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“A Language Perspective to Thinking and Processing – Past, Present and Future”

Language has played a key role in the development and advancement of human civilizations since time immemorial. First and foremost, language is the primary means of expressing thoughts and feelings with clarity. Secondly, it is the basis of communication and understanding for both day-to-day interactions as well as cultural activities. Interestingly, the evolution and development of mathematics across various civilizations are closely linked to the progression of language. Mathematics itself is often regarded as a unique form of language, with its concepts and principles remarkably the same across different cultures. The importance of language and mathematics continued in the Enlightenment period of the 17th and 18th centuries when scientific worldview took shape and reached its zenith in 19th century. The latter half of the 20th century belonged to computational revolution where computational model development reigned supreme as well as its implementation aided by spectacular development of computing hardware. The early 21st century saw neural network models inspired computation that led to development of LLM models that can emulate human “intelligence” tasks such as thinking and writing remarkably well. There is no reason to think that a language-based approach cannot take us any further. In fact, it is the only vital signpost available to us that can lead us to future developments to more closely emulate human “intelligence.”

Language and Thinking in Earlier Civilizations

Communication is a core component for both survival and progress of animal species, in general, human species, in particular. Earlier communication using rudimentary gestures and facial expressions evolved into a more complex form of making vocal sounds. As different cultural communities interacted with each other collective understanding through sounds emerged leading to the formation of languages. Language is used to build relationships with each other and to have a sense of kinship. In a way, the language can be seen as a bridge from subjective knowledge to objective knowledge with tacit sharing of feelings and understanding. As human populations became scattered and diverse for various reasons, new languages came to be developed. Today, we have an estimated 7000 different languages being spoken, each with its own unique characteristics. Subsequently, with advancement in thinking, languages developed grammar, syntax and vocabulary.

In earlier civilizations, oral systems of language communication were done through various means such as storytelling, rituals, poems, instructions etc. While the oral system of language communication had its limitations, such as the potential for information loss or alteration over time, it was remarkably effective in fostering and preserving cultural thoughts. In response to growing needs and complexities of developing societies, writing systems emerged as a reliable method for recording, preservation and transmission of language over longer periods of time. The vast majority of human languages have developed writing systems peculiar to them. Thus, any language can be encoded into media through means of writing, whistling, signing, or braille. In other words, human language is modality independent. The writing artifacts available thus provided a sort of “peep” into the head and heart of earlier civilizations.

The earliest origin of mathematics and commerce begins with the earliest exchange of items among tribes or civilization. It is interesting to note that the basic intuition and understanding of the concept of mathematical numbers “one” to “nine” and basic mathematical operations is the same across civilizations, although its representations and adaptations may differ. So, earlier civilizations associated a “value” to the items and hence born the concept of “barter”. This has necessitated implementing methods of tallying that placed exact value one item to another item based on the quantity. Consequently, unique words or markers are used to identify quantities are developed.

Two important properties of Human languages are productivity and displacement need to be highlighted. The first property of “productivity” allows for the creation of an unlimited number of sentences in a language. The second property of “displacement” allows the language the ability to refer to objects, events, and ideas that are not immediately present. Besides this, social convention dictates how to use human language in day-to-day activities. Furthermore, expertise of a language in speaking, reading and writing can be acquired through learning as evidenced by newborn babies picking up basic language skills within the first few years.

The basic needs of earlier communities were shelter, food, and safety. Shelter has always been a fundamental human need, providing protection from harsh weather conditions and natural disasters as well as a defensive advantage against attacks from predators such as wild animals. Their expertise in agriculture provided surplus food and further safety. Once these basic needs are met, communities spent time in recreation, and in formulating thoughts on God, nature, ethics and humanity. Ancient civilizations across the globe shared a common thread of deep respect for and interdependence with nature. As most ancient civilizations are river-based, their cultures, beliefs, and daily practices were shaped by bounties afforded from the river in best of times and damages caused by floods in worst times. The concept of nature gods is most common among them and integrated with their way of life. Their processing needs are minimal and include technological advancements such as building large houses, complex network of drainages, pottery and making use of metals.

