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InternVL 1.5 Advances Multimodal AI with High-Resolution and Bilingual Capabilities in Open-Source Models - MarkTechPost
Multimodal large language models (MLLMs) integrate text and visual data processing to enhance how artificial intelligence understands and interacts with the world. This area of research focuses on creating systems that can comprehend and respond to a combination of visual cues and linguistic information, mimicking human-like interactions more closely. The challenge often lies in the limited capabilities of open-source models compared to their commercial counterparts. Open-source models frequently exhibit deficiencies in processing complex visual inputs and supporting various languages, which can restrict their practical applications and effectiveness in diverse scenarios. Historically, most open-source MLLMs have been trained at fixed resolutions,
Edge AI and It’s Advantages over Traditional AI - MarkTechPost
Edge artificial intelligence (Edge AI) involves implementing AI algorithms and models on local devices like sensors or IoT devices at the network's periphery. This setup allows for immediate data processing and analysis, reducing dependence on cloud infrastructure. Consequently, it empowers devices to make intelligent decisions quickly and autonomously without the need for data from distant servers or cloud systems. Deep Neural Networks (DNNs) are crucial for AI applications in the 5G era. However, running DNN-based tasks on mobile devices requires more computation resources. Also, traditional cloud-assisted DNN inference suffers from significant wide-area network latency, resulting in poor real-time performance and
Exploring Parameter-Efficient Fine-Tuning Strategies for Large Language Models - MarkTechPost
Large Language Models (LLMs) signify a revolutionary leap in numerous application domains, facilitating impressive accomplishments in diverse tasks. Yet, their immense size incurs substantial computational expenses. With billions of parameters, these models demand extensive computational resources for operation. Adapting them to specific downstream tasks becomes particularly challenging due to their vast scale and computational requirements, especially on hardware platforms limited by computational capabilities. Previous studies have proposed that LLMs demonstrate considerable generalization abilities, allowing them to apply learned knowledge to new tasks not encountered during training, a phenomenon known as zero-shot learning. However, fine-tuning remains crucial to optimize LLM performance
Free LLM Playgrounds and Their Comparative Analysis - MarkTechPost
Free LLM Playgrounds and Their Comparative Analysis As the landscape of AI technology advances, the proliferation of free platforms to test large language models (LLMs) online has greatly increased. These ‘playgrounds’ offer a valuable resource for developers, researchers, and enthusiasts to experiment with different models without requiring extensive setup or investment. Let’s explore a comparative analysis of various free LLM playgrounds based on their features, performance, and accessibility, helping you to decide which platform might best suit your needs. Overview of LLM Playgrounds LLMs have become a cornerstone of modern AI applications, offering capabilities ranging from text generation to sophisticated
This AI Research from Cohere Discusses Model Evaluation Using a Panel of Large Language Models Evaluators (PoLL) - MarkTechPost
Large Language Models (LLMs) are advancing at a very fast pace in recent times. However, the lack of adequate data to thoroughly verify particular features of these models is one of the main obstacles. An additional layer of complication arises when evaluating the precision and caliber of a model's free-form text production on its own. In order to address these issues, a lot of evaluations now use LLMs as judges to score the caliber of results produced by other LLMs. This method often uses one huge model for evaluation, such as the GPT-4. Although this approach has become increasingly popular,
Balancing Innovation and Rights: A Cooperative Game Theory Approach to Copyright Management in Generative AI Technologies - MarkTechPost
The advent of generative artificial intelligence (AI) marks a significant technological leap, enabling the creation of new text, images, videos, and other media by learning from vast datasets. However, this innovative capability brings forth substantial copyright concerns, as it may utilize and repurpose the creative works of original authors without consent. This research addresses the potential for copyright infringement by generative AI technologies, which can produce outputs that might mimic and replace original human-made content. Such infringement risks undermine the economic rights of original content creators and pose legal challenges in creative industries. Traditionally, approaches to mitigate these risks have
REBEL: A Reinforcement Learning RL Algorithm that Reduces the Problem of RL to Solving a Sequence of Relative Reward Regression Problems on Iteratively Collected Datasets - MarkTechPost
Initially designed for continuous control tasks, Proximal Policy Optimization (PPO) has become widely used in reinforcement learning (RL) applications, including fine-tuning generative models. However, PPO's effectiveness relies on multiple heuristics for stable convergence, such as value networks and clipping, making its implementation sensitive and complex. Despite this, RL demonstrates remarkable versatility, transitioning from tasks like continuous control to fine-tuning generative models. Yet, adapting PPO, originally meant to optimize two-layer networks, to fine-tune modern generative models with billions of parameters raises concerns. This necessitates storing multiple models in memory simultaneously and raises questions about the suitability of PPO for such tasks.
