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AI For Advertisers: How Data Analytics Can Change The Maths Of Advertising?

All Images Credit: Freepik

The task of understanding a customer’s journey and designing your marketing strategy accordingly can be difficult in this data-driven world. Today, the customer expresses their needs in myriad forms of requests.

Consumers express their needs and want attitudes, and values in various forms through search, comments, blogs, Tweets, “likes,” videos, and conversations and access such data across many channels like web, mobile, and face to face. Volume, variety, velocity and veracity of the data accumulated through these customer interactions are huge.

BigData and data analytics can be leveraged to understand several phases of the customer journey. There are risks involved in using Artificial Intelligence for the marketing data analysis of data breach and even manipulation. But, AI do have brighter prospects when it comes to marketing and advertiser applications.

As the CEO of a technology firm Chop Dawg and marketer, Joshua Davidson puts it, “AI-powered apps are going to be the future for us, and there are several industries that are ripe for this.” The mobile-first strategy of many enterprises has powered the use of AI for digital marketing and developing technologies and innovations to power industries with intelligent systems.

How AI and Machine learning are affecting customer journeys?

Any consumer journey begins with the recognition of a problem and then stages like initial consideration, active evaluation, purchase, and postpurchase come through up till the consumer journey is over. The need for identifying the purchasing and need patterns of the consumers and finding the buyer personas to strategize the marketing for them.

Need and Want Recognition:

Identifying a need is quite difficult as it is the most initial level of a consumer’s journey and it is more on the category level than at a brand level. Marketers and advertisers are relying on techniques like market research, web analytics, and data mining to build consumer profiles and buyer’s persona for understanding the needs and influencing the purchase of products. AI can help identify these wants and needs in real-time as the consumers usually express their needs and wants online and help build profiles more quickly.

AI technologies offered by several firms help in consumer profiling. Firms like Microsoft offers Azure that crunches billions of data points in seconds to determine the needs of consumers. It then personalizes web content on specific platforms in real-time to align with those status-updates. Consumer digital footprints are evolving through social media status updates, purchasing behavior, online comments and posts. Ai tends to update these profiles continuously through machine learning techniques.

Initial Consideration:

A key objective of advertising is to insert a brand into the consideration set of the consumers when they are looking for deliberate offerings. Advertising includes increasing the visibility of brands and emphasize on the key reasons for consideration. Advertisers currently use search optimization, paid search advertisements, organic search, or advertisement retargeting for finding the consideration and increase the probability of consumer consideration.

AI can leverage machine learning and data analytics to help with search, identify and rank functions of consumer consideration that can match the real-time considerations at any specific time. Take an example of Google Adwords, it analyzes the consumer data and helps advertisers make clearer distinctions between qualified and unqualified leads for better targeting.

Google uses AI to analyze the search-query data by considering, not only the keywords but also context words and phrases, consumer activity data and other BigData. Then, Google identifies valuable subsets of consumers and more accurate targeting.

Active Evaluation: 

When consumers narrow it down to a few choices of brands, advertisers need to insert trust and value among the consumers for brands. A common technique is to identify the higher purchase consumers and persuade them through persuasive content and advertisement. AI can support these tasks using some techniques:

Predictive Lead Scoring: Predictive lead scoring by leveraging machine learning techniques of predictive analytics to allow marketers to make accurate predictions related to the intent of purchase for consumers. A machine learning algorithm runs through a database of existing consumer data, then recognize trends and patterns and after processing the external data on consumer activities and interests, creates robust consumer profiles for advertisers.

Natural Language Generation: By leveraging the image, speech recognition and natural language generation, machine learning enables marketers to curate content while learning from the consumer behavior in real-time scenarios and adjusts the content according to the profiles on the fly.

Emotion AI: Marketers use emotion AI to understand consumer sentiment and feel about the brand in general. By tapping into the reviews, blogs or videos they understand the mood of customers. Marketers also use emotion AI to pretest advertisements before its release. The famous example of Kelloggs, which used emotion AI to help devise an advertising campaign for their cereal, eliminating the advertisement executions whenever the consumer engagement dropped.

Purchase: 

As the consumers decide which brands to choose and what it’s worth, advertising aims to move them out of the decision process and push for the purchase by reinforcing the value of the brand compared with its competition.

Advertisers can insert such value by emphasizing convenience and information about where to buy the product, how to buy the product and reassuring the value through warranties and guarantees. Many marketers also emphasize on rapid return policies and purchase incentives.

AI can completely change the purchase process through dynamic pricing, which encompasses real-time price adjustments on the basis of information such as demand and other consumer-behavior variables, seasonality, and competitor activities.

Post-Purchase: 

Aftersales services can be improved through intelligent systems using AI technologies and machine learning techniques. Marketers and advertisers can hire dedicated developers to design intelligent virtual agents or chatbots that can reinforce the value and performance of a brand among consumers.

Marketers can leverage an intelligent technique known as Propensity modeling to identify the most valuable customers on the basis of lifetime value, likelihood of reengagement, propensity to churn, and other key performance measures of interest. Then advertisers can personalize their communication with these customers on the basis of these data.

Conclusion:

AI has shifted the focus of advertisers and marketers towards the customer-first strategies and enhanced the heuristics of customer engagement. Machine learning and IoT(Internet of Things) has already changed the way customer interact with the brands and this transition has come at a time when advertisers and marketers are looking for new ways to tap into the customer mindset and buyer’s persona.

All Images Credit: Freepik

How can AI and Machine learning impact healthcare industry?

Healthcare industry is a recession-proof one. Even in times of economic meltdown and financial distress, the healthcare industry can hold its own because mankind will always need healthcare. In fact, during the Great Depression in the US, when the economy was facing a severe slowdown, the healthcare industry expanded, adding 852,000 jobs.

Healthcare AI in the US is slated to reach $6.6 billion in value by 2021.

From clinical trials to new drug research & development, and from innovative medical devices to technology like nanoparticles, AI, and ML has touched every point and has the power to transform them completely.

In fact, according to a study by Accenture, AI applications in healthcare can result in global savings to the tune of $150 billion by 2026.

The possibilities are endless, and the results unthinkable if AI can be properly used.

Here are some of the ways AI and ML can impact the healthcare industry:

1. Solving the Iron Triangle

A problem that has plagued the world for many years the triangle aims to solve a fundamental healthcare problem: that of good quality, accessible treatment at low cost.

Providing all three at the same time is a major challenge in healthcare, as the cost of healthcare is usually high. Here, trying to improve one factor harms another.

But AI can solve this problem in the near future without breaking the triangle, by improving the current healthcare cost-structure. The key to it is AI, and smart machines, that the patient can use for self-treatment for the majority of times, cutting down treatment costs drastically, by reducing human contact and improving quality of life.

2. Diagnostics and Imaging

The US FDA has drastically increased investment on AI in radiology and diagnostics. And it’s not without reason.

