Using AI for bouncing off ideas and copy-pasting AI output to completely automate your content creation process is not the same thing. Here’re AI ethical issues you should consider…

Read more: https://www.optimizely.com/insights/blog/ai-ethical-issues/

By sharding the training data across multiple GPUs and training multiple mini-batches in parallel, we aimed to achieve significant reductions in training time. We present empirical results showcasing the observed reduction in training time when we scaled up resources from 1 to N GPUs, and share some future directions we are considering in our continued effort to speed up model training.

Read more: https://multithreaded.stitchfix.com/blog/2023/06/08/distributed-model-training/

High-performing product teams regularly apply and understand data across teams to measure the impact of their strategy.

Read more: https://amplitude.com/blog/data-informed-product-loop

Since averages reduce an entire distribution to a single number, however, any heterogeneity in treatment effects will go unnoticed. Instead, we have found that calculating quantile treatment effects (QTEs) allows us to effectively and efficiently characterize the full distribution of treatment effects and thus capture the inherent heterogeneity in treatment effects when thousands of riders and drivers interact within Uber’s marketplace.

Read more: https://www.uber.com/blog/analyzing-experiment-outcomes/

Mitigating risks in AI software development requires a combination of best practices, careful considerations, and proactive measures. Here are some key steps to help navigate the industry trend.

Read more: https://www.split.io/blog/how-to-mitigate-risk-in-ai-software-development/

ChatGPT is a chat implementation of the research work done by OpenAI. It’s trained by a massive amount of text data across the internet, and to respond to queries conversationally. It’s currently been released as a research preview, but even with that, the community is pretty excited about how powerful it’s proving. 

Read more: https://launchdarkly.com/blog/openai-chatgpt-and-lowering-the-bar-for-code/

To allow for early feedback in a risk-managed manner, we use sequential tests to monitor regressions in the experiments. However, when moving toward smaller and smaller time windows, we’re faced with multiple measurements per unit in our analysis, which is known as longitudinal data.

Read more: https://engineering.atspotify.com/2023/07/bringing-sequential-testing-to-experiments-with-longitudinal-data-part-1-the-peeking-problem-2-0/