What is GPT-3?
GPT-3 (Generative Pretrained Transformer 3) is a third-generation language module developed by OpenAI was developed and based on Natural Language Processing (NLP)-models.
The company, which was co-founded by Tesla CEO Elon Musk, is engaged in the development of Open source solutions in the area of artificial intelligence and has set itself the goal of working out the advantages of this for humanity. For the founders, as well as some scientists, there is a danger of human intelligence being surpassed or replaced by artificial intelligence.
Compared to its predecessors GPT-1 and GPT-2, the current version, which was introduced in May 2020, has achieved several improvements. The first version, GPT-1, was an NLP model that, in contrast to the previous state of the art, did not have to be trained specifically for a particular task, but only required very few examples for high-quality speech output. OpenAI staff further developed this model by expanding the data set behind it, adding more parameters and thus creating GPT-2.
This language module also has the ability to understand instructions as such, for example, to translate texts automatically by text instruction. While GPT-1 and GPT-2 are freely available as open source software, a commercialisation of the product took place with the GPT-3 language model. OpenAI argues this move on the grounds that freely distributing the new version poses too great a risk of spreading misinformation and spam or fraudulently writing academic papers due to its strong performance.
How does the language model work?
Compared to its predecessor, the third version takes into account a hundred times more parameters and uses five data sets (Common Crawl, WebText2, Books1, Books2 and Wikipedia), while GPT-1 (Book Corpus) and GPT-2 (WebText) only use one each. Training data set accesses.
The basic idea of many language modules in generating texts is to use statistical models to predict the next words in a text so that the text makes grammatical and linguistic sense. The AI software GPT-3 does not work with words or letters, but with so-called tokens. Basically speaking, this is a sequence of characters that belong together. In this way, the GPT-3 speech AI manages to bring variance into the speech output, which would be more difficult to represent by looking at whole words.
The language module applies the following models (so-called engines) for the analysis and generation of texts: Davinci, Curie, Babbage and Ada. Each offers advantages and disadvantages for certain areas of application. While Davinci is suitable for the analysis of complex texts, Curie is suitable for the use of a service chatbot. The user specifies this and a few other parameters to determine the output. Such parameters are, for example, the creativity of the output text as well as its length.
The language AI GPT-3 was developed for the English language and can currently only develop its full potential in this language, even though it offers translation options.
In which software is GPT-3 used?
Basically GPT-3 can currently be used via API interface and corresponding payment. Microsoft, on the other hand, has secured the exclusive licence to the source code of GPT-3 in order to integrate the language module into its own products and develop it further. For example, Microsoft Azure OpenAI combines the speech AI with the Microsoft services of security, access management and scaling capacity. This overall solution is said to be of particular interest to companies.
Furthermore, it is possible to GPT-3 for example also in Chatbots so that the software helps to conduct conversations and offer assistance. It is also used in game development to create automatic dialogues and storylines in computer games. Due to the engine's parameterisation, entire marketing texts, landing pages or newsletters can be created and implemented on the basis of a small amount of information.
What is generic programming?
The word generic comes from Latin (genus - genus, gender) and basically describes different properties of an existing object. Sets, classes or genera of different types are also used here. Generic programming, which is used in software libraries, is about designing functions in the general area. The aim here is to create different data structures and types of data.
The integration of various programming languages is done through templates or generic types. One consequence is that programming languages are created that are dynamically designed. The algorithms used here are not created specifically for certain types of data, but only place specific functional requirements on the data types.
Many people also use the term parametric polymorphism in this context. This can be clearly explained and seen in a standard library of the C++ programming language. There, the structures of data and the algorithms used are treated separately from each other, as well as possible, of course, because they are supposed to work independently.
C# Programming language
In the area of generic programming, the C# programming language is also used, which uses templates similar to those found in C++. Among other things, classes and commands are used that are already known, including list, dictionary or hashset. It should also be mentioned that in C++, brackets are always used, which are pointed. The aim is to highlight a declaration of classes and make it visible.
In Java, generic programming has been offered and used since version 1.5. The idea here is to implement one or the other new variable for existing types and data. When classes are used, for example, concretised types in turn replace other variables. One could also see it as a kind of new creation. In this way, programming that is type-specific should usually be made possible.
Artificial intelligence and generic programming are always interrelated and thus mutually dependent. Neural networks, broadly speaking, mimic the human brain and copy the behaviours and executions of their biological counterparts. In the creation of neural networks, generic programming is always given a raison d'être. Various commands, classes and structures are necessary alongside algorithms to create an artificial intelligence. The more extensive and diverse an artificial intelligence is to be, the more detailed and sophisticated generic programming becomes.
Technological progress in the course of advancing globalisation is unstoppable. Due to growing demands, for example in the economic sector, on the stock exchange or in industry, new solutions must be found again and again. In this context, generic programming is becoming more and more complex, but also more error-prone, unless software and hardware are constantly updated.
In principle, the IT sector is one of the few where jobs will be secure in the future, as specialists in the field will always be needed. Further education in generic programming as well as training in this field is becoming more and more important. This is also important in order to be able to keep up and compete with other industrialised nations worldwide in the future.
