Astronomers Are Closing In on the Kuiper Belt's Secrets As next-generation telescopes map this outer frontier, astronomers are bracing for discoveries that could reveal hidden planets, strange structures, and clues to the solar system's chaotic youth. Out beyond the orbit of Neptune lies an expansive ring of ancient relics, dynamical enigmas, and possibly a hidden planet--or two. The Kuiper Belt, a region of frozen debris about 30 to 50 times farther from the sun than the Earth is--and perhaps farther, though nobody knows--has been shrouded in mystery since it first came into view in the 1990s. Over the past 30 years, astronomers have cataloged about 4,000 Kuiper Belt objects (KBOs), including a smattering of dwarf worlds, icy comets, and leftover planet parts. But that number is expected to increase tenfold in the coming years as observations from more advanced telescopes pour in.

Check out all the on-demand sessions from the Intelligent Security Summit here. Classical machine learning (ML) algorithms have proven to be powerful tools for a wide range of tasks, including image and speech recognition, natural language processing (NLP) and predictive modeling. However, classical algorithms are limited by the constraints of classical computing and can struggle to process large and complex datasets or to achieve high levels of accuracy and precision. Enter quantum machine learning (QML). QML combines the power of quantum computing with the predictive capabilities of ML to overcome the limitations of classical algorithms and offer improvements in performance.

Tech Mahindra and Mahindra University have signed a memorandum of understanding (MoU) to set up a new'Makers Lab' for research and development in quantum computing, explainable artificial intelligence, and Metaverse. Tech Mahindra already has 10 Makers Lab across the world and the new unit at Mahindra University will be the 11th facility globally and second in Hyderabad. Emphasising the need to focus on development of quantum computing, Tech Mahindra MD and CEO CP Gurnani said, the industry is looking at data explosion with growth in cloud computing, data centres, and 5G driving the change in the present computing system. "I think the basics of quantum computing is quantum physics. Quantum physics clearly shows there is always this inflection point and then after that, either the current hardware or the quant developers will be able to suddenly create magic. My only personal belief is that the pressure on the systems will come in because of the data explosion," he said.

Rajiv Malhotra was trained initially as a Physicist, and then as a Computer Scientist specializing in AI in the 1970s. After a successful corporate career in the US, he became an entrepreneur and founded and ran several IT companies in 20 countries. Since the early 1990s, as the founder of his non-profit Infinity Foundation (Princeton, USA), he has been researching civilizations and their engagement with technology from a historical, social sciences and mind sciences perspective. He has authored several best-selling books. Infinity Foundation has also published a 14-volume series on the History of Indian Science & Technology.

In a world where a single tweet can make or break a brand, it is crucial for companies and brands to invest in social media automation and analysis to derive actionable insights on brand perception. You would not like to wait for 12 hours to reply to that negative comment while #quit prefixed with your brand name trends on Twitter and Instagram, would you? Studies have shown that customers tend to be more vocal and frank with their views on social media. How they perceive a particular brand, its products/services fundamentally influence their behavior. So, for brands, being able to dig deep into the comments, replies, conversations, etc from customers can help uncover an unbiased view of their customers' behavior and persona, helping them understand customer intent and sentiments better.

Mining consumer perceptions of brands has been a dominant research area in marketing. The marketing literature provides a well-developed rationale for proposing brands as intangible assets that significantly contribute to firm performance. Consumer-brand perceptions typically collected through surveys or focus groups, require recruitment and interaction with a large set of participants; leading to cost, feasibility and validity issues. The advent of web 2.0 opens the door to the application of a wide range of data-centric approaches which can automate and scale beyond the traditional methods used in marketing science. We address this knowledge area by exploiting social media based brand communities to generate a brand network, incorporating consumer perceptions across a broad ecosystem of brands.

The catch, however, was that Khan never actually spoke about these stores. An artificial intelligence (AI) algorithm took a single video of Khan and changed how his lips moved, combined with his tonal qualities, to essentially make him say the names of these neighbourhood stores in subsequent videos. Mondelez is not the only company to try something like this. The use of AI in video is practically changing how firms look at everything from advertising campaigns to corporate learning material. In fact, Ashray Malhotra, co-founder and chief executive of Rephrase.ai--

Artificial intelligence (AI) holds promise for enabling earlier detection of pancreatic cancer, which is crucial to saving lives. The potential of AI is showcased in a study to be presented at the ESMO World Congress on Gastrointestinal Cancer, 1–4 July 2020. Overall, 12 in every 100,000 people develop pancreatic cancer. This means that screening everyone would be inefficient and would expose many people to unnecessary tests and potential side-effects. Between 70-80% of patients are diagnosed at an advanced stage when it is too late for curative treatment and five years after diagnosis, just 6% of patients have survived.

The demand for data scientists has gone up significantly as India embraces digitisation. According to Ashish Malhotra, director at bitgrit Japan, data analytics is a subset of data science. Mathematics and analytical bent of mind are the key skills that form the core of a data science career. The demand for data scientists has gone up significantly as India embraces digitisation. This new coveted job role however, is not limited to data analytics only.

Prognostics or Remaining Useful Life (RUL) Estimation from multi-sensor time series data is useful to enable condition-based maintenance and ensure high operational availability of equipment. We propose a novel deep learning based approach for Prognostics with Uncertainty Quantification that is useful in scenarios where: (i) access to labeled failure data is scarce due to rarity of failures (ii) future operational conditions are unobserved and (iii) inherent noise is present in the sensor readings. All three scenarios mentioned are unavoidable sources of uncertainty in the RUL estimation process often resulting in unreliable RUL estimates. To address (i), we formulate RUL estimation as an Ordinal Regression (OR) problem, and propose LSTM-OR: deep Long Short Term Memory (LSTM) network based approach to learn the OR function. We show that LSTM-OR naturally allows for incorporation of censored operational instances in training along with the failed instances, leading to more robust learning. To address (ii), we propose a simple yet effective approach to quantify predictive uncertainty in the RUL estimation models by training an ensemble of LSTM-OR models. Through empirical evaluation on C-MAPSS turbofan engine benchmark datasets, we demonstrate that LSTM-OR is significantly better than the commonly used deep metric regression based approaches for RUL estimation, especially when failed training instances are scarce. Further, our uncertainty quantification approach yields high quality predictive uncertainty estimates while also leading to improved RUL estimates compared to single best LSTM-OR models.