- AI is a combination of numbers that allows us to represent complex objects or phenomena, such as images, videos, and text.
- GPUs were initially designed for gamers, but have become powerful enough to handle billions of operations and parameters, allowing for the development of multi-layered neurons.
- Data wrangling is a process of getting raw data into a usable format, and data scientists come in at a point where the data already exists and need to understand the data, sit with stakeholders, and clean and curate the data set.
- AI models are becoming increasingly popular, as they are able to process large amounts of data from a variety of sources.
- Big data has been a major breakthrough in AI, but the future may lie in small data.
- The trend in AI research is pushing towards computing at the edge, where AI is done on the device rather than sending data to a large organization.
- Inference is the process of taking a prompt and passing it through the model to get an answer.
- Interdisciplinary collaboration is becoming increasingly important, as it allows experts from different fields to amplify the impact of their work.
- AI models must be designed with data privacy in mind, and individuals should have control over their data and the right to delete it if they choose.
- Smaller AI models are becoming increasingly popular, as they can produce similar results to larger models.
- Organizations must be aware of the legal implications of using data for AI models, as they can face legal action if they use copyrighted data without permission.
- Governments are taking steps to protect user data, such as introducing laws that require websites to get permission from users before saving cookies.
- Evaluating large language models is a difficult task, as the models are becoming increasingly complex.
- AI on the edge is important for privacy, as all processing can be done on the device and offline, without the need to send data to the cloud.
- AI robustness is a new field of research that is gaining attention due to the vulnerability of deep neural networks to attacks.
- Researchers are looking for defense strategies that can detect and prevent attacks, as well as ways to defend against attacks even when they don’t have access to the model parameters.
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