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General General

Sparks of Artificial General Intelligence: Early experiments with GPT-4

ArXiv Preprint

Artificial intelligence (AI) researchers have been developing and refining large language models (LLMs) that exhibit remarkable capabilities across a variety of domains and tasks, challenging our understanding of learning and cognition. The latest model developed by OpenAI, GPT-4, was trained using an unprecedented scale of compute and data. In this paper, we report on our investigation of an early version of GPT-4, when it was still in active development by OpenAI. We contend that (this early version of) GPT-4 is part of a new cohort of LLMs (along with ChatGPT and Google's PaLM for example) that exhibit more general intelligence than previous AI models. We discuss the rising capabilities and implications of these models. We demonstrate that, beyond its mastery of language, GPT-4 can solve novel and difficult tasks that span mathematics, coding, vision, medicine, law, psychology and more, without needing any special prompting. Moreover, in all of these tasks, GPT-4's performance is strikingly close to human-level performance, and often vastly surpasses prior models such as ChatGPT. Given the breadth and depth of GPT-4's capabilities, we believe that it could reasonably be viewed as an early (yet still incomplete) version of an artificial general intelligence (AGI) system. In our exploration of GPT-4, we put special emphasis on discovering its limitations, and we discuss the challenges ahead for advancing towards deeper and more comprehensive versions of AGI, including the possible need for pursuing a new paradigm that moves beyond next-word prediction. We conclude with reflections on societal influences of the recent technological leap and future research directions.

Sébastien Bubeck, Varun Chandrasekaran, Ronen Eldan, Johannes Gehrke, Eric Horvitz, Ece Kamar, Peter Lee, Yin Tat Lee, Yuanzhi Li, Scott Lundberg, Harsha Nori, Hamid Palangi, Marco Tulio Ribeiro, Yi Zhang

2023-03-22

General General

Understanding Social Robots: Attribution of Intentional Agency to Artificial and Biological Bodies.

In Artificial life

Much research in robotic artificial intelligence (AI) and Artificial Life has focused on autonomous agents as an embodied and situated approach to AI. Such systems are commonly viewed as overcoming many of the philosophical problems associated with traditional computationalist AI and cognitive science, such as the grounding problem (Harnad) or the lack of intentionality (Searle), because they have the physical and sensorimotor grounding that traditional AI was argued to lack. Robot lawn mowers and self-driving cars, for example, more or less reliably avoid obstacles, approach charging stations, and so on-and therefore might be considered to have some form of artificial intentionality or intentional directedness. It should be noted, though, that the fact that robots share physical environments with people does not necessarily mean that they are situated in the same perceptual and social world as humans. For people encountering socially interactive systems, such as social robots or automated vehicles, this poses the nontrivial challenge to interpret them as intentional agents to understand and anticipate their behavior but also to keep in mind that the intentionality of artificial bodies is fundamentally different from their natural counterparts. This requires, on one hand, a "suspension of disbelief " but, on the other hand, also a capacity for the "suspension of belief." This dual nature of (attributed) artificial intentionality has been addressed only rather superficially in embodied AI and social robotics research. It is therefore argued that Bourgine and Varela's notion of Artificial Life as the practice of autonomous systems needs to be complemented with a practice of socially interactive autonomous systems, guided by a better understanding of the differences between artificial and biological bodies and their implications in the context of social interactions between people and technology.

Ziemke Tom

2023-Mar-16

Attribution, embodiment, grounding, human–robot interaction, intentionality, observer’s grounding problem, social robots

General General

Artificial intelligence based virtual screening study for competitive and allosteric inhibitors of the SARS-CoV-2 main protease.

