Artificial Intelligence is the technological story of the 2010s, and over time, more AI technologies are on the way. AI was the new charm for all tech people -- but it did not end even in the second decade. No doubt, 2019 was the year of artificial intelligence; however, 2020 has promised more AI miracles. Here are the top ten greatest AI trends in business in 2020. We see artificial intelligence's rate of growth much higher than we would have expected.

Elon Musk says he is quitting Twitter "for a while". The SpaceX and Tesla founder is one of the site's most high-profile users, and regularly sends tweets making major announcements about himself or his companies. But his Twitter account has brought controversy, too, with Mr Musk regularly causing controversy with his posts. "Off Twitter for a while," Mr Musk wrote in the post. It comes just days after SpaceX launched Nasa astronauts into space in a historic mission.

Topological Data Analysis (TDA) provides novel approaches that allow us to analyze the geometrical shapes and topological structures of a dataset. As one important application, TDA can be used for data visualization and dimension reduction. We follow the framework of circular coordinate representation, which allows us to perform dimension reduction and visualization for high-dimensional datasets on a torus using persistent cohomology. In this paper, we propose a method to adapt the circular coordinate framework to take into account sparsity in high-dimensional applications. We use a generalized penalty function instead of an $L_{2}$ penalty in the traditional circular coordinate algorithm. We provide simulation experiments and real data analysis to support our claim that circular coordinates with generalized penalty will accommodate the sparsity in high-dimensional datasets under different sampling schemes while preserving the topological structures.

Traditional clustering identifies groups of objects that share certain qualities. Tangles do the converse: they identify groups of qualities that often occur together. They can thereby discover, relate, and structure types: of behaviour, political views, texts, or viruses. If desired, tangles can also be used for direct clustering of objects. They offer a precise, quantitative paradigm suited particularly to fuzzy clusters, since they do not require any `hard' assignments of objects to the clusters they collectively form. This is a draft of the introductory chapter of a book I am preparing on the application of tangles in the empirical sciences. The purpose of posting this draft early is to give authors of tangle application papers a generic reference for the basic guiding principles underlying tangle applications outside mathematics, so that in their own papers they can concentrate on the ideas specific to their particular application rather than having to repeat the generic story each time. The text starts with three separate generic introductions to tangles in the natural sciences, in the social sciences, and in data science including machine learning. It then gives a short informal description of the abstract notion of tangles that encompasses all these potential applications.

The morality of algorithms and their potential for bias and discrimination are important concerns. A popular approach to machine learning and artificial intelligence is via the numerical optimization of objective functions, and adapting such an approach to handle ethics could seem natural: with a hammer in hand, everything looks like a nail. The hammer of much artificial intelligence is the optimization of objective values, so some might like to treat morality solely through such objective functions. However, relying solely on the optimization of scalar objective values is fraught with unavoidable flaws when dealing with real people.

The molecular dipole moment ($\boldsymbol{\mu}$) is a central quantity in chemistry. It is essential in predicting infrared and sum-frequency generation spectra, as well as induction and long-range electrostatic interactions. Furthermore, it can be extracted directly from high-level quantum mechanical calculations, making it an ideal target for machine learning (ML). In this work, we choose to represent this quantity with a physically inspired ML model that captures two distinct physical effects: local atomic polarization is captured within the symmetry-adapted Gaussian process regression (SA-GPR) framework, which assigns a (vector) dipole moment to each atom, while movement of charge across the entire molecule is captured by assigning a partial (scalar) charge to each atom. The resulting "MuML" models are fitted together to reproduce molecular $\boldsymbol{\mu}$ computed using high-level coupled-cluster theory (CCSD) and density functional theory (DFT) on the QM7b dataset. The combined model shows excellent transferability when applied to a showcase dataset of larger and more complex molecules, approaching the accuracy of DFT at a small fraction of the computational cost. We also demonstrate that the uncertainty in the predictions can be estimated reliably using a calibrated committee model. The ultimate performance of the models depends, however, on the details of the system at hand, with the scalar model being clearly superior when describing large molecules whose dipole is almost entirely generated by charge separation. These observations point to the importance of simultaneously accounting for the local and non-local effects that contribute to $\boldsymbol{\mu}$; further, they define a challenging task to benchmark future models, particularly those aimed at the description of condensed phases.

Drought is a natural creeping threat with numerous damaging effects in various aspects of human life. Accurate drought prediction is a promising step in helping policy makers to set drought risk management strategies. To fulfill this purpose, choosing appropriate models plays an important role in predicting approach. In this study, different models of Artificial Neural Network (ANN) are employed to predict short and long-term of droughts by using Standardized Precipitation Index (SPI) at different time scales, including 3, 6, 12, 24 and 48 months in Tabriz city, Iran. To this end, different combination of calculated SPI and time series of various hydro-meteorological variables, such as precipitation, wind velocity, relative humidity and sunshine hours for years 1992 to 2010 are used to train the ANN models. In order to compare the models performances, some well-known measures, namely RMSE, Mean Absolute Error (MAE) and Correlation Coefficient (CC) are utilized in the present study. The results illustrate that the application of all hydro-meteorological variables significantly improves the prediction of SPI at different time scales.

A British artificial intelligence firm involved in the Vote Leave campaign has been handed a £400,000 contract to tap data from places such as social media sites to help steer the Government's response to Covid-19. Official documents from the Government show Faculty Science was awarded the contract by the Ministry of Housing, Communities and Local Government (MHCLG) in April to provide data scientists who could set up "alternative data sources (e.g. They would, the contract said, apply data science and machine learning to the data, which could help identify trends, and then develop "interactive dashboards" to inform policymakers. It is understood the contract, awarded through the Government's G-Cloud framework, was designed to address an urgent need for the department to analyse real-time data and monitor the effect of Covid-19 on local communities. Faculty's AI technology can be used to process vast amounts of data and in the past was used for polling analysis by the Vote Leave campaign, run by Boris Johnson's adviser Dominic Cummings.

The Defense Department is seeking to adapt artificial intelligence technology it uses to track down terrorists with drones or predict when aircraft need maintenance for a new purpose: screening and testing novel coronavirus treatments and vaccines. The Pentagon plans to boost existing programs with money Congress provided under the virus-relief CARES Act for the "development of artificial intelligence-based models to rapidly screen, prioritize, and test Food and Drug Administration approved therapeutics for new COVID-19 drug candidates." The AI funds would also be tapped for human test trials for vaccines and antibody based treatments, according to the spending plan the department submitted to congressional panels. Dick Durbin (Ill.), the Senate's No. 2 Democrat and ranking member on the Appropriations Defense Subcommittee, pressed for the plan's release. While the amount of money the Pentagon wants to use on these programs is small---close to $1 million--it shows some of the department's urgency to apply new technology to choke off the pandemic.

Even the best inventions and intentions can result in unintended consequences. Email vastly improved many forms of communication and information sharing -- but it also begat spam, phishing, and an entire industry in cybersecurity. Social media connected billions of people and spread democratic ideals -- but it also wrought hacked accounts, stolen data, "fake news," and election meddling. The same is true with today's "advanced technologies" that promise to revolutionize information gathering, data analytics, workplace mobility, and much more during the coming decade. The ethical considerations and possible regulation of artificial intelligence (AI), machine learning, robotics, and other advanced technologies are playing catch-up once again.