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Getting started with Natural Language Processing




Vinay Kumar Paspula

Natural language processing (NLP) is a field dedicated to enabling computers to work with human language. It may surprise you to learn that NLP tools are increasingly ubiquitous in everyday life (you’ve probably used several already today!).

Photo by Lorenzo Herrera on Unsplash

Look at the technologies around us:

  • Spellcheck and autocorrect
  • Auto-generated video captions
  • Virtual assistants like Amazon’s Alexa
  • Autocomplete
  • Your news site’s suggested articles

What do they have in common?

All of these handy technologies exist because of natural language processing! Also known as NLP, the field is at the intersection of linguistics, artificial intelligence, and computer science. The goal? Enabling computers to interpret, analyze, and approximate the generation of human languages (like English or Spanish).

NLP got its start around 1950 with Alan Turing’s test for artificial intelligence evaluating whether a computer can use language to fool humans into believing it’s human.

But approximating human speech is only one of a wide scope of uses for NLP! Applications from recognizing spam messages or biasin tweets to improving accessibility for people with inabilities all rely strongly upon natural language processing procedures.

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2. 🤖 How to talk to Computers: A Framework for building Conversational Agents — Part 1

3. Picture my voiceTop

4. 5 NLP Chatbot Platforms

NLP can be conducted in several programming languages. However, Python has some of the most extensive open-source NLP libraries, including the Natural Language Toolkit or NLTK. Because of this, you’ll be using Python to get your first taste of NLP.

Photo by micah boswell on Unsplash

“You never know what you have… until you clean your data.”

~ Unknown (or possibly made up)

Cleaning and preparation are crucial for many tasks, and NLP is no exception. Text preprocessing is usually the first step you’ll take when faced with an NLP task.

Without preprocessing, your computer interprets "the", "The", and "<p>The" as entirely different words. There is a LOT you can do here, depending on the formatting you need. Lucky for you, Regex and NLTK will do most of it for you! Common tasks include:

Noise removal — stripping text of formatting (e.g., HTML tags).

Tokenization — breaking text into individual words.

Normalization — cleaning text data in any other way:

  • Stemming is a blunt axe to chop off word prefixes and suffixes. “booing” and “booed” become “boo”, but “sing” may become “s” and “sung” would remain “sung.”
  • Lemmatization is a scalpel to bring words down to their root forms. For example, NLTK’s savvy lemmatizer knows “am” and “are” are related to “be.”
  • Other common tasks include lowercasing, stopwords removal, spelling correction, etc.

You now have a preprocessed, clean list of words. Now what? It may be helpful to know how the words relate to each other and the underlying syntax (grammar). Parsing is a stage of NLP concerned with segmenting text based on syntax.

You probably do not want to be doing any parsing by hand and NLTK has a few tricks up its sleeve to help you out:

Part-of-speech tagging (POS tagging) identifies parts of speech (verbs, nouns, adjectives, etc.). NLTK can do it faster (and maybe more accurately) than your grammar teacher.

Named entity recognition (NER) helps identify the proper nouns (e.g., “Natalia” or “Berlin”) in a text. This can be a clue as to the topic of the text and NLTK captures many for you.

Dependency grammar trees help you understand the relationship between the words in a sentence. It can be a tedious task for a human, so the Python library spaCy is at your service, even if it isn’t always perfect.

In English we leave a lot of ambiguity, so syntax can be tough, even for a computer program. Take a look at the following sentence:

“I saw a cow under a tree with binoculars”

Do I have the binoculars? Does the cow have binoculars? Does the tree have binoculars?

Regex parsing, using Python’s re library, allows for a bit more nuance. When coupled with POS tagging, you can identify specific phrase chunks. On its own, it can find you addresses, emails, and many other common patterns within large chunks of text.

How can we help a machine make sense of a bunch of word tokens? We can help computers make predictions about language by training a language model on a corpus (a bunch of example text).

Language models are probabilistic computer models of language. We build and use these models to figure out the likelihood that a given sound, letter, word, or phrase will be used. Once a model has been trained, it can be tested out on new texts.

