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How to Automate Data Extraction and Digitize Your Document Based Processes?

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How to Automate Data Extraction and Digitize Your Document Based Processes?

Is Manual Data Extraction still a thing in 2021?

The moment I read the title of the blog post, the first question that sprung to my mind was: ‘Is Manual data Entry still a thing in 2021?.’ A bit of research and I was pleasantly surprised at the scale of the problem. Many organizations still rely on manual data entry. Most of them don’t invest in setting up an automated data extraction pipeline because manual data entry is extremely cheap and requires almost zero expertise. However, according to a 2018 Goldman Sachs report, the direct and indirect costs of manual data entry amounts to around $2.7 trillion for global businesses.

A potential use case for an automated data extraction pipeline was during the COVID-19 pandemic. A lot of data such as the number of people tested, the test reports of each individual etc. had to be manually entered into a database. Automating the process would have saved a lot of time and manpower.

DRAWBACKS OF MANUAL DATA EXTRACTION:

  1. Errors: When performing a tedious and repetitive task like manual data entry, errors are bound to creep in. Identifying and correcting these errors at a later stage might prove to be a costly affair.
  2. Slow Process: When compared to automated data extraction, manual data entry is an extremely slow process and could stall the entire production pipeline.
  3. Data Security: When dealing with sensitive data, a manual data entry process can lead to data leakages which could in turn compromise the system.

Are you facing manual Data Extraction issues? Want to make your organization’s data extraction process efficient? Head over to Nanonets and see for yourself about how Data Extraction from Documents can be automated.


SECTION 1: THE DATA PIPELINE

To overcome the above mentioned drawbacks, almost all large organisations need to build a data pipeline. The main components of any data pipeline are aptly described by the acronym ETL (Extract, Transform, Load). Data Extraction involves extracting data from various sources, the data transformation stage aims to convert this data into a specific format and data loading refers to the process of storing this data in a data warehouse.

How to Automate Data Extraction and Digitize Your Document Based Processes?
Fig 1. The ETL Process

Being the first stage in the pipeline, data extraction plays a crucial role in any organization. This post explores the various methods and tools that can be used to perform data extraction and how Optical Character Recognition(OCR) can be employed for this task.

SECTION 2: AUTOMATIC DATA EXTRACTION:

Almost all modern day data analytics requires large amounts of data to perform well. For example: Any organization would want to keep tabs on their competitors performance, the general market trends, customer reviews and reactions etc. A way to do this is to make use of data extraction tools that can scrape the web and retrieve data from various sources. The following section highlights a few popular off the shelf data extraction tools.

2.1: DATA EXTRACTION TOOLS
1) Scrapy: Scrapy is an open-source web crawler written in python. Let’s go through a simple example that illustrates how even a complete novice can scrape the web using Scrapy. In the following example, I have used Scrapy to parse the title of the Nanonets blog page.

How to Automate Data Extraction and Digitize Your Document Based Processes?
Fig 2. Title of the Nanonets blog page parsed using Scrapy

Although I used the Scrapy shell for the purpose of parsing, the same behaviour could be achieved using a python script.

How to Automate Data Extraction and Digitize Your Document Based Processes?
Fig 3.Title of the Nanonets blog page parsed by Scrapy

The tool is extremely intuitive and elements from any HTML page can be parsed using CSS. The only downside to the tool from the point of view of a beginner was that parsing dynamic web pages was pretty challenging.

2) Octoparse, Outwit hub, Parsehub etc are other open source tools that provide an intuitive GUI for web scraping.

Apart from these open source tools there are companies that are dedicated to performing data extraction. Small organizations that don’t have the resources to build custom data extraction pipelines can outsource the data extraction process by making use of these data extraction services.

2.2:DATA EXTRACTION TECHNIQUES

The flowchart given below provides a brief explanation about a few data extraction techniques.

How to Automate Data Extraction and Digitize Your Document Based Processes?
Flowchart1. Data extraction techniques

The following sections explore the use of Optical Character Recognition (OCR) to perform the task of data extraction.


Are you facing manual Data Extraction issues? Want to make your organization’s data extraction process efficient? Head over to Nanonets and see for yourself how Data Extraction from Documents can be automated.


SECTION 3: AUTOMATIC DATA EXTRACTION USING OCR:

Optical Character Recognition (OCR) is a technology that identifies characters from printed or handwritten material. By setting up a data extraction pipeline using OCR, organizations can automate the process of extracting and storing data.

THE HEART OF ANY OCR SYSTEM:

Modern OCR tools come with an array of data preprocessing (noise removal, binarization, line segmentation) and postprocessing steps. However, at the core of any OCR system lies two major components:

  1. A Feature Extractor and
  2. A Classifier
How to Automate Data Extraction and Digitize Your Document Based Processes?
Fig 4

The feature extractor extracts features corresponding to each lexeme (character/word). These extracted features are fed as inputs to the classifier that determines the probability of the lexeme belonging to a specific class.

