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From Text to Knowledge: The Information Extraction Pipeline




information extraction

I am thrilled to present my latest project I have been working on. If you have been following my posts, you know that I am passionate about combining natural language processing and knowledge graphs. In this blog post, I will present my implementation of an information extraction data pipeline. Later on, I will also explain why I see the combination of NLP and graphs as one of the paths to explainable AI.

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Information extraction pipeline

What exactly is an information extraction pipeline? To put it in simple terms, information extraction is the task of extracting structured information from unstructured data such as text.

Steps in my implementation of the IE pipeline. Image by author

My implementation of the information extraction pipeline consists of four parts. In the first step, we run the input text through a coreference resolution model. The coreference resolution is the task of finding all expressions that refer to a specific entity. To put it simply, it links all the pronouns to the referred entity. Once that step is finished, it splits the text into sentences and removes the punctuations. I have noticed that the specific ML model used for named entity linking works better when we first remove the punctuations. In the named entity linking part of the pipeline, we try to extract all the mentioned entities and connect them to a target knowledge base. The target knowledge base, in this case, is Wikipedia. Named entity linking is beneficial because it also deals with entity disambiguation, which can be a big problem.

Once we have extracted the mentioned entities, the IE pipeline tries to infer relationships between entities that make sense based on the text’s context. The IE pipeline results are entities and their relationships, so it makes sense to use a graph database to store the output. I will show how to save the IE information to Neo4j.

I’ll use the following excerpt from Wikipedia to walk you through the IE pipeline.

Elon Musk is a business magnate, industrial designer, and engineer. He is the founder, CEO, CTO, and chief designer of SpaceX. He is also early investor, CEO, and product architect of Tesla, Inc. He is also the founder of The Boring Company and the co-founder of Neuralink. A centibillionaire, Musk became the richest person in the world in January 2021, with an estimated net worth of $185 billion at the time, surpassing Jeff Bezos. Musk was born to a Canadian mother and South African father and raised in Pretoria, South Africa. He briefly attended the University of Pretoria before moving to Canada aged 17 to attend Queen's University. He transferred to the University of Pennsylvania two years later, where he received dual bachelor's degrees in economics and physics. He moved to California in 1995 to attend Stanford University, but decided instead to pursue a business career. He went on co-founding a web software company Zip2 with his brother Kimbal Musk.

Text is copied from and is available under CC BY-SA 3.0 license.

Step 1: Coreference resolution

As mentioned, the coreference resolution tries to find all expressions in the text that refer to a specific entity. In my implementation, I have used the Neuralcoref model from Huggingface that runs on top of the SpaCy framework. I have used the default parameters of the Neuralcoref model. One thing I did notice along the way is that the Neuralcoref model doesn’t work well with location pronouns. I have also borrowed a small improvement code from one of the GitHub issues. The code for the coreference resolution part is the following:

import spacy
import neuralcoref # Load SpaCy
nlp = spacy.load('en')
# Add neural coref to SpaCy's pipe
neuralcoref.add_to_pipe(nlp) def coref_resolution(text): """Function that executes coreference resolution on a given text""" doc = nlp(text) # fetches tokens with whitespaces from spacy document tok_list = list(token.text_with_ws for token in doc) for cluster in doc._.coref_clusters: # get tokens from representative cluster name cluster_main_words = set(cluster.main.text.split(' ')) for coref in cluster: if coref != cluster.main: # if coreference element is not the representative element of that cluster if coref.text != cluster.main.text and bool(set(coref.text.split(' ')).intersection(cluster_main_words)) == False: # if coreference element text and representative element text are not equal and none of the coreference element words are in representative element. This was done to handle nested coreference scenarios tok_list[coref.start] = cluster.main.text + doc[coref.end-1].whitespace_ for i in range(coref.start+1, coref.end): tok_list[i] = "" return "".join(tok_list)

If we run our example text through the coref_resolution function, we’ll get the following output:

