abstraction and smart interactions. Thus, it is important that individuals understand how to act in a complicated product environment, and hence the need to consider the context of interests.

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Building the context of interests signify that one needs to be able to figure out data that is relevant and resilient to his own AI components through the use of inter-discipline areas of science such as art, science and engineering. Furthermore, microbatch data can be employed for fast adapting, with batch data being used for purposes of analyzing, iterative learning as well as abstracting. Smart Decisions that Should be Produced By AI Without relevant and resilient data, our AI will not be good enough. Although it is quite possible to produce smart decisions on real complex product world, it’s hard. Consider our prior example of a car for instance. The creation of an autonomous driving behavior on the car will demand greater capabilities and new AI technologies. In this regard, the use of graph engines and geo application programming interface technology make a good system excellent.

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Considerably, the use of AI architecture has the capability of bringing together highly accurate and deterministic AI technology together. I expect that in my next article, I will be able to explore deeper into this chapter so as to discuss the dependency between needed capabilities, fast adoption, one shot technology as well as AI architecture.

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