Executives worry about their businesses.
They often have to navigate, with limited resources, a stormy market made of customers, competitors, and regulators, and the interactions between all these actors make finding answers to business questions a complex process.
But recently, machines have demonstrated their abilities to help shine some light on this chaos and provide, if not direct answers, context clues that help guide executives in using AI to handle business problems.
In this article, we delve into examples of how natural language processing (NLP) business applications can be applied at scale to address 5 pressing business questions. Read More
Monthly Archives: May 2019
Natural Language Processing – Current Applications and Future Possibilities
A 2017 Tractica report on the natural language processing (NLP) market estimates the total NLP software, hardware, and services market opportunity to be around $22.3 billion by 2025. The report also forecasts that NLP software solutions leveraging AI will see a market growth from $136 million in 2016 to $5.4 billion by 2025.
In order to shed more light on the growing applications of NLP solutions, Dan Faggella, the CEO of Emerj, converses with Vlad Sejnoha, the CTO of Nuance Communications, an organization offering AI and NLP solutions in voice, natural language understanding, reasoning and systems integration. Read More
Natural Language Processing projects & startups to watch in 2019
Along with other tech trends, Natural Language Processing became another buzzword in the past years. But not everyone really understands what NLP is and how it can be used to improve efficiency of the process and impact your business in a positive way. In this article I will be briefly explaining what natural language processing is, how it is used, a few benefits on-site search get from doing it and I will mention a some cool startups that are doing natural language processing today. Read More
How to Apply Machine Learning to Business Problems
It’s easy to see the massive rise in popularity for venture investment, conferences, and business-related queries for “machine learning” since 2012 – but most technology executives often have trouble identifying where their business might actually apply machine learning (ML) to business problems.
With new AI buzzwords being created weekly, it can seem difficult to get ahold of what applications are viable, and which are hype, hyperbole or hoax
In this article, we’ll break down categories of business problems that are commonly handled by ML, and we’ll also provide actionable advice to begin a ML initiative with the right approach and perspective (even it’s the first such project you’ve undertaken at your company). Read More
Adversarial Vulnerability of Neural Networks Increases with Input Dimension
Over the past four years, neural networks have been proven vulnerable to adversarial images: targeted but imperceptible image perturbations lead to drastically different predictions. We show that adversarial vulnerability increases with the gradients of the training objective when viewed as a function of the inputs. For most current network architectures, we prove that the`1-norm of these gradients grows as the square root of the input size. These nets therefore become increasingly vulnerable with growing image size. Our proofs rely on the network’s weight distribution at initialization, but extensive experiments confirm that our conclusions still hold after training. Read More
This Startup's Artificial Voice Sounds Almost Indistinguishable From A Human's
An Irish startup has claimed a breakthrough in text-to-speech synthesis that improves on public demonstrations by Google’s DeepMind and Facebook.
The result is an artificial voice that lacks many of the glitches in intonation heard from digital assistants like Siri or Amazon’s Alexa. It sounds eerily human, and shows that you no longer need a multi-billion dollar R&D budget or hundreds of engineers to produce an artificial voice that’s as good as Google’s. Read More
Facebook's virtual assistant, M, is dead. So are chatbots
IT’S DIFFICULT TO remember now, but there was a moment in early 2016 when many in the tech industry believed chatbots—automated text-based virtual assistants—would be the next big platform. Messaging app Kik staked its company’s future on bots and “chatvertising.” Startup studio Betaworks launched an accelerator program called Botcamp. And at its 2016 F8 conference, Facebook pitched bots to developers as the best way to connect with 900 million Messenger users.
Few expected that voice assistants like Amazon’s Alexa and Google Assistant would thrive and text-based chatbots would become a punchline. Betaworks’ accelerator, which the company says was designed as a one-off, has moved on to other themes. Kik pivoted to blockchain technology. And now Facebook says it will shutter M, its buzzy full-service virtual assistant, on Jan. 19. Read More
Narrative Science Employs Natural Language Generation
If you’ve spent much time on Nanalyze, you know that we’re passionate about technology and believe that we’re living in the most exciting times in history. We’re talking cure-for-cancer and sending-humans-to-Mars territory here. Our job is to keep you up-to-date about these changes in a variety of fields, so you can make informed financial decisions about where to invest or not—and learn some pretty cool stuff along the way. We talk about the good, the bad and ugly no matter what.
Then we came across Narrative Science and its natural language generation (NLG) platform Quill, which uses artificial intelligence technology to write everything from financial reports to sports news. In other words, it’s the competition. That’s right: Looks like we’re going from gonzo journalism to robo journalism. We knew that English degree would be obsolete someday. Read More
Introducing Translatotron: An End-to-End Speech-to-Speech Translation Model
Speech-to-speech translation systems have been developed over the past several decades with the goal of helping people who speak different languages to communicate with each other. Such systems have usually been broken into three separate components: automatic speech recognition to transcribe the source speech as text, machine translation to translate the transcribed text into the target language, and text-to-speech synthesis (TTS) to generate speech in the target language from the translated text. Dividing the task into such a cascade of systems has been very successful, powering many commercial speech-to-speech translation products, including Google Translate. Read More
Deep Learning for NLP: Advancements & Trends
Over the past few years, Deep Learning (DL) architectures and algorithms have made impressive advances in fields such as image recognition and speech processing.
Their application to Natural Language Processing (NLP) was less impressive at first, but has now proven to make significant contributions, yielding state-of-the-art results for some common NLP tasks. Named entity recognition (NER), part of speech (POS) tagging or sentiment analysis are some of the problems where neural network models have outperformed traditional approaches. The progress in machine translation is perhaps the most remarkable among all.
In this article I will go through some recent advancements for NLP that rely on DL techniques. I do not pretend to be exhaustive: it would simply be impossible given the vast amount of scientific papers, frameworks and tools available. I just want to share with you some of the works that I liked the most. I think the last months have been great for our field. The use of DL in NLP keeps widening, yielding amazing results in some cases, and all signs point to the fact that this trend will not stop. Read More
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