"Google fires engineer who contended its AI technology was sentient." A new discovery (or debacle) is reported practically every week, sometimes exaggerated, sometimes not. Policymakers struggle to know what to make of AI and it's hard for the lay reader to sort through all the headlines, much less to know what to be believe. Here are four things every reader should know. First, AI is real and here to stay.

It started with a single tweet in November 2019. David Heinemeier Hansson, a high-profile tech entrepreneur, lashed out at Apple's newly launched credit card, calling it "sexist" for offering his wife a credit limit 20 times lower than his own. The allegations spread like wildfire, with Hansson stressing that artificial intelligence – now widely used to make lending decisions – was to blame. "It does not matter what the intent of individual Apple reps are, it matters what THE ALGORITHM they've placed their complete faith in does. And what it does is discriminate. While Apple and its underwriters Goldman Sachs were ultimately cleared by US regulators of violating fair lending rules last year, it rekindled a wider debate around AI use across public and private industries. Politicians in the European Union are now planning to introduce the first comprehensive global template for regulating AI, as institutions increasingly automate routine tasks in an attempt to boost efficiency and ...

There will be more than 1,000 elections in the United States in 2022 at the state and higher level. And as of June 30, 2022, six fundraising committees associated with the Democratic and Republican parties have reported raising a combined $1.3 billion. Raising and spending that money effectively for campaigns is where specialist firms like Sterling Data Company enter the game. Sterling is a national Democratic political data firm focused on fundraising. Whatever your political preferences, Sterling's use of artificial intelligence is instructive for pretty much any organization looking to gain competitive advantage.

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Ryan Steelberg is President of Veritone. The widespread discussion of artificial intelligence (AI) in nearly every industry did not happen overnight. It has been a long time coming as tangible use cases have validated the importance of the technology for both commercial enterprises and government organizations. As adoption continues to rise, questions surrounding the ethical boundaries of the technology will only increase. With the advent of the metaverse, which utilizes AI-based technology such as synthetic media to drive immersive engagements in digital, decentralized worlds, ethical use will become an important point of concern for both brands and users.

Molecular dynamics simulations are an invaluable tool in numerous scientific fields. However, the ubiquitous classical force fields cannot describe reactive systems, and quantum molecular dynamics are too computationally demanding to treat large systems or long timescales. Reactive force fields based on physics or machine learning can be used to bridge the gap in time and length scales, but these force fields require substantial effort to construct and are highly specific to a given chemical composition and application. A significant limitation of machine learning models is the use of element-specific features, leading to models that scale poorly with the number of elements. This work introduces the Gaussian multipole (GMP) featurization scheme that utilizes physically-relevant multipole expansions of the electron density around atoms to yield feature vectors that interpolate between element types and have a fixed dimension regardless of the number of elements present. We combine GMP with neural networks to directly compare it to the widely used Behler-Parinello symmetry functions for the MD17 dataset, revealing that it exhibits improved accuracy and computational efficiency. Further, we demonstrate that GMP-based models can achieve chemical accuracy for the QM9 dataset, and their accuracy remains reasonable even when extrapolating to new elements. Finally, we test GMP-based models for the Open Catalysis Project (OCP) dataset, revealing comparable performance to graph convolutional deep learning models. The results indicate that this featurization scheme fills a critical gap in the construction of efficient and transferable machine-learned force fields.

Today\'s world of space\'s primary concern is the uncontrolled growth of space debris and its probability of collision with spacecraft, particularly in the low earth orbit (LEO) regions. This paper is aimed to design an optimized micro-propulsion system, Cold Gas Thruster, to de-orbit the PSLV debris from 668km to 250 km height after capturing process. The propulsion system mainly consists of a storage tank, pipes, control valves, and a convergent-divergent nozzle. The paper gives an idea of the design of each component based on a continuous iterative process until the design thrust requirements are met. All the components are designed in the CATIA V5, and the structural analysis is done in the ANSYS tool for each component where our cylinder tank can withstand the high hoop stress generated on its wall of it. And flow analysis is done by using the K-$\epsilon$ turbulence model for the CD nozzle, which provides the required thrust to de-orbit PSLV from a higher orbit to a lower orbit, after which the air drag will be enough to bring back to earth\'s atmosphere and burn it. Hohmann\'s orbit transfer method has been used to de-orbit the PSLV space debris, and it has been simulated by STK tools. And the result shows that our optimized designed thruster generates enough thrust to de-orbit the PSLV debris to a very low orbit.

Prior work on language models (LMs) shows that training on a large number of diverse tasks improves few-shot learning (FSL) performance on new tasks. We take this to the extreme, automatically extracting 413,299 tasks from internet tables - orders of magnitude more than the next-largest public datasets. Finetuning on the resulting dataset leads to improved FSL performance on Natural Language Processing (NLP) tasks, but not proportionally to dataset scale. In fact, we find that narrow subsets of our dataset sometimes outperform more diverse datasets. For example, finetuning on software documentation from support.google.com raises FSL performance by a mean of +7.5% on 52 downstream tasks, which beats training on 40 human-curated NLP datasets (+6.7%). Finetuning on various narrow datasets leads to similar broad improvements across test tasks, suggesting that the gains are not from domain adaptation but adapting to FSL in general. We do not observe clear patterns between the datasets that lead to FSL gains, leaving open questions about why certain data helps with FSL.

Neural network-based embeddings have been the mainstream approach for creating a vector representation of the text to capture lexical and semantic similarities and dissimilarities. In general, existing encoding methods dismiss the punctuation as insignificant information; consequently, they are routinely treated as a predefined token/word or eliminated in the pre-processing phase. However, punctuation could play a significant role in the semantics of the sentences, as in "Let's eat\hl{,} grandma" and "Let's eat grandma". We hypothesize that a punctuation-aware representation model would affect the performance of the downstream tasks. Thereby, we propose a model-agnostic method that incorporates both syntactic and contextual information to improve the performance of the sentiment classification task. We corroborate our findings by conducting experiments on publicly available datasets and provide case studies that our model generates representations with respect to the punctuation in the sentence.

The Patent Act requires an "inventor" to be a natural person, the US Court of Appeals for the Federal Circuit said, rejecting computer scientist Stephen Thaler's bid for patents on two inventions he said his DABUS system created. Thaler said in an email Friday that DABUS, which stands for "Device for the Autonomous Bootstrapping of Unified Sentience," is "natural and sentient." His attorney Ryan Abbott of Brown Neri Smith & Khan said the decision "ignores the purpose of the Patent Act" and has "real negative social consequences." He said they plan to appeal. The US Patent and Trademark Office declined to comment on the decision.