With the continuous advancement of high technology, more progress will emerge in the field of artificial intelligence, which will have a huge commercial impact and give birth to multiple applications, such as digital service desks, digital assistants, etc.
Image source: US "Forbes" biweekly website
The COVID-19 pandemic has spawned widespread use of digital twins, metaverses, universes, augmented reality, virtual reality, and mixed reality. With the continuous increase of people's needs and the continuous advancement of technology, more new technologies will emerge. In a recent report by the US "Forbes" magazine website, it shows us the technology development trends in 2022.
data economy
The world has entered the era of data economy. Data provides basic "nutrients" for artificial intelligence, and artificial intelligence helps people obtain meaningful information from data to provide reference for their own behavior and decision-making. This was evident at the 2021 Amazon Cloud Tech Conference. At this technology event, the participants discussed all the value and services that data can provide, and all kinds of enterprises are also trying to make good use of their data.
This is also evidenced by the growing role of the chief data officer and chief analytics officer in the enterprise. The chief data officer oversees a range of data-related functions to ensure that the organization gets a valuable asset. His responsibilities include 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 platform
Most businesses are aware of the importance of data and AI, however, there may be many issues to "transform" into a data-driven business, for example, it takes nearly 8 months to integrate AI models into business applications time. No-code/low-code platforms have emerged from this, helping more people, including non-professionals such as "civilian developers", meet the challenges posed by data and artificial intelligence.
Civilian developers are not professional programmers, but employees of a company who can develop new business applications within the company for use by other employees. In a future where software development can be done by almost anyone with a little technical knowledge, no-code/low-code tools can actively transform ordinary business users into platform developers.
AI at the edge
5G, AI and cybersecurity need to work together to achieve wider penetration. Data from factories and IoT endpoints in autonomous vehicles will create a data tsunami.
Edge AI and federated learning are struggling to meet these challenges, training models on local and centralized datasets without sharing datasets and violating privacy. With the rise of extended detection and response, security information and event management, and security orchestration, automation, and response, coupled with intelligent operations management platforms, security will play a critical role in handling application and data distribution.
Hyperautomation
Hyperautomation 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; hyperautomation 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.
Hyperautomation enables accelerated growth and business resilience by quickly identifying, reviewing and automating as many processes as possible. Gartner research shows that high-performing hyperautomated teams focus on three key priorities: improving the quality of their work, accelerating business processes, and increasing decision-making agility.
data weaving
Data weaving is also one of the technology trends to watch in 2022, published by Gartner.
Data weaving is the next generation of data management, which integrates data from multiple data sources such as data warehouse, data lake, lake-warehouse integration, and data mart. A data lake refers to a repository of raw data in various formats. The integration of lake and warehouse is a new architectural paradigm in the field of data management, combining the best features of data warehouse and data lake. Data analysts and data scientists can operate on data in the same data store, and it also brings more convenience to companies in data governance. The data mart refers to meeting the needs of specific departments or users, storing them in a multi-dimensional manner, and generating data cubes for decision analysis requirements.
Data weaving not only preserves data more persistently, but also leverages artificial intelligence to enable in-place, self-service analysis, classification, and governance of data. As a flexible and elastic data integration method across platforms and business users, data weaving can simplify the data integration infrastructure of enterprise organizations and create an extensible architecture, thereby reducing the difficulty of integration for most data and analysis teams. problems that arise.
explainable artificial intelligence
The company DeepMind has released a new super-large language model called Gopher. "Gopher" can run 280 billion parameters, surpassing the GPT-3 previously released by OpenAI, which can run 175 billion parameters, but inferior to the "Megatron-Figure" released by Nvidia-Microsoft, which can run 530 billion parameters. spirit". The results of the study confirmed that "Megatron-Turing" achieved unprecedented accuracy in a range of natural language tasks, including text prediction, reading comprehension, common sense reasoning, natural language reasoning, and word sense 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 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 find problems in data and feature behavior, it can also provide better decision-making because its interpretation provides additional information to the middleman, allowing it to make informed decisions Act decisively, etc. (Reporter Liu Xia)