Elon Musk is a business magnate, industrial designer, and engineer. 
Elon Musk is the founder, CEO, CTO, and chief designer of SpaceX.
Elon Musk is also early investor, CEO, and product architect of Tesla, Inc. Elon Musk is also the founder of The Boring Company and the co-founder of Neuralink. A centibillionaire, Musk became the richest person in the world in January 2021, with an estimated net worth of $185 billion at the time, surpassing Jeff Bezos. Musk was born to a Canadian mother and South African father and raised in Pretoria, South Africa. Elon Musk briefly attended the University of Pretoria before moving to Canada aged 17 to attend Queen's University. Elon Musk transferred to the University of Pennsylvania two years later, where Elon Musk received dual bachelor's degrees in economics and physics. Elon Musk moved to California in 1995 to attend Stanford University, but decided instead to pursue a business career. Elon Musk went on co-founding a web software company Zip2 with Elon Musk brother Kimbal Musk.

In this example, there are no advanced coreference resolution techniques required. The Neuralcoref model changed a couple of pronouns “He” to “Elon Musk”. While it might seem very simple, this is an important step that will increase the overall efficiency of our IE pipeline.

Step 2: Named Entity Linking

Just recently, I have published a blog post using Named Entity Linking to construct a knowledge graph. Here, I wanted to use a different named entity linking model. I first tried to use the Facebook BLINK model, but I quickly realized it wouldn’t work on my laptop. It needs at least 50GB of free space, which is not a big problem per se, but it also requires 32GB of RAM. My laptop has only 16GB of RAM, and we still need other parts of the pipeline to work. So I reverted to use the good old Wikifier API, which has already shown to be useful. And it’s totally free. If you want to find more information about the API, look at my previous blog post or the official documentation.

Before we run our input text through the Wikifier API, we will split the text into sentences and remove the punctuations. Overall, the code for this step is as follows:

import urllib
from string import punctuation
import nltk ENTITY_TYPES = ["human", "person", "company", "enterprise", "business", "geographic region", "human settlement", "geographic entity", "territorial entity type", "organization"] def wikifier(text, lang="en", threshold=0.8): """Function that fetches entity linking results from API""" # Prepare the URL. data = urllib.parse.urlencode([ ("text", text), ("lang", lang), ("userKey", "tgbdmkpmkluegqfbawcwjywieevmza"), ("pageRankSqThreshold", "%g" % threshold), ("applyPageRankSqThreshold", "true"), ("nTopDfValuesToIgnore", "100"), ("nWordsToIgnoreFromList", "100"), ("wikiDataClasses", "true"), ("wikiDataClassIds", "false"), ("support", "true"), ("ranges", "false"), ("minLinkFrequency", "2"), ("includeCosines", "false"), ("maxMentionEntropy", "3") ]) url = "" # Call the Wikifier and read the response. req = urllib.request.Request(url, data=data.encode("utf8"), method="POST") with urllib.request.urlopen(req, timeout=60) as f: response = response = json.loads(response.decode("utf8")) # Output the annotations. results = list() for annotation in response["annotations"]: # Filter out desired entity classes if ('wikiDataClasses' in annotation) and (any([el['enLabel'] in ENTITY_TYPES for el in annotation['wikiDataClasses']])): # Specify entity label if any([el['enLabel'] in ["human", "person"] for el in annotation['wikiDataClasses']]): label = 'Person' elif any([el['enLabel'] in ["company", "enterprise", "business", "organization"] for el in annotation['wikiDataClasses']]): label = 'Organization' elif any([el['enLabel'] in ["geographic region", "human settlement", "geographic entity", "territorial entity type"] for el in annotation['wikiDataClasses']]): label = 'Location' else: label = None results.append({'title': annotation['title'], 'wikiId': annotation['wikiDataItemId'], 'label': label, 'characters': [(el['chFrom'], el['chTo']) for el in annotation['support']]}) return results

I forgot to mention that the Wikifier API returns all the classes that an entity belongs to. It looks at the INSTANCE_OF and SUBCLASS_OF classes and traverses all the way through the class hierarchy. I decided to filter out entities with categories that would belong to a person, organization, or location. If we run our example text through the Named Entity Linking part of the pipeline, we will get the following output.

