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MIT CSAIL’s TextFooler generates adversarial text to strengthen natural language models

February 9, 2020 Big Data

AI and machine learning algorithms are vulnerable to adversarial samples that have alterations from the originals. That’s especially problematic as natural language models become capable of generating humanlike text, because of their attractiveness to malicious actors who would use them to produce misleading media. In pursuit of a technique that illustrates the extent to which adversarial text can affect model prediction, researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL), the University of Hong Kong, and Singapore’s Agency for Science, Technology, and Research developed TextFooler, a baseline framework for synthesizing adversarial text examples. They claim in a paper that it was able to successfully attack three leading target models, including Google’s BERT.

“If those tools are vulnerable to purposeful adversarial attacking, then the consequences may be disastrous,” said Di Jin, MIT Ph.D. student and lead author on the paper, who noted that the adversarial examples produced by TextFooler could improve the robustness of AI models trained on them. “These tools need to have effective defense approaches to protect themselves, and in order to make such a safe defense system, we need to first examine the adversarial methods.”

The researchers assert that besides the ability to fool AI models, the outputs of a natural language “attacking” system like TextFooler should meet certain criteria: human prediction consistency, such that human predictions remain unchanged; semantic similarity, such that crafted examples bear the same meaning as the source; and language fluency, such that generated examples look natural and grammatical. TextFooler meets all three even when no model architecture or parameters (values that influence model performance) are available — i.e., black-box scenarios.

It achieves this by identifying the most important words for the target models and replacing them with semantically similar and grammatically correct words until the prediction is altered. TextFooler is applied to two different tasks — text classification and entailment (the relationship between text fragments in a sentence) — with the goal of changing the classification or invalidating the entailment judgment of the original models. For instance, given the input “The characters, cast in impossibly contrived situations, are totally estranged from reality,” TextFooler might output “The characters, cast in impossibly engineered circumstances, are fully estranged from reality.”

To evaluate TextFooler, the researchers applied it to text classification data sets with various properties, including news topic classification, fake news detection, and sentence- and document-level sentiment analysis, where the average text length ranged from tens of words to hundreds of words. For each data set, they trained the aforementioned state-of-the-art models on a training set before generating adversarial examples semantically similar to the test set to attack those models.

The team reports that on the adversarial examples, they managed to reduce the accuracy of almost all target models in all tasks to below 10% with fewer than 20% of the original words perturbed. Even for BERT, which attained relatively robust performance compared with the other models tested, TextFooler reduced its prediction accuracy by about 5 to 7 times on a classification task and about 9 to 22 times on an entailment task (where the goal was to judge whether a sentence could be derived from entailment, contradiction, or a neutral relationship).

“The system can be used or extended to attack any classification-based NLP models to test their robustness,” said Jin. “On the other hand, the generated adversaries can be used to improve the robustness and generalization of deep learning models via adversarial training, which is a critical direction of this work.”

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Big Data – VentureBeat

Google open-sources LaserTagger, an AI model that speeds up text generation

February 1, 2020 Big Data

Sequence-to-sequence AI models, which were introduced by Google in 2014, aim to map fixed-length input (usually text) with a fixed-length output where the length of the input and output might differ. They’re used in text-generating tasks including summarization, grammatical error correction, and sentence fusion, and recent architectural breakthroughs have made them more capable than before. But they’re imperfect in that they (1) require large amounts of training data to reach acceptable levels of performance and that they (1) typically generate the output word-by-word (which makes them inherently slow).

That’s why researchers at Google developed LaserTagger, an open source text-editing model that predicts a sequence of edit operations to transform a source text into a target text. They assert that LaserTagger tackles text generation in a fashion that’s less error-prone — and that’s easier to train and faster to execute.

The release of LaserTagger follows on the heels of notable contributions from Google to the field of natural language processing and understanding. This week, the tech giant took the wraps off of Meena, a neural network with 2.6 billion parameters that can handle multiturn dialogue. And earlier this month, Google published a paper describing Reformer, a model that can process the entirety of novels.

