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- ⚡🔋NVIDIA's Nemotron-70B: David Takes on Goliath in the AI Arena
⚡🔋NVIDIA's Nemotron-70B: David Takes on Goliath in the AI Arena
And more : First Robot's Art Auctioned at Sotheby's; How to Use AI to Tackle the Electrician Shortage
Supercharged With AI
October 18, 2024
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Good morning, aspiring leader of the next generation! ☀️
🚀 Here is the most impactful AI news today. Enjoy and we wish you an amazing day:
NVIDIA's Nemotron-70B LLM flexes on GPT-4o and Claude 3.5. The AI muscle contest continues! 💪🧠
Ai-Da, the robot artist, makes history at Sotheby's. Picasso, watch your back! 🎨🤖
Meta's AI chief predicts "human-level AI" in 10 years. Time to start prepping for our new robot overlords? 🕰️👾
Dottxt scores $11.9 million to teach AI models manners. Because even algorithms need finishing school! 🎓💬
Google's NotebookLM now lets you play puppet master with AI conversations. Business pilot included! 🎭🚀
ChatGPT invades Windows. Clippy's feeling pretty nervous right now. 💻😰
NVIDIA Nemotron-70B: New LLM beats GPT-4o and Claude 3.5
NVIDIA has released a new large language model called Nemotron-70B, which has outperformed leading models like GPT-4o and Claude 3.5 Sonnet on certain benchmarks. This open-source model, based on Llama 3.1, has achieved impressive scores despite having only 70 billion parameters, significantly fewer than its competitors.
Nemotron-70B excelled in three key evaluation metrics: Arena Hard (85.0), AlpacaEval 2 LC (57.6), and MT-Bench (8.98). These scores place it at the top of the leaderboard for automatic alignment benchmarks, surpassing both Claude 3.5 Sonnet and GPT-4o.
The model's success is attributed to its use of Reinforcement Learning from Human Feedback (RLHF) using the REINFORCE algorithm, which allows it to learn from human evaluator feedback. Additionally, NVIDIA incorporated two specific reward models: Llama-3.1-Nemotron-70B-Reward and HelpSteer2-Preference Prompts, which guide the model towards producing more helpful and relevant answers.
However, some experts caution against declaring Nemotron-70B the definitive best LLM. Its success is limited to specific benchmarks and doesn't necessarily reflect overall performance across diverse tasks. Unlike GPT-4o, Nemotron lacks multimodal capabilities, and it hasn't been extensively evaluated for coding proficiency, an area where Claude 3.5 Sonnet excels.
Early user feedback suggests that while Nemotron-70B performs well, it may not surpass GPT-4o or Claude 3.5 Sonnet in practical applications. The model also lacks results from traditional machine learning benchmarks that assess overall performance across a wide array of tasks.
NVIDIA has made Nemotron-70B available for free through their NVIDIA NIM platform, allowing users to experience and evaluate the model themselves. This release marks another significant step in the ongoing race to develop more powerful and efficient language models, highlighting the rapid pace of innovation in the field of generative AI.
Ai-Da Makes History: First Robot's Art Auctioned at Sotheby's
Ai-Da, a humanoid robot artist, is making history with her painting "AI God" being auctioned at Sotheby's. Key points include:
The painting, a portrait of Alan Turing, is expected to fetch between £100,000 and £150,000.
This marks the first time a robot-created artwork will be auctioned at a major auction house.
Ai-Da uses AI algorithms, cameras in her eyes, and bionic hands to create art autonomously.
The robot was developed by Aidan Meller with support from experts at Oxford and Birmingham Universities.
The choice of Alan Turing as the subject is symbolic, linking AI's history to its current capabilities.
The auction raises questions about creativity, authorship, and the role of AI in art.
Public reactions are mixed, with some viewing it as a milestone for AI's creative potential, while others remain skeptical.
The event has implications for the art market, copyright laws, and societal perceptions of AI-generated art.
This auction represents a significant moment in the intersection of technology and art, challenging traditional notions of creativity and artistic value.
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Meta’s AI chief says world models are key to ‘human-level AI’ — but it might be 10 years out
Meta's chief AI scientist Yann LeCun believes that achieving human-level AI may be 10 years away and requires developing "world models." Key points:
LeCun argues current AI systems, including large language models, lack true understanding of the three-dimensional world.
He proposes "world models" as a new AI architecture that can perceive and predict the effects of actions in the real world.
World models could enable AI to perform complex tasks like cleaning a room or driving a car more effectively than current systems.
Several AI labs, including Meta's FAIR and the startup World Labs, are pursuing world model research.
LeCun outlines a concept for "objective-driven AI" using world models to achieve human-level AI capabilities.
He estimates it may take years or even a decade to solve the challenges involved in developing effective world models.
Despite some companies' claims, LeCun believes current AI systems are far from achieving human-level intelligence or reasoning.
📔#1 Insights Today on AI. Click the Links to Read
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Unleash your AI innovation! Share how you've creatively integrated AI tools, automations, agents, or copilots into your business processes. We'll select the most inventive use case each week and reward the winner with a special surprise. Don't forget to include your unique AI journey!