One of the most well-known language models is the unigram model, a statistical language model usually known as bag-of-words. As its name recommends, bag-of-words doesn’t have a lot of request to its turmoil! What it has is a count tally of each occurrence for each word. Think about the accompanying content example:

“The squids jumped out of the suitcases.”

Provided some initial preprocessing, bag-of-words would result in a mapping like:

{"the": 2, "squid": 1, "jump": 1, "out": 1, "of": 1, "suitcase": 1}

Now look at this sentence and mapping:

“Why are your suitcases full of jumping squids?”

{"why": 1, "be": 1, "your": 1, "suitcase": 1, "full": 1, "of": 1, "jump": 1, "squid": 1}

You can see how even with different word order and sentence structures, “jump,” “squid,” and “suitcase” are shared topics between the two examples. Bag-of-words can be an excellent way of looking at language when you want to make predictions concerning topic or sentiment of a text. When grammar and word order are irrelevant, this is probably a good model to use.

For parsing entire phrases or conducting language prediction, you will want to use a model that pays attention to each word’s neighbors. Unlike bag-of-words, the n-gram model considers a sequence of some number (n) units and calculates the probability of each unit in a body of language given the preceding sequence of length n. Because of this, n-gram probabilities with larger n values can be impressive at language prediction.Take a look at our revised squid example:

“The squids jumped out of the suitcases. The squids were furious.”

A bigram model (where n is 2) might give us the following count frequencies:

{('', 'the'): 2, ('the', 'squids'): 2, ('squids', 'jumped'): 1, ('jumped', 'out'): 1, ('out', 'of'): 1, ('of', 'the'): 1, ('the', 'suitcases'): 1, ('suitcases', ''): 1, ('squids', 'were'): 1, ('were', 'furious'): 1, ('furious', ''): 1}

There are a couple problems with the n gram model:

  1. How can your language model make sense of the sentence “The cat fell asleep in the mailbox” if it’s never seen the word “mailbox” before? During training, your model will probably come across test words that it has never encountered before (this issue also pertains to bag of words). A tactic known as language smoothing can help adjust probabilities for unknown words, but it isn’t always ideal.
  2. For a model that more accurately predicts human language patterns, you want n (your sequence length) to be as large as possible. That way, you will have more natural sounding language, right? Well, as the sequence length grows, the number of examples of each sequence within your training corpus shrinks. With too few examples, you won’t have enough data to make many predictions.

Enter neural language models (NLM)! Much recent work within NLP has involved developing and training neural networks to approximate the approach our human brains take towards language. This deep learning approach allows computers a much more adaptive tack to processing human language.

We’ve touched on the idea of finding topics within a body of language. But what if the text is long and the topics aren’t obvious?

Topic modeling is an area of NLP dedicated to uncovering latent, or hidden, topics within a body of language.

A common technique is to deprioritize the most common words and prioritize less frequently used terms as topics in a process known as term frequency-inverse document frequency (tf-idf). Say what?! This may sound counter-intuitive at first. Why would you want to give more priority to less-used words? Well, when you’re working with a lot of text, it makes a bit of sense if you don’t want your topics filled with words like “the” and “is.” The Python libraries gensim and sklearn have modules to handle tf-idf.

Whether you use your plain bag of words (which will give you term frequency) or run it through tf-idf, the next step in your topic modeling journey is often latent Dirichlet allocation (LDA). LDA is a statistical model that takes your documents and determines which words keep popping up together in the same contexts (i.e., documents). We’ll use sklearn to tackle this for us.

If you have any interest in visualizing your newly minted topics, word2vec is a great technique to have up your sleeve. word2vec can map out your topic model results spatially as vectors so that similarly used words are closer together. In the case of a language sample consisting of “The squids jumped out of the suitcases. The squids were furious. Why are your suitcases full of jumping squids?”, we might see that “suitcase”, “jump”, and “squid” were words used within similar contexts. This word-to-vector mapping is known as a word embedding.

