Gupshup nabs $240M to power messaging channels


Conversational messaging platform Gupshup today announced that it raised $240 million led by Tiger Global Management, with participation from Fidelity Management, Think Investments, Malabar Investments, Harbor Spring Capital, and others. The tranche, which values the company at $1.64 billion, will be used to build new tools, infrastructure, and services while expanding Gupshup’s global reach, CEO Beerud Sheth said.

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Google testing Duplex feature that adds names to restaurant waitlists

Restaurant booking

Google appears to be testing a new feature that allows users to add themselves and parties to the waitlists of restaurants that would normally require a phone call. Powered by Duplex, Google’s AI-driven natural language processing technology that can converse with business owners over the phone, the waitlist capability could benefit hospitality organizations facing surges in traffic as pandemic fears abate.

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Sentiment & Engagement Analysis from your Slack data

A sneak peek into your Slack space’s emotions. Ever wondered how engaging was the content you delivered? Was it clear or confusing? Or if people misunderstood your message at that company-wide meeting? Remote environments give very little chance for teachers and leaders to gain feedback and optimize their content towards better performance.

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Where is the ROI in Artificial Intelligence deployments?

Miniature robots

Anyone with any doubts about the interest in AI and its use across enterprise technologies only needs to look at the example of the Intelligent Document Processing (IDP) market and the kind of verticals that are investing in it to quash those doubts. According to the Everest Group’s recently published report, Intelligent Document Processing (IDP) State of the Market Report 2021 (purchase required) the market for this segment alone is estimated at $700-750 million in 2020 and expected to grow at a rate of 55-65% over the next year. 

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Natural Language Processing Pipeline

