Supervised Learning

Supervised machine learning builds a model that makes predictions based on evidence in the presence of uncertainty. A supervised learning algorithm takes a known set of input data and known responses to the data (output) and trains a model to generate reasonable predictions for the response to new data. Use supervised learning if you have known data for the output you are trying to predict.

Supervised learning uses classification and regression techniques to develop predictive models.

Classification techniques predict discrete responsesfor example, whether an email is genuine or spam, or whether a tumor is cancerous or benign. Classification models classify input data into categories. Typical applications include medical imaging, speech recognition, and credit scoring.

Use classification if your data can be tagged, categorized, or separated into specific groups or classes. For example, applications for hand-writing recognition use classification to recognize letters and numbers. In image processing and computer vision, unsupervised pattern recognition techniques are used for object detection and image segmentation.

Common algorithms for performing classification include support vector machine (SVM), boosted and bagged decision trees, k-nearest neighbor, Nave Bayes, discriminant analysis, logistic regression, and neural networks.

Regression techniques predict continuous responsesfor example, changes in temperature or fluctuations in power demand. Typical applications include electricity load forecasting and algorithmic trading.

Use regression techniques if you are working with a data range or if the nature of your response is a real number, such as temperature or the time until failure for a piece of equipment.

Common regression algorithms include linear model, nonlinear model, regularization, stepwise regression, boosted and bagged decision trees, neural networks, and adaptive neuro-fuzzy learning.

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What Is Machine Learning? | How It Works, Techniques ...

Machine learning is enabling computers to tackle tasks that have, until now, only been carried out by people.

From driving cars to translating speech, machine learning is driving an explosion in the capabilities of artificial intelligence -- helping software make sense of the messy and unpredictable real world.

But what exactly is machine learning and what is making the current boom in machine learning possible?

At a very high level, machine learning is the process of teaching a computer system how to make accurate predictions when fed data.

Those predictions could be answering whether a piece of fruit in a photo is a banana or an apple, spotting people crossing the road in front of a self-driving car, whether the use of the word book in a sentence relates to a paperback or a hotel reservation, whether an email is spam, or recognizing speech accurately enough to generate captions for a YouTube video.

The key difference from traditional computer software is that a human developer hasn't written code that instructs the system how to tell the difference between the banana and the apple.

Instead a machine-learning model has been taught how to reliably discriminate between the fruits by being trained on a large amount of data, in this instance likely a huge number of images labelled as containing a banana or an apple.

Data, and lots of it, is the key to making machine learning possible.

Machine learning may have enjoyed enormous success of late, but it is just one method for achieving artificial intelligence.

At the birth of the field of AI in the 1950s, AI was defined as any machine capable of performing a task that would typically require human intelligence.

AI systems will generally demonstrate at least some of the following traits: planning, learning, reasoning, problem solving, knowledge representation, perception, motion, and manipulation and, to a lesser extent, social intelligence and creativity.

Alongside machine learning, there are various other approaches used to build AI systems, including evolutionary computation, where algorithms undergo random mutations and combinations between generations in an attempt to "evolve" optimal solutions, and expert systems, where computers are programmed with rules that allow them to mimic the behavior of a human expert in a specific domain, for example an autopilot system flying a plane.

Machine learning is generally split into two main categories: supervised and unsupervised learning.

This approach basically teaches machines by example.

During training for supervised learning, systems are exposed to large amounts of labelled data, for example images of handwritten figures annotated to indicate which number they correspond to. Given sufficient examples, a supervised-learning system would learn to recognize the clusters of pixels and shapes associated with each number and eventually be able to recognize handwritten numbers, able to reliably distinguish between the numbers 9 and 4 or 6 and 8.

However, training these systems typically requires huge amounts of labelled data, with some systems needing to be exposed to millions of examples to master a task.

As a result, the datasets used to train these systems can be vast, with Google's Open Images Dataset having about nine million images, its labeled video repository YouTube-8M linking to seven million labeled videos and ImageNet, one of the early databases of this kind, having more than 14 million categorized images. The size of training datasets continues to grow, with Facebook recently announcing it had compiled 3.5 billion images publicly available on Instagram, using hashtags attached to each image as labels. Using one billion of these photos to train an image-recognition system yielded record levels of accuracy -- of 85.4 percent -- on ImageNet's benchmark.

The laborious process of labeling the datasets used in training is often carried out using crowdworking services, such as Amazon Mechanical Turk, which provides access to a large pool of low-cost labor spread across the globe. For instance, ImageNet was put together over two years by nearly 50,000 people, mainly recruited through Amazon Mechanical Turk. However, Facebook's approach of using publicly available data to train systems could provide an alternative way of training systems using billion-strong datasets without the overhead of manual labeling.

In contrast, unsupervised learning tasks algorithms with identifying patterns in data, trying to spot similarities that split that data into categories.

An example might be Airbnb clustering together houses available to rent by neighborhood, or Google News grouping together stories on similar topics each day.

The algorithm isn't designed to single out specific types of data, it simply looks for data that can be grouped by its similarities, or for anomalies that stand out.

The importance of huge sets of labelled data for training machine-learning systems may diminish over time, due to the rise of semi-supervised learning.

As the name suggests, the approach mixes supervised and unsupervised learning. The technique relies upon using a small amount of labelled data and a large amount of unlabelled data to train systems. The labelled data is used to partially train a machine-learning model, and then that partially trained model is used to label the unlabelled data, a process called pseudo-labelling. The model is then trained on the resulting mix of the labelled and pseudo-labelled data.

The viability of semi-supervised learning has been boosted recently by Generative Adversarial Networks ( GANs), machine-learning systems that can use labelled data to generate completely new data, for example creating new images of Pokemon from existing images, which in turn can be used to help train a machine-learning model.

Were semi-supervised learning to become as effective as supervised learning, then access to huge amounts of computing power may end up being more important for successfully training machine-learning systems than access to large, labelled datasets.

A way to understand reinforcement learning is to think about how someone might learn to play an old school computer game for the first time, when they aren't familiar with the rules or how to control the game. While they may be a complete novice, eventually, by looking at the relationship between the buttons they press, what happens on screen and their in-game score, their performance will get better and better.

An example of reinforcement learning is Google DeepMind's Deep Q-network, which has beaten humans in a wide range of vintage video games. The system is fed pixels from each game and determines various information about the state of the game, such as the distance between objects on screen. It then considers how the state of the game and the actions it performs in game relate to the score it achieves.

Over the process of many cycles of playing the game, eventually the system builds a model of which actions will maximize the score in which circumstance, for instance, in the case of the video game Breakout, where the paddle should be moved to in order to intercept the ball.

Everything begins with training a machine-learning model, a mathematical function capable of repeatedly modifying how it operates until it can make accurate predictions when given fresh data.

Before training begins, you first have to choose which data to gather and decide which features of the data are important.

A hugely simplified example of what data features are is given in this explainer by Google, where a machine learning model is trained to recognize the difference between beer and wine, based on two features, the drinks' color and their alcoholic volume (ABV).

Each drink is labelled as a beer or a wine, and then the relevant data is collected, using a spectrometer to measure their color and hydrometer to measure their alcohol content.

An important point to note is that the data has to be balanced, in this instance to have a roughly equal number of examples of beer and wine.

The gathered data is then split, into a larger proportion for training, say about 70 percent, and a smaller proportion for evaluation, say the remaining 30 percent. This evaluation data allows the trained model to be tested to see how well it is likely to perform on real-world data.

Before training gets underway there will generally also be a data-preparation step, during which processes such as deduplication, normalization and error correction will be carried out.

The next step will be choosing an appropriate machine-learning model from the wide variety available. Each have strengths and weaknesses depending on the type of data, for example some are suited to handling images, some to text, and some to purely numerical data.

Basically, the training process involves the machine-learning model automatically tweaking how it functions until it can make accurate predictions from data, in the Google example, correctly labeling a drink as beer or wine when the model is given a drink's color and ABV.

A good way to explain the training process is to consider an example using a simple machine-learning model, known as linear regression with gradient descent. In the following example, the model is used to estimate how many ice creams will be sold based on the outside temperature.

Imagine taking past data showing ice cream sales and outside temperature, and plotting that data against each other on a scatter graph -- basically creating a scattering of discrete points.

To predict how many ice creams will be sold in future based on the outdoor temperature, you can draw a line that passes through the middle of all these points, similar to the illustration below.

Once this is done, ice cream sales can be predicted at any temperature by finding the point at which the line passes through a particular temperature and reading off the corresponding sales at that point.

Bringing it back to training a machine-learning model, in this instance training a linear regression model would involve adjusting the vertical position and slope of the line until it lies in the middle of all of the points on the scatter graph.

At each step of the training process, the vertical distance of each of these points from the line is measured. If a change in slope or position of the line results in the distance to these points increasing, then the slope or position of the line is changed in the opposite direction, and a new measurement is taken.

In this way, via many tiny adjustments to the slope and the position of the line, the line will keep moving until it eventually settles in a position which is a good fit for the distribution of all these points, as seen in the video below. Once this training process is complete, the line can be used to make accurate predictions for how temperature will affect ice cream sales, and the machine-learning model can be said to have been trained.

While training for more complex machine-learning models such as neural networks differs in several respects, it is similar in that it also uses a "gradient descent" approach, where the value of "weights" that modify input data are repeatedly tweaked until the output values produced by the model are as close as possible to what is desired.

Once training of the model is complete, the model is evaluated using the remaining data that wasn't used during training, helping to gauge its real-world performance.

To further improve performance, training parameters can be tuned. An example might be altering the extent to which the "weights" are altered at each step in the training process.

A very important group of algorithms for both supervised and unsupervised machine learning are neural networks. These underlie much of machine learning, and while simple models like linear regression used can be used to make predictions based on a small number of data features, as in the Google example with beer and wine, neural networks are useful when dealing with large sets of data with many features.

Neural networks, whose structure is loosely inspired by that of the brain, are interconnected layers of algorithms, called neurons, which feed data into each other, with the output of the preceding layer being the input of the subsequent layer.

Each layer can be thought of as recognizing different features of the overall data. For instance, consider the example of using machine learning to recognize handwritten numbers between 0 and 9. The first layer in the neural network might measure the color of the individual pixels in the image, the second layer could spot shapes, such as lines and curves, the next layer might look for larger components of the written number -- for example, the rounded loop at the base of the number 6. This carries on all the way through to the final layer, which will output the probability that a given handwritten figure is a number between 0 and 9.

See more: Special report: How to implement AI and machine learning (free PDF)

The network learns how to recognize each component of the numbers during the training process, by gradually tweaking the importance of data as it flows between the layers of the network. This is possible due to each link between layers having an attached weight, whose value can be increased or decreased to alter that link's significance. At the end of each training cycle the system will examine whether the neural network's final output is getting closer or further away from what is desired -- for instance is the network getting better or worse at identifying a handwritten number 6. To close the gap between between the actual output and desired output, the system will then work backwards through the neural network, altering the weights attached to all of these links between layers, as well as an associated value called bias. This process is called back-propagation.