TRADITIONAL APPROACHES TO SOLVING THE OCR PROBLEM:

  1. Template Matching: A set of templates (images of each character of the alphabet) are collected and stored. Each character of the input image is then matched against this collection of templates. Each comparison is associated with a similarity measure using which the best possible matches are identified.
How to Automate Data Extraction and Digitize Your Document Based Processes?
Fig 5. List of templates for the English Alphabet (Source: https://www.google.com/url?sa=i&url=https%3A%2F%2Fwww.slideshare.net%2FVj84529%2Focr-color&psig=AOvVaw0u4z1m4DwYNIFQEFKlQLqH&ust=1613545352470000&source=images&cd=vfe&ved=0CAIQjRxqFwoTCKiG8Ijr7e4CFQAAAAAdAAAAABAD)

Rule based Methods: As children we were taught to recognise the character ‘H’ as two vertical lines with a horizontal line connecting them. Intuitively this is what rule based methods try to achieve. Certain structural features are extracted from the input images and a rule based system is used to classify them.

Apart from the above-mentioned approaches, various other methods have been developed for performing OCR based on traditional computer vision. However, almost all of them have been replaced by or supplemented by Deep Learning.
Now that we have an idea of what OCR is and some of the traditional approaches used to perform OCR, let’s go deeper…

How to Automate Data Extraction and Digitize Your Document Based Processes?
(Source: https://memegenerator.net/instance/57413687/inception-di-caprio-we-need-to-go-deeper)

SECTION 4: OCR TOOLS

Let’s look into some of the free open source state of the art OCR tools:

  1. Tesseract: Tesseract was initially developed by HP and was released as an open source software in 2005. Since then, its development has been taken over by Google. There are numerous tutorials explaining all the details of tesseract OCR and how it can be used. The following blog on Nanonets provides a comprehensive review of the same https://nanonets.com/blog/ocr-with-tesseract/#introduction
  2. OCRopus: OCRopus is a collection of tools used for performing OCR on images. The general pipeline of OCRopus contains three main blocks as shown in the figure below.
How to Automate Data Extraction and Digitize Your Document Based Processes?
Flowchart2. General pipeline of OCRopus

OCRopus is a full GUI engine and can optionally use tesseract in the backend for performing OCR.

3. Calamari OCR: Calamari OCR is a relatively new line recognition software that uses deep neural networks implemented in TensorFlow. When compared to Tesseract and OCRopus, Calamari OCR has few explanations detailing its network architecture and its inner workings. This seems like a good point to formalize the OCR problem and peer at it through the eyes of Calamari.

Let’s assume that we want to perform Optical Character recognition on the word “Speed” using a Deep Neural Network(DNN) . Let’s also assume that we have created a DNN using Convolutional Neural Nets(CNNs) and Long short-term memory(LSTMs) to perform this task. Our network predicts output probabilities associated with each class at every timestep.

For example: In an ideal scenario

How to Automate Data Extraction and Digitize Your Document Based Processes?
Fig 6. Input Image fed to the Neural Network

How to Automate Data Extraction and Digitize Your Document Based Processes?
Fig 7. The output(if all goes well)

The table below shows the possible probability values associated with each time step.

T0

T1

T2

T3

T4

P(a)

0.001

0.002

0.01

0.01

0.001

P(b)

0.001

0.003

0.003

0.002

0.002

P(c)

0.005

0.005

0.002

0.001

0.001

P(d)

0.002

0.001

0.001

0.003

0.7

P(e)

0.001

0.002

0.7

0.8

0.002

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

P(p)

0.003

0.8

0.002

0.004

0.001

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

P(s)

0.7

0.008

0.002

0.001

0.007

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

   Table 1. Probabilities associated with each class

Taking the maximum probability under each timestep, we get the required output i.e SPEED. What could go wrong with this approach? Let’s take a moment to think about an assumption we have made in our reasoning namely the alignment of each timestep.
We assumed that each timestep occurs exactly between successive alphabets. The output would have been very different if the neural network decides to align the timesteps as shown in figure 8.

How to Automate Data Extraction and Digitize Your Document Based Processes?
Fig 8. Misaligned timesteps

In this scenario, the neural network might predict SSPPEEEEDD as the output. Secondly, preparing the training data for the neural network might prove to be extremely tedious. We would need to specify the exact pixel location at which each alphabet starts and ends.

What seemed like a straightforward task is proving to be extremely frustrating. The problem of misaligned timesteps and training data annotation can be solved by introducing a new loss function.

Connectionist Temporal Classification (CTC)

How to Automate Data Extraction and Digitize Your Document Based Processes?
(Source:https://www.google.com/search?q=memeanimals.com+i+must+go+my+people+need+me&tbm=isch&source=iu&ictx=1&fir=C8adpx9pd63_pM%252C6SVZE5KvuruZIM%252C_&vet=1&usg=AI4_-kR44ME7ZPnrJBaiK3LJUtr-hYlyWw&sa=X&ved=2ahUKEwiiiZ2XqonvAhUkmeYKHQpbCgcQ9QF6BAgMEAE#imgrc=C8adpx9pd63_pM)

CTC helps us in the following ways:

  1. Using the CTC loss, we can train the network without having to specify the pixel wise position of each alphabet. This is achieved by introducing a new character ‘-’. ‘-’ is used to indicate that no character is seen at a given timestep.
    Using this special character ‘-’, the ground truth could be modified to account for all possible positions where the word “speed” occurs in the image. For example, the word “speed” could be written as “—speed”, “–speed-”, “-speed–”, “speed—”. Similarly, since we don’t know how much space each alphabet might take, we add character repetitions to account for varying character lengths i.e. “speed” can be written as “—sspeed” , “—ssspeed”, and so on.
    In the case of actual character repetitions in the ground truth, we need to add a ‘-’ between the characters that are repeated. Thus the word “speed” can be encoded in the following ways: “—spe-ed”, “–spe-ed-” , “-spe-ed–”, “spe-ed–” , “–sspe-ed”, etc. We calculate the score for each possible encoding and the sum of all the individual scores gives us the loss for each (image, ground truth) pair.
  2. Using the CTC decoder is much simpler. Let’s say that the decoder outputs “ssppe-eee-dd. We can simply discard duplicates i.e “ssppe-eee-dd” becomes “spe-e-d”. Finally, we remove the ‘-’ characters to obtain the word “speed”.

