Artificial Intelligence has already moved into many facets of our daily lives from Siri to Cortana, Alexa to Google Duplex, in banks, in CCTV cameras on the street, Conversational AI, Emotional AI, flying drone swarms, Chatbots, language translators, facial recognition and Social media. We are all surrounded by variety of new Artificial Intelligence devices. We have become accustomed to sharing our reality with intelligence simulations. By means of smart algorithms, machines today are capable of doing incredible things with facial and speech recognition. With error rates of under five percent, many systems can perform better than humans.

Elon Musk-run brain-machine interface company Neuralink is preparing to launch clinical trials that will implant brain chips in humans. Elon Musk's brain-interface technology company Neuralink may start implanting microchips in human beings from 2022. Neuralink is preparing to launch clinical trials that will implant brain chips in humans. Cofounded by Elon Musk in 2016, Neuralink is working on a chip that would be implanted in our brains to record and stimulate brain activity. This chip is being created for medical applications such as treating serious spinal cord injuries and neurological disorders.

While most approaches to cybersecurity remain stuck in the past -- using rules, signatures, and other historically defined understandings of threat -- best practice these days is to keep your focus forward, preparing for the unknown and the unpredictable. In that spirit, Darktrace anticipates what 2022 will bring, both in terms of the threat landscape and for evolutions in defensive technologies. Focusing on augmenting the human with AI is just as important as the cutting-edge mathematics that drive AI. The relationship between humans and AI can be improved with explainable artificial intelligence (XAI). In cybersecurity, this means delivering the insights of AI to the security team on a silver platter -- that is, in human-readable language and clear diagrams rather than abstruse code.

In today's China, behemoths like Alibaba Group Holding Ltd. and Tencent Holdings Ltd. are out of favor, but "little giants" are on the rise. That's the designation for a new generation of startups that have been selected under an ambitious government program aimed at fostering a technology industry that can compete with Silicon Valley. These often-obscure companies have demonstrated they're doing something innovative and unique, and they're targeting strategically important sectors like robotics, quantum computing and semiconductors. Wu Gansha won the little giants title for his autonomous driving startup after a government review of his technology. That gave the Beijing company, Uisee, an extra dose of credibility and financial benefits.

In reaction to growing concerns about the potential harms of artificial intelligence (AI), societies have begun to demand more transparency about how AI models and systems are created and used. To address these concerns, several efforts have proposed documentation templates containing questions to be answered by model developers. These templates provide a useful starting point, but no single template can cover the needs of diverse documentation consumers. It is possible in principle, however, to create a repeatable methodology to generate truly useful documentation. Richards et al. [25] proposed such a methodology for identifying specific documentation needs and creating templates to address those needs. Although this is a promising proposal, it has not been evaluated. This paper presents the first evaluation of this user-centered methodology in practice, reporting on the experiences of a team in the domain of AI for healthcare that adopted it to increase transparency for several AI models. The methodology was found to be usable by developers not trained in user-centered techniques, guiding them to creating a documentation template that addressed the specific needs of their consumers while still being reusable across different models and use cases. Analysis of the benefits and costs of this methodology are reviewed and suggestions for further improvement in both the methodology and supporting tools are summarized.

In recent years, the monarch butterfly's iconic migration patterns have come under threat from a number of factors, from climate change to pesticide use. To track trends in their populations, scientists as well as citizen scientists must identify individuals accurately. This is uniquely key for the study of monarch butterflies because there exist other species of butterfly, such as viceroy butterflies, that are "look-alikes" (coined by the Convention on International Trade in Endangered Species of Wild Fauna and Flora), having similar phenotypes. To tackle this problem and to aid in more efficient identification, we present MonarchNet, the first comprehensive dataset consisting of butterfly imagery for monarchs and five look-alike species. We train a baseline deep-learning classification model to serve as a tool for differentiating monarch butterflies and its various look-alikes. We seek to contribute to the study of biodiversity and butterfly ecology by providing a novel method for computational classification of these particular butterfly species. The ultimate aim is to help scientists track monarch butterfly population and migration trends in the most precise and efficient manner possible.

As facial recognition systems are deployed more widely, scholars and activists have studied their biases and harms. Audits are commonly used to accomplish this and compare the algorithmic facial recognition systems' performance against datasets with various metadata labels about the subjects of the images. Seminal works have found discrepancies in performance by gender expression, age, perceived race, skin type, etc. These studies and audits often examine algorithms which fall into two categories: academic models or commercial models. We present a detailed comparison between academic and commercial face detection systems, specifically examining robustness to noise. We find that state-of-the-art academic face detection models exhibit demographic disparities in their noise robustness, specifically by having statistically significant decreased performance on older individuals and those who present their gender in a masculine manner. When we compare the size of these disparities to that of commercial models, we conclude that commercial models -- in contrast to their relatively larger development budget and industry-level fairness commitments -- are always as biased or more biased than an academic model.

We present a memory-augmented approach to condition an autoregressive language model on a knowledge graph. We represent the graph as a collection of relation triples and retrieve relevant relations for a given context to improve text generation. Experiments on WikiText-103, WMT19, and enwik8 English datasets demonstrate that our approach produces a better language model in terms of perplexity and bits per character. We also show that relational memory improves coherence, is complementary to token-based memory, and enables causal interventions. Our model provides a simple yet effective way to combine an autoregressive language model with a knowledge graph for a more coherent and logical generation.

This modern world is suffering from issues regarding cybersecurity and privacy. It is truly difficult and economically unfeasible to create and maintain such systems, as well as to assure that both the network and the accompanying systems are not vulnerable to threats and assaults. Over the last several decades, there has been a surge in the number of illegal acts in networks, in addition to an increase in devious and malicious contentAhamad and Aljumah [2015]. When an individual or an organization intentionally and maliciously attempts to enter the information system of another individual or organization, this is referred to as a cyberattack. While most assaults have an economic aim, various recent operations have included data destruction as a goal. Cybersecurity is the need of today's time. Cybersecurity can be defined as the protection of systems, networks, and data within cyberspacewha.

We propose a novel statistical inference paradigm for zero-inflated multiway count data that dispenses with the need to distinguish between true and false zero counts. Our approach ignores all zero entries and applies zero-truncated Poisson regression on the positive counts. Inference is accomplished via tensor completion that imposes low-rank structure on the Poisson parameter space. Our main result shows that an $N$-way rank-$R$ parametric tensor $\boldsymbol{\mathscr{M}}\in(0,\infty)^{I\times \cdots\times I}$ generating Poisson observations can be accurately estimated from approximately $IR^2\log_2^2(I)$ non-zero counts for a nonnegative canonical polyadic decomposition. Several numerical experiments are presented demonstrating that our zero-truncated paradigm is comparable to the ideal scenario where the locations of false zero counts are known a priori.