The forecasting tool assesses multiple patient-specific biological and clinical factors to predict the degree of response to immune checkpoint inhibitors and survival outcomes. It markedly outperforms individual biomarkers or other combinations of variables developed so far, according to findings published in Nature Biotechnology. With further validation, the tool may help oncologists better identify patients most likely to benefit from ICB. Discerning, prior to treatment, patients for whom ICB would be ineffective could reduce unnecessary expense and exposure to potential side effects. It could also indicate the need to pursue alternate treatment strategies, such as combination therapies. "It's important to know which treatment modalities patients are most suited for," said Dr. Chan, director of Cleveland Clinic's Center for Immunotherapy & Precision Immuno-Oncology.

Raising serious concerns about AI (artificial intelligence) deployments for judicial decisions, he said, the plans to use AI to speed up decision …

Robotics will eventually play a key role in India's "Make in India" strategy, which aims to persuade global businesses to invest. India's position as a true hub of robotics talent is due to this. At this time, the whole population and their daily life are centred on the internet. Everything from buying to schooling to vacation planning can be done with just a few clicks on smartphones and laptops. No one could have predicted digital life a decade ago, and the same can be said for robotics.

Real-time systems using AI for navigation and guidance, coordination, self-healing, target identification and acquisition & munition delivery systems are a strategic asset. Drone warfare is asymmetric and is almost mainstream. A bit like guerrilla warfare as it is low cost. Command, control, communications and intelligence, surveillance and reconnaissance (C4ISR) are becoming a most essential element in modern military operations. Countries with artificial intelligence powered drone have baffled their enemies in the war zone.

One word: AI – Many of the world's leaders in the field of science and technology, including the late Stephen Hawking, Telsa founder Elon Musk, Apple co-founder Steve Wozniak and Microsoft founder Bill Gates, have all expressed concern in recent years over the risks of artificial intelligence (AI) – most notably its potential use in autonomous weapons. Along with many in academia and human rights groups, the science and tech visionaries have warned that in the wrong hands there is a serious danger posed by AI. One concern is that these weapons could be designed to be extremely difficult to simply "turn off," as the Future of Life Institute noted in its report on the development of autonomous weapon platforms. That could result in a scenario straight out of science fiction where humans lose control of their dangerous creations. While it may not mean a world-ending scenario presented in The Terminator, even losing control of a few AI weapons temporarily could result in unnecessary mass causalities or worse.

This manuscript proposes to extend the information set of time-series regression trees with latent stationary factors extracted via state-space methods. First, it allows to handle predictors that exhibit measurement error, non-stationary trends, seasonality and/or irregularities such as missing observations. Second, it gives a transparent way for using domain-specific theory to inform time-series regression trees. As a byproduct, this technique sets the foundations for structuring powerful ensembles. Their real-world applicability is studied under the lenses of empirical macro-finance. Keywords: Ensemble learning, Factor models, State-space models, Time series, Unobserved components.Introduction In time series, the simplicity of regression trees (Morgan and Sonquist, 1963; Breiman et al., 1984; Quinlan, 1986) comes at a cost: irregularities, complicated periodic patterns and non-stationary trends cannot be explicitly modelled, and this is unfortunate given that many real-world examples are subject to them. Following, in spirit, Harvey et al. (1998), this paper proposes to pre-process problematic predictors using state-space representations general enough to deal with all these complexities at once. This operation can be thought as an automated feature engineering process that extracts stationary patterns hidden across multiple predictors, while handling problematic data characteristics. Besides, when the state-space representation is compatible with domain-specific theory, this becomes a transparent way for extracting signals with structural interpretation. The resulting stationary common components, referred hereinbelow as stationary dynamic factors, are then employed as regular predictors for standard time-series regression trees. This manuscript calls them factor-augmented regression trees to stress their dependence on latent components. I thank Matteo Barigozzi and Kostas Kalogeropoulos for their valuable suggestions and supervision; Serena Lariccia and Qiwei Yao for their helpful comments on a preliminary draft of this article.

We review the scholarly contributions that utilise Natural Language Processing (NLP) methods to support the design process. Using a heuristic approach, we collected 223 articles published in 32 journals and within the period 1991-present. We present state-of-the-art NLP in-and-for design research by reviewing these articles according to the type of natural language text sources: internal reports, design concepts, discourse transcripts, technical publications, consumer opinions, and others. Upon summarizing and identifying the gaps in these contributions, we utilise an existing design innovation framework to identify the applications that are currently being supported by NLP. We then propose a few methodological and theoretical directions for future NLP in-and-for design research.

Britain's National Health Service (NHS) is famous for offering free medical treatment to all UK citizens. Despite this, uptake of some services remains low, particularly in certain ethnic demographics. The British government has spent many years trying to reduce these inequalities – and now they are investigating how artificial intelligence (AI) can help bridge the gap. NHSx – the NHS' AI lab and health foundation – has a mission "to ensure NHS patients are amongst the first in the world to benefit from leading AI," and "a responsibility to ensure those technologies don't exacerbate existing health inequalities." As part of these efforts, NHSx has recently identified four AI projects that will benefit from additional investment.

In the world of game theory, we refer to games such as Catan, Risk, and Civilization 6 as large-scale strategy games. The defining trait of these games is their massive number of components and how they interact. Games often give players the option to compete against the computer. These computer players are called artificial intelligence (AIs). The purpose of these AIs is to give players an equal challenge.

The twenty-first century has seen a technological revolution. Several highly commercial and widely used technologies from the early 2000s have completely vanished, and other ones have replaced them. In 2021, many latest technologies will emerge, particularly in the fields of computer science and engineering. These latest technologies are only going to get better in 2021, and they may even make it into the hands of the average individual. These are the key trends or latest technologies to look at whether you're a recent computer science graduate or a seasoned IT professional.