LaserTagger takes advantage of the fact that for many text-generation tasks, there’s often an overlap between the input and the output. For instance, when detecting and fixing grammatical mistakes or when fusing several sentences, most of the input text can remain unchanged — only a small fraction of words need to be modified. LaserTagger, then, produces a sequence of edit operations instead of actual words, like keep (which copies a word to the output, delete (which removes a word), and keep-addx or delete-addx (which adds phrase X before the tagged word and optionally deletes the tagged word).

Added phrases come from a restricted vocabulary that’s been optimized to minimize vocabulary size and maximize the number of training examples. The only words necessary to add to the target text come from the vocabulary alone, preventing the model from adding arbitrary words and mitigating the problem of hallucination (i.e., producing outputs that aren’t supported by the input text). And LaserTagger can predict edit operations in parallel with high accuracy, enabling an end-to-end speedup compared with models that perform predictions sequentially.

Evaluated on several text generation tasks, LaserTagger performed “comparably strong” with, and up to 100 times faster than, a baseline model that used a large number of training examples. Even when trained using only a few hundred or a few thousand training examples, it produced “reasonable” results that could be manually edited or curated.

“The advantages of LaserTagger become even more pronounced when applied at large scale, such as improving the formulation of voice answers in some services by reducing the length of the responses and making them less repetitive,” wrote the team. “The high inference speed allows the model to be plugged into an existing technology stack, without adding any noticeable latency on the user side, while the improved data efficiency enables the collection of training data for many languages, thus benefiting users from different language backgrounds.”

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Big Data – VentureBeat

Google Brain’s AI achieves state-of-the-art text summarization performance

December 24, 2019 Big Data

Summarizing text is a task at which machine learning algorithms are improving, as evidenced by a recent paper published by Microsoft. That’s good news — automatic summarization systems promise to cut down on the amount of message-reading enterprise workers do, which one survey estimates amounts to 2.6 hours each day.

Not to be outdone, a Google Brain and Imperial College London team built a system — Pre-training with Extracted Gap-sentences for Abstractive SUmmarization Sequence-to-sequence, or Pegasus — that leverages Google’s Transformers architecture combined with pretraining objectives tailored for abstractive text generation. They say it achieves state-of-the-art results in 12 summarization tasks spanning news, science, stories, instructions, emails, patents, and legislative bills, and that it shows “surprising” performance on low-resource summarization, surpassing previous top results on six data sets with only 1,000 examples.

As the researchers point out, text summarization aims to generate accurate and concise summaries from input documents, in contrast to executive techniques. Rather than merely copy fragments from the input, abstractive summarization might produce novel words or cover principal information such that the output remains linguistically fluent.

Transformers are a type of neural architecture introduced in a paper by researchers at Google Brain, Google’s AI research division. As do all deep neural networks, they contain functions (neurons) arranged in interconnected layers that transmit signals from input data and slowly adjust the synaptic strength (weights) of each connection — that’s how all AI models extract features and learn to make predictions. But Transformers uniquely have attention. Every output element is connected to every input element, and the weightings between them are calculated dynamically.

The team devised a training task in which whole, and putatively important, sentences within documents were masked. The AI had to fill in the gaps by drawing on web and news articles, including those contained within a new corpus (HugeNews) the researchers compiled.

In experiments, the team selected their best-performing Pegasus model — one with 568 million parameters, or variables learned from historical data — trained on either 750GB of text extracted from 350 million web pages (Common Crawl) or on HugeNews, which spans 1.5 billion articles totaling 3.8TB collected from news and news-like websites. (The researchers say that in the case of HugeNews, a whitelist of domains ranging from high-quality news publishers to lower-quality sites was used to seed a web-crawling tool.)

