How to become an AI all-star: A guide for techies – Vantage Point –

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This is part one of a two-part series on getting up to speed in AI. This part addresses the career and job needs of the technical worker: the AI developer. Next week, part two will address the non-technical worker: the AI user.

I’ve published more than 40 articles, columns, blogs and posts on artificial intelligence in the last year alone, covering general AI topics, components of AI, the business of AI, the ethics of AI, and the case for getting an early jump on AI.

One thing we know: AI is the biggest transformation in history and will, within 12 to 18 months, change everything, including itself. Therefore, this is not a “nice to have.” It’s a “must have.”

How to get there

Learning AI with little or no prior experience will be a challenge, to be sure, so I enlisted the help of six experts. We sat down to answer one question: How does one become an AI all-star? Here’s the panel’s step-by-step guide. Technical today, non-tech next week.

[Note: In the interest of impartiality, objectivity, and transparency, I am refraining from listing online sources by name. I’m sure you’ll find them easily enough.]


  • Understand the basics. Get familiar with the underpinning concepts of AI: a broad overview of what it is, its history, and its main subfields like machine learning and deep learning. There are plenty of good sources online.
  • Learn programming. As a techie, you’re probably down this road already, but we’ll say this anyway. Proficiency in a programming language is crucial for AI development. Python is highly recommended due to its extensive reach, ease of use and community support.
  • Grasp statistics and mathematics. You’ll need a strong understanding of key mathematical concepts like linear algebra, calculus and probability theory. These are fundamental and will lead to your in-depth mastery of the algorithms and models.
  • Learn machine learning (ML) basics. This is your first specific learning objective. Study supervised and unsupervised learning, regression, classification and clustering algorithms. There’s more, but everything else comes after ML.
  • Learn deep learning. DL is a subfield of ML. here you’ll learn about neural networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and their applications.

Ongoing, continuous learning

  • Get Involved in hands-on projects. Nothing cements learning like practice: applying your knowledge by working on projects. Start with simple projects and gradually increase complexity. There online platforms for this, not to mention in-house teams.
  • Master frameworks and tools. Familiarize yourself with popular AI frameworks: TensorFlow and PyTorch. Learn how to build and train models using these tools.
  • Specialize and generalize at the same time. We live in a world of specialization, but AI will demand more. Choose a specific AI subfield or application area of interest – natural language processing (NLP), computer vision, reinforcement learning, or robotics – but keep up with the rest.
  • Make online courses and tutorials a part of your life. Continue your education through online courses, tutorials, and blogs. Well-known platforms of massive open online courses (MOOCs) abound.
  • Opt for formal education. Consider pursuing a formal degree in AI or data science or even linguistics (for natural language processing). In an age where certificates are common, degrees become prized.
  • Stay updated. AI is evolving at unprecedented speed. The old wisdom of technical knowledge staying current and relevant for two years no longer holds true. Six months is more like it in AI.
  • Join communities of practice. Join AI communities on professional networking platforms. Engage and be active. There’s nothing like collegial advice from fellow practitioners.
  • Build and display a portfolio. This is solid advice for anyone in any field, but create a portfolio of your AI. As easy as it is to gain exposure, you should not be without an online presence that allows you to show off.
  • Get real-time experience. Apply your AI skills to real-world problems and challenges. This practical experience will enhance your expertise and make you a more attractive candidate in the job market.
  • Network! Networking is, always has been, and always will be the most effective career advancement strategy. Jump in with both feet.

Remember, learning and staying relevant in AI is a continuous effort – and it’s not achieved overnight. Patience and persistence will lead to proficiency. As Henry Wadsworth Longfellow advised in his poem, “The Ladder of St. Augustine:”

“The heights by great men reached and kept

Were not achieved by sudden flight;

But they, while their companions slept,

Were toiling upward in the night.”

Eli Amdur has been providing individualized career and executive coaching, as well as corporate leadership advice since 1997. For 15 years he taught graduate leadership courses at FDU. He has been a regular writer for this and other publications since 2003. You can reach him at [email protected] or 201-357-5844.

This post was originally published on this site

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