In Journal of biomolecular structure & dynamics

SARS-CoV-2 is a highly contagious and dangerous coronavirus that first appeared in late 2019 causing COVID-19, a pandemic of acute respiratory illnesses that is still a threat to health and the general public safety. We performed deep docking studies of 800 M unique compounds in both the active and allosteric sites of the SARS-COV-2 Main Protease (Mpro) and the 15 M and 13 M virtual hits obtained were further taken for conventional docking and molecular dynamic (MD) studies. The best XP Glide docking scores obtained were -14.242 and -12.059 kcal/mol by CHEMBL591669 and the highest binding affinities were -10.5 kcal/mol (from 444215) and -11.2 kcal/mol (from NPC95421) for active and allosteric sites, respectively. Some hits can bind both sites making them a great area of concern. Re-docking of 8 random allosteric complexes in the active site shows a significant reduction in docking scores with a t-test P value of 2.532 × 10-11 at 95% confidence. Some specific interactions have higher elevations in docking scores. MD studies on 15 complexes show that single-ligand systems are stable as compared to double-ligand systems, and the allosteric binders identified are shown to modulate the active site binding as evidenced by the changes in the interaction patterns and stability of ligands and active site residues. When an allosteric complex was docked to the second monomer to check for homodimer formation, the validated homodimer could not be re-established, further supporting the potential of the identified allosteric binders. These findings could be important in developing novel therapeutics against SARS-CoV-2.Communicated by Ramaswamy H. Sarma.

Charles Ssemuyiga, Edgar Mulumba Pius, Mahapatra Rajani Kanta

2023-Mar-21

Artificial intelligence, COVID-19, SARS-CoV-2 main protease, deep docking, molecular docking, molecular dynamics simulation, neural networks

Surgery Surgery

CranioRate TM: An Image-Based, Deep-Phenotyping Analysis Toolset and Online Clinician Interface for Metopic Craniosynostosis.

In Plastic and reconstructive surgery ; h5-index 62.0

INTRODUCTION : The diagnosis and management of metopic craniosynostosis involves subjective decision-making at the point of care. The purpose of this work is to describe a quantitative severity metric and point-of-care user interface to aid clinicians in the management of metopic craniosynostosis and to provide a platform for future research through deep phenotyping.

METHODS : Two machine-learning algorithms were developed that quantify the severity of craniosynostosis - a supervised model specific to metopic craniosynostosis (Metopic Severity Score) and an unsupervised model used for cranial morphology in general (Cranial Morphology Deviation). CT imaging from multiple institutions were compiled to establish the spectrum of severity and a point-of-care tool was developed and validated.

RESULTS : Over the study period (2019-2021), 254 patients with metopic craniosynostosis and 92 control patients who underwent CT scan between the ages of 6 and 18 months were included. Scans were processed using an unsupervised machine-learning based dysmorphology quantification tool, CranioRate TM. The average Metopic severity score (MSS) for normal controls was 0.0±1.0 and for metopic synostosis was 4.9±2.3 (p<0.001). The average Cranial Morphology Deviation (CMD) for normal controls was 85.2±19.2 and for metopic synostosis was 189.9±43.4 (p<0.001). A point-of-care user interface (craniorate.org) has processed 46 CT images from 10 institutions.

CONCLUSION : The resulting quantification of severity using MSS and CMD has shown an improved capacity, relative to conventional measures, to automatically classify normal controls versus patients with metopic synostosis. We have mathematically described, in an objective and quantifiable manner, the distribution of phenotypes in metopic craniosynostosis.

Beiriger Justin W, Tao Wenzheng, Bruce Madeleine K, Anstadt Erin, Christensen Cameron, Smetona John, Whitaker Ross, Goldstein Jesse

2023-Mar-22

General General

The Role of MicroRNAs in Predicting the Neurological Outcome of Patients with Subarachnoid Hemorrhage: A Meta-analysis.

