What is GPT-4?
GPT-4 is an advanced language model that uses the technology of Natural Language Processing (NLP) models and is used by the company OpenAI was developed. GPT stands for "Generative Pre-trained Transformer" and represents the fourth generation of OpenAI language models is the new GPT-3.5. As with its predecessor GPT-3.5, text entries can be made via ChatGPT can be processed. In addition, an interface via a API (Application Programming Interface) be set up.
Performance and skills
The scope of services of the language model mainly refers to the Processing and output of user requests in the form of human speech. To accomplish this function, GPT-4 was equipped with Training data and then optimised by human feedback. Based on this training model, GPT-4 should be able to output human-like texts and solve complex problems based on the user's input. Tests have shown that GPT-4 is able to pass university entrance exams and other tests.
The training data is currently based on a status of September 2021, which is why events and findings that occur after this date are sometimes not known or output by the model. Like its predecessors, GPT-4 faces reliability limitations. According to a company statement, it is still possible that GPT-4 "hallucinates" facts, i.e. outputs false statements, although the factuality results are slightly better than under GPT-3.5 due to special post-training.
Training data and training of the model
The training data of the language model were taken from both publicly available data such as internet data as well as by the company licensed data created. Thus, the training data contains both correct and incorrect answers, strong and weak arguments, and contradictory as well as consistent statements. Furthermore, a wide variety of ideologies and ideas are included in the data. To optimise the output quality, the behaviour of the model was optimised using Reinforcement Learning from Human Feedback (RLHF). In this process, the training data is made available, through Supervised learning learnt and then passed on through reinforcement learning (reinforcement learning) improved by means of reward models.
Differences between GPT-4 and GPT-3
According to statements on the part of OpenAI, innovations compared to GPT-3 or GPT-3.5 will not be apparent in the output during a simple conversation, but only when a certain threshold in the complexity of the task is exceeded. Accordingly, GPT-4 should be able to, Respond more reliably and creatively and process instructions in a more nuanced way. The new version also shows a Improvement in the output quality of different languagesincluding so-called low-resource languages.
Developments are also evident in the area of tractability of the language model, with GPT-4 being able to adjust the style and verbosity as well as the tone of the output within certain limits. In addition, according to the company, it should be more difficult to detect "bad behaviour" by the Model and thus create so-called jailbreaks by generating content that violates the company's usage content. GPT-4 is known as multimodal language model seen and is able to accept and process both image and text input and report back text output.
GPT-4 Application examples
The use cases of GPT-4 are very versatile and can be applied across industries. For example, the language model can be used in the field of Customer service to take over the communication with customer enquiries there in the form of a chatbot. Due to the variability of the tonality of the output, the conversation can be adapted to a certain extent. Another use case is in the entertainment industry. GPT-4 can be used to create scripts, texts or poems, for example. Application tests have shown that GPT-4 can also understand humour, which increases the possibilities for use in the entertainment industry. Entertainment industry expanded again.
Like its predecessors, GPT-4 is also capable of programming. Due to the language model's image input capability, web pages can now be created and programmed using imported sketches. The possibility of image input also opens up, for example, use cases in the Area of medicineby identifying, categorising and displaying abnormalities in imaging examination procedures. Due to the multimodal structure and variable application areas of GPT-4, there is potential for the application and linking of the language model across several sectors.
How can I use GPT-4?
GPT-4 is to be available via a paid licence version as part of the ChatGPT Plus subscription model at chat.openai.com be available with a usage cap. The company states that this usage cap will be based on demand and system performance in practice and will be variable. In addition, new subscription levels could be introduced or a certain number of free GPT-4 queries could be available for test purposes. Another access option is via a API interfacewhich can be used to implement the functionality of GPT-4 on external applications.
What is Google LaMDA (Language Model for Dialogue Applications)?
LaMDA (Language Model for Dialogue Applications) by Google is a new research breakthrough in the field of language processing. LaMDA is a Conversation-oriented neural network architecturethat can engage in free-flowing dialogue on endless topics. It was developed to overcome the limitations of traditional Chatbots who tend to follow narrow, predefined paths in conversations. LaMDA's ability to engage in meandering conversations could open up more natural ways of interacting with technology and new categories of applications.