ScrapeGraphAI: A Web Scraping Python Library that Uses LLMs to Create Scraping Pipelines for Websites, Documents, and XML Files - MarkTechPost
Extracting information quickly and efficiently from websites and digital documents is crucial for businesses, researchers, and developers. They require specific data from various online sources to analyze trends, monitor competitors, or gather insights for strategic decisions. Collecting this data can be time-consuming and prone to errors, presenting a significant challenge in data-driven industries. Traditionally, web scraping tools have been utilized to automate the process of data extraction. These tools can navigate web pages, identify relevant data based on predefined rules, and efficiently collect this information. However, they often demand a good understanding of programming and web technologies from the user.
This AI Paper from China Introduces TinyChart: An Efficient Multimodal Large Language Models MLLMs for Chart Understanding with Only 3B Parameters - MarkTechPost
Charts have become indispensable tools for visualizing data in information dissemination, business decision-making, and academic research. As the volume of multimodal data grows, a critical need arises for automated chart comprehension, which has garnered increasing attention from the research community. Recent advancements in Multimodal Large Language Models (MLLMs) have demonstrated impressive capabilities in comprehending images and executing instructions effectively. However, existing chart understanding models confront several challenges, including extensive parameter requirements, susceptibility to errors in numerical calculations, and inefficiencies in encoding high-resolution images. To address these limitations, a team of researchers from China has proposed an innovative solution: TinyChart. Despite
Meta AI Introduces CyberSecEval 2: A Novel Machine Learning Benchmark to Quantify LLM Security Risks and Capabilities - MarkTechPost
Large language models (LLMs) are expanding in usage, posing new cybersecurity risks. These risks emerge from their core traits: heightened capability in code generation, heightened deployment for real-time code generation, automated execution within code interpreters, and integration into applications handling untrusted data. This poses the need for a robust mechanism for cybersecurity evaluations. Prior works to evaluate LLMs' security properties include open benchmark frameworks and position papers proposing evaluation criteria. CyberMetric, SecQA, and WMDP-Cyber employ a multiple-choice format similar to educational evaluations. CyberBench extends evaluation to various tasks within the cybersecurity domain, while LLM4Vuln concentrates on vulnerability discovery, coupling LLMs
Enhancing Transformer Models with Filler Tokens: A Novel AI Approach to Boosting Computational Capabilities in Complex Problem Solving - MarkTechPost
Language models based on the transformers are pivotal in advancing the field of AI. Traditionally, these models have been deployed to interpret and generate human language by predicting token sequences, a fundamental process in their operational framework. Given their broad application, from automated chatbots to complex decision-making systems, improving their efficiency and accuracy remains a critical area of research. A notable limitation in current language model methodologies is their reliance on direct response generation or intermediate reasoning steps, known as 'chain-of-thought' tokens. These methods presuppose that adding more tokens representing steps in reasoning inherently enhances the model's problem-solving capabilities. However,
SEED-Bench-2-Plus: An Extensive Benchmark Specifically Designed for Evaluating Multimodal Large Language Models (MLLMs) in Text-Rich Scenarios - MarkTechPost
Evaluating Multimodal Large Language Models (MLLMs) in text-rich scenarios is crucial, given their increasing versatility. However, current benchmarks mainly assess general visual comprehension, overlooking the nuanced challenges of text-rich content. MLLMs like GPT-4V, Gemini-Pro-Vision, and Claude-3-Opus showcase impressive capabilities but lack comprehensive evaluation in text-rich contexts. Understanding text within images requires interpreting textual and visual cues, a challenge yet to be rigorously addressed. SEED-Bench-2-Plus, developed by researchers from Tencent AI Lab, ARC Lab, Tencent PCG, and The Chinese University of Hong Kong, Shenzhen, is a specialized benchmark for evaluating MLLMs' understanding of text-rich visual content. It consists of 2.3K meticulously
