Language of Machines | Future of Natural Language Processing
Language of Machines, How we talk to Machines, Chat GPT and Future of Natural Language Processing! ChatGPT and the Sometimes, when I am very focused on work, the scenes of the film “The Imitation Game” (“Enigma: Turing Code“), which is about the life of Alan Turing, fall to my mind. It must be because it fascinates me so much, my subconscious seems to remind me of the potential of this intense effort, which we will define as producing technology, which creates value for the benefit of humanity.
Language of Machines! Enigma: Turing Code
Directed by Morten Tyldum and written by Graham Moore, this 2014 biopic chronicles the life of British mathematician, computer scientist and cryptanalyst Alan Turing and his spectacular journey with a group of cryptanalysts to decipher the German Enigma code during World War II. Today, historians agree that thanks to Turing’s work, the duration of the war was significantly shortened.
We know that reason and science constantly improve human life, but such groundbreaking scientific advances never cross the fingers of one hand in history. Will the ChatGPT phenomenon we are experiencing today have such a big place in history?
The trajectory seems to be going in that direction. What we are witnessing today with ChatGPT, the ability of the machine to scan a huge amount of data and produce texts that fit descriptions, are meaningful and indistinguishably similar to human-produced texts, will bring about major transformations. So what kind of technology is behind this tool that will have important results, let’s take a brief look.
Chat GPT and Future of Natural Language Processing
ChatGPT is a GPT-3 (Generative Pre-trained Transformer 3) language model developed by OpenAI. This language model, which reached 1 million users in the first week of its life, broke a record in terms of the number of users reached by a technology tool in such a short time and attracted all the attention. Thanks to natural language processing (NLP) technology, this new tool can understand texts with language skills that are very similar to humans and produce responses in real time. Natural language processing is a field of computer science that aims to develop computer systems capable of understanding and processing human language.
The progress of artificial intelligence confirms the concept of paradigm shift that historian, physicist and philosopher of science Thomas Kuhn talks about in The Structure of Scientific Revolutions. We can explain the rapid developments we have experienced in recent years by the “periodic leaps” in scientific knowledge that Kuhn described. However, if we think about ChatGPT in particular, we should not lose sight of the accumulation going back decades behind a technology tool that has gained such great popularity. Let’s take a short trip through history to remember the turning points in this AI process.
Eliza to ChatGPT and Language of Machines
To look at the origins of the natural language processing technology behind ChatGPT, we need to go back a bit. Here it is worth recalling Alan Turing again. Natural language processing technology can be traced to a question posed by the British mathematician and computer scientist Alan Turing in his 1950 paper “Computing Machinery and Intelligence”: Can a machine reach the level of human intelligence? Turing proposed a thought experiment to answer this question. This test, which has been done for various software until today, is called the “Turing Test”. The test is being used to determine whether a computer or artificial intelligence is capable of thinking like a human. Here’s how the test works: A judge conducts a written conversation between a computer (artificial intelligence) and a human.
The task of the judge is to determine which of the participants is a human being and which is a computer. The judge makes his decision by asking the participants various questions and evaluating the answers. If the judge decides that the computer is human, or if the judge is not successful in making a decision, the computer is considered to have passed the Turing Test and exhibited human-level intelligence.
The Turing Test is an important milestone in the development of artificial intelligence and natural language processing technologies. This test sheds light on the main goal of research in natural language processing, which is to achieve human-like language abilities. Today, many competitions and events based on the Turing Test are held. The most well-known of these is the Loebner Prize, which has been held since 1991.
Another milestone in natural language processing is considered to be ELIZA. We can think of ELIZA as the grandmother of ChatGPT. Developed by Joseph Weizenbaum, a computer scientist at MIT (Massachusetts Institute of Technology) between 1964 and 1966, ELIZA was one of the first studies on artificial intelligence and natural language processing, and was able to conduct simple text-based conversations with people, albeit at a level that cannot be compared with today.
Natural language processing and Language of Machines
During this period, rules-based and symbolic approaches were used for the understanding and processing of language. The most famous version of ELIZA was a chatbot named “DOCTOR” that pretended to be a good doctor psychotherapist. ELIZA’s competence was not beyond producing responses to user-entered texts using pre-made templates and simple language processing techniques, but the creation of ELIZA was an important start to the point where humanity has come today as an idea.
The next milestone was the development of a sub-branch of machine learning that we call deep learning, which attempts to model learning processes using artificial neural networks. This discipline, which began to be developed in the 1980s, made great strides in the early 2000s with models such as AlexNet. The application of statistical methods to the field of natural language processing gained popularity during this period. During this time, learning algorithms began to be used in areas such as language models and text classification. These techniques have begun to be used in language models as well as in natural language processing applications such as voice recognition and machine translation.
The Transformer Architecture, introduced in 2017 by Ashish Vaswani and colleagues with the article “Attention is All You Need”, is another important development in the field of natural language processing. Using attention mechanisms, Transformer accelerates the training processes of language models and enables them to make more accurate predictions. The model offers a new neural network architecture that can be trained, scalable and learn complex language structures more quickly and effectively than previous methods.
Generative Pre-trained Transformer
These studies are considered the basis of the ChatGPT (Generative Pre-trained Transformer) series developed by OpenAI. The language models that we can call the technology behind ChatGPT developed by OpenAI, an artificial intelligence research laboratory established in 2015, are called GPT Series. GPT, which uses the Transformer architecture and contains 117 million parameters, was released in 2018 as the first model of this series.
Soon after, in 2019, GPT-2 was released. A larger and more powerful model with 1.5 billion parameters, GPT-2 was noted for its ability to generate and understand text, furthering the success and capabilities of its predecessor, GPT.
