Closely linked to artificial intelligence (AI), it is helping machines do many things that used to be in the human domain alone. "We use artificial intelligence and machine learning to try to teach computers how to interpret images," Rueckert explains. So Rueckert and his team don't just use machine learning to teach their IT systems to spot lesions. In the Imperial College case, one system tries to make fake scans that are so good the other system thinks they are real.
One approach is to design more robust algorithms where the testing error is consistent with the training error, or the performance is stable after adding noise to the dataset. For example, using "r" as a measure of similarity in the registration of low-contrast images can produce cases where "close to unity" means 0.998 and "far from unity" means 0.98, and there's no way to compute a p-value due to the extremely non-Gaussian distributions of pixel values involved. Robust statistics are also called nonparametric precisely because the underlying data can have almost any distribution and they will still produce a number that can be associated with a p-value. So while losing signal information can reduce the statistical power of a method, degrading gracefully in the presence of noise is an extremely nice feature to have, particularly when it comes time to deploy a method into production.
While a new neural net is still in training, clients (or bots) could connect to the web service, query for a piece of training data, and the model it is training for, run the calculation, and return the result to the web service, which will then place it in the right spot in the database. In an example I heard about, people were developing a handwriting classifier, and one of the methods they used to synthesize more training data was having a script download random images from the internet, opening those up in a word document, and printing a letter on the image in a random font. So this would suit itself rather perfectly for distribution, especially because the processes on the Master/Control server would not need to know anything about the synthesizing process itself, all it needs to receive is the newly generated training data, and the expected output, which can be easily provided by the distributed node, once it is ready and sending it to the server. This would really democratize the potential computing power this can deliver to our research.
As a result, the trained model found much higher weights for these features, since they are highly correlated to the target by construction. However, if one splits the training data into 2 pieces, and construct crime by address ratios from piece_1 and merge them with piece_2 (and repeat vice versa from piece_2 to piece_1) then the overfitting could be mitigated. The reason that this works is because the new features are constructed by using out-of-sample target values and so the crime by address ratios of each piece is not memorized. I have also described a method to avoid pitfalls of overfitting when the target variable in the training data is incorporated in feature engineering.
Apart from image recognition, computer vision also includes event detection, object recognition, learning, image reconstruction and video tracking. The major steps in image recognition process are gather and organize data, build a predictive model and use it to recognize images. There are numerous algorithms for image classification in recognizing images such as bag-of-words, support vector machines (SVM), face landmark estimation (for face recognition), K-nearest neighbors (KNN), logistic regression etc. From the business perspective, major applications of image recognition are face recognition, security, and surveillance, visual geolocation, object recognition, gesture recognition, code recognition, industrial automation, image analysis in medical and driver assistance.
We've already taken a look at neural networks and deep learning techniques in a previous post, so now it's time to address another major component of deep learning: data -- meaning the images, videos, emails, driving patterns, phrases, objects and so on that are used to train neural networks. For example, to train a neural network to identify pictures of apples or oranges, it needs to be fed images that are labeled as such. Fortunately, there already is a large number of free and publicly shared labeled data sets that cover a mind-boggling array of categories (this Wikipedia page hosts links to dozens and dozens). On the first test Ned is shown a Spanish word: azul.
The "deep artificial composer", or "DAC" for short, generates brand-new melodies that imitate traditional folk music of Irish or Klezmer origin. EPFL's deep artificial composer avoids traditional music theory altogether. In fact, the EPFL algorithm determines its own composition rules by extracting probability distributions from existing melodies using neural networks, requiring only the computation power of graphic cards that can speed up calculations by a factor of ten compared to standard computers. The generated music is not limited to Irish or Klezmer traditional folk music: any style of music could be used.
Recurrent Neural Networks (RNNs) are Turing-complete. People would count the frequencies of symbols, compare encrypted text to decrypted text, and try to find patterns. A mere 150,000 steps of gradient descent produced a model which decoded the ciphertext with 99% accuracy. Many believe that these breakthroughs will enable machines to perform complex tasks such as driving cars, understanding text, and even reasoning over memory.
As with many unreleased apps in the Android app store, it all started with an invite code, this very afternoon about an hour ago. My employer and I were talking about chatbots--as we often do--and it reminded him of an app that was being demoed to a select group of users (actually the user base is huge at the moment), and he sent me an invite code to try out the app. Of course I understand that if I just start talking to it again from my side it will pick back up, and most likely it will have a few other funnels to try to get me back into training mode. Most bots follow that simple model of question - response, and have no way of recovering from a deadlock in the conversation.
As we noted in our previous post, a necessary transformational development that we can expect in the field of machine learning is a move away from models that perform purely pattern recognition and can only achieve local generalization, towards models capable of abstraction and reasoning, that can achieve extreme generalization. Current AI programs that are capable of basic forms of reasoning are all hard-coded by human programmers: for instance, software that relies on search algorithms, graph manipulation, formal logic. We will have instead a blend of formal algorithmic modules that provide reasoning and abstraction capabilities, and geometric modules that provide informal intuition and pattern recognition capabilities. Figure: A learned program relying on both geometric primitives (pattern recognition, intuition) and algorithmic primitives (reasoning, search, memory).