Eventually this process will settle on values for these weights and biases that will allow the network to reliably perform a given task, such as recognizing handwritten numbers, and the network can be said to have "learned" how to carry out a specific task

An illustration of the structure of a neural network and how training works.

A subset of machine learning is deep learning, where neural networks are expanded into sprawling networks with a huge number of layers that are trained using massive amounts of data. It is these deep neural networks that have fueled the current leap forward in the ability of computers to carry out task like speech recognition and computer vision.

There are various types of neural networks, with different strengths and weaknesses. Recurrent neural networks are a type of neural net particularly well suited to language processing and speech recognition, while convolutional neural networks are more commonly used in image recognition. The design of neural networks is also evolving, with researchers recently devising a more efficient design for an effective type of deep neural network called long short-term memory or LSTM, allowing it to operate fast enough to be used in on-demand systems like Google Translate.

The AI technique of evolutionary algorithms is even being used to optimize neural networks, thanks to a process called neuroevolution. The approach was recently showcased by Uber AI Labs, which released papers on using genetic algorithms to train deep neural networks for reinforcement learning problems.

While machine learning is not a new technique, interest in the field has exploded in recent years.

This resurgence comes on the back of a series of breakthroughs, with deep learning setting new records for accuracy in areas such as speech and language recognition, and computer vision.

What's made these successes possible are primarily two factors, one being the vast quantities of images, speech, video and text that is accessible to researchers looking to train machine-learning systems.

But even more important is the availability of vast amounts of parallel-processing power, courtesy of modern graphics processing units (GPUs), which can be linked together into clusters to form machine-learning powerhouses.

Today anyone with an internet connection can use these clusters to train machine-learning models, via cloud services provided by firms like Amazon, Google and Microsoft.

As the use of machine-learning has taken off, so companies are now creating specialized hardware tailored to running and training machine-learning models. An example of one of these custom chips is Google's Tensor Processing Unit (TPU), the latest version of which accelerates the rate at which machine-learning models built using Google's TensorFlow software library can infer information from data, as well as the rate at which they can be trained.

These chips are not just used to train models for Google DeepMind and Google Brain, but also the models that underpin Google Translate and the image recognition in Google Photo, as well as services that allow the public to build machine learning models using Google's TensorFlow Research Cloud. The second generation of these chips was unveiled at Google's I/O conference in May last year, with an array of these new TPUs able to train a Google machine-learning model used for translation in half the time it would take an array of the top-end GPUs, and the recently announced third-generation TPUs able to accelerate training and inference even further.

As hardware becomes increasingly specialized and machine-learning software frameworks are refined, it's becoming increasingly common for ML tasks to be carried out on consumer-grade phones and computers, rather than in cloud datacenters. In the summer of 2018, Google took a step towards offering the same quality of automated translation on phones that are offline as is available online, by rolling out local neural machine translation for 59 languages to the Google Translate app for iOS and Android.

Perhaps the most famous demonstration of the efficacy of machine-learning systems was the 2016 triumph of the Google DeepMind AlphaGo AI over a human grandmaster in Go, a feat that wasn't expected until 2026. Go is an ancient Chinese game whose complexity bamboozled computers for decades. Go has about 200 moves per turn, compared to about 20 in Chess. Over the course of a game of Go, there are so many possible moves that searching through each of them in advance to identify the best play is too costly from a computational standpoint. Instead, AlphaGo was trained how to play the game by taking moves played by human experts in 30 million Go games and feeding them into deep-learning neural networks.

Training the deep-learning networks needed can take a very long time, requiring vast amounts of data to be ingested and iterated over as the system gradually refines its model in order to achieve the best outcome.

However, more recently Google refined the training process with AlphaGo Zero, a system that played "completely random" games against itself, and then learnt from the results. At last year's prestigious Neural Information Processing Systems (NIPS) conference, Google DeepMind CEO Demis Hassabis revealed AlphaGo had also mastered the games of chess and shogi.

DeepMind continue to break new ground in the field of machine learning. In July 2018, DeepMind reported that its AI agents had taught themselves how to play the 1999 multiplayer 3D first-person shooter Quake III Arena, well enough to beat teams of human players. These agents learned how to play the game using no more information than the human players, with their only input being the pixels on the screen as they tried out random actions in game, and feedback on their performance during each game.

More recently DeepMind demonstrated an AI agent capable of superhuman performance across multiple classic Atari games, an improvement over earlier approaches where each AI agent could only perform well at a single game. DeepMind researchers say these general capabilities will be important if AI research is to tackle more complex real-world domains.

Machine learning systems are used all around us, and are a cornerstone of the modern internet.

Machine-learning systems are used to recommend which product you might want to buy next on Amazon or video you want to may want to watch on Netflix.

Every Google search uses multiple machine-learning systems, to understand the language in your query through to personalizing your results, so fishing enthusiasts searching for "bass" aren't inundated with results about guitars. Similarly Gmail's spam and phishing-recognition systems use machine-learning trained models to keep your inbox clear of rogue messages.

One of the most obvious demonstrations of the power of machine learning are virtual assistants, such as Apple's Siri, Amazon's Alexa, the Google Assistant, and Microsoft Cortana.

Each relies heavily on machine learning to support their voice recognition and ability to understand natural language, as well as needing an immense corpus to draw upon to answer queries.

But beyond these very visible manifestations of machine learning, systems are starting to find a use in just about every industry. These exploitations include: computer vision for driverless cars, drones and delivery robots; speech and language recognition and synthesis for chatbots and service robots; facial recognition for surveillance in countries like China; helping radiologists to pick out tumors in x-rays, aiding researchers in spotting genetic sequences related to diseases and identifying molecules that could lead to more effective drugs in healthcare; allowing for predictive maintenance on infrastructure by analyzing IoT sensor data; underpinning the computer vision that makes the cashierless Amazon Go supermarket possible, offering reasonably accurate transcription and translation of speech for business meetings -- the list goes on and on.

Deep-learning could eventually pave the way for robots that can learn directly from humans, with researchers from Nvidia recently creating a deep-learning system designed to teach a robot to how to carry out a task, simply by observing that job being performed by a human.

As you'd expect, the choice and breadth of data used to train systems will influence the tasks they are suited to.

For example, in 2016 Rachael Tatman, a National Science Foundation Graduate Research Fellow in the Linguistics Department at the University of Washington, found that Google's speech-recognition system performed better for male voices than female ones when auto-captioning a sample of YouTube videos, a result she ascribed to 'unbalanced training sets' with a preponderance of male speakers.

As machine-learning systems move into new areas, such as aiding medical diagnosis, the possibility of systems being skewed towards offering a better service or fairer treatment to particular groups of people will likely become more of a concern.

A heavily recommended course for beginners to teach themselves the fundamentals of machine learning is this free Stanford University and Coursera lecture series by AI expert and Google Brain founder Andrew Ng.

Another highly-rated free online course, praised for both the breadth of its coverage and the quality of its teaching, is this EdX and Columbia University introduction to machine learning, although students do mention it requires a solid knowledge of math up to university level.

Technologies designed to allow developers to teach themselves about machine learning are increasingly common, from AWS' deep-learning enabled camera DeepLens to Google's Raspberry Pi-powered AIY kits.

All of the major cloud platforms -- Amazon Web Services, Microsoft Azure and Google Cloud Platform -- provide access to the hardware needed to train and run machine-learning models, with Google letting Cloud Platform users test out its Tensor Processing Units -- custom chips whose design is optimized for training and running machine-learning models.

This cloud-based infrastructure includes the data stores needed to hold the vast amounts of training data, services to prepare that data for analysis, and visualization tools to display the results clearly.

Newer services even streamline the creation of custom machine-learning models, with Google recently revealing a service that automates the creation of AI models, called Cloud AutoML. This drag-and-drop service builds custom image-recognition models and requires the user to have no machine-learning expertise, similar to Microsoft's Azure Machine Learning Studio. In a similar vein, Amazon recently unveiled new AWS offerings designed to accelerate the process of training up machine-learning models.

For data scientists, Google's Cloud ML Engine is a managed machine-learning service that allows users to train, deploy and export custom machine-learning models based either on Google's open-sourced TensorFlow ML framework or the open neural network framework Keras, and which now can be used with the Python library sci-kit learn and XGBoost.

Database admins without a background in data science can use Google's BigQueryML, a beta service that allows admins to call trained machine-learning models using SQL commands, allowing predictions to be made in database, which is simpler than exporting data to a separate machine learning and analytics environment.

For firms that don't want to build their own machine-learning models, the cloud platforms also offer AI-powered, on-demand services -- such as voice, vision, and language recognition. Microsoft Azure stands out for the breadth of on-demand services on offer, closely followed by Google Cloud Platform and then AWS.

Meanwhile IBM, alongside its more general on-demand offerings, is also attempting to sell sector-specific AI services aimed at everything from healthcare to retail, grouping these offerings together under its IBM Watson umbrella.

Early in 2018, Google expanded its machine-learning driven services to the world of advertising, releasing a suite of tools for making more effective ads, both digital and physical.

While Apple doesn't enjoy the same reputation for cutting edge speech recognition, natural language processing and computer vision as Google and Amazon, it is investing in improving its AI services, recently putting Google's former chief in charge of machine learning and AI strategy across the company, including the development of its assistant Siri and its on-demand machine learning service Core ML.

In September 2018, NVIDIA launched a combined hardware and software platform designed to be installed in datacenters that can accelerate the rate at which trained machine-learning models can carry out voice, video and image recognition, as well as other ML-related services.

The NVIDIA TensorRT Hyperscale Inference Platform uses NVIDIA Tesla T4 GPUs, which delivers up to 40x the performance of CPUs when using machine-learning models to make inferences from data, and the TensorRT software platform, which is designed to optimize the performance of trained neural networks.

There are a wide variety of software frameworks for getting started with training and running machine-learning models, typically for the programming languages Python, R, C++, Java and MATLAB.

Famous examples include Google's TensorFlow, the open-source library Keras, the Python library Scikit-learn, the deep-learning framework CAFFE and the machine-learning library Torch.

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What is machine learning? Everything you need to know | ZDNet

What is Machine Learning?

Machine Learning is a system that can learn from example through self-improvement and without being explicitly coded by programmer. The breakthrough comes with the idea that a machine can singularly learn from the data (i.e., example) to produce accurate results.

Machine learning combines data with statistical tools to predict an output. This output is then used by corporate to makes actionable insights. Machine learning is closely related to data mining and Bayesian predictive modeling. The machine receives data as input, use an algorithm to formulate answers.

A typical machine learning tasks are to provide a recommendation. For those who have a Netflix account, all recommendations of movies or series are based on the user's historical data. Tech companies are using unsupervised learning to improve the user experience with personalizing recommendation.

Machine learning is also used for a variety of task like fraud detection, predictive maintenance, portfolio optimization, automatize task and so on.

