Devansh Bansal is the VP of the Emerging Tech Business Unit at Damco Solutions. The widespread adoption of chatbots was imminent with the stellar rise and consolidation of instant messaging. However, the accelerated pace at which chatbots have evolved from accepting scripted responses to holding natural-sounding conversations has been unprecedented. According to Google Trends, the interest in AI Chatbots has increased ten-fold over the last five years! With chatbots getting smarter, value-driven, and user-friendly, it has fueled customer-led demand for chatbot-driven interaction at every touchpoint.

With the huge growth in e-commerce domain, product recommendations have become an increasing field of interest amongst e-commerce companies. One of the more difficult tasks in product recommendations is size and fit predictions. There are a lot of size related returns and refunds in e-fashion domain which causes inconvenience to the customers as well as costs the company. Thus having a good size and fit recommendation system, which can predict the correct sizes for the customers will not only reduce size related returns and refunds but also improve customer experience. Early works in this field used traditional machine learning approaches to estimate customer and product sizes from purchase history. These methods suffered from cold start problem due to huge sparsity in the customer-product data. More recently, people have used deep learning to address this problem by embedding customer and product features. But none of them incorporates valuable customer feedback present on product pages along with the customer and product features. We propose a novel approach which can use information from customer reviews along with customer and product features for size and fit predictions. We demonstrate the effectiveness of our approach compared to using just product and customer features on 4 datasets. Our method shows an improvement of 1.37% - 4.31% in F1 (macro) score over the baseline across the 4 different datasets.

I can try explaining basic AI concepts, but your brain will probably just get more and more confused: That's a very normal thing in the AI world, which is why… If there is one thing you can take away from this article, it's that AI is hands-on, and trying to learn it by theory will not get you anywhere. This means that instead of reading giant books and watching hours long videos explaining fundamental concepts, get into it right away and start learning by building things! WAIT WHAT⁉ I said anyone can learn AI, and now I am saying there are prerequisites to start? Because AI is all about math and programming (obviously)! NOTE: You will be able to build out projects without understanding the math behind it if you are using libraries such as TensorFlow or PyTorch, but it is always an asset to have the math concepts down as they would help you get a deeper understanding of what you're doing .

Sometimes HR managers and recruiters have a tough time finding the right candidate for a job. Can conversational AI save the day? Recruiting the right talent is one of the most challenging tasks for recruiters. On top of these responsibilities, recruiters have to develop a recruitment strategy that meets business goals, takes into consideration competitor analysis and employee satisfaction rates. In reality, managing this workload effectively is a hard task to do, that's why many companies turned to Artificial Intelligence for extra help. In one of their recent reports, Deloitte covered a use case of a multinational bank struggling to optimize the workload of hundreds of service desk agents, who support HR management processes.

How does your organization compare to its peers in terms of artificial intelligence implementations today? Do you feel as if you are behind? Rest assured, even if you don't have a program in place today, you are in good company. If you are looking for a clear picture of where enterprise organizations are in terms of artificial intelligence implementation today, you're going to get a very mixed picture. Some organizations may be very advanced.

Artificial intelligence and machine learning already make a huge impact on the way we watch movies and television, shop, and travel, but how will these new technology advancements affect you as a sourcing or recruiting professional? It all comes down to being able to quickly analyze huge amounts of data and make decisions and predictions based on that, says Summer Husband, senior director, data science, at Randstad Sourceright, in a presentation at SourceCon, in Anaheim, Calif, last week. In a healthcare setting, Husband says, algorithms can be used to perform survival analysis, a machine learning technique that analyzes time to an event, such as a patient's expected time before recurrence of a disease or a death. This process was developed for medical situations and it's a good analogy to sourcing and recruiting except instead of survival analysis, job posting data is examined. Ultimately, the goal is to answer the question, "what's the time to fill?" "So, we take data on jobs we've filled for clients in the past, how long those took, how many candidates, open roles, information about the company as well as job market data from sources like the BLS and CareerBuilder, for instance, to find out how all of those things impact the'survival rate' of our open jobs. We're obviously flipping the script, because we want our open jobs to die quickly, but the process is the same," Husband says.

DMX-h, Syncsort's ETL /DI product for Hadoop runs natively on Hadoop and integrates very closely with the Map Reduce paradigm to perform high volume ETL batch operations like large JOINS, AGGREGATIONS, etc., which doesn't require users to rip the data out of Hadoop, do the ETL, and put it back into Hadoop as you referenced. DMX-h's ETL engine integrates via Syncsort's contribution to the Apache open source community, patch MAPREDUCE-2454, which introduced a new feature to the Hadoop MapReduce framework to allow alternative implementations of the Sort phase. This engine is the same ETL engine Syncsort offers outside of Hadoop and uses the same graphical UI, thereby making it very easy and seamless for existing ETL developers and architects to make the transition to ETL in Hadoop/Map Reduce – eliminating the need for Java/PIG expertise. The same lightweight DMX-h engine can be used to extract data from disparate source systems (Mainframe, RDBMS, files, etc.), pre-process, cleanse, validate and load it to HDFS, and then be used to implement very efficient and high speed Map Reduce ETL in Hadoop. Why Hadoop means more data savings & less data warehouse.