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Amazon

19h

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Accelerating Articul8’s domain-specific model development with Amazon SageMaker HyperPod

Articul8 is accelerating their training and deployment of domain-specific models using Amazon SageMaker HyperPod, achieving over 95% cluster utilization and a 35% improvement in productivity.
SageMaker HyperPod provides fault-tolerant compute clusters, efficient cluster utilization through observability, and seamless model experimentation using Slurm and Amazon EKS.
Articul8 develops domain-specific models for industry sectors like supply chain, energy, and semiconductors, achieving significant accuracy and performance gains over general-purpose models.
The A8-SupplyChain model achieves 92% accuracy and threefold performance gains over general-purpose models.
SageMaker HyperPod enabled Articul8 to rapidly iterate on DSM training, optimize model training performance, and reduce AI deployment time and total cost of ownership.
The platform offers efficient cluster management, automated failure recovery, and observability through Amazon CloudWatch and Grafana.
Articul8's setup with SageMaker HyperPod and Managed Grafana empowered rapid experimentation, leading to superior real-world performance for domain-specific models.
The cluster setup includes head and compute nodes, shared volumes, local storage, Slurm scheduler, and accounting for job runtime information.
Articul8 confirmed the performance of A100 and achieved near linear scaling with distributed training, reducing training time significantly.
Through SageMaker HyperPod, Articul8's DSMs demonstrated superior performance, accelerated AI deployment time, and lowered total cost of ownership.

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Amazon

19h

Image Credit: Amazon

How VideoAmp uses Amazon Bedrock to power their media analytics interface

VideoAmp collaborated with AWS to develop a prototype of the VideoAmp Natural Language (NL) Analytics Chatbot using Amazon Bedrock for media analytics data analysis.
The AI solution included a natural language to SQL pipeline and an automated testing tool for analytics queries.
VideoAmp, a tech-first measurement company, uses AI to enhance measurement and optimization capabilities for TV, streaming, and digital media.
Their AI journey focuses on providing accurate audience insights, improving measurement, and optimizing ad campaigns in real-time.
VideoAmp is set to launch an AI analytics interface powered by generative AI to offer accessible insights through natural language queries.
The solution aimed to convert natural language questions to SQL, execute queries on performance metrics data, and provide natural language summaries.
Challenges included adapting large language models (LLMs) and developing an automated evaluation pipeline for accurate results.
Amazon Bedrock, with Anthropic’s Claude 3 LLMs, was used for the AI assistant, providing models for SQL generation and data summarization.
The solution connected to a data warehouse and supported various database connections, enhancing analytics capabilities.
The evaluation framework ensured high accuracy, low latency, and cost-effectiveness for user queries, meeting VideoAmp's expectations.

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Medium

19h

Avalanches of Meaning: Applying the Brain's Power Law Geometry to AI Language Models

The article explores the merging of neuroscience principles with Large Language Models (LLMs) in the context of meaning generation.
Neuroscience reveals brain activity follows a 'power law' and operates in high-dimensional geometries, similar to LLM capabilities.
The brain's power-law dynamics allow for stability and flexibility, enabling meaningful cognition through neural avalanches.
The Semantic Collapse Function scores the significance of ideas based on coherence, relevance, and novelty, altering standard LLM outputs.
LLMs typically prioritize statistical likelihood over semantic significance, prompting the proposal to shift to prioritizing meaning.
The Semantic Avalanche Model alters LLM behavior to prioritize profound, meaningful structures over common outputs, simulating human insight.
This model introduces the concept of Semantic Mass and a selection process emphasizing semantic coherence and rarity in outputs.
The proposed architecture mirrors human brain insight processes, aiming to create AI systems that simulate cognitive avalanches of meaning.
The unified equation of the Semantic Avalanche Model guides AI in selecting outputs based on brain-like principles of coherence and power-law distribution.
The article concludes by suggesting that combining neuroscience, semantic resonance mathematics, and structured LLM architectures can lead to the creation of systems that generate highly meaningful symbolic collapses.
Eligible for Web Story: True

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Medium

20h

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Who’s Responsible When AI Fails?