In this basic tutorial, you will learn-

Traditional programming differs significantly from machine learning. In traditional programming, a programmer code all the rules in consultation with an expert in the industry for which software is being developed. Each rule is based on a logical foundation; the machine will execute an output following the logical statement. When the system grows complex, more rules need to be written. It can quickly become unsustainable to maintain.

Machine learning is supposed to overcome this issue. The machine learns how the input and output data are correlated and it writes a rule. The programmers do not need to write new rules each time there is new data. The algorithms adapt in response to new data and experiences to improve efficacy over time.

Machine learning is the brain where all the learning takes place. The way the machine learns is similar to the human being. Humans learn from experience. The more we know, the more easily we can predict. By analogy, when we face an unknown situation, the likelihood of success is lower than the known situation. Machines are trained the same. To make an accurate prediction, the machine sees an example. When we give the machine a similar example, it can figure out the outcome. However, like a human, if its feed a previously unseen example, the machine has difficulties to predict.

The core objective of machine learning is the learning and inference. First of all, the machine learns through the discovery of patterns. This discovery is made thanks to the data. One crucial part of the data scientist is to choose carefully which data to provide to the machine. The list of attributes used to solve a problem is called a feature vector. You can think of a feature vector as a subset of data that is used to tackle a problem.

The machine uses some fancy algorithms to simplify the reality and transform this discovery into a model. Therefore, the learning stage is used to describe the data and summarize it into a model.

For instance, the machine is trying to understand the relationship between the wage of an individual and the likelihood to go to a fancy restaurant. It turns out the machine finds a positive relationship between wage and going to a high-end restaurant: This is the model

When the model is built, it is possible to test how powerful it is on never-seen-before data. The new data are transformed into a features vector, go through the model and give a prediction. This is all the beautiful part of machine learning. There is no need to update the rules or train again the model. You can use the model previously trained to make inference on new data.

The life of Machine Learning programs is straightforward and can be summarized in the following points:

Once the algorithm gets good at drawing the right conclusions, it applies that knowledge to new sets of data.

Machine learning can be grouped into two broad learning tasks: Supervised and Unsupervised. There are many other algorithms

An algorithm uses training data and feedback from humans to learn the relationship of given inputs to a given output. For instance, a practitioner can use marketing expense and weather forecast as input data to predict the sales of cans.

You can use supervised learning when the output data is known. The algorithm will predict new data.

There are two categories of supervised learning:

Imagine you want to predict the gender of a customer for a commercial. You will start gathering data on the height, weight, job, salary, purchasing basket, etc. from your customer database. You know the gender of each of your customer, it can only be male or female. The objective of the classifier will be to assign a probability of being a male or a female (i.e., the label) based on the information (i.e., features you have collected). When the model learned how to recognize male or female, you can use new data to make a prediction. For instance, you just got new information from an unknown customer, and you want to know if it is a male or female. If the classifier predicts male = 70%, it means the algorithm is sure at 70% that this customer is a male, and 30% it is a female.

The label can be of two or more classes. The above example has only two classes, but if a classifier needs to predict object, it has dozens of classes (e.g., glass, table, shoes, etc. each object represents a class)

When the output is a continuous value, the task is a regression. For instance, a financial analyst may need to forecast the value of a stock based on a range of feature like equity, previous stock performances, macroeconomics index. The system will be trained to estimate the price of the stocks with the lowest possible error.

In unsupervised learning, an algorithm explores input data without being given an explicit output variable (e.g., explores customer demographic data to identify patterns)

You can use it when you do not know how to classify the data, and you want the algorithm to find patterns and classify the data for you

Type

K-means clustering

Puts data into some groups (k) that each contains data with similar characteristics (as determined by the model, not in advance by humans)

Clustering

Gaussian mixture model

A generalization of k-means clustering that provides more flexibility in the size and shape of groups (clusters

Clustering

Hierarchical clustering

Splits clusters along a hierarchical tree to form a classification system.

Can be used for Cluster loyalty-card customer

Clustering

Recommender system

Help to define the relevant data for making a recommendation.

Clustering

PCA/T-SNE

Mostly used to decrease the dimensionality of the data. The algorithms reduce the number of features to 3 or 4 vectors with the highest variances.

Dimension Reduction

There are plenty of machine learning algorithms. The choice of the algorithm is based on the objective.

In the example below, the task is to predict the type of flower among the three varieties. The predictions are based on the length and the width of the petal. The picture depicts the results of ten different algorithms. The picture on the top left is the dataset. The data is classified into three categories: red, light blue and dark blue. There are some groupings. For instance, from the second image, everything in the upper left belongs to the red category, in the middle part, there is a mixture of uncertainty and light blue while the bottom corresponds to the dark category. The other images show different algorithms and how they try to classified the data.

The primary challenge of machine learning is the lack of data or the diversity in the dataset. A machine cannot learn if there is no data available. Besides, a dataset with a lack of diversity gives the machine a hard time. A machine needs to have heterogeneity to learn meaningful insight. It is rare that an algorithm can extract information when there are no or few variations. It is recommended to have at least 20 observations per group to help the machine learn. This constraint leads to poor evaluation and prediction.

Augmentation:

Automation:

Finance Industry

Government organization

Healthcare industry

Marketing

Example of application of Machine Learning in Supply Chain

Machine learning gives terrific results for visual pattern recognition, opening up many potential applications in physical inspection and maintenance across the entire supply chain network.

Unsupervised learning can quickly search for comparable patterns in the diverse dataset. In turn, the machine can perform quality inspection throughout the logistics hub, shipment with damage and wear.

For instance, IBM's Watson platform can determine shipping container damage. Watson combines visual and systems-based data to track, report and make recommendations in real-time.

In past year stock manager relies extensively on the primary method to evaluate and forecast the inventory. When combining big data and machine learning, better forecasting techniques have been implemented (an improvement of 20 to 30 % over traditional forecasting tools). In term of sales, it means an increase of 2 to 3 % due to the potential reduction in inventory costs.

Example of Machine Learning Google Car

For example, everybody knows the Google car. The car is full of lasers on the roof which are telling it where it is regarding the surrounding area. It has radar in the front, which is informing the car of the speed and motion of all the cars around it. It uses all of that data to figure out not only how to drive the car but also to figure out and predict what potential drivers around the car are going to do. What's impressive is that the car is processing almost a gigabyte a second of data.

Machine learning is the best tool so far to analyze, understand and identify a pattern in the data. One of the main ideas behind machine learning is that the computer can be trained to automate tasks that would be exhaustive or impossible for a human being. The clear breach from the traditional analysis is that machine learning can take decisions with minimal human intervention.

Take the following example; a retail agent can estimate the price of a house based on his own experience and his knowledge of the market.

A machine can be trained to translate the knowledge of an expert into features. The features are all the characteristics of a house, neighborhood, economic environment, etc. that make the price difference. For the expert, it took him probably some years to master the art of estimate the price of a house. His expertise is getting better and better after each sale.

For the machine, it takes millions of data, (i.e., example) to master this art. At the very beginning of its learning, the machine makes a mistake, somehow like the junior salesman. Once the machine sees all the example, it got enough knowledge to make its estimation. At the same time, with incredible accuracy. The machine is also able to adjust its mistake accordingly.

Most of the big company have understood the value of machine learning and holding data. McKinsey have estimated that the value of analytics ranges from $9.5 trillion to $15.4 trillion while $5 to 7 trillion can be attributed to the most advanced AI techniques.

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Machine Learning Tutorial for Beginners

Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves.

The process of learning begins with observations or data, such as examples, direct experience, or instruction, in order to look for patterns in data and make better decisions in the future based on the examples that we provide. The primary aim is to allow the computers learn automatically without human intervention or assistance and adjust actions accordingly.

Machine learning algorithms are often categorized as supervised or unsupervised.

Machine learning enables analysis of massive quantities of data. While it generally delivers faster, more accurate results in order to identify profitable opportunities or dangerous risks, it may also require additional time and resources to train it properly. Combining machine learning with AI and cognitive technologies can make it even more effective in processing large volumes of information.

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What is Machine Learning? A definition - Expert System

Artificial Intelligence (AI) and Machine Learning (ML) are two very hot buzzwords right now, and often seem to be used interchangeably.

They are not quite the same thing, but the perception that they are can sometimes lead to some confusion. So I thought it would be worth writing a piece to explain the difference.

Both terms crop up very frequently when the topic is Big Data, analytics, and the broader waves of technological change which are sweeping through our world.

In short, the best answer is that:

Artificial Intelligence is the broader concept of machines being able to carry out tasks in a way that we would consider smart.

And,

Machine Learning is a current application of AI based around the idea that we should really just be able to give machines access to data and let them learn for themselves.

Early Days

Artificial Intelligence has been around for a long time the Greek myths contain stories of mechanical men designed to mimic our own behavior. Very early European computers were conceived as logical machines and by reproducing capabilities such as basic arithmetic and memory, engineers saw their job, fundamentally, as attempting to create mechanical brains.

As technology, and, importantly, our understanding of how our minds work, has progressed, our concept of what constitutes AI has changed. Rather than increasingly complex calculations, work in the field of AI concentrated on mimicking human decision making processes and carrying out tasks in ever more human ways.

Artificial Intelligences devices designed to act intelligently are often classified into one of two fundamental groups applied or general. Applied AI is far more common systems designed to intelligently trade stocks and shares, or maneuver an autonomous vehicle would fall into this category.

Neural Networks - Artificial Intelligence And Machine Learning (Source: Shutterstock)

Generalized AIs systems or devices which can in theory handle any task are less common, but this is where some of the most exciting advancements are happening today. It is also the area that has led to the development of Machine Learning. Often referred to as a subset of AI, its really more accurate to think of it as the current state-of-the-art.

The Rise of Machine Learning

Two important breakthroughs led to the emergence of Machine Learning as the vehicle which is driving AI development forward with the speed it currently has.

One of these was the realization credited to Arthur Samuel in 1959 that rather than teaching computers everything they need to know about the world and how to carry out tasks, it might be possible to teach them to learn for themselves.

The second, more recently, was the emergence of the internet, and the huge increase in the amount of digital information being generated, stored, and made available for analysis.

Once these innovations were in place, engineers realized that rather than teaching computers and machines how to do everything, it would be far more efficient to code them to think like human beings, and then plug them into the internet to give them access to all of the information in the world.

Neural Networks

The development of neural networks has been key to teaching computers to think and understand the world in the way we do, while retaining the innate advantages they hold over us such as speed, accuracy and lack of bias.

A Neural Network is a computer system designed to work by classifying information in the same way a human brain does. It can be taught to recognize, for example, images, and classify them according to elements they contain.

Essentially it works on a system of probability based on data fed to it, it is able to make statements, decisions or predictions with a degree of certainty. The addition of a feedback loop enables learning by sensing or being told whether its decisions are right or wrong, it modifies the approach it takes in the future.

Machine Learning applications can read text and work out whether the person who wrote it is making a complaint or offering congratulations. They can also listen to a piece of music, decide whether it is likely to make someone happy or sad, and find other pieces of music to match the mood. In some cases, they can even compose their own music expressing the same themes, or which they know is likely to be appreciated by the admirers of the original piece.

