The term GPT indicates neural networks pre-trained on large unlabeled datasets capable of generating human-like content. Their fascinating architecture is a fundamental building block for generative artificial intelligence. Rarely do three letters enclose such a vast and profound world: GPT is on everyone’s lips thanks to ChatGPT and represents the democratization of AI par excellence. Anyone can use it, and it gives ample display of one’s potential without veils, for better or worse.
It has another purely representative importance, that is, it is actual technology. To be a technology, it must meet four essential requirements: it must be widespread, it must be easy to use, it must be economical and it must be transparent, i.e. users must not know the dynamics that allow it to function. And it is precisely among these dynamics that we dive because, at the bottom, there is a beautiful world.
How GPT Works: The Machine Learning Algorithm
A step back before starting: OpenAI, a foundation that deals with research on artificial intelligence, releases GPT models which are the evolution of previous ones, i.e., more performing precisely under the training. The most recent model, GPT-4, was released in March 2023. GPT is called generative because the neural network of this machine learning model does not simply respond positively or negatively but argues by providing a detailed output that goes into detail about the query launched by the user.
It may seem trivial, but it is necessary to make a discourse that recalls the work of the American philosopher Daniel Dennett, who focuses on cognitive science and evolutionary biology. From this point of view, the reflection to be made regarding GPT is demanding, and – although AIs have no familiarity with logic and cognition – it is necessary to refer to the concept “Competence without comprehension “so dear to Dennett. GPT is this: he has pure skills without knowing.
The proof is soon provided: GPT does not know languages but is able to translate. Evaluating GPT only for its technological nature is a mistake because we are at the dawn of a new era and because the added value is not what it does but the enormous work behind it, which is the sum of the abstraction of thoughts of those who make all this possible.
The GPT model includes initial training data, which is what is called “input.” From here, the algorithm to create content calculates the possibility that a word is after those previously used. GPT chooses the word that is most likely correct in the context. This provides a certain level of understanding of the text.
Still, as fascinating as this hypothesis is, it should be specified that GPT has not achieved the ability to grasp the context and, consequently, the descriptive peculiarities of human skills. All this translates into a certain acerbity in drafting texts that require a profound understanding of the topic covered.
Advantages Of GPT In Natural Language Processing
The use of GPT has several advantages, but, as we will see and as we have already mentioned, the enthusiasm it has aroused must take into account the limitations it still presents. Among the notable assets are:
- Adaptability: GPT can be used for text generation, question answering, text summarization, translation, and more
- Understanding context: The fact that GPT can understand the context of a sentence returns coherent forms of language
- Linguistic precision: the generated text is impeccable from the point of view of grammar and syntax. This favors using the GPT model for online content creation and chatbots.
- Pre-training: GPT is pre-trained on large amounts of data, which allows it to generate accurate answers. This also favors the transferability of GPT skills to other areas and has its versatility.
- Additional training: by training GPT with specific datasets, GPT can be trained to move with greater precision and ease in peculiar contexts. For example, GPT can learn legal or medical terminology by providing industry input data to generate high-quality text. Also, in this case, the tuning operations show a valuable transversality.
But it’s not all gold: GPT has to deal with its limits, which, moreover, are subsidiary to its evolution. On the one hand, you may come up against a wall that is difficult to cross because, at least in its current forms, the GPT model takes information from the web, but it is only sometimes known how accurate this is.
On the other hand, it is necessary to question further the quality of the answers generated because they could be plausible but not precise, if not entirely out of place ( the phenomenon of hallucinations ). Even “toxic” texts cannot be wholly excluded. Although progress is continuous and constant, AI brings with it the schematizations of man, and among these, there are also sexism, racism, and bias of another nature. All this should not demonize the use of AI, but these problems must be overcome.
How The Generative Pre-Trained Transformer Is Advancing The Field Of AI
A broad discussion that finds a synthesis in an important work written by researchers from Chinese, Indian, Lebanese, and Swedish universities. A study whose conclusions point to the future of linguistic models such as GPT is destined to constantly improve to transform how man interacts with technologies and peers.
