This report synthesizes the outcomes of a recent interdisciplinary workshop that brought together leading experts in cognitive psychology, language learning, and artificial intelligence (AI)-based natural language processing (NLP). The workshop, funded by the National Science Foundation, aimed to address a critical knowledge gap in our understanding of the relationship between AI language models and human cognitive processes in text comprehension and composition. Through collaborative dialogue across cognitive, linguistic, and technological perspectives, workshop participants examined the underlying processes involved when humans produce and comprehend text, and how AI can both inform our understanding of these processes and augment human capabilities. The workshop revealed emerging patterns in the relationship between large language models (LLMs) and human cognition, with highlights on both the capabilities of LLMs and their limitations in fully replicating human-like language understanding and generation. Key findings include the potential of LLMs to offer insights into human language processing, the increasing alignment between LLM behavior and human language processing when models are fine-tuned with human feedback, and the opportunities and challenges presented by human-AI collaboration in language tasks. By synthesizing these findings, this report aims to guide future research, development, and implementation of LLMs in cognitive psychology, linguistics, and education. It emphasizes the importance of ethical considerations and responsible use of AI technologies while striving to enhance human capabilities in text comprehension and production through effective human-AI collaboration.

Lego has a long history in the video games sector between licensed titles that feature digital brick versions of iconic movie characters and physical sets like the new Mario Kart one. But after decades of third-party studios making games with the Lego name on them, the company is taking more of a hands-on approach. "We can definitely say as long as we're under the Lego brand we can cover experiences for kids of all ages, digital or physical, Lego CEO Niels Christiansen told the Financial Times. To that end, an in-house game development division "is something we're building up." Per the publication, Lego plowed hundreds of millions of dollars into tripling its number of software developers to more than 1,800. "We have made quite a few investments in the future -- I'd almost rather overinvest.

Recent advances in large language models using deep learning techniques have renewed interest on how languages can be learned from data. However, it is unclear whether or how these models represent grammatical information from the learned languages. In addition, the models must be pre-trained on large corpora before they can be used. In this work, we propose an alternative, more transparent and cognitively plausible architecture for learning language. Instead of using deep learning, our approach uses a minimal cognitive architecture based on sequence memory and chunking. The learning mechanism is based on the principles of reinforcement learning. We test our architecture on a number of natural-like toy languages. Results show that the model can learn these artificial languages from scratch and extract grammatical information that supports learning. Our study demonstrates the power of this simple architecture and stresses the importance of sequence memory as a key component of the language learning process. Since other animals do not seem to have a faithful sequence memory, this may explain why only humans have developed complex languages.

Long-range interactions are essential for the correct description of complex systems in many scientific fields. The price to pay for including them in the calculations, however, is a dramatic increase in the overall computational costs. Recently, deep graph networks have been employed as efficient, data-driven surrogate models for predicting properties of complex systems represented as graphs. These models rely on a local and iterative message passing strategy that should, in principle, capture long-range information without explicitly modeling the corresponding interactions. In practice, most deep graph networks cannot really model long-range dependencies due to the intrinsic limitations of (synchronous) message passing, namely oversmoothing, oversquashing, and underreaching. This work proposes a general framework that learns to mitigate these limitations: within a variational inference framework, we endow message passing architectures with the ability to freely adapt their depth and filter messages along the way. With theoretical and empirical arguments, we show that this simple strategy better captures long-range interactions, by surpassing the state of the art on five node and graph prediction datasets suited for this problem. Our approach consistently improves the performances of the baselines tested on these tasks. We complement the exposition with qualitative analyses and ablations to get a deeper understanding of the framework's inner workings.

Education technology company Udemy has been selling language learning modules made with ChatGPT to help language teachers design their courses. Duolingo, the popular online language learning company, is relying on AI technology to power its Duolingo English Test (DET), an English proficiency exam available online, on demand. The test utilizes ChatGPT to generate text passages for reading comprehension and AI for supporting human proctors in spotting suspicious test-taking behavior. It is also working with teachers to generate lesson content and speed up the process and scale of adding advanced materials to the platform. "Since not everyone in the world has equal access to great teachers and favorable learning conditions, AI gives us the best chance to scale quality education to everyone who needs it," said Klinton Bicknell, Duolingo's head of AI.

Microsoft is detailing how it handles bugs in its software and services using machine learning models. "47,000 developers generate nearly 30,000 bugs a month," explains Scott Christiansen, a senior security program manager at Microsoft. The software maker tracks these bugs across GitHub and AzureDevOps repositories, but it's a lot of issues to track with just traditional labeling and prioritization. Microsoft is now using nearly 20 years of historical data across 13 million work items and bugs to create a machine-learning model that can separate security and non-security bugs 99 percent of the time. It's a model that's designed to help developers accurately identify and prioritize critical security issues that need fixing.

Data science tools now automate various pieces of the analytics process, from data preparation to model selection. And automation will only broaden the future scope of data science. According to most analytics and artificial intelligence experts, trends like augmented analytics will only increase the efficiency and reach of data science within the enterprise. Even with accelerating analytics automation, data scientists will be sitting pretty with job security for a long time. "I think what is happening with AI and a lot of these technologies is they are making our jobs easier," said data science expert Usama Fayyad, co-founder of the Initiative for Analytics and Data Science Standards.

Deep learning constitutes a recent, modern technique for image processing and data analysis, with promising results and large potential. As deep learning has been successfully applied in various domains, it has recently entered also the domain of agriculture. In this paper, we perform a survey of 40 research efforts that employ deep learning techniques, applied to various agricultural and food production challenges. We examine the particular agricultural problems under study, the specific models and frameworks employed, the sources, nature and pre-processing of data used, and the overall performance achieved according to the metrics used at each work under study. Moreover, we study comparisons of deep learning with other existing popular techniques, in respect to differences in classification or regression performance. Our findings indicate that deep learning provides high accuracy, outperforming existing commonly used image processing techniques.

For the first time in Australia, a Melbourne surgeon used robot technology to remove an inoperable tumor from a six-year-old girl's head, reports said Tuesday. The successful operation was recently performed on Freyja Christiansen from Canberra at the Epworth Hospital in Richmond. The six-year-old was diagnosed with a rare sarcoma near the base of her skull in December 2016, along with other tumors in her head and neck. Due to the location of the child's tumor -- between a main artery and the base of her skull -- several specialists refused to operate on her. Due to this, she underwent immunotherapy since last year, which helped shrink the tumors.

Manufacturing operations depend on getting the right information at precisely the right moment, ensuring that products get built on time, to quality specs. With the latest enterprise resource management (ERP) software, this critical data flow is often coming via the cloud, as more manufacturers become comfortable with it as a repository for key manufacturing information. With ERP software delivered via the cloud, Big Data is also more easily leveraged for Industrial Internet of Things (IIoT) applications. In this application, advanced analytics do the data crunching required for processing the flow of data, including operational metrics and inventory information. Along with offering more mobile apps that funnel factory data directly to users' fingertips, many ERP software developers are also testing newer technologies like artificial intelligence, augmented reality/virtual reality (AR/VR) capabilities, and machine learning and advanced analytics that can handle the Big Data inherent with many IIoT/IoT manufacturing data scenarios.