AI is already being used in several markets and it’s going to create new jobs and industries.
AI is already being used in several markets and it’s going to create new jobs and industries. The jobs will look different. As a society, this also means that we need to think about this from the grassroots level and develop fundamental skills to work with AI in the future.
You can look at several examples where AI is applied today – self-driving cars, in assisting doctors with radiology and pathology, in life sciences, in ad-tech, and more broadly in general automation. Even at Doc.ai, an AI first company, we are finding our development processes influenced by AI, especially around infrastructure (security and anomaly detection), and machine learning (auto-ml). All this while we ourselves are creating many AI-based tools for users to take charge of their health – like our selfie2bmi, our mood extractor, edge genetics germline browser and more. The end result is that we are hiring a lot of AI engineering talent (data scientists, mathematicians, machine learning engineers, and infrastructure engineers).
If you are a developer/engineer the transition will be easier if you build start building new skills in these areas now. I would highly recommend reading Software 2.0 by Andrej Karpathy which details some fundamental shifts in how the software will be written. Ability to write code is somewhat of a superpower in the era of AI. For starters, I would focus on learning Python and quickly move into data science. Fortunately today there are many free resources out there to learn and study from. For deep learning, fast.ai is a good place to start, along with free compute provided by Google Colab. There is also a deeplearning course on Coursera by Andrew Ng which is quite good. However, there is no substitute for actually getting your feet wet by building using the tools to build models – you will develop critical thinking by doing. I would also recommend learning machine learning. A good book is the one by Hands-on Machine Learning by Aurélien Géron.
If you are not an engineer you could still develop and augment skills required in your field. Building awareness and intuition of how AI could work and impact your field will be the key. In medicine, I would highly recommend Eric Topol’s Deep Medicine book as a starting point. If you work in healthcare it is important to know how AI can assist you, and reduce human errors in the process. AI cannot be developed by engineers alone – you need domain experts to assist. There is value in going deep in the domain but think “AI-first” on the job. What I mean by this is to develop a good intuition of how AI/tech can help you on the job, then work with engineers to build or improve it.
It is also important to understand the shortcomings of AI today. It is not a panacea and miserably fails in many places. Deep learning can still be a black box. Causal reasoning is not easy. There can be bias in the AI – this is much harder to detect, but being aware of this fact helps. There are also ethical aspects of AI that need to be addressed. In short, there are so many ways as a non-engineer one can learn and take an active role in shaping the future of AI. I think it first starts by being curious about it.
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Fuente: / Source: www.inc.com