Gradformer: A Machine Learning Method that Integrates Graph Transformers (GTs) with the Intrinsic Inductive Bias by Applying an Exponential Decay Mask to the Attention Matrix - MarkTechPost
Graph Transformers (GTs) have successfully achieved state-of-the-art performance on various platforms. GTs can capture long-range information from nodes that are at large distances, unlike the local message-passing in graph neural networks (GNNs). In addition, the self-attention mechanism in GTs permits each node to look at other nodes in a graph directly, helping collect information from arbitrary nodes. The same self-attention in GTs also provides much flexibility and capacity to collect information globally and adaptively. Despite being advantageous over a large variety of tasks, the self-attention mechanism in GTs doesn’t pay more attention to the special features of graphs, such as
Meet Electric Atlas: A New Era of Robotics by Boston Dynamics - MarkTechPost
Boston Dynamics has been at the forefront of robotics innovation for decades, and its latest offering—the fully electric Atlas robot—marks a significant milestone in the field. As it announces the retirement of its hydraulic Atlas, a new era begins with an electric version poised to transform real-world applications across various industries. Image Source A Decade of Groundbreaking Work Atlas's journey started over a decade ago when Boston Dynamics was among the few companies investing heavily in humanoid robots. Today, the landscape has drastically changed. The success of their robots Spot and Stretch has paved the way for Atlas, which builds
Researchers at UC San Diego Propose DrS: A Novel Machine Learning Approach for Learning Reusable Dense Rewards for Multi-Stage Tasks in a Data-Driven Manner - MarkTechPost
The success of many reinforcement learning (RL) techniques relies on dense reward functions, but designing them can be difficult due to expertise requirements and trial and error. Sparse rewards, like binary task completion signals, are easier to obtain but pose challenges for RL algorithms, such as exploration. Consequently, the question emerges: Can dense reward functions be learned in a data-driven manner to address these challenges? Existing research on reward learning often overlooks the importance of reusing rewards for new tasks. In learning reward functions from demonstrations, known as inverse RL, methods like adversarial imitation learning (AIL) have gained traction. Inspired
This AI Paper Introduces a Novel Artificial Intelligence Approach in Precision Text Retrieval Using Retrieval Heads - MarkTechPost
In computational linguistics, much research focuses on how language models handle and interpret extensive textual data. These models are crucial for tasks that require identifying and extracting specific information from large volumes of text, presenting a considerable challenge in ensuring accuracy and efficiency. A critical challenge in processing extensive text data is the model's ability to accurately identify and extract relevant information from vast content pools. This issue is particularly pronounced in tasks where the model needs to discern specific details from large datasets or long documents. Existing research includes models like LLaMA, Yi, QWen, and Mistral, which utilize advanced
Llama-3-based OpenBioLLM-Llama3-70B and 8B: Outperforming GPT-4, Gemini, Meditron-70B, Med-PaLM-1 and Med-PaLM-2 in Medical-Domain - MarkTechPost
With the significant development in the rapidly developing field of Artificial Intelligence driven healthcare, a team of researchers has introduced OpenBioLLM-Llama3-70B & 8B models. These state-of-the-art Large Language Models (LLMs) have the potential to completely transform medical natural language processing (NLP) by establishing new standards for functionality and performance in the biomedical field. The release of these models marks a substantial advancement in medical-domain LLM technology. Their ability to outperform models such as GPT-4, Gemini, Meditron-70B, Med-PaLM-1, and Med-PaLM-2 in biomedical tasks is a testament to their superiority and represents a significant breakthrough in the usability and effectiveness of freely
GPT-4.5 or GPT-5? Unveiling the Mystery Behind the 'gpt2-chatbot': The New X Trend for AI - MarkTechPost
Artificial intelligence is constantly advancing, and there's always something new to be excited about. A few moments ago, a cutting-edge AI model called 'gpt2-chatbot' was making waves in X's AI community (Twitter). This new large language model (LLM) has generated a lot of discussion and curiosity among AI experts and enthusiasts, who are eager to know more about it, constantly trying to find its full potential and speculating about its capabilities. There is no official documentation available on the model, yet the 'gpt2-chatbot' is still gaining massive attention for its impressive reasoning abilities and proficiency in handling complex questions. Its
