Lead the GenAI revolution. Future-proof your skills by understanding AI basics, machine learning, algorithms, ethical practices, and real-world applications. Stay ahead in tech leadership.

In one form or the other, AI is going to be infused in all the tech products. Meaning, that in order to stay a relevant leader, it has become essential to have a solid, broad understanding of AI. While you may not be implementing the solution, you need to speak the language of AI.
 
Whether you’re leading a team on creating AI solutions, or you just want a better understanding of AI, then this course is for you. In this course, I have deconstructed the concepts most relevant for you.
 
You will start by learning the basics of AI, its connection with machine learning, and the different types of algorithms you should be familiar with. You’ll then learn the rules, best practices, and infrastructure for creating great AI products that users trust. Then, you will go through some real case studies of AI application and do a deep dive into Ethical AI. You'll end by getting an outlook into the current status and future of AI. In a nutshell, this course is your no-fuss, comprehensive guide to the essentials of AI.

Grokking AI for Engineering & Product Managers

# AI timeline: A lesson on history

AI has had its rounds of inflated expectations and crushing disappointments. The timeline below shows the history of AI's rises and falls (a.k.a. **AI winters**):

- AI has been around since the beginning of computing. It became a formal discipline in **1956** as described by this statement: ***"Every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it."*** However, it was too early for such statements, which resulted in expectations that were too high for their time. The many disappointments and dead-ends led AI from hype to winter in the 1970s. 

- The **cycle** repeated itself a decade later. Computers simulating human reasoning through *if-then* logic rekindled the hope in the potential of AI. But, again, the technology was not mature enough to back the high hopes, and this led to yet another downfall. 

- **Today**, we are in a much different era of computing. In the last five years, AI development and deployment has hit a tipping point.  Compared to the previous waves of AI, we now have **distributed computing, low-cost cloud services, new improved algorithms, advances in Graphical Processing Units (GPUs), and vast troves of training data** to satisfy the hunger of AI systems. With more and more breakthroughs on the way, the biggest questions of the century are:  ***How far can this technology can go? What's next?***

- For example, although **quantum computing** is still in its very early stages, it promises to disrupt AI in huge ways by giving it unprecedented boosts. Let’s consider autonomous vehicle navigation, for instance. Say we want to optimize the routes in a large crowded city like NYC based on real-time, second-by-second data. Although today this is not possible, adding quantum computing in the picture makes this no longer an unrealistic application. Of course, this kind of revolution will not be limited to any one industry.


 


This lesson provides a history of the highpoints and low points of AI development and utilization.

The Fundamentals

AI in Practice

Real Case Studies

Responsible AI

What's Next

The Highs and Lows of AI

AI timeline: A lesson on history