A nice thing about the wikification process is that we also get the corresponding WikiData ids for entities along with their titles. Having the WikiData ids takes care of the entity disambiguation problem. You might wonder then what happens if an entity does not exist on Wikipedia. In that case, unfortunately, the Wikifier will not recognize it. I wouldn’t worry too much about it, though, as Wikipedia has more than 100 million entities if I recall correctly.

If you look closely at the results, you’ll notice that Pretoria is wrongly classified as an Organization. I tried to solve this issue, but the Wikipedia class hierarchy is complicated and usually spans five or six hops. If there are some Wiki class experts out there, I will happily listen to your advice.

Step 3: Relationship extraction

I have already presented all of the concepts until this point. I have never delved into relationship extraction before. So far, we have only played around with co-occurrence networks. So, I am excited to present a working relationship extraction process. I spend a lot of time searching for any open-source models that might do a decent job. I was delighted to stumble upon the OpenNRE project. It features five open-source relationship extraction models that were trained on either the Wiki80 or Tacred dataset. Because I am such a big fan of everything Wiki, I decided to use the Wiki80 dataset. Models trained on the Wiki80 dataset can infer 80 relationship types. I haven’t tried the models trained on the Tacred dataset. You might try that on your own. In the IE pipeline implementation, I have used the wiki80_bert_softmax model. As the name implies, it uses the BERT encoder under the hood. One thing is sure. If you don’t have a GPU, you are not going to have a good time.

If we look at an example relationship extraction call in the OpenNRE library, we’ll notice that it only infers relationships and doesn’t try to extract named entities. We have to provide a pair of entities with the h and t parameters and then the model tries to infer a relationship.

model.infer({'text': 'He was the son of Máel Dúin mac Máele Fithrich, and grandson of the high king Áed Uaridnach (died 612).', 'h': {'pos': (18, 46)}, 't': {'pos': (78, 91)}})
('father', 0.5108704566955566)

The results output a relationship type as well as the confidence level of the prediction. My not so spotless code for relationship extraction looks like this:

# First get all the entities in the sentence
entities = wikifier(sentence, threshold=entities_threshold)
# Iterate over every permutation pair of entities
for permutation in itertools.permutations(entities, 2): for source in permutation[0]['characters']: for target in permutation[1]['characters']: # Relationship extraction with OpenNRE data = relation_model.infer( {'text': sentence, 'h': {'pos': , source[1] + 1]}, 't': {'pos': [target[0], target[1] + 1]}}) if data[1] > relation_threshold: relations_list.append( {'source': permutation[0]['title'], 'target': permutation[1]['title'], 'type': data[0]})

We have to use the results of the named entity linking as an input to the relationship extraction process. We iterate over every permutation of a pair of entities and try to infer a relationship. As you can see by the code, we also have a relation_threshold parameter to omit relationships with a small confidence level. You will later see why we use permutations and not combinations of entities.

So, if we run our example text through the relationship extraction pipeline, the results are the following:

Relationship extraction is a challenging problem to tackle, so don’t expect perfect results. I must say that this IE pipeline works as well, if not better than some of the commercial solutions out there. And obviously, other commercial solutions are way better.

Step 4: Knowledge graph

As we are dealing with entities and their relationships, it only makes sense to store the results in a graph database. I used Neo4j in my example.

Image by author

Remember, I said that we would try to infer a relationship between all permutations of pairs of entities instead of combinations. Looking at table results, it would be harder to spot why. In a graph visualization, it is easy to observe that while most of the relationships are inferred in both directions, that is not true in all cases. For example, the work location relationship between Elon Musk and the University of Pennsylvania is assumed in a single direction only. That brings us to another shortcoming of the OpenNRE model. The direction of the relationship isn’t as precise as we would like it to be.

A practical example of IE pipeline

To not leave you empty-handed, I will show you how you can use my IE implementation in your projects. We will run the IE pipeline through the BBC News Dataset found on Kaggle. The hardest part about the IE pipeline implementation was to set up all the dependencies. I want you to retain your mental sanity, so I built a docker image that you can use. Run the following command to get it up and running:

docker run -p 5000:5000 tomasonjo/trinityie

On the first run, the OpenNRE models have to be downloaded, so definitely don’t use -rm option. If you want to do some changes to the project and built your own version, I have also prepared a GitHub repository.

