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Arstechnica

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New Apple study challenges whether AI models truly “reason” through problems

Apple researchers published a study suggesting that Simulated Reasoning models produce outputs consistent with pattern-matching, rather than true reasoning, when faced with novel problems.
The study, titled 'The Illusion of Thinking,' evaluated large reasoning models against classic puzzles of varying difficulties.
Results showed that models struggled on tasks requiring extended systematic reasoning, achieving low scores on novel mathematical proofs.
Critics like Gary Marcus found the results 'devastating' to Large Language Models (LLMs) and questioned their logical and intelligent processes.
The study revealed that SR models behave differently from standard models depending on task difficulty, sometimes 'overthinking' and failing on complex puzzles.
An identified scaling limit showed that reasoning models reduce their effort beyond a certain complexity threshold.
Not all researchers agree with the study's interpretation, suggesting that limitations may reflect deliberate training constraints rather than inherent inabilities.
Some critics argue that the study's findings may be measuring engineered constraints rather than fundamental reasoning limits.
The Apple researchers caution against over-extrapolating the study's results, noting that puzzle environments may not capture the diversity of real-world reasoning problems.
While the study challenges claims about AI reasoning models, it does not render these models useless, indicating potential uses for tasks like coding and writing.

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Mit

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Bringing meaning into technology deployment

In a recent event, MIT faculty presented their pioneering research that integrates social, ethical, and technical aspects with seed grants from SERC.
70 proposals were submitted in response to the call for proposals, with winning projects receiving up to $100,000 in funding.
The MIT Ethics of Computing Research Symposium highlighted four key themes: responsible health-care technology, AI governance, technology in society, and digital inclusion.
Projects included improving kidney transplant systems, examining AI-generated social media content ethics, and enhancing civil discourse online using AI.
Dimitris Bertsimas introduced an algorithm for fair kidney transplant allocation, significantly reducing evaluation time.
Adam Berinsky and Gabrielle Péloquin-Skulski discussed the impact of labeling AI-generated social media content on user perception.
Lily Tsai and team focused on using AI to increase civil discourse online through the DELiberation.io platform.
Catherine D’Ignazio and Nikko Stevens established Liberatory AI, functioning as a public think tank exploring all aspects of AI.

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Marktechpost

306

How Do LLMs Really Reason? A Framework to Separate Logic from Knowledge

Advancements in reasoning-focused LLMs like OpenAI's o1/3 and DeepSeek-R1 have improved complex task performance, yet their reasoning processes remain unclear.
Current evaluations of LLMs often focus on final-answer accuracy, masking the reasoning steps and the combination of knowledge and logic.
Factual errors and lack of reasoning depth in math and medicine demonstrate the limitations of current final-answer evaluation methods.
Researchers propose a new framework to assess LLM reasoning by separating factual knowledge and logical steps using the Knowledge Index and Information Gain metrics.
Evaluation of Qwen models across math and medicine tasks shows that reasoning skills do not easily transfer between domains.
The study compares supervised fine-tuning and reinforcement learning in domain-specific tasks, highlighting the impact on accuracy, knowledge retention, and reasoning depth.
Results indicate that while supervised fine-tuning enhances factual accuracy, it may weaken reasoning depth, whereas reinforcement learning improves both reasoning and knowledge.
The framework introduced in the study aims to make LLMs more interpretable and trustworthy, particularly in critical fields like medicine and math.

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Arstechnica

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In landmark suit, Disney and Universal sue Midjourney for AI character theft

Disney and NBCUniversal have filed a lawsuit against AI company Midjourney for copyright infringement, allowing users to create images of characters like Darth Vader and Shrek.
The lawsuit, filed in US District Court in Los Angeles, is the first major legal action by Hollywood studios against a generative AI company.
Midjourney is accused of enabling users to generate personalized images of copyrighted characters using AI image-synthesis.
The studios claim Midjourney trained its AI model on copyrighted content without permission, leading to the creation of unauthorized images.
The legal complaint includes visual examples showing AI-generated versions of characters like Yoda, Wall-E, Stormtroopers, Minions, and more.
Disney's general counsel stated that infringement by an AI company does not make it any less illegal, emphasizing the issue of piracy.
The studios argue that Midjourney actively promotes copyright infringement by displaying copyrighted characters in its platform's 'Explore' section.
Midjourney supposedly has technical protection measures to prevent infringing outputs but has chosen not to implement them.
Prior to the lawsuit, Disney and NBCUniversal tried to address the issue with Midjourney, but the company allegedly continued to release infringed images.
NBCUniversal's executive vice president highlighted the lawsuit's purpose to protect the artists' work and the studios' significant content investments.
The legal action demonstrates Hollywood's new front concerning AI copyright issues, with major studios potentially uniting against tech companies.
Other studios like Amazon, Netflix, Paramount Pictures, Sony, and Warner Bros. are not part of the lawsuit but are members of the Motion Picture Association.
The conflict highlights the studios' efforts to protect intellectual property in the face of AI advancements and potential copyright violations.
Midjourney's platform allows users to submit prompts for AI-generated images, leading to the creation of unauthorized images of well-known characters.
The lawsuit follows similar legal moves in different creative industries, indicating a trend of addressing AI-related copyright concerns.
Various copyrighted characters from different studios were found in the AI-generated images provided as evidence in the legal filing by Disney and NBCUniversal.

