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2022 and Beyond: Quantum AI, Graph Neural Networks, and Personal Data Pods

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Read more about author Jans Aasman.

The new year will bring us many exciting developments in data-enabling technologies including the merging of artificial intelligence with quantum computing and graph neural networks, which will power extremely complex, next-generation algorithms. Knowledge graphs will become lego-like with the ability to be plugged into diverse applications. With mounting concern over social media sites using personal data, expect new ways for users to regain control with “personal data pods.” Enterprises will interweave graphs with document and time-series databases to create a single enterprise-wide data fabric.

Quantum AI Environments Will Emerge

With recent advances in quantum computing, in 2022, we will start to see the convergence of quantum computing with artificial intelligence, knowledge graphs, and programming languages. These distinct technologies will start to morph into a single computing environment operating in one memory space as a fully integrated solution. The separation between programming and AI/analytics will begin to blur as developers use Quantum-based computer languages to generate incredibly complex, next-generation AI algorithms and applications that result in new discoveries based on the quantum acceleration of machine learning and deep learning.

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Graph Neural Networks (GNNs) Will Advance AI Reasoning 

In the past few years, organizations have experienced the advantages of combining graphs with artificial intelligence. In 2022 and beyond, leading companies will apply machine learning’s advanced pattern matching to graph neural networks (GNNs), which are complex high-dimensional, non-Euclidian datasets. By fusing GNN reasoning capabilities with classic semantic inferencing available in AI knowledge graphs, organizations will get two forms of reasoning in one framework. Automatically mixing and matching these two types of reasoning is the next level of AI and produces the best prescriptive outcomes. This “total AI” is swiftly becoming necessary to tackle enterprise-scale applications of mission-critical processes like predicting equipment failure, optimizing healthcare treatment, and maximizing customer relationships.

Facebook Users Will Regain Control Through Personal Data Pods 

There is mounting concern over social media sites like Facebook, advertisers, and other organizations using personal data and freely capitalizing on users’ browsing habits. To combat this issue, in 2022 we will see new ways for users to regain control of their data, such as  “personal data pods” that store an individual’s data gathered from websites, companies, or government institutions within a secure data vault or pod for authentication. Not only do these solutions put data back in users’ control, they also open up an opportunity for a data broker market, where users are paid a micro-payment every time their personal information is used.

Knowledge Graphs Will Become Composable

Organizations are realizing it is unrealistic to have a single enterprise standard for data and analytics. In 2022 and beyond, companies will embrace a lego-like approach to analytics and AI solutions where knowledge graphs become composable and used in multiple, different applications to connect data insights to business actions across the enterprise. Composable knowledge graphs will facilitate agile AI solutions, increase data access, and create sustainable data architectures that future-proof enterprise intelligence.

Graph, Document and Time-Series Databases will Dominate by 2030

The days of relegating graphs to specialty analytics projects and continuing to use relational databases for transactional systems will cease to be reality. Graph technology has gained the performance necessary to execute real-time transactions at scale, enabling graphs to replace relational databases as the central system of record (SOR) for enterprises. By 2030 we will see leading enterprises creating a single data fabric consisting of multiple interwoven graphs, document and time-series databases that are used for real-time transactions, as well as predictive, machine learning analytics as well as real-time transactions and the system of record (SRO).

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