SynthEval: A Novel Open-Source Machine Learning Framework for Detailed Utility and Privacy Evaluation of Tabular Synthetic Data - MarkTechPost
Computer vision, machine learning, and data analysis across many fields have all seen a surge in the usage of synthetic data in the past few years. Synthetic means to mimic complicated situations that would be challenging, if not impossible, to record in the actual world. Information about individuals, such as patients, citizens, or customers, along with their unique attributes, can be found in tabular records at the personal level. These records are ideal for knowledge discovery tasks and the creation of advanced predictive models to help with decision-making and product development. The privacy implications of tabular information are substantial, though,
OpenVoice V2: Evolving Multilingual Voice Cloning with Enhanced Style Control and Cross-Lingual Capabilities - MarkTechPost
Instant Voice Cloning (IVC) in Text-to-Speech (TTS) synthesis, also known as Zero-shot TTS, allows TTS models to replicate the voice of any given speaker with just a short audio sample without requiring additional training on that speaker. While existing methods like VALLE and XTTS can replicate tone color, they need more flexibility in controlling style parameters like emotion, accent, and rhythm. Auto-regressive models, though effective, are computationally expensive and slow. Non-autoregressive approaches like YourTTS and Voicebox offer faster inference but lack comprehensive style control. Additionally, achieving cross-lingual voice cloning demands extensive datasets, hindering the inclusion of new languages. Closed-source projects
Physics-Based Deep Learning: Insights into Physics-Informed Neural Networks (PINNs) - MarkTechPost
Physics-Informed Neural Networks (PINNs) have become a cornerstone in integrating deep learning with physical laws to solve complex differential equations, marking a significant advance in scientific computing and applied mathematics. These networks offer a novel methodology for encoding differential equations directly into the architecture of neural networks, ensuring that solutions adhere to the fundamental laws of physics. Image Source Overview of PINNs Definition and Core Concept: PINNs integrate differential equations into the neural network's loss function, allowing the network to train on data while respecting underlying physical laws. Advantages: This method enhances the network's predictive accuracy, especially in scenarios where
Top Data Science Courses in 2024 - MarkTechPost
As businesses increasingly rely on data-driven decision-making, the ability to extract insights and derive value from data has become quite essential. Acquiring skills in data science enables professionals to unlock new opportunities for innovation and gain a competitive edge in today's digital age. This article lists the top data science courses one should take to master the necessary skills and meet the growing demand for data expertise in various industries. IBM Data Science Professional Certificate This course helps master the practical skills and knowledge necessary for a proficient data scientist. It is a beginner-friendly course that teaches the tools, languages,
This Machine Learning Paper from ICMC-USP, NYU, and Capital-One Introduces T-Explainer: A Novel AI Framework for Consistent and Reliable Machine Learning Model Explanations - MarkTechPost
In the ever-evolving field of machine learning, developing models that predict and explain their reasoning is becoming increasingly crucial. As these models grow in complexity, they often become less transparent, resembling 'black boxes' where the decision-making process is obscured. This opacity is problematic, particularly in sectors like healthcare and finance, where understanding the basis of decisions can be as important as understanding the decisions themselves. One fundamental issue with complex models is their lack of transparency, which complicates their adoption in environments where accountability is key. Traditionally, methods to increase model transparency have included various feature attribution techniques that explain
Cleanlab Introduces the Trustworthy Language Model (TLM) that Addresses the Primary Challenge to Enterprise Adoption of LLMs: Unreliable Outputs and Hallucinations - MarkTechPost
While 55% of organizations are experimenting with generative AI, only 10% have implemented it in production, according to a recent Gartner poll. LLMs face a major obstacle in transitioning to production due to their tendency to generate erroneous outputs, termed hallucinations. These inaccuracies hinder their utilization in applications requiring correct results. Instances like Air Canada's chatbot misinforming customers about refund policies and a law firm's use of ChatGPT to produce a brief filled with fabricated citations illustrate the risks associated with deploying unreliable LLMs. Similarly, New York City's 'MyCity' chatbot has provided incorrect responses to inquiries about local laws, underscoring
Mistral.rs: A Lightning-Fast LLM Inference Platform with Device Support, Quantization, and Open-AI API Compatible HTTP Server and Python Bindings - MarkTechPost
In artificial intelligence, one common challenge is ensuring that language models can process information quickly and efficiently. Imagine you're trying to use a language model to generate text or answer questions on your device, but it's taking too long to respond. This delay can be frustrating and impractical, especially in real-time applications like chatbots or voice assistants. Currently, some solutions are available to address this issue. Some platforms offer optimization techniques like quantization, which reduces the model's size and speeds up inference. However, these solutions may not always be easy to implement or may not support a wide range of
Meet Openlayer: An AI Evaluation Tool that Fits into Development and Production Pipelines to Help Ship High-Quality Models with Confidence - MarkTechPost
Artificial Intelligence (AI) is a rapidly expanding field with new daily applications. However, ensuring these models' accuracy and dependability continues to be a difficult task. Conventional AI assessment techniques are frequently cumbersome and require extensive manual setup, which impedes ongoing development and disrupts developers' workflows. There is no set framework, application, or set of rules for testing and working together on models. Engineers are mostly left to manually sift through failing rows before deployment to comprehend and enhance models. OpenLayer's Innovative Solution Meet Openlayer, an evaluation tool that fits into your development and production pipelines to help you ship high-quality
This AI Paper by DeepMind Introduces Gecko: Setting New Standards in Text-to-Image Model Assessment - MarkTechPost
Text-to-image (T2I) models are central to current advances in computer vision, enabling the synthesis of images from textual descriptions. These models strive to capture the essence of the input text, rendering visual content that mirrors the intricacies described. The core challenge in T2I technology lies in the model's ability to accurately reflect the detailed elements of textual prompts in the generated images. Despite the visual quality of the outputs, there often remains a significant discrepancy between the envisioned description and the actual image produced. Existing research in T2I generation includes frameworks like TIFA160 and DSG1K, which utilize datasets like MSCOCO
DrBenchmark: The First-Ever Publicly Available French Biomedical Large Language Understanding Benchmark - MarkTechPost
A group of researchers in France introduced Dr.Benchmark to address the need for the evaluation of masked language models in French, particularly in the biomedical domain. There have been significant advances in the field of NLP, particularly in pre-trained language models (PLMs), but evaluating these models remains difficult due to variations in evaluation protocols. The scarcity of evaluation benchmarks in the biomedical domain in languages other than English and Chinese has made this even more challenging. These issues created a gap in evaluating the accuracy of the latest French biomedical models. The existing method for evaluating French language models failed
From Lost to Found: INformation-INtensive (IN2) Training Revolutionizes Long-Context Language Understanding - MarkTechPost
Long-context large language models (LLMs) have garnered attention, with extended training windows enabling processing of extensive context. However, recent studies highlight a challenge: these LLMs struggle to utilize middle information effectively, termed the lost-in-the-middle challenge. While the LLM can comprehend the information at the beginning and end of the long context, it often overlooks the information in the middle. This impedes tasks like Needle-in-the-Haystack and passkey retrieval. Consequently, a pressing research question arises: how can long-context LLMs fully utilize the information in the long context? Recent research has significantly advanced the exploration of training large models with extended context windows,