These are all possibilities offered by systems based around ML and neural networks. Thanks in no small part to science fiction, the idea has also emerged that we should be able to communicate and interact with electronic devices and digital information, as naturally as we would with another human being. To this end, another field of AI Natural Language Processing (NLP) has become a source of hugely exciting innovation in recent years, and one which is heavily reliant on ML.

NLP applications attempt to understand natural human communication, either written or spoken, and communicate in return with us using similar, natural language. ML is used here to help machines understand the vast nuances in human language, and to learn to respond in a way that a particular audience is likely to comprehend.

A Case Of Branding?

Artificial Intelligence and in particular today ML certainly has a lot to offer. With its promise of automating mundane tasks as well as offering creative insight, industries in every sector from banking to healthcare and manufacturing are reaping the benefits. So, its important to bear in mind that AI and ML are something else they are products which are being sold consistently, and lucratively.

Machine Learning has certainly been seized as an opportunity by marketers. After AI has been around for so long, its possible that it started to be seen as something thats in some way old hat even before its potential has ever truly been achieved. There have been a few false starts along the road to the AI revolution, and the term Machine Learning certainly gives marketers something new, shiny and, importantly, firmly grounded in the here-and-now, to offer.

The fact that we will eventually develop human-like AI has often been treated as something of an inevitability by technologists. Certainly, today we are closer than ever and we are moving towards that goal with increasing speed. Much of the exciting progress that we have seen in recent years is thanks to the fundamental changes in how we envisage AI working, which have been brought about by ML. I hope this piece has helped a few people understand the distinction between AI and ML. In another piece on this subject I go deeper literally as I explain the theories behind another trending buzzword Deep Learning.

Check out these links for more information on artificial intelligence and many practical AI case examples.

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What Is The Difference Between Artificial Intelligence And ...

Machine learning (ML) is a type of artificial intelligence (AI) that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so. Machine learning algorithms use historical data as input to predict new output values.

Recommendation enginesare a common use case for machine learning. Other popular uses include fraud detection, spam filtering, malware threat detection, business process automation (BPA) andpredictive maintenance.

Classical machine learning is often categorized by how an algorithm learns to become more accurate in its predictions. There are two basic approaches: supervised learning andunsupervised learning. The type of algorithm a data scientist chooses to use is dependent upon what type of data they want to predict.

Supervised machine learning requires thedata scientist to train the algorithm with both labeled inputs and desired outputs. Supervised learning algorithms are good for the following tasks:

Unsupervised ML algorithms do not require data to be labeled. They sift through unlabeled data to look for patterns that can be used to group data points into subsets.Unsupervised learning algorithms are good for the following tasks:

Today, machine learning is used in a wide range of applications. Perhaps one of the most well-known examples of machine learning in action is the recommendation engine that powers Facebook's News Feed.

Facebook uses machine learning to personalize how each member's feed is delivered. If a member frequently stops to read a particular groups posts, the recommendation engine will start to show more of that groups activity earlier in the feed.

Behind the scenes, the engine is attempting to reinforce known patterns in the members online behavior. Should the member change patterns and fail to read posts from that group in the coming weeks, the News Feed will adjust accordingly.

In addition to recommendation engines, other uses for machine learning include the following:

Customer relationship management CRM software can use machine learning models to analyze email and prompt sales team members to respond to the most important messages first. More advanced systems can even recommend potentially effective responses.

Business intelligence- BI and analytics vendors use machine learning in their software to identify potentially important data points, patterns of data points and anomalies.

Human resource information systems HRIS systems can use machine learning models to filter through applications and identify the best candidates for an open position.

Self-driving cars Machine learning algorithms can even make it possible for a semi-autonomous car to recognize a partially visible object and alert the driver.

Virtual assistants- Smart assistants typically combine supervised and unsupervised machine learning models to interpret natural speech and supply context.

The process of choosing the right machine learning model to solve a problem can be time consuming if not approached strategically.

Step 1: Align the problem with potential data inputs that should be considered for the solution. This step requires help from data scientists and experts who have a deep understanding of the problem.

Step 2: Collect data, format it and label the data if necessary. This step is typically led by data scientists, with help from data wranglers.

Step 3: Chose which algorithm(s) to use and >test to see how well they perform. This step is usually carried out by data scientists.

Step 4: Continue to fine tune outputs until they reach an acceptable level of accuracy. This step is usually carried out by data scientists with feedback from experts who have a deep understanding of the problem.

Explaining how a specific ML model works can be challenging when the model is complex. There are some vertical industries where data scientists have to use simple machine learning models because its important for the business to explain how each and every decision was made. This is especially true in industries with heavy compliance burdens like banking and insurance.

Complex models can accurate predictions, but explaining to a lay person how an output was determined can be difficult.

While machine learning algorithms have been around for decades, they've attained new popularity asartificial intelligence(AI) has grown in prominence. Deep learning models, in particular, powers today's most advanced AI applications.

Machine learning platforms are among enterprise technology's most competitive realms, with most major vendors, including Amazon, Google, Microsoft, IBM and others, racing to sign customers up for platform services that cover the spectrum of machine learning activities, including data collection,data preparation, data classification, model building, training and application deployment.

As machine learning continues to increase in importance to business operations and AI becomes ever more practical in enterprise settings, the machine learning platform wars will only intensify.

Continued research into deep learning and AI is increasingly focused on developing more general applications. Today's AI models require extensive training in order to produce an algorithm that is highly optimized to perform one task. But some researchers are exploring ways to make models more flexible and are seeking techniques that allow a machine to apply context learned from one task to future, different tasks.

1642 - Blaise Pascal invents a mechanical machine that can add, subtract, multiply and divide.

1679 - Gottfried Wilhelm Leibniz devises the system of binary code.

1834 - Charles Babbage conceives the idea for a general all-purpose device that could be programmed with punched cards.

1842 - Ada Lovelace describes a sequence of operations for solving mathematical problems using Charles' Babbage's theoretical punch-card machine and becomes the first programmer.

1847 - George Boole creates Boolean logic, a form of algebra in which all values can be reduced to the binary values of true or false.

1936 - English logician and cryptanalyst AlanTuring proposes a Universal Machine that could decipher and execute a set of instructions. His published proof is considered the basis of computer science.

1952 - Arthur Samuel creates a program to help an IBM computer get better at checkers the more it plays.

1959 - MADALINE becomes the first artificial neural network applied to a real-world problem: removing echoes from phone lines.

1985 - Terry Sejnowski and Charles Rosenbergs artificial neural network taught itself how to correctly pronounce 20,000 words in one week.

1997 - IBMs Deep Blue beat chess grandmaster Garry Kasparov.

1999 - A CAD prototype intelligent workstation reviewed 22,000 mammograms and detected cancer 52% more accurately than radiologists did.

2006 - Computer scientist Geoffrey Hinton invents the term deep learning to describe neural net research.

2012 - An unsupervised neural network created by Google learned to recognize cats in YouTube videos with 74.8% accuracy.

2014 - A chatbot passes the Turing Test by convincing 33% of human judges that it was a Ukrainian teen named Eugene Goostman.

2014 - Googles AlphaGo defeats the human champion in Go, the most difficult board game in the world.

2016 - LipNet, DeepMinds artificial-intelligence system, identifies lip-read words in video with an accuracy of 93.4%.

2019 - Amazon controls 70% of the market share for virtual assistants in the U.S.

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What is machine learning (ML)? - Definition from WhatIs.com

Typing what is machine learning? into a Google search opens up a pandoras box of forums, academic research, and false information and the purpose of this article is to simplify the definition and understanding of machine learning thanks to the direct help from our panel of machine learning researchers.

At Emerj, the AI Research and Advisory Company, many of our enterprise clients feel as though they should be investing in machine learning projects, but they dont have a strong grasp of what it is. We often direct them to this resource to get them started with the fundamentals of machine learning in business.

In addition to an informed, working definition of machine learning (ML), we detail the challenges and limitations of getting machines to think, some of the issues being tackled today in deep learning (the frontier of machine learning), and key takeaways for developing machine learning applications for business use-cases.

This article will be broken up into the following sections:

We put together this resource to help with whatever your area of curiosity about machine learning so scroll along to your section of interest, or feel free to read the article in order, starting with our machine learning definition below:

* Machine Learning is the science of getting computers to learn and act like humans do, and improve their learning over time in autonomous fashion, by feeding them data and information in the form of observations and real-world interactions.

The above definition encapsulates the ideal objective or ultimate aim of machine learning, as expressed by many researchers in the field. The purpose of this article is to provide a business-minded reader with expert perspective on how machine learning is defined, and how it works.Machine learning and artificial intelligence share the same definition in the minds of many however, there are some distinct differences readers should recognize as well. References and related researcher interviews are included at the end of this article for further digging.

(Our aggregate machine learning definition can be found at the beginning of this article)

As with any concept, machine learning may have a slightly different definition, depending on whom you ask. We combed the Internet to find five practicaldefinitions from reputable sources:

We sent these definitions to experts whom weve interviewed and/or included in one of our past research consensuses, and asked them to respond with their favorite definition or to provide their own. Our introductory definition is meant to reflect the varied responses. Below are someof their responses:

Dr. Yoshua Bengio,Universit de Montral:

ML should not be defined by negatives (thus ruling 2 and 3). Here is my definition:

Machine learning research is part of research on artificial intelligence, seeking to provide knowledge to computers through data, observations and interacting with the world. That acquired knowledge allows computers to correctly generalize to new settings.

Dr. Danko Nikolic, CSC and Max-Planck Institute:

(edit of number 2 above): Machine learning is the science of getting computers to act without being explicitly programmed, but instead letting them learn a few tricks on their own.

Dr. Roman Yampolskiy, University ofLouisville:

Machine Learning is the science of getting computers to learn as well as humans do or better.

Dr. Emily Fox, University of Washington:

My favorite definition is #5.

There are many different types of machine learning algorithms, with hundreds published each day, and theyretypically grouped by either learning style (i.e. supervised learning, unsupervised learning, semi-supervised learning) or by similarity in form or function (i.e. classification, regression, decision tree, clustering, deep learning, etc.). Regardless of learning style or function, all combinations of machine learning algorithms consist of the following:

Image credit: Dr. Pedro Domingo, University of Washington

The fundamental goal of machine learning algorithms is togeneralize beyond the training samples i.e. successfully interpret data that it has never seen before.

Concepts and bullet points can only take one so far in understanding.When people ask What is machine learning?, they often want to see what it is and what it does. Below are some visual representations of machine learning models, with accompanying links for further information. Even more resources can be found at the bottom of this article.

Decision tree model

Gaussian mixture model

Dropout neural network

Merging chrominance and luminance using Convolutional Neural Networks

There are different approaches to getting machines to learn, from using basic decision trees to clustering to layers of artificial neural networks (the latter of which has given way to deep learning), depending on what task youre trying to accomplish and the type and amount of data that you have available. This dynamic sees itself played out in applications as varyingas medical diagnostics or self-driving cars.