When AI fails, the issue of responsibility arises, as AI systems can make mistakes despite their perceived accuracy.
AI is created by people but behaves differently as it learns from itself, making decisions that may not be fully understood by its creators.
The use of AI in real-life scenarios, such as aiding judges in predicting repeat crimes, has led to instances of bias and errors.
AI, while often perceived as neutral, actually learns from the data it is fed, which can contain biases and prejudices.
Concerns about responsibility in AI are growing, particularly when AI systems make incorrect recommendations or decisions with significant consequences.
Calls for greater transparency in AI development and a shift towards a culture of accountability and ethical considerations are becoming more prominent.
AI can have positive impacts but also poses risks, highlighting the importance of addressing accountability in the development and deployment of AI technologies.
There is a need to consider the implications of AI failures and ensure that mechanisms are in place to assign responsibility when things go wrong.
Building a culture where moral considerations and responsibility are prioritized can help mitigate the potential negative impacts of AI failures.
Ultimately, understanding who holds responsibility for AI failures is crucial for ensuring the ethical and accountable use of AI technologies.

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Medium

20h

100

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Salaries in the Tech Industry — What Influences Your Income?

Tech jobs are highly sought after, and factors influencing developer salaries were analyzed using the Stack Overflow Survey 2024 data.
The survey encompassed insights into programming languages, working conditions, and salaries of over 50,000 developers worldwide.
Salaries in the tech industry vary widely, ranging from six-figure incomes to lower salaries.
Factors like professional experience, education level, and location significantly impact salary levels.
A Random Forest model was built to predict salaries based on different variables.
Location emerged as the most crucial factor influencing salaries, with over 51% influence.
Professional experience followed, accounting for about 20% of salary influence.
The type of developer role, programming languages mastery, and company size also play a significant role in determining salaries.
Education was found to have a lesser impact on salaries compared to practical skills and experience.
Remote work and age were identified as factors with lesser influence on salary levels.
Moving to a country with higher salary levels may be more financially rewarding than focusing solely on educational qualifications or specific programming languages.
These insights can aid both new and experienced developers in career planning and setting realistic salary expectations.
For detailed technical information on the analysis, the complete code and methodology are available on the author's GitHub repository.

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Medium

21h

160

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Why AI Needs an “Oral Defense” — A New Approach to Model Validation

AI systems are advancing rapidly with capabilities like acing medical boards and drafting legal contracts.
There is a concern about how to validate if these AI systems truly understand the tasks they perform.
The author, a physician and immunologist, questions the readiness of AI systems for practical use.
There is a need to move beyond static benchmarks to assess AI models.
The author proposes bringing the oral defense paradigm from academia to the validation of AI models.
Questions about AI models' comprehension and understanding need to be addressed.
The limitations of current evaluation methods need to be acknowledged and tackled.
It's suggested that engaging with complex questions now is better than facing failures later.
The hope is that this approach will lead to new discussions and collaborations in the field of AI validation.

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Global Fintech Series

23h

1.4k

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Automating AML Investigations with AI and Machine Learning

Financial crime, including money laundering and fraud, is evolving rapidly with increasing complexity and sophistication.
Current AML methods relying on manual processes struggle to keep up, resulting in high false positives and missed threats.
AI and ML redefine AML investigations, enabling real-time anomaly detection, automated risk assessment, and proactive fraud prevention.
The article explores how AI and ML are transforming AML investigations by automating processes and reducing false positives.
Financial institutions face challenges in detecting financial crime due to the complexities of modern criminal activities and evolving regulatory landscape.
Cryptocurrencies, DeFi, and cyber threats add layers of complexity, making it harder for institutions to monitor illicit activities.
Financial fraud driven by cybercrime, synthetic identities, and ransomware poses significant challenges for traditional AML systems.
Regulators are increasing scrutiny and penalties for non-compliance, emphasizing the need for more adaptive AML solutions.
AI and ML help overcome AML compliance challenges by providing intelligent pattern detection, reducing false positives, and enhancing customer risk profiling.
The integration of AI in AML processes enhances efficiency, reduces false positives, enables real-time anomaly detection, and offers a scalable approach to financial crime detection.

