How VSpeech.ai’s ML Model Understands Mixed Language Inputs

Ahmedabad-based VSpeech.ai was founded in 2015. The startup sensed an opportunity while working with Interactive Voice Response (IVR) call centres, and soon pivoted to IVR based telephony integrations with Speech products.

Read more:https://analyticsindiamag.com/how-vspeech-ais-ml-model-understands-mixed-language-inputs-accurately/

#ai #ml

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How VSpeech.ai’s ML Model Understands Mixed Language Inputs

How VSpeech.ai’s ML Model Understands Mixed Language Inputs

Ahmedabad-based VSpeech.ai was founded in 2015. The startup sensed an opportunity while working with Interactive Voice Response (IVR) call centres, and soon pivoted to IVR based telephony integrations with Speech products.

Read more:https://analyticsindiamag.com/how-vspeech-ais-ml-model-understands-mixed-language-inputs-accurately/

#ai #ml

Mikel  Okuneva

Mikel Okuneva

1603785600

Microsoft’s Turing Language Model Can Now Interpret 94 Languages

Recently, the developers at Microsoft detailed the Turing multilingual language model (T-ULRv2) and announced that the AI model has achieved the top rank at the Google XTREME public leaderboard.

The Cross-lingual TRansfer Evaluation of Multilingual Encoders, also known as XTREME benchmark includes 40 typologically diverse languages, which span 12 language families. XTREME also consists of nine tasks that require reasoning about different levels of syntax as well as semantics.

The Turing multilingual language model (T-ULRv2) is created by the Microsoft Turing team in collaboration with Microsoft Research. The model is also known to beat the previous best from Alibaba (VECO) by 3.5 points in average score.


Saurabh Tiwary, Vice President & Distinguished Engineer at Microsoft mentioned that in order to achieve this milestone, the team leveraged StableTune, which is a multilingual fine-tuning technique based on stability training along with the pre-trained model. The other popular language models on the XTREME leaderboard include XLM-R, mBERT, XLM, among others. Ming Zhou, Assistant Managing Director at Microsoft Research Asia, stated in a blog post that the Microsoft Turing team has long believed that language representation should be universal. Also, this kind of approach would allow for the trained model to be fine-tuned in one language and applied to a different one in a zero-shot fashion.

For a few years now, unsupervised pre-trained language modelling has become the backbone of all-natural language processing (NLP) models, with transformer-based models at the heart of all such innovation. According to Zhou, this type of models has the capability to overcome the challenge of requiring labelled data to train the model in every language.

How T-ULRv2 Works

The Turing multilingual language model (T-ULRv2) model is the latest cross-lingual innovation at the tech giant. It incorporates the InfoXLM (Information-Theoretic Framework for Cross-Lingual Language Model Pre-Training),which is a cross-lingual pre-trained model for language understanding and generation to create a universal model that represents 94 languages in the same vector space.

TT-ULRv2 is a transformer architecture with 24 layers and 1,024 hidden states. The architecture also includes a total of 550 million parameters. The pre-training of this model includes three different tasks, which are multilingual masked language modelling (MMLM), translation language modelling (TLM) and cross-lingual contrast (XLCo).


#developers corner #google xtreme #microsoft #microsoft ai #microsoft ai model #microsoft turing nlg #t-ulrv2 model #turing multilingual language model

Hertha  Walsh

Hertha Walsh

1602709200

Learning AI/ML: The Hard Way

The Wave and the Curve

Data science, Artificial Intelligence (AI), and Machine Learning (ML), since last five to six years these phrases have made their places in Gartner’s hype cycle curve. Gradually they have crossed the peak and moving toward the plateau. The curve also has few related terms such as Deep Neural Network, Cognitive AutoML etc. This shows that, there is an emerging technology trend around AI/ML which is going to prevail over the software industry during the coming years. Few of their predecessors such as Business Intelligence, Data Mining and Data Warehousing were there even before these years.

Finding the Crystal Ball in the Jungle

Prediction and forecasting being my favorite topics, I started finding a way to get into this world of data and algorithms back in early 2019. Another driving force for me to learn AI/ML was my fascination on neural networks that was haunting me since I started learning about computer science. I collected few books, learned some python skills to dive into the crystal ball.