Pegasus achieved high linguistic quality in terms of fluency and coherence, according to the researchers, and it didn’t require countermeasures to mitigate disfluencies. Moreover, in a low-resource setting with just 100 example articles, it generated summaries at a quality comparable to a model that had been trained on a full data set ranging from 20,000 to 200,000 articles.

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Big Data – VentureBeat

Google launches AutoML Natural Language with improved text classification and model training

December 13, 2019 Big Data

Earlier this year, Google took the wraps off of AutoML Natural Language, an extension of its Cloud AutoML machine learning platform to the natural language processing domain. After a months-long beta, AutoML today launched in general availability for customers globally, with support for tasks like classification, sentiment analysis, and entity extraction, as well as a range of file formats, including native and scanned PDFs.

By way of refresher, AutoML Natural Language taps machine learning to reveal the structure and meaning of text from emails, chat logs, social media posts, and more. It can extract information about people, places, and events both from uploaded and pasted text or Google Cloud Storage documents, and it allows users to train their own custom AI models to classify, detect, and analyze things like sentiment, entities, content, and syntax. It furthermore offers custom entity extraction, which enables the identification of domain-specific entities within documents that don’t appear in standard language models.

AutoML Natural Language has over 5,000 classification labels and allows training on up to 1 million documents up to 10MB in size, which Google says makes it an excellent fit for “complex” use cases like comprehending legal files or document segmentation for organizations with large content taxonomies. It has been improved in the months since its reveal, specifically in the areas of text and document entity extraction — Google says that AutoML Natural Language now considers additional context (such as the spatial structure and layout information of a document) for model training and prediction to improve the recognition of text in invoices, receipts, resumes, and contracts.

Additionally, Google says that AutoML Natural Language is now FedRAMP-authorized at the Moderate level, meaning it has been vetted according to U.S. government specifications for data where the impact of loss is limited or serious. It says that this — along with newly introduced functionality that lets customers create a data set, train a model, and make predictions while keeping the data and related machine learning processing within a single server region — makes it easier for federal agencies to take advantage.

Already, Hearst is using AutoML Natural Language to help organize content across its domestic and international magazines, and Japanese publisher Nikkei Group is leveraging AutoML Translate to publish articles in different languages. Chicory, a third early adopter, tapped it to develop custom digital shopping and marketing solutions for grocery retailers like Kroger, Amazon, and Instacart.

The ultimate goal is to provide organizations, researchers, and businesses who require custom machine learning models a simple, no-frills way to train them, explained product manager for natural language Lewis Liu in a blog post. “Natural language processing is a valuable tool used to reveal the structure and meaning of text,” he said. “We’re continuously improving the quality of our models in partnership with Google AI research through better fine-tuning techniques, and larger model search spaces. We’re also introducing more advanced features to help AutoML Natural Language understand documents better.”

Notably, the launch of AutoML follows on the heels of AWS Textract, Amazon’s machine learning service for text and data extraction, which debuted in May. Microsoft offers a comparable service in Azure Text Analytics.

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Big Data – VentureBeat

Facebook trains AI to generate worlds in a fantasy text adventure

November 24, 2019 Big Data

Procedurally generating an interesting video game environment isn’t just challenging — it’s incredibly time-consuming. Tools like Promethean AI, which tap machine learning to generate scenes, promise to ease the design burden somewhat. But barriers remain.

That’s why researchers at Facebook, the University of Lorraine, and the University College London in a preprint research paper investigated an AI approach to creating game worlds. Using content from LIGHT, a fantasy text-based multiplayer adventure, they designed models that could compositionally arrange locations and characters and generate new content on the fly.

“We show how [machine learning] algorithms can learn to assemble … different elements, arranging locations and populating them with characters and objects,” wrote the study’s coauthors. “[Furthermore, we] demonstrate that these … tools can aid humans interactively in designing new game environments.”