Language and Thinking in the Enlightenment era

The next significant human progress was made by Greek civilization which formed the basis for Western scientific development later. Greeks introduced important concepts in mathematics, medicine, astronomy and physics. Plato, Aristotle, Archimedes, and Hippocrates laid a solid foundation for Western science and philosophy. There were also significant contributions to literature and theater. stop, Roman empire took over Greeks and this has put a stop to their progress. Subsequently, in medieval Europe, the Church wielded a significant political influence over kings and emperors. The Church had a significant impact on Western civilization, shaping laws, beliefs, and cultural norms for centuries. Elsewhere, Islamic invasion for political and religious supremacy did not foster new thoughts in literature, math and science. 

Enlightenment thinkers such as John Locke and Jean-Jacques Rousseau emphasized the importance of clear communication through language, leading to greater attention on linguistic study. There was a push towards standardizing languages, particularly in Europe. Efforts were made to establish dictionaries, grammar, and rules of usage. Enlightenment thinkers engaged in extensive philosophical discourse, which contributed to the development and refinement of language. Philosophers like Voltaire, Rousseau, and Kant wrote influential works that shaped not only philosophical thought but also the language used to express it. 

Copernicus heralded a revolution in astronomy by proposing a heliocentric model of the solar system. Francis Bacon advocated an organized system for obtaining nature’s knowledge with humanitarian goals and hence regarded as a major figure in scientific methodology and natural philosophy during the Renaissance period. Galileo made further progress on the ideas of Copernicus, optics and motion. Isaac Newton and Leibniz introduced calculus methodology to denote rates of change, laying the groundwork for much of modern science and engineering. Newton proposed gravity as the force that holds objects together and with his calculus approach synthesized developments done by his predecessors in his famous laws of motion. Newton’s pioneering work also had implications for other fields, such as engineering and mathematics. Significant progress in transport, navigation and military warfare was based on the inventions and discoveries of Newton. To summarize, Newton helped establish modern physics as a scientific discipline and provide practical applications to astronomy, engineering, and mathematics.

In the early days of enlightenment deductive reasoning and the primacy of the mind played a crucial role. However, empiricism gained prominence as the Enlightenment progressed. Empiricism refers to the belief that knowledge comes primarily from experience or observation. Descartes’ work helped to establish the idea that scientific knowledge is based on empirical evidence, rather than on traditional beliefs or dogmas. Philosophers such as Locke and Kant strongly argued that knowledge is to be based on empirical evidence. This helped establish idea that the scientific knowledge should be based on careful observation and experimentation, rather than on theoretical speculation or metaphysical assumptions. This led to the development of new scientific methods and tools, such as the use of telescopes, microscopes, and other instruments for observing and studying the natural world.

Enlightenment mathematicians emphasized the importance of rigor and precision in mathematical reasoning. Mathematicians such as Leonhard Euler and Pierre-Simon Laplace contributed to the development of rigorous mathematical methods and formal proofs, which elevated the status of mathematics as a discipline. Another interesting development is the application of mathematical methods to the study of the natural world. Mathematicians worked closely with scientists to develop mathematical models and theories to explain natural phenomena, leading to advancements in fields such as physics, astronomy, and mechanics.

Overall, the Enlightenment period was characterized by a pursuit of knowledge and reason, which manifested in both the development of language through philosophical discourse and the advancement of mathematics through rigorous reasoning and application to the natural sciences. Newtonian physics became the established paradigm and role model for science practitioners.

Computer Revolution and Language

In the early 19th century, Michael Faraday’s significant discovery of electromagnetic induction laid the foundation for the modern understanding of electricity and magnetism and led to the development of electric generators and transformers. A little later, pioneering work of James Clerk Maxwell has unified the theories of electricity and magnetism, laying the groundwork for modern electromagnetism and paving the way for future radio technologies. Marconi’s development of wireless telegraphy in the late 19th and early 20th centuries paved the way for radio communications.