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Medium

297

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How to Generate Synthetic Time-Series Data on Databricks

Leveraging time-series datasets involves challenges like variability, representativity, and granularity for time-dependent variables, hindering AI model development.
Synthetic data helps overcome these challenges by providing diverse, privacy-compliant datasets for accurate time-series analysis on the Databricks platform.
Generating synthetic time-series data is crucial for capturing observed patterns and complexities in datasets like Walmart store sales data from Kaggle.
Tools like TimeGAN and DoppelGANger offer solutions but can be hard to tune; YData Fabric simplifies time-series synthetic data generation in Databricks.
Using ydata-sdk in Databricks enables data profiling, synthetic data exploration, and efficient training of generative models for time-series data.
Configuring and training the synthetic data generator with YData Fabric involves optimizing model selection and parameters based on metadata search.
Understanding dataset aspects like entities and time-series behaviors is crucial for generating multiple synthetic samples with fidelity.
Combining original and synthetic data sets allows for applications like building forecasting models for weekly sales in retail scenarios.
The integration of ydata-sdk with Databricks streamlines data quality and privacy compliance, enabling synthetic time-series data generation for advanced predictive models.
This integration enhances data robustness, reduces overfitting, and simplifies workflow for data access and preparation in large-scale scenarios.

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Arxiv

243

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Llama-Affinity: A Predictive Antibody Antigen Binding Model Integrating Antibody Sequences with Llama3 Backbone Architecture

Antibody-facilitated immune responses are crucial for defense against pathogens and viruses.
Bioengineering advancements have accelerated therapeutic antibody development.
AI and machine learning have revolutionized affinity prediction for antibodies.
A new model, LlamaAffinity, integrates antibody sequences with Llama 3 backbone architecture.
The model outperforms existing methods like AntiFormer and AntiBERTa in affinity prediction.
LlamaAffinity achieved high accuracy, F1-score, precision, recall, and AUC-ROC values.
It demonstrated computational efficiency with significantly lower training time compared to previous studies.

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Arxiv

262

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Enhanced Whole Page Optimization via Mixed-Grained Reward Mechanism-Adapted Language Models

Whole Page Optimization (WPO) is crucial for improving user experience by optimizing search and recommendation results.
Pre-trained Large Language Models (LLMs) are effective in generating relevant content, but fine-tuning them for complex tasks like WPO is challenging.
This study introduces PageLLM, a reward-based fine-tuning approach for LLMs using user feedback as supervision.
PageLLM utilizes a mixed-grained reward mechanism integrating page-level and item-level rewards to optimize presentation.
Page-level reward assesses quality and coherence, while item-level reward focuses on accuracy and relevance of recommendations.
User feedback is noisy and less precise compared to manually labeled datasets, posing a challenge that PageLLM addresses.
PageLLM was tested on public and industrial datasets, surpassing baselines and showing a 0.44% GMV increase in an online A/B test.
The dual-reward structure of PageLLM enhances both the overall quality and the individual components of WPO.
Fine-tuning LLMs for WPO using user feedback reduces reliance on costly human-annotated data.
PageLLM's success in real-world applications highlights its effectiveness in improving user engagement and system performance.

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Arxiv

259

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LLM-ML Teaming: Integrated Symbolic Decoding and Gradient Search for Valid and Stable Generative Feature Transformation

Feature transformation is crucial for enhancing data representation by creating new features from the original data.
Generative AI shows promise in this area but struggles with stable and error-free output generation.
Existing methods have limitations in ensuring both valid syntax and stable performance.
A new framework is proposed that combines LLMs' symbolic generation with ML's gradient optimization.
The proposed framework includes steps such as generating high-quality samples, embedding and searching for better feature transformations, distilling knowledge between LLMs, and combining ML and LLM probabilities for stable generation.
Experiments on various datasets show that this framework can improve downstream performance by 5% and reduce error cases by nearly half.
The results highlight the effectiveness and robustness of the collaborative approach.
The study also unveils interesting insights into LLMs' ability to understand original data.