By way of refresher, LIGHT — which was proposed in a March paper published by the same team of scientists — is a research environment in the form of a text-based game within which AI and humans interact as player characters. All told, it comprises crowdsourced natural language descriptions of 663 locations based on a set of regions and biomes, along with 3,462 objects and 1,755 characters.

In this latest study, the team built a model to generate game worlds, which entail crafting location names and descriptions including background information. They trained it using example neighboring locations partitioned into test and validation sets, such that the locations were distinct in each set. Two ranking models were considered — one where models had access to the location name only and a second where they had access to the location description information — and architected so that when a new world was constructed at test time, the placed location was the highest scoring candidate of several.

To create a map for a new game, the models predicted the neighboring locations of each existing location, and for each location added, they filled in the surroundings. A location could connect to up to four neighboring locations (though not all connections needed to be filled), and locations couldn’t appear multiple times in one map.

A separate set of models produced objects, or items with which characters could interact. (Each object has a name, description, and a set of affordances that represent object properties, such as “gettable” and “drinkable.”) Using characters and objects associated with locations from LIGHT, the researchers created data sets to train algorithms that placed both objects and characters in locations, as well as objects within objects (e.g., coins inside a wallet).

Yet another family of models that had been fed the corpora from the world construction task created new game elements — either a location, character, or object — by leveraging a Transformer architecture pretrained on 2 billion Reddit comments, which were chosen because of their “closeness to natural human conversation” and because they exhibit “elements of creativity and storytelling.” It predicted a background and description given a location name; a persona and description given an object name; or a description and affordances given an object name.

So how did it all work in concert? First, an empty map grid was initialized to represent the number of possible locations, with a portion of grid positions marked inaccessible to make exploration more interesting. The central location was populated randomly, and the best-performing model iteratively filled in neighboring locations until the entire grid was populated. Then, for each placed location, a model predicted which characters and objects should populate that location before another model predicted if objects should be placed inside existing objects.

The researchers also propose a human-aided design paradigm, where the models could provide suggestions for which elements to place. If human designers enter names of game elements not present in the data set, the generative models would write descriptions, personas, and affordances.

In experiments, the team used their framework to generate 5,000 worlds with a maximum size of 50 arranged locations. Around 65% and 60% of characters and objects in the data set, respectively, were generated after the full 5,000 maps. The most commonly placed location was “the king’s quarters” (in 34% of the generated worlds), while the least commonly placed location was “brim canal,” and 80% of the worlds had more than 30 locations.

Despite the fact that the generative models didn’t tap the full range of entities available to them, the researchers say that the maps they produced were generally cohesive, interesting, and diverse. “These steps show a path to creating cohesive game worlds from crowd-sourced content, both with model-assisted human creation tooling and fully automated generation,” they wrote.

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Big Data – VentureBeat

IBM’s Lambada AI generates training data for text classifiers

November 15, 2019 Big Data

What’s a data scientist to do if they lack sufficient data to train a machine learning model? One potential avenue is synthetic data generation, which researchers at IBM Research advocate in a newly published preprint paper. They used a pretrained machine learning model to artificially synthesize new labeled data for text classification tasks. They claim that their method, which they refer to as language-model-based data augmentation (Lambada for short), improves classifiers’ performance on a variety of data sets and significantly improves upon state-of-the-art techniques for data augmentation.

“Depending upon the problem at hand, getting a good fit for a classifier model may require abundant labeled data. However, in many cases, and especially when developing AI systems for specific applications, labeled data is scarce and costly to obtain,” wrote the paper’s coauthors. “Depending upon the problem at hand, getting a good fit for a classifier model may require abundant labeled data. However, in many cases, and especially when developing AI systems for specific applications, labeled data is scarce and costly to obtain.”

Generating synthetic training data tends to be more challenging in the text domain than the visual domain, the researchers note, because the transformations used in simpler methods usually distort the text, making it grammatically and semantically incorrect. That’s why most text data augmentation techniques — including those detailed in the paper — involve replacing a single word with a synonym, deleting a word, or changing the word order.

