Large language models are increasingly deployed across diverse applications. This often includes tasks LLMs have not encountered during training. This implies that enumerating and obtaining the high-quality training data for all tasks is infeasible. Thus, we often need to rely on transfer learning using datasets with different characteristics, and anticipate out-of-distribution requests. Motivated by this practical need, we propose an analysis framework, building a transfer learning matrix and dimensionality reduction, to dissect these cross-task interactions. We train and analyze 10 models to identify latent abilities (e.g., Reasoning, Sentiment Classification, NLU, Arithmetic) and discover the side effects of the transfer learning. Our findings reveal that performance improvements often defy explanations based on surface-level dataset similarity or source data quality. Instead, hidden statistical factors of the source dataset, such as class distribution and generation length proclivities, alongside specific linguistic features, are actually more influential. This work offers insights into the complex dynamics of transfer learning, paving the way for more predictable and effective LLM adaptation.

In e-commerce, opinion summarization is the process of summarizing the consensus opinions found in product reviews. However, the potential of additional sources such as product description and question-answers (QA) has been considered less often. Moreover, the absence of any supervised training data makes this task challenging. To address this, we propose a novel synthetic dataset creation (SDC) strategy that leverages information from reviews as well as additional sources for selecting one of the reviews as a pseudo-summary to enable supervised training. Our Multi-Encoder Decoder framework for Opinion Summarization (MEDOS) employs a separate encoder for each source, enabling effective selection of information while generating the summary. For evaluation, due to the unavailability of test sets with additional sources, we extend the Amazon, Oposum+, and Flipkart test sets and leverage ChatGPT to annotate summaries. Experiments across nine test sets demonstrate that the combination of our SDC approach and MEDOS model achieves on average a 14.5% improvement in ROUGE-1 F1 over the SOTA. Moreover, comparative analysis underlines the significance of incorporating additional sources for generating more informative summaries. Human evaluations further indicate that MEDOS scores relatively higher in coherence and fluency with 0.41 and 0.5 (-1 to 1) respectively, compared to existing models. To the best of our knowledge, we are the first to generate opinion summaries leveraging additional sources in a self-supervised setting.

At the US Patent & Trademark Office in Alexandria, Virginia, artificial intelligence (AI) projects are expediting the patent classification process, helping detect fraud, and expanding examiners' searches for similar patents, enabling them to search through more documents in the same amount of time. And every project started with a pilot project. "Proofs of concept (PoCs) are a key approach we use to learn about new technologies, test business value assumptions, de-risk scale project delivery, and inform full production implementation decisions," says USPTO CIO Jamie Holcombe. Once the pilot proves out, he says, the next step is to determine if it can scale. Indian e-commerce vendor Flipkart has followed a similar process before deploying projects that allow for text and visual search through millions of items for customers who speak 11 different languages.

Artificial Intelligence or Machine learning (AI/ML) is a broad set of software capabilities that make software smarter. AI/ML is fundamentally changing how businesses operate across all sectors, including manufacturing, healthcare, IT, and transportation. Companies like Google, Amazon, Netflix, Facebook, Flipkart, Ola and Swiggy, who are using AI/ML, have witnessed huge growth and profits. Over 35% of revenue for Amazon and Flipkart is generated by product recommendations. Companies like Intel, Microsoft, Twitter, Ford, Intel and Apple have successfully integrated AI/ML into their products and are seeing a growth of about 30% improvement in areas like revenue, growth rate and efficiency.

For the past few years, artificial intelligence has become a buzzword in the tech sphere. As more advancements in technology are taking center stage, more companies and people are jumping into the pool of artificial intelligence. With exploding population and high-end experts, India is one of the front-running countries that are striving to streamline artificial intelligence. Because of the country's never-ending efforts, artificial intelligence recruiters and recruiting are also mushrooming. AI recruiters in India, especially, from big companies are seeking talented candidates in machine learning engineering, robotic scientist, data scientist, research analyst, business intelligence developer, etc. Analytics insight has listed the top 10 artificial intelligence recruiters from top-notch companies who could brighten your future.

