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Beijing September 15-September 14-16, 2023 World Computing Conference sponsored by the people's Government of Hunan Province and the Ministry of Industry and Information Technology was held in Changsha. Chen Xudong, chairman and general manager of IBM Greater China, was invited to attend the opening ceremony and opening forum and delivered a keynote speech. He shared the evolution of artificial intelligence in the past 70 years and the trend in the next decade, as well as IBM's ongoing exploration of enterprise-level artificial intelligence, including the newly released enterprise-class AI and data platform watsonx.

He believes that the development history of artificial intelligence is basically synchronized with the development of computers, and continues to benefit from the enhancement of computing power, the optimization of algorithms and the explosive growth of data. Today's IBM has a comprehensive enterprise-class AI product portfolio based on machine learning and basic models, and with the help of co-creation methods such as IBM garage innovation and a wide range of partner ecology, it has become the best partner for enterprises to implement AI applications.

Chen Xudong, Chairman and General Manager of IBM Greater China (Photo: 2023 World Computing Congress)

The following is an excerpt from his speech:

Evolution and trend of artificial Intelligence Technology

The development history of artificial intelligence is basically synchronized with the development of computers, benefiting from the enhancement of computer computing power, the optimization of algorithms and the explosive growth of data. We generally regard the Dartmouth Conference (Dartmouth Workshop) in 1956 as the beginning of artificial intelligence, mainly thanks to the promotion of computer scientist Alan Turing, mathematician John McCarthy and so on. IBM is also an important participant in this meeting. The research in this period is mainly to explore how to make computers simulate human intelligence, including reasoning, problem-solving and learning capabilities.

Looking back at the development history of 67 years, we can roughly divide the development of artificial intelligence into four stages:

The first stage, expert system stage: expert system is the representative of early artificial intelligence, they use the knowledge of specific domain experts to solve specific problems. Typical application fields include diagnosis, chemical analysis, engineering problems and so on. The calculation at this stage can support the machine to play checkers.

The second stage, machine learning stage, roughly from the 1980s, the rise of machine learning marks a new era of artificial intelligence. In this stage, many technologies have emerged, including decision tree, support vector machine, random forest and so on, which enable computers to learn and predict from data. Machine learning has a wide range of applications, including speech recognition, image processing, natural language processing and recommendation systems. The arithmetic of this stage can support the machine to play chess and beat the human world champion.

The third stage, the deep learning stage, roughly began after 2000. Deep learning is a sub-field of machine learning, which uses deep neural networks to simulate the neuronal structure of the human brain. Deep learning has made remarkable progress in the fields of image recognition, natural language processing and speech recognition. Well-known deep learning models include convolution neural network (CNN) and cyclic neural network (RNN). The power of calculation at this stage can support machines to play go and defeat humans.

The fourth stage, the basic model stage, from 2017 to the present, the basic model refers to any model that trains on a wide range of data (usually using large-scale self-monitoring methods) and can adapt to a variety of downstream tasks (such as fine-tuning). The basic model adopts pre-training from a large number of unlabeled data, uses limited label data, can quickly adapt to enterprise-specific tasks, and explores complex scenes such as autopilot and robot control through interactive learning between agents and the environment.

The computing power of this stage appears explosive growth, at the same time, due to the rapid development of the Internet, especially the mobile Internet, the data also ushered in exponential growth. This provides the guarantee of data and computing power for large-scale self-supervised learning methods, which makes the large language model a great success and breaks through the language barrier between man and machine.

At the same time, let the machine have the ability of understanding, reasoning, learning and interaction. The application of these capabilities will quickly improve and optimize the human-computer interaction scene, for example, the enterprise's automatic customer service system can be upgraded to improve customer satisfaction. In addition, with the help of the large model, the work efficiency of language and programming tasks will be greatly improved.

From an enterprise perspective, AI adoption increased nearly 2.5 times in just five years from 2017 to 2022. Looking to the future, the rapid development of artificial intelligence in the next few years has become a foregone conclusion. We predict that by 2030, omnipotent and multimodal artificial intelligence will be further popularized, and human production and life will enter a new era of man-machine cooperation.

Exploration and contribution of IBM to Enterprise artificial Intelligence

Today's IBM is a technology company focused on hybrid cloud and AI, and is an important driver of AI technology and AI applications around the world. In addition to the Dartmouth conference mentioned earlier, another milestone in artificial intelligence was driven by IBM: in 1997, when the IBM Deep Blue computer defeated the then chess champion, artificial intelligence began to enter the public eye. In 2011, IBM artificial Intelligence participated in the knowledge contest and won the championship, which means that 12 years ago, IBM has reached a high level of speech recognition and semantic understanding. In 2019, IBM artificial intelligence debaters have begun to compete with human beings, and their language expression ability has attracted the attention of the world. In May this year, IBM released a new generation of enterprise AI and data platform watsonx.

In fact, IBM launched the enterprise AI product Watson as early as 2011, which optimizes business processes and supports intelligent decision-making through AI technologies such as natural language processing, machine learning and data analysis. Watson has been widely used in more than 40,000 customers around the world.

Today, we see that "making AI the core productivity" has become an urgent need for business leaders. IBM Business value Research Institute recently released a survey of more than 3000 CEO in more than 30 countries and regions around the world.

According to the survey report, 3/4 of the CEO surveyed believe that the deployment of advanced generative artificial intelligence will bring competitive advantage to enterprises.

However, 61% of CEO expressed concern about the data sources used in the generative AI. This worry reflects that enterprises are facing many challenges on the road to AI: the first is technical challenges, especially the preparation, application and governance of data; the second is talent challenges, in which enterprises need to quickly realize the transformation and upgrading of personnel skills to embrace the AI wave; the third is cultural challenges, the transformation of skills is often accompanied by the renewal of organizational culture, how to make the two achieve each other and improve productivity.

In what ways can enterprise AI help the development of enterprises? The most direct value is to help enterprises optimize business processes, thereby reducing costs and efficiency, improving productivity, and improving customer experience. In order to meet the needs of enterprise AI, IBM has released enterprise AI and data platform watsonx based on decades of accumulation in the field of AI.

Watsonx can not only help enterprises to use large models and generative AI, but also solve the three major challenges of enterprise AI applications: finding the needed data, establishing appropriate models, and supervising the operation of the system. Moreover, IBM has opened up some of the models and datasets of watsonx, working with the community and enterprises to create them.

Join hands with IBM to create Enterprise AI applications

In the future, enterprises will use multiple models on multiple clouds, and IBM watsonx can make AI the core productivity of the enterprise. We can provide customers with models developed by IBM, including a large collection of basic models for specific industries; we can also support customers to use models from open source or other companies, as well as their own models; and we can also provide pre-trained basic models according to the needs of different tasks, including traditional machine learning and the latest generative AI functions.

No matter what choice is made, IBM can help customers try and experiment, including tuning, building, and evaluating the model, and help them deploy and tune the model on any cloud. This is also consistent with IBM's hybrid cloud and AI strategy.

Today's IBM also has the industry's most comprehensive enterprise-class AI portfolio based on machine learning and basic models. With IBM's extensive partner ecosystem, it has been deployed to a large number of users in more than 20 industries. For example, 25 joint intelligent industry solutions built by SAP and IBM Consulting are serving customers around the world.

IBM strives to become the best partner for enterprises to land AI applications, and is fully prepared to meet customers with strong needs to come to IBM to participate in a half-day "watsonx strategy workshop"; and according to the results of the workshop, to carry out a four-week "watsonx pilot project", the use of IBM garage innovation methods to help customers start an enterprise-level AI + trip.

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