Kategorie: Allgemein

Exciting new project

I got the chance to work on a new book with de Gruyter: Data Science in Chemistry.

I will be author as well as editor. Meanwhile most contributors are on board. The goal of the book is to give practice and useful guidelines to the chemists.

Very interesting where the contributors come from. No one from industry and just a few from academic. I know that in in digital chemistry all companies and institutes have the same working principles. But everyone things he has the holy grail to defend his IP. Somehow funny to see.

We are looking forward releasing the book in 2020 in August in San Francisco during the ACS autumn conference.

Autochemistry

Artificial Intelligence technologies affect every domain and process in industry. Many solutions with different maturity levels have been created or are in development.

With this paper we collect initiatives in the domain of chemical science and bring these resources together into a common process model.

We define ten building blocks, analyse their role in the architecture and evaluate their impact to the current system. Finally we discuss the changes and the transition that occurs to the lab worker and the chemist.

This paper introduces Autochemistry as a meme and for further development and discussion. We just can provide a first sketch to this exciting new area of scientific principles changing the anthropocentric fundament of chemistry research to a technocentric one.

https://chemrxiv.org/articles/Autochemistry_A_New_Research_Paradigm_Based_on_Artificial_Intelligence_and_Big_Data/7264103/3

Some more design principles of X.0 laboratories

1. INVENTORY AND NOTATION FOR ACTIONS AND MICROACTIONS

For mapping symbolic and subsymbolic data in neural nets in the symbolic world the available information must be structured available. Introducing a notation analog to Labannotation we can cover movements in wet-laboratories.

2. CONTINUOUS ELN ON BLOCKCHAIN IS THE NEW ERA OF SCIENTIFIC PUBLISHING

We introduce the paradigm to “keep everything”. We use blockchain to transform the ELN into a “wet-laboratory data lake” from where every knowledge may be derived: Positive results – just they are published today (in the classical historic pattern of science) – or negative results, that may lead to new forms of forming knowledge and completes the logical positivism, that was up to now limited by resources, bringing the cognitive meaningfulness to a new level.

3. INTELLIGENCE LABEL FOR AUTONOMOUS MACHINES

Conciousness and Creativity: Learning without knowledge

Abstract. This paper discusses possible patterns of creating of levels of self-awareness in machines by implementing mechanisms like dreaming and self-observation, not by using knowledge.

It is still is asked what analogy actually dreams and consciousness have in the construction of thinking machines.

Selfconstruction of higher dimensions in ML. As the standard mechanism of learning is based on knowledge here might be the key to another construction pattern.

Move 37. In the construction of Alpha go there Google did a remarkable approach. They set a initial set of knowledge in the system (Recorded GO matches). But after that the cloned the AI and a few hundred thoughts began to play with each other. With this they created some kind of new BIAS different to human.

Claustrum. „There are three main types of cells.

The first, which is deemed Type 1, is large and covered with dendritic spines. These cells receive input as well as project back toward various regions, both laterally and medially.

The other two types of cells do not have spines, but can be told apart based on the cell body size. However, both are restricted to the claustrum and, thus, are labeled interneurons.“ (Wikipedia)

Laboratory 4.0: The cognitive approach

In everyday life 90% of all tasks wet laboratories worldwide are repetitive manual tasks, short documentation fragments and manual inventory management. No integrated IT systems as well as seamless robotic solutions support the worker. Data integration like fieldbus or standardized exchange formats still does not exist. In the last decade several attempts for developing „Laboratory 4.0“ were taken. Always robotic solutions and automation were in focus. All suggested solutions were technology-centric. We introduce the cognitive laboratory. It assists the worker for integration by the user’s perspective. The cognitive laboratory becomes context aware. It can hear and see. The cognitive laboratory implements the strategy of an artificial intelligence platform for wet laboratories. The system has a picture of the situation, means the system has all properties tracked and cached and so available just in time. We call it „identity“. If a situation is unclear the system is asking questions. It is designed to be multiuser capable (each worker has is ‚personal‘ assistant) as well it has shared resources to access common tasks. Work safety is significantly improved and the logistics of the basic substances, glassware and chemicals are simplified. The system uses voice commands, visual analysis, augmented reality and has IT back office integration