The IDx-DR became the first AI system cleared by the US FDA to provide diagnostic decisions. It was a breakthrough discovery to detect early mild diabetic retinopathy. The device was accurate 87.5% of the times, and also detected patients who didn’t have the condition, correctly up to 89.5% of times.

The US FDA also permitted marketing of the Viz.AI a type of clinical decision support system designed to analyze CT scan results to identify possibilities of a stroke in the patients and send the results to a specialist to identify any block.

In fact, diagnostics is fast becoming one of the significant drivers of AI investment in healthcare.

These advances can impact the healthcare industry in a novel way. As more and more devices become AI-enabled, the landscape of healthcare delivery will change.

3. Early screening 

Early screening in case of most diseases can drastically improve the mortality rates of patients and cut down treatment costs by over 50%.

Let’s take the example of colorectal cancer.

The 5-year survival for Stage 1 CRC is around 90%, as compared to only 10% for Stage 4.

Early detection of CRC can be ideally treated with a minimally invasive endoscopy at a low cost of less than $5,000 per year. However, in the case of late-stage CRC, it requires multidisciplinary treatment with multiple surgeries, chemotherapy, and radiation, skyrocketing the costs.

And that is why early detection is essential, and that’s exactly what AI can do. There are already apps on the market that are doing this. For example, Autism & Beyond is a revolutionary app that leveraged the power of Apple’s ResearchKit to gather videos of children and detect their preference for the development of autism, using AI software.

AI used for early screening can save billions of taxpayer dollars of taxpayer money every year, and reduce out of pocket expenditure in the US drastically.

4. Drug research & development

According to the California Biomedical Research Association, it takes around 12 years for a drug to be conceived in the laboratory and go to the patient.

Only 1 out of 5000 drugs that are selected for pre-clinical testing are then used for human testing, and only 20% of them make it to the market for human use.

(image)

The cost to develop a new drug now is more than $2.5 billion.

It is only recently that AI is being used in drug research and discovery. The power of AI can be leveraged to streamline the drug discovery and drug repurposing processes. It can identify patients best suited to the trial, can identify patients in the most need for new medications and can predict any side-effects and idiosyncrasies beforehand.

All of these, for a start, can lead to much safer clinical trials with no unwanted drug reactions.

And then, there is the question of lowering costs. In fact, a study by Carnegie Mellon and a German university estimated that AI could lower drug discovery costs by as much as 70%.

This, in turn, will be transferred to patients in the form of lower drug prices, which will increase accessibility to better medications for patients and improve population health in general.

5. Surgery 

AI-enabled robotic-assisted surgeries are taking over the US. They are increasingly being used to reduce surgeon variations and improve quality.

‘Artificial intelligence can help surgeons perform better’ quotes Dr. John Birkmeyer, a chief clinical officer at Sound Physicians.

Advanced analytics and machine learning techniques are being used concomitantly used to unleash critical insights from the billions of data elements associated with robotic-assisted surgery. If used properly, this can help overcome attendant inefficiencies and improve patient health outcomes.

Artificial intelligence helps surgeons make better clinical decisions in real-time during surgery, and helps them understand the dynamics of the patient, especially during complex operations. It also reduces the length of stay of patients by 21%.

This is ultimately reflected in the patient’s post-operative care and long-term health. It also prevents patient readmissions, saving millions of dollars annually.

A study involving 379 orthopedic patients found out that AI-assisted robotic surgery resulted in five times fewer complications as compared to surgeons working alone.

According to Accenture, AI-assisted robotic surgery could save the US healthcare industry $40 billion annually, by 2026.

6. AI-assisted virtual nurses

AI-assisted virtual nurses could well end up saving the US healthcare industry $20 billion annually, by 2026.

They are available 24/7 to answer any patient queries, monitor patients, and guide them in any way they might want.

Currently, they act as a bridge for information exchange between care providers (doctors) and care receivers (patients), to decide what medications to start, the current health status, the most recent test results, and many other things.

It can save the patient many physical appointments with doctors, and also prevent high hospital readmission rates through simple, engaging, and intelligent care.

Care Angel is one of the finest virtual nurses around. Apart from all of the above, it can also provide wellness checks through voice and AI.

Wrap-Up 

AI and ML in healthcare are still at its infancy. Adoption at a large-scale is missing as of yet. To be successful in the healthcare domain, AI and ML need the endorsement of healthcare providers like physicians and nurses.

However, considerable investment is being made in AI in healthcare, and its increasing at a good rate.

AI in healthcare is currently aimed at improving patient outcomes, taking care of the interests of various stakeholders involved, increasing accessibility, and reducing healthcare costs.

In the near future, however, AI and ML, along with technologies like Data Science will take up a much more holistic role to drive healthcare forward.

Visual Question Answering with Keras – Part 1

This is Part I of II of the Article Series Visual Question Answering with Keras

Making Computers Intelligent to answer from images

If we look closer in the history of Artificial Intelligence (AI), the Deep Learning has gained more popularity in the recent years and has achieved the human-level performance in the tasks such as Speech Recognition, Image Classification, Object Detection, Machine Translation and so on. However, as humans, not only we but also a five-year child can normally perform these tasks without much inconvenience. But the development of such systems with these capabilities has always considered an ambitious goal for the researchers as well as for developers.

In this series of blog posts, I will cover an introduction to something called VQA (Visual Question Answering), its available datasets, the Neural Network approach for VQA and its implementation in Keras and the applications of this challenging problem in real life. 

Table of Contents:

1 Introduction

2 What is exactly Visual Question Answering?

3 Prerequisites

4 Datasets available for VQA

4.1 DAQUAR Dataset

4.2 CLEVR Dataset

4.3 FigureQA Dataset

4.4 VQA Dataset

5 Real-life applications of VQA

6 Conclusion

 

  1. Introduction:

Let’s say you are given a below picture along with one question. Can you answer it?

I expect confidently you all say it is the Kitchen without much inconvenience which is also the right answer. Even a five-year child who just started to learn things might answer this question correctly.

Alright, but can you write a computer program for such type of task that takes image and question about the image as an input and gives us answer as output?

Before the development of the Deep Neural Network, this problem was considered as one of the difficult, inconceivable and challenging problem for the AI researcher’s community. However, due to the recent advancement of Deep Learning the systems are capable of answering these questions with the promising result if we have a required dataset.

Now I hope you have got at least some intuition of a problem that we are going to discuss in this series of blog posts. Let’s try to formalize the problem in the below section.

  1. What is exactly Visual Question Answering?:

We can define, “Visual Question Answering(VQA) is a system that takes an image and natural language question about the image as an input and generates natural language answer as an output.”

VQA is a research area that requires an understanding of vision(Computer Vision)  as well as text(NLP). The main beauty of VQA is that the reasoning part is performed in the context of the image. So if we have an image with the corresponding question then the system must able to understand the image well in order to generate an appropriate answer. For example, if the question is the number of persons then the system must able to detect faces of the persons. To answer the color of the horse the system need to detect the objects in the image. Many of these common problems such as face detection, object detection, binary object classification(yes or no), etc. have been solved in the field of Computer Vision with good results.