I found the following resources extremely helpful when learning about the CTC loss.https://distill.pub/2017/ctc/        https://dl.acm.org/doi/abs/10.1145/1143844.1143891

Implementing the network is straightforward. According to the paper(https://arxiv.org/pdf/1807.02004.pdf), the default network has the following specifications:

Architecture: Conv layer -> Max-Pooling -> Conv layer -> Max Pooling -> LSTM.  

Loss: CTC loss                                                                                                    

Optimizer: Adam with a learning rate of 0.001

Phew! That was a lot of theory. Let’s get our hands dirty by implementing Optical Character recognition using Calamari.

Getting started from the Calamari github page https://github.com/Calamari-OCR/calamari is an easy task and I had no problem during the installation process. I decided to use a model trained on the uw3-modern-english dataset. Figure 9 shows the input fed to the network and Figure 10 shows the corresponding output.

How to Automate Data Extraction and Digitize Your Document Based Processes?
Fig 9. Input image to Calamari
How to Automate Data Extraction and Digitize Your Document Based Processes?
Fig 10. Output from Calamari OCR

Calamari produced the output (Fig 10) with a confidence of 97%. It performs very well in most cases and can easily be fine-tuned to suit your specific use case.
NOTE: Calamari performs OCR on a single line of text at a time. If you want to perform OCR on an entire document some preprocessing (layout analysis, line segmentation etc.) is required prior to feeding the image to Calamari.
Apart from the abovementioned free open source OCR tools, there are several paid tools such as Google cloud vision, Microsoft Computer Vision API and Amazon Textract.

The next section talks about how OCR can be used to solve practical problems in various industries and organizations.


Do you have a Data Extraction requirement? Head over to Nanonets and see how you can automate Data Extraction from documents like PDFs, Receipts, Invoices, Forms and More.


SECTION 5: PRACTICAL USE CASES OF DATA EXTRACTION USING OCR:

Using the generic OCR pipeline shown in FlowChart3, some of the problems that can be solved using OCR are elucidated below.

How to Automate Data Extraction and Digitize Your Document Based Processes?
Flowchart 3. OCR Pipeline

OCR based Data Extraction Techniques for the Healthcare Sector

The problem: Ever since I was a little boy, the following sequence of steps would be performed whenever I visited the hospital. The receptionist would first ask for my ID number. She would then dive into a huge stack of diaries which were sorted in some fashion. Usually, after a prolonged period of searching, I would get my diary and a token number. The doctor would examine the cause of my illness and write down a prescription in my diary. Upon handing over the prescription to the pharmacy, I would receive the required medicines. I assume that this is the routine followed in most local hospitals within the country.

Solution: Using our OCR pipeline, all the information could be digitized and stored in a database. A simple way to implement this would be to hand over forms to each patient which are scanned and fed into the OCR pipeline. The advantages of doing this are manyfold:

  1. Patients’ medical history can be stored in a common database which the doctors can access at their will. This information could help the doctor diagnose the illness.
  2. The hospital could analyze the data and allocate its resources accordingly. For example: If the data indicates that the gynaecology section has a maximum number of patients, the hospital can choose to employ more doctors and nurses in this section.

Potential pitfalls:

  1. As you might have guessed, deciphering doctors’ prescriptions using OCR is no small challenge. However, by using good quality training data along with some domain-specific information (names of well-known medicines) in the post-processing step, the solution can be made robust to most errors.

Automated Data Extraction Services that can benefit the Government

The Problem:  During the past year, the COVID-19 pandemic has brought along with it an array of problems. I was quite surprised to learn that manual data entry was one of them. When the pandemic was at its peak, lakhs of tests were being conducted every day and all the results had to be manually entered into a database.

Solution: OCR could have been easily employed in this scenario. A scanned copy of the lab report can be fed into the OCR pipeline. For example, Fig 11 shows the test report which is fed as an input to the pipeline and Fig 12 is the corresponding result.

How to Automate Data Extraction and Digitize Your Document Based Processes?
Fig 11. Scanned copy of a COVID test report(https://www.lalpathlabs.com/SampleReports/N228.pdf)
How to Automate Data Extraction and Digitize Your Document Based Processes?
Fig 12. Result of OCR on the COVID test report

The problem could be simplified further by concentrating on the fields that are important and ignoring the rest. In this case, the Name of the individual and the result of the test must be extracted reliably. Since the results of the test are binary i.e. either negative or positive, they could be matched using regular expressions. Similarly, the name field could be replaced by a unique identification number to ensure reliable character recognition.

