"Many researchers … speculate that the information-processing abilities of biological neural systems must follow from highly parallel processes operating on representations that are distributed over many neurons. [Artificial neural networks] capture this kind of highly parallel computation based on distributed representations"
– from Machine Learning (Section 4.1.1; page 82) by Tom M. Mitchell, McGraw Hill Companies, Inc. (1997).
Deep learning is a sub-field of machine learning and an aspect of artificial intelligence. To understand this more easily, understand that it is meant to emulate the learning approach that humans use to acquire certain types of knowledge. This is somewhat different from machine learning, often people get confused in this and machine learning. Deep learning uses a sequencing algorithm while machine learning uses a linear algorithm. To understand this more accurately, understand this example that if a child is identified with a flower, then he will ask again and again, is this flower?
Using those primitives, DeepMind generated a dataset known as Procedurally Generated Matrices(PGM) that consists of triplets [progression, shape, color]. The relationship between the attributes in a triplet represent an abstract challenge. For instance, if the first attribute is progression, the values of the other two attributes must along rows or columns in the matrix. In order to show signs of abstract reasoning using PGM, a neural network must be able to explicitly compute relatioships between different matrix images and evaluate the viability of each potential answer in parallel. To address this challenge, the DeepMind team created a new neural network architecture called Wild Relation Network(WReN) in recognition of John Rave's wife Mary Wild who was also a contributor to the original IQ Test. In the WReN architecture, a convolutional neural network(CNN) processes each context panel and an individual answer choice panel independently to produce 9 vector embeddings. This set of embeddings is then passed to an recurrent network, whose output is a single sigmoid unit encoding the "score" for the associated answer choice panel.
For the visualization of features, the authors use deconvolutional networks (deconvnet). Think of deconvnet as decoder part of the autoencoders. It does the reverse of a normal convolutional network, it uses unpooling and filters to recover the pixels from the features. The only confusing part in this network is how it is undoing the pooling because when any pooling is done, only one value remains out of N² values given NxN filter was used. That whole data cannot be recovered but the max value is still there but it is no use if we don't know where it is located in the output of the convolutional layer.
A good example of solving for the right problems can be seen in Formula One World Championship Ltd. The motorsport company was looking for new ways to deliver race metrics that could change the way fans and teams experience racing, but had more than 65 years of historical race data to sift through. After aligning their technical and domain experts to determine what type of untapped data had the most potential to deliver value for its teams and fans, Formula 1 data scientists then used Amazon SageMaker to train deep learning models on this historical data to extract critical performance statistics, make race predictions and relay engaging insights to their fans into the split-second decisions and strategies adopted by teams and drivers.
Our initial attempts at representing wave functions with neural networks were met with frustration when we couldn't reach the accuracy of even the standard Hartree–Fock method of quantum chemistry. Not even on the smallest molecules, where such a calculation takes only a few seconds on a modern computer. This eventually motivated us to build the Hartree–Fock baseline and other components enforcing the correct physics of wavefunctions into our architecture, which we dubbed PauliNet--a neural network that obeys the Pauli exclusion principle.2 We were then quite surprised in September 2019 when we found out that researchers at DeepMind, who pursued the same idea in parallel, were able to reach impressive accuracy without building any physics into their architecture, called FermiNet, although at the cost of using much larger networks and hence requiring more computational resources.3
This will be an interactive post using Google Colab notebooks. If you have not used Google Colab before, there is a quick-start tutorial at tutorialspoint. You can access the notebook at this link: Train your first DL model. First, make a copy and save it into your Drive so that you can access it and make changes. Next, make sure the runtime is set to GPU so you can make use of the free resources provided by Google.
Artificial intelligence (AI) machine learning can have a considerable carbon footprint. Deep learning is inherently costly, as it requires massive computational and energy resources. Now researchers in the U.K. have discovered how to create an energy-efficient artificial neural network without sacrificing accuracy and published the findings in Nature Communications on August 26, 2020. The biological brain is the inspiration for neuromorphic computing--an interdisciplinary approach that draws upon neuroscience, physics, artificial intelligence, computer science, and electrical engineering to create artificial neural systems that mimic biological functions and systems. The human brain is a complex system of roughly 86 billion neurons, 200 billion neurons, and hundreds of trillions of synapses.
These days we are hearing a lot about AI, but have you ever heard about EDGE AI ..? What does it mean and what is it used for? Network edge or edge, where data resides and collected. Edge computing processes data on local places like computers, IoT devices or Edge servers, here we are doing computation to a network edge which indeed reduces long-distance communication between client and server. Edge AI, where AI algorithms will locally process sensor data or signals that are created on hardware devices in less than a few milliseconds by providing real-time information. Most of the time the AI algorithms are being processed in cloud data centers with deep learning models, which consume heavy compute capacity.
Artificial Intelligence (AI) is the study of "intelligent agents" which can be define as any device that perceives its environment and takes appropriate action that makes the highest probability of achieving its goals. Additionally, it can also be define as a system's ability to interpret external data, learn from gathered data and use those learnings to realize specific goals through adaptation. It is also called as machine intelligence and attributed to the nature of intelligence demonstrated by machines. Some of the features of artificial intelligence are; successfully understanding human language, contending at the highest level in strategic games systems such as chess and go, autonomously operating cars, intelligent routing in content delivery networks and military simulations and others. To solve the problem of learning and perceiving the immediate environment, many approaches have been taken such as statistical methods, computational intelligence, versions of search and mathematical optimization, artificial neural networks, and methods based on statistic, probability and economics.