ChatGPT and OpenAI: Everything you need to know about this technology



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Developed by OpenAI, ChatGPT is a machine learning model based on the GPT (Generative Pre-trained Transformer) architecture. It is a kind of text generator whose interface resembles chatting with a human. The popularity of ChatGPT is record-breaking – the number of active users exceeded one million people in just 3 days from the start. How does this technology work? How to use it? 

Behind ChatGPT is a team of engineers and researchers specializing in machine learning, programming and natural language processing. Their work was used to “train” the algorithm so that it would be able to understand the text entered by Internet users and react to it in the right way. The sources of data were materials from from the Internet and books, which allowed us to better understand the human language and the way we use it every day. The algorithm uses this knowledge to generate human-like text when prompted or asked.

How ChatGPT works

OpenAI engineers used a neural network, a type of machine learning algorithm. It is modeled on the structure and function of the human brain. It consists of layers of interconnected nodes, called artificial neurons, which are used to process and analyze input data. The basic building block of a neural network is an artificial neuron, which is modeled on biological neurons in the human brain. The artificial neuron receives the input, processes it and generates the output. The inputs are connected to the artificial neuron via synapses, which are similar to the connections between biological neurons.

A neural network consists of layers of artificial neurons, with the input layer receiving the raw input and the output layer producing the final output. Intermediate layers, called hidden layers, process the input data, model it, and pass it on to the next layer. The number of layers and the number of neurons in each layer may vary depending on the specific application of the neural network.

The primary function of a neural network is to learn from inputs and make predictions or decisions based on that input. This is done by adjusting the strength of the connections between neurons, called weights, in a process called training. During training, the neural network is presented with a set of inputs and their corresponding desired outputs, and the weights are adjusted to minimize the difference between the predicted and desired output. As an example, we can use a simplified model in which we train an algorithm to perform a given task, e.g. driving a winding road from place a to b. The algorithm tries to perform the task in, for example, 20 ways. Based on the data, at the end of each trial, for example, the 3 best methods are selected and the rest rejected. From these three best methods, new ones evolve and such a simulation can be repeated even tens of millions of times. As you can guess, each subsequent attempt will generate better and better results.

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How can you teach a machine to think?

One of the most common methods of machine learning is the so-called supervised learning. In this method, the machine is equipped with a labeled data set that contains the input data and the corresponding desired output data. The machine uses this information to learn the relationship between them. It can then make predictions or make decisions based on the new data received.

Another common method of machine learning is unsupervised learning. In this method, the machine is supplied with an unlabeled data set that contains only the input information. The machine uses this dataset to learn patterns and structures, and then it can make predictions or make decisions based on new inputs.

Reinforcement learning is another method of machine learning. This is done by providing it with a set of rules or rewards for certain actions. The machine learns by trial and error, adjusting its actions based on the rewards or punishments it receives. This method is often used in robotics and gaming applications.

Transfer learning, on the other hand, is a method of teaching a machine by using the knowledge gained from one task to improve the performance of another but related task. This method can be useful when data is missing for a specific job, but there is a lot of data for a related job.

Man-in-the-loop is a method of teaching a machine by allowing humans to provide feedback and guidance during the training process. This can be done by having people label data, giving feedback on machine performance, or adjusting machine parameters.

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How a machine thinks?

ChatGPT uses a technique called “masked language modeling”, which means that the algorithm is trained to predict the next word in a sentence given the context of previous words. This allows the model to generate text that is relevant to the input and has a natural flow. Interestingly, its features include translation of input data (e.g. a question can be asked in Polish and will be understood by the English-language interface), or summarizing or summarizing the answers. It can also be adapted to specific areas such as customer service, technical support and creative writing – depending on the commands used, we can generate advertisements, movie scripts or finished articles. The machine makes decisions based on the information provided to it and the algorithms it has been programmed with. The specific process may vary depending on the type of machine learning algorithm and the task at hand, but here are some common steps:

  • Input: The machine receives input, which can be images, text, or numeric values.
    Processing: Input data is processed and analyzed by algorithms. This may include tasks such as recognizing patterns or features in data, comparing data to known examples, or calculating probabilities.
  • Decision making: Based on the processed input data, the machine makes a decision or prediction. This decision can be a simple yes/no answer, a numerical value, or a classification into one of many categories.
  • Output: The machine generates output, which can be numerical values, predictions, or decisions.
  • Feedback: The machine receives feedback on the accuracy of its decision via a labeled set of data, and this feedback is used to adjust the parameters of the algorithm and improve the decision-making process.