OCR Based Data Extraction Software for Invoice Automation

THE PROBLEM: Deep within the accounts section of any organization lies a group of people whose job is to manually enter data from invoices into the company’s database. This is a highly repetitive and mundane task that can be automated thanks to our OCR pipeline.

SOLUTION: Performing OCR on the given invoice can automate the task of manual data entry. A lot of work has already been done in this area and developing a robust solution mainly hinges upon reliably extracting tables and amounts accurately from the invoice.

The following blogposts https://nanonets.com/blog/table-extraction-deep-learning/ and https://nanonets.com/blog/extract-structured-data-from-invoice/ provide comprehensive explanations of the same.

SECTION 6:  THE LATEST RESEARCH:

  1. ScrabbleGAN: Semi-Supervised Varying Length Handwritten Text Generation(https://arxiv.org/abs/2003.10557)(CVPR-2020):

This paper addresses the problem of handwritten text recognition (HTR). Although state of the art OCR tools performs well on printed text, handwritten text recognition is still a developing field. The authors attribute this gap to the lack of training data i.e., the lack of annotated handwritten text. The authors propose a DNN that can generate handwritten images of varying styles.

How to Automate Data Extraction and Digitize Your Document Based Processes?
Fig 13. Architecture of ScrabbleGAN

Fig 13. Illustrates the architecture of ScrabbleGAN. The generator generates synthetic images which are fed to a recognizer in addition to the discriminator. The discriminator forces the generator to generate real looking images while the recognizer makes sure that meaningful words are generated by the generator.

How to Automate Data Extraction and Digitize Your Document Based Processes?
Fig 14. Different styles of the word “supercalifragilisticexpialidocious”

The network is trained in a semi supervised manner and two metrics namely the Word Error Rate (WER) and normalized edit distance(NED) are used for evaluation.

2. OrigamiNet: OrigamiNet: Weakly-Supervised, Segmentation-Free, One-Step, Full Page Text Recognition by learning to unfold(https://arxiv.org/abs/2006.07491)(CVPR-2020):

The very first OCR architectures tried to segment each character from the input image and classify each segmented character. This progressed to segmentation free approaches where an entire word was segmented and classified. Today, most state-of-the-art approaches operate on an entire line of text.

In this paper, the authors propose a simple set of operations that enable OCR to be performed on an entire page in a single forward pass through the network. The major constraint in performing OCR on an entire page is that the CTC loss function requires the input to be 1D. This is clearly illustrated in Fig 15, where the input is down sampled and converted to 1D before the loss calculation stage.

How to Automate Data Extraction and Digitize Your Document Based Processes?
Fig 15. A fully convolutional single line recognizer

Since CNNs perform well on tasks such as image to image translation, the authors make use of a CNN to learn the 2D to 1D transformation. The feature map from the generic fully convolutional neural network is upsampled vertically and downsampled horizontally in two successive stages before the pooling operation is performed.

How to Automate Data Extraction and Digitize Your Document Based Processes?
Fig 16. Generic CNN used for performing OCR on a single line of text augmented with additional stages to perform multi-line recognition

The final tall feature map contains all of the lines of text from the input image. The authors argue that providing the model with sufficient spatial capacity allows it to easily learn the required 2D to 1D transformation.
The authors evaluate their work by using standard CNNs such as ResNet, VGG and GTR

CONCLUSION:

In this post we looked at data extraction in detail and how Optical character recognition can be used to solve this problem. Section1 contains a brief introduction of the data extraction problem. In Section2 we took a look at some data extraction tools and techniques. Section3 gave an overview of the OCR problem and some of the traditional methods used to solve it. In Section4 we explored some popular open-source tools used to perform OCR and understood the CTC loss function. Section5 contains several practical use cases where OCR can be used to solve the data extraction problem. Finally, we looked at the current state of the art research in the field of OCR.

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How to Automate Data Extraction and Digitize Your Document Based Processes?

Source: https://nanonets.com/blog/automating-data-extraction-and-digitizing-document-based-processes/

Artificial Intelligence

Deep Learning vs Machine Learning: How an Emerging Field Influences Traditional Computer Programming

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When two different concepts are greatly intertwined, it can be difficult to separate them as distinct academic topics. That might explain why it’s so difficult to separate deep learning from machine learning as a whole. Considering the current push for both automation as well as instant gratification, a great deal of renewed focus has been heaped on the topic.

Everything from automated manufacturing worfklows to personalized digital medicine could potentially grow to rely on deep learning technology. Defining the exact aspects of this technical discipline that will revolutionize these industries is, however, admittedly much more difficult. Perhaps it’s best to consider deep learning in the context of a greater movement in computer science.

Defining Deep Learning as a Subset of Machine Learning

Machine learning and deep learning are essentially two sides of the same coin. Deep learning techniques are a specific discipline that belong to a much larger field that includes a large variety of trained artificially intelligent agents that can predict the correct response in an equally wide array of situations. What makes deep learning independent of all of these other techniques, however, is the fact that it focuses almost exclusively on teaching agents to accomplish a specific goal by learning the best possible action in a number of virtual environments.

Traditional machine learning algorithms usually teach artificial nodes how to respond to stimuli by rote memorization. This is somewhat similar to human teaching techniques that consist of simple repetition, and therefore might be thought of the computerized equivalent of a student running through times tables until they can recite them. While this is effective in a way, artificially intelligent agents educated in such a manner may not be able to respond to any stimulus outside of the realm of their original design specifications.

