Mira Murati's new AI startup is set to be valued at $9 billion, sources say
Mira Murati, the former CTO of OpenAI, has launched a new startup called Thinking Machines Lab, which aims to raise $1 billion at a valuation of approximately $9 billion. Despite being less than a year old, the startup has garnered significant investor interest, particularly due to Murati's background in AI, including her work on ChatGPT.
Thinking Machines Lab focuses on making AI systems more accessible and customizable. Murati has assembled a team of engineers and researchers from OpenAI, Meta, and Anthropic to further this mission, positioning the lab as a key player in advancing AI technology.
Mira Murati, the former CTO of OpenAI, has launched a new startup called Thinking Machines Lab, which aims to raise $1 billion at a valuation of approximately $9 billion. Despite being less than a year old, the startup has garnered significant investor interest, particularly due to Murati's background in AI, including her work on ChatGPT.
Thinking Machines Lab focuses on making AI systems more accessible and customizable. Murati has assembled a team of engineers and researchers from OpenAI, Meta, and Anthropic to further this mission, positioning the lab as a key player in advancing AI technology.
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ChatGPT & Free AI Resources
🚨 Breaking: OpenAI just released GPT-4.5, the startup's largest AI model to date. Available now to Pro ($200/mo tier) users and developers on paid tiers via API. Everything else you need to know about the highly-anticipated launch⤵️⤵️
1/ GPT-4.5 is a leap forward in scaling unsupervised learning.
Reasoning models (like o1 + o3-mini) are great for STEM/logic.
Unsupervised learning increases world model accuracy and intuition — leading to better EQ and a broader knowledge base (and less hallucinations)
Reasoning models (like o1 + o3-mini) are great for STEM/logic.
Unsupervised learning increases world model accuracy and intuition — leading to better EQ and a broader knowledge base (and less hallucinations)
ChatGPT & Free AI Resources
1/ GPT-4.5 is a leap forward in scaling unsupervised learning. Reasoning models (like o1 + o3-mini) are great for STEM/logic. Unsupervised learning increases world model accuracy and intuition — leading to better EQ and a broader knowledge base (and less…
2/ OpenAI says early testers reported GPT 4.5 interactions feeling more natural and intuitive.
In human preference testing:
— 70.8% preferred GPT-4.5 for professional tasks
— 58.4% chose it for creative work
— 55.9% picked it for everyday queries
In human preference testing:
— 70.8% preferred GPT-4.5 for professional tasks
— 58.4% chose it for creative work
— 55.9% picked it for everyday queries
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ChatGPT & Free AI Resources
2/ OpenAI says early testers reported GPT 4.5 interactions feeling more natural and intuitive. In human preference testing: — 70.8% preferred GPT-4.5 for professional tasks — 58.4% chose it for creative work — 55.9% picked it for everyday queries
3/ With a focus on unsupervised learning over reasoning, 4.5 isn't a step up from previous models on math or science.
However, it does surpass o3-mini and o1 on SWE-Lancer, OpenAI's recently released freelance coding task benchmark
However, it does surpass o3-mini and o1 on SWE-Lancer, OpenAI's recently released freelance coding task benchmark
ChatGPT & Free AI Resources
3/ With a focus on unsupervised learning over reasoning, 4.5 isn't a step up from previous models on math or science. However, it does surpass o3-mini and o1 on SWE-Lancer, OpenAI's recently released freelance coding task benchmark
GPT-4.5 currently supports search, file/image uploads, and canvas for writing/code — no multimodal features like Voice Mode, video, or screensharing yet.
OpenAI expects to roll out the new model more broadly to Plus and Team plans next week.
The acceleration continues 🗣
OpenAI expects to roll out the new model more broadly to Plus and Team plans next week.
The acceleration continues 🗣
Developer: I trained AI. (2015) ✅
AI: Now I train you. (2024) 😂🔥
Free AI Resources: 👇
https://lnkd.in/dyEZQwXv
AI: Now I train you. (2024) 😂🔥
Free AI Resources: 👇
https://lnkd.in/dyEZQwXv
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If you’re not getting the AI responses you want, it might be because your prompts need refining.
Here's how to fix them.
Mastering the art of prompt crafting is key to unlocking the full potential of GPT and other AI models.
Here are some powerful frameworks to elevate your prompts:
↳ ERA: Expectation, Role, Action
↳ APE: Action, Purpose, Expectation
↳ TAE: Task, Action, Goal …and more!
Here's how to fix them.
Mastering the art of prompt crafting is key to unlocking the full potential of GPT and other AI models.
Here are some powerful frameworks to elevate your prompts:
↳ ERA: Expectation, Role, Action
↳ APE: Action, Purpose, Expectation
↳ TAE: Task, Action, Goal …and more!
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Preparing for a machine learning interview as a data analyst is a great step.
Here are some common machine learning interview questions :-
1. Explain the steps involved in a machine learning project lifecycle.
2. What is the difference between supervised and unsupervised learning? Give examples of each.
3. What evaluation metrics would you use to assess the performance of a regression model?
4. What is overfitting and how can you prevent it?
5. Describe the bias-variance tradeoff.
6. What is cross-validation, and why is it important in machine learning?
7. What are some feature selection techniques you are familiar with?
8.What are the assumptions of linear regression?
