Machine Learning Skills (2)

Goals

To obtain an understanding of the deficits in machine learning skills in technology employment, to inform recruitment and marketing strategies.

Early Findings

Deep Learning Skills

  • Deep learning is considered a subset of machine learning, specifically related to AI.
  • Deep learning is part of the skills of creating unsupervised learning capabilities, using unlabeled or unstructured data, and this learning is growing in demand.
  • This learning skill is also referred to as deep neural learning or networking.

Need for Deep Learning — UK Study

  • 83% of Artificial Intelligence (AI) decision-makers report a deficit in deep learning skills in the workforce, according to a study conducted in the UK with AI decision-makers in companies employing over 1000 persons.
  • 93% of respondents noted the data scientists they currently employ are overworked due to the skills shortage.
  • 49% of respondents noted project delays directly related to the deficit in deep learning skills.
  • Results of this study recommended capitalizing on transferable talent within the company, to expose more persons to deep learning technology.

Machine Learning Needs — US and Canada

  • A 2019 survey focused on the United States and Canada.
  • This survey found 53% of the tech companies plan to utilize AI and other machine learning processes within the next 2 years.
  • Additionally, 80% of these companies said they will need additional employees with these skills (AI, machine learning) to keep up with their technology goals.

Technology Trends

  • Experts predict multiple technology trends will affect lives in 2020, which will require additional machine learning expertise.
  • Artificial Intelligence is expected to play a bigger role in manufacturing in the immediate future.
  • The space industry is expected to return to the forefront of technology, making aerospace technology skills essential.
  • The healthcare industry is likely to require additional technology skills for testing, research, and digital health. This trend was noted in 2019 and likely becoming more relevant with the current health crisis.
  • The agricultural industry is beginning to use AI, computer vision technology, and big data to keep up with competition.

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