The fast rise of Deep Learning - from a fancy experiment with cat videos in 2012 to industry applications - happened at an unprecedented speed. Especially the job market was hit very hard by this sudden, shift as traditional channels for education and training were not able to churn out enough graduates.
In typical fashion for a new field, the job and hiring profiles in AI are relatively vague. Most HR departments have no clear understanding if they require machine learning engineers, data scientists or big data analysts.
While this lack of blueprints is a challenge for HR, it provides a higher degree of freedom to prospective students and graduates. There are many ways to build up an excellent AI CV e.g. through portfolio projects, contribution to open source projects and participation in MOOCS such as Udacity or Coursera.
While at first glance AI and deep learning appear like a hard break from traditional computer science the opposite is true. In order to successfully improve on existing deep learning techniques a fundamental understanding of classical disciplines such as mathematics, statistics and computer science is essential.
When recently asked on the key aspects of succeeding in AI Shane Legg, co-founder and chief scientist at deepmind, stated
1. linear algebra well (e.g. matrix math)
2. calculus to an ok level (not advanced stuff)
3. prob. theory and stats to a good level
4. theoretical computer science basics
5. to code well in Python and ok in C++
What can companies do to find and retain the innovative employees of tomorrow? One way to combat the current talent crunch is to rely on professionals with good coding skills, and further develop their skillset towards AI. Fortunately there are many high-quality learning materials and prepared data-sets available in this field.
Full article in TUCareer Magazine (in German): https://www.tucareer.com/Aktuelles/News/Computer-Science
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