NEW DELHI: As data science gets set to drive the artificial intelligence (AI) market in 2017, a few Indian startups are initiating development of conversational bots, speech recognition tools, intelligent digital assistants and conversational services to be built over social media channels, a joint study by PwC-Assocham said on Thursday. Organizations are looking to leverage AI capabilities for predictive modelling. "Online shopping portals have extensively been using predictive capabilities to gauge consumer interest in products by building a targeted understanding of preferences through collection of browsing and click-stream data, and effectively targeting and engaging customers using a multi-channel approach," the report added. To enable consumers to find better products at low prices, machine learning algorithms are being deployed for better matching of supply with consumer demand. Some of the areas where AI can improve legal processes, said the findings, include improved discovery and analysis based on law case history and formulation of legal arguments based on identification of relevant evidence.

"We are applying AI to a number of problems such as speech recognition, natural language understanding, question answering, dialog systems," Rastogi said.Rajeev Rastogi, who heads the Machine Learning team at Amazon, explains how the global ecommerce giant employs Artificial Intelligence to improve the online shopping experience.Edited excerpts: In which areas does Amazon use AI? We are applying AI to a number of problems such as speech recognition, natural language understanding, question answering, dialog systems, product recommendations, product search, forecasting future product demand, among others. We have used Deep Learning to do better speech recognition. We use neural networks to convert speech (spoken by users) to text with very high accuracy. The speech recognition and understanding technology in Alexa (Amazon's voice-controlled virtual assistant) is powered by Deep Learning.

The next time you shop on fashion website Myntra, you might end up choosing a t-shirt designed completely by a software - the pattern, colour and texture-without any intervention from a human designer. The first set of these t-shirts went on sale four days ago. This counts as a significant leap for Artificial Intelligence in e-commerce. For customers, buying online might seem simple--click, pay and collect. Behind the scenes, from the warehouses to the websites, artificial intelligence plays a huge role in automating processes.

It is very important to make sales forecasting in the supply chain management. In our previous post, we considered different approaches for time series forecasting. The most important thing is to make a decision how many products should be supplied into each store. If we can predict future sales precisely, the amount of products we need to supply is equal to our precise prediction. But in the real life we cannot make precise prediction, we rather can predict product consumption value with some confidential interval.

Human chemistry feels complicated, but the intricacies of relationships are ripe for machine learning. How do you feel about this person, how do they feel about you? A great deal of human interaction can be patterned and captured in a very large AI system. Laurie predicts we will casually use the data we know about us and the people around us to manipulate our body chemistry. The surprise is that it will be easy.

Voice is a Big Deal in tech this year. Amazon has probably sold 10m Echos, you couldn't move for Alexa partnerships at CES, Google has made its own and, it seems, this is the new platform. There are a couple of different causes for this explosion, and, also, a couple of problems. First, voice is a big deal because voice input now works in a way that it did not until very recently. The advances in machine learning in the past couple of years mean (to simplify hugely) that computers are getting much better at recognizing what people are saying.

At the heart of AI systems are statistical models that have no concept of social inequality, fairness, or hardships. In Cathy O'Neil's book, Weapons of Math Destruction (WMD), she points out that big data is discriminating nearly at every juncture of our society and pummeling the poor at each opportunity. Her book points to many avenues of misuse of data, but most offensive is through the use of proxies. Data statistics that are designed for one purpose but are repurposed to be used for economic or convenience sake. There are a number of examples of this.

Which is the most interesting, latest, and easy Machine Learning project available now? What is the most I/O(more interested in write I/O) bound machine learning algorithm? What are the best sources to pick up projects for beginners who are interested in the field of machine learning and data science? What is the best or most interesting aspect of machine learning to work in? What are some of the most interesting machine learning ideas that you came up with that were unsuccessful?

WASHINGTON/BOSTON One of two current members of the U.S. Securities and Exchange Commission raised questions on Thursday for companies like Snap Inc that offer shareholders unequal voting rights, saying the agency should "focus on how some innovations may prove detrimental to investors."

Editor's note: Chloe (as well as other members of SVDS) will be speaking at TDWI Accelerate in Boston. Find more information, and sign up to receive our slides here. From the outside, data science is often thought to consist wholly of advanced statistical and machine learning techniques. However, there is another key component to any data science endeavor that is often undervalued or forgotten: exploratory data analysis (EDA). At a high level, EDA is the practice of using visual and quantitative methods to understand and summarize a dataset without making any assumptions about its contents.