You see, Mattheij decided he wanted in on the profitable cottage industry of online Lego reselling, and after placing a bunch of bids for the colorful little blocks on eBay, he came into possession of 2 tons (4,400 pounds) of Lego -- enough to fill his entire garage. As Mattheij explains in his blog post, resellers can make up to €40 ($45) per kilogram for Lego sets, and rare parts and Lego Technic can fetch up to €100 ($112) per kg. Instead of spending an eternity sifting through his own, intimidatingly large collection, Mattheij set to work on building an automated Lego sorter powered by a neural network that could classify the little building blocks. "By the end of two weeks I had a training data set of 20,000 correctly labeled images."
Bitville has launched a major research project to investigate various alternatives about how Artificial Intelligence (AI) can be harnessed to assist in human learning. The research project is funded partly by Tekes, and it belongs to the Team Finland Augmented Intelligence campaign. The solutions will be based on deep neural networks, also known as "deep learning". The solutions described above will use a wide array of deep learning technologies including recurrent neural networks and memory augmented neural networks.
Andrej Karpathy's post "The Unreasonable Effectiveness of Recurrent Neural Networks" made splashes last year. The basic premise is that you can create a recurrent neural network to learn language features character-by-character. First, let's play a variation of the Imitation Game with generated text from Karpathy's tinyshakespeare dataset. The hidden layer maintains state over the training set.
A few notable exceptions, like DeepMind's recently released Kinetics dataset, try to alleviate this by focusing on shorter clips, but since they show high-level human activities taken from YouTube videos, they fall short of representing the simplest physical object interactions that will be needed for modeling visual common sense. To generate the complex, labelled videos that neural networks need to learn, we use what we call "crowd acting". Predicting the textual labels from the videos therefore requires strong visual features that are capable of representing a wealth of physical properties of the objects and the world. The videos show human actors performing generic hand gestures in front of a webcam, such as "Swiping Left/Right," "Sliding Two Fingers Up/Down," or "Rolling Hand Forward/Backward."
Kimberly Powell, who leads Nvidia's efforts in health care, says the company is working with medical researchers in a range of areas and will look to expand these efforts in coming years. Most notably, a machine-learning technique called deep learning is being applied to processing medical images and sifting through large amounts of medical data. Nvidia is, for example, working with Bradley Erickson, a neuro-radiologist at the Mayo Clinic, to apply deep learning to brain images. There are, however, significant challenges in applying techniques like deep learning to medicine.
A California-based startup called Instrumental developed an intelligent AI inspection system to help manufactures identify product defects on the assembly line. Instrumental makes a hardware box that goes on the assembly line and takes a photo of every device that passes through and they recently announced their deep learning software called Detect which highlights units that appear defective or anomalous, giving our customers a significant edge in discovering and resolving product issues. Using TITAN X GPUs and cuDNN with the TensorFlow deep learning framework, they are able to process hundreds of units in seconds and identify the most interesting units to review. According to their blog, an engineer using Detect remotely caught an assembly process issue still in progress on the line and was able to inform the factory to correct it right away.
Artificial Intelligence is, locally, a computer algorithm tasked with solving input problems based on accessible data and operational parameters, with respect to the amount of computational power available to the algorithm. With the vast field of AI are specific concepts like machine learning and deep learning. A subset of AI, Machine Learning focuses on learning abilities, or how to make machines learn on their own. As a subfield of Machine Learning, and a sub-subset of AI, Deep Learning is automatic learning technology based on deep neural networks.
IBM's chips are still too experimental to be used in mass production, but they've shown promise in running a special type of neural network called a spiking neural network. The chips are designed in such a way that researchers can run a single neural net on multiple data sets or run multiple neural nets on a single data set. Similar to other experimental computing hardware like chips enabling quantum annealing, IBM's TrueNorth approach has drawn criticism from some leaders of the field who say it offers limited advantages over more conventional custom chips, FPGAs and GPUs. He went on to argue that IBM's chips are designed for spiking neural networks, a type of network that hasn't shown as much promise as convolutional neural networks on common tasks like object recognition.
Typically, this consists of n-gram language models combined with Hidden Markov models (HMM). This article reviews the main options for free speech recognition toolkits that use traditional HMM and n-gram language models. However, Kaldi does cover both the phonetic and deep learning approaches to speech recognition. We didn't dig as deeply into the other three packages, but they all come with at least simple models or appear to be compatible with the format provided on the VoxForge site, a fairly active crowdsourced repository of speech recognition data and trained models.
My first recollection of an effective Deep Learning system that used feedback loops where in "Ladder Networks". In an architecture developed by Stanford called "Feedback Networks", the researchers explored a different kind of network that feeds back into itself and develops the internal representation incrementally: In an even more recently published research (March 2017) from UC Berkeley have created astonishingly capable image to image translations using GANs and a novel kind of regularization. The major difficulty of training Deep Learning systems has been the lack of labeled data. So the next time you see some mind boggling Deep Learning results, seek to find the strange loops that are embedded in the method.