Cloud computing and online platforms have long been the norm for enterprises in the last decade. With this evolution, computing power has increased dramatically while simultaneously producing new data that these systems can analyze. The move from enterprise AI 1.0 to 2.0 laid the groundwork for future automation and analytics. It enabled deeper insights to progress.
AI 2.0 overtakes enterprise AI 1.0’s simple automation and shallow learning approaches. End-to-end process intelligence coupled with machine learning and focused solutions boosts operational efficiency and productivity exponentially. The organization has begun to embrace these changes from the top down, starting with leaders who recognize the importance of digital transformation for future growth. They are the driving force behind this change. Here is how companies move towards enterprise AI 2.0.
Dynamic forces driving AI 2.0
The forces that influenced AI 2.0 came from four different areas.
- With the rise of mobile devices, the Internet, sensor networks, vehicular networks, and wearable devices, the information environment has dramatically changed in the 21st century. Throughout the globe, networks continue to expand rapidly, reflecting and aggregating global demands, knowledge, and capabilities.
- As a result of social developments, AI research is changing quickly from being promoted by academic curiosity to being stimulated by external demands. Changing goals and new problems call for AI development across industries. Therefore, many enterprises have actively promoted AI 2.0 research.
- AI has transformed from using a PC to spiralling human intelligence. Ever since AI 2.0 is taking new shapes in technology, it is only expected to improve hybrid intelligence systems, new crowd intelligence systems, and more complex intelligence systems.
- Finally, data resources related to AI are changing. AI creates a new information environment based on data-driven algorithms. A growing amount of big data, sensors, and networks will contribute to these calculations. These environmental changes will enable significant advancements in Artificial Intelligence.
New technologies driving AI 2.0
Various technological advancements have appeared and continue to drive AI development in the new external environment. What are they? Scroll down!
Internet crowd intelligence
Recent advancements have been made in Internet crowd intelligence. A crowd that exhibits extraordinary intelligence participates and interacts on the Internet, constituting a new type of intelligent system. According to their difficulty level, there are three crowds: task allocation, complex workflows, and problem-solving ecosystems.
In recognition of their external environment, cross-media intelligence is regarded as a cornerstone of AI. Although cross-media intelligence is still in its infancy, it is anticipated to become an essential part of artificial intelligence in the future. Language, sight, and hearing will play a central role in smart behaviour, including association, design, and creation.
Human intelligence differs from artificial intelligence because it is a form of natural biological intelligence. Human intelligence may eventually be surpassed by machine intelligence. It is possible to accomplish this in specialized fields, but it is not likely to happen in general intelligence within the next 60 years. Simulations of human intelligence by computers are essential. However, hybrid intelligence systems combine computers and humans to create enhanced intelligence.
Robotics has always been the prime focus of AI development, and bionics is a vital development trajectory. The majority of bionic robots developed over the past 60 years failed in their application. After an initial attempt at designing a four-legged walking machine called a “mechanical mule,” the US military has returned to developing unmanned combat vehicles. Unmanned aircraft and vehicles are other well-known examples that have grown rapidly and far surpassed robotic progress.
AI-based big data intelligence is not just a means of enhancing communication between different areas but also transforming and developing new technology. Utilizing big data and transforming it into knowledge, then into intelligent behaviour, calls for the integration of data from different fields and the creation of innovative connections. Research is necessary to link its relationship and behaviour to define this new knowledge system and its function in CPH.
AI 2.0 era is characterized by dynamic forces and innovative technologies that hold core values and quality standards. It is, therefore, necessary to discuss, clarify, and implement ethical guidelines. However, it is not about limiting the economic potential of AI and its innovation power. Regulators must be defined based on specific application scenarios and a sense of proportion. A transparent and precise risk assessment must inform the development of new regulations based on already existing measures. How is your business marching towards AI 2.0 development? Let us know in the comments.