The scientific progress in the early 20th century witnessed revolutionary developments of theory of relativity and atomic physics. Physicists started to take a look at atomic structure and their behavior. They developed quantum mechanics as a comprehensive theoretical framework to explain atomic and subatomic phenomena. These developments lead physics to look for fundamental forces of nature and a grand unification theory to explain them. To add to this, atomic physics laid foundational knowledge and tools that significantly influenced progress in biology. They enabled scientists to explore life at the molecular and atomic levels, leading to groundbreaking discoveries in molecular biology, genetics, biochemistry, and medicine. Further the progress of atomic physics required development of sophisticated instruments like telescopes, microscopes, spectroscopy equipment and so on. Instrumentation in turn influenced the computational revolution of the latter half of 20th century.

The invention of the triode vacuum tube by Lee De Forest in 1906 marked a significant advancement in electronic technology, enabling amplification and signal processing. The discovery of the semiconductor effect and the invention of the transistor in 1947 revolutionized electronics by replacing bulky vacuum tubes with smaller, compact and more efficient solid-state devices. The next significant invention of integrated circuit (IC) in the late 1950s enabled the miniaturization of electronic components, leading to the development of more and more sophisticated electronic devices.
Boolean logic and algebra invented by George Boole in the mid-19th century, laid the groundwork for the development of computers. It stipulates that the information in digital systems can be represented with only two possible values: 0 or 1. This binary representation is central to how computers store, process, and transmit data with high precision and efficiency. The boolean logic has inspired creation of electronic logic gates to efficiently implement the boolean functions. The development of complex digital circuits based on the Boolean logic has led to the creation of integrated circuits and eventually to microprocessor architectures. The latter formed the backbone of modern computers that enable execution of complex algorithms and efficient processing of vast amounts of data. Boolean logic has influenced programming paradigms and software development as well. The modern computing systems make use of expressions and operators of Boolean logic for decision making, flow control and data processing. The latter digital revolution with the integration of digital computers and communication systems has totally transformed society and the way information is processed, stored, and communicated.

Significant advancements in telecommunications with cellular networks, fiber optic cables, and satellite communications systems along with advanced computer networks made possible faster data transfer and the emergence of the Internet. Further, rapid expansion of wireless communication technologies allowed for seamless connectivity between devices, making it possible to access the Internet from anywhere, anytime.

The advent of digital technology caused a shift from analog to digital signal processing. In 1948, Claude Shannon’s seminal work laid the theoretical foundation for digital signal processing (DSP) and information theory. In 1960-1970 era, efficient algorithms for digital signal processing were developed and digital computers were used for signal processing tasks, though they were limited by the computational power of the time. The need for faster and efficient signal processing led to the development of special purpose DSP chips. The 1980s witnessed a boom in the development of algorithms and DSP applications, fueled by advancements powerful computing devices, especially in audio and speech processing and image processing.

Artificial intelligence (AI) and machine learning (ML) have emerged as crucial technologies for signal processing as they enable algorithms to learn from data and improve performance over time. Signal processing techniques have been useful in cleaning or denoising data as a preprocessing step, feature extraction from raw data and converting data into a form that is more effectively utilized by AI/ML algorithms. Further, synthetic data generation needed for robust training of AI/ML need signal processing methods.

Language Processing and Generative AI models

Natural Language Processing (NLP) started in the 1950s when computers were employed to understand, translate and generate human language. The earlier NLP models were using rule-based approach to handle language translation and sentiment analysis tasks. These systems relied on human-defined rules, lacked contextual understanding and could not handle complex language patterns. In the 1970s and 1980s, NLP models began to incorporate probabilistic methods for language processing and used statistical techniques to learn from data. The 1990s saw the rise of neural network-based NLP models, which used deep learning algorithms to process natural language. These models were able to learn complex patterns in language data and achieve notable proficiency in NLP tasks.

The next progress is made by long short-term memory (LSTM) networks which afford a powerful tool for sequence modeling tasks and have been successfully applied to various NLP tasks, and time series prediction. The complex architecture and high computational complexity of LSTM lead to the development of transformer-based encoder and decoder models. The latter transformer architectures have the ability to parallelly process sequential data that captures context and have achieved remarkable success in a wide range of NLP tasks.

The next breakthrough is the development of Generative AI models which generate novel content in response to prompts without having no predefined rules. In other words, they can create new content, such as text, images, or music, that is not directly copied from existing examples. More specifically, Generative AI systems are used to generate human-like responses in conversational agents, composing articles or stories, or even generating code. 

