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Prediction Errors, Bias, and Variance; Top 5 AI resume builders
SoftBank to build a $100B AI chip venture
Ishaan Mahajan
February 21, 2024
Welcome to learning edition of the Data Pragmatist, your dose of all things data science and AI.
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Today we are talking about comprehending prediction errors, bias, and variance is fundamental for achieving accurate models. As part of our learning series, Top 5 AI resume builders.
📝 Top 5 AI resume builders in 2024
Rezi
Features: Real-time optimization based on keyword analysis, 300 templates, error detection, Chrome extension for LinkedIn profile integration.
Pricing:
Free basic plan
Pro plan for $29 per month
Lifetime plan for a one-time payment of $129
Kickresume
Features: GPT-4 powered, various templates, ATS-friendly, cover letter builder, resume to website conversion.
Pricing:
Free plan
Monthly plan for $19
Yearly plan for $5 per month
ChatGPT
Features: Personalised advice, keyword identification, ATS compatibility, natural language processing.
Pricing:
Free plan
ChatGPT-4 for $20 per month
Skillroads
Features: Free resume writing tool, AI-driven suggestions, paid resume writing services, cover letter builder, ATS checker.
Pricing:
Free for AI resume builder tool
Paid resume writing services available
Jobscan
Features: Resume optimization, comparison to job listings, soft and hard skills matching, ATS compliance.
Pricing:
Free plan
Monthly plan for $49.95
🧠 Understanding Prediction Errors, Bias, and Variance
In machine learning, comprehending prediction errors, bias, and variance is fundamental for achieving accurate models.
Bias: The Discrepancy between Predictions and Actual Values
Bias refers to the difference between the model's predictions and the actual values. High bias leads to significant errors in both training and testing data, resulting in underfitting, where the model's predictions lack complexity to accurately represent the dataset.
In such a problem, a hypothesis looks like follows.
Variance: The Variability in Model Predictions
Variance measures the variability in model predictions for a given data point, indicating the spread of data. Models with high variance exhibit complex fits to training data but struggle to generalize to unseen data, leading to overfitting.
In such a problem, a hypothesis looks like follows.
The Bias-Variance Tradeoff
The bias-variance tradeoff necessitates finding a balance between model complexity (variance) and simplicity (bias). Overly simplistic models result in high bias and low variance, prone to underfitting, while excessively complex models display high variance and low bias, susceptible to overfitting.
Understanding bias and variance aids in navigating the bias-variance tradeoff, crucial for developing models that generalize well to unseen data. By optimizing model complexity, machine learning practitioners can mitigate underfitting and overfitting, ultimately enhancing the model's predictive accuracy and reliability.
🤖 Google unveils Gemini 1.5 LINK
Google has launched Gemini 1.5, an upgraded version of its large language model, featuring improvements like a "Mixture of Experts" technique for faster, more efficient processing, and availability for developers and enterprise users ahead of a broader consumer rollout.
A standout feature of Gemini 1.5 is its significantly expanded context window of 1 million tokens, greatly surpassing the capacity of its predecessors and competitors, enabling it to process and analyze extensive amounts of information in a single query.
The enhanced context window facilitates a range of new applications, from analyzing entire movies for potential reviews to reviewing extensive financial records for businesses.
🔍 OpenAI reportedly developing AI web search to compete with Google
OpenAI is reportedly developing its own web search product that will compete directly with Google and may be based in part on Google's Bing search.
It is unclear whether the web search will be a separate product from ChatGPT, which already integrates Bing and summarizes web content in about 100 words.
A standalone search product from OpenAI could be linked to an AI agent that independently performs tasks on the web, such as booking movie tickets. OpenAI is reportedly working on such an agent.
💰 SoftBank to build a $100B AI chip venture
SoftBank’s Masayoshi Son is seeking $100 billion to create a new AI chip venture, aiming to compete with industry leader Nvidia.
The new venture, named Izanagi, will collaborate with Arm, a company SoftBank spun out but still owns about 90% of, to enter the AI chip market.
SoftBank plans to raise $70 billion of the venture's funding from Middle Eastern institutional investors, contributing the remaining $30 billion itself.
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