AI News, 5 Neural Network Use Cases That Will Help You Understand the Technology Better
- On Thursday, June 7, 2018
- By Read More
5 Neural Network Use Cases That Will Help You Understand the Technology Better
Every day, highly advanced artificial neural networks (ANNs) and deep learning algorithms scan through millions of queries and dig through the endless flow of big data.
Machine learning is the instrument through which these newborn computer-based bits of synthetic intelligence process all the info they're nourished with, much like the five senses help a human toddler learn and experience the world.
We've spent the last 30 years dreaming of cyberpunk dystopian worlds where androids who dream of electric sheep run from captors by jumping on driverless vehicles.
For years, human-driven cars have been equipped with an array of cameras and sensors that record everything from driving patterns to road obstacles, traffic lights, and road signs.
However, modern technologies have made a huge leap forward, and revolutionary machine learning algorithms can mundanely perform complex tasks such as predicting faults and scheduling fixes.
AI is exceptionally efficient at allocating network resources where they're most needed by autonomously analyzing traffic data, and they possess the agility required to integrate themselves with the many internet of things (IoT) devices connected to the network architecture.
Yet, no more than 10 percent of the files change from iteration to iteration, so algorithm-based learning models that can predict these variations are able to detect which files are malware with amazing accuracy.
More in general, neural nets could be used to detect any change or anomaly in network traffic to identify potentially malicious activities such as brute-force attacks, unusual failed logins and file exfiltration.
(To see how AI is fighting crime in the real world, see How AI Is Helping in the Fight Against Crime.) One of the traditional fears of those who oppose technology is that machines will eventually replace human labor and drive millions of people into poverty.
The machine learning-driven model is able to analyze past data to predict the duration of unemployment for each potential candidate, while devising new, smart ways to direct the government's limited resources where they’re truly needed to boost the economy.
AIs are incredibly good at detecting quality issues inside and beyond the assembly line, for instance by identifying patterns in the free-text fields of warranty registration cards.
We can only look forward to the day when they will be able to build and perfect their own learning methods and reach their university phase, but in the meantime, the goals they have already achieved are nonetheless amazing.
What’s the Difference Between Artificial Intelligence, Machine Learning, and Deep Learning?
This is the first of a multi-part series explaining the fundamentals of deep learning by long-time tech journalist Michael Copeland.
The easiest way to think of their relationship is to visualize them as concentric circles with AI — the idea that came first — the largest, then machine learning — which blossomed later, and finally deep learning — which is driving today’s AI explosion — fitting inside both.
It also has to do with the simultaneous one-two punch of practically infinite storage and a flood of data of every stripe (that whole Big Data movement) – images, text, transactions, mapping data, you name it.
Let’s walk through how computer scientists have moved from something of a bust — until 2012 — to a boom that has unleashed applications used by hundreds of millions of people every day.
Back in that summer of ’56 conference the dream of those AI pioneers was to construct complex machines — enabled by emerging computers — that possessed the same characteristics of human intelligence.
So rather than hand-coding software routines with a specific set of instructions to accomplish a particular task, the machine is “trained” using large amounts of data and algorithms that give it the ability to learn how to perform the task.
Machine learning came directly from minds of the early AI crowd, and the algorithmic approaches over the years included decision tree learning, inductive logic programming.
But, unlike a biological brain where any neuron can connect to any other neuron within a certain physical distance, these artificial neural networks have discrete layers, connections, and directions of data propagation.
Attributes of a stop sign image are chopped up and “examined” by the neurons — its octogonal shape, its fire-engine red color, its distinctive letters, its traffic-sign size, and its motion or lack thereof.
In our example the system might be 86% confident the image is a stop sign, 7% confident it’s a speed limit sign, and 5% it’s a kite stuck in a tree ,and so on — and the network architecture then tells the neural network whether it is right or not.
Still, a small heretical research group led by Geoffrey Hinton at the University of Toronto kept at it, finally parallelizing the algorithms for supercomputers to run and proving the concept, but it wasn’t until GPUs were deployed in the effort that the promise was realized.
It needs to see hundreds of thousands, even millions of images, until the weightings of the neuron inputs are tuned so precisely that it gets the answer right practically every time — fog or no fog, sun or rain.
Today, image recognition by machines trained via deep learning in some scenarios is better than humans, and that ranges from cats to identifying indicators for cancer in blood and tumors in MRI scans.
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Developed by AWS and Microsoft, Gluon provides a clear, concise API for defining machine learning models using a collection of pre-built, optimized neural network components.