Most of us have a decent autocorrect story. Our phone’s messenger unobtrusively trades one letter for another as we type and out of nowhere the significance of our message has changed (to our shock or delight). However, addressing text similarity, tending to content similarity — including spelling correction — is a significant test inside natural language processing.

Addressing word similarity and misspelling for spellcheck or autocorrect often involves considering the Levenshtein distance or minimal edit distance between two words. The distance is calculated through the minimum number of insertions, deletions, and substitutions that would need to occur for one word to become another. For example, turning “bees” into “beans” would require one substitution (“a” for “e”) and one insertion (“n”), so the Levenshtein distance would be two.

Phonetic similarity is also a major challenge within speech recognition. English-speaking humans can easily tell from context whether someone said “euthanasia” or “youth in Asia,” but it’s a far more challenging task for a machine! More advanced autocorrect and spelling correction technology additionally considers key distance on a keyboard and phonetic similarity (how much two words or phrases sound the same).

It’s also helpful to find out if texts are the same to guard against plagiarism, which we can identify through lexical similarity (the degree to which texts use the same vocabulary and phrases). Meanwhile, semantic similarity (the degree to which documents contain similar meaning or topics) is useful when you want to find (or recommend) an article or book similar to one you recently finished.

How does your favorite search engine complete your search queries? How does your phone’s keyboard know what you want to type next? Language prediction is an application of NLP concerned with predicting text given preceding text. Auto suggest, autocomplete, and suggested replies are common forms of language prediction.

Your first step to language prediction is picking a language model. Bag of words alone is generally not a great model for language prediction; no matter what the preceding word was, you will just get one of the most commonly used words from your training corpus.

If you go the n-gram route, you will most likely rely on Markov chains to predict the statistical likelihood of each following word (or character) based on the training corpus. Markov chains are memory-less and make statistical predictions based entirely on the current n-gram on hand.

For example, let’s take a sentence beginning, “I ate so many grilled cheese”. Using a trigram model (where n is 3), a Markov chain would predict the following word as “sandwiches” based on the number of times the sequence “grilled cheese sandwiches” has appeared in the training data out of all the times “grilled cheese” has appeared in the training data.

A more advanced approach, using a neural language model, is the Long Short Term Memory (LSTM) model. LSTM uses deep learning with a network of artificial “cells” that manage memory, making them better suited for text prediction than traditional neural networks.

Believe it or not, you’ve just scratched the surface of natural language processing. There are a slew of advanced topics and applications of NLP, many of which rely on deep learning and neural networks.

  • Naive Bayes classifiers are supervised machine learning algorithms that leverage a probabilistic theorem to make predictions and classifications. They are widely used for sentiment analysis (determining whether a given block of language expresses negative or positive feelings) and spam filtering.
  • We’ve made enormous gains in machine translation, but even the most advanced translation software using neural networks and LSTM still has far to go in accurately translating between languages.
  • Some of the most life-altering applications of NLP are focused on improving language accessibility for people with disabilities. Text-to-speech functionality and speech recognition have improved rapidly thanks to neural language models, making digital spaces far more accessible places.
  • NLP can likewise be utilized to recognize inclination recorded as a hard copy and discourse. Have an inclination that a political applicant, book, or news source is one-sided yet can’t placed precisely how? Natural language processing can assist you with recognizing the language at issue.

As you’ve seen, there are a vast array of applications for NLP. However, as they say, “with great language processing comes great responsibility” (or something along those lines). When working with NLP, we have several important considerations to take into account:

  • Different NLP tasks may be more or less difficult in different languages. Because so many NLP tools are built by and for English speakers, these tools may lag behind in processing other languages. The tools may also be programmed with cultural and linguistic biases specific to English speakers.
  • Imagine a scenario where your Amazon Alexa could just understand wealthy men from coastal zones of the United States. English itself is definitely not a homogeneous body. English changes by individual, by tongue, and by numerous sociolinguistic elements. When we fabricate and train NLP instruments, would we say we are just building them for one kind of English speaker?
  • You can have the best intentions and still inadvertently program a bigoted tool. While NLP can limit bias, it can also propagate bias. As an NLP developer, it’s important to consider biases, both within your code and within the training corpus. A machine will learn the same biases you teach it, whether intentionally or unintentionally.
  • As you become someone who builds tools with natural language processing, it’s vital to take into account your users’ privacy. There are many powerful NLP tools that come head-to-head with privacy concerns. Who is collecting your data? How much data is being collected and what do those companies plan to do with your data?