To summarize a good VQA system must be able to address the typical problems of CV as well as NLP.

To get a better feel of VQA you can try online VQA demo by CloudCV. You just go to this link and try uploading the picture you want and ask the related question to the picture, the system will generate the answer to it.

 

  1. Prerequisites:

In the next post, I will walk you through the code for this problem using Keras. So I assume that you are familiar with:

  1. Fundamental concepts of Machine Learning
  2. Multi-Layered Perceptron
  3. Convolutional Neural Network
  4. Recurrent Neural Network (especially LSTM)
  5. Gradient Descent and Backpropagation
  6. Transfer Learning
  7. Hyperparameter Optimization
  8. Python and Keras syntax
  1. Datasets available for VQA:

As you know problems related to the CV or NLP the availability of the dataset is the key to solve the problem. The complex problems like VQA, the dataset must cover all possibilities of questions answers in real-world scenarios. In this section, I will cover some of the datasets available for VQA.

4.1 DAQUAR Dataset:

The DAQUAR dataset is the first dataset for VQA that contains only indoor scenes. It shows the accuracy of 50.2% on the human baseline. It contains images from the NYU_Depth dataset.

Example of DAQUAR dataset

Example of DAQUAR dataset

The main disadvantage of DAQUAR is the size of the dataset is very small to capture all possible indoor scenes.

4.2 CLEVR Dataset:

The CLEVR Dataset from Stanford contains the questions about the object of a different type, colors, shapes, sizes, and material.

It has

  • A training set of 70,000 images and 699,989 questions
  • A validation set of 15,000 images and 149,991 questions
  • A test set of 15,000 images and 14,988 questions

Image Source: https://cs.stanford.edu/people/jcjohns/clevr/?source=post_page

 

4.3 FigureQA Dataset:

FigureQA Dataset contains questions about the bar graphs, line plots, and pie charts. It has 1,327,368 questions for 100,000 images in the training set.

4.4 VQA Dataset:

As comapred to all datasets that we have seen so far VQA dataset is relatively larger. The VQA dataset contains open ended as well as multiple choice questions. VQA v2 dataset contains:

  • 82,783 training images from COCO (common objects in context) dataset
  • 40, 504 validation images and 81,434 validation images
  • 443,757 question-answer pairs for training images
  • 214,354 question-answer pairs for validation images.

As you might expect this dataset is very huge and contains 12.6 GB of training images only. I have used this dataset in the next post but a very small subset of it.

This dataset also contains abstract cartoon images. Each image has 3 questions and each question has 10 multiple choice answers.

  1. Real-life applications of VQA:

There are many applications of VQA. One of the famous applications is to help visually impaired people and blind peoples. In 2016, Microsoft has released the “Seeing AI” app for visually impaired people to describe the surrounding environment around them. You can watch this video for the prototype of the Seeing AI app.

Another application could be on social media or e-commerce sites. VQA can be also used for educational purposes.

  1. Conclusion:

I hope this explanation will give you a good idea of Visual Question Answering. In the next blog post, I will walk you through the code in Keras.

If you like my explanations, do provide some feedback, comments, etc. and stay tuned for the next post.

Understanding Dropout and implementing it on MNIST dataset

Over-fitting is a major problem in deep learning and a plethora of techniques have been introduced to prevent it. One of the most effective one is called “dropout”.  Let’s use the analogy of a person going to gym for understanding this. Let’s say the person going to gym mostly uses his dominant arm, say his right arm to pick up weights. After some time, he notices that his dominant arm is developing a large muscle, but not the other arm. So, what can he do? Obviously, he needs to involve both his arms while training. Sometimes he should stop using his right arm, and use the left arm to lift weights and vice versa.

Something like this happens commonly in neural networks. Sometime one part of the network has very large weights and ends up dominating the training. While other part of the network remains weak and does not really play a role in the training. So, what dropout does to solve this problem, is it randomly shuts off some nodes and stop the gradients flowing through it. So, our forward and back propagation happen without those nodes. In that case the rest of the nodes need to pick up the slack and be more active in the training. We define a probability of the nodes getting dropped. For example, P=0.5 means there is a 50% chance a node will be dropped.

Figure 1 demonstrates the dropout technique, taken from the original research paper.

Dropout in a neuronal Net

Our network can never rely on any given node because it can be squashed at any given time. Hence the network is forced to learn redundant representation for everything to make sure at least some of the information remains. Redundant representation leads our network to be more robust. It also acts as ensemble of many networks, since at every epoch random nodes are dropped, each time our network will be different. Ensemble of different networks perform better than a single network since they capture more randomness. Please note, only non-output nodes are dropped.

Let’s, look at the python code to implement dropout in a neural network:

from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets(".", one_hot=True, reshape=False)

import tensorflow as tf

# Parameters
learning_rate = 0.00001
epochs = 10
batch_size = 128

# Number of samples to calculate validation and accuracy
test_valid_size = 256

# Network Parameters
n_classes = 10  # MNIST total classes (0-9 digits)
dropout = 0.75  # Dropout, probability to keep units


# layers weight & bias
weights = {
    'wc1': tf.Variable(tf.random_normal([5, 5, 1, 32])),
    'wc2': tf.Variable(tf.random_normal([5, 5, 32, 64])),
    'wd1': tf.Variable(tf.random_normal([7*7*64, 1024])),
    'out': tf.Variable(tf.random_normal([1024, n_classes]))}

biases = {
    'bc1': tf.Variable(tf.random_normal([32])),
    'bc2': tf.Variable(tf.random_normal([64])),
    'bd1': tf.Variable(tf.random_normal([1024])),
    'out': tf.Variable(tf.random_normal([n_classes]))}

#function that implements Convolution layer
def conv2d(x, W, b, strides=1):
    x = tf.nn.conv2d(x, W, strides=[1, strides, strides, 1], padding='SAME')
    x = tf.nn.bias_add(x, b)
    return tf.nn.relu(x)

#defining a function to implement maxpool layers
def maxpool2d(x, k=2):
    return tf.nn.max_pool(x, ksize=[1, k, k, 1], strides=[1, k, k, 1], padding='SAME')

#Function that defines all the convolution layers.
def conv_net(x, weights, biases, dropout):
    # Layer 1 - 28*28*1 to 14*14*32
    conv1 = conv2d(x, weights['wc1'], biases['bc1'])
    conv1 = maxpool2d(conv1, k=2)

    # Layer 2 - 14*14*32 to 7*7*64
    conv2 = conv2d(conv1, weights['wc2'], biases['bc2'])
    conv2 = maxpool2d(conv2, k=2)


    # Fully connected layer - 7*7*64 to 1024
    fc1 = tf.reshape(conv2, [-1, weights['wd1'].get_shape().as_list()[0]])
    fc1 = tf.add(tf.matmul(fc1, weights['wd1']), biases['bd1'])
    fc1 = tf.nn.relu(fc1)
    fc1 = tf.nn.dropout(fc1, dropout)  # Implementing the dropout layer