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AI is not just about creating intelligent machines, but about expanding our capacity to solve problems. It's a tool that can help us tackle challenges at a scale and complexity that were previously unimaginable, from personalized medicine to global climate modeling.
Yann André LeCun is a French-American computer scientist working primarily in the fields of machine learning, computer vision, mobile robotics and computational neuroscience.
⬆️ ️ Trends: How Businesses Implement AI
Treehouse: Using AI to Tackle the Electrician Shortage in the Green Energy Boom
As the United States races towards a greener future, a surprising obstacle has emerged: a shortage of electricians. With unfilled jobs in the field expected to grow by 11% annually over the next decade, the country faces a critical challenge in its electrification efforts. Enter Treehouse, a startup leveraging artificial intelligence to make the existing electrician workforce more efficient.
The problem is rooted in the lengthy training process for electricians, who typically need to log 8,000 hours before obtaining their license. This creates a significant lag in addressing the shortage, even as demand surges for installations of solar panels, heat pumps, and electric vehicle chargers.
Eric Owski, co-founder and CEO of Treehouse, recognized that waiting for new electricians to enter the workforce wasn't a viable solution. "That pipeline takes a long time to build," he explains. "We have to make the existing workforce more efficient."
Treehouse's approach focuses on eliminating one of the most time-consuming aspects of electrical work: the initial site visit to scope and quote a job. By leveraging AI models trained on data about housing footprints and various electrical jobs, Treehouse can often provide accurate quotes without an in-person visit.
This is particularly effective for simpler jobs like EV charger installations. Treehouse can provide quotes for over 90% of these jobs without a preliminary site visit, asking customers a few key questions about their home's electrical usage and desired charger location. This efficiency is crucial, especially when customers need quick quotes while at car dealerships.
For more complex jobs like heat pump installations, Treehouse may require additional information or a virtual visit. But the goal remains the same: minimize "truck rolls" and maximize efficiency.
Treehouse's model goes beyond just quoting. The company serves as the lead contractor, either subcontracting to independent electricians or, in some markets, employing its own. This approach allows Treehouse to maintain quality control while scaling its operations.
The startup's innovative approach has attracted significant investment, recently raising a $16 million Series A round led by Flourish Ventures. With this funding, Treehouse plans to improve its AI models, expand geographically, and hire more in-house electricians in key markets.
As the U.S. continues its shift towards electrification, Treehouse sees a long-term opportunity. "I think we're going through a multi-decade super cycle," Owski says. "EVs are going to transform the way the average consumer thinks about home energy."
In a world where the green energy transition is hampered by a shortage of skilled workers, Treehouse's AI-driven approach offers a glimpse of how technology can help bridge the gap, making the path to a more sustainable future a little smoother.
💎Challenge 2: AI Image RecognitionObjective: There's more to this image than meets the eye. Use technology to unveil the unseen. |
The hint of suggested tools to help you find the answer:
Code Translators:
Binary to Text Converter: Binary Translator
Morse Code Translator: Morse Code World
Caesar Cipher Decoder: Cryptii
Metadata Viewers:
ExifTool: Download ExifTool
Online Metadata Viewer: Metapicz
Steganography Tools:
OpenPuff: OpenPuff Steganography
Steganography Online: Styles Teganography
QR Code Scanners:
For Smartphones: Built-in camera apps or downloadable QR scanner apps.
What do you think is the most exciting application of AI?What are the most daring problems you think we can resolve with AI for a better future in the world? |
This series of AI Challenges is designed to help you dive deeper into the world of AI and figure out how to use AI tools firsthand. Good luck, winners!
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⚡ AI Playground: Code & Craft
Work smarter, not harder!
Today’s Tool: ClipAnything (OpusClip)
What is this tool best at?
ClipAnything is best at converting long-form videos into multiple short, viral-ready clips in an automated manner. It uses AI to identify the most engaging moments and formats them for various social platforms.
Why is this tool better than other tools?
Unlike traditional video editing software, ClipAnything is powered by AI that auto-curates and optimizes clips for virality. It saves time by automatically reframing videos for different aspect ratios, adding animated captions, and even incorporating AI-generated B-roll. This makes the video production process significantly faster and more efficient for creators and businesses.
What does it do best?
ClipAnything excels at turning long videos into multiple short-form clips that are tailored for different social media platforms. Its AI-driven features, such as virality scoring and auto-posting, ensure that content is optimized for maximum engagement.
What are some applications of this tool?
Vlogging: Clip exciting moments from travel or food videos.
Social Media Management: Automatically extract and post viral-worthy clips from live streams or recorded content.
Podcasting: Create short, shareable clips of the most engaging Q&A or interview moments.
Content Creation: ClipAnything helps with rapid content generation for platforms like TikTok, Instagram Reels, YouTube Shorts, and more.
It’s ideal for creators and businesses looking to grow their social presence by quickly repurposing long-form content into viral-ready clips.
I hope you find these resources helpful. Let me know if you have any questions, if there's anything else I can do to assist you, or what you would like to learn more about in AI.
Have an amazing day!
No more playing catch-up. It's time to GET AHEAD!!! 🚀🚀🚀
🙌🏼
Elena
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