Skip to main content

AI Rivalries : Flipkart enroute in creating Alexa's Nemesis?

Hey Readers!
A recent development caught my eye in the field of E-commerce rivalries! Have a look at the following article clipping:

 Journalism Credits : Business Today Group
Walmart-backed Flipkart has just issued a challenge to Amazon's Alexa and Google Assistant. The home-grown e-commerce giant today announced that it has acquired Bengaluru-based artificial intelligence (AI) startup Liv.ai, which has developed a platform that converts speech-to-text in nine regional languages apart from English. With this move, the e-tailer hopes to soon offer an end-to-end conversational shopping experience for its users.
"Given the complexities in typing on vernacular keyboards, voice will become a preferred interface for new shoppers. One does understand that building a voice interface is complex, and is especially challenging in Indian context given multiple languages and accents," Flipkart CEO Kalyan Krishnamurthy said in a statement. "Ultimately, we want to give our customers a conversational e-commerce experience and believe that with the voice interface the opportunities are endless including discovery, search, engagement, transactions etc. With Liv, we're one step closer to making e-commerce accessible to emerging users."


According to The Economic Times, the deal is expected to be valued at around $40 million and, with this acquisition, voice integration could be available in some parts of the Flipkart platform as early as 6-8 months.
Launched in 2015 by three IIT-Kharagpur graduates, Subodh Kumar, Kishore Mundra and Sanjeev Kumar, Liv.ai claims to be the only company in India to be able to convert speech to text in nine Indian languages, including Bengali, Punjabi, Marathi, Gujarati, Kannada, Tamil, Telugu and Hindi.
"While Flipkart has been solving for quintessential Indian problems, it is an exciting time for us to join in and solve it together. We are excited with the opportunity that is being presented to scale this up further and make it available to millions of consumers and help them have a deep level e-commerce experience," Liv.ai CEO Subodh Kumar said on the occasion.
According to him, voice is "a much better and effortless interface" and it not only improves ease of use on an e-commerce platform but also increases the consumers buying propensity.
According to Flipkart, once the acquisition is complete, Liv.ai will be integrated into Flipkart Center of Excellence for Voice Solutions. As a part of the deal, the entire 20-member team of Liv, including its founders, will work under Flipkart Vice President, Ravish Sinha, to develop voice solutions, integration with Flipkart app and work on developing use cases for various categories.
The acquisition is a timely move, given that the next phase of the country's accelerated digital transformation will focus on Tier 2 towns and beyond. According to the daily, that translates to 200-300 million online shoppers. And a whopping majority of them speak vernacular languages, not English.
A recent report released by Bain & Company, Google and Omidyar Network, claims that India has the potential to unlock over $50 billion in online commerce in India by driving awareness, usage and transactions among the current and next set of internet users and shoppers.
Significantly, the study found that 54 million Indian users opted out after their first online purchase due to issues with user experience and pegged the potential uplift in e-commerce from reengaging this group at $6 billion, or even double that. Breaking down the language barrier, obviously, would be a key step in improving user experience.
Despite the huge digital divide, India already boasts the second highest active internet user base with 1 out of 3 people online. A lot of credit for that goes to the smartphone revolution. Now imagine the potential of the voice revolution that is already underway.

For the Geeks

You may want to try out the liv.ai working for yourself. This excellent venture carries out Liv.ai speech API enables developers to convert speech-to-text by using Powerful Neural Network Models with exceptional accuracy and minimal latency. The API recognizes 9 major Indian Languages - English, Hindi, Bengali, Punjabi, Marathi, Gujarati, Kannada, Tamil and Telugu. Our system works with most accents and performs remarkably well in no
isy environments.

Labels:

Comments

Post a Comment

Total Pageviews

Popular posts from this blog

Kaggle Dataset Analysis : Is your Avocado organic or not?

Hey readers! Today, allow me to present you yet another dataset analysis of a rather gluttony topic, namely Avocado price analysis. This Data set  represents the historical data on avocado prices and sales volume in multiple US markets. Our prime objectives will be to visualize the dataset, pre-process it and ultimately test multiple sklearn classifiers to checkout which one gives us the best confidence and accuracy for our Avocado's Organic assurance! Note : I'd like to extend the kernel contribution to Shivam Negi . All this code belongs to him. Data Visualization This script must procure the following jointplot  While a similar joint plot can be drawn for conluding the linearly exponent relations between extra large bags and the small ones. Pre Processing The following script has been used for pre processing the input data. Model Definition and Comparisons We will be looking mostly at three different models, namely ran
Post a Comment
Read more

Your help in Fashion : 7 layer CNN at your service (~92% accurate)

Hey Readers! Welcome to yet another post where I play with a self designed neural network. This CNN would be tackling a variant of classical MNIST known as Fashion MNIST dataset  . Before we start exploring what is the approach for this dataset, let's first checkout what this dataset really is. Fashion-MNIST is a dataset of Zalando's article images—consisting of a training set of 60,000 examples and a test set of 10,000 examples. Each example is a 28x28 grayscale image, associated with a label from 10 classes. Zalando intends Fashion-MNIST to serve as a direct drop-in replacement for the original MNIST dataset for benchmarking machine learning algorithms. It shares the same image size and structure of training and testing splits. The original MNIST dataset contains a lot of handwritten digits. Members of the AI/ML/Data Science community love this dataset and use it as a benchmark to validate their algorithms. In fact, MNIST is often the first dataset researchers try. &q
Post a Comment
Read more

IOT Breakthrough : TensorFlow 1.9 Officially Supports the Raspberry Pi

Hey Readers! Good news for all the "cheap fair power" computer fans, as a result of a major collaboration effort between TensorFlow and Raspberry Pi foundation, one can now install tensorflow precompiled binaries using Python's pip package system !  When TensorFlow was first launched in 2015, they wanted it to be an “ open source machine learning framework for everyone ”. To do that, they needed to run on as many of the platforms that people are using as possible. They have long supported Linux, MacOS, Windows, iOS, and Android, but despite the heroic efforts of many contributors, running TensorFlow on a Raspberry Pi has involved a lot of work. If one is using Rasbian9 they can simply use these 2 commands to install tensorflow on their machine! According to an excerpt from TensorFlow's medium article page :  " We’re excited about this because the Raspberry Pi is used by many innovative developers, and is also widely used in education to
1 comment
Read more