    # Output Layer - class prediction - 1024 to 10
    out = tf.add(tf.matmul(fc1, weights['out']), biases['out'])
    return out

# tf Graph input
x = tf.placeholder(tf.float32, [None, 28, 28, 1])
y = tf.placeholder(tf.float32, [None, n_classes])
keep_prob = tf.placeholder(tf.float32) # Keep probability for dropout layers

# Model
logits = conv_net(x, weights, biases, keep_prob)

# Define loss and optimizer
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=y))
optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate).minimize(cost)

# Accuracy
correct_pred = tf.equal(tf.argmax(logits, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))

# Initializing the variables
init = tf.global_variables_initializer()

# Launch the graph
with tf.Session() as sess:
    sess.run(init)

    for epoch in range(epochs):
        for batch in range(mnist.train.num_examples//batch_size):
            batch_x, batch_y = mnist.train.next_batch(batch_size)
            sess.run(optimizer, feed_dict={x: batch_x, y: batch_y, keep_prob: dropout})

            # Calculate batch loss and accuracy
            loss = sess.run(cost, feed_dict={x: batch_x, y: batch_y, keep_prob: 1.})
            valid_acc = sess.run(accuracy, feed_dict={
                x: mnist.validation.images[:test_valid_size],
                y: mnist.validation.labels[:test_valid_size],
                keep_prob: 1.}) #we want to keep all nodes while training so keep prob is 1.

            print('Epoch {:>2}, Batch {:>3} - Loss: {:>10.4f} Validation Accuracy: {:.6f}'.format(
                epoch + 1,
                batch + 1,
                loss,
                valid_acc))

    # Calculate Test Accuracy
    test_acc = sess.run(accuracy, feed_dict={
        x: mnist.test.images[:test_valid_size],
        y: mnist.test.labels[:test_valid_size],
        keep_prob: 1.})
    print('Testing Accuracy: {}'.format(test_acc))

 


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Sentiment Analysis of IMDB reviews

 

Download the Data Sources

The data sources used in this article can be downloaded here:

Deep Learning and Human Intelligence – Part 1 of 2

Many people are under the impression that the new wave of data science, machine learning and/or digitalization is new, that it did not exist before. But its history is as long as the history of humanity and/or science itself.  The scientific discovery could hardly take place without the necessary data. Even the process of discovering the numbers included elements of machine learning: pattern recognition, comparison between different groups (ranking), clustering, etc. So what differentiates mathematical formulas from machine learning and how does it relate to artificial intelligence?

There is no difference between the two if seen from the perspective of formulas however, such a perspective limits the type of data to which they can be applied. Data stored via tables consist of structured data and are stored in so-called relational databases. The reason for such a data storage is the connection between different fields that assume a well-established structure in advance, such as a company’s sales or balance sheet. However, with the emergence of personal computers, many of the daily activities have been digitalized: music, pictures, movies, and so on. All this information is stored unrelated to other data and therefore called unstructured data.

IEEE International Conference on Computer Vision (ICCV), 2015, DOI: 10.1109/ICCV.2015.428

Copyright: IEEE International Conference on Computer Vision (ICCV), 2015, DOI: 10.1109/ICCV.2015.428

The essence of scientific discoveries was and will be structure. Not surprisingly, the mathematical formulas revolve around relations between variables – information, in general. For example, Galileo derived the law of falling balls from measuring the successive hight of a falling ball. The main difficulty was to obtain measurements at regular time intervals. What about if the data is not structured, which mathematical formula should be applied then? There is a distribution of people’s height, but no distribution for the pictures taken in all holidays for the last year, there is an amplitude for acoustic signals, but no function that detects the similarity between two songs. This is one of the reasons why machine learning focuses heavily on clustering and classification.

Roughly speaking, these simple examples are enough to categorize the difference between scientific discovery and machine learning. Science is about discovering relationships between different variables, Machine Learning tries to automatize processes. Every technical improvement is part of the automation, so why is everything different in this case? Because the current automation deals with human intelligence. The car automates the walking, the kitchen stove the fire, but Machine Learning parts of the human intelligence. There is a difference between the previous automation steps and those of human intelligence. All the previous ones are either outside the human body – such as Fire – or unconsciously executed (once learned) – walking, spinning, etc. The automation induced by Machine Learning affects a part of the human intelligence that we consciously perceive. Of course, today’s machine learning tools are unable to automate all human intelligence, but it is a fascinating step in that direction.

A breakthrough in Machine Learning tasks was achieved in 2012 when the first Deep Learning algorithm for detecting types of images, reached near-human accuracy. It could appreciate the likelihood that the image is a human face, a train, a ball or a fish without having “seen” the picture before. Such an algorithm can be used in various areas:  personally – facial recognition in pictures and/or social media – as tagging of images or videos, medicine – cancer detection, etc. For understanding such cutting-edge issues of classification, one cannot avoid understanding how Deep Learning works. To see the beauty of such algorithms and, at the same time, to be able to comprehend the difficulty of working with them, an example will be the best guide.

The building blocks of Deep Learning are neurons, operational units, which perform mathematical operations or logical operations like AND, OR, etc., and are modelled after the neurons in the brain. Already in the 1950’s two neuroscientist, Hubel and Wiesel, observed that not all neurons in the brain are responding in the same fashion to visual stimuli. Some responded only to horizontal lines, whereas others to vertical lines, with other words, the brain is constructed with specialized neurons. Groups of such neurons are called, in the Machine Learning community, layers. Like in the brain, neurons with different properties are clustered in different layers. This implies that layers have also specific properties and have to be arranged in a specific way, called architecture. It is this architecture which differentiates Deep Learning from Artificial Neuronal Networks (ANN are similar to a layer).

Unfortunately, scientists still haven’t figured out how the brain works, thus to discover how to train Deep Learning from data was not an easy task, and is also the reason why another example is used to explain the training of Deep Learning: the eye. One has always to remember: once it is known how Deep Learning works, it is simple to find example which illustrates the working mechanism.  For such an analogy, it is sufficient for someone without any knowledge about Deep Learning, to keep in mind only the elements that compose such architectures: input data, different layers of neurons, output layers, ReLu’s.

Input data are any type of information, in our example it is light. Of course, that Deep Learning is not limited only to images or videos, but also to sound and/or time series, which would imply that the example would be the ear and sound waves, or the brain and numbers.

Layers can be seen as cells in the eye. It is well known that the eye is formed of different layers connected to each other with each of them having different properties, functionalities. The same is true also for the layers of a Deep Learning architecture: one can see the neurons as cells of the layer as the tissue. While, mathematically, the neurons are nothing more than simple operations, usually linear weight functions, they can be seen as the properties of individual cells. Each layer has one weight matrix, which gives the neuron (and layer) specific properties depending on the data and the task at hand.

It is here that the architecture becomes very important. What Deep Learning offers is a default setting of the layers with unknown weights. One can see this as trying to build an eye knowing that there are different types of cells and different ways how tissues of such cells can be arranged, but not which cell exactly is needed (with what properties) and which arrangement of layers works best. Such an approach has the advantage that one is capable of building any type of organ desired, but the disadvantage is also very obvious: it is time consuming to find the appropriate cell properties and layers arrangements.

