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Generative AI tools, such as ChatGPT, have gained popularity and have been used for various purposes, including writing papers, generating music, and solving math problems. However, these tools can also produce misinformation and amplify biases. The development of AI systems has a long history, with early programs like ELIZA attempting to simulate conversation. While AI systems like ChatGPT show promise in reasoning, they are not equivalent to human thinking. The field of generative AI is evolving, with researchers exploring applications in fields like medicine and the military. However, there are concerns about the risks associated with these tools, such as cybersecurity and privacy issues. While AI can enhance efficiency, human ingenuity is still necessary to address the challenges that arise. [Extracted from the article]

Dmitry Kurouski, an associate professor of biomedical engineering in the department of Biochemistry and Biophysics at Texas A&M University in College Station, Texas, USA, recently spoke to Spectroscopy about Raman spectroscopy’s role in determining crop yield of key food items as the world population continues to increase. His paper on the subject, “Raman Spectroscopy and Machine Learning for Agricultural Applications: Chemometric Assessment of Spectroscopic Signatures of Plants as the Essential Step Toward Digital Farming,” co-authored by student Charles Farber, appeared in the journal Frontiers in Plant Science in April 2022 (1). The following is transcribed from an online video interview between Spectroscopy and Kurouski in January 2024 with the assistance of artificial intelligence (AI) software, and has been lightly edited for length and clarity. [ABSTRACT FROM AUTHOR]

Apple is focusing on vision AI because it allows for understanding, contextualizing, decision-making, and altering the appearance of what is seen. This technology is already being used in various ways, such as text recognition, location identification, image descriptions, and translation. Apple's recent research paper describes a Multimodal Model for Text and Image Data, which combines text and images to train large language models and achieve strong context-learning capabilities. The company's vision for Generative Visual AI, particularly in the creation of digital replicas of spaces, has significant implications for industries like architecture, design, and health. This highly visual AI deployment surpasses the futuristic visions depicted in movies like Minority Report, making Apple an industry leader in this field. [Extracted from the article]

The article provides information on the 2024 annual meeting concluded by the Association for Research in Vision and Ophthalmology (ARVO) in Seattle, Washington with content and presentations that showed the level of innovation on the field of ophthalmology. Topics include number of posters and paper sessions featured in the event, mission of ARVO, and the meeting's focus on the impact of innovation, technology, and information on vision research.

Machine learning has emerged as a promising paradigm to study the quantum dissipative dynamics of open quantum systems. To facilitate the use of our recently published ML-based approaches for quantum dissipative dynamics, here we present an open-source Python package MLQD (https://github.com/Arif-PhyChem/MLQD), which currently supports the three ML-based quantum dynamics approaches: (1) the recursive dynamics with kernel ridge regression (KRR) method, (2) the non-recursive artificial-intelligence-based quantum dynamics (AIQD) approach and (3) the blazingly fast one-shot trajectory learning (OSTL) approach, where both AIQD and OSTL use the convolutional neural networks (CNN). This paper describes the features of the MLQD package, the technical details, optimization of hyperparameters, visualization of results, and the demonstration of the MLQD 's applicability for two widely studied systems, namely the spin-boson model and the Fenna–Matthews–Olson (FMO) complex. To make MLQD more user-friendly and accessible, we have made it available on the Python Package Index (PyPi) platform and it can be installed via ▪. In addition, it is also available on the XACS cloud computing platform (https://XACScloud.com) via the interface to the MLatom package (http://MLatom.com). Program Title: MLQD CPC Library link to program files: https://doi.org/10.17632/yxp37csy5x.1 Developer's repository link: https://github.com/Arif-PhyChem/MLQD Code Ocean capsule: https://codeocean.com/capsule/5563143/tree Licensing provisions: Apache Software License 2.0 Programming language: Python 3.0 Supplementary material: Jupyter Notebook-based tutorials External routines/libraries: Tensorflow, Scikit-learn, Hyperopt, Matplotlib, MLatom Nature of problem: Fast propagation of quantum dissipative dynamics with machine learning approaches. Solution method: We have developed MLQD as a comprehensive framework that streamlines and supports the implementation of our recently published machine learning-based approaches for efficient propagation of quantum dissipative dynamics. This framework encompasses: (1) the recursive dynamics with kernel ridge regression (KRR) method, as well as the non-recursive approaches utilizing convolutional neural networks (CNN), namely (2) artificial intelligence-based quantum dynamics (AIQD), and (3) one-shot trajectory learning (OSTL). Additional comments including restrictions and unusual features: 1. Users can train a machine learning (ML) model following one of the ML-based approaches: KRR, AIQD and OSTL. 2. Users have the option to propagate dynamics with the existing trained ML models. 3. MLQD also provides the transformation of trajectories into the training data. 4. MLQD also supports hyperparameter optimization using MLATOM's grid search functionality for KRR and Bayesian methods with Tree-structured Parzen Estimator (TPE) for CNN models via the HYPEROPT package. 5. MLQD also facilitates the visualization of results via auto-plotting. 6. MLQD is designed to be user-friendly and easily accessible, with availability on the XACS cloud computing platform (https://XACScloud.com) via the interface to the MLATOM package (http://MLatom.com). In addition, it is also available as a pip package which makes it easy to install. Future outlook: MLQD will be extended to more realistic systems along with the incorporation of other machine learning-based approaches as well as the traditional quantum dynamics methods. [ABSTRACT FROM AUTHOR]