Check out how much you’ve learned about natural language processing!

  • Natural language processing combines computer science, linguistics, and artificial intelligence to enable computers to process human languages.
  • NLTK is a Python library used for NLP.
  • Text preprocessing is a stage of NLP focused on cleaning and preparing text for other NLP tasks.
  • Parsing is a stage of NLP concerned with breaking up text based on syntax.
  • Language models are probabilistic machine models of language use for NLP comprehension tasks. Common models include bag-of-words, n-gram models, and neural language modeling.
  • Topic modeling is the NLP process by which hidden topics are identified given a body of text.
  • Text similarity is a facet of NLP concerned with semblance between instances of language.
  • Language prediction is an application of NLP concerned with predicting language given preceding language.
  • There are many social and ethical considerations to take into account when designing NLP tools.


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

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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

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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

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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.

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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?

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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

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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

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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.

How is edtech spending its extra capital?

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Edtech unicorns have boatloads of cash to spend following the capital boost to the sector in 2020. As a result, edtech M&A activity has continued to swell.

The idea of a well-capitalized startup buying competitors to complement its core business is nothing new, but exits in this sector are notable because the money used to buy startups can be seen as an effect of the pandemic’s impact on remote education.

But in the past week, the consolidation environment made a clear statement: Pandemic-proven startups are scooping up talent — and fast.

Tech in Mexico: A confluence of Latin America, the US and Asia

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Knowledge transfer is not the only trend flowing in the U.S.-Asia-LatAm nexus. Competition is afoot as well.

Because of similar market conditions, Asian tech giants are directly expanding into Mexico and other LatAm countries.

How we improved net retention by 30+ points in 2 quarters

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There’s certainly no shortage of SaaS performance metrics leaders focus on, but NRR (net revenue retention) is without question the most underrated metric out there.

NRR is simply total revenue minus any revenue churn plus any revenue expansion from upgrades, cross-sells or upsells. The greater the NRR, the quicker companies can scale.

5 mistakes creators make building new games on Roblox

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Even the most experienced and talented game designers from the mobile F2P business usually fail to understand what features matter to Robloxians.

For those just starting their journey in Roblox game development, these are the most common mistakes gaming professionals make on Roblox.

CEO Manish Chandra, investor Navin Chaddha explain why Poshmark’s Series A deck sings

CEO Manish Chandra, investor Navin Chaddha explain why Poshmark’s Series A deck sings image

“Lead with love, and the money comes.” It’s one of the cornerstone values at Poshmark. On the latest episode of Extra Crunch Live, Chandra and Chaddha sat down with us and walked us through their original Series A pitch deck.

Will the pandemic spur a smart rebirth for cities?

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Cities are bustling hubs where people live, work and play. When the pandemic hit, some people fled major metropolitan markets for smaller towns — raising questions about the future validity of cities.

But those who predicted that COVID-19 would destroy major urban communities might want to stop shorting the resilience of these municipalities and start going long on what the post-pandemic future looks like.

The NFT craze will be a boon for lawyers

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There’s plenty of uncertainty surrounding copyright issues, fraud and adult content, and legal implications are the crux of the NFT trend.

Whether a court would protect the receipt-holder’s ownership over a given file depends on a variety of factors. All of these concerns mean artists may need to lawyer up.

Viewing Cazoo’s proposed SPAC debut through Carvana’s windshield

It’s a reasonable question: Why would anyone pay that much for Cazoo today if Carvana is more profitable and whatnot? Well, growth. That’s the argument anyway.

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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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