Machine learning is looking for patterns in data. If you start with racist data, you will end up with even more racist models. This is a real problem.
Interpretation
The quote emphasizes the importance of data quality in machine learning and warns against biases in data.
Oren Etzioni's quote highlights a critical issue in the field of machine learning: the impact of biased data. When the data used to train algorithms contains inherent biases, the models developed as a result will also reflect and potentially amplify these biases, leading to problematic outcomes. This serves as a reminder that the integrity of the data is paramount in developing ethical and fair AI systems.
In practice
In a discussion about AI ethics, this quote can be used to illustrate the consequences of using biased data.
With our technology, with objects, literally three people in a garage can blow away what 200 people at Microsoft can do. Literally can blow it away. Corporate America has a need that is so huge and can save them so much money, or make them so much money, or cost them so much money if they miss it, that they are going to fuel the object revolution.
I love what the Valley does. I love company building. I love startups. I love technology companies. I love new technology. I love this process of invention. Being able to participate in that as a founder and a product creator, or as an investor or a board member, I just find that hugely satisfying.
Good information architecture makes users less alienated and suppressed by technology. It simultaneously increases human satisfaction and your company's profits. Very few jobs allow you to do both at the same time, so enjoy.
Technology has moved away from sharing and toward ownership. This suits software and hardware companies just fine: They create new, bloated programs that require more disk space and processing power. We buy bigger, faster computers, which then require more complex operating systems, and so on.
In engineering, as in other creative arts, we must learn to do analysis to support our efforts in synthesis. One cannot build a beautiful and functional bridge without a knowledge of steel and dirt, and a considerable mathematical technique for using this knowledge to compute the properties of structures. Similarly, one cannot build a beautiful computer system without a deep understanding of how to "previsualize" the process generated by the code one writes.
It is not necessary to conceal anything from a public insensible to contradiction and narcotized by technological diversions.
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