In a proliferated, multi-channel world, every brand needs to win the heart and mind of the consumer to acquire and retain them. They need to set up a foundation of empathy and connectedness. Artificial intelligence combined with a human-centric approach to marketing might seem like a contrarian model. But the truth is that machine learning, AI and automation are vital for brands today to transform data into empathetic, customer-centric experiences. For marketers, AI-based solutions serve as a scalable and customizable tool capable of understanding the motive behind consumer interactions.

Artificial Intelligence (AI) is much more than just a buzzword nowadays. It powers facial recognition in smartphones and computers, translation between foreign languages, systems which filter spam emails and identify toxic content on social media, and can even detect cancerous tumours. These examples, along with countless other existing and emerging applications of AI, help make people's daily lives easier, especially in the developed world. As of October 2021, 44 countries were reported to have their own national AI strategic plans, showing their willingness to forge ahead in the global AI race. These include emerging economies like China and India, which are leading the way in building national AI plans within the developing world.

Lilt, a provider of AI-powered business translation software, today announced that it raised $55 million in a Series C round led by Four Rivers, joined by new investors Sorenson Capital, CLEAR Ventures and Wipro Ventures. The company says that it plans to use the capital to expand its R&D efforts as well as its customer footprint and engineering teams. "Lilt [aims to] build a solution that [will] combine the best of human ingenuity with machine efficiency," CEO Spence Green told TechCrunch via email. "This new funding will … [reduce our] unit economics [to make] translation more affordable for all businesses. It will also [enable us to add] a sales team to our existing production team in Asia. We are in three regions -- the U.S., Europe, the Middle East and Africa (EMEA) and Asia -- and look to have both sales and production teams in each of these regions."

JOHANN JUNGWIRTH, vice president of mobility-as-a-service at Intel-owned company Mobileye, says he spends two to three hours per day on the road. It's a long commute, especially given he's sitting behind the driver's wheel--except for the fact that he's not the one making decisions on the road. "I just push the Go button, and then, you know, I let it drive itself," Jungwirth tells WIRED after a Mobileye robotaxi drove him from Jerusalem to Tel Aviv. Driving enthusiasts can now check out what that experience looks like, thanks to a 45-minute long unedited video of Mobileye's seven-seater electric van ferrying ride-hailing passengers around Jerusalem's narrow, winding roads. As the robotaxi, which comes equipped with Mobileye's True Redundancy sensing system, drives to different drop-off and pick-up points, it easily dodges the jaywalkers, gives way to cars suddenly interrupting its route, and navigates around parked cars and other obstacles blocking the way.

"Computers are able to see,hear and learn.Welcome to the future." According to the World Economic Forum,more than 65% of students will work in jobs that don't even exist today.We want to help prepare them for that future by getting them excited about what computer science (CS) can take them.With a focus on girls and others who are underrepresented in the field today. Robotics and automation are dramatically reshaping the global economy.From delivering faster customer service to better quality products and efficient operations, robotics and automation provide enormous value for organizations that adopt them at scale. "Robots and automation will take 800 million jobs by 2030."-McKinsey.Using AI, the company hopes to teach the robot to copy human movements automatically, so that it can operate without a pilot. From the initially reported outbreak of coronavirus (COVID-19) in China to the spread of it across the globe, Medtech companies are rolling out robots and drones to help fight it and provide services and care to those quarantined or practicing social distancing. This pandemic has fast-tracked the "testing" of robots and drones in public as officials seek out the most expedient and safe way to grapple with the outbreak and limit contamination and spread of the virus.

In the pooled data problem we are given a set of $n$ agents, each of which holds a hidden state bit, either $0$ or $1$. A querying procedure returns for a query set the sum of the states of the queried agents. The goal is to reconstruct the states using as few queries as possible. In this paper we consider two noise models for the pooled data problem. In the noisy channel model, the result for each agent flips with a certain probability. In the noisy query model, each query result is subject to random Gaussian noise. Our results are twofold. First, we present and analyze for both error models a simple and efficient distributed algorithm that reconstructs the initial states in a greedy fashion. Our novel analysis pins down the range of error probabilities and distributions for which our algorithm reconstructs the exact initial states with high probability. Secondly, we present simulation results of our algorithm and compare its performance with approximate message passing (AMP) algorithms that are conjectured to be optimal in a number of related problems.

Starting from the COMET methodology by Bosselut et al. (2019), generating commonsense knowledge directly from pre-trained language models has recently received significant attention. Surprisingly, up to now no materialized resource of commonsense knowledge generated this way is publicly available. This paper fills this gap, and uses the materialized resources to perform a detailed analysis of the potential of this approach in terms of precision and recall. Furthermore, we identify common problem cases, and outline use cases enabled by materialized resources. We posit that the availability of these resources is important for the advancement of the field, as it enables an off-the-shelf-use of the resulting knowledge, as well as further analyses on its strengths and weaknesses.

Predicting the results of matches in sport is a challenging and interesting task. In this paper, we review a selection of studies from 1996 to 2019 that used machine learning for predicting match results in team sport. Considering both invasion sports and striking/fielding sports, we discuss commonly applied machine learning algorithms, as well as common approaches related to data and evaluation. Our study considers accuracies that have been achieved across different sports, and explores whether evidence exists to support the notion that outcomes of some sports may be inherently more difficult to predict. We also uncover common themes of future research directions and propose recommendations for future researchers. Although there remains a lack of benchmark datasets (apart from in soccer), and the differences between sports, datasets and features makes between-study comparisons difficult, as we discuss, it is possible to evaluate accuracy performance in other ways. Artificial Neural Networks were commonly applied in early studies, however, our findings suggest that a range of models should instead be compared. Selecting and engineering an appropriate feature set appears to be more important than having a large number of instances. For feature selection, we see potential for greater inter-disciplinary collaboration between sport performance analysis, a sub-discipline of sport science, and machine learning.

In a previous article of February 6, 2020, we discussed the EPO Receiving Section's refusal, in January 2020, of two European patent applications where an AI system called DABUS was indicated as the inventor1 . We then looked at the grounds of the decisions2 (concerning applications EP 18 275 163 and EP 18 275 174 for "food container" and "devices and methods for attracting enhanced attention"), and predicted that the EPO Board of Appeal (BoA) was bound to shed light on the novel and intriguing legal issue of whether a non-human, such as an artificial intelligence (AI), could be named as inventor in the system of the EPC. The BoA has now issued its decision, which is worth commenting. The applicant, one Mr. Stephen Thaler, had filed his appeals against the refusal (cases J 8/20 and J 9/20), along with an auxiliary request whereby no person was allegedly identified as inventor, but a natural person was indicated to hold "the right to the European Patent by virtue of being the owner and creator of" the DABUS AI system. By decision of December 21, 20213, the BoA dismissed the appeal, confirming that the EPC required the inventor to be a person with legal capacity.

It's not a coincidence that two global multinational investment banks and financial services companies, Morgan Stanley and Goldman Sachs, agrees that the nascent metaverse market could be worth $8 trillion in the future. In its latest Technology Vision 2022 report, titled Meet me in the metaverse, multinational information technology services company, Accenture surveyed more than 4,600 business and technology leaders across 23 industries in 35 countries. Like an arms race, futuristic big tech companies Microsoft, Facebook (FB now Meta), and Apple Inc, Google (now Alphabet) amongst others, are scrambling to sweep up the metaverse. Facebook (now Meta) describes the metaverse as "a set of virtual spaces where you can create and explore with other people who aren't in the same physical space as you". CEO Mark Zuckerberg says Meta is working on egocentric data, which involves seeing worlds from a first-person perspective.