While emphasis is often placed on choosing the best learning algorithm, researchers have found that some of the most interesting questions arise out of none of the available machine learning algorithms performing to par. Most of the time this is a problem with training data, but this also occurs when working with machine learning in new domains.

Research done when working on real applications often drives progress in the field, and reasons are twofold: 1. Tendency to discover boundaries and limitations of existing methods 2. Researchers and developers working with domain experts andleveraging time and expertise to improve system performance.

Sometimes this also occurs by accident. We might consider model ensembles, or combinations of many learning algorithms to improve accuracy, to be one example. Teams competing for the 2009 Netflix Price found that they got their best results when combining their learners with other teams learners, resulting in an improved recommendation algorithm (read Netflixs blog for more on why theydidnt end up using this ensemble).

One important point (based on interviews and conversations with experts in the field), in terms of application within business and elsewhere, is that machine learning is not just, or even about, automation, an often misunderstood concept. If you think this way, youre bound to miss the valuable insights that machines can provide and the resulting opportunities (rethinking an entire business model, for example, as has been in industries like manufacturing and agriculture).

Machines that learn are useful to humans because, with all of their processing power, theyre able to more quickly highlight or find patterns in big (or other) data that would have otherwise been missed by human beings. Machine learning is a tool that can be used to enhance humans abilities to solve problems and make informed inferences on a wide range of problems, from helping diagnose diseases to coming up with solutions for global climate change.

Machine learning cant get something from nothingwhat it does is get more from less. Dr. Pedro Domingo, University of Washington

The two biggest, historical (and ongoing) problems in machine learning have involved overfitting (in which the model exhibits bias towards the training data and does not generalize to new data, and/or variance i.e. learns random things when trained on new data) and dimensionality (algorithms with more features work in higher/multiple dimensions, making understanding the data more difficult). Having access to a large enough data set has in some cases also been a primary problem.

One of the most common mistakes among machine learning beginners is testing training data successfully and having the illusion of success; Domingo (and others) emphasize the importance of keeping some of the data set separate when testing models, and only using that reserved data to test a chosen model, followed by learning on the whole data set.

When a learning algorithm (i.e. learner) is not working, often the quicker path to success is to feed the machine more data, the availability of which is by now well-known as a primary driver of progress in machine and deep learning algorithms in recent years; however, this can lead to issues with scalability, in which we have more data but time to learn that data remains an issue.

In terms of purpose, machine learning is not an end or a solution in and of itself. Furthermore, attempting to use it as a blanket solution i.e. BLANKis not a useful exercise; instead, coming to the table with a problem or objective is often best driven bya more specific question BLANK.

Deep learning involves the study and design of machine algorithms for learning good representation of data at multiple levels of abstraction (ways of arranging computer systems). Recent publicity of deep learning through DeepMind, Facebook, and other institutionshas highlighted it as the next frontier of machine learning.

The International Conference on Machine Learning (ICML) is widely regarded as one of the most important in the world. This years took place in June in New York City, and it brought together researchers from all over the world who are working on addressing the current challenges in deep learning:

Deep-learning systems have made great gains over the past decade in domains like bject detection and recognition, text-to-speech, information retrieval and others. Research is now focused on developingdata-efficient machine learning i.e. deep learning systems that can learn more efficiently, with the same performance in less time and with less data, in cutting-edge domains like personalized healthcare, robot reinforcement learning, sentiment analysis, and others.

Below is a selection of best-practices and concepts of applying machine learning that weve collated from our interviews for out podcast series, and from select sources cited at the end of this article. We hope that some of these principles will clarify how ML is used, and how to avoid some of the common pitfalls that companies and researchers might be vulnerable to in starting off on an ML-related project.

Emerj helps businesses get started with artificial intelligence and machine learning. Using our AI Opportunity Landscapes, clients can discover the largest opportunities for automation and AI at their companies and pick the highest ROI first AI projects. Instead of wasting money on pilot projects that are destined to fail, Emerj helps clients do business with the right AI vendors for them and increase their AI project success rate.

1 http://homes.cs.washington.edu/~pedrod/papers/cacm12.pd

2 http://videolectures.net/deeplearning2016_precup_machine_learning/

3 http://www.aaai.org/ojs/index.php/aimagazine/article/view/2367/2272

4 https://research.facebook.com/blog/facebook-researchers-focus-on-the-most-challenging-machine-learning-questions-at-icml-2016/

5 https://sites.google.com/site/dataefficientml/

6 http://www.cl.uni-heidelberg.de/courses/ws14/deepl/BengioETAL12.pdf

One of the best ways to learn about artificial intelligence concepts is to learn from the research and applications of the smartest minds in the field. Below is a brief list of some of our interviews with machine learning researchers, many of which may be of interest for readers who want to explore these topics further:

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What is Machine Learning? | Emerj

Monday, March 23, 2020

To help users be more productive and deliberate in their actions while emailing, the web version of Outlook and the Outlook for iOS and Android app have introduced suggested replies, a new feature powered by Azure Machine Learning service.

Tuesday, January 21, 2020

Microsoft Azure Machine Learning (ML) is addressing complex business challenges that were previously thought unsolvable and is having a transformative impact across every vertical.

Tuesday, November 5, 2019

Enterprises today are adopting artificial intelligence (AI) at a rapid pace to stay ahead of their competition, deliver innovation, improve customer experiences, and grow revenue. AI and machine learning applications are ushering in a new era of transformation across industries from skillsets to scale, efficiency, operations, and governance.

Monday, October 28, 2019

Azure Machine Learning is the center for all things machine learning on Azure, be it creating new models, deploying models, managing a model repository and/or automating the entire CI/CD pipeline for machine learning. We recently made some amazing announcements on Azure Machine Learning, and in this post, Im taking a closer look at two of the most compelling capabilities that your business should consider while choosing the machine learning platform.

Wednesday, July 17, 2019

Today we are announcing the open sourcing of our recipe to pre-train BERT (Bidirectional Encoder Representations from Transformers) built by the Bing team, including code that works on Azure Machine Learning, so that customers can unlock the power of training custom versions of BERT-large models for their organization. This will enable developers and data scientists to build their own general-purpose language representation beyond BERT.

Tuesday, June 25, 2019

The next time you see your physician, consider the times you fill in a paper form. It may seem trivial, but the information could be crucial to making a better diagnosis. Now consider the other forms of healthcare data that permeate your lifeand that of your doctor, nurses, and the clinicians working to keep patients thriving.

Monday, June 10, 2019

Data scientists have a dynamic role. They need environments that are fast and flexible while upholding their organizations security and compliance policies. Notebook Virtual Machine (VM), announced in May 2019, resolves these conflicting requirements while simplifying the overall experience for data scientists.

Thursday, June 6, 2019

Build more accurate forecasts with the release of capabilities in automated machine learning. Have scenarios that require have gaps in training data or need to apply contextual data to improve your forecast or need to apply lags to your features? Learn more about the new capabilities that can assist you.

Tuesday, June 4, 2019

The automated machine learning capability in Azure Machine Learning service allows data scientists, analysts, and developers to build machine learning models with high scalability, efficiency, and productivity all while sustaining model quality.

Wednesday, May 22, 2019

During Microsoft Build we announced the preview of the visual interface for Azure Machine Learning service. This new drag-and-drop workflow capability in Azure Machine Learning service simplifies the process of building, testing, and deploying machine learning models for customers who prefer a visual experience to a coding experience.

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Machine Learning | Azure Blog and Updates | Microsoft Azure

Machine learning is sub-categorized to three types:

Supervised Learning Train Me!

Unsupervised Learning I am self sufficient in learning

Reinforcement Learning My life My rules! (Hit & Trial)

Supervised Learning is the one, where you can consider the learning is guided by a teacher. We have a dataset which acts as a teacher and its role is to train the model or the machine. Once the model gets trained it can start making a prediction or decision when new data is given to it.

The model learns through observation and finds structures in the data. Once the model is given a dataset, it automatically finds patterns and relationships in the dataset by creating clusters in it. What it cannot do is add labels to the cluster, like it cannot say this a group of apples or mangoes, but it will separate all the apples from mangoes.

Suppose we presented images of apples, bananas and mangoes to the model, so what it does, based on some patterns and relationships it creates clusters and divides the dataset into those clusters. Now if a new data is fed to the model, it adds it to one of the created clusters.

It is the ability of an agent to interact with the environment and find out what is the best outcome. It follows the concept of hit and trial method. The agent is rewarded or penalized with a point for a correct or a wrong answer, and on the basis of the positive reward points gained the model trains itself. And again once trained it gets ready to predict the new data presented to it.

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What is Machine Learning? | Types of Machine Learning ...

Machines, most often computers, are given rules to follow known as algorithms. They are also given an initial set of data to explore when they first begin learning. That data is called training data.

Computers start to recognize patterns and make decisions based on algorithms and training data. Depending on the type of machine learning being used, they are also given targets to hit or they receive rewards when they make the right decision or take a positive step towards their end goal.

As they build this understanding or learn, they work through a series of steps to transform new inputs into outputs which may consist of brand-new datasets, labeled data, decisions, or even actions.

The idea is that they learn enough to operate without any human intervention. In this way they start to develop and demonstrate what we call artificial intelligence. Machine learning is one of the main ways artificial intelligence is created.

Other examples of artificial intelligence include robotics, speech recognition, and natural language generation, all of which also require some element of machine learning.

There are many different reasons to implement machine learning and ways to go about it. There are also a variety of machine learning algorithms and types and sources of training data.

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Machine Learning Overview | What is Machine Learning?

Introduction to Machine Learning Course

Machine Learning is a first-class ticket to the most exciting careers in data analysis today. As data sources proliferate along with the computing power to process them, going straight to the data is one of the most straightforward ways to quickly gain insights and make predictions.

Machine learning brings together computer science and statistics to harness that predictive power. Its a must-have skill for all aspiring data analysts and data scientists, or anyone else who wants to wrestle all that raw data into refined trends and predictions.

This is a class that will teach you the end-to-end process of investigating data through a machine learning lens. It will teach you how to extract and identify useful features that best represent your data, a few of the most important machine learning algorithms, and how to evaluate the performance of your machine learning algorithms.

This course is also a part of our Data Analyst Nanodegree.

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Introduction to Machine Learning Course | Udacity

COVID-19 will change how the majority of us live and work, at least in the short term. Its also creating a challenge for tech companies such as Facebook, Twitter and Google that ordinarily rely on lots and lots of human labor to moderate content. Are A.I. and machine learning advanced enough to help these firms handle the disruption?

First, its worth noting that, although Facebook has instituted a sweeping work-from-home policy in order to protect its workers (along with Googleand a rising number of other firms), it initially required its contractors who moderate content to continue to come into the office. That situation only changed after protests,according toThe Intercept.