Google's research breakthrough has New standards in the field of speech processing and the technology can be used in a variety of areas, such as customer service, education and even entertainment.
Functions and capabilities
LaMDA is based on the Transformer architectureinvented by Google Research and launched in 2017 as the Open Source was published. In contrast to most other language models, LaMDA is used to trained by means of dialogues, which allows the model to recognise different nuances that distinguish open conversations from other forms of speech. LaMDA learns from dialogue to generate responses that are both sensitive and specific to the context of the conversation.
The meaningfulness of LaMDA responses is based on how well they make sense in the context of the conversation. For example, if someone says, "I just started taking guitar lessons," an appropriate response might be, "How exciting! My mother has an old Martin she likes to play." The specificity of LaMDA's response clearly relates to the context of the conversation. For example, if someone says, "I'm going to the beach," a specific response would be, "Don't forget to put on sunscreen!"
LaMDA's conversational capabilities were developed over years of work, building on previous Google research that showed that Transformer-based language models trained on dialogue can learn to talk about virtually anything. LaMDA can be fine-tuned to significantly improve the sensitivity and specificity of its responses. Google also looks at dimensions such as "interestingness", assessing whether answers are insightful, unexpected or funny, and factuality, which refers to whether LaMDA's answers are not only convincing but also accurate.
Google attaches great Value the safety and ethical use of its technologiesand Google LaMDA is no exception. Language models can encourage abuse by internalising prejudice, reflecting hateful statements or reproducing misleading information. Even if the language is carefully vetted, the model itself can be abused. Google is working to minimise such risks and has developed and released resources that allow researchers to analyse the models and their behaviour.
The "Blake Lemoine" case
In 2022, the testimony of software engineer Blake Lemoine caused quite a stir: he claimed that the Artificial intelligence LaMDA a consciousness and feelings of its own has developed. This has stimulated much ethical debate among experts and cost Lemoine his job at Google.
It all started when Lemoine, as part of Google's Responsible AI team, was given the task of testing whether LaMDA disadvantages or discriminates against minorities. Since LaMDA, according to Lemoine, has been trained with almost all data from the internet and can even read Twitter, there is a risk that the Chatbot provides inappropriate answers.
Basically, LaMDA learns the patterns of people's communication and evaluates them statistically to generate a response. Therefore, Lemoine regularly chatted with Google LaMDA and he got very surprising answers for him. For example, LaMDA was able to describe a self-image of itself in which it described itself as a glowing ball of energy. The chatbot also described its own fear of death and that it wanted to be seen as a collaborator, not a machine.
Through these answers, Lemoine is convinced that LaMDA has developed its own consciousness with feelings and fears. He took the issue to his superiors, who did not take it seriously. He was subsequently given a paid leave of absence. The Washington Post picked up the story, sparking techno-philosophical discussion in the public. Lemoine's dismissal followed, but he is undeterred and continues to fight for LaMDA's rights.
What is Google GLaM (Generalist Language Model)?
The Generalist Language Model (GLaM for short) was developed as a Efficient method for scaling language models with a Mixture-of-Experts (MoE) model. introduced by Google. GLaM is a Model with trillions of weights that can be trained and operated efficiently through sparsity while achieving competitive performance on multiple "few-shot" learning tasks. It was evaluated against 29 public benchmarks for natural language processing (Natural Language Processing, NLP for short) evaluated in seven categories ranging from language completion to open-ended question answering and natural language inference tasks.
To develop GLaM, Google created a dataset of 1.6 trillion tokens representing a wide range of use cases for the model. A filter was then created to assess the quality of web page content by training GLaM with text from reputable sources such as Wikipedia and books. This filter was then used to select a subset of web pages, which were combined with content from books and Wikipedia to produce the final Training data set to create.