Still, the strategy of Deep Learning is a significant departure from the Machine Learning approaches. The performance of Machine Learning methods is as good as the features engineering performed by Data Scientists, and thus depending on the creativity of the Data Scientist. In the case of Deep Learning the engineers of the features is performed automatically as part of the model building. This is a huge improvement, as the only difficult task is to have enough data and computer power to find the right weights matrices. Such an endeavor was performed also by nature for the eye — and is also the reason why one can choose it as an example for Deep Learning — evolution. It is not surprising that Deep Learning is one of the best direction scientists have of Artificial Intelligence today.

The evolution of the eye can be seen, from the perspective of Data Scientists, as the continuous training of a Deep Learning architecture which enables to recognize and track one or more objects. The performance of the evolutional process can be summed up as the fine tuning of the cells which are getting more and more susceptible to light and the adaptation of layers to enable a better vision. Different animals in different environments and different targets — as the hawk and the fly — developed different eyes than humans, but they all work according to the same principle. The tasks that Deep Learning is performing today are similar, for example it can be used to drive cars but there is still a difference:  there is no connection to other organs. Deep Learning is not the approximation of an Artificial Organism, like an android, but a simplified Artificial Organ that can work on its own.

Returning to the working mechanism of the Deep Learning architecture, we can already follow the analogy of what happens if a ray of light is hitting the eye. Once the eye is fully adapted to the task, one can followed how the information enters the Deep Learning architecture (Artificial Eye) by penetrating the input layer. already here arises the question, what kind of eye is the best? One where a small source of light can reach as many neurons as possible, or the one where the light sources reaches only few neurons? In order to take such a decision, a last piece of the puzzle is required: ReLu. One can see them as synapses between neurons (cells) and/or similarly for tissue. By using continuous functions, such as the shape of the latter ‘S’ (called sigmoid), the information from one neuron will be distributed over a large number of other neurons. If one uses the maximum function, then only few neurons are updated with processed information from earlier layers.

Such sparse structures between neurons, was a major improvement in the development of the technique of training Deep Learning architectures. Again, it has a strong evolutionary analogy: energy efficiency. By needing less neurons, the tissues and architecture are both kept to a minimal size which enables flexibility in development and less energy. As the information is process by the different layers, the Artificial Eye is gathering more and more complex (non-linear) structures — the adapted features –, which help to decide, from past experience, what kind of object is detected.

This was part 1 of 2 of the article series. Continue with Part 2.

Ways AI & ML Are Changing How We Live

From Amazon’s Alexa, a personal assistant that can do anything from making your to-do list to giving a wide range of real-time information about the world around you, to Google’s DeepMind that has very recently made headlines for possibly being able to predict the future, AI and ML are the biggest development in human history.

Machine Learning Used by Hospitals

We hear a lot about Artificial Intelligence (AI) in the realm of insurance Big Data, but there isn’t much buzz around how AI and ML are revolutionising hospitals. The national health expenditures were around $3.4 trillion and estimated to increase from 17.8 percent of GDP to 19.9 percent between 2015 and 2025. By 2021, industry analysts have predicted that the AI health market will reach $6.6 billion. By 2026, such increases in AI technology in the healthcare sector will save the economy around $150 billion annually.

Some of the most popular Artificial Intelligence applications used in hospitals now are:

  • Predictive Health Trackers – Technology that has the ability to monitor patients’ health status using real-time data collection. One such technology is the Health and Environmental Tracker (HET) which can predict if someone is about to have an asthma attack.
  • Chatbots – It isn’t only retail customer service that uses chatbots to deal with consumers. Now hospitals have automated physicians that inquire and route clinicians to the right specialists.
  • Predictive AnalyticsCleveland Clinics have partnered with Microsoft (Cortana) while John Hopkins has partnered up with GE in order to create Machine Learning technology that has the ability to monitor patients and prevent patient emergencies before they happen. It does this by analysing data for primary indicators of potential risks.

Cognitive Marketing – Content Marketing on Steroids

Customer experience and content marketing are terms often tossed around in the world of business and advertising these days. Why do we bring them up now, you ask? Well, things are about to be kicked into sixth gear, thanks to Cognitive Marketing. To explain what that is, let’s go back a bit: remember when Google’s DeepMind AlphaGo bested the top human player at the game? This wasn’t some computer beating a bored office clerk at the game of Solitaire. In order to achieve that victory, Google’s AI had to “actually show its cognitive capability to ‘think’ like humans, because to win the game, ‘intuition’ was needed rather than just ‘logical reasoning’.” Similar algorithm-powered AI’s are enabling machines to learn and grow on their own. Soon, they’ll reach the potential to create content for marketeers at a massive scale. Not only that, but they’ll always deliver the right content, to the right kind of audience, at just the right time.

More Ways Than One: How Retail Is Harnessing AI & ML

  1. Developing Store That Don’t Need Checkout Lines

Tech companies and online retail giants such as Amazon want to create cashier-free stores, at least they are trying to. Last year Amazon launched its Amazon Go which uses sensors and hundreds of cameras to track what customers pick up and then charge the amount to an application on their smart phone, put simply. But only months into the experiment Amazon has said they need to work out some kinks in the system. As of now, Amazon Go’s system can only handle 20 or so customers at a time.

Among other issues, The Guardian, citing an unnamed source, wrote in an article, stated “…if an item has been moved from its specific spot on the shelf.”  Located in Seattle, Washington, Amazon Go is now running in “beta mode” only for Amazon employees as it tests its systems. And these tests are showing that Amazon’s attempt at a cashier-free brick-and-mortar convenience store is far from ready for the real world. A Journal report stated, “For now, the technology functions flawlessly only if there are a small number of customers present, or when their movements are slow.”

  1. Could Drones Be Delivering Goods to Your Home One Day?

Imagine ordering something online from, let’s say, Amazon, and it arrives at your door in 30 minutes or so via drone. Does that sound like something out of the movie The Fifth Element? Maybe, but this technology is already is already here.

Amazon Prime Air made its first delivery to a customer via a GPS-guided flying drone on December 7th, 2016. It only took 13 minutes for the drone to deliver the merchandise to the customer. This sort of technology will be a huge game changer for retail. The supply chain industry is headed for a revolution – drone delivery is coming, and retailers who want to keep up really should adopt such technologies.

Even in 2016, consumers were totally ready to accept drone delivery. The Walk Sands Future of Retail 2016 Study showed that 79 percent of US consumers said they would be “very likely” or “somewhat likely” to choose drone delivery if their product could be delivered within an hour. For me, I’d choose it just to see how cool it was. I think it would be pretty rad to have a drone land in my yard with my package, don’t you? Furthermore, other consumers stated they would pay up to $10 for a drone delivery. Lastly, 26 percent of consumers are already expecting to have their packages delivered to them in the next two years or so.

