Blog - Introductions

Large Language Models (LLMs) are a highly discussed topic in current strategy meetings of organizations across all industries. This article is the first part of two, providing some guidelines for organizations to determine their LLM strategy. It will help you identify the strategy with the most benefits while finding ways of solving associated complexities. For more content on LLMs, see our LLM hub .

At the end of 2022, OpenAI released ChatGPT (a Transformer-based language model) to the public. Although based on the already widely discussed GPT-3, it launched an unprecedented boom in generative AI. It is capable of generating human-like text and has a wide range of applications, including language translation, language modeling, and generating text for applications such as chatbots. Feel free to also read our introduction to LLMs . ChatGPT seems to be so powerful that many people consider it to be a substantial step towards artificial general intelligence. The main reason for the recent successes of language models such as ChatGPT lies in their size (in terms of trainable parameters). But making language models bigger does not inherently make them better at following a user's intent. A bigger model can also become more toxic and more likely to "hallucinate". To mitigate these issues and to more generally align models to user intentions, one option is to apply Reinforcement Learning. In this blog post, we will present an overview of the training process of ChatGPT, and have a closer look at the use of Reinforcement Learning in language modeling. Also interesting: Our aggregated collection of LLM content .

Machine Learning has been solving complex problems for decades. Just think about how Computer Vision methods can reliably predict life-threatening diseases, self-driving cars are on their way to revolutionize traffic safety, or automatic translation gives us the ability to talk to just about anyone on the planet. The power of Machine Learning has been embraced by many branches of industry and science. There are some areas however where the potential of Machine Learning is harder to see and also less utilized. One of these is environmental protection. Protecting the natural environment is one of the biggest challenges our generation is facing, with pressing issues such as climate change, plastic pollution or resource depletion. Let us now look at how Machine Learning has been and can be used as a tool in environmental protection.

When building Python applications for production, it's good practice to pin all dependency versions, a process also known as “freezing the requirements”. This makes the deployments reproducible and predictable. (For libraries and user applications, the needs are quite different; in this case, one should support a large range of versions for each dependency, in order to reduce the potential for conflicts.) In this post, we explain how to manage a layered requirements setup without forgoing the improved conflict resolution algorithm introduced recently in pip. We provide a Makefile that you can use right away in any of your projects!

In this blog post, I give an overview and provide some Python code for several collaborative filtering techniques. This is the second blog post in a series of articles about recommendation engines. Check out the first article if you want to get an overview of recommendation systems in general or need a refresher on the terminology. The Jupyter notebook I used for creating the plots will be made available soon. The techniques will be illustrated on the famous MovieLens-100K dataset. It contains 100.000 user-movie rating pairs from 943 users on 1682 movies. For most of the algorithms, I have used an existing implementation from the surprise library for Python. Even though it needs some getting used to, I think it is a nice library that you should check out if you are starting to play around with recommendation engines.

Probably the most common form of problem we tackle with machine learning is classification, that is taking new data points and putting them into one of a number of fixed sets or classes. But what if we don’t necessarily know all the classes when we train the model? A good example of this is face recognition where we want a system that can store faces and then identify if any new images it sees contain that face. Obviously, we can’t retrain the model every time we add someone new to the database so we need a better solution. One way to solve this problem is metric learning. In metric learning, our goal is to learn a metric or distance measure between different data points. If we train our model correctly then this distance measure will put examples of the same class close together and different classes further apart.

Recommendation systems are everywhere. We use them to buy clothes, find restaurants and choose which TV show to watch. In this blog post, I will give an overview of the underlying basic concepts, common use cases and discuss some limitations. This is the first of a series of articles about recommendation engines. Stay tuned for the follow-ups, where we will explore some of the mentioned concepts in much more detail! Already in 2010, 60 % of watch time on Youtube came from recommendations [1] and personalized recommendations are said to increase conversion rates on e-commerce sites by up to 5 times [2]. It is safe to say that if customers are presented with a nice pre-selection of products they will be less overwhelmed, more likely to consume something and have an overall better experience on the website. But how do recommendation engines work? Let's dive right in.

Many machine learning (ML) projects are doomed to fail. This can be due to various reasons and often they occur in combination. To avoid failure, all involved stakeholders need to understand the technical and organizational requirements of the project. Besides all preliminary discussions that define the project, it is important to summarize the project-relevant information in a comprehensive proposal. It should cover the technical and organizational requirements, possible problem areas and technical restrictions. In this article, I will describe the most important modules in machine learning project proposals. For a software provider like dida, the project proposal is the first step towards meeting the needs of the customer.

In this blog post, I'll explain how Codex from OpenAI works, and in particular how it differs from GPT-3. I will outline why I think it should be used with caution and is not ready yet to revolutionize the software development process.