That’s why deep learning specialists have developed alternative algorithms that are considered to be somewhat superior to this method, though they are admittedly far more hardware intensive in many ways. Subrountines used by deep learning agents may be based around generative adversarial networks, convolutional neural node structures or a practical form of restricted Boltzmann machine. These stand in sharp contrast to the binary trees and linked lists used by conventional machine learning firmware as well as a majority of modern file systems.

Self-organizing maps have also widely been in deep learning, though their applications in other AI research fields have typically been much less promising. When it comes to defining the deep learning vs machine learning debate, however, it’s highly likely that technicians will be looking more for practical applications than for theoretical academic discussion in the coming months. Suffice it to say that machine learning encompasses everything from the simplest AI to the most sophisticated predictive algorithms while deep learning constitutes a more selective subset of these techniques.

Practical Applications of Deep Learning Technology

Depending on how a particular program is authored, deep learning techniques could be deployed along supervised or semi-supervised neural networks. Theoretically, it’d also be possible to do so via a completely unsupervised node layout, and it’s this technique that has quickly become the most promising. Unsupervised networks may be useful for medical image analysis, since this application often presents unique pieces of graphical information to a computer program that have to be tested against known inputs.

Traditional binary tree or blockchain-based learning systems have struggled to identify the same patterns in dramatically different scenarios, because the information remains hidden in a structure that would have otherwise been designed to present data effectively. It’s essentially a natural form of steganography, and it has confounded computer algorithms in the healthcare industry. However, this new type of unsupervised learning node could virtually educate itself on how to match these patterns even in a data structure that isn’t organized along the normal lines that a computer would expect it to be.

Others have proposed implementing semi-supervised artificially intelligent marketing agents that could eliminate much of the concern over ethics regarding existing deal-closing software. Instead of trying to reach as large a customer base as possible, these tools would calculate the odds of any given individual needing a product at a given time. In order to do so, it would need certain types of information provided by the organization that it works on behalf of, but it would eventually be able to predict all further actions on its own.

While some companies are currently relying on tools that utilize traditional machine learning technology to achieve the same goals, these are often wrought with privacy and ethical concerns. The advent of deep structured learning algorithms have enabled software engineers to come up with new systems that don’t suffer from these drawbacks.

Developing a Private Automated Learning Environment

Conventional machine learning programs often run into serious privacy concerns because of the fact that they need a huge amount of input in order to draw any usable conclusions. Deep learning image recognition software works by processing a smaller subset of inputs, thus ensuring that it doesn’t need as much information to do its job. This is of particular importance for those who are concerned about the possibility of consumer data leaks.

Considering new regulatory stances on many of these issues, it’s also quickly become something that’s become important from a compliance standpoint as well. As toxicology labs begin using bioactivity-focused deep structured learning packages, it’s likely that regulators will express additional concerns in regards to the amount of information needed to perform any given task with this kind of sensitive data. Computer scientists have had to scale back what some have called a veritable fire hose of bytes that tell more of a story than most would be comfortable with.

In a way, these developments hearken back to an earlier time when it was believed that each process in a system should only have the amount of privileges necessary to complete its job. As machine learning engineers embrace this paradigm, it’s highly likely that future developments will be considerably more secure simply because they don’t require the massive amount of data mining necessary to power today’s existing operations.

Image Credit: toptal.io

Coinsmart. Beste Bitcoin-Börse in Europa
Source: https://datafloq.com/read/deep-learning-vs-machine-learning-how-emerging-field-influences-traditional-computer-programming/13652

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Artificial Intelligence

Extra Crunch roundup: Tonal EC-1, Deliveroo’s rocky IPO, is Substack really worth $650M?

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For this morning’s column, Alex Wilhelm looked back on the last few months, “a busy season for technology exits” that followed a hot Q4 2020.

We’re seeing signs of an IPO market that may be cooling, but even so, “there are sufficient SPACs to take the entire recent Y Combinator class public,” he notes.

Once we factor in private equity firms with pockets full of money, it’s evident that late-stage companies have three solid choices for leveling up.

Seeking more insight into these liquidity options, Alex interviewed:

  • DigitalOcean CEO Yancey Spruill, whose company went public via IPO;
  • Latch CFO Garth Mitchell, who discussed his startup’s merger with real estate SPAC $TSIA;
  • Brian Cruver, founder and CEO of AlertMedia, which recently sold to a private equity firm.

After recapping their deals, each executive explains how their company determined which flashing red “EXIT” sign to follow. As Alex observed, “choosing which option is best from a buffet’s worth of possibilities is an interesting task.”

Thanks very much for reading Extra Crunch! Have a great weekend.

Walter Thompson
Senior Editor, TechCrunch
@yourprotagonist


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The Tonal EC-1

Image Credits: Nigel Sussman

On Tuesday, we published a four-part series on Tonal, a home fitness startup that has raised $200 million since it launched in 2018. The company’s patented hardware combines digital weights, coaching and AI in a wall-mounted system that sells for $2,995.

By any measure, it is poised for success — sales increased 800% between December 2019 and 2020, and by the end of this year, the company will have 60 retail locations. On Wednesday, Tonal reported a $250 million Series E that valued the company at $1.6 billion.

