Miguel Fierro

One of the best way to find mentors is by contributing to open source. Let's be honest, the best people in the world don't have time to mentor anyone. However, if you contribute to a top opensource repository: 1) You'll get feedback of the best people in the world for free. 2) You can use your contributions as a presentation letter for jobs. 3) You'll learn a ton.

Bias in AI are systematic errors in the output of an AI system that disproportionately affect certain individuals or groups based on their characteristics such as race, gender, age, or other protected attributes. It can be produced from biased training data, biased algorithms, or biased human intervention in the machine learning process. Fairness involves ensuring that AI systems do not discriminate against any individual or group based on their attributes. Fair AI systems provide unbiased outcomes and equal treatment, promoting equitable opportunities and treatment for all individuals regardless of their background or characteristics.

One of the most difficult aspects of reinforcement learning is that, in general, it has a real-time nature. Reinforcement learning needs to perform an action, measure the new state, and calculate the optimal new action. Even though reinforcement learning can be used with non real time data, implementing this technique in real time can be challenging.

In a company context, it is important to ensure that there is a business outcome of the days story. One of the biggest mistakes I see is that people in data don't tie their solutions to a business metric. Ask yourself this, is my data helping to make a business decision?

One of the main differences of that BI is related to the area of data analytics, whereas AI is required to machine learning. In BI, we get some data and analyze it with some visuals to extract insights. This is a forward action. In contrast, in AI, we get some data, train a machine learning model, and then perform an action or return some data. This is a closed loop, a forward plus a backward action.

One important aspect of AI is to be able to generate a model of the world. When a human sees a picture of a chair, we automatically have a lot of knowledge associated. We know that a chair is something to sit on, that they are normally in a room. You typically put multiple chairs around a table to have lunch. This world model is what help us reason. An advanced AI needs to have a model of the world to become intelligent.