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Medium

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What is Alignment in AI?

Alignment in AI refers to synchronizing an AI system's goals with human intentions and values.
Humans often struggle to clearly articulate their values, making alignment challenging.
AI could misinterpret vague objectives, leading to harmful outcomes.
Real-world examples include social media algorithms amplifying harmful content and Amazon's biased recruitment algorithm.
Alignment involves implementing feedback loops, testing with diverse perspectives, and embedding ethics from the start.
Product managers can foster alignment by taking specific steps.
Understanding AI alignment is crucial for anyone interacting with technology.
Aligning AI with human values is a significant challenge and opportunity in our interconnected world.

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Medium

213

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RAG in Practice: Exploring Versioning, Observability, and Evaluation in Production Systems

The article discusses the concept of RAG systems and their evolution alongside LLM-powered applications.
It explores the practical implications of LLMOps, essentially MLOps tailored for large language models, with a focus on RAG systems.
Key questions addressed include data tracking in RAG systems, evaluation of retrieval quality, and system architecture choices.
The article highlights the importance of observability, evaluation/testing, reproducibility, modularity, and versioning in RAG systems.
The author details building a containerized RAG system orchestrated with Docker Compose and utilizing a microservice architecture.
Challenges in data versioning, traceability, and system evaluation are discussed within the context of RAG systems.
The exploration includes incorporating monitoring with Prometheus and Grafana, and discussing evaluation methodologies using tools like RAGAS and MLflow.
The article delves into model deployment considerations, system design choices, and the implications of using hosted LLMs versus self-hosted models.
Future considerations involve event-driven architectures, enhanced evaluation infrastructure, user feedback mechanisms, and database optimization for RAG systems.
The author emphasizes the ongoing evolution and learning process in working with RAG systems, seeking feedback and further insights.
The project serves as a practical exploration of deploying RAG systems, aiming to grasp the nuances of achieving 'production-ready' status in this domain.

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Medium

205

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I’m Sharing the Exact 8-Step Playbook I’d Use to Get Rich with AI Today

AI is a rapidly evolving technology that presents numerous opportunities for those who explore it.
AI is likened to a new continent full of potential for wealth creation, similar to the early days of the internet.
The key to getting rich with AI is not luck but intelligence in leveraging the technology to address actual problems.
The provided playbook offers a step-by-step guide on utilizing AI to create valuable solutions.

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Mit

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Photonic processor could streamline 6G wireless signal processing

MIT researchers have developed a novel AI hardware accelerator for wireless signal processing, using an optical processor that performs machine-learning computations at the speed of light, classifying wireless signals in nanoseconds.
The photonic chip is significantly faster and more energy-efficient than digital alternatives, making it scalable and flexible for various high-performance computing applications, including future 6G wireless technologies like cognitive radios.
This new hardware accelerator enables edge devices to perform real-time deep-learning computations, potentially revolutionizing applications like autonomous vehicles' reactions to environmental changes or continuous monitoring by smart pacemakers.
The optical neural network architecture, named MAFT-ONN, encodes signal data and conducts all machine-learning operations in the frequency domain, achieving high efficiency and scalability for signal processing.
MAFT-ONN can fit 10,000 neurons on a single device, performing necessary multiplications efficiently through photoelectric multiplication and achieving signal classification with high accuracy in nanoseconds.
The researchers aim to enhance MAFT-ONN further by implementing multiplexing schemes for increased computations, expanding into complex deep learning architectures, and optimizing performance for future applications.

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Medium

Illusion of Thinking — what’s next?

Apple's results show that for low complexity problems, standard Large Language Models (LLMs) outperform Less-Resource Models (LRMs), while LRMs have an advantage in medium complexity problems. However, both models fail for high complexity problems.
The author agrees with Apple's results, stating that both LLMs and LRMs, being based on learned patterns from data, do not capture the 'thinking' process of the human mind effectively.
Drawing a parallel to Schrödinger's thought experiment, where outcomes are probabilistic, the author suggests that the human mind cannot be modeled in a binary way, but rather needs a quantum approach.
The author proposes that modeling the human mind with a quantum approach, leading to probabilistic outcomes, may be more reflective of actual human thinking processes.

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Hackernoon

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Kaggle Champions Swear by XGBoost — And You Can Too

XGBoost is a prominent model in machine learning, known for dominating Kaggle competitions and being efficient at handling tabular data.
XGBoost iteratively learns from mistakes, is optimized for speed and accuracy, and performs well on large datasets.
To get started with XGBoost, one can install it using 'pip install xgboost' and check the version for confirmation.
Training a model with XGBoost on the Iris dataset involves prepping the data, converting it into XGBoost's optimized format, and training the model.
A key component in XGBoost is DMatrix, which allows for efficient data handling before model training.
Performance evaluation of an XGBoost model can be done using metrics like accuracy.
GridSearchCV can be employed to fine-tune model parameters for better performance.
Feature importance analysis in XGBoost can be visualized to understand which features the model relies on most.
SHAP can be used for explaining model predictions in XGBoost, enhancing model interpretability.
XGBoost can be utilized for regression and binary classification tasks in addition to its use for classification.
Advanced users can explore distributed training options with XGBoost, including multi-GPU training and utilizing frameworks like Dask or Spark.
XGBoost is recommended for structured data tasks requiring speed, power, and flexibility, with possibilities for advanced fine-tuning and scalability.

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Arstechnica

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With the launch of o3-pro, let’s talk about what AI “reasoning” actually does

OpenAI launched o3-pro, a new simulated reasoning model, available to ChatGPT Pro and Team users, replacing o1-pro.
o3-pro focuses on mathematics, science, coding, web search, file analysis, image analysis, and Python execution.
The model is slower but recommended for complex problems where accuracy is more important than speed.
Price reductions for o3-pro make it 87% cheaper than o1-pro, addressing concerns about high costs.
o3-pro utilizes chain-of-thought simulated reasoning for technical challenges, prioritizing accuracy over speed.
OpenAI reports positive feedback on o3-pro's performance and preferences over o3 in various domains.
Research suggests that reasoning models like o3-pro allocate more resources for improved analytical task performance.
AI models using chain-of-thought techniques enhance accuracy by constraining outputs and considering context.
However, Transformer-based AI models primarily rely on pattern matching from training data.
While pattern-matching and reasoning are not mutually exclusive, limitations exist in current AI models' logical reasoning abilities.

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Amazon

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Adobe enhances developer productivity using Amazon Bedrock Knowledge Bases

Adobe Inc. offers a suite of creative tools for digital artists, designers, and developers worldwide. Adobe's Developer Platform team created Unified Support to centralize developer information, improving efficiency and reducing support costs.
Adobe partnered with the AWS Generative AI Innovation Center to enhance their developer support system using Amazon Bedrock Knowledge Bases.
The solution resulted in a 20% increase in retrieval accuracy, empowering developers to access Adobe-specific guidelines and best practices efficiently.
Adobe's project focused on developing a robust document retrieval engine and scalable, automated deployment using Amazon Bedrock Knowledge Bases.
The solution enabled multi-tenancy through metadata filtering and streamlined the retrieval process using the Retrieve API.
Experimental testing included optimizing data chunking strategies to improve retrieval performance, with the fixed-size 400-token strategy proving most effective.
The integration of metadata filtering and the Amazon Titan V2 embedding model significantly increased retrieval accuracy for Adobe's developer support.
Key individuals involved in the project include Kamran Razi from Amazon and Nay Doummar, Varsha Chandan Bellara, Jan Michael Ong from Adobe, among others.
The collaboration between Adobe and AWS led to a scalable, efficient developer support solution, laying a foundation for continuous improvement in developer support at Adobe.
The Amazon Bedrock Knowledge Bases solution offers advanced data chunking, retrieval capabilities, and metadata filtering to enhance developer productivity at Adobe.

