Well, I signed up for Andrew Ng’s Machine Learning Course at Stanford. It began a few weeks ago, and is a next generation to lectures by Ng circulating on YouTube. I’m going to basically audit the course, since I started a little late, but I plan to take several of the exams and work up a few of the projects. This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI). The course will also draw from numerous case studies and applications, so that you’ll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas. I like the change in format. The YouTube videos circulating on the web are lengthly, and involve Ng doing derivations on white boards. This is a more informal, expository format. Here is a link to a great short introduction to neural networks. Click on the link above this picture, since the picture itself does not trigger a YouTube. Ng’s introduction on this topic is fairly short, so here is the follow-on lecture, which starts the task of representing or modeling neural networks. I really like the way Ng approaches this is grounded in biology. I believe there is still time to sign up. Comment on Neural Networks and Machine Learning I can’t do much better than point to Professor Ng’s definition of machine learning – Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. More importantly, you’ll learn about not only the theoretical underpinnings of learning, but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems. Finally, you’ll learn about some of Silicon Valley’s best practices in innovation as it pertains to machine learning and AI. And now maybe this is the future – the robot rock band.
In a brilliant, early article on whether machines can “think,” Alan Turing, the genius behind a lot of early computer science, suggested that if a machine cannot be distinguished from a human during text-based conversation, that machine could be said to be thinking and have intelligence.
Every year, the Loebner Prize holds this type of Turing comptition. Judges, such as those below, interact with computer programs (and real people posing as computer programs). If a
computer program fools enough people, the program is elible for various prizes.
The 2013 prize was won by Mitsuki Chatbox advertised as an artificial lifeform living on the web.
She certainly is fun, and is an avatar of a whole range of chatbots which are increasingly employed in customer service and other business applications.
Mitsuku’s botmaster, Steve Worswick, ran a music website with a chatbot. Apparently, more people visited to chat than for music so he concentrated his efforts on the bot, which he still regards as a hobby. Mitsuku uses AIML (Artificial Intelligence Markup Language) used by members of pandorabot.
Mitsuki is very cute, which perhaps one reason why she gets worldwide attention.
It would be fun to develop a forecastbot, capable of answering basic questions about which forecasting method might be appropriate. We’ve all seen those flowcharts and tabular arrays with data characteristics and forecast objectives on one side, and recommended methods on the other.