Now, Facebook is paying those contractors while they sit at home, since the nature of their work (scanning peoples posts for content that violates Facebooks terms of service) is extremely privacy-sensitive. Heres Facebooks statement:

For both our full-time employees and contract workforce there is some work that cannot be done from home due to safety, privacy and legal reasons. We have taken precautions to protect our workers by cutting down the number of people in any given office, implementing recommended work from home globally, physically spreading people out at any given office and doing additional cleaning. Given the rapidly evolving public health concerns, we are taking additional steps to protect our teams and will be working with our partners over the course of this week to send all contract workers who perform content review home, until further notice. Well ensure that all workers are paid during this time.

Facebook, Twitter, Reddit, and other companies are in the same proverbial boat: Theres an increasing need to police their respective platforms, if only to eliminate fake news about COVID-19, but the workers who handle such tasks cant necessarily do so from home, especially on their personal laptops. The potential solution? Artificial intelligence (A.I.) and machine-learning algorithms meant to scan questionable content and make a decision about whether to eliminate it.

HeresGoogles statement on the matter, via its YouTube Creator Blog.

Our Community Guidelines enforcement today is based on a combination of people and technology: Machine learning helps detect potentially harmful content and then sends it to human reviewers for assessment. As a result of the new measures were taking, we will temporarily start relying more on technology to help with some of the work normally done by reviewers. This means automated systems will start removing some content without human review, so we can continue to act quickly to remove violative content and protect our ecosystem, while we have workplace protections in place.

To be fair, the tech industry has been heading in this direction for some time. Relying on armies of human beings to read through every piece of content on the web is expensive, time-consuming, and prone to error. But A.I. and machine learning are still nascent, despite the hype. Google itself, in the aforementioned blog posting, pointed out how its automated systems may flag the wrong videos. Facebook is also receiving criticism that its automated anti-spam system is whacking the wrong posts, including those thatoffer vital information on the spread of COVID-19.

If the COVID-19 crisis drags on, though, more companies will no doubt turn to automation as a potential solution to disruptions in their workflow and other processes. That will force a steep learning curve; again and again, the rollout of A.I. platforms has demonstrated that, while the potential of the technology is there, implementation is often a rough and expensive processjust look at Google Duplex.

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Nonetheless, an aggressive embrace of A.I. will also create more opportunities for those technologists who have mastered A.I. and machine-learning skills of any sort; these folks may find themselves tasked with figuring out how to automate core processes in order to keep businesses running.

Before the virus emerged, BurningGlass (which analyzes millions of job postings from across the U.S.), estimated that jobs that involve A.I. would grow 40.1 percent over the next decade. That percentage could rise even higher if the crisis fundamentally alters how people across the world live and work. (The median salary for these positions is $105,007; for those with a PhD, it drifts up to $112,300.)

If youre trapped at home and have some time to learn a little bit more about A.I., it could be worth your time to explore online learning resources. For instance, theres aGooglecrash coursein machine learning. Hacker Noonalso offers an interesting breakdown ofmachine learningandartificial intelligence.Then theres Bloombergs Foundations of Machine Learning,a free online coursethat teaches advanced concepts such as optimization and kernel methods.

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Will COVID-19 Create a Big Moment for AI and Machine Learning? - Dice Insights

Along with artificial intelligence (A.I.), machine learning is regarded as one of the most in-demand areas for tech employment at the moment. Machine learning engineers develop algorithms and models that can adapt and learn from data. As a result, those who thrive in this discipline are generally skilled not only in computer science and programming, but also statistics, data science, deep learning, and problem solving.

According to Burning Glass, which collects and analyzes millions of job postings from across the country, the prospects for machine learning as an employer-desirable skill are quite good, with jobs projected to rise 36.5 percent over the next decade. Moreover, even those with relatively little machine-learning experience can pull down quite a solid median salary:

Dice Insights spoke with Oliver Sulley, director of Edge Tech Headhunters, to figure out how you should prepare, what youll be asked during an interviewand what you should say to grab the gig.

Youre going to be faced potentially by bosses who dont necessarily know what it is that youre doing, or dont understand ML and have just been [told] they need to get it in the business, Sulley said. Theyre being told by the transformation guys that they need to bring it on board.

As he explained, that means one of the key challenges facing machine learning engineers is determining what technology would be most beneficial to the employer, and being able to work as a cohesive team that may have been put together on very short notice.

What a lot of companies are looking to do is take data theyve collected and stored, and try and get them to build some sort of model that helps them predict what they can be doing in the future, Sulley said. For example, how to make their stock leaner, or predicting trends that could come up over they year that would change their need for services that they offer.

Sulley notes that machine learning engineers are in rarified air at themomentits a high-demand position, and lots of companies are eager to show theyve brought machine learning specialists onboard.

If theyre confident on their skills, then a lot of the time they have to make sure the role is right for them, Sulley said. Its more about the soft skills that are going to be important.

Many machine learning engineers are strong on the technical side, but they often have to interact with teams such as operations; as such, they need to be able to translate technical specifics into laymans terms and express how this data is going to benefit other areas of the company.

Building those soft skills, and making sure people understand how you will work in a team, is just as important at this moment in time, Sulley added.

There are quite a few different roles for machine learning engineers, and so its likely that all these questions could come upbut it will depend on the position. We find questions with more practical experience are more common, and therefore will ask questions related to past work and the individual contributions engineers have made, Sulley said.

For example:

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A lot of data engineering and machine learning roles involve working with different tech stacks, so its hard to nail down a hard and fast set of skills, as much depends on the company youre interviewing with.(If youre just starting out with machine learning, here are some resources that could prove useful.)

For example, if its a cloud based-role, a machine learning engineer is going to want to have experience with AWS and Azure; and for languages alone, Python and R are the most important, because thats what we see more and more in machine learning engineering, Sulley said. For deployment, Id say Docker, but it really depends on the persons background and what theyre looking to get into.

Sulley said ideal machine learning candidates posses a really analytical mind, as well as a passion for thinking about the world in terms of statistics.

Someone who can connect the dots and has a statistical mind, someone who has a head for numbers and who is interested in that outside of work, rather than someone who just considers it their job and what they do, he said.

As you can see from the following Burning Glass data, quite a few jobs now ask for machine-learning skills; if not essential, theyre often a nice to have for many employers that are thinking ahead.

Sulley suggests the questions you ask should be all about the technologyits about understanding what the companies are looking to build, what their vision is (and your potential contribution to it), and looking to see where your career will grow within that company.

You want to figure out whether youll have a clear progression forward, he said. From that, you will understand how much work theyre going to do with you. Find out what theyre really excited about, and that will help you figure out whether youll be a valued member of the team. Its a really exciting space, and they should be excited by the opportunities that come with bringing you onboard.

Continued here:
Machine Learning Engineer Interview Questions: What You Need to Know - Dice Insights

Roundup Let's get cracking with some machine-learning news.

Starksy Robotics is no more: Self-driving truck startup Starsky Robotics has shut down after running out of money and failing to raise more funds.

CEO Stefan Seltz-Axmacher bid a touching farewell to his upstart, founded in 2016, in a Medium post this month. He was upfront and honest about why Starsky failed: Supervised machine learning doesnt live up to the hype, he declared. It isnt actual artificial intelligence akin to C-3PO, its a sophisticated pattern-matching tool.

Neural networks only learn to pick up on certain patterns after they are faced with millions of training examples. But driving is unpredictable, and the same route can differ day to day, depending on the weather or traffic conditions. Trying to model every scenario is not only impossible but expensive.

In fact, the better your model, the harder it is to find robust data sets of novel edge cases. Additionally, the better your model, the more accurate the data you need to improve it, Seltz-Axmacher said.

More time and money is needed to provide increasingly incremental improvements. Over time, only the most well funded startups can afford to stay in the game, he said.

Whenever someone says autonomy is ten years away thats almost certainly what their thought is. There arent many startups that can survive ten years without shipping, which means that almost no current autonomous team will ever ship AI decision makers if this is the case, he warned.

If Seltz-Axmacher is right, then we should start seeing smaller autonomous driving startups shutting down in the near future too. Watch this space.

Waymo to pause testing during Bay Area lockdown: Waymo, Googles self-driving car stablemate, announced it was pausing its operations in California to abide by the lockdown orders in place in Bay Area counties, including San Francisco, Santa Clara, San Mateo, Marin, Contra Costa and Alameda. Businesses deemed non-essential were advised to close and residents were told to stay at home, only popping out for things like buying groceries.

It will, however, continue to perform rides for deliveries and trucking services for its riders and partners in Phoenix, Arizona. These drives will be entirely driverless, however, to minimise the chance of spreading COVID-19.

Waymo also launched its Open Dataset Challenge. Developers can take part in a contest that looks for solutions to these problems:

Cash prizes are up for grabs too. The winner can expect to pocket $15,000, second place will get you $5,000, while third is $2,000.

You can find out more details on the rules of the competition and how to enter here. The challenge is open until 31 May.

More free resources to fight COVID-19 with AI: Tech companies are trying to chip in and do what they can to help quell the coronavirus pandemic. Nvidia and Scale AI both offered free resources to help developers using machine learning to further COVID-19 research.

Nvidia is providing a free 90-day license to Parabricks, a software package that speeds up the process of analyzing genome sequences using GPUs. The rush is on to analyze the genetic information of people that have been infected with COVID-19 to find out how the disease spreads and which communities are most at risk. Sequencing genomes requires a lot of number crunching, Parabricks slashes the time needed to complete the task.

Given the unprecedented spread of the pandemic, getting results in hours versus days could have an extraordinary impact on understanding the viruss evolution and the development of vaccines, it said this week.

Interested customers who have access to Nvidias GPUs should fill out a form requesting access to Parabricks.

Nvidia is inviting our family of partners to join us in matching this urgent effort to assist the research community. Were in discussions with cloud service providers and supercomputing centers to provide compute resources and access to Parabricks on their platforms.

Next up is Scale AI, the San Francisco based startup focused on annotating data for machine learning models. It is offering its labeling services for free to any researcher working on a potential vaccine, or on tracking, containing, or diagnosing COVID-19.

Given the scale of the pandemic, researchers should have every tool at their disposal as they try to track and counter this virus, it said in a statement.

Researchers have already shown how new machine learning techniques can help shed new light on this virus. But as with all new diseases, this work is much harder when there is so little existing data to go on.

In those situations, the role of well-annotated data to train models o diagnostic tools is even more critical. If you have a lot of data to analyse and think Scale AI could help then apply for their help here.

PyTorch users, AWS has finally integrated the framework: Amazon has finally integrated PyTorch support into Amazon Elastic Inference, its service that allows users to select the right amount of GPU resources on top of CPUs rented out in its cloud services Amazon SageMaker and Amazon EC2, in order to run inference operations on machine learning models.

Amazon Elastic Inference works like this: instead of paying for expensive GPUs, users select the right amount of GPU-powered inference acceleration on top of cheaper CPUs to zip through the inference process.