While I was going through the online articles, videos and books, I discovered lots of readily available tools, libraries and APIs for AI/ML. It was like someone who is trying to learn cycling and given a car to drive. Due to my interest in neural networks, I got attracted to most the most interesting sub-set of AI/ML, Deep Learning, which deals with deep neural networks. I couldn’t stop myself from directly jumping into Google Tensorflow (a free Google ML tool) and got overwhelmed by a huge collection of its APIs. I could follow the documentation, write code and even made it work. But there was a problem, I was unable understand why I am doing what I am doing. I was completely drowning with the terms like bios, variance, parameters, feature selection, feature scaling, drop out etc. That’s when I took a break, rewind and learn about the internals of AI/ML rather than just using the APIs and Libs blindly. So, I took the hard way.

On one side, I was allured by the readily available smart AI/ML tools and on the other side, my fascination on neural networks was attracting me to learn it from scratch. Meanwhile, I have spent around a month or two just looking for a path to enter the subject. A huge pool of internet resources made me thoroughly confused in identifying the doorway to the heart of puzzle. I realized, why it is a hard nut for people to learn. Janakiram MSV pointed out the reasons correctly in his article.

However, some were very useful, such as an Introduction to Machine Learning by Prof. Grimson from MIT OpenCourseWare. Though its little long but helpful.

#machine learning #ai #artificial intelligence (ai) #ml #ai guide #ai roadmap

Otho  Hagenes

Otho Hagenes

1619511840

Making Sales More Efficient: Lead Qualification Using AI

If you were to ask any organization today, you would learn that they are all becoming reliant on Artificial Intelligence Solutions and using AI to digitally transform in order to bring their organizations into the new age. AI is no longer a new concept, instead, with the technological advancements that are being made in the realm of AI, it has become a much-needed business facet.

AI has become easier to use and implement than ever before, and every business is applying AI solutions to their processes. Organizations have begun to base their digital transformation strategies around AI and the way in which they conduct their business. One of these business processes that AI has helped transform is lead qualifications.

#ai-solutions-development #artificial-intelligence #future-of-artificial-intellige #ai #ai-applications #ai-trends #future-of-ai #ai-revolution

Shradha Singh

1609159431

AI Models Are Making the World a Better Place

Artificial Intelligence (AI) is not a future trend; it is very much a part of our present and is steering our everyday lives. From the posts we see on our social media profiles to the movies we are recommended by Netflix and products Amazon suggests to us, we actively use AI technology.

Further on, with big companies and makers like NVIDIA, Intel, Qualcomm, and others, innovating the underlying technology (semiconductors), AI models are becoming smarter and better. Here we explore a few ways in which AI is changing our world and making it more advanced and simpler.

AI and the Human World: 4 Big Futuristic Changes
AI Will Improve Remote Learning

Distance learning has existed for many years. But its sudden introduction came as a shocker to the parents as well as teachers as it forced them to learn to teach and learn through the screen.

Artificial Intelligence professionals can help education leaders reduce costs and make education more effective by delivering successful online lessons. It will allow teachers to delegate mundane tasks and take up creative assessments. Planning, assessment, scheduling, and even teaching of facts can be taken care of by the AI.

The tech will allow teachers to focus on building students’ curiosity levels, critical thinking abilities, and creativity. China is leading the way in this with AI solutions in e-Learning with its 9 EdTech unicorns.

It can deliver a learning experience that is customized to a child’s needs.

**AI Will Introduce Physical Interfaces Between Humans and Machines **
Platforms and machines today are better at interacting with us due to AI. However, it is yet to go beyond the software styled interface. In the years to come, AI will go beyond real-world interfaces through which we will talk and interact with AI machines.

Autonomous vehicles are one such example.

Currently, such automation can only be seen in closed doors of factories and warehouses. Plus, these machines are narrow in their activities and rigid. AI-driven automated interfaces and machines will be more sensitive to our needs and intelligence. Artificial Intelligence professionals working in this arena will be high in-demand in future economies.

Latest developments in machine learning and AI models can successfully beat humans through reinforcement learning in games such as Go and DOTA, where an infinite amount of data is generated. This raises hopes for intelligent real-world AI becoming a reality provided enough data and simulations are provided.

#ai models #machine learning #ai #ai machines #ai solutions #futuristic