Industrial AI Podcast Pixel Komma Ton

Zeki's insights are purely data-driven, based on a carefully curated dataset that brings together unique technologies and data to provide insights into 140,000 of the world's top AI scientists and engineers, all with advanced skills and expertise, most of whom are at the early stages of their careers.

The market for top AI talent is diversifying and deepening. Large companies hire five times more top AI scientists and engineers than the Big Five (Amazon, Apple, Google, Meta, Microsoft). New national AI champions are emerging outside the US.
The healthcare sector, particularly in Europe, has attracted twenty times more top AI talent over the past 10 years. The global market is also shifting away from the strong dominance of the US. National champions are emerging in some major economies, anchoring talent in their own countries. The characteristics of the best AI talent who choose to go abroad or stay close to home. There are consistent global trends that emerge early in the careers of top AI talent.

Thanks for listening. We welcome suggestions for topics, criticism and a few stars on Apple, Spotify and Co.

We thank our partner **SIEMENS** their Industrial AI approach -> [here ](https://www.siemens.com/global/en/products/automation/topic-areas/artificial-intelligence-in-industry.html)


Our guest: Thomas Hurd [more](https://www.linkedin.com/in/thomas-hurd-1128031b8/)

The report [more](https://www.thezeki.com/the-state-of-ai-talent-2024)

Many companies use GenAI to generate code. Josef Waltl and his team take the opposite approach. They have old code translated. Josef explains how they do this in the podcast interview.

Thanks for listening. We welcome suggestions for topics, criticism and a few stars on Apple, Spotify and Co.

We thank our partner **SIEMENS**
https://www.siemens.de/de/

Our guest Josef Waltl [more ](https://www.linkedin.com/in/josefwaltl/)

Fabian Bause was actually very sceptical at our AI in the Forest event, which was all about AutoML. Then he called us a few weeks ago and we were surprised. Beckhoff offers its customers and also non-Beckhoff users an AutoML tool. The automation company is thus breaking new ground. Has he changed his mind? And if so, why? Fabian explains it to us.

We can't give anything away yet, but anyone who was at our AI in the Forest event will remember. You'll have to be patient until Monday. See you at the fair.

Silo AI and TurkuNLP are developing a family of multilingual open source LLMs with the aim of strengthening Europe's digital sovereignty and democratising access to LLMs. Developing baseline models that are in line with European values is crucial to this effort to ensure that they are based on data and information that accurately represents the different languages, citizens, organisations and cultural landscape of the European Union. This approach is not only in line with European values, but also enables sovereignty over how downstream applications and value are created.

The success was attributed to the combination of the low-resource Finnish language with resource-rich languages. The team worked to determine the optimal frequency of data reuse for low-resource languages during training, and integrated translated text pairs from English and Finnish. This strategy, which relies on a cross-linguistic signal to improve the model's understanding of the relationships between the languages, proved crucial in achieving excellent performance in the low-resource languages without compromising performance in English.

AINode is the third internal node after ConfigNode and DataNode in Apache IoTDB. This node extends the capability of IoTDB to perform machine learning analysis on time series data by interacting with the DataNode and ConfigNode clusters. AINode allows the integration of existing machine learning models by registering them and executing time series analysis tasks through simple SQL statements on specified time series data. This seamlessly combines model creation, management, and inference within the database engine. The framework currently supports common machine learning algorithms or proprietary models for typical time series analysis scenarios like time series prediction and anomaly detection.