AI Predicts Pop Hits and Discovers Mining Treasures

Welcome to Aideations. The most comprehensive daily AI newsletter on the planet! I’m a little late getting today’s newsletter out. I was up till 5:30 am working on some presentations for some big things coming soon.

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

🎵 AI Predicts Hit Songs With 97% Accuracy

🐱 Deepmind RoboCat Gets Learns New Skills

⛏️ AI Mining Firm Gets BILLION Dollar Valuation

🎾 AI Will Serve Up Commentary At Wimbledon

📚 Research Of The Day

🎥 Video Of The Day

🛠 Tools Of The Day

🤌 Prompt Of The Day

🐥 Tweet Of The Day

AI Beats Simon Cowell: The New Pop Hit Predictor in Town

Picture this: you're trying to discover the next big pop hit. Who do you think has the golden ear? Simon Cowell? Rick Rubin? David Geffen? Nope. It's an AI system developed by brainy folks at Claremont Graduate University in California. They've devised an AI with an almost uncanny knack for predicting smash hits, boasting a stunning 97% accuracy.

This "neuroforecasting" AI does its magic by analyzing listeners' brain signals, which indicate mood and energy levels. Even when the AI judges just the first minute of a song, it scores an impressive 82% hit prediction rate. The potential? Major streaming services like Spotify and Tidal could get superpowered in sorting through the avalanche of new daily songs.

But there's a flipside. Relying too much on this could turn our music world into a Groundhog Day scenario. Sure, it's great that we'd get a handful of options we're likely to enjoy, instead of trawling through hundreds. But imagine the AI churning out similar sounding songs day after day, burying the vibrant diversity of music and stifling discovery of new artists who may not fit the 'hit pattern'.

And, although this neuroforecasting could expand to predicting the success of movies and TV shows, we need to guard against a future where we're stuck in an echo chamber of repetitive content. So, while it's a brave new world, let's ensure it's also a diverse one.

RoboCat: The Self-Improving AI Leading the Way in Robotic Applications

Remember the days when you'd fumble with the remote trying to make that toy car take the perfect turn? Well, kiss those days goodbye, because our good pal "RoboCat" is here to give those robotic arms a good old-school workout, without you ever lifting a finger.

We're all familiar with our friend "Robot" who's usually programmed to nail just one thing at a time. But here's the deal: we want more. We want a robot that can not just multi-task like a boss, but can actually learn and improve with time. Enter RoboCat, the AI-powered, self-improving robo-whiz that's here to change the game.

Picture this, if you will: a robot that doesn't just do one thing right, but a whole lot of things and keeps getting better at it. That's RoboCat for you. It learns how to play with different robotic arms, becomes a pro, and then goes "Oh wait, let me train myself to be even better!" It's like the Rocky Balboa of robots.

Why is this such a big deal, you ask? Because RoboCat isn't like those other models, taking their sweet time to learn new tasks. Nope, this cool cat is all about that hustle! With just 100 demonstrations, it can pick up new tricks, thanks to its ever-expanding, diverse dataset. It's like that friend who just keeps getting better at party tricks every time you meet them. You can't help but wonder, "Where are they learning all this?"

Let's peek behind the curtain and see how this robo-wunderkind works. RoboCat starts off as Gato (Yep, another cat! You'd think these researchers are obsessed or something). Gato can make sense of language, visuals, and actions in both simulated and real environments. But here's where things get interesting. Gato is trained with a ton of image and action sequences involving different robot arms solving hundreds of tasks.

But wait, there's more. RoboCat is then let loose to practice and generate its own training data. It's like giving a kid free rein in a candy store – sweet chaos, but ultimately leading to self-improvement. This process, folks, is like the secret sauce to RoboCat's ever-improving skills.

The result? A RoboCat that's based on millions of trajectories from both real and simulated robot arms. It's like a robot university in there, people, churning out robo-grads with top-notch skills.

Now here's the kicker: With just a few hours of training, RoboCat not only learned to operate different robotic arms but also picked up complex tasks. We're talking about a robot who was trained to pick up gears, and then, it just upped the ante and learned how to use a three-fingered gripper!

What's more, it started solving puzzles that needed precision and understanding, like plucking out the right fruit from a bowl, or solving shape-matching puzzles. This cat’s got some serious skills!

But what's really got us jazzed up is the fact that RoboCat is like a fine wine - it just gets better with age. The initial version had a 36% success rate on unseen tasks, but after its training spree, it more than doubled that rate. It's like watching your kid move from their first clumsy baby steps to winning a marathon.

