AI Takeover: Crafting the Future of Jobs, Music & Coding

Welcome to Aideations. The most comprehensive daily AI newsletter on the planet! How do I know? Because I read over 50 of them, plus the news, so you don’t have to. It’s my goal to make this the most-read newsletter on AI. I can’t do that without your support and feedback. It was a busy weekend. Today is packed. Let’s DIVE IN!

Here's what we've got in store for you today:

🚛 How AI Is Transforming Labor & Logistics

🏆 Grammy Awards Weigh In On AI

👾 GitHub CEO Predicts 80% Of Code To Be AI Generated

🎙️ Meta Continues To Launch New AI : Enter VOICEBOX

📚 Research Of The Day

🎥 Video Of The Day

🛠 Tools Of The Day

🤌 Prompt Of The Day

🐥 Tweet Of The Day

Labor and Logistics: How AI is Changing the Landscape

As I contemplate the pace of AI, it strikes me that we're on the verge of a transformative shift that could completely overhaul both the manufacturing and transportation industries. I'm talking not just about delivery robots and self-driving trucks, but also about a revival of the 'Made in USA' tag in a completely new avatar.

The economics of manufacturing might experience a radical change with AI's rise. Labor costs, a significant part of the offshoring equation, could dramatically decrease as AI eliminates a large portion of the human workforce in manufacturing. We're picturing fewer people, more robots, and unprecedented efficiency and productivity levels. This could lead to a renaissance of American manufacturing, akin to reshoring on steroids.

Now let's journey to the transportation sector, where AI is flexing muscles that even Arnold in his prime would envy. Morgan Stanley predicts hundreds of autonomous trucks will hit U.S. roads by 2024, cutting costs by an impressive 25% to 30%. With AI and machine learning, we could predict and avoid supply chain disruptions, ending the era of empty shelves.

Consider global shipping giant Maersk, betting big on AI. They're talking about "predictive cargo arrival" models, improving reliability and helping customers better plan their supply chains. But let's also address the threat from high tech digital newcomers. AI could make third-party logistics firms about as useful as a chocolate teapot.

Despite the palpable excitement around AI, there's a nagging concern about the job scenario. While we may see the advent of "knowledge assistants" and new roles like 'AI Ethics Compliance Officer' or 'Robotic Interaction Designer', the traditional jobs we stand to lose might not balance out. Consider, for instance, the planning function in businesses: A team of 1,000 could shrink to 100 or fewer due to AI. Moreover, the monumental task of reskilling lies ahead, one that far surpasses previous technological shifts.

The AI revolution is a rollercoaster ride, full of thrills, speed, and a dash of fear. Yet, like rollercoasters, we often find ourselves eager to get back on at the end of the ride. Buckle up, as we brace for this wild ride into an AI-led future. With its promise and uncertainties, AI’s at the wheel, steering us toward a future we're both eager and cautious to meet.

New Grammy Rules Pull the Plug on AI Artists

Picture it: The Grammy Awards. The nominees for the best song are… a funky fresh rapper, a pop princess, a moody indie band, and a... robot?

Wait, let's rewind that back.

AI has been showing off. It's penning radio scripts, turning plain old text into jaw-dropping art. And now, it's getting musical. But, can a robot compose a Grammy-winning song?

Well, as it stands, the Recording Academy – the puppet masters behind the glitz and glam of the Grammy Awards – has just laid down some new law: only flesh-and-blood, human beings can win their coveted golden gramophones.

Sure, songs with a dash of AI flavor are still on the menu, as long as there's a legit human artist stirring the pot and adding their own spices

Harvey Mason Jr., the big cheese at the Recording Academy, gave us the scoop: AI-voiced songs and AI-powered instrumentation are all good in the 'hood. But when we're talking songwriting, it's got to have the human touch.

Now, don't get it twisted, Mason isn't throwing shade at our AI amigos. On the contrary, he's pretty convinced they'll be the future beat of the music industry. Rather than kicking the can down the road, he reckons the Grammy Awards should go full steam ahead into the AI question. Set the standards, lay down the rules, and figure out just how our silicon counterparts fit into the musical landscape.

On the creative side, the U.S. Copyright Office has rolled out the red carpet for AI, with new guidelines for copyrighting AI-assisted masterpieces.

For now, we can rest easy knowing that the Grammy will still go to a talented human being. Sorry, Siri, you've got a bit more singing practice ahead of you.