The academic collaboration is expected to bring real world industry exposure to students and scholars of IIT Patna, and to provide an opportunity to the faculty members to work closely with Flipkart on research projects. As a part of this MoU, IIT Patna will undertake a number of programs such as joint research activities, writing research papers, organizing seminars, internship / mentorship opportunities, etc. "The aim behind this collaboration is to create industry-focused applied research which could help reach e-commerce to more consumers and sellers alike. With this MoU, we aim to establish deeper academia collaborations which could help students and the academia to leverage our data and platform knowledge to work on India specific e-commerce challenges, in addition to publishing research papers," Mayur Datar, Chief Data Scientist Flipkart, said. IIT Patna's computer science professor Asif Ekbal said the research is particularly aimed at developing robust machine translation techniques for translating the large amount of user reviews written in English to the Indian vernacular languages. Flipkart works closely with academia through some of the leading institutes including Indian Institute of Science (IISC), IIT (Kharagpur, Bombay and Kanpur), IIM (Ahmedabad and Kolkata) and a few foreign universities like Carnegie Mellon University and University of California, San Diego.

The models H71 and K71 are available exclusively on Flipkart, starting at an appealing price of INR 49,999 and INR 25,499, respectively. Additionally, the first 250 customers will give 1 year Sony Liv subscription for free, along with some special banking offer from Flipkart. Some of the common features that both models come equipped with include hands-free voice control. With far-field voice control, users can control TV, set reminders, and much more totally hands-free – Just say "Hey Google", or "Ok Google". Another common feature is micro dimming, which is designed to dim particular parts of the screen while leaving the rest in a brighter tone for an enhanced and optimized LED viewing.

Have you ever thought what would our lives be like in a world without Artificial Intelligence? Recall how you spend an average day of your life- you get up, then you check your smartphone. You reach your workplace, and then start working over the internet. Remember, most of your work takes place over cloud computing and other services the internet provides. Now picture that you have to look for an answer to something. For how long and in how many books are you going to keep searching for the answer? Let's take another example, you come back home and decide to order food online. Who really places the order if you are behind the screen? Before going to sleep, you probably use a voice to text assistant that's present in your phone to set an alarm for the next day.

Walk into any one of the many start-up events organized across India, and inevitably the image of an Indian bazaar comes to mind: people rushing around, shouting, bargaining, answering phones with great excitement, laughing loudly, boasting, blushing, and generally being optimistic, as if they are at the beginning of a rising trend of well-being. Such optimism might seem justified. According to data compiled by Fortune magazine,a from just eight'unicorns' in 2015, the number of start-ups in India valued at more than $1 billion has grown to 26. What is interesting is that in 2018 alone, India added eight unicorns to the club. These include diverse entities such as Ola, started in India as a competitor to Uber and has since expanded its footprint into the U.K. (and is eyeing Australia); an insurance aggregator called PolicyBazaar; the e-commerce site Paytm Mall; an eyewear retailer called Lenskart; food technology aggregators such as Swiggy and Zomato, and hotel-room aggregators like OYO and FabHotels. Thousands of entrepreneurs start up every year and aspire to become one of the new unicorns.

With the festive time of the year, everyone is going crazy about the mind-blowing sales going on in Flipkart Big Billion Day and Amazon India Great India Festival Sale. Diwali is celebrated to mark the triumph of Lord Rama over Ravana or the triumph of good over evil. However, for businesses in India, Diwali is the go-to make money festival. It is known as the big Indian Festive Season with eye-popping deals on from apparel, add-ons, shoes, consumer electronics, home appliances, furniture and also traveling. But did we ever imagined, how these e-commerce companies have prepared for these big sale days or how their courier partners are ensuring that last-mile delivery to the customers?