Lambada leverages a generative model (OpenAI’s GPT) that’s pretrained on large bodies of text, enabling it to capture the structure of language such that it produces coherent sentences. The researchers fine-tuned their model on an existing, small data set, and used the fine-tuned model to synthesize new labeled sentences. Independently, they trained a classifier on the aforementioned data set and had it filter the synthesized corpus, retaining only data that appeared to be “qualitative enough” before re-training the classifier on both the existing and synthesized data.

To validate their approach, the researchers tested three different classifiers — BERT, a support vector machine, and a long short-term memory network — on three data sets by running experiments in which they varied the training samples per class. The corpora in question contained queries on flight-related information, open-domain and fact-based questions in several categories, and data from telco customer support systems.

They report that Lambada statically improved all three classifiers’ performance on small data sets, which they attribute in part to its controls over the number of samples per class. Said controls allowed them to invest more time in generating samples for classes that are under-represented in the original data set, they said.

“Our augmentation framework does not require additional unlabeled data … Surprisingly, for most classifiers, LAMBADA achieves better accuracy compared to a simple weak labeling approach,” wrote the coauthors. “Clearly, the generated data set contributes more to improving the accuracy of the classifier than … samples taken from the original data set.”

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Big Data – VentureBeat

AI uses genre metadata to track the evolution of Ancient Greek text

March 14, 2019 Big Data

Ageōmétrētos mēdeìs eisítō. That’s Greek for “Let no one untrained in geometry enter” — the motto famously inscribed above the entrance to Plato’s Academy. It’s straightforward enough (if you’re fluent), but not every case is so clear-cut. Interpreting ancient languages involves guesswork about semantics, as well as polysemy (the coexistence of many possible meanings for a word or phrase), and context.

Researchers at the Alan Turing Institute, the University of Warwick, the University of Helsinki, and Amazon propose a novel solution in a newly published paper. The idea involves neural networks, or layered mathematical functions that model biological neurons. Dubbed Genre-Aware Semantic Change for Ancient Greek (GASC), it leverages categorical metadata about target texts’ genres to uncover the evolution of meanings in Ancient Greek data sets.

AI systems that model semantic change aren’t new, per se — researchers have employed a range of topic-based and graph-based natural language processing models for interpretation tasks. But the authors note that few focus on ancient languages and most don’t account for language variation features. By contrast, the team’s work goes beyond literary data sets and historical language data and directly addresses questions about genre — i.e., which genre is most likely associated with a given sense, what is an unusual sense for a genre, and which genres have the most similar senses.

“Over time, new words enter the lexicon, others become obsolete, and existing words acquire new senses,” the paper’s authors write. “For example, in Old English ‘thing’ meant ‘a public assembly’, and currently it more generally means ‘entity’ … The close relationship between innovation and variation is well known in historical linguistics, and critical to ancient languages, for which balanced corpora are not available due to the limited amount of data at our disposal; therefore models need to explicitly account for confounding variables, like genre.”

The researchers first compiled a preprocessed corpus — the Diorises Annotated Ancient Greek Corpus — containing over 10 million words from 820 poems, dramas, oratories, philosophies, essays, narratives, atlases, religious scripts, and letters dated between the 8th century BC and the 5th century AD. Each was lemmatized (grouped together in the inflected forms) and part-of-speech tagged, and the model’s task was to detect the sense associated with target words in given contexts and describe their evolution over time.

“In technical texts, we expect polysemous words to have a technical sense,” the team explains. “On the other hand, in works more closely representing general language (comedy, oratory, historiography) we expect the words to appear in their more concrete and less metaphorical senses; in a number of genres, such as philosophy and tragedy, we cannot assume that this distribution holds.”

