This blog explains how gpts can be used as a part of your Generative AI journey and exploration towards your Ai strategy.

This post explains why implementing ai tools without any strategy and business view can be detrimental and lead to not successful ai projects.

This post is abou the major generative AI trends and investment areas in 2024

universities and schools need to change learining approach due to generative AI. How schools and universities can use Generative AI

A comprehensive Guide to Alternatives of GPTs and Assistant API from OpenAI

Many companies are reluctant when implementing llm-based products because they fear bein confronted with high costs. Especially for medium-sized companies which have not the ressouces or enough capacity to deploy and oprimize their AI models nor to set up an own infrastructure with MLOps. As described in our article about sustainability of Gen. AI applications , cloud and performance costs of running an llm can become very high. What are the cost types when implementing OpenAI or other llms? T here are four types of costs related to llms: Inference Costs Setup and Maintenance Costs Costs depending on the Use Case Other Costs related to Generative AI products What are inference costs? An llm has been trained on a huge library of books, articles, and websites. Now, when you ask it something, it uses all that knowledge to make its best guess or create something new that fits what you asked for. That process of coming up with answers or creating new text based on what it has learned is called inference in LLMs . Usually, developers would call a large language model like GPT-4. But here comes the "but": usually not only large language models account to the total costs when running the final product. To explain: LLMs can be used to classify data (e.g undestand that the text talks about "searching a new car insurance"), for summarization, for translation and for many other tasks. Download the ultimative Gen. AI Task Overview to learn where llms make sense.

this article helps companies like enterprises and sme to successfully implement generative AI by providing best-in-breed frameworks.

In this blog you will learn about the alternatives to ChatGPT and OpenAI. Where is Bard better than ChatGPT? Bard is the response to OpenAI's ChatGPT. What makes Bard so different to OpenAI? It is free! So you can try it out here whereas ChatGPT costs $20 per month. Another advantage is the microphone on the desktop version to directly speak in your question and get a response. Bard has internet access whereas ChatGPT you need to jump from one service (Web Browsing) to the other Bard covers far more languages (265 as of October 2023) Some drawbacks: it is not able to generate pictures. With ChatGPT DALL E-3 you can generate pictures. Bard only offers you a nice description. Where is Claude better than ChatGPT? Claude is the version of ChatGPT developed by the company Anthropic. This tool is currently accessible only in the UK and US, and not yet available in Switzerland. You might consider using Nord VPN to explore its functionality in your country. Claude has one big advantage to ChatGPT: It can process more "context" ( Generative AI from A to Z ), meaning the input token (100 token equals around 75 words) can be up to 100'000 tokens (75'000 words!). GPT-3 has a limit of 4096 tokens (3072 words) and GPT-4 of 8192 tokens (= 6000 words). So when you want to upload huge files, use Claude.

With the advances in Generative AI companies should consider how to be "on top" when it comes to new technologies such as ChatGPT and Generative AI assistants. While Google SEO was one of the main drivers before the launch of ChatGPT by OpenAI, today companies should optimize towards the next-generation of search engines based on large language models and advanced search models. Why are Generative AI searches so intelligent? Intelligent searches basically understand the semantics in a sentence. So old index-based searches used to be sufficient for keyword entries but did often fall short when users did not enter exact the correct keywords or did spelling errors. Also AI based searches with older NLP classification models before the launch of OpenAI were handling some of these challenges but did never achieve the level of intelligence of so-called embeddings. With new vectorbased embeddings ( which are also available by OpenAI ) companies can build intelligent searches that can give to their users the best solution across all their websites. What are embeddings? Basically you can think of a vector represenation. A user question is translated into a vector representation and this vector is used to query a vector database. Think about when you play with your toys. You might group them together based on what they are or how you use them. You might put all your cars together because they all have wheels and can move around. Then you might put all your action figures together because you can play make-believe with them. And your board games would be in another group because you play them on a table. This is similar to how vector embeddings work in computers. But instead of toys, we have words. Just like how you group your toys, a computer groups words that are similar. For example, words like "cat", "dog", and "hamster" could be in one group because they are all pets. But how does a computer know which words are similar? It learns from reading a lot, like how you learn from playing and studying. If the computer sees the word "dog" being used in similar places as the word "cat", it will think these words are related and put them close together in its group. So, in the end, vector embeddings are like a big, organized toy box for a computer, but with words instead of toys. Just like how you can more easily pick a toy to play with when your toy box is organized, a computer can more easily understand and use words when they are nicely grouped by vector embeddings. How can companies create their own Generative AI search? OpenAI did not only change the way companies could search with ChatGPT but it also changed the way companies can create their own page search. There are several paid and open-source models (embeddings from HuggingFace or OpenAI and Meta) to create your own intelligent search. If you need support with the development of your own search contact our team . This graph from TheAiEdge.io nicely illustrates how embeddings work: