Wilkinson recently had a close call when a shark trailed him, only inches away. Wilkinson recently had a close call when a shark trailed him, only inches away. The surfer had no idea a shark was trailing him. Near Sharpes Beach in Australia, professional surfer Matt Wilkinson was paddling on his board on Wednesday. Unbeknownst to him, a shark quickly surfaced and began stalking the surfing world champion, at one point only inches away.

Nvidia CEO Jensen Huang is confident that the Nvidia-Arm deal, the biggest in semiconductor history at $40 billion, will get past global regulators. Speaking during a "fireside chat" as part of Arm Dev Summit, the company's developer conference, Huang said that the two companies are complementary and as such, the acquisition would drive innovation forward and be good for customers. We are confident that [the Nvidia-Arm deal] is going to go through," Huang said. "As soon as we explain the rationale of the transaction and our plans to regulators around the world, they will realize that these are two complementary companies." Arm CEO Simon Segars backed Huang up, while noting that the regulatory process will take some time. "This is a deal that's about expanding," Segars said. It's about putting that technology in the hands of people who are going to build really cool stuff with it. So from that point of view, this is about enabling more people to do more things. That's the positive thing, that as regulators do scrutinize this, they're going to be able to see." Extended reach Huang's reasoning behind the Nvidia-Arm deal is that the combination of the world leader in AI compute with the world's most popular CPU architecture will bring AI capabilities to the Arm ecosystem as well as give Nvidia's accelerated computing extended reach.

There has been considerable progress made towards conversational models that generate coherent and fluent responses; however, this often involves training large language models on large dialogue datasets, such as Reddit. These large conversational models provide little control over the generated responses, and this control is further limited in the absence of annotated conversational datasets for attribute specific generation that can be used for fine-tuning the model. In this paper, we first propose and evaluate plug-and-play methods for controllable response generation, which does not require dialogue specific datasets and does not rely on fine-tuning a large model. While effective, the decoding procedure induces considerable computational overhead, rendering the conversational model unsuitable for interactive usage. To overcome this, we introduce an approach that does not require further computation at decoding time, while also does not require any fine-tuning of a large language model. We demonstrate, through extensive automatic and human evaluation, a high degree of control over the generated conversational responses with regard to multiple desired attributes, while being fluent.

Biometric-based personal authentication systems have seen a strong demand mainly due to the increasing concern in various privacy and security applications. Although the use of each biometric trait is problem dependent, the human ear has been found to have enough discriminating characteristics to allow its use as a strong biometric measure. To locate an ear in a 2D side face image is a challenging task, numerous existing approaches have achieved significant performance, but the majority of studies are based on the constrained environment. However, ear biometrics possess a great level of difficulties in the unconstrained environment, where pose, scale, occlusion, illuminations, background clutter etc. varies to a great extent. To address the problem of ear localization in the wild, we have proposed two high-performance region of interest (ROI) segmentation models UESegNet-1 and UESegNet-2, which are fundamentally based on deep convolutional neural networks and primarily uses contextual information to localize ear in the unconstrained environment. Additionally, we have applied state-of-the-art deep learning models viz; FRCNN (Faster Region Proposal Network) and SSD (Single Shot MultiBox Detecor) for ear localization task. To test the model's generalization, they are evaluated on six different benchmark datasets viz; IITD, IITK, USTB-DB3, UND-E, UND-J2 and UBEAR, all of which contain challenging images. The performance of the models is compared on the basis of object detection performance measure parameters such as IOU (Intersection Over Union), Accuracy, Precision, Recall, and F1-Score. It has been observed that the proposed models UESegNet-1 and UESegNet-2 outperformed the FRCNN and SSD at higher values of IOUs i.e. an accuracy of 100\% is achieved at IOU 0.5 on majority of the databases.

Simulated environments with procedurally generated content have become popular benchmarks for testing systematic generalization of reinforcement learning agents. Every level in such an environment is algorithmically created, thereby exhibiting a unique configuration of underlying factors of variation, such as layout, positions of entities, asset appearances, or even the rules governing environment transitions. Fixed sets of training levels can be determined to aid comparison and reproducibility, and test levels can be held out to evaluate the generalization and robustness of agents. We introduce Prioritized Level Replay, a general framework for estimating the future learning potential of a level given the current state of the agent's policy. We find that temporal-difference (TD) errors, while previously used to selectively sample past transitions, also prove effective for scoring a level's future learning potential in generating entire episodes that an agent would experience when replaying it. We report significantly improved sample-efficiency and generalization on the majority of Procgen Benchmark environments as well as two challenging MiniGrid environments. Lastly, we present a qualitative analysis showing that Prioritized Level Replay induces an implicit curriculum, taking the agent gradually from easier to harder levels. Environments generated using procedural content generation (PCG) have garnered increasing interest in RL research, leading to a surge of PCG environments such as MiniGrid (Chevalier-Boisvert et al., 2018), the Obstacle Tower Challenge (Juliani et al., 2019), the Procgen Benchmark (Cobbe et al., 2019), and the NetHack Learning Environment (Küttler et al., 2020).

The SIGTYP 2020 shared task (Bjerva et al., 2020) We reach the accuracy of 70.7% on the test data and rank first in the shared task. The task specification envisions a constrained The World Atlas of Language Structures (WALS) and an unconstrained track, where the constrained (Dryer and Haspelmath, 2013) is a database of systems can use only the provided WALS data, over 2,000 languages, which lists structural properties while an unconstrained system can use additional ('features') of each language, gathered from external resources, such as texts or pre-trained word reference grammars.

Text-based games have emerged as an important test-bed for Reinforcement Learning (RL) research, requiring RL agents to combine grounded language understanding with sequential decision making. In this paper, we examine the problem of infusing RL agents with commonsense knowledge. Such knowledge would allow agents to efficiently act in the world by pruning out implausible actions, and to perform look-ahead planning to determine how current actions might affect future world states. We design a new text-based gaming environment called TextWorld Commonsense (TWC) for training and evaluating RL agents with a specific kind of commonsense knowledge about objects, their attributes, and affordances. We also introduce several baseline RL agents which track the sequential context and dynamically retrieve the relevant commonsense knowledge from ConceptNet. We show that agents which incorporate commonsense knowledge in TWC perform better, while acting more efficiently. We conduct user-studies to estimate human performance on TWC and show that there is ample room for future improvement.

Interest in artificial intelligence continues to explode across every industry, but few areas offer more opportunities for drastic improvement of human life than the application of machine learning and AI in healthcare and the medical field. Let's begin first with a definition. AI in healthcare and medicine means using data more effectively through machine learning algorithms to produce positive patient outcomes. The sheer amount of data created through IoT-enabled devices, the electronic medical record (EMR), and ever-expanding quantities of genetic data has made possible a large number of applications of artificial intelligence in healthcare. Check out the Harvard Business Review ranking of the potential value that these applications could bring to the healthcare industry. The underlying value of artificial intelligence is to enhance human decision-making and automate processes that are time- or resource-intensive for humans to perform.

ABBYY, a Digital Intelligence company, announced it acquired Pericom Singapore, part of the Pericom Group, a leading solution provider based in Singapore. The acquisition strengthens ABBYY's presence in Asia Pacific following the opening of its Hong Kong office in 2019, long-established office in Japan, and a strong partner network throughout the region. Singapore ranks first in the Asian Digital Transformation Index and is considered the trading crossroads for innovations in cloud computing, artificial intelligence, data analytics and other technologies that span healthcare, security, energy, aviation, defense, smart cities and education. As more Asia Pacific executives look to accelerate their digital business initiatives post-COVID, including 84% of Singapore businesses who have increased their budgets, ABBYY's growing presence signifies its readiness to meet their digital transformation needs. "We have had many successful large-scale implementations in the Asia Pacific market working closely with our valuable partners and large system integrators," commented Ulf Persson, CEO of ABBYY.

Automation Anywhere, Inc., a global leader in robotic process automation (RPA), announced AARI (Automation Anywhere Robotic Interface) – a smart digital assistant designed for a new era of work that brings consumer experiences to the enterprise. Available via Automation Anywhere's award-winning, cloud-native RPA platform, Enterprise A2019, AARI makes it easy for anyone to participate in the automation of day-to-day business tasks, through business-friendly user interfaces. Like the popular digital assistants Siri and Alexa that have become ubiquitous in our personal lives, AARI provides an easy-to-use, bot-to-human interface that oversees various business processes. AARI enables all users to further simplify everyday tasks, improve collaboration between teams, and provide best-in-class customer service – either on-premises or in the cloud. Now, every employee can participate in the automation economy from the device or application of their choice – from data lookups across multiple systems to complex escalation scenarios.