To evaluate the AI system’s performance, the researchers created a secondary data set and framework: They chose 50 target words in the corpus that could be identified as polysemous, including 17 words from Greek technical vocabulary and 33 words from the highest-frequency lemmas (forms chosen to represent the lexemes, or units of meaning) in the Diorisis corpus. For each word, they randomly divided the source corpora into training (80 percent) and test sets (20 percent) and devised a way to automatically match lists of words associated with each sense by the model to sense labels assigned by a group of expert human annotators.

In experiments, the researchers report that GASC was able to provide “interpretable representations” of the evolution of word sense and that it achieves improved predictive performance compared to the state of the art. “To our knowledge, no previous work has systematically compared the estimates from a statistical model to manual semantic annotations of ancient texts,” the coauthors wrote. “This work can be seen as a step toward the development of richer evaluation schemes and models that can embed expert judgments.”

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Big Data – VentureBeat

Jan 10 Webinar: Text Recognition (OCR) using Cognitive Service, Microsoft Flow, and Power Apps by Leila Etaati

January 9, 2019 Self-Service BI

Join Charles Sterling and Power BI MVP Leila Etaati, in this webinar as she covers how Text Recognition (OCR) using Cognitive Service, Microsoft Flow, and Power Apps.

Webinar details:

Cognitive service is a pre-build AI tool that can be used with Microsoft Flow and Power Apps to create intelligence applications in an hour. In this one hour session, an end-to-end demo on how to create a mobile application for detecting text from an image such as a picture of an invoice and then send to share point folders of shows in Power BI service. The process of how to create this application using Power Apps, Microsoft Flow, and cognitive service will be shown and demonstrates. The audience also gets some understanding of cognitive services, Flow and Power Apps.

When: Jan 10th 2019 10AM PST

Where: https://www.youtube.com/watch?v=11MG-RJ-L04

About Leila Etaati

Leila Jan 10 Webinar: Text Recognition (OCR) using Cognitive Service, Microsoft Flow, and Power Apps by Leila Etaati

Leila is Data Scientist, PhD, MVP, and BI Consultant, and Speaker. She has over 10 years’ experience working with databases and software systems. She was involved in many large-scale projects for big sized companies. Leila has PhD of Information System department, University of Auckland, MS and BS in computer science.

She worked in Industries including banking financial, power and utility, manufacturing … She is a lecturer and trainer in Business intelligence and data base design course in University of Auckland.

Leila speaks in international SQL Server and BI conferences such as Microsoft Ignite, PASS Summit, PASS Business Analytics, PASS Rally, and many SQL Saturdays in USA, Europe, Australia, and New Zealand on Machine Learning and Analytics topics.

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Microsoft Power BI Blog | Microsoft Power BI

PowerApps: Sending Text Messages

January 3, 2019 Microsoft Dynamics CRM

So much can be accomplished with PowerApps! In today’s blog post, we will walk you through the incredibly simple process of using PowerApps to send text messages with prefilled content using your own phone’s native messaging app. It’s just one example of the potential of PowerApps.

With PowerApps, we can generate text messages in a couple different ways:

Using a Flow to send an automated message. Typically, this requires the use of a 3^rd party action in Flow which will incur additional costs.
Using the sender’s own device to send the message through its native texting application. This option uses existing carrier rates to send the text message, which for most plans is free due to unlimited texting – therefore, it is the preferred method.

You can even configure the body of the text message in PowerApps to make the whole process only two clicks:

010219 1621 PowerAppsSe1 PowerApps: Sending Text Messages

1. Click the Send on-route text message button in Power Apps.

2. Click Send.

010219 1621 PowerAppsSe2 PowerApps: Sending Text Messages

Slick, right? Now let’s look at how we can automate this process with a very simple formula.

