When it comes to AI, Elon Musk has a name in treating it like some aliens' attack or God's wrath upon us, even without an exaggeration. Speaking at MIT in 2014, he called AI humanity's "biggest existential threat" and compared it to "summoning the demon." He is very optimistic about all other technologies like neurotechnology, self-driving cars, Mars colonization, and others, but Artificial Intelligence always seems to scare him. I wonder if it is something so dreadful as he says it is. Today, we will talk about the things he has said in the past years about AI implications and sees if they are convincing enough.

The long-simmering debate over facial recognition technology is taking on new urgency during the pandemic, as companies rush to pitch face-scanning systems to track the movements of Covid-19 patients. That's playing out in California, where state legislators on Tuesday will debate legislation that would regulate the use of the technology. Its most controversial element: It would permit companies and public agencies to feed people's facial data into a recognition system without their consent if there is probable cause to believe they've engaged in criminal activity. The bill isn't specifically meant for the coronavirus response, but if enacted, could shape the way that people with Covid-19 and their contacts are tracked and traced in the coming months. The legislation has won the support of Microsoft, but it has garnered opposition from more than 40 civil rights and privacy groups and from 18 public health scholars.

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Bay Area startup Cerebras Systems recently unveiled the largest computer chip in history, ... [ ] purpose-built for AI. The semiconductor is the foundational technology of the digital age. It gave Silicon Valley its name. It sits at the heart of the computing revolution that has transformed every facet of society over the past half-century. The pace of improvement in computing capabilities has been breathtaking and relentless since Intel introduced the world's first microprocessor in 1971.

More than 3 billion people–around 50 percent of the world's population–engage with and post content online. Some of that content is misleading and potentially harmful, whether by design or as a side effect of its spread and manipulation. With the billions of daily active users on social media platforms, even if a mere 0.1 percent of total content contains mis or disinformation, there is a vast volume of content to review. In response to this challenge, automated content review technologies have emerged as an enticing and scalable solution to help triage mis/disinformation online. Yet, while many technology companies and social media platforms have promoted artificial intelligence (AI) as an omnipotent tactic to address mis/disinformation, AI is not a panacea for information challenges.

Artificial intelligence is believed to have the ability to function and perform like that of human intelligence and possess the capability of reasoning, arguing, perceiving and acting rationally. We can say to an extent it can imitate all human functioning but this belief in itself is paradoxical. As we talk about it on the global platform itself, the views are bifurcated some believe it as next disruptive technology which would lead to development and growth whereas some hold the view that it may lead to job losses and increase unemployment. Researches have been made on AI towards developing such machines which can imitate human cognitive and logical skills. Many countries have already adopted AI in judicial litigations, according to CEPEJ and the court administration of Latvia held a conference on "Artificial Intelligence" at the service of the judiciary, on 27th Sep 2018, it formed a platform which collaborated representatives of the academic world, professional justices, from different European countries to discuss the relevance of AI in the judicial arena, to ensure delivery of improved quality of justices, while maintaining the key fundamental principles and further highlighted the directives on which application of AI will be based upon in judicial system.

It has been shown that machine learning models, especially deep neural networks, are vulnerable to small adversarial perturbations, i.e., a small carefully crafted perturbation added to the input may significantly change the prediction results (Szegedy et al., 2014; Goodfellow et al., 2015; Biggio and Roli, 2018; Fawzi et al., 2018). Therefore, the problem of finding those perturbations, also known as adversarial attacks, has become an important way to evaluate the model robustness: the more difficult to attack a given model, the more robust it is. Depending on the information an adversary can access, the adversarial attacks can be classified into white-box and black-box settings. In the white-box setting, the target model is completely exposed to the attacker, and adversarial perturbations could be easily crafted by exploiting the first-order information, i.e., gradients with respect to the input (Carlini and Wagner, 2017; Madry et al., 2018). Despite of its efficiency and effectiveness, the white-box setting is an overly strong and pessimistic threat model, and white-box attacks are usually not practical when attacking real-world machine learning systems due to the invisibility of the gradient information. Instead, we focus on the problem of black-box attacks, where the model structure and parameters (weights) are not available to the attacker.

This paper reviews current literature in the field of predictive maintenance from the system point of view. We differentiate the existing capabilities of condition estimation and failure risk forecasting as currently applied to simple components, from the capabilities needed to solve the same tasks for complex assets. System-level analysis faces more complex latent degradation states, it has to comprehensively account for active maintenance programs at each component level and consider coupling between different maintenance actions, while reflecting increased monetary and safety costs for system failures. As a result, methods that are effective for forecasting risk and informing maintenance decisions regarding individual components do not readily scale to provide reliable sub-system or system level insights. A novel holistic modeling approach is needed to incorporate available structural and physical knowledge and naturally handle the complexities of actively fielded and maintained assets.

Many tasks aim to measure machine reading comprehension (MRC), often focusing on question types presumed to be difficult. Rarely, however, do task designers start by considering what systems should in fact comprehend. In this paper we make two key contributions. First, we argue that existing approaches do not adequately define comprehension; they are too unsystematic about what content is tested. Second, we present a detailed definition of comprehension -- a "Template of Understanding" -- for a widely useful class of texts, namely short narratives. We then conduct an experiment that strongly suggests existing systems are not up to the task of narrative understanding as we define it.

I survey recent progress on a classic and challenging problem in social choice: the fair division of indivisible items. I discuss how a computational perspective has provided interesting insights into and understanding of how to divide items fairly and efficiently. This has involved bringing to bear tools such as those used in knowledge representation, computational complexity, approximation methods, game theory, online analysis and communication complexity