A Quote by Fred Ehrsam

AIs are only as good as the data they are trained on. — © Fred Ehrsam
AIs are only as good as the data they are trained on.
AIs are only as good as the data they are trained on. And while many of the tech giants working on AI, like Google and Facebook, have open-sourced some of their algorithms, they hold back most of their data.
AIs trained on open data are more likely to be neutral and trustworthy instead of biased by the interests of the corporation who created and trained them.
We should always be suspicious when machine-learning systems are described as free from bias if it's been trained on human-generated data. Our biases are built into that training data.
There is a reasonable concern that posting raw data can be misleading for those who are not trained in its use and who do not have the broader perspective within which to place a particular piece of data that is raw.
The biggest mistake is an over-reliance on data. Managers will say if there are no data they can take no action. However, data only exist about the past. By the time data become conclusive, it is too late to take actions based on those conclusions.
One of the myths about the Internet of Things is that companies have all the data they need, but their real challenge is making sense of it. In reality, the cost of collecting some kinds of data remains too high, the quality of the data isn't always good enough, and it remains difficult to integrate multiple data sources.
Biases and blind spots exist in big data as much as they do in individual perceptions and experiences. Yet there is a problematic belief that bigger data is always better data and that correlation is as good as causation.
In the past, Google has used teams of humans to 'read' its street address images - in essence, to render images into actionable data. But using neural network technology, the company has trained computers to extract that data automatically - and with a level of accuracy that meets or beats human operators.
When I was young I trained a lot. I trained my mind, I trained my eyes, trained my thinking, how to help people. And it trained me how to deal with pressure.
Disruptive technology is a theory. It says this will happen and this is why; it's a statement of cause and effect. In our teaching we have so exalted the virtues of data-driven decision making that in many ways we condemn managers only to be able to take action after the data is clear and the game is over. In many ways a good theory is more accurate than data. It allows you to see into the future more clearly.
The characteristics of a good musician can be summarized as follows: 1. A well-trained ear. 2. A well-trained intelligence. 3. A well-trained heart. 4. A well-trained hand. All four must develop together, in constant equilibrium. As soon as one lags behind or rushes ahead, there is something wrong. So far most of you have met only the requirement of the fourth point: the training of your fingers has left the rest far behind. You would have achieved the same results more quickly and easily, however, if your training in the other three had kept pace.
It's very difficult when you have learned something a certain way. Not only your mind absorbs it; your eyes also are trained to see in a certain way because eyes are only as good as you've been trained to see.
We are all trained to be data driven people, but no hard data exist about the future. Therefore, the only way to look into the future with any degree of accuracy is to use theory, statements of what causes what and why. If executives have the right theories in their heads, they can very quickly interpret market developments. They can identify what matters and why, and act accordingly. So we suggest decision-makers should start by gaining a deep understanding of the relevant collection of theories, and then be alert for signals that indicate certain types of developments.
To understand how quickly we're cooking the planet, we need good data. To have good data, we need good satellites.
We're in this period where we're getting good data rates. I would say we're getting data rates that are like the data rates we got when we launched RealAudio in 1995.
We're being trained through our incarnations--trained to seek love, trained to seek light, trained to see the grace in suffering.
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