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Artificial Intelligence will gift us with more benefits and advantages than any other invention or discovery in history. On that, everyone agrees.
But it will also require more skills and mastery than anything else. It will place that onus not just on each of us, but also on those in leadership positions who will have to see to their individual transformations and to the transformations of their organizations.
Critical AI SKills For Everyone
In my previous post (Forbes.com – 8/10/23) I discussed 11 skills necessary for the AI user. In consultation with six respected colleagues, we compiled a sweeping overview of the skill set we’ll all to be effective AI users. No tech involved in that list, just user skills.
Now let’s turn to the skills that AI techies will need – a far more imposing list, for sure. And a quick disclaimer: As the quintessential non-techie, I contributed nothing to this list. I did, however, identify a former career coaching client of mine who has developed a consultancy in the field. Considering the sensitivity of her work and of her clients’ identities, she asked that I refer to her only as Christine, her first name.
“Technical AI skills range from the conceptual and abstract,” explained Christine, “to the concrete and predictable.” Here, in no special order, is her list of 17, the last four of which are recognized as soft skills, a fundamental necessity across the board, no matter what your profession.
1. Programming Languages.
Proficiency in programming languages like Python is crucial, as it’s widely used in AI development due to its rich libraries and frameworks.
2. AI Frameworks and Libraries.
Such as: TensorFlow, PyTorch, scikit-learn, and Keras for building and training models.
3. Neural Networks and Deep Learning.
Dig deep into neural network architectures, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers.
4. Machine Learning.
Understand the fundamentals of machine learning: supervised learning, unsupervised learning, reinforcement learning, and deep learning.
5. Mathematics.
Understanding algorithms and models through a strong foundation in mathematics, especially linear algebra, calculus, and probability/statistics.
6. Data Manipulation and Analysis.
Skills in data preprocessing, cleaning, and manipulation are vital for working with real-world data, as AI models heavily rely on quality data. [Note: AI is currently doing a pretty poor job of checking the veracity of data, putting out “hallucinations” more often than not.]
7. Natural Language Processing (NLP).
Learn about tokenization, word embeddings, language models, and sentiment analysis.
8. Computer Vision.
This includes image classification, object detection, image generation, and related technologies.
9. Reinforcement Learning.
Making sequential decisions in dynamic environments.
10. Version Control:
Particularly critical in collaborating on code.
11. Cloud Computing.
Develop strength on cloud platforms like AWS, Azure, or Google Cloud; gain valuable resources needed to train and deploy AI models at scale.
12. Model Evaluation and Hyperparameter Tuning.
You’ll need techniques for evaluating model performance and optimizing hyperparameters.
13. Deployment and Scaling.
Develop skills in deploying AI models to production environments and optimizing them for real-world usage.
14. AI Ethics and Bias.
Understand the ethical implications of AI and machine learning, including bias in data and algorithms. ”Actually,” says Christine, “this should be #1 on everybody’s list.”
15. Collaboration and Communication.
Effective communication is essential for collaboratin ing, especially spanning cross-functional teams and explaining AI concepts to non-technical stakeholders. [Author’s note: I taught two graduate leadership and communication courses for 15 years, and did corporate advising for 26. Can’t overemphasize the communication issue.]
16. Ongoing Learning.
Stay up to date on AI’s comings and goings. The nature, pace, and scope of change in AI will certainly make our heads spin, but we have no choice. AI is rapidly transforming from a strategic advantage to an operational advantage.
17. Problem-Solving and Creativity
. Developing AI solutions often requires creative problem-solving to address complex challenges. Keep your teams diverse and listen to new voices.
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