The generative capability has come as a surprise. Even AI experts and developers, who have engineered and trained neural network implementation, are clueless as to how they do this as the algorithms are self-developed and tuned as the system is trained. As of today, it is too complex a task to decipher what goes on inside the neural network layers. Another viewpoint is that Generative AI’s ability to produce new original content appears to be an emergent property of their structure and training. 

The Generative Pre-Training (GPT) model introduced by OpenAI in 2018 has had a significant impact on the field of generative AI research. GPT was one of the first deep learning models to be trained on massive amounts of text data to learn from unsupervised data. This approach allowed the model to generate coherent and realistic text without the need for explicit supervision or human intervention. GPT has demonstrated the power of deep learning models to learn complex patterns in language data and generate coherent text that rivals human-generated output. Its ability to learn from unsupervised data and generate coherent output has made it a valuable tool for exploring the limits of generative AI.

GPT success has inspired the creation of many subsequent generative AI models. Google has come out with Gopher, Chinchilla and lately Gemini models. Meta has taken the lead to support Open community development and made available parameters related to various models OPT, Bloom and Llama for research and development. These models have further improved GPT’s performance by incorporating additional pre-training objectives and fine-tuning on specific NLP tasks. Generative AI can be used to power chatbots and virtual assistants that can engage in natural, human-like conversations with users. These systems generate responses based on the context of the conversation and can provide helpful information or assistance. Generative AI models are useful to automatically generate content for various purposes, such as writing articles, product descriptions, or in creative writing. They are also employed for succinct text summarization and automatic translation to other languages.

Impact and Future of Generative AI

The astonishing abilities of LLMs to emulate human reasoning and communication has encouraged AI customization of LLM technology in healthcare, finance, entertainment industries. These applications are designed to integrate business proprietary data with LLM to provide contextually accurate responses. This enables providing more efficient and personalized business solutions. As an example, retail business is able adopt region-specific AI models trained on customer preferences and cultural nuances to take well informed business decisions. The potential of Generative AI is immense and with advances in deep learning and neural networks, it is possible to generate highly realistic images and videos that are difficult to discern from the real ones. The latter has applications in games and animated films.

Adoption of Generative AI is not without risk. It is well known that LLM’s are susceptible to attacks such as prompt injection, poisoned training data, model theft and supply chain attacks. Good news is it is possible to have defenses to mitigate or even reduce the above attack. However, there is another danger – which both the experts and critics have warned – that is bad elements misusing AI power to control others and cause large scale damages. Once the mighty power of AI is placed in the miscreant’s hands it is difficult to predict and control the consequences. A few AI pioneers have advocated government control in production usage of AI. As things stand now, neither there are guaranteed methods to prevent misuse of AI nor guidelines and regulations to contain AI research and growth. 

The progress of AI has triggered speculations on the possibility of Artificial General Intelligence (AGI) which will automate complex tasks such as decision making or problem solving. There are many challenges that need to be overcome to accomplish AGI. To begin with, although the current LLMs have a good model for language applications, they do not have planning (end goal) and decision-making ability. To make predictions about the real world we have to have all possible scenarios and we don’t have a representative probability distribution model which is essentially for high dimensional continuum space. We need to develop models that can handle ambiguous situations and make decisions based on incomplete or conflicting information. A radically new look at language models is necessary to go beyond the current limitations of LLM framework. Just as programming languages are created to do efficient computation, a new language needed to evolve for human-like intelligence to emerge. As always, the clarity and communication of a language to human thinking is essential to developing super-intelligent processing models.

Related to the above considerations, the success of AI has raised philosophical question whether machine can be considered conscious. Generative AI has exhibited its language skills to showcase its intelligence and can further evolve through innovations to emulate more human capabilities of intelligence. Another notable development is Stephen Wolfram proposing a computational viewpoint to model the universe. While these discussions/developments may not immediately impact, these cannot be ignored altogether. With this exciting research, we are poised to a deeper understanding of human intelligence vis-à-vis artificial intelligence.

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