Asking questions from the user can be an effective way to teach the machine and improve the performance of its algorithms. User questions can be used to label data, which is a key step in supervised learning. By asking users to label data, the machine can learn the relationship between input and valid output. This tagged data can then be used to train the machine and improve its performance. ChatGPT can also learn actively by responding to users’ questions with questions asking for clarification. By asking users to verify a machine’s decisions or predictions, the machine can be assured that it will not mislead a human, and the human can provide additional context or information that the machine may not have taken into account.

Can a machine lie to us?

The machine can determine whether it is giving the correct answers or not by comparing its output against a set of labeled data, or so-called ground truth. This labeled data is a dataset that has been prepared by experts and includes inputs and corresponding valid outputs.

Basic truth, or ground truth

The underlying truth refers to the true or correct information or values that the machine learning model is trying to predict or estimate. It is used as a benchmark to evaluate the performance of the model. Basic truth can take many forms, such as labeled data, expert materials, or physical measurements. In autonomous vehicles, the underlying truth may be physical measurements of the position and speed of the car obtained using GPS.

Base truth is an important part of machine learning because it allows the model to know the relationship between inputs and correct outputs, and it allows you to evaluate the performance of the model by comparing its predictions to the base truth. However, even the basic truth is not always perfect and can sometimes contain errors or biases. This is especially true when the basic truth is created by humans. It is important to be aware of these limitations and take them into account when evaluating model performance.

Once the machine has been trained, it can be tested using a separate set of data (i.e. test data) that it has not seen before. It is marked and allows you to compare the machine’s predictions or decisions with the underlying truth and evaluate its performance. This process is called evaluation, and it helps measure how well the machine is able to generalize new data. This is an important aspect to know that the machine is not misleading.

Another way to ensure that the machine does not mislead is to use a “man in the loop” approach where a human expert is involved in the decision making process. This can be done by having a human verify a decision, a machine’s prediction, or by allowing a human to provide feedback on the machine’s performance.

Can a machine stop learning?

The article often mentions that the machine learns new things and thus increases efficiency. Can it reach such a level that science becomes redundant? It depends on the specific machine learning algorithm and the task at hand. Some machine learning algorithms, such as supervised learning algorithms, can reach the end of the learning stage, also known as convergence, where the performance of the machine on the task reaches a satisfactory level and further improvement is not possible.

However, other machine learning algorithms, such as unsupervised learning algorithms, may have no end of learning stage. They can still actively respond to new data. In addition, in other cases, such as in reinforcement learning, the machine is constantly learning and adapting to the changing environment. Also note that even if the machine learning algorithm converges, model performance may degrade over time if the model is not updated or tuned with new data, or if the distribution of the data changes.

Application and criticism of ChatGPT

It quickly turned out that the technology, which was intended primarily for scientists (ChatGPT is in the phase of free tests, which are designed to refine the algorithm and “feed it” with additional data), became the favorite tool of practically everyone. The applications are enormous, as are the possibilities of this technology. There are already reports of solving matriculation tests or professional exams (e.g. legal applications) using ChatGPT. Students no longer want to write essays on their own, because an algorithm can do it for them – quickly, efficiently and practically undetectable with the naked eye. Texts developed so far by copyrighters, PR specialists or marketers can also be created entirely by ChatGPT. The machine helps programmers revise the code, or suggests how it can be improved. If you want, he can also write the text of an advertisement, a movie script of any genre, or even the lyrics of a rap song. The first job offers for people who specialize in using ChatGPT and other content creation technologies using AI appear on the web. Technology began to develop so fast that the biggest players in the technology market felt threatened. According to The New York Times, Google was scared by the huge increase in interest in ChatGPT. After all, the world’s largest content search engine earns money from advertising in search results, which are increasingly of dubious quality (cheated by SEO specialists, etc.). Meanwhile, the work of experts from OpenAI allows you to get an immediate answer to the question asked, and it is served in an accessible way, often accompanied by a comparison and summary. These are things Google has never been able to do.

ChatGPT doesn’t work

Due to the high interest in this technology around the world, we may see an error message when entering the ChatGPT website. This is due to the huge number of requests from users overloading the service’s servers. All that remains is to wait for the failure or sign up for the list of notifications.

Jakub Markiewicz
Jakub Markiewicz
Hi, I am the author of the blog and series of thematic portals since 2013. I have nearly 15 years of experience in working in the media, marketing, public relations and IT. If you are interested in cooperation, you would like me to write about something or test a product - let me know.
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