Our deep dive examines Tonal’s origins, product development timeline, its go-to-market strategy and other aspects that combined to spark investor interest and customer delight.

We call this format the “EC-1,” since these stories are as comprehensive and illuminating as the S-1 forms startups must file with the SEC before going public.

Here’s how the Tonal EC-1 breaks down:

We have more EC-1s in the works about other late-stage startups that are doing big things well and making news in the process.

What to make of Deliveroo’s rough IPO debut

Why did Deliveroo struggle when it began to trade? Is it suffering from cultural dissonance between its high-growth model and more conservative European investors?

Let’s peek at the numbers and find out.

Kaltura puts debut on hold. Is the tech IPO window closing?

The Exchange doubts many folks expected the IPO climate to get so chilly without warning. But we could be in for a Q2 pause in the formerly scorching climate for tech debuts.

Is Substack really worth $650M?

A $65 million Series B is remarkable, even by 2021 standards. But the fact that a16z is pouring more capital into the alt-media space is not a surprise.

Substack is a place where publications have bled some well-known talent, shifting the center of gravity in media. Let’s take a look at Substack’s historical growth.

RPA market surges as investors, vendors capitalize on pandemic-driven tech shift

Business process organization and analytics. Business process visualization and representation, automated workflow system concept. Vector concept creative illustration

Image Credits: Visual Generation / Getty Images

Robotic process automation came to the fore during the pandemic as companies took steps to digitally transform. When employees couldn’t be in the same office together, it became crucial to cobble together more automated workflows that required fewer people in the loop.

RPA has enabled executives to provide a level of automation that essentially buys them time to update systems to more modern approaches while reducing the large number of mundane manual tasks that are part of every industry’s workflow.

E-commerce roll-ups are the next wave of disruption in consumer packaged goods

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Source: https://techcrunch.com/2021/04/02/extra-crunch-roundup-tonal-ec-1-deliveroos-rocky-ipo-is-substack-really-worth-650m/

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What did COVID do to all our models?

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What did COVID do to all our models?

An interview with Dean Abbott and John Elder about change management, complexity, interpretability, and the risk of AI taking over humanity.


By Heather Fyson, KNIME

What did COVID do to all our models?

After the KNIME Fall Summit, the dinosaurs went back home… well, switched off their laptops. Dean Abbott and John Elder, longstanding data science experts, were invited to the Fall Summit by Michael to join him in a discussion of The Future of Data Science: A Fireside Chat with Industry Dinosaurs. The result was a sparkling conversation about data science challenges and new trends. Since switching off the studio lights, Rosaria has distilled and expanded some of the highlights about change management, complexity, interpretability, and more in the data science world. Let’s see where it brought us.

What is your experience with change management in AI, when reality changes and models have to be updated? What did COVID do to all our models?

 
[Dean] Machine Learning (ML) algorithms assume consistency between past and future. When things change, the models fail. COVID has changed our habits, and therefore our data. Pre-COVID models struggle to deal with the new situation.

[John] A simple example would be the Traffic layer on Google Maps. After lockdowns hit country after country in 2020, Google Maps traffic estimates were very inaccurate for a while. It had been built on fairly stable training data but now that system was thrown completely out of whack.

How do you figure out when the world has changed and the models don’t work anymore?

 
[Dean] Here’s a little trick I use: I partition my data by time and label records as “before” and “after”. I then build a classification model to discriminate the “after” vs. the “before” from the same inputs the model uses. If the discrimination is possible, then the “after” is different from the “before”, the world has changed, the data has changed, and the models must be retrained.

How complicated is it to retrain models in projects, especially after years of customization?

 
[John] Training models is usually the easiest step of all! The vast majority of otherwise successful projects die in the implementation phase. The greatest time is spent in the data cleansing and preparation phase. And the most problems are missed or made in the business understanding / project definition phase. So if you understand what the flaw is and can obtain new data and have the implementation framework in place, creating a new model is, by comparison, very straightforward.

Based on your decades-long experience, how complex is it to put together a really functioning Data Science application?

 
[John] It can vary of course, by complexity. Most of our projects get functioning prototypes at least in a few months. But for all, I cannot stress enough the importance of feedback: You have to talk to people much more often than you want to. And listen! We learn new things about the business problem, the data, or constraints, each time. Not all us quantitative people are skilled at speaking with humans, so it often takes a team. But the whole team of stakeholders has to learn to speak the same language.

[Dean] It is important to talk to our business counterpart. People fear change and don’t want to change the current status. One key problem really is psychological. The analysts are often seen as an annoyance. So, we have to build the trust between the business counterpart and the analytics geeks. The start of a project should always include the following step: Sync up domain experts / project managers, the analysts, and the IT and infrastructure (DevOps) team so everyone is clear on the objectives of the project and how it will be executed. Analysts are number 11 on the top 10 list of people they have to see every day! Let’s avoid embodying data scientist arrogance: “The business can’t understand us/our techniques, but we know what works best”. What we don’t understand, however, are the domains experts are actually experts in the domain we are working in! Translation of data science assumptions and approaches into language that is understood by the domain experts is key!

The latest trend now is deep learning, apparently it can solve everything. I got a question from a student lately, asking “why do we need to learn other ML algorithms if deep learning is the state of the art to solve data science problems”?

