Riiid, a Seoul, South Korea-based startup developing AI test prep solutions, today closed a $41.8 million pre-series D financing round, bringing its total venture capital raised to date to $70.2 million. CEO YJ Jang says the funding will be used to advance Riiid's technology that offers personalized study solutions based on big data analysis, and to bolster the company's expansion across the U.S., South America, and the Middle East as it establishes an R&D lab -- Riiid Labs -- in Silicon Valley. The pandemic has forced the shutdown of schools in countries around the world; cramped indoor classrooms are seen as a major threat vector. Despite inequities with regard to internet access and the widening achievement gap, it's the belief of educators that the health pros outweigh the cons. Riiid, which offers its services exclusively online, has been a beneficiary of the shift.

Both races are setting the stage for the next dominant world power. While research into AI and quantum technologies is being developed on a worldwide scale, with advances coming from different countries, China and the United States (US) are at the forefront of both races, with these technologies forming important stepping stones for geopolitical power accumulation. Indeed, China is currently playing the game for supremacy on both quantum technologies and AI, trying to surpass the US and become the leading world power (Smith-Goodson, 2019). If China wins the race for quantum supremacy then it will be in a leading geostrategic position, since it will become the major dominant power in the next technological infrastructure, if, along with quantum supremacy, China achieves AI supremacy (both classical and quantum), then it may topple the US, Russia, Europe and Asian geopolitical competition vectors. On the other hand, this race is not restricted to countries, it is a global geostrategic and geoeconomic race that includes cooperative networks involving the academia and the private sectors as well, indeed, the US geostrategic position depends strongly upon the private sector's US-based large technology companies' investment in quantum technologies. Regarding the issue of quantum supremacy, it is relevant to consider Kirkland (2020)'s reflection, quoting: "(…) One thing remains unchanged (…) and that is the glaring reality that those who manage to successfully harness the power of quantum mechanics will have supremacy over the rest of the world. How do you think they will use it?"

The number of AI startups in India has increased tremendously over the years. Apart from being adopted in major industries, Artificial intelligence has become a way of doing business in other niche areas such as farming or even security. To recognise the unconventional startups in the AI space, Analytics India Magazine comes with a list of 10 such exceptional startups that are leading the AI race every year. In this year's list, we have covered startups that are not more than 3 to 4 years old and have headquarters in India. Most of these startups are funded externally and are working hard to bring about exceptional transformation in the Indian tech ecosystem.

Nowadays, journalism is facilitated by the existence of large amounts of digital data sources, including many Open Data ones. Such data sources are extremely heterogeneous, ranging from highly struc-tured (relational databases), semi-structured (JSON, XML, HTML), graphs (e.g., RDF), and text. Journalists (and other classes of users lacking advanced IT expertise, such as most non-governmental-organizations, or small public administrations) need to be able to make sense of such heterogeneous corpora, even if they lack the ability to de ne and deploy custom extract-transform-load work ows. These are di cult to set up not only for arbitrary heterogeneous inputs , but also given that users may want to add (or remove) datasets to (from) the corpus. We describe a complete approach for integrating dynamic sets of heterogeneous data sources along the lines described above: the challenges we faced to make such graphs useful, allow their integration to scale, and the solutions we proposed for these problems. Our approach is implemented within the ConnectionLens system; we validate it through a set of experiments.

In this work, we show that neural networks can be represented via the mathematical theory of quiver representations. More specifically, we prove that a neural network is a quiver representation with activation functions, a mathematical object that we represent using a {\em network quiver}. Also, we show that network quivers gently adapt to common neural network concepts such as fully-connected layers, convolution operations, residual connections, batch normalization, and pooling operations. We show that this mathematical representation is by no means an approximation of what neural networks are as it exactly matches reality. This interpretation is algebraic and can be studied with algebraic methods. We also provide a quiver representation model to understand how a neural network creates representations from the data. We show that a neural network saves the data as quiver representations, and maps it to a geometrical space called the {\em moduli space}, which is given in terms of the underlying oriented graph of the network. This results as a consequence of our defined objects and of understanding how the neural network computes a prediction in a combinatorial and algebraic way. Overall, representing neural networks through the quiver representation theory leads to 13 consequences that we believe are of great interest to better understand what neural networks are and how they work.

Google on Wednesday announced the launch of an online tool that uses artificial intelligence to decode and translate Egyptian hieroglyphics. The tool -- called fabricius -- will be useful for presenting an interactive experiment to globalists to help them learn hieroglyphics more efficiently. It will also help increase Egyptologists' awareness of the history and heritage of the ancient Egyptian civilization. The tool was made available on the Google Arts and Culture platform, which allows users to learn about arts and cultural artifacts from roughly 2,000 cultural institutions worldwide. The tool uses machine learning to ease the collection, indexing and understanding of hieroglyphic symbols. The tool to help Egyptologists translate hieroglyphics into English is now available, with the Arabic tool set to unveil soon.

This paper provides a review of past approaches to the use of deep-learning frameworks for the analysis of discrete irregular-patterned complex sequential datasets. A typical example of such a dataset is financial data where specific events trigger sudden irregular changes in the sequence of the data. Traditional deep-learning methods perform poorly or even fail when trying to analyse these datasets. The results of a systematic literature review reveal the dominance of frameworks based on recurrent neural networks. The performance of deep-learning frameworks was found to be evaluated mainly using mean absolute error and root mean square error accuracy metrics. Underlying challenges that were identified are: lack of performance robustness, non-transparency of the methodology, internal and external architectural design and configuration issues. These challenges provide an opportunity to improve the framework for complex irregular-patterned sequential datasets.

Ubisoft Hieroglyphs Initiative teamed up with Google's Arts and Culture in 2017 and set to launch a hieroglyph translator at the British Museum previously. This month they posted an important update on the project. The project aimed to explore the possibility of using machine learning algorithms to translate the logographs of Ancient Egypt. The main contributors of the project include Berlin-Brandenburg Academy of Sciences and Humanities, Brown and Harvard Universities, Université du Québec à Montréal, Macquarie University, along with countless Egyptologists around the globe. Ubisoft says "We started this project as a way of saying thank you to all the academics that had helped us make Assassin's Creed Origins so realistic, and for assisting us in developing the hugely popular Discovery Tour."

Hosted by Dylan Doyle-Burke and Jessie J Smith, Radical AI is a podcast featuring the voices of the future in the field of artificial intelligence ethics. In this episode Jess and Dylan chat to Abeba Birhane about "Robot Rights? Should we grant robots rights? What is moral relationality and how can it be useful for designing machine learning algorithms? What is the algorithmic colonization of Africa and why is it harmful?

Predicting the potential success of a book in advance is vital in many applications. This could help both publishers and readers in their decision making process whether or not a book is worth publishing and reading, respectively. This prediction could also help authors decide whether a book draft is good enough to send to a publisher. We propose a model that leverages Convolutional Neural Networks along with readability indices. Unlike previous methods, our method includes no count-based, lexical, or syntactic hand-crafted features. Instead, we make use of a pre-trained sentence encoder to encode the book sentences. We highlight the connection between this task and book genre identification by showing that embeddings that are good at capturing the separability of book genres are better for the book success prediction task. We also show that only the first 1K sentences are good enough to predict the successability of books. Our proposed model outperforms strong baselines on this task by as large as 6.4% F1-score.