More seasoned data scientists and researchers will value the ability to build prototypes quickly and utilize dynamic neural network graphs for entirely new model architectures, all without sacrificing training speed.
Machine learning is a subset of artificial intelligence in the field of computer science that often uses statistical techniques to give computers the ability to 'learn' (i.e., progressively improve performance on a specific task) with data, without being explicitly programmed. The name machine learning was coined in 1959 by Arthur Samuel. Evolved from the study of pattern recognition and computational learning theory in artificial intelligence, machine learning explores the study and construction of algorithms that can learn from and make predictions on data – such algorithms overcome following strictly static program instructions by making data-driven predictions or decisions,:2 through building a model from sample inputs.
Mitchell provided a widely quoted, more formal definition of the algorithms studied in the machine learning field: 'A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E.' This definition of the tasks in which machine learning is concerned offers a fundamentally operational definition rather than defining the field in cognitive terms.
Machine learning tasks are typically classified into two broad categories, depending on whether there is a learning 'signal' or 'feedback' available to a learning system: Another categorization of machine learning tasks arises when one considers the desired output of a machine-learned system::3 Among other categories of machine learning problems, learning to learn learns its own inductive bias based on previous experience.
Developmental learning, elaborated for robot learning, generates its own sequences (also called curriculum) of learning situations to cumulatively acquire repertoires of novel skills through autonomous self-exploration and social interaction with human teachers and using guidance mechanisms such as active learning, maturation, motor synergies, and imitation.
Probabilistic systems were plagued by theoretical and practical problems of data acquisition and representation.:488 By 1980, expert systems had come to dominate AI, and statistics was out of favor. Work on symbolic/knowledge-based learning did continue within AI, leading to inductive logic programming, but the more statistical line of research was now outside the field of AI proper, in pattern recognition and information retrieval.:708–710;
Machine learning and data mining often employ the same methods and overlap significantly, but while machine learning focuses on prediction, based on known properties learned from the training data, data mining focuses on the discovery of (previously) unknown properties in the data (this is the analysis step of knowledge discovery in databases).
Much of the confusion between these two research communities (which do often have separate conferences and separate journals, ECML PKDD being a major exception) comes from the basic assumptions they work with: in machine learning, performance is usually evaluated with respect to the ability to reproduce known knowledge, while in knowledge discovery and data mining (KDD) the key task is the discovery of previously unknown knowledge.
Jordan, the ideas of machine learning, from methodological principles to theoretical tools, have had a long pre-history in statistics. He also suggested the term data science as a placeholder to call the overall field. Leo Breiman distinguished two statistical modelling paradigms: data model and algorithmic model, wherein 'algorithmic model' means more or less the machine learning algorithms like Random forest.
Multilinear subspace learning algorithms aim to learn low-dimensional representations directly from tensor representations for multidimensional data, without reshaping them into (high-dimensional) vectors. Deep learning algorithms discover multiple levels of representation, or a hierarchy of features, with higher-level, more abstract features defined in terms of (or generating) lower-level features.
In machine learning, genetic algorithms found some uses in the 1980s and 1990s. Conversely, machine learning techniques have been used to improve the performance of genetic and evolutionary algorithms. Rule-based machine learning is a general term for any machine learning method that identifies, learns, or evolves 'rules' to store, manipulate or apply, knowledge.
They seek to identify a set of context-dependent rules that collectively store and apply knowledge in a piecewise manner in order to make predictions. Applications for machine learning include: In 2006, the online movie company Netflix held the first 'Netflix Prize' competition to find a program to better predict user preferences and improve the accuracy on its existing Cinematch movie recommendation algorithm by at least 10%.
A joint team made up of researchers from AT&T Labs-Research in collaboration with the teams Big Chaos and Pragmatic Theory built an ensemble model to win the Grand Prize in 2009 for $1 million. Shortly after the prize was awarded, Netflix realized that viewers' ratings were not the best indicators of their viewing patterns ('everything is a recommendation') and they changed their recommendation engine accordingly. In 2010 The Wall Street Journal wrote about the firm Rebellion Research and their use of Machine Learning to predict the financial crisis.
 In 2012, co-founder of Sun Microsystems Vinod Khosla predicted that 80% of medical doctors jobs would be lost in the next two decades to automated machine learning medical diagnostic software. In 2014, it has been reported that a machine learning algorithm has been applied in Art History to study fine art paintings, and that it may have revealed previously unrecognized influences between artists. Although machine learning has been very transformative in some fields, effective machine learning is difficult because finding patterns is hard and often not enough training data are available;
as a result, machine-learning programs often fail to deliver. Classification machine learning models can be validated by accuracy estimation techniques like the Holdout method, which splits the data in a training and test set (conventionally 2/3 training set and 1/3 test set designation) and evaluates the performance of the training model on the test set.
