Someone once asked AI researchers what their secret to effective financial analysis was. Their answer will surprise the sh*t out of you. Here’s what they had to say: Predicting Earnings with AI The study aimed to see if GPT-4 could analyze financial statements and predict future earnings like a pro human analyst. They fed it standardized balance sheets and income statements. At first, it seemed like a wild idea with no real-world application. But years later, when GPT-4 tackled financial analysis, it all made sense. The AI far outperformed human analysts. Chain-of-Thought Magic The researchers used a Chain-of-Thought (CoT) prompt to guide GPT-4 through the step-by-step analysis, just like a human analyst would think. This approach sustained GPT-4 through complex financial scenarios. Its systematic thinking enabled it to handle challenges and deliver accurate predictions. The data: From 1968 to 2021, the study analyzed data from 15,401 firms, including 150,678 firm-year observations —just pure fundamental analysis. With just the numbers, GPT-4 predicted future earnings with impressive accuracy, proving that AI could match and even surpass human analysts in some cases. Benchmark Comparisons For comparison, the study used logistic regression, an advanced neural network, and consensus forecasts by financial analysts. GPT-4 with CoT outperformed them all. This teaches us that structured numerical data, when analyzed properly, can yield powerful insights. Have the courage to trust data-driven approaches. AI's Analytical Edge GPT-4’s ability to analyze large quantities of unstructured data quickly gave it a significant edge over traditional models and human analysts. When the results were in, GPT-4 had a 60% accuracy rate compared to the naive model’s 49%. This leap showcased AI’s potential in financial analysis. Future of Financial Analysts With AI taking over data-heavy tasks, financial analysts might shift towards more strategic and interpretative roles, focusing on insights rather than just data crunching.

After teaching practical AI for more than 8 months with a successful first course called "The 4 Hour AI Workweek" I gathered tons of feedback from students and people relying on AI for productivity gains. By far the biggest takeaway is NO FLUFF, HIGH SIGNAL, APPLICABLE AI workflows and tutorials that bring about immediate change in the way people work and complete tasks. In 12 hours I will release a beta mini course at a silly discount which will form part of a larger course release end of year. Tomorrow's release can be completed this weekend and will drastically change the way you work. You'll save hours every day and maximize your efficiency to the point where your old work routine is in the forgotten past. So, if you want no-fluff AI tutorials, packed with value - be on the lookout for tomorrow's release. I would urge you to grab the special discount while it's available. Thanks, see you tomorrow!

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We all hear about benchmarks these days And that LLMs are measured against them But what exactly are these benchmarks? Here's an explainer: (🔖Bookmark for later) 1. HumanEval and MultiPL-E HumanEval and MultiPL-E are benchmarks used to evaluate the performance of AI models in generating and understanding code. These benchmarks are designed to assess how well a model can complete programming tasks, understand code syntax, and generate accurate code solutions. HumanEval: Purpose: Measures the ability of AI models to generate correct code solutions for given programming problems. Tasks: Typically involves coding challenges where the model must write functional code snippets based on a problem description. Performance Indicator: Higher scores indicate better understanding and generation of code, reflecting the model’s capability in code-related tasks. MultiPL-E: Purpose: Evaluates the model's performance across multiple programming languages. Tasks: Similar to HumanEval but expanded to include various programming languages, testing the model's versatility and multilingual coding proficiency. Performance Indicator: Outperforming Llama 3.1 405B instruct and scoring just below GPT-4o suggests that Mistral Large 2 is highly proficient in coding tasks and can handle multiple languages effectively. 2. MATH (0-shot, without CoT) MATH (0-shot, without CoT) is a benchmark designed to assess the mathematical reasoning and problem-solving abilities of AI models without prior contextual examples (0-shot) and without chain-of-thought (CoT) prompting. Purpose: Tests the model's inherent ability to solve mathematical problems without additional hints or step-by-step guidance. Tasks: Includes a variety of math problems ranging from basic arithmetic to more complex mathematical reasoning. Performance Indicator: Falling only behind GPT-4o indicates that Mistral Large 2 has strong mathematical reasoning abilities, capable of solving problems independently without guided thinking processes.

As project management evolved, artificial intelligence (AI) is revolutionizing how teams operate, making processes more efficient and outcomes more predictable. AI tools are designed to automate repetitive tasks, provide real-time insights, and optimize resource allocation, thereby freeing up project managers to focus on strategic decision-making. This article explores the top AI tools that can significantly enhance your project management capabilities in 2024. Frequently Asked Questions About AI Tools for Project Management What are AI Project Management Tools? AI project management tools integrate AI capabilities with traditional project management techniques to streamline processes, automate tasks, and improve decision-making. They analyze vast amounts of data to provide predictive analytics, optimize resource allocation, and enhance overall project outcomes. How Do AI Tools Benefit Project Managers? AI tools offer several benefits: - Automation: They handle routine tasks like scheduling, data entry, and task tracking, reducing the burden on project managers. - Real-time Insights: AI provides real-time data analysis, helping managers make informed decisions quickly. - Risk Mitigation: AI tools can predict potential risks by analyzing historical data and current trends. - Cost Savings: By optimizing resource allocation, AI tools help in reducing project costs. Can AI Tools Integrate with Other Software? Yes, AI project management tools can integrate with various other software and tools such as collaboration platforms (e.g., Slack, Microsoft Teams), document management systems, CRM software, and accounting tools. This integration enhances the overall efficiency of project management processes. Top AI Tools for Project Management in 2024 1. Forecast: Best Overall AI Project Management Software Overview:Forecast provides a unified platform for project creation, budgeting, resource allocation, task management, invoicing, and reporting. Its AI capabilities offer smart insights into project data, helping managers make well-informed decisions.Key Features: