Google last week unpacked a host of critical new artificial intelligence (AI) advancements, particularly in the areas around generative AI such as language analysis, text-to-video, and assisted writing capabilities. Along with other enticing developments around ethical AI, Google revealed it would be working on AI in at least one thousand languages going forward. At the Google AI event held at the company's Pier 57 offices in New York City on November 2, the global technology titan highlighted areas of focus to include democratizing AI development pathways, "building for everyone" responsible, controlled models which can identify generative AI -- unsupervised artificial intelligence learning algorithms that can create new digital audio, text, imagery, video, or code -- along with advancing language translation, disaster management, and health AI. Google shared the first rendering of a video that shares both of the company's complementary text-to-video research approaches -- Imagen Video and Phenaki. The interest of global communities was piqued by the announcement that language abilities are being developed using AI for the world's one thousand most spoken languages, showcasing another major area of artificial intelligence focus as tech giants compete to dominate the internet's next battleground.

Burkina Faso is a multi-cultural and diverse country with a rich history. In this article, we explore how personal names can be interpreted to reflect regional, ethnic appartenance within the country. Then we illustrate how the use of a personal name can affect a black-box Artificial Intelligence – such as OpenAI's DALL-E. This is a first article in our series of blog posts with tag #thisnamedpersondoesnotexist.

Blockchain and AI, particularly machine learning, are two quite recent revolutionary technologies that are being adopted by Governments and Businesses in all sorts of ways. In a series of articles (in 4 parts) I reflect in what ways these technologies have the potential to impact the lives of ordinary citizens. A lot of discussion has been going on about how blockchain and AI affects governments and businesses. If AI has been around for quite a long time, the more recent Blockchain technology, has taken the world by storm, as being a database system that provides us with a simple protocol that allows transactions to be simultaneously anonymous and secure, peer-to-peer, immediate and in constant flow. The beautiful promise of blockchain is that is distributes the trust which is currently allocated to centralized, large and powerful intermediaries, to a large global network of people engaged in massive collaboration, facilitated by clever coding and cryptography.

Artificial intelligence (AI)-based investment advisory platform JARVIS Invest has launched its first brand campaign'AI in investing'. Through the campaign, the brand aims to highlight the benefits of AI and its application in stock advisory services. As per the company, the campaign will run across various digital platforms for five months using a programmatic approach. JARVIS Invest has recently crossed assets under advisory of over Rs 100 crores and the AI tool'JARVIS' has also been able to successfully predict the recent market crash of March 2020 and January 2022, Sumit Chanda, CEO and founder, Jarvis Invest said. "While investors have been receptive to the idea of AI, there is still certain resistance from the prospects of trusting AI completely and using AI in their journey of wealth creation. This triggered us to devise a campaign that could address this apprehension. Through this campaign, early-age investors will now be able to gain confidence about creating their custom portfolios using AI and fulfil their financial goals," he added.

When it comes to creativity, is artificial intelligence a powerful new tool or an existential threat? A San Francisco gallery is taking on this question in a new exhibition: "Artificial Imagination" features eight artists who used A.I. image generators to create the pieces on display. The artists' methods vary: Some fed their A.I. tool of choice phrases to generate their entire piece, while others created illustrations or sculptures based on the tool's recommendations. The show is on view at bitforms' West Coast gallery through the end of the year. From robots that make their own art to image-generation tools that mimick history's greatest painters, A.I. is quickly permeating creative spaces--and generating lots of questions.