NLP pipeline

A map for your studies and projectsBruno RodriguesJust now·6 min readIntroductionIn this article, I cover the development, or rather, a pipeline of an Natural Language Processing (NLP). Thus, interested parties can benefit from a guiding content in the development stages of the study or project.For those looking for a definition, NLP is a sub-area of ​​machine learning that works with natural language, whether dealing with text or audio. In technical terms, it studies the capabilities and limitations of machines to understand human language. A pratical application now is that while the text is written in English language, you probably are reading it in your native language, if not English. Is it truth? I also emphasize that Machine Learning (AM) is a sub-area of ​​Artificial Intelligence (AI).As shown in the image above, NLP is the result of the interdisciplinarity between Linguistics and ML. Goind deeper, NLP even uses Deep Learning (DL).There are two interesting concepts to mention: Natural Language Generation Systems (NLGS’s) and Natural Language Understanding Systems (NLUS’s). As far as NLG’s are concerned, they convert information from computer databases into human-understanble language and, as compared to the NLUS’s, theses convert human language occurances into more formal representations more easily manipulated by computer programs.Human language is not scientifically simple for a machine. One of its main challenging features is semantic ambiguity. We as humans have two fundamental factors that separate us from machines in terms of natural language: common cultural knowledge and prior experience.Other important factors are the context and tone of voice. Here you are faced with issues about feelings and emotions together. Machines do not contain feelings and emotions, nor can intrinsically and truly understand them.That said, it is possible to realize that NLP is a brige between human and machine. Furthermore, it is a great tool to help human beings in processes and automations of tasks that would streamline certain human needs, such as, for example, producing specific and everyday documents.Some other applications are sentiment analysis, contract review, machine translation, among others.Now, let’s start!PipelineWe can divide the NLP development process into five stages: i) data acquisition, ii) data cleaning, iii) pre-processing, iv) training, and v) data evaluation.Let’s start with data acquisition.Data acquisitionIn technical terms, data acquistion, when referring to NLP, is denominated corpus acquisition, for dataset in NLP is that way named. Some methods of acquiring data are web scraping and crawling. Selenium, Requests and Beautiful Soup are tools for this. It is possible to have and use data from databases such as SQL and Sparks. There is also the possibility of using structured corpus by third parties, such as COLT, IMDB reviws and Standford Sentiment Treebank. From theses, you develop a NLP initial model to put in interectaion with users or clients, to, thus, improve the model.You must be logical when acquiring a corpus, for the data must assist the needs of your project or study. Imagine two models, one aimed at education and the other at identifying toxicity. While you must eliminate offensive words for a model, for the other, you allow them. Another factor to be clean up is stereotyping trends.Data cleaningStopWords RemovalGenerally, prepositions and articles are removed. The idea is that words connecting ideas are removed. However, for other models, they are important, such as Translation, Question and Answer (Q&A) and Natural Language Understanding (NLU) tasks, wich can suffer from the loss of these words. Again, you must be logical in your work. Differently from these, a model that fits well this procedure is a spam detector.Below is an example of this procedure:Offensive words fit into this example, as stated above.Pre-processingBag-of-Words (BOW)This is simply the count of terms present in text. The procedure is you create a dictionary or list referring to the words present in your text and count their repetitions. Below is an illustration:Note that there are five sentences on the left side of the frame: i)It is a puppy, ii)It is a kitten, iii)It is a cat, iv)That is a dog and this is a pen and v)It is a matrix. Above the frame, there are words encounted from some of the sentences. Below them there is the presence count of those words, metioned above the frame, within sentences. If you add up all columns, you will receive the number of times that each word appear in the texts. Above we have structured data and unstructured data. From the unstructured data, the raw material, we build the structured data, the middle and fundamental part of the NLP process. It is necessary to pay attention for this method. It is not an absolute rule, it is necessary to master the method to know when to apply it and how to apply it.Through this method you need to be aware of the Curse of the Dimensionality so that your model is not inefficient. Another issue is the need to normalize the importance of words. Finally, I highlight that this method does not differentiate common words from more specific words. Imagine that a text has 100 words “very” and only one “basketball”. Although “very” appears 100 times in the text, nothing can be inferred about its content. However, just reading the word “basketball” once, it is possible to infer that the text talks about this sport. To solve this problem, the TF-IDF (Term Frequency-Inverse Document Frequency) was developed.TF-IDFThe idea behind this method is to first count an “a” term within an “x” document and divide it by the number of terms within the “x” document. It considers repetition of terms. After, there is the division between the number of documents by the number of documents, in wich “a” appears, being the quotient treated by log. Below is the mathematical formula:But, this model deals not with Curse of Dimensionality, as its approach considers the entire document to the point where many representations are equal to zero, not being significantly different from the Bag-of-Words. To solve this issue, the Word2Vec algorithm.Word2VecIt is an neural networks algorithm that does not represent an entire document. Its approach is to consider that the meaning of the words is given by the context, or rather by the neighboring words. I will not go into details about this algoritm, but I would like to point out that, for the subject of this topic, it has been the main tool.Finally I provide this explanatory schema, got on Google, of its objetive function:While the window scrolls through the text, the probability calculation for each word is given by the following formula:TrainingThere are numerous possibilities to apply NLP. To decide wich model to use, if in doubt, start by consulting the literature. Make a baseline. Use NLTK, Gensim and Spacy. It is not advisable to define something here when dealing with such comprehensive content.EvaluationJust as there are many tasks, there are many metrics. So, just like what was said above, consult the literature. There is already a considerable amount of work done demonstrating the performance of models and evaluation methods.Final wordsTo have a successful career, in terms of quality, it takes commitment, planning, study and action. I hope we can develop the world that way.Make your observations, corrections, tips below. I’m waiting for more learning.Grateful for your reading!

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Native Voice nabs $14M to convert devices into branded voice assistants

Native Voice

Native Voice, a startup developing an SDK for audio device manufacturers to integrate third-party voice assistants like Alexa and Google Assistant, today announced that it closed a $14 million seed round led by Gutbrain Ventures, PBJ Capital, Signal Peak Ventures with participation from Revel Partners, Ideaship, TechNexus, and others. According to CEO John Goscha, the proceeds will be used to propel the company onto more devices, expand its suite of voice services, and prove and scale its go-to-market model.

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Top 10 Natural Language Processing (NLP) Tools in 2021

Natural Language Processing is the fastest-growing subset of AI that applies linguistics and computer science to make human language understandable to machines. There are new advancements every year. New tools of NLP are evolving and the old ones are being updated with more developed features. Before going with the top 10 NLP tools services, it is important to mention that all the tools are either recently released or are upgraded with new features. The tools named below are free and open-source instruments.

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The Automated Writing Assistance Landscape in 2021

Automated Writing

Automated writing assistance — a category that encompasses a variety of computer-based tools that help with writing — has been around in one form or another for 60 years, although it’s always been a relatively minor part of the NLP landscape. But the category has been given a substantial boost from recent advances in deep learning. We review some history, look at where things stand today and consider where things might be going.

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Ethics sheet for Automatic Emotion Recognition and Sentiment Analysis

Emotions play a central role in our lives. Automatic Emotion Recognition (AER) — or “giving emotional abilities to computers” as Dr. Rosalind Picard described it in her seminal book Affective Computing) — is a sweeping interdisciplinary area of study exploring many foundational research questions and many commercial applications. However, some of the recent commercial and governmental uses of emotion recognition have garnered considerable criticism.

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Artificial intelligence and the analysis of text data — how NLP helps impact investors

Impact investing

With extensive datasets available and massive computing power, there are also misconceptions relating to what values are relevant for impact investing. The result is that critical information may be overlooked. One of the myths relating to investment decisions is that only numerical data can be analysed using artificial intelligence.

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Follow the Money June 2021: 50 Funded Machine Learning Companies

Follow the Money June 2021: 50 Funded Machine Learning Companies

June 2021 latest funding covering artificial intelligence, machine learning, robotics, and innovation.

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How PepsiCo uses AI to create products consumers don’t know they want


If you imagine how a food and beverage company creates new offerings, your mind likely fills with images of white-coated researchers pipetting flavors and taste-testing like mad scientists. This isn’t wrong, but it’s only part of the picture today. More and more, companies in the space are tapping AI for product development and every subsequent step of the product journey. At PepsiCo, for example, multiple teams tap AI and data analytics in their own ways to bring each product to life.

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Watching contents with the eyes of a human: the Document AI

Media content

As promised, our journey in the world of Machine Learning has reached the chapter regarding the Document AI. After explaining what is intended by the expression of machine readable and also why this specification is essential for NLP systems — which work only on plain text — now it’s time to discover what technologies make contents themselves accessible to machines.

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A brief into to NLP in the Media & Communication Industry

Natural Language Processing (NLP) possesses a massive influence on the media and communication industry. The ability to track people’s choice, filter irrelevant information, speed, and accuracy makes this technology standing apart in the industry. In this write-up, we will understand the role of NLP in the media industry, its impact, and how it will help to clear out the issues which are hampering the overall growth.

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Amex bets on AI and NLP for customer service

Woman a laptop and binary graphic

The customer draws the AI roadmap at American Express (Amex), at least according to two of the company’s top AI leaders. When describing their latest project, Josh Pizzaro, the company’s director of AI, and Cong Liu, the VP of natural language processing and conversational AI, couldn’t stress this enough.

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Top 10 Natural Language Processing (NLP) Tools for beginners

Glass window

Natural Language Processing (NLP) is one of the most interesting and fast-growing branches of Artificial Intelligence to work with. This realm is developing rapidly. Every year or even month there are new advancements. New tools are appearing, and existing ones are being updated with more progressive features. Having some experience in this field, I decided to share my best tools for NLP. My goal is not to provide you with dry analysis, but to advise you of the instruments that I enjoyed myself. Here, I gathered well-suited stuff for beginners. All links with documentations and guides are added.

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