Authors: Maarit Widmann and Rosaria Silipo (KNIME). As first published in The New Stack.
The full big data explosion has convinced us that more is better. While it is of course true that a large amount of training data helps the machine learning model to learn more rules and better generalize to new data, it is also true that an indiscriminate addition of low-quality data and input features might introduce too much noise and, at the same time, considerably slow down the training algorithm.
By Felix Kergl-Räpple and Maarit Widmann (KNIME)
By Rosaria Silipo, KNIME. As first published in InfoWorld.
Bias in artificial intelligence (AI) is hugely controversial these days. From image classifiers that label people’s faces improperly to hiring bots that discriminate against women when screening candidates for a job, AI seems to inherit the worst of human practices when trying to automatically replicate them.
The risk is that we will use AI to create an army of racist, sexist, foul-mouthed bots that will then come back to haunt us. This is an ethical dilemma. If AI is inherently biased, isn’t it dangerous to rely on it? Will we end up shaping our worst future?
Author: Martyna Pawletta (KNIME)
Authors: Jaime Rodríguez-Guerra, Dominique Sydow, Andrea Volkamer (Volkamer Lab, Institute of Physiology, Charité Universitätsmedizin Berlin)
Jupyter Notebooks offer an incredible potential to disseminate technical knowledge thanks to its integrated text plus live code interface. This is a great way of understanding how specific tasks in the Computer-Aided Drug Design (CADD) world are performed, but only if you have a basic coding expertise. While users without a programming background can simply execute the code blocks blindly, this rarely provides any useful feedback on how a particular pipeline works. Fortunately, more visual alternatives like KNIME workflows are better suited for this kind of audience.
In this blog post we want to introduce our new collection of tutorials for computer-aided drug design (Sydow and Wichmann et al., 2019). Building on our Notebook-based TeachOpenCADD platform (Sydow et al., 2019), our TeachOpenCADDKNIME pipeline consists of eight interconnected workflows (W1-8), each containing one topic in computer-aided drug design.
By Michael Berthold, KNIME. As first published in Techopedia.
There is a lot of talk about data science these days, and how it affects essentially all types of businesses. Concerns are raised by management teams about the lack of people to create data science, and promises are made left and right on how to simplify or automate this process.
Yet, little attention is paid to how the results can actually be put into production in a professional way.
Authors: Lionel Colliandre & Eric Le Roux, Discngine
The authors of this article, Lionel Colliandre and Eric Le Roux are both from Discngine, a company that operates in the life science field. The company serves the needs of chemists and biologists in research, helping organize their data, provide informatics services, screening, data acquisition, and enable better decision making.
By: Rosaria Silipo and Mykhailo Lisovyi. As first published in The Next Web.
Machine learning algorithms are used everywhere from smartphones to spacecraft. They tell you the weather forecast for tomorrow, translate from one language into another, and suggest what TV series you might like next on Netflix.
Author: Jim Falgout (KNIME)
Organizations are using cloud services more and more to attain top levels of scalability, security, and performance. In recent years, the Amazon Web Services (AWS) cloud platform has released several services that support machine learning (ML) and artificial intelligence (AI) capabilities to enable developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale.
In our latest release of KNIME Analytics Platform, we added more functionality to our KNIME Amazon Machine Learning Integration. Think of KNIME as a quick and powerful start to AWS Services now enabling greater interaction between the various AWS services.
Authors: Kathrin Melcher, Tobias Schmidt, Christian Dietz (KNIME).
Do you remember the blog post “The KNIME Hub - Share and Collaborate”, where we introduced the new KNIME Hub and its first features? Since then our developers have been implementing a lot of additional functionality to make it even easier for you to find and share insights with the community. Learn more in this article.