This paper revisits the work of Rauch et al. (1965) and develops a novel method for recursive maximum likelihood particle filtering for general state-space models. The new method is based on statistical analysis of incomplete observations of the systems. Score function and conditional observed information of the incomplete observations/data are introduced and their distributional properties are discussed. Some identities concerning the score function and information matrices of the incomplete data are derived. Maximum likelihood estimation of state-vector is presented in terms of the score function and observed information matrices. In particular, to deal with nonlinear state-space, a sequential Monte Carlo method is developed. It is given recursively by an EM-gradient-particle filtering which extends the work of Lange (1995) for state estimation. To derive covariance matrix of state-estimation errors, an explicit form of observed information matrix is proposed. It extends Louis (1982) general formula for the same matrix to state-vector estimation. Under (Neumann) boundary conditions of state transition probability distribution, the inverse of this matrix coincides with the Cramer-Rao lower bound on the covariance matrix of estimation errors of unbiased state-estimator. In the case of linear models, the method shows that the Kalman filter is a fully efficient state estimator whose covariance matrix of estimation error coincides with the Cramer-Rao lower bound. Some numerical examples are discussed to exemplify the main results.

Robots' visual qualities (VQs) impact people's perception of their characteristics and affect users' behaviors and attitudes toward the robot. Recent years point toward a growing need for Socially Assistive Robots (SARs) in various contexts and functions, interacting with various users. Since SAR types have functional differences, the user experience must vary by the context of use, functionality, user characteristics, and environmental conditions. Still, SAR manufacturers often design and deploy the same robotic embodiment for diverse contexts. We argue that the visual design of SARs requires a more scientific approach considering their multiple evolving roles in future society. In this work, we define four contextual layers: the domain in which the SAR exists, the physical environment, its intended users, and the robot's role. Via an online questionnaire, we collected potential users' expectations regarding the desired characteristics and visual qualities of four different SARs: a service robot for an assisted living/retirement residence facility, a medical assistant robot for a hospital environment, a COVID-19 officer robot, and a personal assistant robot for domestic use. Results indicated that users' expectations differ regarding the robot's desired characteristics and the anticipated visual qualities for each context and use case.

Increasing number of COVID-19 research literatures cause new challenges in effective literature screening and COVID-19 domain knowledge aware Information Retrieval. To tackle the challenges, we demonstrate two tasks along withsolutions, COVID-19 literature retrieval, and question answering. COVID-19 literature retrieval task screens matching COVID-19 literature documents for textual user query, and COVID-19 question answering task predicts proper text fragments from text corpus as the answer of specific COVID-19 related questions. Based on transformer neural network, we provided solutions to implement the tasks on CORD-19 dataset, we display some examples to show the effectiveness of our proposed solutions.

The increased importance of mobile photography created a need for fast and performant RAW image processing pipelines capable of producing good visual results in spite of the mobile camera sensor limitations. While deep learning-based approaches can efficiently solve this problem, their computational requirements usually remain too large for high-resolution on-device image processing. To address this limitation, we propose a novel PyNET-V2 Mobile CNN architecture designed specifically for edge devices, being able to process RAW 12MP photos directly on mobile phones under 1.5 second and producing high perceptual photo quality. To train and to evaluate the performance of the proposed solution, we use the real-world Fujifilm UltraISP dataset consisting on thousands of RAW-RGB image pairs captured with a professional medium-format 102MP Fujifilm camera and a popular Sony mobile camera sensor. The results demonstrate that the PyNET-V2 Mobile model can substantially surpass the quality of tradition ISP pipelines, while outperforming the previously introduced neural network-based solutions designed for fast image processing. Furthermore, we show that the proposed architecture is also compatible with the latest mobile AI accelerators such as NPUs or APUs that can be used to further reduce the latency of the model to as little as 0.5 second. The dataset, code and pre-trained models used in this paper are available on the project website: https://github.com/gmalivenko/PyNET-v2

Mathematical models have been used to understand the spread patterns of infectious diseases such as Coronavirus Disease 2019 (COVID-19). The transmission component of the models can be modelled in an age-dependent manner via introducing contact matrix for the population, which describes the contact rates between the age groups. Since social contact patterns vary from country to country, we can compare and group the countries using the corresponding contact matrices. In this paper, we present a framework for clustering countries based on their contact matrices with respect to an underlying epidemic model. Since the pipeline is generic and modular, we demonstrate its application in a COVID-19 model from R\"ost et. al. which gives a hint about which countries can be compared in a pandemic situation, when only non-pharmaceutical interventions are available.