With the rapid advancements in technology, 2023 promises to be a pivotal year for innovation. From quantum computing making strides in real-world applications, to AI models becoming more interpretable and transparent, and the rise of decentralized finance platforms leveraging blockchain — the horizon is buzzing with potential. Given this backdrop, which emerging technology are you most eagerly anticipating for 2023, and could you share specific technical aspects or potential applications that fuel your excitement?
The Spatial web (Check under IEEE)
The protocol here is an upgrade to the transport layer of internet. Allowing other emmerging technologies to emerge, as the internet layer is currently clearly one of the weakest parts of the chain, and no longer fit to purpose to support next generation connectivity and content provision.
Among the myriad of emerging technologies, the advancement of quantum computing stands out as a particularly transformative frontier for 2024. This excitement stems not only from its potential to solve complex problems far beyond the reach of classical computers—such as in drug discovery, climate modeling, and optimization problems—but also from its strides towards practical, real-world applications. Quantum computing's ability to process vast amounts of data at unprecedented speeds offers a leap forward in artificial intelligence, cryptography, and materials science. The technical aspects that fuel this excitement include developments in quantum error correction, the scaling of qubits (quantum bits), and the integration of quantum processors into traditional computing infrastructures, paving the way for quantum advantage where quantum computers can outperform their classical counterparts on specific tasks.
Another emerging technology that promises significant impact in 2024 is the continued evolution of AI, particularly in making models more interpretable and transparent. This progress is crucial for expanding AI's application in sensitive areas like healthcare, finance, and autonomous systems, where understanding and trusting AI's decision-making processes are paramount. Technical advancements in explainable AI (XAI) are making it possible to unveil the "black box" of AI algorithms, providing insights into how models arrive at their conclusions. This not only enhances the reliability and safety of AI systems but also aligns with increasing regulatory requirements for transparency and accountability in automated decision-making. The potential for these technologies to revolutionize industries by making AI decisions more transparent and trustable, thereby accelerating the adoption of AI in critical decision-making processes, is particularly intriguing.