Systems which are trained on datasets collected with biases may exhibit these biases upon use (algorithmic bias), thus digitizing cultural prejudices. For example, using job hiring data from a firm with racist hiring policies may lead to a machine learning system duplicating the bias by scoring job applicants against similarity to previous successful applicants. Responsible collection of data and documentation of algorithmic rules used by a system thus is a critical part of machine learning.
There is huge potential for machine learning in health care to provide professionals a great tool to diagnose, medicate, and even plan recovery paths for patients, but this will not happen until the personal biases mentioned previously, and these 'greed' biases are addressed. Software suites containing a variety of machine learning algorithms include the following :
Machine learning? Neural networks? Here’s your guide to the many flavors of A.I.
is everywhere at the moment, and it’s responsible for everything from the virtual assistants on our smartphones to the self-driving cars soon to be filling our roads to the cutting-edge image recognition systems reported on by yours truly.
Right now, artificial intelligence is to Silicon Valley what One Direction is to 13-year-old girls: an omnipresent source of obsession to throw all your cash at, while daydreaming about getting married whenever Harry Styles is finally ready to settle down.
To help you make sense of some of the buzzwords and jargon you’ll hear when people talk about A.I., we put together this simple guide help you wrap your head around all the different flavors of artificial intelligence —
means (some people suggest it’s simply cool things computers can’t do yet), but most would agree that it’s about making computers perform actions which would be considered intelligent were they to be carried out by a person.
Like the heading of A.I., machine learning also has multiple subcategories, but what they all have in common is the statistics-focused ability to take data and apply algorithms to it in order to gain knowledge.
As brain-inspired systems designed to replicate the way that humans learn, neural networks modify their own code to find the link between input and output —
The concept of artificial neural networks actually dates back to the 1940s, but it was really only in the past few decades when it started to truly live up to its potential: aided by the arrival of algorithms like “backpropagation,” which allows neural network to adjust their hidden layers of neurons in situations where the outcome doesn’t match what the creator is hoping for.
(For instance, a network designed to recognize dogs, which misidentifies a cat.) This decade, artificial neural networks have benefited from the arrival of deep learning, in which different layers of the network extract different features until it can recognize what it is looking for.
In that case, the function may be to come up with a solution capable of fitting in a 10cm x 10cm box, capable of radiating a spherical or hemispherical pattern, and able to operate at a certain Wi-Fi band.
AI Is Not the End of Software Developers
Even Google CEO Sundar Pichai has talked about software that “automatically writes itself.” And certainly if you consider software development to be little more than the creation of oft-repeated segments of code, then the rapid advances in AI would give software engineers pause.
But Software 2.0 recognizes that — with advances in deep learning — we can build a neural network that learns which instructions or rules are needed for a desired outcome.
In this scenario, we can imagine the role of software engineer morphing into “data curator” or “data enabler.” Whatever we call ourselves, we’ll be people who are no longer writing code.
“A large portion of programmers of tomorrow do not maintain complex software repositories, write intricate programs or analyze their running times,” wrote Karpathy in a recent post titled Software 2.0.
Perez, author of The Deep Learning AI Playbook, writes that while “I agree with Kaparthy that teachable machines are indeed ‘Software 2.0’, what is clearly debatable is whether these new kinds of systems are different from other universal computing machinery.” Personally, I don’t think software engineering will go away anytime soon.
Even if a new role evolves — call it Software 2.0 engineer or data scientist 2.0 or whatever — there are ways in which this technology shift will empower the practitioner of Software 1.0.
Yes, we’ll have help from deep learning neural network systems, but they’ll help us do our current job better rather than replace us entirely.
As you’re writing code, your machine partner might determine what kind of function you’re writing and fill the rest in for you, based on your style, using high-level predictive analysis.
Maybe this is a new role for software engineers, similar to what Andrej alludes to in his post: monitoring the code and helping the machine learning system achieve closer to 100% accuracy rate.
Now that we’ve outlined the conceivable benefits, the next question arises: what parts of software programming can be moved to the deep learning 2.0 framework and what should remain in the traditional 1.0 framework?
Today, it’s clear that these deep learning neural networks do well in supervised learning settings, if they’re provided training data with good examples and bad examples so they can learn what to output correctly.
And, as one of my colleagues pointed out, improving a model’s performance frequently involves improving the underlying code and deployment environment, as well as improving the training data.
- On Wednesday, September 18, 2019
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