💡 Meta is falling behind

Plus: Write a job application cover letter

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

Hey AI Family, happy Monday! 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: Meta falling behind & Can Ai comply with EU

  • 💻 𝟙 Practical Use of AI: Write a job application cover letter

Let’s jump in!

💎 𝟛 Tools to Give You The Edge

Dofollow.io: Use Dofollow to turn your website or link into a lead generating machine with premium link building

TalkBerry: Learn and speak a new language with AI tutors

BlueWillow: A free Midjourney alternative

🚨 Breaking AI News

Once at the forefront of AI development, Meta seems to be losing its edge, as a staggering 33% of its AI researchers have chosen to leave the company over the last year. Exhaustion and fading confidence in Meta's AI prospects are the driving factors.

Despite recruiting the esteemed AI veteran Yann LeCun back in 2013, inside sources indicate that during LeCun's tenure, the company found it challenging to promote the development of advanced AI models like ChatGPT.

Confidence in leadership within Meta is currently low, with an internal survey revealing that only 26% of the workforce has faith in the existing executives. This is after massive layoffs in November 2022, with over 11,000 employees being dismissed.

On top of this, The White House excluded Meta from a summit of AI innovators. Will Meta be able to recover? Maybe they should change their name again😂

The European Union is in the final stages of its AI Act. “It will be the world’s first comprehensive regulation to govern AI.” Yesterday, the European Parliament adopted a draft of the Act by a vote of 499 in favor, 28 against, and 93 abstentions.

Regulatory action is imperative; here are three big reasons why

  1. Lack of Compliance: The majority of foundation model providers do not meet the draft requirements of the EU AI Act. Issues include insufficient disclosure about copyrighted training data, hardware and emissions from training, and how models are evaluated and tested.

  2. Need for Transparency: Policymakers need to prioritize transparency, and echo the AI Act’s requirements. It suggests that it's possible for providers to meet these requirements, and doing so would enhance transparency throughout the ecosystem.

  3. Persistent Challenges: Poor compliance among many providers is a major issue, and it includes handling copyrighted data, reporting on energy use, risk mitigation strategies, and standards for model evaluation and testing.

(The closer to 48 the better)

💻 Real Life Use Case

Chat GPT Prompt: Write a job application cover letter

Use this ChatGPT prompt to give you a basis when writing or editing your cover letter, you’ll thank me later.

🔏 Copy and paste the prompt below ⏬ 

As an expert job search cover letter writer, your task is to create a concise and compelling cover letter that aligns with the specified #KeySkills and incorporates relevant industry experience denoted by #YearsOfExperience. The letter should be tailored to the provided job description (#JobDescription) and showcase the applicability of the key skills to the roles and responsibilities outlined in the job description. The language used should be natural and straightforward, avoiding any hyperbole. Please ensure that the cover letter does not exceed 200 words, thus demonstrating the ability to communicate effectively and succinctly.

Here’s a link to the results: ⬇️

ChatGPT

A conversational AI system that listens, learns, and challenges

chat.openai.com/share/8214b62a-e66f-441f-93f9-944448004295

Daily Definition

Early & Late Fusion

'Early and Late Fusion Scenarios' are typically applied in the realm of multimodal machine learning, where an AI system processes two or more types of input, such as text and images, to make a prediction. 'Fusion' here refers to the way these different types of data are combined or 'fused' together by the AI system to make sense of them.

- Early Fusion: In 'Early Fusion', multiple types of input data are combined at the beginning of the processing pipeline. Imagine being at a party and hearing multiple conversations at once - 'Early Fusion' would be like trying to understand all these conversations simultaneously, as one big mixed sound.

- Late Fusion: Conversely, in 'Late Fusion', each type of data is processed separately, and the results are combined at the end of the pipeline. Back to the party analogy, 'Late Fusion' would be like listening to each conversation individually and then piecing together the whole picture afterwards.

The choice between 'Early Fusion' and 'Late Fusion' depends on the specific task and the nature of the data. Some tasks might benefit from the data being interwoven from the start (early fusion), while others might do better with a separate initial analysis of each type of data (late fusion).

Remember, whether it's 'Early' or 'Late,' it's all about how different types of data are combined in a machine learning model to make the most sense of the world.

🔮 AI Inspiration

This is OP Art: A form of visual art that creates the illusion of movement (Credit)
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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