As we will be storing the results into Neo4j, you will also have to download and set it up. In the above example, I have used a simple graph schema, where nodes represent entities and relationships represent, well, relationships. Now we will refactor our graph schema a bit. We want to store entities and relationships in the graph but also save the original text. Having an audit trail is very useful in real-world scenarios as we already know that the IE pipeline is not perfect.

Image by author

It might be a bit counter-intuitive to refactor a relationship into an intermediate node. The problem we are facing is that we can’t have a relationship pointing to another relationship. Given this issue, I have decided to refactor a relationship into an intermediate node. I could have used my imagination to produce better relationship types and node labels, but it is what it is. I only wanted for the relationship direction to retain its function.

The code to import 500 articles in the BBC news dataset to Neo4j is the following. You’ll have to have the trinityIE docker running for the IE pipeline to work.

import json
import urllib
import pandas as pd
from neo4j import GraphDatabase driver = GraphDatabase.driver('bolt://localhost:7687', auth=('neo4j', 'letmein')) def ie_pipeline(text, relation_threshold=0.9, entities_threshold=0.8): # Prepare the URL. data = urllib.parse.urlencode([ ("text", text), ("relation_threshold", relation_threshold), ("entities_threshold", entities_threshold)]) url = "http://localhost:5000?" + data req = urllib.request.Request(url, data=data.encode("utf8"), method="GET") with urllib.request.urlopen(req, timeout=150) as f: response = response = json.loads(response.decode("utf8")) # Output the annotations. return response import_refactored_query = """
UNWIND $params as value
CREATE (a:Article{content:value.content})
FOREACH (rel in | MERGE (s:Entity{name:rel.source}) MERGE (t:Entity{}) MERGE (s)-[:RELATION]->(r:Relation{type:rel.type})-[:RELATION]->(t) MERGE (a)-[:MENTIONS_REL]->(r))
WITH value, a
UNWIND as entity
MERGE (e:Entity{name:entity.title})
SET e.wikiId = entity.wikiId
WITH entity, e
CALL apoc.create.addLabels(e,[entity.label]) YIELD node
RETURN distinct 'done' """ with driver.session() as session: params = [] for i,article in list(data.iterrows())[:500]: content = article['content'] ie_data = ie_pipeline(content) params.append({'content':content, 'ie':ie_data}) if (len(params) % 100 == 0):, {'params':params}) params = [], {'params':params})

The code is also available in the form of a Jupyter Notebook on GitHub. Depending on your GPU capabilities, the IE pipeline might take some time. Let’s now inspect the output. Obviously, I chose results that make sense. Run the following query:

MATCH p=(e:Entity{name:'Enrico Bondi'})-[:RELATION]->(r)-[:RELATION]->(), 


Results of IE extraction on BBC news dataset. Image by author

We can observe that Enrico Bondi is an Italian citizen. He held a position at Italy’s Chamber of Deputies. Another relationship was inferred that he also owns Parmalat. After a short Google search, it seems that this data is more or less at least in the realms of possible.

Path to explainable AI

You might wonder, what has this got to do with explainable AI. I’ll give you a real-world example. This research paper is titled Drug Repurposing for COVID-19 via Knowledge Graph Completion. I’m not a doctor, so don’t expect a detailed presentation, but I can give a high-level overview. There are a lot of medical research papers available online. There are also online medical entities databases such as MeSH or Ensembl. Suppose you run a Named Entity Linking model on biomedical research papers and use one of the online medical databases as a target knowledge base. In that case, you can extract mentioned entities in articles. The more challenging part is the relationship extraction. Because this is such an important field, great minds have come together and extracted those relationships.

Probably there are more projects, but I am aware of the SemMedDB project, which was also used in the mentioned article. Now that you have your knowledge graph, you can try to predict new purposes for existing drugs. In network science, this is referred to as link prediction. When you are trying to predict links as well as their relationship types, then the scientific community calls it knowledge graph completion. Imagine we have predicted some new use cases for existing drugs and show our results to a doctor or a pharmacologist. His response would probably be, that’s nice, but what makes you think this new use case will work? The machine learning models are a black box, so that’s not really helpful. But what you can give to the doctor is all the connections between the existing drug and the new disease it could treat. And not only direct relationships, but also those that are two or three hops away. I’ll make up an example, so it might not make sense to a biomedical researcher. Suppose the existing drug inhibits a gene that is correlated to the disease. There might be many direct or indirect connections between the drug and the disease that might make sense. Hence, we have embarked on a step towards an explainable AI.