This AI Paper from Apple Introduces a Weakly-Supervised Pre-Training Method for Vision Models Using Publicly Available Web-Scale Image-Text Data - MarkTechPost
In recent times, contrastive learning has become a potent strategy for training models to learn efficient visual representations by aligning image and text embeddings. However, one of the difficulties with contrastive learning is the computation needed for pairwise similarity between image and text pairs, especially when working with large-scale datasets. In recent research, a team of researchers has presented a new method for pre-training vision models with web-scale image-text data in a weakly supervised manner. Called CatLIP (Categorical Loss for Image-text Pre-training), this approach solves the trade-off between efficiency and scalability on web-scale image-text datasets with weak labeling. By extracting
This AI Paper from Google DeepMind Introduces Enhanced Learning Capabilities with Many-Shot In-Context Learning - MarkTechPost
In-context learning (ICL) in large language models (LLMs) utilizes input-output examples to adapt to new tasks without altering the underlying model architecture. This method has transformed how models handle various tasks by learning from direct examples provided during inference. The problem at hand is the limitation of a few-shot ICL in handling intricate tasks. These tasks often demand a deep comprehension that few-shot learning cannot provide, as it operates under the restriction of minimal input data. This scenario could be better for applications requiring detailed analysis and decision-making based on extensive data sets, such as advanced reasoning or language translation.
Top Artificial Intelligence AI Courses for Beginners in 2024 - MarkTechPost
The popularity of AI has skyrocketed in the past few years, with new avenues being opened up with the rise in the use of large language models (LLMs). Having knowledge of AI has now become quite essential as recruiters are actively looking for candidates with a strong foundation in the same. This article lists the top AI courses for beginners to take to help them make a shift in their careers and gain the necessary skills. Google AI for Anyone 'Google AI for Anyone' is a beginner-friendly course that teaches about artificial intelligence (AI). The course covers how AI is
Cohere AI Open-Sources 'Cohere Toolkit': A Major Accelerant for Getting LLMs into Production within an Enterprise - MarkTechPost
Cohere AI has made a major advancement in the field of Artificial Intelligence (AI) development by releasing the Cohere Toolkit, a comprehensive open-source repository designed to accelerate the development of AI applications. Cohere, which is a leading enterprise AI platform, has released the toolkit with future extensions to incorporate new platforms. This toolkit enables developers to make use of Cohere's advanced models, Command, Embed, and Rerank, across several platforms, including AWS, Azure, and Cohere's own platform. By providing a set of production-ready apps that can be easily deployed across cloud providers, the Cohere Toolkit enables developers to comply with strict
How Scientific Machine Learning is Revolutionizing Research and Discovery - MarkTechPost
Scientific Machine Learning (SciML) is an innovative field at the crossroads of ML, data science, and computational modeling. This emerging discipline utilizes powerful algorithms to propel discoveries across various scientific domains, including biology, physics, and environmental sciences. Image Source Expanding the Horizons of Research Accelerated Discovery and Innovation SciML allows for the quick processing and analysis of massive datasets, drastically reducing the time from hypothesis generation to experimental verification. This rapid cycle is pivotal in fields like pharmacology, where algorithms streamline the drug development process by analyzing vast databases of chemical compounds for potential drug efficacy and safety. Sophisticated Predictive
The Representative Capacity of Transformer Language Models LMs with n-gram Language Models LMs: Capturing the Parallelizable Nature of n-gram LMs - MarkTechPost
Neural language models (LMs) have become popular due to their extensive theoretical work mostly focusing on representational capacity. An earlier study of representational capacity using Boolean sequential models helps in a proper understanding of its lower and upper bound and the potential of the transformer architecture. LMs have become the backbone of many NLP tasks, and most state-of-the-art LMs are based on transformer architecture. In addition, formal models of computation offer a smooth and accurate formulation to study different aspects of probability distributions that LMs can handle. However, LM architecture is mostly examined in the context of binary language recognition,
