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Arxiv
1d
357
Image Credit: Arxiv
On the dimension of pullback attractors in recurrent neural networks
- Recurrent Neural Networks (RNNs) are capable of learning functions on sequence data.
- Reservoir computers, a class of RNNs, trained on dynamical system observations can be interpreted as embeddings.
- An upper bound for the fractal dimension of the reservoir state space during training and prediction phase is established.
- The fractal dimension of the subset is bounded above by the dimension of the input sequences in a nonautonomous dynamical system.
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Arxiv
1d
310
Image Credit: Arxiv
PhD Knowledge Not Required: A Reasoning Challenge for Large Language Models
- A new benchmark for large language models (LLMs) has been developed, which focuses on general knowledge rather than specialized 'PhD-level' knowledge.
- The benchmark consists of 594 problems based on the NPR Sunday Puzzle Challenge and is challenging for both humans and models.
- OpenAI o1 outperforms other reasoning models on the benchmark, revealing capability gaps in existing benchmarks.
- The analysis of reasoning outputs exposes new types of failures in models, such as conceding with 'I give up' before providing known incorrect answers.
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Medium
1d
0
Image Credit: Medium
This Is How AI Thinks (It’s Not What You Think)
- AI models like Claude or ChatGPT are often described as black boxes.
- Research by Anthropic has revealed that AI models like Claude actually think and plan.
- Claude demonstrates the ability to formulate plans in advance to ensure coherence.
- Internally, AI models like Claude don't separate languages but activate conceptual links for translation.
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Medium
1d
38
Image Credit: Medium
Will AI Steal Your Job or Make It Better?
- AI is making things faster, cheaper, and more efficient, but it doesn't replace jobs, it replaces tasks.
- AI is used in various fields like marketing and graphic design to automate repetitive tasks.
- Jobs involving data entry, basic analysis, and predictable workflows are at the highest risk.
- Adapting, learning new skills, and embracing AI as a tool can lead to an exciting future.
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Towards Data Science
2d
323
A Simple Implementation of the Attention Mechanism from Scratch
- The Attention Mechanism is crucial in tasks like Machine Translation to focus on important words for prediction.
- It helped RNNs mitigate the vanishing gradient problem and capture long-range dependencies among words.
- Self-attention in Transformers provides information on the correlation between words in the same sequence.
- It generates attention weights for each token based on other tokens in the sequence.
- By multiplying query and key vectors and applying softmax, attention weights are obtained.
- Multi-head Self-Attention in Transformers uses multiple sets of matrices to capture diverse relationships among tokens.
- The dense vectors from each head are concatenated and linearly transformed to get the final output.
- The implementation involves generating query, key, and value vectors for each token and calculating attention scores.
- Softmax is applied to get attention weights, and the final context-aware vector is computed for each token.
- A multi-head attention mechanism with separate weight matrices for each head is used to improve relationship capture.
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Medium
2d
105
Image Credit: Medium
Dataset Definition, Types, Benefits, and Use Cases
- Before the rise of machine learning (ML), data science focused on traditional statistical analysis, manual data handling, data visualization, and predictive analytics without ML.
- Data science before machine learning primarily used predefined models and rules to derive insights.
- Structured datasets are well-organized and stored in a table-like format, typically in relational databases or spreadsheets.
- Unstructured data refers to datasets that aren't stored in a structured format, including audio and video datasets.
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Medium
2d
328
Image Credit: Medium
How AI is Revolutionizing Asset Allocation: A Hands-On Guide with Python
- AI is revolutionizing asset allocation through the use of large language models (LLMs) and quantitative models like modern portfolio theory (MPT).
- A hands-on guide with Python demonstrates building an AI-powered asset allocation system.
- The process involves combining LLM-generated market insights with Markowitz’s optimization model.
- This approach enables investors to make smarter, data-driven financial decisions.
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Towards Data Science
2d
100
My Learning to Be Hired Again After a Year… Part 2
- After a year of unemployment, the author reflects on finding meaning and identity outside of a job title or company name.
- Deciding to pivot to machine learning engineering, the author sought advice from friends who had made a similar career move.
- Despite the competitive job market, the author chose to try for machine learning engineer positions even at entry level.
- By focusing on self-improvement and showing value in interviews, the author gained three job offers for senior MLE roles.
- The author emphasizes the importance of not begging for jobs but rather selling oneself effectively.
- Through mock interviews and practice, the author improved behavioral interview skills and changed their mindset towards job interviews.
- Struggling with nervousness and judgment during interviews, the author learned to pause, breathe, and refocus to regain control.
- Utilizing the Mnookin Two-Pager exercise helped the author clarify job preferences and goals, leading to a better job fit at Disney.
- Writing as a way to reflect, the author invites readers to follow their posts on TDS and subscribe to their newsletter for more insights and humor.