AI and machine learning have entered significantly into our day-to-day lives. For example, we use search queries and are startled or even angered if the algorithm did not understand what we were actually looking for. Just image what an effort it would be to process all those queries by human individuals. In case you can't imagine, CollegeHumor already prepared a vision of that: Fortunately, we taught machines --- at least to some degree --- to "understand" human language. This branch of machine learning is called natural language processing (NLP). We already gave an introduction , if you want to review the basics. However, since search engines, chat bots, and other NLP algorithms are not humans, we can employ them on large scale, i.e. on global scale. Since there are ubiquitous and used by very different people in various contexts, we want them to be objective and neutral (and not to be an annoyed and skeptical man as in the video above). But what if they are not the neutral number crunchers? What if they are subjective and even carry harmful stereotypes against specific groups?

In the first part of this article I have described the basic idea behind GPT-3 and given some examples of what it is good at. This second and final part is dedicated to the “beyond” in the title. Here you will learn in which situations GPT-3 fails and why it is far from having proper natural language understanding, which approaches can help to mitigate the issues and might lead to the next breakthrough, what alternatives to GPT-3 there are already, and, in case you are wondering, what's the connection between GPT-3 and an octopus. Update February 14th '22: I have also included a section about OpenAI's new InstructGPT.

GPT-3 is a neural network capable of solving a wide range of natural language processing (NLP) tasks, which has been presented by OpenAI in summer 2020 (upscaling the previous models GPT and GPT-2). For various tasks it has set new state-of-the-art performances and is considered by many as a substantial step into the direction of artificial general intelligence. “General intelligence” refers to the capability of not only behaving intelligently with respect to one set task, but also being able to adapt to and accomplish new, unforeseen tasks. This blog article is the first of a two-article-series on GPT-3. In this first article I will explain how GPT-3 works, what it is good at and why some people think it’s dangerous, and how you can try out a GPT-3-like model for free. The second part will deal with GPT-3’s weaknesses and where to expect the next breakthrough in the future.

Since its first introduction in late 2017, the Transformer has quickly become the state of the art architecture in the field of natural language processing (NLP). Recently, researchers started to apply the underlying ideas to the field of computer vision and the results suggest that the resulting Visual Transformers are outperforming their CNN-based predecessors in terms of both speed and accuracy. In this blogpost, we will have a closer look at how to apply transformers to computer vision tasks and what it means to tokenize an image.

Automating processes using machine learning (ML) algorithms can increase the efficiency of a system beyond human capacity and thus becomes more and more popular in many industries. But between an idea and a well-defined project there are several points that need to be considered in order to properly assess the economic potential and technical complexity of the project. Especially for companies like dida that offer custom workflow automation software, a well-prepared project helps to quickly assess the feasibility and the overall technical complexity of the project goals -which, in turn, makes it possible to deliver software that fulfills the client's requirements. In this article, we discuss which topics should be considered in advance and why the questions we ask are important to start a successful ML software project.

What is called open-domain question answering in machine learning papers is nothing else than answering a question based on a large collection of texts, such as answering the question of a visitor of a large website, using the website's content. Due to recent progress in machine reading comprehension, open-domain question answering systems have drastically improved. They used to rely on redundancy of information but now they are able to “read” more carefully. Modern systems are able to quote a section of text that answers the question or even reformulate it. What is still an aspiration is to generate longer, paragraph-length answers or to use multiple sources to puzzle together an answer. Google recently implemented such a feature into their search engine. If they find a passage that answers the question typed into the search field, the first result shows the corresponding website with the passage highlighted. There are many different systems that tackle open-domain question answering, here I will go into detail on one system in particular, DrQA (by Chen et al. 2017 ). This particular system splits the task into two parts for each of which it is easier to get data than for the combined task. I will also explain how this idea can be used to create a question answering system for a website from an already existing search function.

Unsurprisingly, a major requirement that makes mining endeavours successful is the right location - one where the enterprise knows with confidence that the soil bears high grade minerals of interest. Finding such a site, however, poses a significant challenge. Conventionally, when mining enterprises pursue greenfield exploration, field studies and drillings are conducted. As these are very expensive, they should only serve as a last assurance after potentially interesting regions have been identified. This is where Remote Sensing comes into play. In this article, we will have a look at the possibilities that spaceborne imaging provides for greenfield exploration. Let’s have a satellite scout promising spots.