Microsoft's GeckOpt Optimizes Large Language Models: Enhancing Computational Efficiency with Intent-Based Tool Selection in Machine Learning Systems - MarkTechPost
Large language models (LLMs) are the backbone of numerous computational platforms, driving innovations that impact a broad spectrum of technological applications. These models are pivotal in processing and interpreting vast amounts of data, yet they are often hindered by high operational costs and inefficiencies related to system tool utilization. Optimizing LLM performance without prohibitive computational expenses is a significant challenge in this field. Traditionally, LLMs operate under systems that engage various tools for any given task, regardless of the specific needs of each operation. This broad tool activation drains computational resources and significantly increases the costs associated with data processing
This AI Paper Proposes FLORA: A Novel Machine Learning Approach that Leverages Federated Learning and Parameter-Efficient Adapters to Train Visual-Language Models VLMs - MarkTechPost
Traditional methods for training vision-language models (VLMs) often require the centralized aggregation of vast datasets, which raises concerns regarding privacy and scalability. Federated learning offers a solution by allowing models to be trained across a distributed network of devices while keeping data locally but adapting VLMs to this framework presents unique challenges. To address these challenges, a team of researchers from Intel Corporation and Iowa State University introduced FLORA (Federated Learning with Low-Rank Adaptation) to address the challenge of training vision-language models (VLMs) in federated learning (FL) settings while preserving data privacy and minimizing communication overhead. FLORA fine-tunes VLMs like
Advancing Time Series Forecasting: The Impact of Bi-Mamba4TS's Bidirectional State Space Modeling on Long-Term Predictive Accuracy - MarkTechPost
Time series forecasting is increasingly vital across numerous sectors, such as meteorology, finance, and energy management. Its relevance has grown as organizations aim to predict future trends and patterns more accurately. This type of forecasting is instrumental in enhancing decision-making processes and optimizing resource allocation over long periods. However, making accurate long-term forecasts is complex due to the inherently unpredictable nature of the datasets involved and the substantial computational resources required for processing them. Historically, recurrent neural networks (RNNs) and convolutional neural networks (CNNs) have been employed to manage these predictions. While RNNs are adept at processing data sequentially, they
TD3-BST: A Machine Learning Algorithm to Adjust the Strength of Regularization Dynamically Using Uncertainty Model - MarkTechPost
Reinforcement learning (RL) is a type of learning approach where an agent interacts with an environment to collect experiences and aims to maximize the reward received from the environment. This usually involves a looping process of experience collecting and enhancement, and due to the requirement of policy rollouts, it is called online RL. Both on-policy and off-policy RL need online interaction, which can be impractical in certain domains due to experimental or environmental constraints. Offline RL algorithms are framed so that they can extract optimal policies from static datasets. Offline RL algorithms are used to learn effective and well-applicable policies
China’s Vidu Challenges Sora with High-Definition 16-Second AI Video Clips in 1080p - MarkTechPost
The 2024 Zhongguancun Forum in Beijing saw the introduction of Vidu, an advanced AI model that can generate 16-second 1080p video clips with a simple prompt. Developed by ShengShu-AI and Tsinghua University, Vidu is set to compete with OpenAI's Sora, marking a significant milestone for China's generative AI capabilities and ambition to lead in emerging technologies. Vidu's primary technology is the Universal Vision Transformer (U-ViT), which combines two AI models - Transformer and Diffusion. This integration enables Vidu to produce dynamic video content that closely resembles the physical world in terms of detail and realism. This includes intricate facial expressions and complex lighting effects. Vidu has been thoughtfully designed with a deep