The hottest Results Substack posts right now

The Iowa Caucus is a significant event in the presidential election process.
The outcome of the caucus can provide insights into the rest of the Republican primary.
For candidates like Trump, meeting expectations and gaining momentum is crucial.

Chess is a game of skill, not luck. Players have full control over their moves.
There is still some luck involved in chess, such as external distractions or unexplained variations in performance.
To improve in chess, focus on the process, consider long-term results, and seek outside perspectives for feedback.

Education systems have changed, resulting in complex impacts on intelligence and educational outcomes over time.
The duration and intensity of schooling play crucial roles in student performance, with balanced effects observed when intensity increases with reduced school lengths.
Reducing time in school can give young adults back time to start their careers, form relationships, and enjoy their adult lives.

People's qualities are similar but their results vary greatly.
In the real world, winners often take the most rewards.
Investing in improvement can lead to significant rewards, especially in competitive environments.

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Large Language Models tend to produce more incorrect answers when primed with previous incorrect answers
Explicitly flattering the AI does not significantly increase the likelihood of it producing incorrect answers
Statistical tests show strong evidence that incorrectness cascades can occur in AI language models

Results are achieved through holding people accountable.
Building trust through candid conversations is key in professional relationships.
Starting with generosity can lead to better accountability and results.

The experiment tested if adding false information to a language model affects its output.
Results showed mixed evidence for simulator theory, with the model not always producing incorrect completions as expected.
Research aims to understand if small instances of bad behavior in language models can lead to more significant issues.

GANs like StyleGAN2 can produce highly realistic images, but training them requires significant resources like powerful GPUs and large datasets.
Building a dataset for training a model like StyleGAN2 can involve sourcing high-quality imagery, using tools like GIS, and ensuring data cleanliness.
Even with limited resources, it's possible to achieve reasonable performance on state-of-the-art networks by optimizing training methods and creatively working around constraints.

Learning is really important for long-term success. Understanding how things work helps you solve problems better in the future.
Sometimes, learning can be hard and frustrating. But even when you fail, you learn what not to do, which can help you next time.
Optimizing for quick results might seem tempting, but it can limit your growth. Balancing learning and results is key to becoming better at what you do.