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9. Google unveils PaLM 2. It has powerful multilingual, reasoning, and coding capabilities trained on 100+ languages. PaLM 2 comes in 4 sizes: - Gecko (10 billion parameters) - Otter (100 billion parameters) - Bison (1 trillion parameters) - Unicorn (10 trillion parameters)

123,288 次观看 • 3 年前 •via X (Twitter)

18 条评论

Barsee 🐶 的头像
Barsee 🐶3 年前

Google wins the internet with its biggest breakthroughs in AI. It takes their generative AI capabilities to a whole new level. Here are the 10 revolutionary innovations they unveiled today:

Barsee 🐶 的头像
Barsee 🐶3 年前

1. Bard Google Bard's new features: -Dark theme -Visual features to search from photos -Support 20+ programming languages -Works with other apps -Write in Gmail, Export into Docs -Generate spreadsheet

Barsee 🐶 的头像
Barsee 🐶3 年前

2. Google unveils a list of future plugins for Bard.

Barsee 🐶 的头像
Barsee 🐶3 年前

3. Google releases "Help me write" in Gmail. Email has changed forever. Just write one-line prompt, AI will automatically generates the whole email for you in seconds. There are also many options to refine your emails.

Barsee 🐶 的头像
Barsee 🐶3 年前

4. Bard + Adobe Firefly announced Bard will be able to generate completely new images using Adobe Firefly.

Barsee 🐶 的头像
Barsee 🐶3 年前

5. Google Unveils Gemini This is Google's next-generation multimodal foundation model. It will directly rival with @OpenAI GPT. Bard will transition to the Google Deepmind Gemini model.

Barsee 🐶 的头像
Barsee 🐶3 年前

6. Google X Adobe Google and Adobe have teamed up to power up Adobe Aero, a tool for AR creators! This partnership brings Google's real-world modeling into Adobe's creative design tools.

Barsee 🐶 的头像
Barsee 🐶3 年前

7. Google Photos' new "Magic Editor" This new feature lets you change: -The lighting of a scene -Get rid of elements you don't like -Adjust your position in the photo.

Barsee 🐶 的头像
Barsee 🐶3 年前

8. Google just announced Immersive View for Maps It helps you: -Know the actual weather and traffic on your route -It calculates the fastest path in less than 2 sec.

Barsee 🐶 的头像
Barsee 🐶3 年前

10. This is how many times Google's CEO says AI

Barsee 🐶 的头像
Barsee 🐶3 年前

If you found this helpful or interesting. Please retweet the tweet below to share it with others:

Barsee 🐶 的头像
Barsee 🐶3 年前

I will cover more about its use cases in my free newsletter today.

Dickson Pau 的头像
Dickson Pau3 年前

These parameters count cannot be correct. Please do better.

Marco Pinnisi 的头像
Marco Pinnisi3 年前

@heyBarsee Source for these model sizes?

APOideas 的头像
APOideas3 年前

How can I use PaLM2?

Rafa is a northern father ✊🏻 的头像
Rafa is a northern father ✊🏻3 年前

@DotCSV

Adam Lieziert 的头像
Adam Lieziert3 年前

Nice- I’ve waited a long time for this upgrade!

Chris Vanderloo 的头像
Chris Vanderloo3 年前

A 10bil parameter model isn’t light enough to run on a mobile device. Gecko is probably 2 orders of magnitude smaller

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Andrej Karpathy just made one of the most interesting arguments about AI model design that most people are completely missing. His take is that frontier AI models are not too big because the technology is complex and too big because the training data is garbage. When you or I think of the internet, we picture Wall Street Journal articles, Wikipedia entries, serious writing. That is not what a pretraining dataset looks like. When researchers at frontier labs look at random documents from the actual training corpus, it is stock ticker symbols, broken HTML, spam, gibberish. One estimate puts Llama 3's information compression at just 0.07 bits per token meaning the model has only a hazy recollection of most of what it trained on. So we build trillion parameter models not because we need a trillion parameter brain but because we need a trillion-parameter compression engine to squeeze some intelligence out of a firehose of noise. Most of those parameters are doing memory work, not cognitive work. Karpathy's prediction is separate the two entirely. Build a cognitive core, a model that contains only the algorithms for reasoning and problem-solving, stripped of encyclopedic memorization and pair it with external memory that it can query when it needs facts. He thinks a cognitive core trained on high-quality data could hit genuine intelligence at around one billion parameters. For reference, today's flagship models run between 200 billion and 1.8 trillion parameters with most of that weight dedicated to remembering the internet's slop. The trend is already moving his direction. GPT-4o operates at roughly 200 billion parameters and outperforms the original 1.8 trillion-parameter GPT-4. Inference costs for GPT-3.5-level performance dropped 280-fold between 2022 and 2024 driven almost entirely by smaller, cleaner, better-architected models. The real bottleneck in AI right now is not compute but rather data quality.

Milk Road AI

200,719 次观看 • 5 个月前