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🌟 16 Changes for Enterprise AI: From Experiment to Mainstream

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Good morning, Wednesday!

As companies dive deeper into AI, pouring more into its growth, we're seeing it everywhere. But as ChatGPT becomes a favorite, not everyone's convinced about its take on elections. Curious? Let's explore together.

What are on FIRE 🔥

🏢 16 Changes for Enterprise AI: From Experiment to Mainstream
🚀 AI's Role in Next-Gen Software Engineering
📈 AI Stock Daily
🌟 AI Highlights
💸 Daily AI Fundraising
🏅 New Empowered AI Tools
⚡ 5 AI Quick Hits
✏️ AI Tutorials: Build & Train an AI Model using webAI’s Navigator
🎯 Build the Best ChatGPT Trading Bots with “DEBOPIE” Framework
💼 4 AI Jobs

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Tap into the value of clean and structured data for market research, ML/AI development, and strategic decision-making. With our scalable solutions, you can efficiently enhance your data strategy, ensuring you're always one step ahead. Start with our free trial and see the difference yourself.

AI INSIGHTS

🏢 16 Changes for Enterprise AI: From Experiment to Mainstream

Source: a16z Growth

With increasing investment and strategic shifts, enterprises are mainstreaming generative AI, focusing on production deployment and open-source models.

Key Takeaways:

  • Measuring ROI and securing specialized talent are seen as major challenges in the widespread adoption of AI.

  • Enterprises are adopting a multi-model strategy, enhancing agility and technological integration.

  • A significant 46% of enterprises are shifting towards open-source AI models in 2024 for greater control and customization.

  • The average enterprise spend on generative AI was $7M in 2023, with plans to increase this investment by 2x to 5x in 2024, reaching $18M.

  • There's a strategic move from one-time 'innovation' budgets to more sustainable software investment lines.

Why it matters: Overcoming these obstacles is crucial for enterprises to fully leverage generative AI's potential, suggesting a significant opportunity for solutions that address these gaps.

AI INSIGHTS  

👥 Diverse ChatGPT Use and Trust Concerns

Source: Pew Research Center

While ChatGPT sees higher usage rates, trust in its election content remains low among Americans.

Key Takeaways:

  • 23% of U.S. adults have used ChatGPT, up from 18%.

  • 38% of Americans distrust ChatGPT's election information.

  • Only 2% trust ChatGPT's election info greatly.

  • 43% of adults under 30 have used ChatGPT.

  • Usage for work rose to 20% among employed Americans.

  • About 31% of employed adults under 30 say they have used it for tasks at work – up 19 points from a year ago.

Why it matters: Increased ChatGPT use across demographics contrasts sharply with its low trustworthiness on election matters, highlighting a trust challenge in AI applications.

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AI STOCK

TODAY IN AI

AI HIGHLIGHTS

  • Researchers at KU Leuven improve beer taste using AI.

  • AI analyzes 250 Belgian beers across 22 styles.

  • The study involved a tasting panel of 16 over three years.

  • Machine learning models predict beer taste preferences.

  • Experiment enhances commercial beer based on AI predictions.

  • NVIDIA's Earth-2 predicts extreme weather using AI.

  • Earth-2 can simulate Earth, offering high-resolution climate images.

  • CorrDiff AI model produces images 12.5x higher resolution.

  • Earth-2 generates forecasts 1,000x faster.

  • Utilizes CUDA-X microservices for efficient simulations.

  • Apple selects Baidu's Ernie Bot for iPhone 16 and other products in China.

  • Tim Cook opens a new retail store in Shanghai.

  • Apple also held talks with Alibaba and Tsinghua University's AI company.

  • Apple to use its own AI model outside China.

  • iPhone sales are down 24% year on year in China.

  • Sam Altman suggests nuclear fusion could power future AI.

  • AI's energy consumption is rising sharply.

  • Experts are skeptical about fusion's near-term viability.

  • Fusion is decades away from commercial use.

  • Demand for AI could double electricity needs soon.

DAILY AI FUNDRAISING

Activeloop has secured $11M in Series A funding to advance its AI database, Deep Lake, which simplifies data management for major industries, enhancing AI’s accuracy and efficiency.

Thoras.ai has secured $1.5M to innovate cloud computing, focusing on smart, automated scaling to improve efficiency and reliability for businesses.

NEW EMPOWERED AI TOOLS

  1. 🌞 Tracecat is the AI-native, open-source automation platform for security teams.

  2. 🎃 Sonny9 assists CPAs, tax pros, and auditors with efficiency.

  3. 🌿 Appomate transforms your life and work with custom-built AI apps and agents

  4. 🌀 Sonia is an AI-powered therapist that provides emotional support and guidance, helping you overcome life's challenges.

  5. ⚙️ Jumprun is AI-powered research as stunning, interactive canvases.

AI QUICK HITS

1. 🚀 Cerebras reveals huge AI chip, 900K cores, and 4T transistors (Read more)

2. 🤖 Musk says Premium X users getting AI Grok soon (Read more)

3. 🍎 Apple Developer Event back June 10, 2024 (Read more)

4. 📞 Microsoft Teams' Copilot AI gets new features for better meeting summaries (Read more)

5. 🚫 Why AI search engines won't beat Google (Read more)

AI TUTORIAL

Build & Train an AI Model Using WebAI’s Navigator *

Learn to connect data, assign workloads & effortlessly deploy any AI Model

1: Train A Vision Model

Easy training on your local compute; now anyone can train & deploy computer vision models.

2: Select your Inputs & Outputs

Spend your time focusing on building the best model. Not the best camera integration. Utilizing no-code functionality, drag and drop your previously created model onto canvas

3: Connect your model together & Test

Connect elements together to finish your flow & test your AI Model.

* indicates a promoted tool, if any

AI KNOWLEDGE

Build the Best ChatGPT Trading Bots with “DEBOPIE” Framework

By Henrique Centieiro & Bee Lee (Medium)

DEBOPIE stands for:

Define, Engineer, Backtest, Optimization, Pilot, Implement, and Extend.

By Henrique Centieiro & Bee Lee
  1. Define your strategy.
    Identify the type of strategy (mean reversion, indicator-based, momentum). Select the assets you want to trade and define your entry and indicator-base strategy, position sizing, and risk level. Write it down.

  2. Engineer the strategy.
    Ask ChatGPT to develop exactly what you want according to your defined strategy.

  3. Backtesting.
    Do a ton of backtesting. Backtest different years and different assets and perhaps do a Monte Carlo backtest. Stress test it and see if the strategy can survive different market conditions.

  4. Optimization.
    Learn from the backtesting and optimize your strategy parameters. You can adjust the indicators used and buy and sell levels. Adjust and optimize until you get the parameters that give the best results.

  5. Pilot.
    Paper trading testing. Before using real money, allow the bot to trade using a virtual account for a few months.

  6. Implement.
    Go live with the strategies. Use a very small percentage of your savings (say 1%).

  7. Extend and scale up.
    If the bot is consistently profitable, you can finally consider increasing your capital allocations.

AI JOBS

  • Nigel Frank International: AI & Data Science Consultant (Link)

  • Microsoft: Senior Researcher - Machine Learning/Generative AI (Link)

  • Confidential: Product owner Gen AI (Link)

  • Capgemini Invent: AI Regulation & Governance Senior/Managing Consultant (Link)

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