Anil Ananthaswamy's book, "Why Machines Learn: The Elegant Math Behind Modern AI," attempts to explain how artificial intelligence (AI) works by scanning data for patterns and making predictions. However, the book's detailed breakdown of probability, principal component analysis, and eigenvectors may be difficult for readers without an advanced math degree to understand. While there are some interesting examples, such as how statisticians determined the authorship of the Federalist Papers, the book is primarily filled with complex equations and proofs. General readers may find it challenging to follow. [Extracted from the article]

We've called for nationwide laws that set out the obligations businesses have to prevent AI risks, along with the rights consumers have when interacting with AI systems. Once confined to academic papers and science fiction, 2023 seems like the year artificial intelligence (AI) officially moved out of the research labs and into the consumer market. AI-based tools ChatGPT and DALL-E have become household names, and AI promises to deliver both productivity and fun. [Extracted from the article]

The article focuses on Trimble's announcement that its Viewpoint Spectrum and Viewpoint Vista construction enterprise resource planning (ERP) software now includes Automatic Invoicing capability, integrating Azure AI Document Intelligence to automate the processing of paper and PDF invoices, reducing errors, enhancing accuracy, and streamlining financial workflows for construction projects.

Schumer's SAFE effort has a better chance of forcing AI companies to safeguard against misuse of their technology because Congress might, in fact, be able to pass laws that at least are enforceable and penalize those who violate them, according to Litan. White House promises on AI regulation called "vague" and "disappointing": US President Joe Biden's latest move to wrangle AI and keep Americans safe while also allowing innovation are not enforceable, meaning AI companies still have carte blanche to do what they want The "voluntary commitments" from seven leading AI tech companies to help address safety, security, and trust risks associated with their ever-evolving technologies aren't worth the paper they could have been written on, according to tech industry experts. [Extracted from the article]

The UK's Competition and Markets Authority (CMA) has opened an initial review into the market for artificial intelligence systems, looking at the underlying foundational large language models that power chatbots such as ChatGPT alongside the opportunities and risks that AI could present. The CMA added that the review is in line with the UK government's aim to support "open, competitive markets", as outlined in a white paper published in March. [Extracted from the article]

"As a result, the absence of an AI framework hasthe likely potential to create a more unequal and unjustsociety, enhancing the market and power asymmetries betweenthose who dominate digital tools and those who are impactedby AI technology." News In its white paper outlining its policy on AIregulation, the UK government has called for "responsibleuse" while avoiding the introduction of what it callsinnovation-stifling "heavy-handed legislation.". [Extracted from the article]

The article reports that National Oceanic and Atmospheric Administration and other government agencies are using the Saildrone design for lengthy marine data gathering missions, and one became the first uncrewed vessel to successfully circumnavigate Antarctica. It mentions that Solar panels power scientific instruments and equipment with vessels are monitored on their voyages via satellite, but they operate independently through the course of their travels.