In the previous post we introduced the basics of reinforcement learning (RL) and the type of problem it can be applied to. The discussed setting was limited in the sense that we were dealing with a single agent acting in a stationary environment. Now we will take it one step further and discuss Multi-Agent Reinforcement Learning ( MARL ). Here we deal with multiple explicitly modeled agents in the same environment, hence every agent is part of the environment as it is perceived by all others. Since all agents learn over time and start to behave differently, the assumption of a stationary environment is violated.

In this article, we are going to have a closer look at BERT - a state-of-the-art model for a range of various problems in natural language processing. BERT was developed by Google and published in 2018 and is for example used as a part of Googles search engine . The term BERT is an acronym for the term Bidirectional Encoder Representations from Transformers , which may seem quiet cryptic at first. The article is split up into two parts: In the first part we are going to see how BERT works and in the second part we will have a look at some of its practical applications - in particular, we are going to examine the problem of automated question answering .

In the first part of this blog post we saw how the BERT architecture works in general. Now we are going to examine a particular practical application of BERT: automated question answering .

Machine Learning concerns itself with solving complicated tasks by having a software learn the rules of a process from data. One can try to discover structure in an unknown data set (unsupervised learning) or one can try to learn a mathematical function between related quantities (supervised learning). But what if you wanted the algorithm to learn to react to its environment and to behave in a particular way? No worries, machine learning’s got you covered! This branch of Machine Learning (ML) is called Reinforcement Learning (RL). In this post we will give a quick introduction to the general framework and look at a few basic solution attempts in more detail. Finally, we will give a visual example of RL at work and discuss further approaches. In the second part of the blog post we will discuss Multi-Agent Reinforcement Learning (MARL).

It seems to be a common mistake to believe that machine learning is usually an unsupervised task : you have data (without pre-existing labels) that you train e.g. a neural network on for tasks like classification or image segmentation. The truth is that most models in machine learning are supervised , that is, they rely on labeled training data . But labeling often takes a lot of time and can be very tedious. In this blog post I want to find out if I am able to perform the same classification task once with labels, once without. For this task I will use the famous MNIST data set , which contains 60,000 training and 10,000 validation images of handwritten digits, all of them labeled. Every image consists of 28x28 greyscale pixels and contains only one digit, located in the center of the image. To make things easier, I use the CSV version of the data set.

In my previous blog post I have started to explain how Bayesian Linear Regression works. So far, I have introduced Bayes' Theorem , the Maximum Likelihood Estimator (MLE) and Maximum A-Posteriori (MAP) . Now we will delve into the mathematical depths of the details behind Bayesian Linear Regression.

Bayesian regression methods are very powerful, as they not only provide us with point estimates of regression parameters, but rather deliver an entire distribution over these parameters. This can be understood as not only learning one model, but an entire family of models and giving them different weights according to their likelihood of being correct. As this weight distribution depends on the observed data, Bayesian methods can give us an uncertainty quantification of our predictions representing what the model was able to learn from the data. The uncertainty measure could be e.g. the standard deviation of the predictions of all the models, something that point estimators will not provide by default. Knowing what the model doesn't know helps to make AI more explainable. To clarify the basic idea of Bayesian regression, we will stick to discussing Bayesian Linear Regression (BLR). BLR is the Bayesian approach to linear regression analysis. We will start with an example to motivate the method. To make things clearer, we will then introduce a couple of non-Bayesian methods that the reader might already be familiar with and discuss how they relate to Bayesian regression. In the following I assume that you have elementary knowledge of linear algebra and stochastics. Let's get started!

Artificial intelligence (AI) and in particular computer vision promise to be valuable aids for diagnosing diseases based on medical imaging techniques . For humans, it takes years of academic and on-the-job training to e.g. perform medical diagnosis from X-ray images. As we will see, it is also quite a challenge for intelligent algorithms. At this year's KIS-RIS-PACS and DICOM convention organized by the Department of Medicine at the University of Mainz, Germany, researchers from radiology and adjacent fields gathered to discuss the state-of-the-art of AI in their field. Philipp Jackmuth from dida was the speaker of choice for this topic and here we will discuss key points of his talk.

Natural Language Processing (short: NLP , sometimes also called Computational Linguistics ) is one of the fields which has undergone a revolution since methods from Machine Learning (ML) have been applied to it. In this blog post I will explain what NLP is about and show how Machine Learning comes into play. In the end you will have learned which problems NLP deals with, what kinds of methods it uses and how Machine Learning models can be adapted to the specific structure of natural language data.

The terms Artificial Intelligence (AI) , Machine Learning (ML) and Deep Learning (DL) are used with staggering frequency. However, it is not obvious what they are supposed to describe and how they relate to each other. This blog article is meant to elucidate some of the terminology used in these fields for the tech-enthusiastic layman, as well as to give some high-level notion of how these technologies can be used in Computer Vision (CV) .