Released in 2020, GPT-3 was found to perform at a human level in a variety of tasks with 175 billion parameters. The model behind ChatGPT, which has made a name for itself in a short period of time such as a week to a considerable world population, is GPT-3.5 and GPT-4. GPT-4, the most advanced model in the GPT series offered by OpenAI, performs closest to human (perhaps superior to human) in many professional and academic fields. OpenAI describes GPT-4 as “more creative and collaborative.”
OpenAI says ChatGPT is a successor to InstructGPT, a GPT model released in January 2022. InstructGPT has been trained to understand, appropriately inform, and execute the instructions it receives from users. Despite having a large database, it is stated by OpenAI that InstaructGPT’s database is limited to training data and lacks in information topicality, accuracy and consistency.
Where we are today about Language of Machines?
OpenAI is constantly improving the different language models in the GPT series, continuously improving the model in terms of its size, capabilities in understanding and generating responses, and its performance. Throughout the GPT series, each new model has a larger and more complex structure, and is trained on larger and more diverse data sets, ensuring that each new model has more advanced natural language processing capabilities than the previous one. In summary, ChatGPT is compared to previous models:
It has better language understanding and context comprehension, which makes it possible for users to provide more appropriate answers to their questions and requests.
It offers more natural and consistent results when it comes to producing and completing text.
It excels at a variety of natural language processing tasks such as summarizing, translating, and question-and-answer.
OpenAI states that ChatGPT is a new model that can interact in a dialogic way. This conversational experience is enriched with different features beyond the simple question and answer structure, as well as the comprehension success of natural language processing. For example, when you ask ChatGPT follow-up questions linked to the previous question in a dialogue flow, it remembers the previous part of the conversation and answers them in the natural flow of the chat.
When you enter a statement that is not scientifically true, it states that it is not true, corrects you. In some cases, he admits that with a humble approach, he can make mistakes himself. By displaying an honest stance, he does not come up with an identity that he is not, and he rejects the requests he cannot make with an appropriate language.
ChatGPT and Machine Learning and Language of Machines!
ChatGPT, which has been rising above a great technological power accumulated by humanity for a long time, was nothing more than the showcase of this accumulation and the point where natural language processing technology has come. Thanks to ChatGPT, millions of people have come up with very creative test scenarios, understanding the capabilities of natural language processing technology and the areas where it promises transformation.
ChatGPT’s creative team emphasizes that there have been general use chatbot studies before them, but they have dared to do it with great confidence in front of the public. Given OpenAI’s current GPT series, we know that APIs were ready to use even a year ago, but this only concerned a select professional group. With the launch of ChatGPT, for the first time in the world, such a large language model was made available to ordinary people who do not have any expertise in this field with a simple chat interface, creating a great awareness. In fact, this alone reveals the revolutionary nature of ChatGPT. Now, people write with artificial intelligence, chat, do homework, print texts, answer questions, often forget that they have software in front of them.
The GPT 3.5 used in ChatGPT was actually trained with a technique called “Reinforcement Learning from Human Feedback (RLHF)”, which, like its predecessors, draws on human feedback. But in ChatGPT, instead of AI giving the first answer that comes to mind, it is based on how people like to hear answers. After a few months of beta testing, ChatGPT was launched.
Of course, ChatGPT is not perfect and has entered our lives with missing points and controversial issues. But the goal of OpenAI was not to make the work perfect or take it out, but to make it available to the public when it was “good enough” and continue the development process. The team emphasizes that tens of millions of people are following a strategy of improving through an iterative method by tracking, analyzing and better testing the results of its use.
It’s better than that, but how?
GPT-4 has already set the bar very high for expanding the boundaries of natural language processing technology compared to the previous version, solving more difficult problems with a higher accuracy rate, creativity and analysis ability.
Big tech companies are announcing that they are starting to use this model one after another. Now the magical world is taking its place as the natural language processing system behind the tools and platforms that many of us use in our daily lives. But there are still critical steps ahead of this technology, and I have no doubt that these steps will be taken. Let’s talk about a few predictions about these.
Successful AI models
More sophisticated and comprehensively successful models will be developed. Language models will better understand the complexity and nuances of language, analyze texts more accurately, and respond more appropriately to users’ intentions and emotions.
The models will be able to apply the knowledge and skills they have already learned more effectively to new situations. This will allow language models to be used in a wider range of tasks.
It will move towards general artificial intelligence systems. Natural language processing systems will be better integrated with other AI components, and more general AI systems will emerge.
Natural language processing models will be trained in more languages and dialects, thus expanding the technology’s sphere of influence.
As the impact of artificial intelligence and natural Language of Machines processing technology grows, the need for regulation of ethics, bias and security issues will increase.
The new models will focus more on protecting users’ privacy and security, and will create regulations and policies to limit biased and harmful content.
The interaction between human and artificial intelligence will become more natural and artificial intelligence systems will become widespread and become part of the tools and platforms used in daily life.
For tomorrow
With ChatGPT everyone has their own individual experience, it is possible to learn something new about its capabilities every day. But ChatGPT and the Language of Machines models behind it offer a vision far beyond what it can do with artificial intelligence. Artificial intelligence, as of the point it has reached today, seems to bring a transformation as much as the microprocessor, personal computer, internet or mobile phone into our lives, perhaps beyond these.
All areas of business will adapt themselves to this new world, and the strength and competence to use the tools and technologies in this new world will be distinctive for both organizations and individuals. Apart from the business world, artificial intelligence and Language of Machines will play a key role in bringing solutions to global problems such as global warming, income distribution inequality and the expansion of educational opportunities.
Still, given the broad vision it offers us, imagining what AI can do for humanity in 10 years gives me hope.