In Cellular and molecular neurobiology

Subarachnoid hemorrhage (SAH) is a hemorrhagic cerebrovascular disease with an extremely poor prognosis. The molecular mechanism and biomarkers involved in neurological outcome after SAH still need to be explored. This study assessed the microRNA expression characteristics of SAH patients with different neurological outcomes by meta-analysis. Public databases were searched from database inception until December 2022. The study reported that microRNA expression data in SAH patients with different neurological outcomes were included in the analysis. The differential expression of miRNAs was evaluated by meta-analysis. Overrepresentation analysis was performed for the targeted genes of significant miRNAs. The XGBoost algorithm was used to assess the predictive ability for neurological outcomes with clinical characteristics and significantly expressed miRNAs. Seven studies were finally included in the meta-analysis. The results showed that the levels of miR-152-3p (SMD: - 0.230; 95% CI - 0.389, - 0.070; padj = 0.041), miR-221-3p (SMD: - 0.286; 95% CI - 0.446, - 0.127; padj = 0.007), and miR-34a-5p (SMD: - 0.227; 95% CI - 0.386, - 0.067; padj = 0.041) were significantly lower in SAH patients with good neurological outcomes than in those with poor neurological outcomes. The PI3K-AKT signaling pathway may have an important role in neurological recovery after SAH. Based on the XGBoost algorithm, the neurological outcome could be accurately predicted with clinical characteristics plus the three miRNAs. The expression levels of miR-152-3p, miR-221-3p, and miR-34a-5p were significantly lower in patients with good neurological outcomes than in those with poor outcomes. These miRNAs can serve as potential predictive biomarkers for neurological outcomes. The molecular mechanism and biomarkers involved in neurological outcome after SAH still need to be explored. Our study analyzed microRNA expression characteristics of SAH patients with different neurological outcomes by meta-analysis. After analyze studies reporting the microRNA expression data in SAH patients with different neurological outcomes, results show that the levels of miR-152-3p, miR-221-3p, and miR-34a-5p were significantly lower in SAH patients with good neurological outcomes than in those with poor neurological outcomes. The PI3K-AKT signaling pathway may have an important role in neurological recovery after SAH. Based on the XGBoost algorithm, the neurological outcome could be accurately predicted with clinical characteristics plus the three miRNAs.

Li Jian, Liu Wei, Anniwaer Ankaerjiang, Li Bo, Chen Yutang, Yu Zhaoxia, Yu Xiangyou

2023-Mar-21

Machine learning, Meta analysis, Subarachnoid hemorrhage, microRNAs

Surgery Surgery

Deep learning-based high-accuracy detection for lumbar and cervical degenerative disease on T2-weighted MR images.

In European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society

PURPOSE : To develop and validate a deep learning (DL) model for detecting lumbar degenerative disease in both sagittal and axial views of T2-weighted MRI and evaluate its generalized performance in detecting cervical degenerative disease.

METHODS : T2-weighted MRI scans of 804 patients with symptoms of lumbar degenerative disease were retrospectively collected from three hospitals. The training dataset (n = 456) and internal validation dataset (n = 134) were randomly selected from the center I. Two external validation datasets comprising 100 and 114 patients were from center II and center III, respectively. A DL model based on 3D ResNet18 and transformer architecture was proposed to detect lumbar degenerative disease. In addition, a cervical MR image dataset comprising 200 patients from an independent hospital was used to evaluate the generalized performance of the DL model. The diagnostic performance was assessed by the free-response receiver operating characteristic (fROC) curve and precision-recall (PR) curve. Precision, recall, and F1-score were used to measure the DL model.

RESULTS : A total of 2497 three-dimension retrogression annotations were labeled for training (n = 1157) and multicenter validation (n = 1340). The DL model showed excellent detection efficiency in the internal validation dataset, with F1-score achieving 0.971 and 0.903 on the sagittal and axial MR images, respectively. Good performance was also observed in the external validation dataset I (F1-score, 0.768 on sagittal MR images and 0.837 on axial MR images) and external validation dataset II (F1-score, 0.787 on sagittal MR images and 0.770 on axial MR images). Furthermore, the robustness of the DL model was demonstrated via transfer learning and generalized performance evaluation on the external cervical dataset, with the F1-score yielding 0.931 and 0.919 on the sagittal and axial MR images, respectively.

CONCLUSION : The proposed DL model can automatically detect lumbar and cervical degenerative disease on T2-weighted MR images with good performance, robustness, and feasibility in clinical practice.

Yi Wei, Zhao Jingwei, Tang Wen, Yin Hongkun, Yu Lifeng, Wang Yaohui, Tian Wei

2023-Mar-21

Deep learning, Degenerative disc disease, Magnet resonance imaging, Spine