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Arxiv

251

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Spiking Neural Models for Decision-Making Tasks with Learning

Decision-making tasks in cognition are commonly modeled using Drift Diffusion Models (DDMs) and Poisson counter model.
These models lack a learning mechanism and are limited to tasks where participants have prior knowledge of the categories.
A proposal for a Spiking Neural Network (SNN) model for decision-making is made to bridge the gap between cognitive and biological models.
The SNN model incorporates a learning mechanism and neuron activities are modeled by a multivariate Hawkes process.
A coupling result between DDM and the Poisson counter model is shown, indicating similar categorizations and reaction times.
The DDM can be approximated by spiking Poisson neurons.
A particular DDM with correlated noise can be derived from a Hawkes network of spiking neurons governed by a local learning rule.
An online categorization task was designed to evaluate the model predictions.
The work aims to integrate biologically relevant neural mechanisms into cognitive models for a deeper understanding of neural activity and behavior.

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Arxiv

160

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Integrating Asynchronous AdaBoost into Federated Learning: Five Real World Applications

This paper introduces an enhanced asynchronous AdaBoost framework for federated learning (FL) with applications in computer vision, blockchain, mobile personalization, IoT anomaly detection, and healthcare diagnostics.
The algorithm incorporates adaptive communication scheduling and delayed weight compensation to reduce synchronization frequency and communication overhead while maintaining or enhancing model accuracy.
The study evaluates improvements in communication efficiency, scalability, convergence, and robustness in each domain through comparative metrics such as training time, communication overhead, convergence iterations, and classification accuracy.
Empirical results demonstrate notable reductions in training time (20-35%) and communication overhead (30-40%) compared to baseline AdaBoost, with faster convergence in boosting rounds.
The research provides mathematical formulations for adaptive scheduling and error-driven synchronization thresholds, illustrating enhanced efficiency and robustness in various FL scenarios.

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Arxiv

152

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CUDA-LLM: LLMs Can Write Efficient CUDA Kernels

Large Language Models (LLMs) are being utilized for efficient CUDA kernel generation for GPUs.
The challenge lies in creating deeply hardware-specific, performance-critical code for massively parallel GPUs.
A novel framework called Feature Search and Reinforcement (FSR) is introduced for CUDA program optimization.
FSR optimizes compilation, functional correctness, and runtime performance of CUDA programs.
The framework is validated through extensive test cases and actual GPU kernel execution latency measurements.
LLMs using FSR can generate syntactically and semantically correct CUDA code while refining it for efficiency.
Evaluation of FSR on various CUDA kernels shows correctness rates and significantly improved execution speeds.
Automatically generated kernels outperform human-written code by up to 179 times in execution speeds.
The results indicate the potential of combining LLMs with performance reinforcement for GPU programming.
LLMs empowered with FSR can streamline GPU programming for architecture-aware and performance-sensitive applications.

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Arxiv

137

Image Credit: Arxiv

Intra-Trajectory Consistency for Reward Modeling

Reward models play a crucial role in enhancing large language models, especially in reinforcement learning from human feedback or inference-time verification.
Current reward modeling methods primarily rely on overall response scores for learning outcome rewards, which limits generalization on unseen responses.
A new approach is proposed in this paper that utilizes generation probabilities to establish intra-trajectory consistency in the response trajectory.
This approach allows for fine-grained signals to propagate across processes, aiding in reward learning.
An intra-trajectory consistency regularization is developed to ensure consistent rewards between adjacent processes with higher next-token generation probabilities.
The proposed regularization is applied to an advanced outcome reward model, leading to improved performance on RewardBench.
The reward model trained with the new regularization demonstrates better DPO-aligned policies and achieves improved best-of-N (BON) inference-time verification results.
The code for the proposed approach is available at https://github.com/chaoyang101/ICRM.

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