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Amazon

314

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Amazon Nova Lite enables Bito to offer a free tier option for its AI-powered code reviews

Bito, an innovative startup, utilizes AI agents for software developers, offering AI-powered code reviews.
Bito's AI Code Review Agent accelerates PR time-to-merge by 89%, reduces regressions by 34%, and works in over 50 programming languages.
With 100,000+ active developers globally, Bito's AI tools enhance code review processes and increase productivity.
To overcome developer wariness of AI tools, Bito introduces a free tier option for its AI Code Review Agent.
Choosing Amazon Nova Lite for the Free Plan, Bito ensures cost-effectiveness with high performance.
Implementing Amazon Bedrock as its standardized platform, Bito seamlessly shifts between models for different tiers of offerings.
Amazon Nova Lite's low cost allows Bito to attract more prospective customers, driving significant growth.
Bito extends the use of Amazon Nova Lite to power its latest AI agentic technology, Bito Wingman.
Bito's strategic use of Amazon Nova Lite and Amazon Bedrock showcases the value of quality and cost-effectiveness in AI integration.
Amar Goel, Co-Founder and CEO of Bito, emphasizes the importance of Amazon Nova Lite in delivering new value to customers.
Bito's success story with AI-powered tools highlights the potential of leveraging AI for software development.
Overall, Bito's adoption of Amazon Nova Lite has enabled it to enhance code review processes, attract new customers, and drive business growth.
Eshan Bhatnagar leads Amazon AGI's Product Management, while Amar Goel, Bito's Co-Founder, focuses on using GenAI for software development innovation.
The successful partnership between Bito and Amazon Nova Lite demonstrates the benefits of AI integration in streamlining software development processes.

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Amazon

335

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How Gardenia Technologies helps customers create ESG disclosure reports 75% faster using agentic generative AI on Amazon Bedrock

Gardenia Technologies partners with AWS to develop Report GenAI, automating ESG reporting with generative AI on Amazon Bedrock, reducing reporting time by up to 75%.
ESG reporting has become a burden for many organizations, with excessive administrative work hindering strategic initiatives.
Gardenia's Report GenAI automates data collection from various sources like databases, documents, and web searches, enhancing the ESG reporting process.
The solution involves an agentic search approach, utilizing tools like Retrieval Augmented Generation (RAG) and text-to-SQL capabilities for efficient reporting.
The process includes steps like setup, batch-fill, review, edit, and repeat, streamlining ESG data collection and response curation.
The Report GenAI architecture employs components such as a UI, generative AI executor, web search tool, text-to-SQL tool, RAG tool, and embedding generation pipeline.
Evaluation of agent performance combines human expertise with AI validation to ensure accuracy and reliability in ESG reporting.
A case study with Omni Helicopters International showcases a 75% reduction in reporting time using Gardenia's Report GenAI solution.
The dual-validation approach in Report GenAI offers a robust quality assurance framework involving human oversight and AI-powered quality assessment.
Authors from Gardenia Technologies and AWS share insights on the development and implementation of Report GenAI for automated and impactful ESG reporting.
The post highlights the collaboration between Gardenia and AWS to build and offer Report GenAI on the AWS Marketplace, enabling organizations to simplify ESG reporting.

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Medium

104

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Spark vs SQL: A Comprehensive Comparison

Spark SQL and SQL both offer capabilities to query structured data and support ANSI SQL syntax.
SQL originated in the 1970s and has been fundamental in relational databases and enterprise reporting, while Spark was developed as a faster alternative to Hadoop MapReduce, focusing on in-memory cluster computing.
SQL is commonly used for OLTP, OLAP, and business intelligence, while Spark is utilized for ETL pipelines, real-time stream processing, machine learning, and more.
Marketwise, SQL caters to traditional enterprise software and RDBMS, whereas Spark addresses Big Data analytics, AI/ML operations, and real-time data processing.
The SQL database market is projected to reach over $75 billion by 2028, while the Big Data and Spark-related market is estimated to surpass $130 billion by 2026.
Spark extensively benefits from NVIDIA GPU compatibility through the RAPIDS Accelerator, providing significant speed enhancements for data processing and ML executions.
Spark integrates well with various ML frameworks like TensorFlow, XGBoost, and PyTorch, while SQL primarily focuses on basic analytics and lacks strong AI integration.
In terms of future developments, Apache Spark aims to expand GPU acceleration, enhance Python support, improve Kubernetes integration, and enable AI-native workflows.
On the other hand, SQL ecosystems are pushing toward cloud-native data warehousing, serverless execution, and increasing AI SQL extensions like Pinecone and Milvus.
Key players in the SQL ecosystem include Oracle, Microsoft, IBM, Google, Snowflake, and open-source solutions like PostgreSQL and MySQL.
Within the Spark ecosystem, notable players include Databricks, Cloudera, Amazon EMR, Google Cloud Dataproc, NVIDIA, and Microsoft Azure Synapse Spark.
Ultimately, while SQL is favored for structured data and enterprise reporting, Apache Spark is recognized for modern data engineering, real-time analytics, and AI workflows, showing strength in GPU-intensive and AI processing tasks.

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