Robotics Field has revolutionized today's world. Sophia Humanoid robot is attending television interviews, appearing on the cover of ELLE magazine. She was imitated on HBO as the first non-human "innovation champion" of the UN. In a tech conference held soon after its awakening, the Kingdom of Saudi Arabia even gave citizenship to Sophia. A humanoid robot is a robot with its body shape built to resemble the human body. The design may be for functional purposes, such as interacting with human tools and environments, for experimental purposes, such as the study of bipedal locomotion, or for other purposes.

This is the web version of Eye on A.I., Fortune's weekly newsletter covering artificial intelligence and business. To get it delivered weekly to your in-box, sign up here. In January 2020, in a Fortune magazine cover story, I chronicled the corporate race for artificial general intelligence, a kind of human-like or even superhuman A.I. that is the staple of science fiction. The pursuit of AGI, as it's more commonly called, has led to many of the machine learning innovations that underpin the current A.I. boom. But that boom is centered around narrow A.I--software that can perform one, specific task well.

Artificial intelligence is taking the stage as smart cities become not just an idea for the future, but a present reality. Advanced technologies are at the forefront of this change, driving valuable strategies and optimising the industry across all operations. These technologies are quickly becoming the solution for fulfilling smart city and clean city initiatives, as well as net-zero commitments. AI is becoming well integrated with the development of smart cities. Implementation of AI is rapidly being recognised as the not-so-secret ingredient helping major energy providers accomplish their lowest-carbon footprints yet, along with unparalleled sustainability and attractive profit margins. What makes a city'smart' is the collection and analysis of vast amounts of data across numerous sectors, from metropolitan development and utility allocation all the way down to manual functions like city services.

The University of the United Arab Emirates has completed projects of artificial intelligence laboratories that work to create a mental health monitoring system for senior citizens and to contribute to the early detection of incurable brain diseases such as dementia and Alzheimer's disease. The Artificial Intelligence and Robotics Laboratory operates at the University as part of the Fourth Industrial Revolutionary Organization, which follows the University of the United Arab Emirates, by establishing five major laboratories to support the march of the Fourth Industrial Revolution in the country. . Dr. Fadi Al-Najjar, Associate Professor in the Department of Computer Science and Software Engineering and Director of the Laboratory of Artificial Intelligence and Robotics, stressed that the UAE University is keen to accelerate and improve the development of educational technologies. The role of artificial intelligence in modern educational studies and programs seeks to improve project releases and curricula that meet the needs of the country's strategic plans and programs for the next fifty years. He said: The projects of the artificial intelligence laboratories at the university have been completed, creating a psychiatric monitoring system for senior citizens, predicting the future development of their cases, relying on the robot "Abu Chief", designed and developed in the laboratory, and with the technical support of Microsoft in the Middle East.

Language identification is a critical component of language processing pipelines (Jauhiainen et al.,2019) and is not a solved problem in real-world settings. We present a lightweight and effective language identifier that is robust to changes of domain and to the absence of copious training data. The key idea for classification is that the reciprocal of the rank in a frequency table makes an effective additive feature score, hence the term Reciprocal Rank Classifier (RRC). The key finding for language classification is that ranked lists of words and frequencies of characters form a sufficient and robust representation of the regularities of key languages and their orthographies. We test this on two 22-language data sets and demonstrate zero-effort domain adaptation from a Wikipedia training set to a Twitter test set. When trained on Wikipedia but applied to Twitter the macro-averaged F1-score of a conventionally trained SVM classifier drops from 90.9% to 77.7%. By contrast, the macro F1-score of RRC drops only from 93.1% to 90.6%. These classifiers are compared with those from fastText and langid. The RRC performs better than these established systems in most experiments, especially on short Wikipedia texts and Twitter. The RRC classifier can be improved for particular domains and conversational situations by adding words to the ranked lists. Using new terms learned from such conversations, we demonstrate a further 7.9% increase in accuracy of sample message classification, and 1.7% increase for conversation classification. Surprisingly, this made results on Twitter data slightly worse. The RRC classifier is available as an open source Python package (https://github.com/LivePersonInc/lplangid).

