São Paulo, January 2020 - Virtual assistants are increasingly present in people's routine, whether to help, answer questions and facilitate daily life. What they all have in common are women's names and the standard female voice, such as Lu, Siri, Alexa, Nat, Bia, etc. According to a study entitled "I'd Blush If I Could" published by UNESCO in May 2019, virtual assistants via Artificial Intelligence suffer from high levels of gender prejudice, although they usually answer with tolerant, subservient and passive phrases. Based on this context, the "Hey Update My Voice" movement was launched in partnership with UNESCO with the objective of drawing attention to cyber education and respect for virtual assistants, and ask companies to update their assistants' responses. If even virtual assistants are harassed, can you imagine how many women are victims of this kind of violence?

Will the proliferation of AI and machine learning reinforce the worldwide digital divide? It's one of the questions the Global Talent Competitiveness Index (GTCI) and Global Cities Talent Competitiveness Index (GCTCI) seek to answer by benchmarking the ability of countries and cities to compete for talent. An answer has historically proven elusive, but the 7th annual reports published by Insead, Adecco Group, and Google suggest it might instead provide "significant" opportunities despite the fact that AI skills are "scarce" and "unequally distributed" across nations. "AI is changing many facets of business and society and, if properly used and governed, has potential to foster sustainable development," said Katell Le Goulven, executive director of the Insead Hoffmann Global Institute for Business and Society. "The GTCI report argues that with multi-stakeholder cooperation the technology could help achieve some of the SDGs [the United Nations' Sustainable Development Goals] such as those related to health (via personalized remote diagnosis and big data analysis to track and reduce endemic disease). But it also points to the imperative of closing the global digital skills gap to harness the potential of AI for good."

Clustering analysis has become a ubiquitous information retrieval tool in a wide range of domains, but a more automatic framework is still lacking. Though internal metrics are the key players towards a successful retrieval of clusters, their effectiveness on real-world datasets remains not fully understood, mainly because of their unrealistic assumptions underlying datasets. We hypothesized that capturing {\it traces of information gain} between increasingly complex clustering retrievals---{\it InfoGuide}---enables an automatic clustering analysis with improved clustering retrievals. We validated the {\it InfoGuide} hypothesis by capturing the traces of information gain using the Kolmogorov-Smirnov statistic and comparing the clusters retrieved by {\it InfoGuide} against those retrieved by other commonly used internal metrics in artificially-generated, benchmarks, and real-world datasets. Our results suggested that {\it InfoGuide} can enable a more automatic clustering analysis and may be more suitable for retrieving clusters in real-world datasets displaying nontrivial statistical properties.

How to assess the potential of liking a city or a neighborhood before ever having been there. The concept of urban quality has until now pertained to global city ranking, where cities are evaluated under a grid of given parameters, or either to empirical and sociological approaches, often constrained by the amount of available information. Using state of the art machine learning techniques and thousands of geotagged satellite and perspective images from diverse urban cultures, this research characterizes personal preference in urban spaces and predicts a spectrum of unknown likeable places for a specific observer. Unlike most urban perception studies, our intention is not by any means to provide an objective measure of urban quality, but rather to portray personal views of the city or Cities of Indexes.

In June, 1972, Ángel Parra, Chile's leading folksinger, wrote a song titled "Litany for a Computer and a Baby About to Be Born." Computers are like children, he sang, and Chilean bureaucrats must not abandon them. The song was prompted by a visit to Santiago from a British consultant who, with his ample beard and burly physique, reminded Parra of Santa Claus--a Santa bearing a "hidden gift, cybernetics." The consultant, Stafford Beer, had been brought in by Chile's top planners to help guide the country down what Salvador Allende, its democratically elected Marxist leader, was calling "the Chilean road to socialism." Beer was a leading theorist of cybernetics--a discipline born of midcentury efforts to understand the role of communication in controlling social, biological, and technical systems.

Governments across the world are increasingly promoting AI technology through investments in R&D and by developing education programs to train the workforce with AI skills, which can support businesses across industries. Retail, BFSI, and manufacturing are a few of the major industries that are increasing their investments in AI to automate business functions. Many key countries have initiated AI development plans to drive economic and technological growth. Some instances include the launch of Germany's digital strategy on AI called "AI Made in Germany" in November 2018, and China's announcement of its "Next Generation Artificial Intelligence Development Plan", in 2017. These strategies focus on the development of talent and education, government investments, and research and collaborative partnerships in AI.

ABOUT A CENTURY ago, engineers created a new sort of space: the control room. Before then, things that needed control were controlled by people on the spot. But as district heating systems, railway networks, electric grids and the like grew more complex, it began to make sense to put the controls all in one place. Dials and light bulbs brought the way the world was working into the room. Levers, stopcocks, switches and buttons sent decisions back out. By the 1960s control rooms had become a powerful icon of the modern. At Mission Control in Houston, young men in horn rimmed glasses and crewcuts sent commands to spacecraft heading for the Moon. In the space seen through television sets, travellers exploring strange new worlds did so within an iconic control room of their own: the bridge of Star Trek's USS Enterprise. A hexagonal room built in Santiago de Chile a decade later fitted right into the same philosophy--and aesthetic. It had an array of screens full of numbers and arrows. It was linked to a powerful computer. It had futuristic swivel chairs, complete with geometric buttons in the armrests to control the displays. Unlike the Johnson Space Centre and the Enterprise, it even had a small bar where occupants could serve themselves drinks after a hard day's controlling.

Zinier, a company that uses artificial intelligence (AI) to automate field work, has raised $90 million in a Series C funding round, bringing its total amount raised to $120 million. The startup plays heavily in the telecom sector -- 80% of its existing customers are in the space, including network operators, equipment vendors and suppliers, contractors, and engineers, according to Zinier's co-founder and CEO Arka Dhar. That's also reflected by the firms that returned to invest in this latest round, including Nokia-backed NGP Capital and Qualcomm Ventures. New investor Iconiq Capital led the round with participation from Tiger Global Management, Accel, Founders Fund, and Newfund Capital. "Zinier is going to play a very, very important role there," Dhar said in a phone interview.

The global deep learning market is expected to grow at a CAGR of 51.1% from forecast period 2019 to 2026 and expected to reach the value of around US$ 56,427.2 Deep learning is a subdivision of machine learning in artificial intelligence (AI) concerned with the algorithm inspired by the functioning of human brain termed as artificial neural networks. It is also termed as deep neural learning or deep neural network. Deep learning is evolved with the increasing amount of unstructured data due to digitalization. The available amount of data is utilized in deep learning to process or understand that data for effective decision making in various industry verticals including healthcare, manufacturing, automotive, agriculture, retail, security, human resources, marketing, law, and fintech.

In bipartite networks, community structures are restricted to being disassortative, in that nodes of one type are grouped according to common patterns of connection with nodes of the other type. This makes the stochastic block model (SBM), a highly flexible generative model for networks with block structure, an intuitive choice for bipartite community detection. However, typical formulations of the SBM do not make use of the special structure of bipartite networks. In this work, we introduce a Bayesian nonparametric formulation of the SBM and a corresponding algorithm to efficiently find communities in bipartite networks without overfitting. The biSBM improves community detection results over general SBMs when data are noisy, improves the model resolution limit by a factor of $\sqrt{2}$, and expands our understanding of the complicated optimization landscape associated with community detection tasks. A direct comparison of certain terms of the prior distributions in the biSBM and a related high-resolution hierarchical SBM also reveals a counterintuitive regime of community detection problems, populated by smaller and sparser networks, where non-hierarchical models outperform their more flexible counterpart.