Large language models continue to improve, providing valid support to AI, which, it should be remembered, is a construct based on critical elements such as Machine learning and Deep learning. The contribution of models like GPT to the development of AI will facilitate progress in the following areas:
- recognition and understanding of the test
- linguistic skills to be integrated into the applications, which will increase in type and number
- of personalization and fine-tuning, i.e., the activities of refining the inputs and, therefore, the results
- of decreasing costs
- versatility, scalability, and accessibility
- of problem-solving, primarily related to the biases and data analysis that fuel AI
All this is within a virtuous circle in which the continuous improvements of the models will return greater capacity for growth and innovation. And this represents a more than valid reason to use models like GPT responsibly.
Challenges And Critical Issues In Using The Generative Pre-Trained Transformer
Even when we talk about technologies, we must consider the ethical and social repercussions they entail. Ethics, which acts as a supervision of morality, suggests dealing in the first instance with the primary effects without ignoring the secondary ones. This means that technologies must be used responsibly, and GPT models have great potential for critical issues.
There are challenges related to the distortion of the data used to train the models, privacy, security, the impact that the creativity entrusted to AI has on human imagination, and, last but not least, the age-old problem of the replacement if not even the cancellation of jobs and, at the same time, any difficulties that may arise in the collaboration between man and machines.
The critical issues and challenges lie within themselves: we need to ensure that GPT and other models are used in a way that generates benefits for society and, since they learn from data, paying attention to how they are selected and enriched will allow for more seamless use of AI in general, so that they can be used reducing potential dangers for the community.
How To Implement GPT In Your Company: Practical Advice
Everything revolves around the data and the use you want to make of it. Implementing GTP in the company can mean different things, just as the objectives can vary. The sectors and flows in which GPT can be used are:
- staff training
- planning of business strategies
- marketing and sales
Any business sector can enjoy the assets provided by GPT models, but each needs to be pre-trained with specific data and others expected for multiple industries. Regardless of use, you need to have an excellent knowledge of prompting techniques, which become essential for interacting with models like GPT. It must also be decided on which data GPT should be trained on, respecting privacy regulations, compliance, and emphasizing security.
It is advisable to start with the data and carry out tests on a small scale, then increase the amount of data valuable for pre-training. Perspectives and developments within the Generative Pre-Trained Transformer There is a profound debate that addresses the topic of GPT models in the world of healthcare, and it is a debate full of meanings because it unfolds through three conceptual areas, namely:
- what can be delegated to GPT models
- what cannot be left to GPT models
- realistic but demanding and not without challenges applications of GPT models
Healthcare is a crucial sector for the development of AI and, therefore, also of GPT models because it is susceptible from every point of view: it is sensitive in terms of privacy, data reuse, the use of algorithms and hardware devices and sensors which, in turn, collect and analyze data, even in real-time.
Suppose the healthcare sector makes good use of GPT models. In that case, all the other sectors will enjoy the benefits achieved, and, nevertheless, healthcare is the key sector to eliminate the doubts that are still harbored towards AI in general and the cornerstones on which it is built.
Ethics And Responsibility In The Use Of GPT
Any question raised by a crucial issue is answered in ethics. It is not something new; Thomas Aquinas was already talking about it in the last decades of the 1200s, and although he can do a light sort of philosophy of ethics on a head of technology, it is honest to admit that, although more than eight centuries have passed, there is no we have significantly deviated from the thought of the 13th-century Italian theologian and philosopher.
The ethical headaches that arise from GPT models are under discussion due to the negative impacts they could have on the community. Most of these, however, focus on the dangers arising from the spread of prejudices and fears that they could cause a revolution in the world of work that could marginalize some professions.
We are moving on my terrain; hiding behind a finger does not solve the problems that are real and tangible. They can be overcome by following the instructions of Thomas Aquinas, who appeals to the right to participation and explanation to the disclosure of what is happening.
A separate discussion but no less important is the responsibility of the developers and companies that use GPT models, invited for transparency and ethics, to discuss and evaluate what is happening in the AI sector not only from a scientific point of view but also from a philosophical, psychological and sociological one. Suppose there is a division between computer science, philosophy, and sociology. In that case, it is more complex than one might believe: the three branches, different from each other, are part of the same whole.