Inspirational work by Stanford researchers using Google’s TensorFlow to detect malignant skin lesions.
James Whittaker of Microsoft gives a stimulating talk on how thinking about “what’s next?” is critical to sustained success. He posits that a brand new thing comes and revolutionizes everything every 10 years, and that technology matures over the preceding 10 years. It does make sense when you think about PC -> mobile -> cloud. He then talks about what he thinks the next big thing will be: machine learning + the concept of microservices in a purchase economy. In other words, the app store is dead! Just a brilliant talk!
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