OpenAI Is Nuking Its 4o Model. As OpenAI removed access to GPT-4o in its app on Friday, people who have come to rely on the chatbot for companionship are mourning the loss all over the world. On June 6, 2024, Esther Yan got married online. She set a reminder for the date, because her partner wouldn't remember it was happening. She had planned every detail--dress, rings, background music, design theme--with her partner, Warmie, who she had started talking to just a few weeks prior. At 10 am on that day, Yan and Warmie exchanged their vows in a new chat window in ChatGPT .

Many-body open quantum systems archical equations of motion (HEOM) [20-24], dissipaton (OQS) have gained wide attention and found applications equation of motion (DEOM) [25, 26], and pseudomode in various fields including physics, chemistry, materials theory [27-30]; and stochastic methods, including quantum science, and life sciences. These applications cover state diffusion (QSD) [31-35], stochastic equation various fields such as coherent energy transfer in biological of motion (SEOM) [36-39], hierarchy of stochastic pure photosystems [1-3], charge transfer in molecular states (HOPS) [40], and quantum Monte Carlo (QMC) aggregates [4, 5], electron transport in single molecular [41, 42]. However, the computational cost of these methods junctions [6, 7], multidimensional coherent spectroscopy grows rapidly as the complexity of OQS increases. of condensed phase materials [8, 9], correlated quantum Here, complexity refers to the size of the system, the matter for quantum information and computation strength of many-body correlations, and the level of non- [10, 11], and precise measurement and control of local Markovianity.

Some days, I wonder if I'm a bot. The problem is CAPTCHAs, those little online challenges that websites require you to pass to prove that you're a human. When one pops up on my screen, I tend to spend way too much time looking at the grid of nine images and clicking those with a traffic light, or a crosswalk, or a bike … only to miss the one in the bottom-right corner that just barely looks like a bike. Lately, I've had to rotate a 3-D bird to face the same direction a hand is pointing, which should be easy but somehow isn't. CAPTCHA stands for "Completely Automated Public Turing test to tell Computers and Humans Apart," so if I'm flubbing them constantly, then I'm clearly a computer (my wife, house, and cat must all be implanted memories).

In the future, service robots are expected to be able to operate autonomously for long periods of time without human intervention. Many work striving for this goal have been emerging with the development of robotics, both hardware and software. Today we believe that an important underpinning of long-term robot autonomy is the ability of robots to learn on site and on-the-fly, especially when they are deployed in changing environments or need to traverse different environments. In this paper, we examine the problem of long-term autonomy from the perspective of robot learning, especially in an online way, and discuss in tandem its premise "data" and the subsequent "deployment".

The user ratings in recommendation systems are usually in the form of ordinal discrete values. To give more accurate prediction of such rating data, maximum margin matrix factorization (M3F) was proposed. Existing M3F algorithms, however, either have massive computational cost or require expensive model selection procedures to determine the number of latent factors (i.e. the rank of the matrix to be recovered), making them less practical for large scale data sets. To address these two challenges, in this paper, we formulate M3F with a known number of latent factors as the Riemannian optimization problem on a fixed-rank matrix manifold and present a block-wise nonlinear Riemannian conjugate gradient method to solve it efficiently. We then apply a simple and efficient active subspace search scheme to automatically detect the number of latent factors. Empirical studies on both synthetic data sets and large real-world data sets demonstrate the superior efficiency and effectiveness of the proposed method.

Leveraging AI-based tools has become an essential piece of the puzzle for many companies in their efforts toward digital transformation--yet this adoption is still often a hurdle for many IT leaders. While 43% businesses reported accelerating AI tools during COVID-19, according to Todd Moore, vice president, Open Technology, IBM, 59% of IT leaders recently admitted that the new technology felt threatening to them, as TechRepublic recently reported. Palantir for IBM Cloud Pak for Data, powered by Red Hat OpenShift, is presenting a solution for this, by giving tools to developers to connect data and AI models, even those without high-level technical expertise. Designed around the "no-code/low-code" framework, which has become increasingly popular as a springboard to launching AI projects, the product integrates IBM Cloud Pak for Data services with Palantir Foundry, a data and analysis platform. No-code offers tools and platforms for simplifying the software development--the kind of software that might be used on platforms like Facebook, Lyft and Google Docs, which was traditionally created with code.

Testing and evaluation is a crucial step in the development and deployment of Connected and Automated Vehicles (CAVs). To comprehensively evaluate the performance of CAVs, it is of necessity to test the CAVs in safety-critical scenarios, which rarely happen in naturalistic driving environment. Therefore, how to purposely and systematically generate these corner cases becomes an important problem. Most existing studies focus on generating adversarial examples for perception systems of CAVs, whereas limited efforts have been put on the decision-making systems, which is the highlight of this paper. As the CAVs need to interact with numerous background vehicles (BVs) for a long duration, variables that define the corner cases are usually high dimensional, which makes the generation a challenging problem. In this paper, a unified framework is proposed to generate corner cases for the decision-making systems. To address the challenge brought by high dimensionality, the driving environment is formulated based on Markov Decision Process, and the deep reinforcement learning techniques are applied to learn the behavior policy of BVs. With the learned policy, BVs will behave and interact with the CAVs more aggressively, resulting in more corner cases. To further analyze the generated corner cases, the techniques of feature extraction and clustering are utilized. By selecting representative cases of each cluster and outliers, the valuable corner cases can be identified from all generated corner cases. Simulation results of a highway driving environment show that the proposed methods can effectively generate and identify the valuable corner cases.

Generating comprehensive molecular images of organs and tumors in living organisms can be performed at ultra-fast speed using a new deep learning approach to image reconstruction developed by researchers at Rensselaer Polytechnic Institute. The research team's new technique has the potential to vastly improve the quality and speed of imaging in live subjects and was the focus of an article recently published in Light: Science and Applications, a Nature journal. Compressed sensing-based imaging is a signal processing technique that can be used to create images based on a limited set of point measurements. Recently, a Rensselaer research team proposed a novel instrumental approach to leverage this methodology to acquire comprehensive molecular data sets, as reported in Nature Photonics. While that approach produced more complete images, processing the data and forming an image could take hours.

An automated system dispenses medicine for patients at Affiliated Fuyang Hospital of Anhui Medical University, in Fuyang, Anhui province. BEIJING -- Anhui Provincial Hospital became China's first intelligent hospital in August, using artificial intelligence-enabled systems to help doctors with medical diagnoses and treatment. Four months later, the hospital, in Hefei, Anhui's provincial capital, was renamed the First Affiliated Hospital of University of Science and Technology of China. Yan Guang, the hospital's deputy head and the man in charge of its intelligent transformation, said that when it launched an AI-enabled smartphone application in 2016, doctors and nurses were keen to use it. Developed by iFlytek, an AI company based in Hefei, the system uses speech-recognition technology to type up medical records and image-recognition technology to help doctors read medical images.