The car's underlying AI technology, known as deep learning, has proved very powerful at solving problems in recent years, and it has been widely deployed for tasks like image captioning, voice recognition, and language translation. There is now hope that the same techniques will be able to diagnose deadly diseases, make million-dollar trading decisions, and do countless other things to transform whole industries. But this won't happen--or shouldn't happen--unless we find ways of making techniques like deep learning more understandable to their creators and accountable to their users. Otherwise it will be hard to predict when failures might occur--and it's inevitable they will. That's one reason Nvidia's car is still experimental.

Artificial intelligence (AI) is already part of our daily lives and is playing a key role in defining the economic and social shape of the future. In 2018, the European Commission introduced its AI strategy able to compete in the next years with world powers such as China and US, but relying on the respect of European values and fundamental rights. As a result, most of the Member States have published their own National Strategy with the aim to work on a coordinated plan for Europe. In this paper, we present an ongoing study on how European countries are approaching the field of Artificial Intelligence, with its promises and risks, through the lens of their national AI strategies. In particular, we aim to investigate how European countries are investing in AI and to what extent the stated plans can contribute to the benefit of the whole society. This paper reports the main findings of a qualitative analysis of the investment plans reported in 15 European National Strategies

A Cambridge driverless car start-up that has emerged as one of Britain's brightest prospects in the cutting edge sector has secured backing from Sir Richard Branson's Virgin Group as it seeks to accelerate its plans. Wayve Technologies, founded by 28-year-old Alex Kendall with Amar Shah, is building artificial intelligence technology that uses machine learning techniques pioneered by DeepMind to improve self-driving cars. Its latest funding has seen it secure a further $20m (£15m) from current and new investors. According to Mr Kendall, its chief executive, Wayve's technology could leapfrog US giants such as Google's Waymo and Uber. Mr Kendall said: "The incumbents started off the back of DARPA [the US defence agency] challenges in the mid 2000s. I think those challenges set the industry back about 10 years."

Traditionally, economists have modelled output as a function of labour and capital (production factors), and material inputs. For AI, the production factors are high-skill specialist talent and Information and Communication Technologies (ICT) infrastructure for computing and storage, and data is the key input. Is data then the new oil? While data does need to be'extracted' and then'refined' before further use, its availability grows with the volume of output. Data is also specific, not fungible.

As reported by the Wall Street Journal, analysts and members of the Information Technology and Innovation Foundation (ITIF) expect that the presidency of Joe Biden will continue to make research and development for AI and quantum computing technologies a priority, although aspects of Biden's approach to regulation and spending are expected to differ. While federal investments in R&D for the Information Technology sector have fallen over the course of the last few decades, in February the White House announced a plan to increasing spending on AI and quantum technologies, and the Biden presidency is expected to continue the commitment. At the moment, total federal research and development funding sits at around $134.1 billion, while the Trump administration had proposed an increase to $142.4 billion for total federal R&D funding. In February the Trump administration announced a plan to increase annual spending on AI by more than $2 billion dollars over the course of the next two years. This was to be accompanied by an increase in funding for quantum information science to the tune of $860 million dollars over the same period.

Works on the use of algorithms, based on artificial intelligence, has been predicting the possibility of a pandemic for many years, whilst models developed by researchers have been used effectively in the fight against infectious diseases, thus limiting their development. An example of such activity are the achievements of AIME company (Artificial Intelligence and Medical Epidemiology), which since 2012 has been conducting research on the possibilities of using AI to predict the course of infectious disease epidemics. In 2017, the models, trained on a huge amount of data, reached 86% effectiveness in predicting the locations where the Zika and dengue virus outbreaks occurred within the following three months. Bill Gates TED Talk in 2015 is known primarily among people who consider the COVID-19 a global conspiracy. In fact, it is impossible not to notice similarities between the course of the current epidemic and the hypothetical super-virus pandemic described by Gates in his speech.

This idea could be interpreted as being rather bleak; are we doomed to repeat the errors of the past until we correct them? We certainly do need to learn and re-learn life lessons--whether in our work, relationships, finances, health, or other areas--in order to grow as people. Zooming out, the same phenomenon exists on a much bigger scale--that of our collective human history. We like to think we're improving as a species, but haven't yet come close to doing away with the conflicts and injustices that plagued our ancestors. What might happen over the course of this year, and what information would we use to make educated guesses about it? The editorial team at The Economist took a unique approach to answering these questions.

Chinese technicians were making final preparations Monday for a mission to bring back material from the moon's surface for the first time in more than four decades – an undertaking that could boost human understanding of the moon and of the solar system more generally. Chang'e 5 – named for the Chinese moon goddess – is the country's most ambitious lunar mission yet. If successful, it would be a major advance for China's space program, and some experts say it could pave the way for bringing samples back from Mars or even a crewed lunar mission. The China National Space Administration said in a statement that the Long March-5Y rocket began fueling up on Monday, ahead of a launch scheduled for between 3 p.m. and 4 p.m. EST Monday at the Wenchang launch center on the southern island province of Hainan. The typically secretive administration had previously only confirmed the launch would be in late November.

High-dimensional representations for words, text, images, knowledge graphs and other structured data are commonly used in different paradigms of machine learning and data mining. These representations have different degrees of interpretability, with efficient distributed representations coming at the cost of the loss of feature to dimension mapping. This implies that there is obfuscation in the way concepts are captured in these embedding spaces. Its effects are seen in many representations and tasks, one particularly problematic one being in language representations where the societal biases, learned from underlying data, are captured and occluded in unknown dimensions and subspaces. As a result, invalid associations (such as different races and their association with a polar notion of good versus bad) are made and propagated by the representations, leading to unfair outcomes in different tasks where they are used. This work addresses some of these problems pertaining to the transparency and interpretability of such representations. A primary focus is the detection, quantification, and mitigation of socially biased associations in language representation.

Early risk diagnosis and driving anomaly detection from vehicle stream are of great benefits in a range of advanced solutions towards Smart Road and crash prevention, although there are intrinsic challenges, especially lack of ground truth, definition of multiple risk exposures. This study proposes a domain-specific automatic clustering (termed Autocluster) to self-learn the optimal models for unsupervised risk assessment, which integrates key steps of risk clustering into an auto-optimisable pipeline, including feature and algorithm selection, hyperparameter auto-tuning. Firstly, based on surrogate conflict measures, indicator-guided feature extraction is conducted to construct temporal-spatial and kinematical risk features. Then we develop an elimination-based model reliance importance (EMRI) method to unsupervised-select the useful features. Secondly, we propose balanced Silhouette Index (bSI) to evaluate the internal quality of imbalanced clustering. A loss function is designed that considers the clustering performance in terms of internal quality, inter-cluster variation, and model stability. Thirdly, based on Bayesian optimisation, the algorithm selection and hyperparameter auto-tuning are self-learned to generate the best clustering partitions. Various algorithms are comprehensively investigated. Herein, NGSIM vehicle trajectory data is used for test-bedding. Findings show that Autocluster is reliable and promising to diagnose multiple distinct risk exposures inherent to generalised driving behaviour. Besides, we also delve into risk clustering, such as, algorithms heterogeneity, Silhouette analysis, hierarchical clustering flows, etc. Meanwhile, the Autocluster is also a method for unsupervised multi-risk data labelling and indicator threshold calibration. Furthermore, Autocluster is useful to tackle the challenges in imbalanced clustering without ground truth or priori knowledge