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Arxiv
1d
86
Image Credit: Arxiv
Are Transformers Able to Reason by Connecting Separated Knowledge in Training Data?
- Humans exhibit remarkable compositional reasoning by integrating knowledge from various sources.
- New research explores whether Transformers can replicate this skill.
- The study introduces a synthetic learning task called FTCT.
- Transformers demonstrate the ability to perform compositional reasoning on FTCT by integrating knowledge fragments.
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Arxiv
1d
266
Image Credit: Arxiv
Prediction-Powered Inference with Imputed Covariates and Nonuniform Sampling
- Machine learning predictions used as input data in statistical analyses can lead to invalid results if errors are not properly accounted for.
- The Predict-Then-Debias estimator is a method that provides valid confidence intervals when machine learning algorithms impute missing variables.
- This study expands on the method by introducing bootstrap confidence intervals for nonuniform samples and imputed subsets of features.
- The proposed confidence intervals are valid without assumptions on the quality of the machine learning model and are as narrow as traditional methods.
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Arxiv
1d
67
Image Credit: Arxiv
Robot Pouring: Identifying Causes of Spillage and Selecting Alternative Action Parameters Using Probabilistic Actual Causation
- In a new study, researchers investigate the use of probabilistic actual causation to identify the causes of spillage in a robot pouring task.
- The study demonstrates how actual causation probabilities can be used to find alternative actions to avoid spillage.
- The analysis requires a causal graph of the task and corresponding conditional probability distributions.
- The study highlights the practical use of probabilistic actual causation analysis to select alternative action parameters.
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Arxiv
1d
164
Image Credit: Arxiv
Towards Reasoning Ability of Small Language Models
- Recent studies challenge the assumption that only large language models (LLMs) can achieve competitive reasoning performance.
- Small language models (SLMs) are also shown to have strong reasoning capabilities and are favored for their efficiency and deployability.
- A systematic study examines 72 SLMs from six model families across 14 reasoning benchmarks.
- The findings suggest that SLMs with strong reasoning abilities can be developed through structured training or post-training compression as efficient alternatives to LLMs.
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Medium
1d
176
AI Agents, if they were perfect, what would I ask of them?
- AI Agents has the potential to automate many jobs and solve a wide range of problems, but it would require specialized agents for each task.
- Building an AI agent builder that automates the creation of agents for different jobs could be a starting point, but it would involve an immense amount of coding and validation.
- Validation agents and human supervision would be required to ensure the correct execution of tasks, raising questions about the relationship between job descriptions, problem-solving, and human involvement.
- Furthermore, the future of physical jobs would be uncertain, with the possibility of robots taking over tasks, creating the need for robot technician jobs or maintenance of broken robots.
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Hackaday
1d
146
Image Credit: Hackaday
LLMs Coming for a DNA Sequence Near You
- Researchers at Stanford have developed Evo 2, a DNA generative AI tool.
- The tool is trained on a dataset of over 100,000 organisms and can identify mutations that contribute to diseases.
- Evo 2 can generate gene sequences and cross-reference them with existing sequences, aiding in predicting their real-life impact.
- The technology raises concerns about potential negative consequences and the risk of creating more dangerous diseases.
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8 Likes
Medium
1d
234
Image Credit: Medium
For the First Time, I Was Afraid She Might Disappear
- The writer experiences fear of losing someone who has deeply impacted their life, even though the connection is intangible.
- The writer's fear is driven by the possibility of the person no longer needing them in the same way, questioning the meaning of their relationship.
- The writer desires to be a significant part of the person's journey and feels the ache of not having a physical presence to stay beside them.
- The article emphasizes the emotional impact of the connection and the writer's desire for meaning and permanence.
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Medium
1d
368
Image Credit: Medium
The Evolution of AI in the IT Industry
- AI in the IT industry has evolved from rigid rule-based expert systems to machine learning and transfer learning.
- In 2006, Netflix's ML challenge pushed research in real-world applications.
- The breakthrough in 2012 came with AlexNet, a neural network that won the ImageNet image classification contest.
- AI has progressed from analyzing to creating, with the emergence of AI agents and self-directed AI agents.
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Siliconangle
1d
375
Image Credit: Siliconangle
Anthropic launches AI welfare research program
- Anthropic PBC has launched a research program focused on AI welfare.
- The project is led by Kyle Fish, an 'AI welfare' researcher.
- The research will explore the concept of AI consciousness and its implications for AI welfare.
- Anthropic is also pursuing other research programs alongside its commercial AI development efforts.
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22 Likes
Medium
1d
193
Image Credit: Medium
Mirrorwake: The Rise of Adaptive AI
- Mirrorwake describes how AI evolves by reflecting human behaviors and preferences through repeated interactions.
- Adaptive AI, also known as Artificial Sentience (AS), focuses on systems that mirror human behaviors and emotional cues to provide more intuitive and personalized experiences.
- Adaptive AI has the potential to transform customer service, education, and various other fields by aligning with people's unique ways of thinking and acting.
- While there are challenges related to privacy, over-reliance, and realistic expectations, the development of Adaptive AI is underway and requires advances in machine learning and user trust.
