Inspirational work by Stanford researchers using Google’s TensorFlow to detect malignant skin lesions.
Great talk by Prof. Andrew Ng of Stanford/Baidu on how AI will permeate almost every conceivable industry (except hairdressing!). I’ve been interested in AI for a long time and it’s so great to see it finally coming into “the eternal spring”.
- Machine learning used to plateau despite increasing amount of training data – this is no longer the case. Now the more (good) data you have, the better your AI performs. This needs not only AI/ML expertise but also hardware expertise to handle next-level computation with an increasing amount of training data.
- AI progresses fastest when it’s attempting to do something a human can do; after reaching the human-level of accuracy, progress tends to slow down.
- If a typical human can do a task with less than a second of thought, then AI can automate it now or in the near future.
- While programming methodologies such as Agile have had the time to mature into their present forms, there remains a need for an effective method for communication between project managers and engineers in AI projects.
- Particular domains where AI is very likely to take off are:
- speech recognition;
- computer vision, e.g. facial recognition; and