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Siliconangle
2d
86
Image Credit: Siliconangle
Alphabet spinout Isomorphic Labs raises $600M for its AI drug design engine
- Isomorphic Labs, an Alphabet spinout, has raised $600 million in funding for its AI drug design engine.
- The funding round was led by Thrive Capital and joined by Alphabet and GV.
- Isomorphic Labs' AI software streamlines the process of designing small molecules for therapeutic applications.
- The funding will be used to enhance the software and support in-house drug development programs.
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Medium
2d
290
Image Credit: Medium
Unlocking New Income Streams: AI-Assisted Content Creator
- AI tools like ChatGPT, Grammarly, and Hemingway Editor have revolutionized content creation by assisting in generating ideas, refining grammar, and enhancing readability.
- To thrive as an AI-assisted writer, focusing on essential skills is crucial.
- While embracing AI in content creation, maintaining the human touch and adding unique insights is emphasized to ensure authenticity and engagement.
- Integrating AI into the writing process can unlock multiple income streams, as demonstrated by the experience of my friend.
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Medium
2d
286
Image Credit: Medium
I Just Discovered A Dirty Little Secret About ChatGPT
- ChatGPT has a hidden data control option in the settings.
- The author discovered this hidden feature by exploring the ChatGPT settings.
- The data control option allows users to have control over their data.
- The feature was overlooked by the author and is considered a dirty little secret.
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Medium
2d
110
Image Credit: Medium
Data Science: The Sexy Job That’s Not So Sexy After All
- Data science is often not the glamorous job it's made out to be.
- A significant portion of the work involves data cleaning and wrangling.
- Data scientists heavily rely on online resources and problem-solving skills.
- The job requires a mix of technical expertise, statistical knowledge, and patience.
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Amazon
2d
235
Image Credit: Amazon
Build agentic systems with CrewAI and Amazon Bedrock
- The enterprise AI landscape is shifting towards agentic systems becoming essential business assets, with a projected market growth from $5.1 billion in 2024 to $47.1 billion by 2030.
- CrewAI's open source agentic framework, combined with Amazon Bedrock, allows for the development of complex multi-agent systems for business transformations.
- AI agents are autonomous systems using large language models to perform tasks independently with minimal human intervention, enabling adaptive decision-making and contextual understanding.
- CrewAI's suite simplifies AI automation through flows and crews of AI agents, promoting team collaboration, delegation of tasks, and enhanced problem-solving.
- CrewAI's key concepts include agents with defined roles, goals, backstory, and tools, tasks that outline specific actions, and tools that extend agent capabilities.
- CrewAI's integration with Amazon Bedrock enhances AI Flows by providing access to powerful foundation models and enabling adaptive, intelligent automation at scale.
- Real-world impacts of CrewAI include legacy code modernization with a 70% improvement in code generation speed and back-office automation leading to a 75% reduction in processing time at a global CPG company.
- Amazon Bedrock integration with CrewAI allows for the creation of production-grade AI agents using state-of-the-art language models, benefiting from enterprise-grade security, compliance, and scalability.
- Operational excellence when deploying agentic applications with CrewAI on AWS includes application-level observability, model-level performance metrics, and agent-level monitoring for efficient system operations.
- The solution architecture combines CrewAI agents to streamline security assessments, automating mapping, vulnerability analysis, and report generation with Amazon Bedrock.
- The post highlights the collaboration of authors from CrewAI, AWS, and other technology firms, showcasing expertise in AI development and solutions architecture.
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Hackernoon
2d
23
Image Credit: Hackernoon
Meet Keymakr: HackerNoon Company of the Week
- Keymakr, a company focusing on image and video annotation services, is the featured Company of the Week.
- Keymakr's proprietary annotation platform, Keylabs, handles large and complex datasets across multiple industries.
- With a four-level quality assurance system and a global presence, Keymakr ensures 99.9% accuracy in annotations.
- Keymakr has partnered with HackerNoon for their business blogging program.
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Medium
2d
197
Image Credit: Medium
What are Neural Networks? (All Basics Covered)
- Neurons in a Neural Network (NN) are variables that hold numeric values representing data inputs.
- We adjust the importance of neurons through Weights and Biases, similar to knobs adjusted by a DJ.
- Changing weights and biases influences the output of a neural network, which aims to minimize error.
- Gradient Descent helps find the best set of weights by moving towards the minima of the error curve.
- Stochastic Gradient Descent improves efficiency and helps avoid local minima in training neural networks.
- Neural Network layers include Input, Hidden, and Output layers, with Hidden layers performing the core computations.
- Softmax function converts NN outputs into probabilities, aiding in classification tasks.
- Backpropagation adjusts weights and biases by comparing actual and expected outputs to increase accuracy.
- Epoch in NN training refers to one cycle of forward and backward propagation to improve model accuracy.
- Understanding neural networks and methodologies like backpropagation can lead to improved predictions and model accuracy.
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