How AI Is Pushing The Frontiers Of The Fourth Industrial Revolution
The world economic forum calls artificial intelligence the next big thing of the 21st century that will drive the engine of change in this technological era. In his book titled The fourth industrial revolution, Klaus Schwab mentions that the adoption of artificial intelligence in various domains would become unstoppable by 2030. The proliferation of artificial intelligence would give birth to technologies that would redefine the dimensions of the innovation fabric for the times to come. Supplementing the studies of Klaus Schwab, a survey by the McKinsey state of AI concluded that more than 75% of the digital organizations would be benefited from AI functions by the end of the decade. The survey also noted that the biggest beneficiaries of the assets of AI would be the academics and the research industry. While we would see phenomenal growth in applied AI courses, we would also witness spontaneous breakthroughs in innovations related to artificial intelligence systems. In this article, we take a look at the various artificial intelligence trends that would push the frontiers of the technological revolution in the long run.
The juxtaposition of AI innovations and cloud environs
By the end of 2025, more than three fourth of the businesses would have migrated their operations to the cloud environments. On the other hand, simultaneous breakthroughs in systems powered by artificial intelligence would be witnessed. The juxtaposition of cloud technologies and AI innovations would lead to better management of cloud resources and faster processing of voluminous amounts of data. The artificial intelligence systems would also stimulate innovation in the cloud interface by extending the domain of various services. We have already been the beneficiaries of infrastructure as a service and platform as a service including software as a service. Artificial intelligence holds the capacity to push the limits of cloud services to data as a service and function as a service.
The frontier of augmented intelligence and decision sciences
It is believed that the human brain is the most complex natural machine with a very high level of cognitive capacity. It is difficult to devise intelligent systems powered by deep learning that can mimic the functions of the human brain in its entirety. However, we have been able to conceive a thinking pattern that is centered around the human brain and works in consonance with artificial intelligence to make powerful decisions driven by concrete facts. This is what is known as augmented Intelligence and it is changing the frontier of decision sciences in the direction of innovative technologies.
Augmented intelligence is often viewed as an aggregate of data analytics, artificial intelligence, and humans judgments. The approach involves a blend of AI-powered analytics, advisory functions, and data-driven insights. It is believed that augmented intelligence has the capacity to transform our decision-making skills and made the decision-making processes fully automated. As such, industry lead augmented intelligence and research-powered augmented intelligence are the two biggest trends that we would witness in the next decade.
A creative cycle of artificial intelligence in product engineering
The accelerated progress of artificial intelligence systems has enabled us to make tremendous innovations in digital industries and products. At the present time, about 40% off of the investment in artificial intelligence is usually reserved for product engineering. In addition to this, about 35% of venture capitalists in the AI industry are exclusively investing in product engineering. This suggests that the future of product engineering can only blossom in the crevices of artificial intelligence. Be it the powerful innovations in embedded systems or the research orientation towards product life cycles, it is artificial intelligence that is taking all these innovative processes towards a logical conclusion.
We are seeing the rise of AI-assisted operations in retail and management. The complicated computational processes are lucidly driven by a set of machine learning algorithms and this is leading to seamless integration between various functions and functionaries. Not only is artificial intelligence proving to be a critical link in merchandise promotion, but it is also serving as the nerve center of inventory assortment and product management. Other domains where artificial intelligence is supplementing the creative lifecycle of product engineering include financial technology, cybersecurity systems, and logistics networks.
An exploration into edge intelligence and edge analytics
In a smart environment powered by the internet of things, such devices have been conceived that have become relatively independent and have acquired a certain level of intelligence as well. These devices process data over the cloud and this is what enables them to communicate and synchronize with their surrounding environment. When we look at the process of cloud computing in detail, we find that the architectural capabilities do not allow the processing of data close to the source. In fact, the distributed nature of infrastructure leaves the option of processing data only in the environs of the cloud. This may expose our data pipeline to various kinds of threats and vulnerabilities. To overcome this, we have come up with technologies like edge intelligence and edge analytics. While both technologies are interrelated, edge intelligence is a slight advancement over edge analytics.
Edge analytics enables us to collect and process information very close to the source from which this data flows. On the other hand, edge intelligence gives us the capability to take important decisions related to such datasets after careful inspection, processing, and analysis. This serves two important purposes. Firstly, it reduces the latency and helps in the faster processing of data as compared to the cloud ecosystem. Secondly, it makes our sensitive datasets relatively immune to attacks as data processing takes very close to the data pipeline. In addition to this, edge intelligence and analytics are also important for real-time analytics that various research industries and businesses are looking forward to. Some of the other important benefits of edge intelligence include the data storage capabilities at very low bandwidth, linear scalability, and a considerable reduction in operational costs.
Future prospects and concluding remarks
In the future, artificial intelligence would serve as a nerve center for various innovation ecosystems. It would impact the graph of the growth of future technologies in a holistic way. It is believed that artificial intelligence would drive a wave of technology that changes the way we look at extreme breakthroughs from the human lens.