I am really delighted with how this project worked out. I’ve been tinkering with combining NLP and Knowledge graphs for the last year or so, and now I have poured all of my knowledge into a single post. I hope you enjoyed it!

P.S. If you want to make some changes to the IE pipeline, the code is available as a Github repository. The code for reproducing this blog post is also available as a Jupyter Notebook.

This article was originally published on Towards Data Science and re-published to TOPBOTS with permission from the author.

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Artificial Intelligence

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




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.

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Artificial Intelligence

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




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

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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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This year is all about the roll-ups, the aggregation of smaller companies into larger firms, creating a potentially compelling path for equity value. The interest in creating value through e-commerce brands is particularly striking.

Just a year ago, digitally native brands had fallen out of favor with venture capitalists after so many failed to create venture-scale returns. So what’s the roll-up hype about?

Hack takes: A CISO and a hacker detail how they’d respond to the Exchange breach

3d Flat isometric vector concept of data breach, confidential data stealing, cyber attack.

Image Credits: TarikVision (opens in a new window) / Getty Images

The cyber world has entered a new era in which attacks are becoming more frequent and happening on a larger scale than ever before. Massive hacks affecting thousands of high-level American companies and agencies have dominated the news recently. Chief among these are the December SolarWinds/FireEye breach and the more recent Microsoft Exchange server breach.

Everyone wants to know: If you’ve been hit with the Exchange breach, what should you do?

5 machine learning essentials nontechnical leaders need to understand

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Machine learning has become the foundation of business and growth acceleration because of the incredible pace of change and development in this space.

But for engineering and team leaders without an ML background, this can also feel overwhelming and intimidating.

Here are best practices and must-know components broken down into five practical and easily applicable lessons.

Embedded procurement will make every company its own marketplace

Businesswomen using mobile phone analyzing data and economic growth graph chart. Technology digital marketing and network connection.

Image Credits: Busakorn Pongparnit / Getty Images

Embedded procurement is the natural evolution of embedded fintech.

In this next wave, businesses will buy things they need through vertical B2B apps, rather than through sales reps, distributors or an individual merchant’s website.

Knowing when your startup should go all-in on business development

One red line with arrow head breaking out from a business or finance growth chart canvas.

Image Credits: twomeows / Getty Images

There’s a persistent fallacy swirling around that any startup growing pain or scaling problem can be solved with business development.

That’s frankly not true.

Dear Sophie: What should I know about prenups and getting a green card through marriage?

lone figure at entrance to maze hedge that has an American flag at the center

Image Credits: Bryce Durbin/TechCrunch

Dear Sophie:

I’m a founder of a startup on an E-2 investor visa and just got engaged! My soon-to-be spouse will sponsor me for a green card.

Are there any minimum salary requirements for her to sponsor me? Is there anything I should keep in mind before starting the green card process?

— Betrothed in Belmont

Startups must curb bureaucracy to ensure agile data governance

Image of a computer, phone and clock on a desk tied in red tape.

Image Credits: RichVintage / Getty Images

Many organizations perceive data management as being akin to data governance, where responsibilities are centered around establishing controls and audit procedures, and things are viewed from a defensive lens.

That defensiveness is admittedly justified, particularly given the potential financial and reputational damages caused by data mismanagement and leakage.

Nonetheless, there’s an element of myopia here, and being excessively cautious can prevent organizations from realizing the benefits of data-driven collaboration, particularly when it comes to software and product development.

Bring CISOs into the C-suite to bake cybersecurity into company culture

Mixed race businesswoman using tablet computer in server room

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Cyber strategy and company strategy are inextricably linked. Consequently, chief information security officers in the C-Suite will be just as common and influential as CFOs in maximizing shareholder value.

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What did COVID do to all our models?




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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The AI Trends Reshaping Health Care




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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