 
[Dean] Deep learning sucked a lot of the oxygen out of the room. It feels so much like the early 1990s when neural networks ascended with similar optimism! Deep Learning is a set of powerful techniques for sure, but they are hard to implement and optimize. XGBoost, Ensembles of trees, are also powerful but currently more mainstream. The vast majority of problems we need to solve using advanced analytics really don’t require complex solutions, so start simple; deep learning is overkill in these situations. It is best to use the Occam’s razor principle: if two models perform the same, adopt the simplest.

About complexity. The other trend, opposite to deep learning, is ML interpretability. Here, you greatly (excessively?) simplify the model in order to be able to explain it. Is interpretability that important?

 
[John] I often find myself fighting interpretability. It is nice, sure, but often comes at too high a cost of the most important model property: reliable accuracy. But many stakeholders believe interpretability is essential, so it becomes a barrier for acceptance. Thus, it is essential to discover what kind of interpretability is needed. Perhaps it is just knowing what the most important variables are? That’s doable with many nonlinear models. Maybe, as with explaining to credit applicants why they were turned down, one just needs to interpret outputs for one case at a time? We can build a linear approximation for a given point. Or, we can generate data from our black box model and build an “interpretable” model of any complexity to fit that data.

Lastly, research has shown that if users have the chance to play with a model – that is, to poke it with trial values of inputs and see its outputs, and perhaps visualize it – they get the same warm feelings of interpretability. Overall, trust – in the people and technology behind the model – is necessary for acceptance, and this is enhanced by regular communication and by including the eventual users of the model in the build phases and decisions of the modeling process.

[Dean] By the way KNIME Analytics Platform has a great feature to quantify the importance of the input variables in a Random Forest! The Random Forest Learner node outputs the statistics of candidate and splitting variables. Remember that, when you use the Random Forest Learner node.

There is an increase in requests for explanations of what a model does. For example, for some security classes, the European Union is demanding verification that the model doesn’t do what it’s not supposed to do. If we have to explain it all, then maybe Machine Learning is not the way to go. No more Machine Learning?

 
[Dean]  Maybe full explainability is too hard to obtain, but we can achieve progress by performing a grid search on model inputs to create something like a score card describing what the model does. This is something like regression testing in hardware and software QA. If a formal proof what models are doing is not possible, then let’s test and test and test! Input Shuffling and Target Shuffling can help to achieve a rough representation of the model behavior.

[John] Talking about understanding what a model does, I would like to raise the problem of reproducibility in science. A huge proportion of journal articles in all fields — 65 to 90% — is believed to be unreplicable. This is a true crisis in science. Medical papers try to tell you how to reproduce their results. ML papers don’t yet seem to care about reproducibility. A recent study showed that only 15% of AI papers share their code.

Let’s talk about Machine Learning Bias. Is it possible to build models that don’t discriminate?

 
[John] (To be a nerd for a second, that word is unfortunately overloaded. To “discriminate” in the ML world word is your very goal: to make a distinction between two classes.) But to your real question, it depends on the data (and on whether the analyst is clever enough to adjust for weaknesses in the data): The models will pull out of the data the information reflected therein. The computer knows nothing about the world except for what’s in the data in front of it. So the analyst has to curate the data — take responsibility for those cases reflecting reality. If certain types of people, for example, are under-represented then the model will pay less attention to them and won’t be as accurate on them going forward. I ask, “What did the data have to go through to get here?” (to get in this dataset) to think of how other cases might have dropped out along the way through the process (that is survivor bias). A skilled data scientist can look for such problems and think of ways to adjust/correct for them.

[Dean] The bias is not in the algorithms. The bias is in the data. If the data is biased, we’re working with a biased view of the world. Math is just math, it is not biased.

Will AI take over humanity?!

 
[John] I believe AI is just good engineering. Will AI exceed human intelligence? In my experience anyone under 40 believes yes, this is inevitable, and most over 40 (like me, obviously): no! AI models are fast, loyal, and obedient. Like a good German Shepherd dog, an AI model will go and get that ball, but it knows nothing about the world other than the data it has been shown. It has no common sense. It is a great assistant for specific tasks, but actually quite dimwitted.

[Dean] On that note, I would like to report two quotes made by Marvin Minsky in 1961 and 1970, from the dawn of AI, that I think describe well the future of AI.

“Within our lifetime some machines may surpass us in general intelligence” (1961)

“In three to eight years we’ll have a machine with the intelligence of a human being” (1970)

These ideas have been around for a long time. Here is one reason why AI will not solve all the problems: We’re judging its behavior based on one number, one number only! (Model error.) For example, predictions of stock prices over the next five years, predicted by building models using root mean square error as the error metric, cannot possibly paint the full picture of what the data are actually doing and severely hampers the model and its ability to flexibly uncover the patterns. We all know that RMSE is too coarse of a measure. Deep Learning algorithms will continue to get better, but we also need to get better at judging how good a model really is. So, no! I do not think that AI will take over humanity.

We have reached the end of this interview. We would like to thank Dean and John for their time and their pills of knowledge. Let’s hope we meet again soon!

