AI Chatbots Overview: What Google and Co. Offer
Battle of the Tech Giants: We show you which AI chatbots are available and what you can use them for.
AI as far as the eye can see: Hardly any topic has shaken up the digital industry in recent months as much as that of artificial intelligence. Especially since the hype around ChatGPT by OpenAI, every tech giant seems to come around the corner with their own AI language model. To help you keep track, we introduce the artificial intelligences of the "Big Player" Microsoft, Meta and Google, and we show what functions and application fields they offer individually.
What is an AI chatbot?
In this article, we focus mainly on Chatbots, which serve to process user input and give responses using artificial intelligence. Basically, what you might already know from OpenAI's ChatGPT.
Technically, such chatbots are based on language models. An AI language model is an artificial neural network based on Natural Language Processing (NLP) and is used for automated processing of texts or speech.
The purpose of an AI chatbot, like that of BOTfriends often consists in automating and improving customer service, interaction with users or the execution of business transactions. A chatbot can help reduce the workload of customer service representatives while simultaneously providing a quick and effective response to user requests. A chatbot can also be used as a marketing tool to increase customer loyalty and customer satisfaction. Additionally, chatbots – like ChatGPT – are increasingly being used in the context of search engines or for text generation.
To be considered an AI chatbot, a chatbot must be able to understand and respond to natural language. It must also be able to recognize the intentions of the users and generate relevant responses. This differentiates it from traditional chatbots which merely draw from a predefined set of response options. An AI chatbot should be capable of conducting context-related conversations and discussing a variety of topics. It should also have the ability to improve its capabilities over time by learning from user feedback and adapting its algorithms accordingly.
How does an AI language model work?
Technically speaking, AI language models are based on machine learning algorithms that specialize in processing natural language (Natural Language Processing, NLP).
The basics of AI language models are based on statistical models that are trained to process language data and recognize patterns in texts and sentences. The models use various data analysis techniques to examine texts. These include, for example, tokenization to divide sentences into words or word groups, part-of-speech tagging to classify words into grammatical categories, and named entity recognition to identify named entities such as persons, places, and organizations.
The training process of AI language models is based on large amounts of text data, usually taken from the internet or specific corpora. This text data feeds into the training process and the model learns to recognize patterns in the data and make automatic predictions when it encounters new data. The machine learning algorithms allow the model to learn based on feedback and continuously improve.
Deep learning and neural networks are important technologies used in AI language models to improve their performance. Deep learning is a special form of information processing that consists of many layers of artificial neural networks to model complex patterns. The layers are hierarchically arranged so that the lower layers recognize simple features like letters or words, while the higher layers recognize more complex features like sentence structure or semantics.
In short: AI language models are systems that are based on processing natural language and have been developed based on machine learning and deep learning. The training process uses large amounts of text data to recognize patterns in language, while neural networks and other algorithms are used to identify complex patterns in the data.
The AI chatbots of Google, Meta and Microsoft
Microsoft & ChatGPT
Actually, there's not much more to write: ChatGPT is after all THE AI chatbot that triggered the hype about artificial intelligences at the end of 2022. Thanks to simple operability and mostly convincing results, the software is one of the most popular AI text generators. The program comes from the company OpenAI. In January 2023, however, tech giant Microsoft announced it would invest 10 billion dollars in the technology as part of a collaboration.
For a long time, the technical basis of ChatGPT was OpenAI's GPT-3 model. The successor GPT4 was released in mid-March 2023, which should also be used in Microsoft's Bing Bot. Important innovations are that the engine will now be able to respond to image inputs and the results should become more precise and factual. In addition, ChatGPT with the new language model should be able to process up to 25,000 words in the future, instead of only 3,000 as before. However, access is initially limited, users of the paid ChatGPT Plus will be given priority access to the new features.
ChatGPT can react to a wide range of topics and generates natural language responses aimed at simulating human-like conversation. The chatbot can be used in various application areas, such as customer service, education or entertainment. We show you how to perfectly use the program in our list with 10 Tricks for ChatGPT.
In the meantime, countless people have discovered ChatGPT for themselves and use the AI for a variety of tasks - from blogging to HTML coding to writing academic papers. Microsoft, as mentioned before, has announced plans to integrate the technology into its own search engine, Bing.
Bard by Google
With the hype about ChatGPT, Google was under pressure. So it's little surprise that the company announced its own AI speech assistant short time later: Google Bard is currently only available to selected testers, but in the future it is supposed to perform similar tasks for search queries, such as Microsoft plans with ChatGPT for Bing.
Unlike ChatGPT, the focus with Bard is primarily on answering search queries. The creation of longer or even creative texts and coding is secondary with Bard. Technically, Bard is based on the LaMDA language model (Language Model for Dialog Applications), which was also developed by Google.
Llama by Meta
Llama is an AI language model developed by Meta, the company behind Facebook, for processing natural language. This model also uses advanced deep learning methods to understand the meaning and intention of user requests in different languages and generate corresponding responses.
According to Meta, Llama is mainly aimed at the research field where the AI is supposed to be continuously trained and improved by using it. Access to the model is therefore provided on a case-by-case basis to academic researchers, members of government, civil society and scientific organizations, and industrial research labs around the world.
A difference to GPT-3 or ChatGPT lies in the number of parameters: While OpenAI's AI is based on 175 billion parameters, Meta's language model is based on “only” 65 billion parameters. Parameters are, simplistically put, the number of variants that the AI processes to answer user input. The more parameters there are, the more precise the answer might be. However, a higher number of parameters also means considerably more storage space required and consequently more computing capacity.
4 Tools with AI Power
Do you want to try the power of AI yourself? Then test one of the following tools, for which you can also find reviews on OMR Reviews. All of these solutions offer an API, so they can be integrated into your own infrastructure via an interface - for example, as AI chatbots:
Conclusion
The development of AI is progressing rapidly. Hardly a week goes by without a (seemingly) groundbreaking innovation being announced. Almost daily, one reads about the creative ways users interact with the artificial intelligences.
Which AI chatbot will ultimately win the race remains to be seen. Besides intuitive operability, the quality of the results for the users will likely play a role. We certainly remain excited to see what possibilities AI will have in the months and years to come.
Nils ist SEO-Texter bei OMR Reviews und darüber hinaus ein echter Content-Suchti. Egal, ob Grafik, Foto, Video oder Audio – wenn es um digitale Medien geht, ist Nils immer ganz vorne mit dabei. Vor seinem Wechsel zu OMR war er fast 5 Jahre lang als Content-Manager und -Creator in einem Immobilienunternehmen tätig und hat zudem eine klassische Ausbildung als Werbetexter.
