💡 Parallel Domain is revolutionizing datasets

Plus: Maximize ChatGPT's writing

Read time: 3 minutes (Click underlined topics or headings for links.)

Hey AI Family, happy Tuesday! We like to keep things short and productive at The Edge, so you can get back to your busy life!

Here’s The Breakdown:

  • 💎 𝟛 Tools to Give You The Edge

  • 🚨 𝟚 AI Updates: Parallel Domain & LinkedIn to roll out new AI

  • 💻 𝟙 Practical Use of AI: Maximize ChatGPT’s writing

Let’s jump in!

💎 𝟛 Tools to Give You The Edge

Inflection Pi: The first personalized chatbot designed to answer your questions

Prompt Storm: Chrome extension that allows you to use ChatGPT to its full potentially

AgentGPT: Deploy your own autonomous AI agent

🚨 Breaking AI News

Parallel Domain launched an API called “Data Lab,” which allows machine learning engineers to generate synthetic datasets to simulate any scenario. Now using Python, engineers can use the API to generate objects that weren't available before!

The “self serve” API lets customers form new datasets almost instantly. I’m not a developer, but I know this will be game-changing. What would previously take weeks or months to create can now be formed in minutes.

Parallel Domain found that autonomous vehicle models performed better when trained on synthetic data compared to real-world data. This technology could be adopted in any sector where computer vision technology is prominent, such as agriculture, retail, or manufacturing.

Preview of AI chat

LinkedIn is testing a new feature for its premium users that uses AI to generate messages regarding job opportunities. It’s like your personal LinkedIn assistant. But during testing, the AI often exaggerated the user's experience by suggesting they had more years of experience than they actually did.

But like with most current AI models, they require human editing to ensure accuracy. LinkedIn sends out two warnings before sending to a job recruiter: "If you haven't already, be sure to review the draft before sending" and to ensure the message is “accurate and appropriate.”

Apparently this can increase the chances of hiring by up to three times, I don’t know about all that…😂 but I guess we have to wait and see.

💻 Real Life Use Case

Chat GPT Prompt: Maximize ChatGPT’s Writing

We’ve all been there, sitting at the computer, hopelessly trying to get ChatGPT to give us the output we need.

With this guidance prompt, you can alleviate some of your writing pain.

🔏 Copy and paste the prompt below ⏬ 

Please construct an engaging and versatile writing prompt for me. This prompt should offer a unique topic or scenario that spurs creativity and encourages thoughtful exploration. It could be related to a specific genre, style, or thematic element. Ultimately, the aim is to facilitate the production of a compelling piece of writing, regardless of whether it's fiction, non-fiction, poetry, or prose.

Here’s a link to the results ⬇️

ChatGPT

A conversational AI system that listens, learns, and challenges

chat.openai.com/share/919c8ca7-6121-406f-a3d5-24bb17ae02f1

Daily Definition

Synthetic Datasets

'Synthetic Datasets' are artificial data sets that are computer-generated, rather than collected from real-world events. They are created to simulate various scenarios or conditions that might not be readily available or easy to collect in the real world, or to augment existing datasets to improve machine learning model performance.

Think of synthetic datasets as a kind of 'virtual reality' for your AI. They can be designed to mimic any situation and contain any variables that you find useful. This is especially helpful in areas where data is difficult to obtain, such as medical imaging, or in cases where privacy is a concern.

For example, if an AI needs to learn how to recognize dogs in pictures, but we don't have enough dog pictures to train it effectively, we could generate synthetic images of dogs to help the AI learn.

However, creating synthetic datasets comes with its own set of challenges. They need to be carefully designed to accurately reflect the complexity of real-world data. If not done properly, the models trained on synthetic data may fail to generalize well when they encounter real-world data.

🔮 AI Inspiration

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That’s all for today!

Thanks for your time today. If it is your first time here, welcome. I hope you found value in today’s Edge edition. If you are returning, thank you. It means the world to me that you spend a few minutes of your day with me. If you have any ideas you’d like me to cover in the future, reply to this email.

Thanks for reading see you tomorrow,

Best,

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