1. In the “OnSelect” event of our button, we will add the Launch() formula Launch(Address, Label, value). As the name suggests, Launch is used to open or launch an application on your device – it could be your phone’s native SMS/texting app, a phone call app, or even a browser. In our example, we will pass the SMS: along with the customer’s phone number in the Address parameter and then pass “body” in the Label parameter to denote the location of pre-populated text of our choice, which is the Value parameter. The pre-populated text is a concatenated string of text along with fields from the Customer record to personalize the message.

2. Note that in terms of the necessary code, there is a slight difference in the syntax between IOS and Android devices. Android follows the comma-separated syntax describe above: Launch(Address, Body, Content). However, Apple devices use a slightly different syntax that uses ampersands (&) and equal signs (=) instead of the commas, yielding the following syntax: Launch(Address & Label=Content). To handle the specific syntax for Apple, we concatenate the three Launch parameters into the first (Address) parameter. We add the ampersands and equal sign syntax into the string and the Apple device will interpret the syntax and will open a new message.

Android: Set the syntax by using the format provided in the hover over, which is Launch(Address, Label, value). Below is the Android syntax used in our example:
Launch(“SMS:”& Gallery1.Selected.address1_telephone2, “body”, “Hey ” & Gallery1.Selected.name & “! We are en-route to your location for the appointment. We will be there in less than an hour!”)
IOS: The entire syntax is located in the address parameter of the Launch formula , with the ampersand and equal sign being passed after the telephone number as part of the body string (highlighted below):
Launch(“SMS:”& Gallery1.Selected.address1_telephone2 & “&body=Hey ” & Gallery1.Selected.name & “! We are en-route to your location for the appointment. We will be there in less than an hour!”)

So, there you have it! Although simple, the Launch formula can help you not only automate your processes, but also save you a lot of money by using your carrier’s (unlimited) texting plan.

Tip: You can also use the Launch() function without the “body” seen in the examples above. If you just say Launch(“SMS” & Gallery1.Selected.address1_telephone2), it will launch the SMS/texting app on your phone with the customer’s phone number filled in. This gives you the ability to seamlessly transition from the PowerApps to your native texting app to send personalized messages to your customer.

We hope this helps. Don’t forget to subscribe to our blog for more!

Happy PowerApps’ing!

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PowerObjects- Bringing Focus to Dynamics CRM

“LinkConnect::linkc: — Message text not found” on startup, removing ~/.Mathematica helps

December 10, 2018 BI News and Info

We have a university license of Mathematica. In /usr/remote/Wolfram/Mathematica/10.3/Configuration/Licensing/mathpass we have the content:

!wolframlicense.physik.uni-bonn.de

One user experiences that Mathematica cannot be started, instead the following output is emitted on the console:

$   mathematica
$  

LinkConnect::linkc: -- Message text not found -- (LinkObject[8u2xs_shm, 3, 1])
LinkConnect::linkc: -- Message text not found -- (LinkObject[s88jc_shm, 3, 1])

She told me that deleting ~/.Mathematica fixes the issue. It seems to be caused by improperly logging off or having Mathematica crash. I did not experience that with my user account.

From what I found online it seems that this LinkConnect is used by remote kernels and also the licensing server communication. Since it is coupled to files in the home directory, it might be some stale socket to the university’s license server?

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Recent Questions – Mathematica Stack Exchange

Tag Archives: Text

MIT CSAIL’s TextFooler generates adversarial text to strengthen natural language models

Google open-sources LaserTagger, an AI model that speeds up text generation

Google Brain’s AI achieves state-of-the-art text summarization performance

Google launches AutoML Natural Language with improved text classification and model training

Facebook trains AI to generate worlds in a fantasy text adventure

IBM’s Lambada AI generates training data for text classifiers

AI uses genre metadata to track the evolution of Ancient Greek text

Jan 10 Webinar: Text Recognition (OCR) using Cognitive Service, Microsoft Flow, and Power Apps by Leila Etaati

PowerApps: Sending Text Messages

“LinkConnect::linkc: — Message text not found” on startup, removing ~/.Mathematica helps

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