GitHub CEO Predicts: Copilot to Author 80% of Code in Near Future

Alright, let's dive straight into the core insights from the recent interview with GitHub CEO, Thomas Dohmke, published on Freethink.com. The central point? GitHub's Copilot, an AI tool powered by OpenAI's Codex, isn't just a convenience—it's a game-changer that's fundamentally reshaping the programming ecosystem.

Dohmke explains that the role of developers will be to guide AI in the future, rather than coding every single line from scratch. The significance? It's not about replacing developers with AI, but leveraging AI to bolster human creativity and efficiency.

One of Copilot's intriguing effects, Dohmke notes, is its role in revitalizing the fun element in coding. By reducing the manual coding load, developers can focus more on the creative and innovative side of their work. The result is a significant productivity boost, with startups reportedly experiencing up to 20-25% productivity gains.

Education, too, is set for an overhaul. Dohmke envisages Copilot changing the focus from rote learning to teaching students how to interact with and direct AI.

As for the longevity of traditional programming languages, Dohmke affirms their endurance. Copilot, in fact, can help modernize legacy code in older languages like COBOL, freeing developers to concentrate on innovation rather than maintenance.

These takeaways show that Copilot isn't merely a tool—it's an integral part of the evolution of the coding world. In essence, it's about increased efficiency, renewed creativity, and a revolutionary approach to coding.

Meta's Voicebox: The AI Ventriloquist

Let's talk about Voicebox, since everybody and their momma is, the brainchild of Meta Platforms’ AI lab. This machine learning model is like the Hermione Granger of text-to-speech conversion - it's top of the class and can do things even it hasn't been trained for. Editing, noise removal, style transfer - it's like the Swiss Army knife of speech generation.

Here's where it gets extra nerdy: Voicebox was trained using Meta's snazzy 'Flow Matching' technique. This isn't some new-age yoga routine, but an efficient method that helps the model "learn from varied speech data without the need for them to be carefully labeled." Think of it like learning to cook from your grandmother - there aren't exact measurements, but the dish still turns out delicious.

This freeform training technique allowed the researchers to throw a whopping 50,000 hours of speech and transcripts from audiobooks at Voicebox. The model uses something called "text-guided speech infilling" as its training goal. Imagine it's like a game of Mad Libs, but instead of words, Voicebox fills in the gaps in the audio using the surrounding context and its transcript.

Now, let's talk capabilities. Ever wanted your Alexa to have Morgan Freeman's voice? Voicebox can use a two-second voice sample to generate speech for new text. This could be a game-changer for people unable to speak or for those looking to spice up their in-game NPC dialogue.

But the fun doesn't stop there. Voicebox can also perform style transfers across different languages, potentially enabling cross-lingual communication with a familiar, personalized twist. Kind of like having your own interpreter in a foreign land, but instead of sounding robotic, it uses your best friend's voice. Talk about friendly guidance!

Ever had Fido bark during your recording? Voicebox can fill in the noisy gaps with clean speech matching the transcript. It can even edit out your flubs, swapping in a corrected word or phrase that seamlessly matches your voice and tone.

But hey, no one's perfect, right? Voicebox does have some flaws. It doesn't vibe well with casual conversation or non-verbal sounds, since its training was strictly bookish. It also can't fully control the attributes of the generated speech like style, tone, or emotion. But fear not, Meta's AI nerds are hard at work trying to iron out these wrinkles.

As Spidey's Uncle Ben said, "with great power comes great responsibility," and Meta is acutely aware of it. There's a dark side to AI, with cybercriminals already using it for voice-based scams. Enter Voicebox, a tool that in the wrong hands, could be a serious tool for deception. That's why Meta isn't releasing the model just yet, focusing on developing safeguards to prevent its misuse.

In a nutshell, we’re witnessing AI shaping up to be quite the ventriloquist, breathing life into text in a whole new way. We'll be watching to see if Voicebox becomes the voice of the future or just another AI concept that whispers sweet nothings into the ether. Until then, fingers crossed for my personal assistant with the voice of Beyoncé!