In order to use the service, however, users will have to convert their PyTorch code into TorchScript, another framework. You can run your models in any production environment by converting PyTorch models into TorchScript, Amazon said this week. That code is then processed by an API in order to use Amazon Elastic Inference.

The instructions to convert PyTorch models into the right format for the service have been described here.

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Self-driving truck boss: 'Supervised machine learning doesnt live up to the hype. It isnt C-3PO, its sophisticated pattern matching' - The Register

A masterpiece is how then-Deputy Defense Secretary Patrick Shanahan infamously described the Fiscal Year 2020 budget request. It would, he said, align defense spending with the U.S. National Defense Strategy both funding the future capabilities necessary to maintain an advantage over near-peer powers Russia and China, and maintaining readiness for ongoing counter-terror campaigns.

The result was underwhelming. While research and development funding increased in 2020, it did not represent the funding shift toward future capabilities that observers expected. Despite its massive size, the budget was insufficient to address the departments long-term challenges. Key emerging technologies identified by the department such as hypersonic weapons, artificial intelligence, quantum technologies, and directed-energy weapons still lacked a clear and sustained commitment to investment. It was clear that the Department of Defense did not make the difficult tradeoffs necessary to fund long-term modernization. The Congressional Budget Office further estimated that the cost of implementing the plans, which were in any case insufficient to meet the defense strategys requirements, would be about 2 percent higher than department estimates.

Has anything changed this year? The Department of Defense released its FY2021 budget request Feb. 10, outlining the departments spending priorities for the upcoming fiscal year. As is mentioned every year at its release, the proposed budget is an aspirational document the actual budget must be approved by Congress. Nevertheless, it is incredibly useful as a strategic document, in part because all programs are justified in descriptions of varying lengths in what are called budget justification books. After analyzing the 10,000-plus programs in the research, development, testing and evaluation budget justification books using a new machine learning model, it is clear that the newest budgets tepid funding for emerging defense technologies fails to shift the departments strategic direction toward long-range strategic competition with a peer or near-peer adversary.

Regardless of your beliefs about the optimal size of the defense budget or whether the 2018 National Defense Strategys focus on peer and near-peer conflict is justified, the Department of Defenses two most recent budget requests have been insufficient to implement the administrations stated modernization strategy fully.

To be clear, this is not a call to increase the Department of Defenses budget over its already-gargantuan $705.4 billion FY2021 request. Nor is this the only problem with the federal budget proposal, which included cuts to social safety net programs programs that are needed now more than ever to mitigate the effects from COVID-19. Instead, my goal is to demonstrate how the budget fails to fund its intended strategy despite its overall excess. Pentagon officials described the budget as funding an irreversible implementation of the National Defense Strategy, but that is only true in its funding for nuclear capabilities and, to some degree, for hypersonic weapons. Otherwise, it largely neglects emerging technologies.

A Budget for the Last War

The 2018 National Defense Strategy makes clear why emerging technologies are critical to the U.S. militarys long-term modernization and ability to compete with peer or near-peer adversaries. The document notes that advanced computing, big data analytics, artificial intelligence, autonomy, robotics, directed energy, hypersonics, and biotechnology are necessary to ensure we will be able to fight and win the wars of the future. The Government Accountability Office included similar technologies artificial intelligence, quantum information science, autonomous systems, hypersonic weapons, biotechnology, and more in a 2018 report on long-range emerging threats identified by federal agencies.

In the Department of Defenses budget press release, the department argued that despite overall flat funding levels, it made numerous hard choices to ensure that resources are directed toward the Departments highest priorities, particularly in technologies now termed advanced capabilities enablers. These technologies include hypersonic weapons, microelectronics/5G, autonomous systems, and artificial intelligence. Elaine McCusker, the acting undersecretary of defense (comptroller) and chief financial officer, argued, Any place where we have increases, so for hypersonics or AI for cyber, for nuclear, thats where the money went This budget is focused on the high-end fight. (McCuskers nomination for Department of Defense comptroller was withdrawn by the White House in early March because of her concerns over the 2019 suspension of defense funding for Ukraine.) Deputy Defense Secretary David L. Norquist noted that the budget request had the largest research and development request ever.

Despite this, the FY2021 budget is not a significant shift from the FY2020 budget in developing advanced capabilities for competition against a peer or near-peer. I analyzed data from the Army, Navy, Air Force, Missile Defense Agency, Office of the Secretary of Defense, and Defense Advanced Research Projects Agency budget justification books, and the department has still failed to realign its funding priorities toward the long-range emerging technologies that strategic documents suggest should be the highest priority. Aside from hypersonic weapons, which received already-expected funding request increases, most other types of emerging technologies remained mostly stagnant or actually declined from FY2020 request levels.

James Miller and Michael OHanlon argued in their analysis of the FY2020 budget, Desires for a larger force have been tacked onto more crucial matters of military innovation and that the department should instead prioritize quality over quantity. This criticism could be extended to the FY2021 budget, along with the indictment that military innovation itself wasnt fully prioritized either.

Breaking It Down

In this brief review, I attempt to outline funding changes for emerging technologies between the FY2020 and FY2021 budgets based on a machine learning text-classification model, while noting cornerstone programs in each category.

Lets start with the top-level numbers from the R1 document, which divides the budget into seven budget activities. Basic and applied defense research account for 2 percent and 5 percent of the overall FY2021 research and development budget, compared to 38 percent for operational systems development and 27 percent for advanced component development and prototypes. The latter two categories have grown from 2019, in both real terms and as a percentage of the budget, by 2 percent and 5 percent, respectively. These categories were both the largest overall budget activities and also received the largest percentage increases.

Federally funded basic research is critical because it helps develop the capacity for the next generation of applied research. Numerous studies have demonstrated the benefit of federally funded basic science research, with some estimates suggesting two-thirds of the technologies with the most far-reaching impact over the last 50 years [stemmed] from federally funded R&D at national laboratories and research universities. These technologies include the internet, robotics, and foundational subsystems for space-launch vehicles, among others. In fact, a 2019 study for the National Bureau of Economic Researchs working paper series found evidence that publicly funded investments in defense research had a crowding in effect, significantly increasing private-sector research and development from the recipient industry.

Concerns over the levels of basic research funding are not new. A 2015 report by the MIT Committee to Evaluate the Innovation Deficit argued that declining federal basic research could severely undermine long-term U.S. competitiveness, particularly for research areas that lack obvious real-world applications. This is particularly true given that the share of industry-funded basic research has collapsed, with the authors arguing that U.S. companies are left dependent on federally-funded, university-based basic research to fuel innovation. This shift means that federal support of basic research is even more tightly coupled to national economic competitiveness. A 2017 analysis of Americas artificial intelligence strategy recommended that the government [ensure] adequate funding for scientific research, averting the risks of an innovation deficit that could severely undermine long-term competitiveness. Data from the Organization for Economic Cooperation and Development shows that Chinese government research and development spending has already surpassed that of the United States, while Chinese business research and development expenditures are rapidly approaching U.S. levels.

While we may debate the precise levels of basic and applied research and development funding, there is little debate about its ability to produce spillover benefits for the rest of the economy and the public at large. In that sense, the slight declines in basic and applied research funding in both real terms and as a percentage of overall research and development funding hurt the United States in its long-term competition with other major powers.

Clean, Code, Classify

The Defense Departments budget justification books contain thousands of pages of descriptions spread across more than 20 separate PDFs. Each program description explains the progress made each year and justifies the funding request increase or decrease. There is a wealth of information about Department of Defense strategy in these documents, but it is difficult to assess departmental claims about funding for specific technologies or to analyze multiyear trends while the data is in PDF form.

To understand how funding changed for each type of emerging technology, I scraped and cleaned this information from the budget documents, then classified each research and development program into categories of emerging technologies (including artificial intelligence, biotechnologies, directed-energy weapons, hypersonic weapons and vehicles, quantum technologies, autonomous and swarming systems, microelectronics/5G, and non-emerging technology programs). I designed a random forest machine learning model to sort the remaining programs into these categories. This is an algorithm that uses hundreds of decision trees to identify which variables or words in a program description, in this case are most important for classifying data into groups.

There are many kinds of machine learning models that can be used to classify data. To choose one that would most effectively classify the program data, I started by hand-coding 1,200 programs to train three different kinds of models (random forest, k-nearest neighbors, and support vector machine), as well as for a model testing dataset. Each model would look at the term frequency-inverse document frequency (essentially, how often given words appear adjusted for how rarely they are used) of all the words in a programs description to decide how to classify each program. For example, for the Armys Long Range Hypersonic Weapon program, the model might have seen the words hypersonic, glide, and thermal in the description and guessed that it was most likely a hypersonic program. The random forest model slightly outperformed the support vector machine model and significantly outperformed the k-nearest neighbors model, as well as a simpler method that just looked for specific keywords in a program description.

Having chosen a machine-learning model to use, I set it to work classifying the remaining 10,000 programs. The final result is a large dataset of programs mentioned in the 2020 and 2021 research and development budgets, including their full descriptions, predicted category, and funding amount for the year of interest. This effort, however, should be viewed as only a rough estimate of how much money each emerging technology is getting. Even a fully hand-coded classification that didnt rely on a machine learning model would be challenged by sometimes-vague program descriptions and programs that fund multiple types of emerging technologies. For example, the Applied Research for the Advancement of S&T Priorities program funds projects across multiple categories, including electronic warfare, human systems, autonomy, and cyber advanced materials, biomedical, weapons, quantum, and command, control, communications, computers and intelligence. The model took a guess that the program was focused on quantum technologies, but that is clearly a difficult program to classify into a single category.

With the programs sorted and classified by the model, the variation in funding between types of emerging technologies became clear.

Hypersonic Boost-Glide Weapons Win Big

Both the official Department of Defense budget press release and the press briefing singled out hypersonic research and development investment. As one of the departments advanced capabilities enablers, hypersonic weapons, defenses, and related research received $3.2 billion in the FY2021 budget, which is nearly as much as the other three priorities mentioned in the press release combined (microelectronics/5G, autonomy, and artificial intelligence).

In the 2021 budget documents, there were 96 programs (compared with 60 in the 2020 budget) that the model classified as related to hypersonics based on their program descriptions, combining for $3.36 billion an increase from 2020s $2.72 billion. This increase was almost solely due to increases in three specific programs, and funding for air-breathing hypersonic weapons and combined-cycle engine developments was stagnant.

The three programs driving up the hypersonic budget are the Armys Long-Range Hypersonic Weapon, the Navys Conventional Prompt Strike, and the Air Forces Air-Launched Rapid Response Weapon program. The Long-Range Hypersonic Weapon received a $620.42 million funding increase to field an experimental prototype with residual combat capability. The Air-Launched Rapid Response Weapons $180.66 million increase was made possible by the removal of funding for the Air Forces Hypersonic Conventional Strike Weapon in FY2021 which saved $290 million compared with FY2020. This was an interesting decision worthy of further analysis, as the two competing programs seemed to differ in their ambition and technical risk; the Air-Launched Rapid Response Weapon program was designed for pushing the art-of-the-possible while the conventional strike weapon was focused on integrating already mature technologies. Conventional Prompt Strike received the largest 2021 funding request at $1 billion, an increase of $415.26 million over the 2020 request. Similar to the Army program, the Navys Conventional Prompt Strike increase was fueled by procurement of the Common Hypersonic Glide Body that the two programs share (along with a Navy-designed 34.5-inch booster), as well as testing and integration on guided missile submarines.

