In this paper, we give an in-depth error analysis for surrogate models generated by a variant of the Sparse Identification of Nonlinear Dynamics (SINDy) method. We start with an overview of a variety of non-linear system identification techniques, namely, SINDy, weak-SINDy, and the occupation kernel method. Under the assumption that the dynamics are a finite linear combination of a set of basis functions, these methods establish a matrix equation to recover coefficients. We illuminate the structural similarities between these techniques and establish a projection property for the weak-SINDy technique. Following the overview, we analyze the error of surrogate models generated by a simplified version of weak-SINDy. In particular, under the assumption of boundedness of a composition operator given by the solution, we show that (i) the surrogate dynamics converges towards the true dynamics and (ii) the solution of the surrogate model is reasonably close to the true solution. Finally, as an application, we discuss the use of a combination of weak-SINDy surrogate modeling and proper orthogonal decomposition (POD) to build a surrogate model for partial differential equations (PDEs).

We introduce the task of microblog opinion summarisation (MOS) and share a dataset of 3100 gold-standard opinion summaries to facilitate research in this domain. The dataset contains summaries of tweets spanning a 2-year period and covers more topics than any other public Twitter summarisation dataset. Summaries are abstractive in nature and have been created by journalists skilled in summarising news articles following a template separating factual information (main story) from author opinions. Our method differs from previous work on generating gold-standard summaries from social media, which usually involves selecting representative posts and thus favours extractive summarisation models. To showcase the dataset's utility and challenges, we benchmark a range of abstractive and extractive state-of-the-art summarisation models and achieve good performance, with the former outperforming the latter. We also show that fine-tuning is necessary to improve performance and investigate the benefits of using different sample sizes.

It is now abundantly evident in the post-Covid era that things will no longer be like before and adoption of technology and embracing automation is a must for the survival in the new normal world. We are also observing that there is hardly a day in Bangladesh when a politician, business leader or a civil servant does not talk about the fourth industrial revolution and the effects of artificial intelligence on our economy. Prime Minister Sheikh Hasina also said on December 11 in 2021 that the government is preparing the country to take advantage of the potential presented by the fourth industrial revolution (4IR) in order to boost economic growth to the desired level. Now the debate that has been going for so long whether artificial intelligence will take away jobs from humans or will it create more jobs? When the first industrial revolution used water and steam power to mechanise production, people thought that people will lose job and machine will replace human, but actually it did not happen.

AI is frequently implemented as a hardware and software hybrid system. From a software perspective, algorithms are the major focus of AI. Creating AI algorithms can be conceptualized using an Artificial Neural Network. It is a simulation of the human brain made up of a network of neurons connected by weighted communication pathways. Artificial intelligence is used in computers to refer to a computer program's ability to carry out operations linked to human intellect, such as reasoning and learning.

The Chinese government has already jumped on the metaverse bandwagon, that immersive digital world being developed by companies like Meta. But the country's leaders don't intend to compete with the US for primacy in this new race – they want to build a domestic metaverse tailored to Chinese Communist Party (CCP) objectives. It's a vision that enables the private sector to develop key technology for the Asian giant, but also maintains what the government euphemistically calls "social peace." The state machinery's wheels are already turning. In 2021, more than 10,000 metaverse-related trademarks were registered in China, compared to less than 1,000 in 2020 and 2019. So far in 2022, 16,000 trademark applications have been submitted.

In brief AI progresses rapidly. Just months after the release of the most advanced text-to-image models, developers are showing off text-to-video systems. Meta announced a multimodal algorithm named Make-A-Video that allows its users to type a text description of a scene as input and get a short computer-generated animated clip as output, typically depicting what was described. Other types of data, such as an image or a video, can be used as an input prompt, too. The text-to-video system was trained on public datasets, according to a non-peer reviewed paper [PDF] describing the software.

Establishing a data-driven pipeline for the discovery of novel materials requires the engineering of material features that can be feasibly calculated and can be applied to predict a material's target properties. Here we propose a new class of descriptors for describing crystal structures, which we term Robust One-Shot Ab initio (ROSA) descriptors. ROSA is computationally cheap and is shown to accurately predict a range of material properties. These simple and intuitive class of descriptors are generated from the energetics of a material at a low level of theory using an incomplete ab initio calculation. We demonstrate how the incorporation of ROSA descriptors in ML-based property prediction leads to accurate predictions over a wide range of crystals, amorphized crystals, metal-organic frameworks and molecules. We believe that the low computational cost and ease of use of these descriptors will significantly improve ML-based predictions.

Mobileye, the self-driving tech firm that Intel had purchased for $15.3 billion back in 2017, has filed for an IPO with the Securities and Exchange Commission. When Intel first announced its plans to take Mobileye public late last year, the autonomous driving firm was expected to have a valuation of over $50 billion. Now according to Bloomberg, Intel expects Mobileye to be valued at around $30 billion, due to soaring inflation rates and poor market conditions. Regardless, it's still bound to become one of the biggest offerings in the US for 2022 if the listing takes place this year. Intel intends to retain a majority stake in Mobileye, but Chief Executive Pat Gelsinger previously said that taking it public would give it the ability to grow more easily.

Here, it isn't about fitting into our culture, it's about adding to it – and we can't wait to see what you'll bring.

The head of the UK's largest and most advanced robotics centre has said that society needs to prepare for the increased integration of robots but shouldn't fear the rise of artificial intelligence (AI). Stewart Miller, the chief executive of the National Robotarium, which opens today in Edinburgh, told Sky News that "Inevitably there will be more robots in everybody's life. They'll be helping you at home, when you go out shopping, when you go to a hotel, they'll be involved in hospitality, when you go to a theatre, everything. Internationally, some scientists have expressed concerns over rapid progress in the field of artificial intelligence. A new survey of researchers from the New York University Centre for Data Science found that more than a third (36%) of respondents that had published recent papers in the field thought that AI could produce catastrophic outcomes in this century, "on the level of all-out nuclear war". Mr Miller said that "the thing to remembers is that we, the humans, are in control.