 📰 News From The Front Lines: 📰

📚 RESEARCH 📚

Title: Speed Is All You Need: On-Device Acceleration of Large Diffusion Models via GPU-Aware Optimizations

Authors: Yu-Hui Chen, Raman Sarokin, Juhyun Lee, Jiuqiang Tang, Chuo-Ling Chang, Andrei Kulik, Matthias Grundmann

Summary:

The research paper presents a series of implementation optimizations for large diffusion models, specifically focusing on improving the performance of these models on GPU-equipped mobile devices. The authors discuss the challenges of deploying large diffusion models on devices due to restricted computational and memory resources. They propose a set of GPU-aware optimizations, including specialized kernels for Group Normalization and Gaussian Error Linear Unit (GELU), enhancing the efficiency of the attention module, and employing Winograd Convolution. These optimizations collectively achieve groundbreaking latency figures for executing large diffusion models on various devices.

Pros:

• The paper introduces a set of GPU-aware optimizations that significantly reduce the latency of large diffusion models on mobile devices.

• The authors provide a detailed analysis of each optimization technique, explaining how they contribute to the overall performance improvement.

• The research presents a balance between computational efficiency and memory utilization, which is a crucial aspect in on-device deployment of large models.

Cons:

• The optimizations proposed are specific to GPU-equipped devices, limiting their applicability to other types of devices.

• The paper does not provide a comparative analysis with other existing optimization techniques.

Limitations:

• The paper focuses on the optimization of large diffusion models, and the techniques may not be applicable or effective for other types of models.

• The optimizations are designed for specific hardware (GPU-equipped devices), and their effectiveness on other hardware configurations is not discussed.

• The research does not address the potential impact of these optimizations on the accuracy or quality of the model outputs.

Potential Use Cases / Implications:

• The research can be beneficial for developers and researchers working on deploying large diffusion models on mobile devices, as it provides effective techniques to improve performance.

• The optimization techniques can be used to enhance the user experience across a wide range of devices by reducing the latency of AI applications.

• The research can contribute to the broader field of on-device AI, providing insights into how to effectively manage computational and memory resources.

 📼 Video Of The Day 📼

🛠️ Tools Of The Day 🛠️

CheatLayer - solves impossible business automation problems using a custom-trained GPT-4 machine learning model to function as your personal AI software engineer.

Thunkable - No code IOS & Android apps made easy

Profile Pro - Optimize your google business listing for free!

DoFollow - Turn Your Website Into A Traffic & Lead Gen Machine With The World's Most Powerful Backlinks

Creasquare - All-in-one platform to create content, generate captions with Al, and schedule content on social media

Webotify - ChatGPT trained specifically for your website

🤌 Prompt Of The Day 🤌

Today’s Prompt Of The Day is brought to you by The AI Solpreneur

Act as Alex Hormozi and rate my offer.

For context, my offer is to:

[DESCRIBE YOUR OFFER]


---

You should rate my offer based on Alex Hormozi's 4-part value equation framework:

1) How desirable is this offer's dream outcome from a scale of 1-100? ("Dream Score")
2) How high is the offer's perceived likelihood of achievement on a scale of 1-100? ("Success Score")
3) How high is the offer's perceived time delay between purchasing the product and reaching the promised achievement on a scale from 0 to 1? ("Time Score") The higher the time delay, the higher the score. Ideally, the perceived time delay should be as low as possible.
4) How high is the offer's perceived effort and sacrifice on a scale of 0 to 1? ("Effort Score") The higher the perceived effort, the higher the score. Ideally, the perceived effort and sacrifice should be as low as possible.

After rating each of the 4 components, calculate an "offer score", which is calculated like this:

1) Multiply "Dream Score" with  "Success Score" 
2) Multiply "Time score" with "Effort Score"
3) Divide the product of the Dream & Success score with the product of the Time and Effort Score to get the "offer score"

In your output, provide actionable advice for how I can tweak my offer to get a higher score on each of the 4 components of the value equation framework.

Also advice me on 2 other offer structures with higher offer score that I could consider instead of my current one, and explain why they have a higher score.

Step 2: Let Hormozi create your perfect offer for you. Without the need to hunt him down or call him up and ask.

Tweak this offer as much as you want so that it reaches a  perfect offer score of 1,000,000 

(Dream Score = 100, Success Score = 100, Time Score = 0.1, Effort Score = 0.1)

Would you take me up on this offer? If the answer is overwhelmingly yes, then upgrade to premium today!

🐥 Tweet Of The Day 🐥

A little shameless self-promotion here, but I created a new community on twitter where I’ll be sharing video breakdowns and more useful prompts as they come up so you can all follow along.

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