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🤖 AI: Power with Constraints 🔒
As AI reshapes industries, it’s crucial to address its constraints:
📉 Data Limitations: AI is only as good as the data it’s trained on.
⚖️ Ethical Challenges: Biases in algorithms can have real-world consequences.
🔋 Compute Dependency: High energy and computing power requirements.
🤔 Lack of Generalization: AI struggles with tasks outside its training scope.
To unlock AI’s full potential, we need innovation paired with responsibility.
Let’s build a future where technology serves humanity sustainably! 🌍
#ArtificialIntelligence#AIChallenges#EthicsInTech#Innovation#Sustainability#TechForGood#AIConstraints
The Economist’s view on the challenges AI and and AI datacentres will bring in 2025.
Good to read a non tech journal originated opinion. Sometimes I think we all get “drunk” on writing and reading the same stuff.
#ai#blackwell#gpu#datacentre#powerThe Economisthttps://lnkd.in/gB5duC2q
AI for 2025 - will not be so easy to deploy as we think. The Economist explores some of the challenges it believes we will have.
https://lnkd.in/gB5duC2q
NVIDIA is pushing the boundaries of AI across energy efficiency, autonomous decision-making, virtual training, and space exploration.
Key Highlights:
1. Transition to Intention-Level Computing:
• NVIDIA is moving from traditional instruction-based computing to intention-level computing, where AI systems understand and act based on user intent, leading to more natural and proactive human-computer interaction.
2. Agentic AI:
• The introduction of agentic AI, where AI agents autonomously manage and optimize industrial processes in real time. These agents can communicate across various business areas (e.g., manufacturing and supply chains) to enhance efficiency and productivity.
3. Energy Efficiency Gains:
• NVIDIA has achieved a 100,000x reduction in energy consumption over the last decade for certain AI processes, making their systems much more energy-efficient. This includes a 200x reduction in energy consumption for AI training.
4. Blackwell Platform:
• The Blackwell platform is NVIDIA’s next-generation AI infrastructure, designed for accelerated computing and full-stack optimization. This includes the use of AI superclusters and the CUDA libraries for advanced AI workloads.
5. Omniverse and Digital Twins:
• The Omniverse platform allows the use of virtual environments to train AI systems that will operate in the real world. This is crucial for industries like manufacturing, where AI can be trained virtually before deployment in physical environments.
6. Confidential Computing for AI Security:
• NVIDIA has integrated confidential computing to protect large language models (LLMs) and ensure the security of AI data. This is critical for safeguarding sensitive information during AI training and usage.
7. Real-Time Detection of Fast Radio Bursts (FRBs):
• NVIDIA’s technology is enabling real-time detection of FRBs from distant galaxies, enhancing the search for extraterrestrial life by analyzing these high-energy signals from deep space almost instantaneously.
Our colleague Nathan Key had a chance to attend the "Woodstock of AI" – NVIDIA GTC AI Conference in San Jose, CA.
The announcements and partnerships unveiled at the conference are set to redefine what AI can achieve, promising a future where technology and human ingenuity fuse to create remarkable value.
Nathan has shared 10 fascinating insights with our blog readers; please check them out to discover the critical role AI plays in tackling some of the world's most pressing challenges. ⤵
#nvidiagtc#gtc24#ai#artificialintelligence#aievents
Quote from Thomas Wolf co-founder and chief science officer at Hugging Face. “We’ve already exhausted the internet as a source of training data a few months ago.”
It's fascinating to observe the creation of AI models so expansive that they utilize all available and reliable data. I take pleasure in working with my own AI models on my modest Dell server rack, yet it's hard to fathom a model capable of efficiently employing all accessible data from the internet. The article in question discusses the potential future of AI, and like many, I believe what we're witnessing now is just a precursor to what's ahead. I'm eager to be at the forefront when we transition from current binary computing to future quantum computing. At that point, machines will begin to exhibit their own personalities, guiding us into the future of science.
All eyes will be on NVIDIA GTC this week. One term we will hear about a lot is "sovereign AI".
Is it a thing? Do we need it? New Interconnected post shares my latest thinking on this topic. tl;dr: I was initially skeptical, but coming around to its necessity, especially when human biology and generative AI combine.
Already seeing progress on that front with Evo, a new DNA foundation model jointly built by Arc Institute, Stanford University, Together AIhttps://lnkd.in/e3UPkaCG
Breaking Barriers:
Elon Musk's xAI Supercomputer Reshapes the AI Landscape
In the ever-evolving world of artificial intelligence, we've just witnessed a breakthrough that many experts deemed impossible. Elon Musk's xAI has unveiled a supercomputer that doesn't just push the boundaries of what's possible – it shatters them entirely.
#xAI#supercomputer#ai#elonmusk