To be sure, the increase in hypersonic funding in the 2021 budget request is important for long-range modernization. However, some of the increases were already planned, and the current funding increase largely neglects air-breathing hypersonic weapons. For example, the Navys Conventional Prompt Strike 2021 budget request was just $20,000 more than anticipated in the 2020 budget. Programs that explicitly mention scramjet research declined from $156.2 million to $139.9 million.

In contrast to hypersonics, research and development funding for many other emerging technologies was stagnant or declined in the 2021 budget. Non-hypersonic emerging technologies increased from $7.89 billion in 2020 to only $7.97 billion in 2021, mostly due to increases in artificial intelligence-related programs.

Biotechnology, Quantum, Lasers Require Increased Funding

Source: Graphic by the author.

Directed-energy weapons funding fell slightly in the 2021 budget to $1.66 billion, from $1.74 billion in 2020. Notably, the Army is procuring three directed-energy prototypes to support the maneuver-short range air defense mission for $246 million. Several other programs are also noteworthy. The High Energy Power Scaling program ($105.41 million) will finalize designs and integrate systems into a prototype 300 kW-class high-energy laser, focusing on managing thermal blooming (a distortion caused by the laser heating the atmosphere through which it travels) for 300 and eventually 500 kW-class lasers. Second, the Air Forces Directed Energy/Electronic Combat program ($89.03 million) tests air-based directed-energy weapons for use in contested environments.

Quantum technologies funding increased by $109 million, to $367 million, in 2021. In general, quantum-related programs are more exploratory, focused on basic and applied research rather than fielding prototypes. They are also typically funded by the Office of the Secretary of Defense or the Defense Advanced Research Projects Agency rather than by the individual services, or they are bundled into larger programs that distribute funding to many emerging technologies. For example, several of the top 2021 programs that the model classified as quantum research and development based on their descriptions include the Office of the Secretary of Defenses Applied Research for the Advancement of S&T Priorities ($54.52 million), or the Defense Advanced Research Projects Agencys Functional Materials and Devices ($28.25 million). The increase in Department of Defense funding for quantum technologies is laudable, but given the potential disruptive ability of quantum technologies, the United States should further increase its federal funding for quantum research and development, guarantee stable long-term funding, and incentivize young researchers to enter the field. The FY2021 budgets funding increase is clearly a positive step, but quantum technologies revolutionary potential demands more funding than the category currently receives.

Biotechnologies increased from $969 million in 2020 to $1.05 billion in 2021 (my guess is that the model overestimated the funding for emerging biotech programs, by including research programs related to soldier health and medicine that involve established technologies). Analyses of defense biotechnology typically focus on the defense applications of human performance enhancement, synthetic biology, and gene-editing technology research. Previous analyses, including one from 2018 in War on the Rocks, have lamented the lack of a comprehensive strategy for biotechnology innovation, as well as funding uncertainties. The Center for Strategic and International Studies argued, Biotechnology remains an area of investment with respect to countering weapons of mass destruction but otherwise does not seem to be a significant priority in the defense budget. These concerns appear to have been well-founded. Funding has stagnated despite the enormous potential offered by biotechnologies like nanotubes, spider silk, engineered probiotics, and bio-based sensors, many of which could be critical enablers as components of other emerging technologies. For example, this estimate includes the interesting Persistent Aquatic Living Sensors program ($25.7 million) that attempts to use living organisms to detect submarines and unmanned underwater vehicles in littoral waters.

Programs classified as autonomous or swarming research and development declined from $3.5 billion to $2.8 billion in 2021. This includes the Army Robotic Combat Vehicle program (stagnant at $86.22 million from $89.18 million in 2020). The Skyborg autonomous attritable (a low-cost, unmanned system that doesnt have to be recovered after launch) drone program requested $40.9 million and also falls into the autonomy category, as do the Air Forces Golden Horde ($72.09 million), Office of the Secretary of Defenses manned-unmanned teaming Avatar program ($71.4 million), and the Navys Low-Cost UAV Swarming Technology (LOCUST) program ($34.79 million).

The programs sorted by the model into the artificial intelligence category increased from $1.36 billion to $1.98 billion in 2021. This increase is driven by an admirable proliferation of smaller programs 161 programs under $50 million, compared with 119 in 2020. However, as the Department of Defense reported that artificial intelligence research and development received only $841 million in the 2021 budget request, it is clear that the random forest model is picking up some false positives for artificial intelligence funding.

Some critics argue that federal funding risks duplicating artificial intelligence efforts in the commercial sector. There are several problems with this argument, however. A 2017 report on U.S. artificial intelligence strategy argued, There also tends to be shortfalls in the funding available to research and start-ups for which the potential for commercialization is limited or unlikely to be lucrative in the foreseeable future. Second, there are a number of technological, process, personnel, and cultural challenges in the transition of artificial intelligence technologies from commercial development to defense applications. Finally, the Trump administrations anti-immigration policies hamstring U.S. technological and industrial base development, particularly in artificial intelligence, as immigrants are responsible for one-quarter of startups in the United States.

The Neglected Long Term

While there are individual examples of important programs that advance the U.S. militarys long-term competitiveness, particularly for hypersonic weapons, the overall 2021 budget fails to shift its research and development funding toward emerging technologies and basic research.

While recognizing that the overall budget was essentially flat, it should not come as a surprise that research and development funding for emerging technologies was mostly flat as well. But the United States already spends far more on defense than any other country, and even with a flat budget, the allocation of funding for emerging technologies does not reflect an increased focus on long-term planning for high-end competition compared with the 2020 budget. Specifically, the United States should increase its funding for emerging technologies other than hypersonics directed energy, biotech, and quantum information sciences, as well as in basic scientific research even if it requires tradeoffs in other areas.

The problem isnt necessarily the year-to-year changes between the FY2020 and FY2021 budgets. Instead, the problem is that proposed FY2021 funding for emerging technologies continues the previous years underwhelming support for research and development relative to the Department of Defenses strategic goals. This is the critical point for my assessment of the budget: despite multiple opportunities to align funding with strategy, emerging technologies and basic research have not received the scale of investment that the National Defense Strategy argues they deserve.

Chad Peltier is a senior defense analyst at Janes, where he specializes in emerging defense technologies, Chinese military modernization, and data science. This article does not reflect the views of his employer.

Image: U.S. Army (Photo by Monica K. Guthrie)

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Put Your Money Where Your Strategy Is: Using Machine Learning to Analyze the Pentagon Budget - War on the Rocks

Watch Dr. Yonit Hoffman's Machine Learning Conference session

Accidents at sea happen all the time. Their costs in terms of lives, money and environmental destruction are huge. Wouldnt it be great if they could be predicted and perhaps prevented? Dr. Yonit Hoffmans Machine Learning Conference session discusses new ways of preventing sea accidents with the power of data science.

Does machine learning hold the key to preventing accidents at sea?

With more than 350 years of history, the marine insurance industry is the first data science profession to try to predict accidents and estimate future risk. Yet the old ways no longer work, new waves of data and algorithms can offer significant improvements and are going to revolutionise the industry.

In her Machine Learning Conference session, Dr. Yonit Hoffman will show that it is now possible to predict accidents, and how data on a ships behaviour such as location, speed, maps and weather can help. She will show how fragments of information on ship movements can be gathered and taken all the way to machine learning models. In this session, she discusses the challenges, including introducing machine learning to an industry that still uses paper and quills (yes, really!) and explaining the models using SHAP.

Dr. Yonit Hoffman is a Senior Data Scientist at Windward, a world leader in maritime risk analytics. Before investigating supertanker accidents, she researched human cells and cancer at the Weizmann Institute, where she received her PhD and MSc. in Bioinformatics. Yonit also holds a BSc. in computer science and biology from Tel Aviv University.

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Data to the Rescue! Predicting and Preventing Accidents at Sea - JAXenter

Deep Learning Super Sampling (DLSS) is one of the marquee features for Nvidias RTX video cards, but its also one people tend to overlook or outright dismiss. The reason for that is because many people equate the technology to something like a sharpening filter that can sometimes reduce the jagged look of lower-resolution images. But DLSS uses a completely different method with much more potential for improving visual quality, and Nvidia is ready to prove that with DLSS 2.0.

Nvidia built the second-generation DLSS to address all of the concerns with the technology. It looks better, gives players much more control, and should support a lot more games. But at its core, DLSS 2.0 is still about using machine learning to intelligently upscale a game to a higher resolution. The idea is to give you a game that, for example, looks like it is running at 4K while actually rendering at 1080p or 1440p. This drastically improves performance. And, in certain games, it can even produce frames that contain more detail than native rendering.

For DLSS, Nvidia inputs a game into a training algorithm to determine what the visuals are supposed to look like at the sharpest possible fidelity. And this is one of the areas where DLSS 2.0 is a significant leap forward. Nvidia originally needed a bespoke training model for every game. DLSS 2.0, however, uses the same neural network for every game. This means Nvidia can add DLSS support to more games at a more rapid pace.

Then using that deep-learning data, DLSS can then use the Tensor GPU cores on Nvidias RTX cards to process what a 1080p frame should look like at 4K. And this method is so much more powerful than sharpening because it is rebuilding from data that isnt even necessarily present in each frame. Heres the result:

MechWarrior 5: Mercenaries and Control are the first two games to support DLSS 2.0. They will get the benefit of the more efficient AI network. This version of the tech is twice as fast on the Tensor cores already available in RTX cards like the RTX 2060 up to the RTX 2080 Ti.

Nvidia has also added temporal feedback to its DLSS system. This enables the super-sampling method to get information about how objects and environments change over time. DLSS 2.0 can then use that temporal feedback to improve the sharpness and stability from one frame to the next.

But the advantages go beyond improved processing. DLSS 2.0 also turns over more control to the player. One of the disadvantages of DLSS in many games is that it was often a binary choice. Either it was on or off, and developers got to decide how DLSS behaved.

DLSS 2.0 flips that by giving three presets: Quality, Balanced, and Performance. In Performance mode, DLSS 2.0 can take a 1080p frame and upscale it all the way up to 2160p (4K). Quality mode, meanwhile, may upscale 1440p to 2160p.

But you dont necessarily need a 4K display to get the advantages of DLSS 2.0. You can use the tech on a 1080p or 1440p display, and it will often provide better results than native rendering.

Again, this is possible because DLSS 2.0 is working from more data than a native 1080p frame. And all of this is going to result in higher frame rates and playable games even when using ray tracing.

DLSS 2.0 is rolling out soon as part of a driver update for RTX cards.

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Nvidias DLSS 2.0 aims to prove the technology is essential - VentureBeat