The big picture: A US judge ruled this week that an artificial intelligence cannot be listed as the inventor of a patent. This ruling is the latest on an issue that has come before judges in multiple countries. A court in Alexandria, Virginia, ruled that inventions can only be patented under the name of a "natural person." The decision was made against someone who tried to list two designs under the name of an AI as part of a broader project to gain worldwide recognition of AI-powered inventions. Imagination Engines, Inc. CEO Stephen Thaler built an AI called DEBUS, which independently designed a new kind of drink holder and flashing light (used to get someone's attention). The name "DEBUS," along with "Invention generated by artificial intelligence," was used in the attempted patent filing for the inventions.

AI 2041: Ten Visions for Our Future is an unusual book. Each chapter consists of a short story, penned by science fiction writer Chen Qiufan, and a related analysis piece from Kai-Fu Lee, CEO of Sinovation Ventures and author of the nonfiction bestseller AI Superpowers. Chen, who also is founder of Thema Mundi, a content development studio, spoke with Fast Company on the eve of the release of AI 2041 about his collaboration with Lee, his own experiences with artificial intelligence, and what machine learning will mean for artists and writers. This interview was edited for length and clarity. Fast Company: How did this project come about?

Ultrasonic methods have great potential applications to detect and characterize defects in multi-layered bonded composites. However, it remains challenging to quantitatively reconstruct defects, such as disbonds and kissing bonds, that influence the integrity of adhesive bonds and seriously reduce the strength of assemblies. In this work, an ultrasonic method based on the supervised fully convolutional network (FCN) is proposed to quantitatively reconstruct defects hidden in multi-layered bonded composites. In the training process of this method, an FCN establishes a non-linear mapping from measured ultrasonic data to the corresponding velocity models of multi-layered bonded composites. In the predicting process, the trained network obtained from the training process is used to directly reconstruct the velocity models from the new measured ultrasonic data of adhesively bonded composites. The presented FCN-based inversion method can automatically extract useful features in multi-layered composites. Although this method is computationally expensive in the training process, the prediction itself in the online phase takes only seconds. The numerical results show that the FCN-based ultrasonic inversion method is capable to accurately reconstruct ultrasonic velocity models of the high contrast defects, which has great potential for online detection of adhesively bonded composites.

Sequential modelling entails making sense of sequential data, which naturally occurs in a wide array of domains. One example is systems that interact with users, log user actions and behaviour, and make recommendations of items of potential interest to users on the basis of their previous interactions. In such cases, the sequential order of user interactions is often indicative of what the user is interested in next. Similarly, for systems that automatically infer the semantics of text, capturing the sequential order of words in a sentence is essential, as even a slight re-ordering could significantly alter its original meaning. This thesis makes methodological contributions and new investigations of sequential modelling for the specific application areas of systems that recommend music tracks to listeners and systems that process text semantics in order to automatically fact-check claims, or "speed read" text for efficient further classification.

Developers of text generation models rely on automated evaluation metrics as a stand-in for slow and expensive manual evaluations. However, image captioning metrics have struggled to give accurate learned estimates of the semantic and pragmatic success of output text. We address this weakness by introducing the first discourse-aware learned generation metric for evaluating image descriptions. Our approach is inspired by computational theories of discourse for capturing information goals using coherence. We present a dataset of image$\unicode{x2013}$description pairs annotated with coherence relations. We then train a coherence-aware metric on a subset of the Conceptual Captions dataset and measure its effectiveness$\unicode{x2014}$its ability to predict human ratings of output captions$\unicode{x2014}$on a test set composed of out-of-domain images. We demonstrate a higher Kendall Correlation Coefficient for our proposed metric with the human judgments for the results of a number of state-of-the-art coherence-aware caption generation models when compared to several other metrics including recently proposed learned metrics such as BLEURT and BERTScore.

A multi-turn dialogue always follows a specific topic thread, and topic shift at the discourse level occurs naturally as the conversation progresses, necessitating the model's ability to capture different topics and generate topic-aware responses. Previous research has either predicted the topic first and then generated the relevant response, or simply applied the attention mechanism to all topics, ignoring the joint distribution of the topic prediction and response generation models and resulting in uncontrollable and unrelated responses. In this paper, we propose a joint framework with a topic refinement mechanism to learn these two tasks simultaneously. Specifically, we design a three-pass iteration mechanism to generate coarse response first, then predict corresponding topics, and finally generate refined response conditioned on predicted topics. Moreover, we utilize GPT2DoubleHeads and BERT for the topic prediction task respectively, aiming to investigate the effects of joint learning and the understanding ability of GPT model. Experimental results demonstrate that our proposed framework achieves new state-of-the-art performance at response generation task and the great potential understanding capability of GPT model.

Traffic flow forecasting is a crucial task in urban computing. The challenge arises as traffic flows often exhibit intrinsic and latent spatio-temporal correlations that cannot be identified by extracting the spatial and temporal patterns of traffic data separately. We argue that such correlations are universal and play a pivotal role in traffic flow. We put forward spacetime interval learning as a paradigm to explicitly capture these correlations through a unified analysis of both spatial and temporal features. Unlike the state-of-the-art methods, which are restricted to a particular road network, we model the universal spatio-temporal correlations that are transferable from cities to cities. To this end, we propose a new spacetime interval learning framework that constructs a local-spacetime context of a traffic sensor comprising the data from its neighbors within close time points. Based on this idea, we introduce spacetime neural network (STNN), which employs novel spacetime convolution and attention mechanism to learn the universal spatio-temporal correlations. The proposed STNN captures local traffic patterns, which does not depend on a specific network structure. As a result, a trained STNN model can be applied on any unseen traffic networks. We evaluate the proposed STNN on two public real-world traffic datasets and a simulated dataset on dynamic networks. The experiment results show that STNN not only improves prediction accuracy by 15% over state-of-the-art methods, but is also effective in handling the case when the traffic network undergoes dynamic changes as well as the superior generalization capability.

The Transform Technology Summits start October 13th with Low-Code/No Code: Enabling Enterprise Agility. Women in the AI field are making research breakthroughs, launching exciting companies, spearheading vital ethical discussions, and inspiring the next generation of AI professionals. And that's why we created the VentureBeat Women in AI Awards -- to emphasize the importance of their voices, work, and experiences, and to shine a light on some of these leaders. We first announced the six winners at Transform 2021 in July, and ever since, we've been catching up with each of them for deeper discussions around their work and emerging challenges in the field. Our conversations have touched on everything from regulation and dealing with messy real world data to how to approach AI more responsibly.

While TV shows and movies adapted from games remain, generally, rubbish, there is no such curse the other way round. This seminal James Bond tie-in is the best example, showing that first-person shooters – previously the esoteric concern of hefty PCs – could excel on consoles. Its four-player split screen also taught an entire generation how to swear wholeheartedly at their peers. And to settle it once and for all: Oddjob is too small. Therefore playing as him is definitely – definitely – cheating.

Striking a blow to patent applicants seeking to assert inventorship by artificial intelligence ("AI") systems, the U.S. District Court for the Eastern District of Virginia ruled on September 3, 2021 that an AI machine cannot qualify as an "inventor" under the Patent Act. The fight is now expected to move to the Federal Circuit on appeal. Proskauer has been closely monitoring the quickly-developing legal treatment of AI systems, especially in view of their implications for life sciences patents. AI's presence in life sciences innovation is well established, for example, to predict biological targets of prospective drug molecules and to identify drug design candidates (among many other applications). As we reported in August, two countries--Australia and South Africa--have already permitted AI systems to qualify as "inventors" in patent applications. However, hope for a worldwide trend have been dashed, at least for now.