Is the Republic of Robots from Star Wars Hypothetically Possible? Here's Everything about AI’s Future

Introduction

If you have ever watched the Star Wars franchise, then the future world has possibly attracted you. But does it make sense at all, is it possible that robots and droids can build their republic? If you are a sci-fi enthusiast, then you might want to go through this article to understand to what extent AI can go in the future.

It wasn’t Star Wars behind the Creation of AI and Robots

The 1956 AI (Artificial Intelligence) conference was a seminal event in the history of artificial intelligence. It was held at Dartmouth College in Hanover, New Hampshire, from August 31 to September 11, 1956, and is widely considered to be the birth of the field of AI. The conference was organized by John McCarthy, Marvin Minsky, Nathaniel Rochester, and Claude Shannon, who invited researchers from various fields to discuss the possibility of creating “thinking machines”. At the conference, the participants explored topics such as problem-solving, learning, natural language processing, and creativity, laying the groundwork for the development of AI as a field of study and research. The conference was a major milestone in the history of computer science and technology and has had a profound impact on the development of AI and its applications in various industries.

AI’s Evolution from Academia to Mainstream Technology:

Although till now, Unlike Star Wars, AI has not formed its Republic, it is speculative and sounds fictitious; but with the assistance of humans, AI has ameliorated. Earlier algorithms at the time allowed computers to speak English and solve algebraic problems. As a crucial component of social and economic mainstream technologies today, such as speech recognition, medical diagnosis, and voice-activated assistance, artificial intelligence has progressed from being an academic field. AI is simply a computer system that mimics human cognition by accessing data from numerous sources. Henceforth making judgments and learning from the outcomes.

Another definition of AI is a computer’s capacity to spot patterns and take decisions based on the information at hand and statistical models. Artificial intelligence has demonstrated superior performance in a wide range of areas, including voice and pattern recognition algorithms, monitoring processes in industries, fault detection, forecasting, and especially in the healthcare sector to improve treatment processes (e.g., Amazon’s Alexa, Apple’s Siri, and Google Assistant).

Beyond the Myths: Unveiling the Truth about AI Progress

People frequently gripe that AI is not progressing as quickly as they would want. People assert that AI progressed rapidly during its initial years, but its growth rate has slowed down significantly. In reality, we are still quickly creating new, beneficial systems for identifying patterns and controlling processes. Modern hardware is also so quick and dependable that we can use practically any program we can design. Every year, effective new systems are developed for various “expert” applications.

Despite the significant advancements in technology, the field of natural language understanding has been progressing slowly. This is primarily because computers lack access to the meanings of most commonly used words and phrases. For instance, words like “rope” or “string” cannot be comprehended by computers as they cannot understand the various ways in which these objects can be used. While a child can come up with numerous applications of a string or rope, computers are incapable of doing so. This limitation applies to thousands of other ordinary words as well, which is something that experts in the field must be aware of.

The Quest for Common Sense in AI: Understanding the Challenges

The lack of understanding of natural language by computers has resulted in a significant limitation in the communication abilities of our “expert systems.” While programs for playing chess and recognizing characters exist, they cannot perform tasks outside their designated domains. For instance, a chess program cannot read text, and an OCR system cannot play chess. Furthermore, getting two different programs to collaborate is a challenging task due to this limitation. The root of this problem lies in the absence of “common sense” capabilities in current AI systems, which involve resolving various types of issues. Once we overcome these obstacles, we can expect to benefit significantly from the advanced AI systems of the future.

Building an Encyclopedia of Objects and Functions for Future AI Systems:

The primary challenge in developing a common sense for AI systems is the creation of a comprehensive knowledge base that includes information about everyday objects, such as strings, roads, tools, energy, books, houses, and clothing. However, a significant portion of this knowledge is so fundamental that people do not even need words to describe it, which makes its integration into machine learning difficult. In addition, future AI systems will need to understand the functions and uses of these objects to solve real-world problems effectively. This requires an in-depth understanding of human psychology to identify acceptable solutions for various tasks. While some research is currently underway in this area, such as the project at CM in the United States and other attempts in Japan, there is still limited progress in this field. As a result, the full potential of AI applications remains untapped.

The Cognition Conundrum:

An additional challenge in creating intelligent systems is the selection of an appropriate method for representing knowledge within a computer. This process involves choosing a suitable data structure to build the knowledge into the memory of the machine. Unfortunately, the Computer Science community has been unable to agree on a standardized representation, leading to ongoing debates on the topic. Some researchers argue in Favor of Logic, while others prefer Neural Networks, and still, others advocate for Semantic Networks. Each approach has its advantages and drawbacks. For instance, while some consider Formal Logic to be the best option due to its precision, others argue that Semantic Networks offer more flexibility by linking concepts instead of numbers. Despite the ongoing debate, progress in this area is crucial to the future development of intelligent systems.

Conclusion

In conclusion, the future of AI is both exciting and uncertain. As AI continues to gain strength and knowledge, it has the potential to transform many industries, from healthcare to transportation. However, there are concerns about the impact of AI on the job market and whether it will lead to significant unemployment. While AI can replace human labour in some areas, it is important to remember that it also has the potential to create new jobs and industries.

As AI systems become more advanced, we must develop strategies to mitigate the negative effects of unemployment and inequality. This may involve creating new forms of education and training programs to prepare people for the changing job market. Additionally, policymakers must ensure that AI is developed and used ethically and responsibly.

Despite these challenges, there is no doubt that AI will continue to play a significant role in shaping the future of humanity. With its ability to learn and adapt, AI has the potential to solve some of the world’s most pressing problems, from climate change to disease. As we move forward, it is important to approach AI with both optimism and caution and to work together to ensure that its potential benefits are realized while minimizing its potential drawbacks.