Five key trends which will shape the future of AI

The tech sphere is one of the most dynamic and fast-changing industries in the world, being the uncontested champion when it comes to its ability to grow and support key areas of society like healthcare, finance, media, retail, the list is almost inexhaustible. As such, when it comes to technology, it’s no surprise that what is new quickly becomes outdated, as the market is eager to adopt new trends, techniques and concepts. There are, of course, exceptions to this rule. One of them is artificial intelligence, a technology that has remained a constant presence in the enterprise and business sector since the 1950s. And as technological research and consulting firm giant Gartner points out, AI will continue to be a foundational catalyst for digital business.

Spurred by an exponential increase of computational power over the years, as well as the emergence of big data, new machine learning techniques, and the hegemony of cloud computing has created fertile ground for further developments in the AI sector, making this technology permeate into almost every aspect of our lives. is helping ignite the inevitable AI revolution by adopting a web 3 mindset in which control and ownership of AI are placed in the hands of humans. By leveraging two of the most innovative technologies available on the market, blockchain and artificial intelligence, has made it its mission to put a real human being behind every AI, creating a complex, multifaceted ecosystem in which every decision made by AI is verified and validated by regular people.

After analyzing the pulse of the market, Gartner has formulated a report in which it outlines 5 key trends which will have a significant role in shaping the future of artificial intelligence:

Gartner points out in the report that it is imperative for technology leaders, if they want to remain relevant, to adapt their approach and include strategies that help accommodate the democratization of artificial intelligence and accommodate its use in a responsible manner while also focusing on generative, composite and edge AI techniques to unlock newfound value.

Democratized AI is a new approach to artificial intelligence which aims to make the technology more accessible to a wider audience of people. AI is the most powerful tool that mankind has at its disposal, so it would be foolhardy to reserve it only for data scientists or researchers. Companies need to become aware that in order to extract the maximum value from this technology, they need to make it accessible to more employees across different departments, regardless if they have specialized knowledge of AI or not. It’s no surprise that AI development is expensive, demanding expert-level knowledge in the field, as well as powerful equipment and computer frameworks to maintain it. has made its goal to democratize artificial intelligence by providing a user-friendly ecosystem that facilitates a streamlined artificial intelligence development process with pre-built AI algorithms and intuitive interfaces, making the creation of artificial intelligence products a breeze for people with no technical background.

Responsible AI is a catch-all term that describes the process of designing, developing and deploying AI responsibly to empower businesses, employees, customers and society as a whole by facilitating the propagation of trust and confidence in AI. Artificial intelligence is a powerful tool which has a direct impact on people’s lives. As such, AI ethics, data governance, trust and legality are burning questions that need to be addressed if we want to adhere to a future dominated by the presence of AI. Accenture’s 2022 Tech Vision research highlights the fact that 35% of global consumers trust how AI is being implemented by companies and organizations, but at the same time, 77% of respondents think that organizations must be held accountable for their misuse of AI. addresses the question of responsible AI by utilizing blockchain, a technology that manages to provide through its inherent characteristics like decentralization, distribution, immutability and transparency, unparalleled levels of trust and granular ownership of all the data it stores. By unifying blockchain and artificial intelligence, has created Proof of Human, a complex governance, consensus and verification mechanism which ensures that AI is used responsibly by giving humans control over how AI is utilized.

Generative AI is an umbrella term used to describe any type of artificial intelligence that uses unsupervised learning algorithms to produce content that is entirely created by machines. In short, it’s a technique that allows machines to use an input like text, audio and images to produce content that is plausible and, in some cases, even indistinguishable from the real deal. MIT has described generative AI as one of the most promising advances in the world of artificial intelligence in the past decade. Some examples of generative AI are: has taken the almost unlimited potential of generative AI and put it at the disposal of regular humans. Through the Humans Studio, an all-encompassing AI development ecosystem with an extensive library of AI algorithms to choose from, everyone can let their imagination run wild to create synthetic media content.

According to Gartner, composite AI is the “combination of different AI techniques to achieve the best result”. AI is a very broad and flexible term that encompasses a lot of things like deep learning, neural networks, natural language processing, optimization techniques and graph techniques, etc. Following this logic, composite AI denotes the ability to use the right AI technique for the right use case to increase efficiency and maximize the technology’s versatility and adaptability.

Edge AI is a fusion between edge computing and artificial intelligence that runs machine learning tasks directly on connected edge devices. The name edge AI means that the AI computation is done near the user at the edge of the network, close to where the data is located, rather than centrally at a cloud provider or private data centre. Edge AI significantly reduces the privacy issue of transmitting millions of data and storing it in the cloud, as well as the bandwidth and latency limitations that reduce data transmission capacity. Gartner estimates that by 2025, over 50% of all data analysis by deep neural networks will occur at the edge, up from less than 10% in 2021.

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