Schneider Electric announced the impact of a new multi-million dollar investment in machine learning tools and data science – Press Release

Schneider Electric announced the impact of a new multi-million dollar investment in machine learning tools and data science. This investment brings AI-assisted advising to its energy and sustainability services offer, giving its clients access to next-generation digital tools. This improves the insights and analysis of a company’s energy and sustainability portfolio, driving more efficient value and bottom-line impact in pursuit of climate change resilience and other resource-reduction related targets.

The investment optimizes corporate resource consumption and reduction data during the dramatic worldwide energy and climate transition. Most companies today still face data challenges that limit their sustainability approach. Companies struggle with inconsistent, incomplete, overabundant, and poor-quality resource consumption and cost data. The introduction of AI allows companies to get more value out of the data they produce and provides more accurate and efficient analysis as the foundation of an energy and sustainability strategy.

Continuing to add machine learning and data

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Schneider Electric announced multi-million dollar investment in machine learning tools and data science – Press Release

Schneider Electric announced the impact of a new multi-million dollar investment in machine learning tools and data science. This investment brings AI-assisted advising to its energy and sustainability services offer, giving its clients access to next-generation digital tools. This improves the insights and analysis of a company’s energy and sustainability portfolio, driving more efficient value and bottom-line impact in pursuit of climate change resilience and other resource-reduction related targets.

The investment optimizes corporate resource consumption and reduction data during the dramatic worldwide energy and climate transition. Most companies today still face data challenges that limit their sustainability approach. Companies struggle with inconsistent, incomplete, overabundant, and poor-quality resource consumption and cost data. The introduction of AI allows companies to get more value out of the data they produce and provides more accurate and efficient analysis as the foundation of an energy and sustainability strategy.

Continuing to add machine learning and data

Read More

Schneider Electric announces multi-million dollar investment in machine learning tools and data science – Press Release

Schneider Electric announced the impact of a new multi-million dollar investment in machine learning tools and data science. This investment brings AI-assisted advising to its energy and sustainability services offer, giving its clients access to next-generation digital tools. This improves the insights and analysis of a company’s energy and sustainability portfolio, driving more efficient value and bottom-line impact in pursuit of climate change resilience and other resource-reduction related targets.

The investment optimizes corporate resource consumption and reduction data during the dramatic worldwide energy and climate transition. Most companies today still face data challenges that limit their sustainability approach. Companies struggle with inconsistent, incomplete, overabundant, and poor-quality resource consumption and cost data. The introduction of AI allows companies to get more value out of the data they produce and provides more accurate and efficient analysis as the foundation of an energy and sustainability strategy.

Continuing to add machine learning and data

Read More

Control Engineering | Bridging the artificial intelligence skills gap for machine manufacturers

The cyclic nature of artificial intelligence and machine learning (AI/ML) projects, as presented in Dimecc’s Machine Learning Academy. Courtesy: Dimecc Ltd.

 

Learning Objectives

  • An AI skills gap exists for ML applications.
  • For Industry 4.0 environments more AI and ML knowledge is needed.
  • Education needs to change to help AI and ML.

Artificial intelligence (AI) talent is difficult to find, and few industrial companies have enough in-house AI talent. AI will transform many jobs, and companies should give every employee the knowledge they will need to adapt to new AI-enhanced roles. AI resources help implement new business models and better services, but user acceptance is required.

During the last decade, AI design, development and implementation has expanded in many sectors. Organizations are struggling with AI business potential understanding and with finding AI talent.

A growing number of countries have recognized the opportunities provided by artificial intelligence and have prepared a national

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