Peter Liu ⚡️ Profile picture
Mar 28 9 tweets 5 min read Twitter logo Read on Twitter
Day 5: Google's @DeepMind.

🧠 While @OpenAI grabs headlines, we wanted to dig into @DeepMind, one of the OG AI research institutions acquired for ~$500M by @Google (a steal!). Its breakthroughs have made a huge impact on the AI field. Let's take a look! 🚀 #AllInAI (1/9)
🎮 @DeepMind's AlphaGo made history in 2016 by defeating Lee Sedol in Go. Combining deep learning & reinforcement learning with Monte Carlo Tree Search, AlphaGo soon learned to play complex games entirely through self-play, without human guidance. (2/9)
🎼 WaveNet, another @DeepMind invention, uses a convolutional neural network (CNN) to generate realistic human-like speech. It powers text-to-speech applications like @Google Assistant and has pushed the boundaries of speech synthesis, music generation, and voice cloning. (3/9)
🧬 @DeepMind's AlphaFold is a game-changer in biology and medicine, accurately predicting protein structures. This breakthrough has the potential to unlock new insights into diseases and drug development...this was one of their impressive talks at @NeurIPSConf '22 in NOLA (4/9)
📚 @DeepMind has also contributed significantly to Transformer-based architectures, the backbone of modern LLMs. These architectures are critical to the success of models like OpenAI's GPT-3/4 and @Google's BERT, revolutionizing natural language processing as we know it. (5/9)
🌍 @DeepMind's research team has a strong focus on solving real-world problems. They've developed AI systems like DeepMind for Google data centers to reduce energy consumption by up to 40% for cooling and improve overall energy efficiency by 15%. Great real world use case. (6/9)
🤖 @DeepMind does seem to prioritize AI safety research more than others. They've developed techniques like "Reward Modeling" and "AI-Driven Debate" to help align AI behavior with human values, encouragoing collaboration between AI agents. Both are prob worth looking into. (7/9)
💤 We beleive @DeepMind is currently a sleeping giant in the AI world that sits on a goldmine of research, data, and compute infrastructure. While it feels like it's behind others who are shipping so quickly, it has all the ingredients to be a formidable player. (8/9)
🔭 With @DeepMind's track record of groundbreaking research and @Google's vast resources and audience, I'm looking forward to seeing what amazing commercial products are released by both. 👀 #AllInAI (9/9)

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More from @NewOrleansVC

Mar 30
1/ Day 7 of #AllInAI: Generative Adversarial Networks (#GANs)

Yesterday, we explored synthetic data, which led us to GANs - the tech behind creating realistic synthetic data. After learning more, there are several other real world applications that have come from GANs research… twitter.com/i/web/status/1…
2/ 👊GANs, or Generative Adversarial Networks, are a type of AI model architecture/technique. They consist of two separate neural networks, a Generator and a Discriminator, that are trained together in a process called Adversarial Training. The goal is to generate new, realistic… twitter.com/i/web/status/1…
3/ 🤖✍️ Imagine a GAN model generating realistic human signatures. The Generator creates a random signature, while the Discriminator classifies it as "real" or "fake." They improve over time, and eventually, the Generator creates signatures that look authentic & are hard to… twitter.com/i/web/status/1…
Read 8 tweets
Mar 29
1/ Day 6 of #AllinAI 🧪 Introducing #SyntheticData - an incredibly powerful tool that's quickly becoming a must-have for AI teams. Synethic data can help companies overcome some of the key issues of AI & data collection, such as privacy, biases, and data scarcity.
2/ 💡 What is Synthetic Data? It's artificially generated data that mimics the characteristics of real-world data. It's created using algorithms, simulations, and generative models like GANs (which pit AI vs. AI to create and authenticate "fake" data).
3/ 📈 Why companies should consider Synthetic Data in their AI stack:

1. Reduces data privacy concerns
2. Helps overcome data scarcity
3. Enhances dataset diversity
4. Reduces biases in data
5. Facilitates edge cases testing
6. Speeds up model development
Read 9 tweets
Mar 28
Open sourcing @RevelryVC's AI Data Strategy DD Checklist.

1/ Data is the fuel for building AI so we're sharing our "AI Data Strategy" DD Checklist to gather feedback and insights to improve our evaluation process and provide value to other investors and founders. #AllInAI
2/ We've identified 7 key areas to focus on when evaluating a startup's data strategy. These areas are critical to ensure that the AI system is effective and that the company's approach aligns with its overall goals.

Here are the 7 areas of our DD process:
3/ Data Acquisition: The process of collecting, sourcing, and obtaining relevant data. A strong data acquisition strategy ensures a diverse and reliable dataset that accurately represents the target use case and can be a critical moat over time.
Read 10 tweets

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