Driverless Delivery Vehicles Already Here as Well

There was a movie I watched some months ago – you most likely heard of it or even watched it. It was the latest movie about Wolverine titled Logan. There was a certain scene that never left my memory (basically because I found it awesome) where Logan and his companions were driving along a freeway full of driverless tractor trailers that had no tractor.

In an article written for pastemagazine.com, Carlos Alvarez of Getty wrote: “… Logan’s writer and director James Mangold’s inclusion of the self-driving trucking machines make it clear that the filmmaker understands the writing on the wall about the future of shipping. It’s a future without truck drivers.” He continues to explain that the movie takes place a little over 10 years from now in 2029.

“The change may well be here long before 2029. It’s only 2017, and already we’re seeing the beginnings of automated trucking taking over the industry. At the 2017 Consumer Electronics Show this January, Peloton Technology demonstrated “platooning,” where trucks are kept in a row on the highway to reduce wind resistance and save fuel. The trucks are controlled by computers on a “Level One” of autonomous driving,” Alvarez continued in his article.

Now in Germany, Mercedes-Benz is has been developing and testing their Actros truck which is fitted with a ‘highway pilot’ system, which acts like an auto-pilot and includes a radar and stereo camera system. So far, German carmaker Daimler has restricted testing on a German autobahn. The autobahn is generally safer than testing in city conditions since the curves are not as steep. Since the tests have started, this autonomous truck has already driven over 20,000 kilometres.

Did I Say Flying Taxis? Huh, Yeah I Did!

But, if you are still not amazed, then I am about to blow your socks off. Dubai has promised to build a fully autonomous public transportation system by 2030, including autonomous flying drone taxis! Now that is really something. And it isn’t a matter of when they’ll be produced and in use because they already are.

Manufactured in China by the drone-making firm EHang, these really freaking cool quad drones on steroids can carry one person weighing up to 100 kilogrammes (I weigh over that, guess I’m walking) plus maybe a backpack or suitcase. They can fly about 30 kilometres (or 19 miles), at a speed of 60 miles per hour, give or take. And, if that isn’t the cool part, you won’t need any lessons on how to fly it. Simply push a button and it flies you from point A to point B. Whether or not you have to give it directions, don’t know. Either way, this is mostly likely the coolest piece of tech out there right now.

Copyright @ CBS Interactive Inc.

Statistical Relational Learning – Part 2

In the first part of this series onAn Introduction to Statistical Relational Learning”, I touched upon the basic Machine Learning paradigms, some background and intuition of the concepts and concluded with how the MLN template looks like. In this blog, we will dive in to get an in depth knowledge on the MLN template; again with the help of sample examples. I would then conclude by highlighting the various toolkit available and some of its differentiating features.

MLN Template – explained

A Markov logic network can be thought of as a group of formulas incorporating first-order logic and also tied with a weight. But what exactly does this weight signify?

Weight Learning

According to the definition, it is the log odds between a world where F is true and a world where F is false,

and captures the marginal distribution of the corresponding predicate.

Each formula can be associated with some weight value, that is a positive or negative real number. The higher the value of weight, the stronger the constraint represented by the formula. In contrast to classical logic, all worlds (i.e., Herbrand Interpretations) are possible with a certain probability [1]. The main idea behind this is that the probability of a world increases as the number of formulas it violates decreases.

Markov logic networks with its probabilistic approach combined to logic posit that a world is less likely if it violates formulas unlike in pure logic where a world is false if it violates even a single formula. Consider the case when a formula with high weight i.e. more significance is violated implying that it is less likely in occurrence.

Another important concept during the first phase of Weight Learning while applying an MLN template is “Grounding”. Grounding means to replace each variable/function in predicate with constants from the domain.

Weight Learning – An Example

Note: All examples are highlighted in the Alchemy MLN format

Let us consider an example where we want to identify the relationship between 2 different types of verb-noun pairs i.e noun subject and direct object.

The input predicateFormula.mln file contains

  1. The predicates nsubj(verb, subject) and dobj(verb, object) and
  2. Formula of nsubj(+ver, +s) and dobj(+ver, +o)

These predicates or rules are to learn all possible SVO combinations i.e. what is the probability of a Subject-Verb-Object combination. The + sign ensures a cross product between the domains and learns all combinations. The training database consists of the nsubj and dobj tuples i.e. relations is the evidence used to learn the weights.

When we run the above command for this set of rules against the training evidence, we learn the weights as here:

Note that the formula is now grounded by all occurrences of nsubj and dobj tuples from the training database or evidence and the weights are attached to it at the start of each such combination.

But it should be noted that there is no network yet and this is just a set of weighted first-order logic formulas. The MLN template we created so far will generate Markov networks from all of our ground formulas. Internally, it is represented as a factor graph.where each ground formula is a factor and all the ground predicates found in the ground formula are linked to the factor.

Inference

The definition goes as follows:

Estimate probability distribution encoded by a graphical model, for a given data (or observation).

Out of the many Inference algorithms, the two major ones are MAP & Marginal Inference. For example, in a MAP Inference we find the most likely state of world given evidence, where y is the query and x is the evidence.

which is in turn equivalent to this formula.

Another is the Marginal Inference which computes the conditional probability of query predicates, given some evidence. Some advanced inference algorithms are Loopy Belief Propagation, Walk-SAT, MC-SAT, etc.

The probability of a world is given by the weighted sum of all true groundings of a formula i under an exponential function, divided by the partition function Z i.e. equivalent to the sum of the values of all possible assignments. The partition function acts a normalization constant to get the probability values between 0 and 1.

Inference – An Example

Let us draw inference on the the same example as earlier.

After learning the weights we run inference (with or without partial evidence) and query the relations of interest (nsubj here), to get inferred values.

Tool-kits

Let’s look at some of the MLN tool-kits at disposal to do learning and large scale inference. I have tried to make an assorted list of all tools here and tried to highlight some of its main features & problems.

For example, BUGS i.e. Bayesian Logic uses a Swift Compiler but is Not relational! ProbLog has a Python wrapper and is based on Horn clauses but has No Learning feature. These tools were invented in the initial days, much before the present day MLN looks like.

ProbCog developed at Technical University of Munich (TUM) & the AI Lab at Bremen covers not just MLN but also Bayesian Logic Networks (BLNs), Bayesian Networks & ProLog. In fact, it is now GUI based. Thebeast gives a shell to analyze & inspect model feature weights & missing features.

Alchemy from University of Washington (UoW) was the 1st First Order (FO) probabilistic logic toolkit. RockIt from University of Mannheim has an online & rest based interface and uses only Conjunctive Normal Forms (CNF) i.e. And-Or format in its formulas.

Tuffy scales this up by using a Relational Database Management System (RDBMS) whereas Felix allows Large Scale inference! Elementary makes use of secondary storage and Deep Dive is the current state of the art. All of these tools are part of the HAZY project group at Stanford University.

Lastly, LoMRF i.e. Logical Markov Random Field (MRF) is Scala based and has a feature to analyse different hypothesis by comparing the difference in .mln files!

