2022: These technological trends cannot be ignored

With the continuous advancement of high technology, more progress will emerge in the field of artificial intelligence, which will bring huge business impact and spawn multiple applications, such as digital service desks, digital assistants, etc.

Image source: Forbes Biweekly website

The tide of scientific and technological innovation is in the world

◎ Reporter Liu Xia

The COVID-19 pandemic has spawned widespread use of digital twins, metaversities, universal universes, augmented reality, virtual reality, and mixed reality. As people's needs continue to increase and technology continues to advance, more new technologies will emerge. In a recent report, the website of Forbes Magazine in the United States showed us the technology development trend in 2022.

Data economy

The world has entered the era of the data economy. Data provides basic "nutrients" for AI, which helps people derive meaningful information from data to inform their own behavior and decisions. This was evident at Amazon Cloud 2021. At this technology event, the participants discussed all about the value and services that data can provide, and all kinds of companies are looking for ways to make the most of their data.

The growing position of chief data officers and chief analytics officers in the enterprise is also evidenced by this. The Chief Data Officer oversees a range of data-related functions to ensure the organization receives its most valuable assets, including projects such as improving data quality, data governance, and master data management, as well as developing information strategy, data science, and business analytics.

No-code/low-code platforms

Most businesses are aware of the importance of data and AI, however, there are many problems that can be faced in "transforming" into a data-driven enterprise, such as the nearly 8-month period of time it takes to integrate AI models into business applications. This is where no-code/low-code platforms emerge to help more people, including non-professionals such as "civilian developers," meet the challenges posed by data and artificial intelligence.

Civilian developers are not professional programmers, they are employees of the company, and they can develop new business applications within the company for other employees to use. In the future, almost anyone with just a modicum of technical knowledge can do software development, and no-code/low-code tools can actively transform ordinary business users into platform developers.

Edge ARTIFICIAL INTELLIGENCE

5G, artificial intelligence and cybersecurity need to work together to achieve broader penetration. Data from IoT endpoints in factories and autonomous vehicles will spark a data tsunami.

AI at the edge and federated learning are grappling with these challenges, training models on local and centralized datasets without sharing datasets and invading privacy. With the rise of scalable detection and response, security information and incident management, and security orchestration, automation, and response, coupled with intelligent O&M management platforms, security will play a vital role in handling application and data distribution.

Super automated

Super automation is both a way of thinking and a collection of technologies: that is, any business in an organization that can be automated should be automated; super automation is a collection of innovative technologies, including robotic process automation, artificial intelligence, machine learning, and other technologies to help organizations improve operational efficiency and save time.

Hyper-automation accelerates growth and business resilience by quickly identifying, reviewing, and automating as many processes as possible. Gartner research shows that the best-performing hyper-automated teams focus on three key priorities: improving the quality of work, speeding up business processes, and enhancing decision agility.

Data weaving

Data weaving is also one of the top technology trends to watch for 2022 released by Gartner.

Data weaving is the next generation of data management, which integrates data from multiple data sources such as data warehouses, data lakes, lake warehouses, and data marts. A data lake is a repository of raw data in various formats. Lake warehouse integration is a new architectural paradigm in the field of data management, combining the best features of data warehousing and data lakes. Data analysts and data scientists can manipulate data in the same data store, while also making it easier for companies to govern their data. The data mart refers to meeting the needs of specific departments or users, storing in a multi-dimensional way, and generating a data cube for decision analysis needs.

Data weaving not only preserves data more durably, but also leverages AI to enable in-place, self-service analysis, classification, and governance of data. As a flexible, elastic approach to data integration across platforms and business users, data weaving simplifies an organization's data consolidation infrastructure and creates a scalable architecture that reduces the problems that most data and analytics teams experience as integration becomes more difficult.

Interpretable artificial intelligence

DeepThinking recently released a new very large language model called Gopher. The "gopher" can run 280 billion parameters, surpassing OpenAI's previous GPT-3, which can run 175 billion parameters, but inferior to NVIDIA-Microsoft's "Weizhentian-Turing" that can run 530 billion parameters. The results of the study confirm that "Wei Zhentian-Turing" has achieved unprecedented accuracy in a series of natural language tasks, including text prediction, reading comprehension, common sense reasoning, natural language reasoning, and word meaning disambiguation.

However, AI has challenges in overcoming bias, protecting privacy, and gaining trust, which has led to the rise of explainable artificial intelligence (XAI). XAI is an emerging branch of artificial intelligence that is used to explain the logic behind every decision made by artificial intelligence. XAI can improve the performance of AI models because XAI's interpretation helps to find problems in data and feature behavior, and it can also provide better decision-making because its interpretation provides additional information for the middleman to act wisely and decisively, etc.

Source: Science and Technology Daily