Causal effect estimation for dynamic treatment regimes (DTRs) contributes to sequential decision making. However, censoring and time-dependent confounding under DTRs are challenging as the amount of observational data declines over time due to a reducing sample size but the feature dimension increases over time. Long-term follow-up compounds these challenges. Another challenge is the highly complex relationships between confounders, treatments, and outcomes, which causes the traditional and commonly used linear methods to fail. We combine outcome regression models with treatment models for high dimensional features using uncensored subjects that are small in sample size and we fit deep Bayesian models for outcome regression models to reveal the complex relationships between confounders, treatments, and outcomes. Also, the developed deep Bayesian models can model uncertainty and output the prediction variance which is essential for the safety-aware applications, such as self-driving cars and medical treatment design. The experimental results on medical simulations of HIV treatment show the ability of the proposed method to obtain stable and accurate dynamic causal effect estimation from observational data, especially with long-term follow-up. Our technique provides practical guidance for sequential decision making, and policy-making.

In pro-drop language like Arabic, Chinese, Italian, Japanese, Spanish, and many others, unrealized (null) arguments in certain syntactic positions can refer to a previously introduced entity, and are thus called anaphoric zero pronouns. The existing resources for studying anaphoric zero pronoun interpretation are however still limited. In this paper, we use five data augmentation methods to generate and detect anaphoric zero pronouns automatically. We use the augmented data as additional training materials for two anaphoric zero pronoun systems for Arabic. Our experimental results show that data augmentation improves the performance of the two systems, surpassing the state-of-the-art results.

Fact-checking is the process (human, automated, or hybrid) by which claims (i.e., purported facts) are evaluated for veracity. In this article, we raise an issue that has received little attention in prior work - that some claims are far more difficult to fact-check than others. We discuss the implications this has for both practical fact-checking and research on automated fact-checking, including task formulation and dataset design. We report a manual analysis undertaken to explore factors underlying varying claim difficulty and categorize several distinct types of difficulty. We argue that prediction of claim difficulty is a missing component of today's automated fact-checking architectures, and we describe how this difficulty prediction task might be split into a set of distinct subtasks.

Market volatility and rising protectionism in countries like the USA, where much of India's IT outsourcing work comes from, saw Cognizant's revenue grow at its slowest pace in two decades last year, and its peers in the Indian IT industry are in the same boat. Since the 1990s Indian firms have carried out back-office tasks, and IT services like data entry, running call centers, and testing software for foreign companies at cut-price rates by throwing cheap labor at them. But as machines become adept at this repetitive, rule-based work, the low-skill jobs – where the bulk of Indian IT workers are employed – are the most at risk. "It's been happening for the last two or three years in an accelerated fashion," says Gopinathan Padmanabhan, head of innovation at IT company Mphasis. This shift will go hand-in-hand with new opportunities in emerging areas – data science, artificial intelligence, and big data – but these will require new skills and probably fewer employees.

Pune, India, Sept. 16, 2021 (GLOBE NEWSWIRE) -- The global Artificial Intelligence (AI) market size is expected to gain momentum by reaching USD 360.36 billion by 2028 while exhibiting a CAGR of 33.6% between 2021 to 2028. In its report titled, "Artificial Intelligence (AI) Market Size, Share & COVID-19 Impact Analysis, By Component (Hardware, Software, and Services), By Technology (Computer Vision, Machine Learning, Natural Language Processing, and Others), By Deployment (Cloud, On-premises), By Industry (Healthcare, Retail, IT & Telecom, BFSI, Automotive, Advertising & Media, Manufacturing, and Others), and Regional Forecast, 2021-2028" Fortune Business Insights mentions that the market stood at USD 35.92 billion in 2020. Artificial Intelligence has become immensely popular, and industries across the globe are rapidly incorporating it into their processes to improve business operations and customer experience. Not only the big companies but also the small and medium businesses are investing in this technology. Besides, the advancement and implementation of 5G, cloud computing, and a huge database are the factors, which are propelling its demand.