 

Hope you enjoyed the read. The content starts from basic concepts and ends up highlighting key tools. In the final part of this 3 part blog series I would explain an application scenario and highlight the active research and industry players. Any feedback as a comment below or through a message is more than welcome!

Back to Part I – Statistical Relational Learning

Additional Links:

[1] Knowledge base files in Logical Markov Random Fields (LoMRF)

[2] (still) nothing clever Posts categorized “Machine Learning” – Markov Logic Networks

[3] A gentle introduction to statistical relational learning: maths, code, and examples

Statistical Relational Learning

An Introduction to Statistical Relational Learning – Part 1

Statistical Relational Learning (SRL) is an emerging field and one that is taking centre stage in the Data Science age. Big Data has been one of the primary reasons for the continued prominence of this relational learning approach given, the voluminous amount of data available now to learn interesting and unknown patterns from data. Moreover, the tools have also improved their processing prowess especially, in terms of scalability.

This introductory blog is a prelude on SRL and later on I would also touch base on more advanced topics, specifically Markov Logic Networks (MLN). To start off, let’s look at how SRL fits into one of the 5 different Machine Learning paradigms.

Five Machine Learning Paradigms

Lets look at the 5 Machine Learning Paradigms: Each of which is inspired by ideas from a different field!

  1. Connectionists as they are called and led by Geoffrey Hinton (University of Toronto & Google and one of the major names in the Deep Learning community) think that a learning algorithm should mimic the brain! After all it is the brain that does all the complex actions for us and, this idea stems from Neuroscience.
  2. Another group of Evolutionists whose leader is the late John Holland (from the University of Michigan) believed it is not the brain but evolution that was precedent and hence the master algorithm to build anything. And using this approach of having the fittest ones program the future they are currently building 3D prints of future robots.
  3. Another thought stems from Philosophy where Analogists like Douglas R. Hofstadter an American writer and author of popular and award winning book – Gödel, Escher, Bach: an Eternal Golden Braid believe that Analogy is the core of Cognition.
  4. Symbolists like Stephen Muggleton (Imperial College London) think Psychology is the base and by developing Rules in deductive reasoning they built Adam – a robot scientist at the University of Manchester!
  5. Lastly we have a school of thought which has its foundations rested on Statistics & Logic, which is the focal point of interest in this blog. This emerging field has started to gain prominence with the invention of Bayesian networks 2011 by Judea Pearl (University of California Los Angeles – UCLA) who was awarded with the Turing award (the highest award in Computer Science). Bayesians as they are called, are the most fanatical of the lot as they think everything can be represented by the Bayes theorem using hypothesis which can be updated based on new evidence.

SRL fits into the last paradigm of Statistics and Logic. As such it offers another alternative to the now booming Deep Learning approach inspired from Neuroscience.

Background

In many real world scenario and use cases, often the underlying data is assumed to be independent and identically distributed (i.i.d.). However, real world data is not and instead consists of many relationships. SRL as such attempts to represent, model, and learn in the relational domain!

There are 4 main Models in SRL

  1. Probabilistic Relational Models (PRM)
  2. Markov Logic Networks (MLN)
  3. Relational Dependency Networks (RDN)
  4. Bayesian Logic Programs (BLP)

It is difficult to cover all major models and hence the focus of this blog is only on the emerging field of Markov Logic Networks.
MLN is a powerful framework that combines statistics (i.e. it uses Markov Random Fields) and logical reasoning (first order logic).

 

markov-random-fields-first-order-logic

Academia

Some of the prominent names in academic and the research community in MLN include:

  1. Professor Pedro Domingos from the University of Washington is credited with introducing MLN in his paper from 2006. His group created the tool called Alchemy which was one of the first, First Order Logic tools.
  2. Another famous name – Professor Luc De Raedt from the AI group at University of Leuven in Belgium, and their team created the tool ProbLog which also has a Python Wrapper.
  3. HAZY Project (Stanford University) led by Prof. Christopher Ré from the InfoLab is doing active research in this field and Tuffy, Felix, Elementary, Deep Dive are some of the tools developed by them. More on it later!
  4. Talking about academia close by i.e. in Germany, Prof. Michael Beetz and his entire team moved from TUM to TU Bremen. Their group invented the tool – ProbCog
  5. At present, Prof. Volker Tresp from Ludwig Maximilians University (LMU), Munich & Dr. Matthias Nickles at Technical University of Munich (TUM) have research interests in SRL.

Theory & Formulation

A look at some background and theoretical concepts to understand MLN better.

A. Basics – Probabilistic Graphical Models (PGM)

The definition of a PGM goes as such:

A PGM encodes a joint p(x,y) or conditional p(y|x) probability distribution such that given some observations we are provided with a full probability distribution over all feasible solutions.

A PGM helps to encode relationships between a set of random variables. And it achieves this by making use of a graph! These graphs can be either be Directed or Undirected Graphs.

B. Markov Blanket

A Markov Blanket is a Directed Acyclic graph. It is a Bayesian network and as you can see the central node A highlighted in red is dependent on its parents and parents of descendents (moralization) by the circle drawn around it. Thus these nodes are the only knowledge needed to predict node A.

C. Markov Random Fields (MRF)

A MRF is an Undirected graphical model. Every node in an MRF satisfies the Local Markov property of Conditional Independence, i.e. a node is conditionally independent of another node, given its neighbours. And now relating it to Markov Blanket as explained previously, a Markov blanket for a node is simply its adjacent nodes!

Intuition

We now that Probability handles uncertainty whereas Logic handles complexity. So why not make use of both of them to model relationships in data that is both uncertain and complex. Markov Logic Networks (MLN) precisely does that for us!

MLN is composed of a set of pairs of  <w, F> where F is the formula (written in FO logic) and weights (real numbers identifying the strength of the constraint).

MLN basically provides a template to ground a Markov network. Grounding would be explained in detail in the next but one section on “Weight Learning”.

It can be defined as a Log linear model where probability of a world is given by the weighted sum of all true groundings of a formula i under an exponential function. It is then divided by Z which is termed as the partition function and used to normalize and get probability values between 0 and 1.

propability_of_a_world_x

The MLN Template

Rules or Predicates

The relation to be learned is expressed in FO logic. Some of the different possible FO logical connectives and quantifiers are And (^), Or (V), Implication (→), and many more. Plus, Formulas may contain one or more predicates, connected to each other with logical connectives and quantified symbols.

Evidence

Evidence represent known facts i.e. the ground predicates. Each fact is expressed with predicates that contain only constants from their corresponding domains.

Weight Learning

Discover the importance of relations based on grounded evidence.

Inference

Query relations, given partial evidence to infer a probabilistic estimate of the world.

More on Weight Learning and Inference in the next part of this series!

Hope you enjoyed the read. I have deliberately kept the content basic and a mix of non technical and technical so as to highlight first the key players and some background concepts and generate the reader’s interest in this topic, the technicalities of which can easily be read in the paper. Any feedback as a comment below or through a message are more than welcome!