What is a genetic algorithm?
The genetic algorithm was made socially acceptable by John H. Holland. Problem indications in the binary domain form the basis here, which is why genotype-phenotype mapping is used. This means nothing other than that binary candidates in the solution domain must first of all be transformed and changed.
The aim is to ensure evaluation with the existing fitness function. Due to this peculiarity, they form a parallel to the biological counterpart. Our genetic material resembles quasi binary numbers due to 4 coded existing nucleic acids. Recombination and mutations are thus easily possible.
The phenotype (appearance) is created through processes and procedures that consist of many steps. Genotype-phenotype mapping is similar. Binary representations are clearly suitable for rapid processing in complex computer systems. Roughly summarised, one can say that genetic algorithms want to copy and imitate the basic principle of biological evolution in some form.
Application of the genetic algorithm
When it comes to solving problems, a genetic algorithm can be useful. When building bridges, a genetic algorithm can optimise the shape, the weight of components or even their position. At neural networks these also find an optimal application, for example when adjusting the existing clamp potential vector.
Things to know
In various initial situations, the aim is always to achieve a desired result at some point with the help of genetic algorithms, which is realised by constantly sorting out and selecting as well as changing information. But the results that are achieved in this way are not always optimal and tailored to the exact problem; there are sometimes deviations.
The reason for this is that the respective changes and selections may not have been pronounced enough; the operations, which are genetic, are to blame. Instead of presenting optimal solutions, suboptimal results are thus produced. There is also another disadvantage. A genetic algorithm always generates a large computational effort, thus also a longer runtime. However, this can be reduced and minimised through optimisation.
Economy and everyday life
When it comes to evolutionary, effective mechanisms, the term genetic algorithms comes up again and again. These have been gaining in importance for decades, as they find a useful application in the banking sector or stock market trading, for example.
Especially when it comes to hedging decisions or valuations of portfolios of different types, genetic algorithms are in demand like never before. They are also used all the time in large areas such as medicine, digital healthcare and research.
What does the future hold?
Due to increasingly complex processing procedures within IT systems, genetic algorithms are also becoming more and more extensive. It can therefore happen that faulty mutations creep in and unusable populations are created.
Thus, it is important that the processes of this kind of evolution continue to be optimised so that, for example, commercial enterprises or the research sector that use algorithms in this way in everyday life do not suffer any disadvantages. The bottom line is that genetic algorithms are a good thing. They make processes efficient, faster and ensure that operations run as smoothly as possible. Complex calculations like this could never be done manually by humans, but genetic algorithms make them possible.
What is Generative Adversarial Network?
Many images you see on the internet are often fake computer-generated images. In real life, there is no such image of a dog, butterfly or mushroom. You have seen many examples of this kind recently; human faces, voices, paintings, writings, etc. that are indistinguishable from reality. Most of these studies are done with an artificial neural network (artificial neural network) model called Generative Adversarial Network GAN.
Generative Adversarial Network - the genesis
The idea for Generative Adversarial Network GAN was born in 2014 during a conversation Ian Goodfellow had with his friends at a party and was subsequently published as an article. This article, which became popular after its publication, has received more than 15000 citations in 5 years and the number of articles published at major conferences in the field exceeds 500 per year.
If you give the Generative Adversarial Network GAN model a sufficient number of samples from the data to be generated, you can have it generate new samples of the same data type. If you give the system thousands of bird photos, the system learns what a bird should look like and generates new bird photos. There are 2 different artificial neural networkswhich are in conflict with each other in GANs.
These are called generator and discriminator networks. The generative network produces new data that looks like reality (images, sounds, models, etc.), while the discriminator network tries to distinguish between fake and real data. As these two neural networks compete with each other, the discriminator network gradually begins to distinguish better between real and fake images.
The generative network creates more realistic fake images. We can compare this command to a counterfeiter and a police officer. Over time, the police get better at catching fake coins to find counterfeiters. The counterfeiter starts to produce more realistic fake coins to beat the police.
Is it possible to ensure authenticity?
The discriminant network calculates the probabilities that the received images are real and assigns them a value between 0 and 1. A real image should have a probability of 1 and a fake image 0. The discriminant network is trained with the loss value, which is the difference between the probability values it gives and the values it should be.
For example, if the Distinctive network gave a real image a value of 0.7, it made an error of 1 to 0.7 = 0.3. On the contrary, it gave a fake image a value of 0.7, but made an error of 0.7-0 = 0.7. The values of the Discriminant network are updated at each iteration during training to reduce these error values to 0. On the contrary, the producer network wants the fake images it produces to be true, i.e. to be assigned a value of 1.
If the self-generated image gave the discriminating network a score of 0.6, the generating network made an error of 1 to 0.6 = 0.4. The generating network also wants to reduce this error to 0 at each iteration. In this way, as the training process progresses, the discriminating network becomes more successful at distinguishing between real and fake images. The generative network produces more realistic fake images.