9. How does regularization help in linear models?
10. Explain the difference between classification and regression.
11. What are some common algorithms used for dimensionality reduction?
12. Describe how a decision tree works.
13. What are ensemble methods, and why are they useful?
14. How do you handle missing or corrupted data in a dataset?
15. What are the different kernels used in Support Vector Machines (SVM)?
These questions cover a range of fundamental concepts and techniques in machine learning that are important for a data scientist role.
Good luck with your interview preparation!
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
Like if you need similar content 😄👍
Here are some common machine learning interview questions :-
1. Explain the steps involved in a machine learning project lifecycle.
2. What is the difference between supervised and unsupervised learning? Give examples of each.
3. What evaluation metrics would you use to assess the performance of a regression model?
4. What is overfitting and how can you prevent it?
5. Describe the bias-variance tradeoff.
6. What is cross-validation, and why is it important in machine learning?
7. What are some feature selection techniques you are familiar with?
8.What are the assumptions of linear regression?
9. How does regularization help in linear models?
10. Explain the difference between classification and regression.
11. What are some common algorithms used for dimensionality reduction?
12. Describe how a decision tree works.
13. What are ensemble methods, and why are they useful?
14. How do you handle missing or corrupted data in a dataset?
15. What are the different kernels used in Support Vector Machines (SVM)?
These questions cover a range of fundamental concepts and techniques in machine learning that are important for a data scientist role.
Good luck with your interview preparation!
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
Like if you need similar content 😄👍
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Generative AI isn't easy!
It’s the groundbreaking technology that creates new content—whether it’s images, text, music, or even entire virtual worlds.
To truly master Generative AI, focus on these key areas:
0. Understanding the Basics: Learn the foundational concepts of generative models, including GANs (Generative Adversarial Networks), VAEs (Variational Autoencoders), and diffusion models.
1. Mastering Neural Networks: Dive deep into the types of neural networks used in generative AI, such as convolutional neural networks (CNNs) for image generation and transformer models for text.
2. Exploring Text Generation Models: Understand the mechanics behind language models like GPT and BERT, and how they generate human-like text.
3. Creating Images with AI: Learn how models like DALL-E and Stable Diffusion generate realistic images from textual prompts.
4. Working with Audio and Music Generation: Explore models like Jukedeck and OpenAI’s MuseNet to create music and sound using AI.
5. Building Custom AI Models: Get hands-on experience with frameworks like TensorFlow, PyTorch, and Hugging Face to train your own generative models.
6. Fine-Tuning Pre-Trained Models: Learn how to adapt large pre-trained models to specific tasks by fine-tuning them with domain-specific data.
7. Ethics and Bias in Generative AI: Understand the ethical implications of creating content using AI, including issues of plagiarism, bias, and misinformation.
8. Evaluating and Enhancing Generated Content: Learn how to assess the quality of generated content and fine-tune models to improve their results.
9. Staying Updated with Cutting-Edge Developments: Generative AI is rapidly evolving—keep up with new advancements, techniques, and applications in the field.
Generative AI is a creative force that blends technology with imagination.
💡 Embrace the challenge of creating innovative, AI-powered content that can transform industries and art.
⏳ With practice, patience, and creativity, you’ll unlock the potential of generative AI to create something truly unique!
#genai
It’s the groundbreaking technology that creates new content—whether it’s images, text, music, or even entire virtual worlds.
To truly master Generative AI, focus on these key areas:
0. Understanding the Basics: Learn the foundational concepts of generative models, including GANs (Generative Adversarial Networks), VAEs (Variational Autoencoders), and diffusion models.
1. Mastering Neural Networks: Dive deep into the types of neural networks used in generative AI, such as convolutional neural networks (CNNs) for image generation and transformer models for text.
2. Exploring Text Generation Models: Understand the mechanics behind language models like GPT and BERT, and how they generate human-like text.
3. Creating Images with AI: Learn how models like DALL-E and Stable Diffusion generate realistic images from textual prompts.
4. Working with Audio and Music Generation: Explore models like Jukedeck and OpenAI’s MuseNet to create music and sound using AI.
5. Building Custom AI Models: Get hands-on experience with frameworks like TensorFlow, PyTorch, and Hugging Face to train your own generative models.
6. Fine-Tuning Pre-Trained Models: Learn how to adapt large pre-trained models to specific tasks by fine-tuning them with domain-specific data.
7. Ethics and Bias in Generative AI: Understand the ethical implications of creating content using AI, including issues of plagiarism, bias, and misinformation.
8. Evaluating and Enhancing Generated Content: Learn how to assess the quality of generated content and fine-tune models to improve their results.
9. Staying Updated with Cutting-Edge Developments: Generative AI is rapidly evolving—keep up with new advancements, techniques, and applications in the field.
Generative AI is a creative force that blends technology with imagination.
💡 Embrace the challenge of creating innovative, AI-powered content that can transform industries and art.
⏳ With practice, patience, and creativity, you’ll unlock the potential of generative AI to create something truly unique!
#genai
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