About Dean Abbott and John Elder

What did COVID do to all our models Dean Abbott is Co-Founder and Chief Data Scientist at SmarterHQ. He is an internationally recognized expert and innovator in data science and predictive analytics, with three decades of experience solving problems in omnichannel customer analytics, fraud detection, risk modeling, text mining & survey analysis. Included frequently in lists of pioneering data scientists and data scientists, he is a popular keynote speaker and workshop instructor at conferences worldwide, also serving on Advisory Boards for the UC/Irvine Predictive Analytics and UCSD Data Science Certificate programs. He is the author of Applied Predictive Analytics (Wiley, 2014) and co-author of The IBM SPSS Modeler Cookbook (Packt Publishing, 2013).


What did COVID do to all our models John Elder founded Elder Research, America’s largest and most experienced data science consultancy in 1995. With offices in Charlottesville VA, Baltimore MD, Raleigh, NC, Washington DC, and London, they’ve solved hundreds of challenges for commercial and government clients by extracting actionable knowledge from all types of data. Dr. Elder co-authored three books — on practical data mining, ensembles, and text mining — two of which won “book of the year” awards. John has created data mining tools, was a discoverer of ensemble methods, chairs international conferences, and is a popular workshop and keynote speaker.


 
Bio: Heather Fyson is the blog editor at KNIME. Initially on the Event Team, her background is actually in translation & proofreading, so by moving to the blog in 2019 she has returned to her real passion of working with texts. P.S. She is always interested to hear your ideas for new articles.

Original. Reposted with permission.

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Source: https://www.kdnuggets.com/2021/04/covid-do-all-our-models.html

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The AI Trends Reshaping Health Care

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Click to learn more about author Ben Lorica.

Applications of AI in health care present a number of challenges and considerations that differ substantially from other industries. Despite this, it has also been one of the leaders in putting AI to work, taking advantage of the cutting-edge technology to improve care. The numbers speak for themselves: The global AI in health care market size is expected to grow from $4.9 billion in 2020 to $45.2 billion by 2026. Some major factors driving this growth are the sheer volume of health care data and growing complexities of datasets, the need to reduce mounting health care costs, and evolving patient needs.

Deep learning, for example, has made considerable inroads into the clinical environment over the last few years. Computer vision, in particular, has proven its value in medical imaging to assist in screening and diagnosis. Natural language processing (NLP) has provided significant value in addressing both contractual and regulatory concerns with text mining and data sharing. Increasing adoption of AI technology by pharmaceutical and biotechnology companies to expedite initiatives like vaccine and drug development, as seen in the wake of COVID-19, only exemplifies AI’s massive potential.

We’re already seeing amazing strides in health care AI, but it’s still the early days, and to truly unlock its value, there’s a lot of work to be done in understanding the challenges, tools, and intended users shaping the industry. New research from John Snow Labs and Gradient Flow, 2021 AI in Healthcare Survey Report, sheds light on just this: where we are, where we’re going, and how to get there. The global survey explores the important considerations for health care organizations in varying stages of AI adoption, geographies, and technical prowess to provide an extensive look into the state of AI in health care today.               

One of the most significant findings is around which technologies are top of mind when it comes to AI implementation. When asked what technologies they plan to have in place by the end of 2021, almost half of respondents cited data integration. About one-third cited natural language processing (NLP) and business intelligence (BI) among the technologies they are currently using or plan to use by the end of the year. Half of those considered technical leaders are using – or soon will be using – technologies for data integration, NLP, business intelligence, and data warehousing. This makes sense, considering these tools have the power to help make sense of huge amounts of data, while also keeping regulatory and responsible AI practices in mind.

When asked about intended users for AI tools and technologies, over half of respondents identified clinicians among their target users. This indicates that AI is being used by people tasked with delivering health care services – not just technologists and data scientists, as in years past. That number climbs even higher when evaluating mature organizations, or those that have had AI models in production for more than two years. Interestingly, nearly 60% of respondents from mature organizations also indicated that patients are also users of their AI technologies. With the advent of chatbots and telehealth, it will be interesting to see how AI proliferates for both patients and providers over the next few years.

In considering software for building AI solutions, open-source software (53%) had a slight edge over public cloud providers (42%). Looking ahead one to two years, respondents indicated openness to also using both commercial software and commercial SaaS. Open-source software gives users a level of autonomy over their data that cloud providers can’t, so it’s not a big surprise that a highly regulated industry like health care would be wary of data sharing. Similarly, the majority of companies with experience deploying AI models to production choose to validate models using their own data and monitoring tools, rather than evaluation from third parties or software vendors. While earlier-stage companies are more receptive to exploring third-party partners, more mature organizations are tending to take a more conservative approach.                      

Generally, attitudes remained the same when asked about key criteria used to evaluate AI solutions, software libraries or SaaS solutions, and consulting companies to work with.Although the answers varied slightly for each category,technical leaders considered no data sharing with software vendors or consulting companies, the ability to train their own models, and state-of-the art accuracy as top priorities. Health care-specific models and expertise in health care data engineering, integration, and compliance topped the list when asked about solutions and potential partners. Privacy, accuracy, and health care experience are the forces driving AI adoption. It’s clear that AI is poised for even more growth, as data continues to grow and technology and security measures improve. Health care, which can sometimes be seen as a laggard for quick adoption, is taking to AI and already seeing its significant impact. While its approach, the top tools and technologies, and applications of AI may differ from other industries, it will be exciting to see what’s in store for next year’s survey results.

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Source: https://www.dataversity.net/the-ai-trends-reshaping-health-care/

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