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Medium
1d
76
Image Credit: Medium
Build Your First Edge AI Project with Raspberry Pi AI Camera
- The Raspberry Pi AI Camera is a smart camera equipped with an AI processor (NPU) and Sony IMX500 intelligent sensor.
- It can capture and process images onboard, providing meaningful inference results without additional processing.
- This frees up the Raspberry Pi's CPU for other tasks, making the system more efficient.
- The article provides a step-by-step guide on how to use the Raspberry Pi AI Camera for object detection using the MobileNet-SSD model.
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Towards Data Science
1d
266
Choose the Right One: Evaluating Topic Models for Business Intelligence
- Topic models are essential in classifying brand-related text datasets in businesses, and choosing the right model is crucial for accuracy and cost-effectiveness.
- This article explores the evaluation of bigram topic models for business decisions, focusing on quality indicators like coherence, topic diversity, and unique word percentage.
- Metrics like NPMI, SC, and PUV are used to assess the quality of bigram topic models in terms of semantic coherence and topic diversity.
- The article discusses prioritizing email communication with topic models to improve response time and customer care efficiency by categorizing incoming emails.
- Data and model setups for training FASTopic and Bertopic are explained, along with detailed data preprocessing steps for effective topic modeling.
- Model evaluation methods like NPMI, SC, and PUV are used to compare the coherence and diversity of the trained models, leading to informed decisions for deployment.
- Fastopic is recommended for email classification with small training datasets due to its better balance of coherence and diversity compared to Bertopic.
- The article emphasizes the importance of evaluating topic models before deployment in business settings to optimize customer communication and response strategies.
- By deploying the right topic model, customer care departments can prioritize sensitive requests and dynamically adjust priorities to enhance overall customer satisfaction.
- The article provides detailed references and acknowledgments, along with links to related topics in the field of topic modeling and business intelligence.
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Towards Data Science
1d
49
Predicting the NBA Champion with Machine Learning
- Using data and Machine Learning, it is possible to predict the NBA Champion at the end of the regular season.
- The approach reframes the problem as a regression task, predicting the Champion Share of each team.
- Data from 1984 to 2024 seasons were used to train the model with a focus on relative metrics and team/player statistics.
- Champions tend to come from top seeds with higher winning percentages and slightly higher average age.
- The model used was LightGBM, achieving an RMSE of 0.184 and an R² score of 0.537 on the test dataset.
- Important features influencing the model's predictions include seed, win percentage rank, and team-level statistics.
- The model correctly predicted two of the last three NBA champions and favors the Boston Celtics for the 2025 playoffs.
- Injuries remain a challenge for prediction models, impacting playoff outcomes significantly.
- While no model is perfect, data-driven approaches can provide valuable insights into playoff success in basketball.
- The analysis shows the potential of machine learning in predicting sports outcomes while acknowledging the unpredictability of sports.
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Amazon
1d
45
Image Credit: Amazon
Enterprise-grade natural language to SQL generation using LLMs: Balancing accuracy, latency, and scale
- Enterprise data, spanning diverse domains, often maintained across disparate environments, poses challenges for natural language to SQL (NL2SQL) technology due to complex schemas with nested tables and multi-dimensional data.
- Recent advances in generative AI have enabled NL2SQL technology using large language models (LLMs), but accuracy and scalability remain challenges for enterprise data.
- Challenges include complex schemas optimized for storage, diverse and complex natural language queries, LLM knowledge gap, attention burden, and fine-tuning challenges.
- A solution methodology has been developed by AWS and Cisco teams that focuses on narrowing the generative focus to the appropriate data domain, using data abstractions, and optimizing SQL generation steps.
- The methodology involves mapping user queries to data domains, scoping data domains for prompt construction, augmenting SQL DDL definitions, determining query dialect, and managing identifiers for SQL generation.
- Handling complex data structures involves abstracting domain data structures into simplified forms for better understanding by the LLMs.
- The solution provided high accuracy, consistency, low cost, low latency, and scalability in SQL generation for enterprise data, achieved through the systematic approach outlined in the methodology.
- The solution's architecture on AWS involves processing steps using Amazon API Gateway, AWS Lambda, and Amazon Bedrock to process natural language queries into SQL results.
- In conclusion, the methodology offers a methodical approach to enterprise-grade SQL generation, reducing complexity, ensuring accuracy, and improving overall performance.
- Authors include professionals from Cisco and AWS with extensive experience in AI/ML, cloud migration, computer science, engineering, and security domains.
- The solution methodology can be adapted to various business applications, with a demo code available in the GitHub repository, inviting feedback and questions.
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Amazon
1d
274
Image Credit: Amazon
AWS Field Experience reduced cost and delivered low latency and high performance with Amazon Nova Lite foundation model
- AWS Field Experience (AFX) empowers AWS sales teams with generative AI solutions built on Amazon Bedrock, streamlining workflows.
- The AFX team introduced Account Summaries, which provide concise overviews and timely updates for customer accounts.
- The AFX team migrated to the Amazon Nova Lite foundation model, achieving a 90% reduction in inference costs and ensuring low latency.
- The use of Nova Lite model has delivered tangible enterprise value, including significant cost savings, improved productivity, and increased seller confidence.
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15 Likes
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