Continue reading with Statistical Relational Learning – Part II.

References

Neural Nets: Time Series Prediction

Artificial neural networks are very strong universal approximators. Google recently defeated the worlds strongest Go (“chinese chess”) player with two neural nets, which captured the game board as a picture. Aside from these classification tasks, neural nets can be used to predict future values, behaviors or patterns solely based on learned history. In the machine learning literature, this is often referred to as time series prediction, because, you know, values over time need to be predicted. Hah! To illustrate the concept, we will train a neural net to learn the shape of a sinusoidal wave, so it can continue to draw the shape without any help. We will do this with Scala. Scala is a great lang, because it is strongly typed but feels easy like Python. Throughout this article, I will use the library NeuroFlow, which is a simple, lightweight library I wrote to build and train nets. Because Open Source is the way to go, feel free to check (and contribute to? :-)) the code on GitHub.

Introduction of the shape

If we, as humans, want to predict the future based on historic observations, we would have no other chance but to be guided by the shape drawn so far. Let’s study the plot below, asking ourselves: How would a human continue the plot?

sinuspredictdr
f(x) = sin(10*x)

Intuitively, we would keep on oscillating up and down, just like the grey dotted line tries to rough out. To us, the continuation of the shape is reasonably easy to understand, but a machine does not have a gut feeling to ask for a good guess. However, we can summon a Frankenstein, which will be able to learn and continue the shape based on numbers. In order to do so, let’s have a look at the raw, discrete data of our sinusoidal wave:

x f(x)
0.0 0.0
0.05 0.479425538604203
0.10 0.8414709848078965
0.15 0.9974949866040544
0.20 0.9092974268256817
0.25 0.5984721441039564
0.30 0.1411200080598672
0.35 -0.35078322768961984
0.75 0.9379999767747389

Ranging from 0.0 until 0.75, these discrete values drawn from our function with step size 0.05 will be the basis for training. Now, one could come up with the idea to just memorize all values, so a sufficiently reasonable value can be picked based on comparison. For instance, to continue at the point 0.75 in our plot, we could simply examine the area close to 0.15, noticing a similar value close to 1, and hence go downwards. Well, of course this is cheating, but if a good cheat is a superior solution, why not cheat? Being hackers, we wouldn’t care. What’s really limiting here is the fact that the whole data set needs to be kept in memory, which can be infeasible for large sets, plus for more complex shapes, this approach would quickly result in a lot of weird rules and exceptions to be made in order to find comprehensible predictions.

Net to the rescue

Let’s go back to our table and see if a neural net can learn the shape, instead of simply memorizing it. Here, we want our net architecture to be of kind [3, 5, 3, 1]. Three input neurons, two hidden layers with five and three neurons respectively, as well as one neuron for the output layer will capture the data shown in the table.

sinuspredictnet

A supervised training mode means, that we want to train our net with three discrete steps as input and the fourth step as the supervised training element. So we will train a, b, c -> d and e, f, g -> h et cetera, hoping that this way our net will capture the slope pattern of our sinusoidal wave. Let’s code this in Scala:

import neuroflow.core.Activator.Tanh 
import neuroflow.core.WeightProvider.randomWeights 
import neuroflow.nets.DynamicNetwork.constructor

First, we want a Tanh activation function, because the domain of our sinusoidal wave is [-1, 1], just like the hyperbolic tangent. This way we can be sure that we are not comparing apples with oranges. Further, we want a dynamic network (adaptive learning rate) and random initial weights. Let’s put this down:

val fn = Tanh.apply
val sets = Settings(true, 10.0, 0.0000001, 500, None, None, Some(Map("τ" -> 0.25, "c" -> 0.25)))
val net = Network(Input(3) :: Hidden(5, fn) :: Hidden(3, fn) :: Output(1, fn) :: Nil, sets)

No surprises here. After some experiments, we can pick values for the settings instance, which will promise good convergence during training. Now, let’s prepare our discrete steps drawn from the sinus function:

val group = 4
val sinusoidal = Range.Double(0.0, 0.8, 0.05).grouped(group).toList.map(i => i.map(k => (k, Math.sin(10 * k))))
val xsys = sinusoidal.map(s => (s.dropRight(1).map(_._2), s.takeRight(1).map(_._2)))
val xs = xsys.map(_._1)
val ys = xsys.map(_._2)
net.train(xs, ys)

We will draw samples from the range with step size 0.05. After this, we will construct our training values xs as well as our supervised output values ys. Here, a group consists of 4 steps, with 3 steps as input and the last step as the supervised value.

[INFO] [25.01.2016 14:07:51:677] [run-main-5] Taking step 499 - error: 1.4395661497489177E-4  , error per sample: 3.598915374372294E-5
[INFO] [25.01.2016 14:07:51:681] [run-main-5] Took 500 iterations of 500 with error 1.4304189739640242E-4  
[success] Total time: 4 s, completed 25.01.2016 14:20:56

After a pretty short time, we will see good news. Now, how can we check if our net can successfully predict the sinusoidal wave? We can’t simply call our net like a sinus function to map from one input value to one output value, e. g. something like net(0.75) == sin(0.75). Our net does not care about any x values, because it was trained purely based on the function values f(x), or the slope pattern in general. We need to feed our net with a three-dimensional input vector holding the first three, original function values to predict the fourth step, then drop the first original step and append the recently predicted step to predict the fifth step, et cetera. In other words, we need to traverse the net. Let’s code this:

val initial = Range.Double(0.0, 0.15, 0.05).zipWithIndex.map(p => (p._1, xs.head(p._2)))
val result = predict(net, xs.head, 0.15, initial)
result.foreach(r => println(s"${r._1}, ${r._2}"))

with

@tailrec def predict(net: Network, last: Seq[Double], i: Double, results: Seq[(Double, Double)]): Seq[(Double, Double)] = {
  if (i < 4.0) {
    val score = net.evaluate(last).head
    predict(net, last.drop(1) :+ score, i + 0.05, results :+ (i, score))
  } else results
}

So, basically we don’t just continue to draw the sinusoidal shape at the point 0.75, we draw the entire shape right from the start until 4.0 – solely based on our trained net! Now, let’s see how our Frankenstein will complete the sinusoidal shape from 0.75 on:

sinuspredictfintwo

I’d say, pretty neat? Keep in mind, here, the discrete predictions are connected through splines. Another interesting property of our trained net is its prediction compared to the original sinus function when taking the limit towards 4.0. Let’s plot both:

sinuspredictfin

The purple line is the original sinusoidal wave, whereas the green line is the prediction of our net. The first steps show great consistency, but slowly the curves diverge a little over time, as uncertainties will add up. To keep this divergence rather low, one could fine tune settings, for instance numeric precision. However, if one is taking the limit towards infinity, a perfect fit is illusory.

Final thoughts

That’s it! We have trained our net to learn and continue the sinusoidal shape. Now, I know that this is a rather academic example, but to train a neural net to learn more complex shapes is straightforward from here.

Thanks for reading!