Functions and capabilities
The MoE model consists of different sub-models, wherebyi each sub-model or expert specialises in different inputs is. The gating network controls the experts in each layer and selects the two most appropriate experts to process the data for each token. The full version of GLaM has 1.2T total parameters for 64 experts per MoE layer with a total of 32 MoE layers, but only activates a subnet of 97B parameters per token prediction during inference.
Google GLaM allows Different experts activated on different types of inputs which is a collection of E x (E-1) various Feedforward network combinations for each MoE layer, resulting in greater computational flexibility. The final learned representation of a token is the weighted combination of the outputs of the two experts. To allow scaling to larger models, each expert within the GLaM architecture can span multiple computational units.
GLaM was evaluated using a zero-shot and one-shot setting where tasks are never seen during training. It performed competitively on 29 public NLP benchmarks ranging from cloze and completion tasks to open-ended question answering, Winograd-like tasks, commonsense reasoning, in-context reading comprehension, SuperGLUE tasks and natural language inference. The Performance of GLaM is comparable to a dense language model, such as GPT-3 (175B), with significantly improved learning efficiency in the 29 public NLP benchmarks. GLaM reduces to a fundamentally dense, on Transformer based language model architecture when each MoE layer has only one expert. The performance and scaling properties of GLaM were investigated and compared with baseline Dense models trained on the same datasets.
What is Google PaLM (Pathways Language Model)?
The Pathways Language Model (abbreviated to PaLM) from Google is a powerful language modeldeveloped for understanding and generating speech. PaLM is a dense decoder-only transformer model trained with the Pathways system. It is a 540 Billion Parameter Modeltrained on multiple TPU v4 pods, making it extremely efficient.
PaLM was trained with a combination of English and multilingual datasets, including web documents, books, Wikipedia, conversations and GitHub code. The vocabulary was also adapted to preserve all spaces, split Unicode characters not included in the vocabulary into bytes, and split numbers into individual tokens, allowing for effective training.
Google PaLM is an important milestone on the way to realising Google Research's vision for Pathways: a single Modelthat can be generalised across domains and tasks and is highly efficient at the same time.
Functions and capabilities
PaLM achieved impressive breakthroughs on a variety of language, reasoning and code tasks. In the evaluation of 29 English-language natural language processing tasks (Natural Language Processing, NLP for short), PaLM outperformed many previous models in 28 of the 29 tasks. In addition, it showed strong performance on multilingual NLP benchmarks, including translation, even though only 22% of the training corpus is non-English.
In addition, Google PaLM showed impressive performance in several BIG Bench tasks. Natural language comprehension and production skills. For example, the model was able to distinguish cause and effect, understand conceptual combinations in appropriate contexts and even guess the film from an emoji.
PaLM also has several Breakthrough skills in code tasks. It can generate high-quality code (text-to-code) that can be executed directly, it can understand natural language explanations of code, and it can provide code completion and error correction (code-to-code). PaLM has shown that it is also capable of generating code for tasks such as sorting, searching and web scraping. PaLM can solve all these tasks, although only 5 % of code are included in its pre-training dataset.
Of particular note is the ability to perform well in few-shot scenarios, which is comparable to the fine-tuned Codex 12B model, although with 50 times less Python code was trained. This result supports earlier findings that larger models can be more effective when it comes to, Transfer learning from both programming languages and natural language data, improving their sampling efficiency compared to smaller models.
PaLM's training efficiency is impressive, with a hardware FLOPs utilisation of 57.8 %, the highest yet achieved for LLMs of this size. This is due to a combination of the parallelism strategy and a reformulation of the Transformer blocks due to the parallel computation of attention and Feedforward layers is made possible. This enables speed increases through TPU compiler optimisations.
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. It is the predecessor model to GPT-4.
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. The best-known example of the use in chatbots is the Microsoft-owned service ChatGPT. Furthermore, it is also used in game development to create automatic dialogue and storylines in computer games. Due to the engine parameterisation, entire marketing texts, landing pages or newsletters, for example, can also be created and implemented on the basis of a small amount of information.