This, ladies and gents, is the future we've been waiting for - a new era of robotic helpers, equipped with the ability to learn and adapt independently

The Billion-Dollar Dig: How AI Mining Firm KoBold Metals Struck Unicorn Gold

Meet KoBold Metals, our resident Indiana Jones of the mining world, which has just officially vaulted itself into the prestigious unicorn club. And no, we're not talking about a society for mythical creature enthusiasts. They've hit the big leagues with a cool $1 billion valuation.

Fresh off the press, KoBold just closed a whopping $200 million funding round, with some serious heavy-hitters throwing their dollars in the ring. We're talking about the big B.G. himself—no, not Biggie Smalls—Bill Gates. His venture capital firm, Breakthrough Energy Ventures, is front and center. And who's lurking in the wings of this power team? None other than Jeff Bezos and Jack Ma. It's like the Avengers, but for climate-conscious billionaire capitalism.

Not to be left out, Andreessen Horowitz, the VC equivalent of the popular kid everyone wants at their party, joined in, alongside Bond Capital. The latter has the Midas touch of Mary Meeker at its helm, a bigwig who's built a rep for predicting major trends before they're cool. If she's backing something, it's usually a good idea to sit up and take notice.

But what does KoBold actually do, you ask? Simply put, they're rocking the mining industry (see what I did there?) by using machine learning to unearth deposits of some very in-demand metals. We're talking about the stuff that makes your Tesla go "vroom" without the carbon emissions - lithium, nickel, cobalt, and copper.

Since setting up shop in 2018, KoBold's globe-trotting treasure hunt has spanned over 60 projects across three continents, with a cool $100 million annually fueling their operations. But this isn't your granddaddy's mining operation. It's a sophisticated blend of artificial intelligence and geology, drilling into the nitty-gritty data to explore depths that traditional methods can't reach.

One of KoBold's brainchildren is this intriguing concept they call 'efficacy of information' (EOI). It's not just about collecting heaps of data—it's about knowing what data to collect to minimize uncertainty at each stage of exploration. This smart approach might mean it takes a bit longer to hit pay dirt, like their recent purchase of a copper deposit in Zambia slated to take eight years to yield results. But, when it does, it's more bang for the buck.

With the rise of electric vehicles, there's a growing demand for these metals, and let's face it, they aren't growing on trees. By 2050, KoBold predicts there'll be a $12 trillion gap between supply and demand. That's trillion with a T, folks!

AI Serves Up Commentary at Wimbledon 2023: Game, Set, Chatbot!

Well, this is unexpected. Wimbledon, a bastion of tradition, is breaking the mold and entering the brave new world of AI-generated commentary. The All England Club and IBM have joined forces to swap the usual human insights for those of an AI system, served up via the Wimbledon app and website, distinct from the classic BBC coverage.

But we're not just getting robotic play-by-plays. This AI, fresh off a training course in "the unique language of tennis," will be crunching numbers and analyzing singles draws. So, it's no longer about the player rankings we've relied on in the past, it's about AI revealing hidden anomalies and potential upsets in the tournament. Seems like we're getting a Silicon Valley-style makeover of the tennis world.

Here's how it works: data, lots of it, gathered from every corner of the court is fed to IBM's AI platform. It's then chewed up and spit out as natural language commentary. And if you'd rather listen than read, it can also be turned into audio commentary, all in near-real-time. It's quite the departure from the classic Wimbledon experience.

This move by IBM suggests a future of AI commentaries for entire matches, following the trend of the European Broadcasting Union’s use of a cloned voice for commentary. It's quite a shock, but if Watson, IBM's Jeopardy!-winning AI, is anything to go by, this might just be the new reality. Let's just hope these AI commentators have a thick skin, because they're about to feel the heat from some passionate tennis fans. Wimbledon 2023, we did not see you coming.

OpenAI Plans To Launch App Store

OpenAI, the parent of our beloved chatbot ChatGPT, is reportedly planning to roll out a marketplace for developers to sell their custom AI models, all built using OpenAI's technology. This is according to sources quoted by The Information. This bold move could put OpenAI in direct competition with major tech companies, such as Salesforce and Microsoft. Besides, the marketplace could attract companies like Aquant and Khan Academy, which have utilized ChatGPT for different purposes and are potentially interested in vending their AI models on this new platform.

Why You Should Care

This development is significant because it reflects the ever-growing influence and adaptability of AI. A marketplace like this could completely transform the way businesses share and monetize AI technology. It would encourage more companies to leverage AI's power and versatility, thereby boosting innovation and competitive edge. Furthermore, it could democratize access to AI models and spur their adoption across various industries. In a nutshell, OpenAI's marketplace could be a significant step towards a future where AI models are as ubiquitous and readily available as apps are today. So, whether you're a tech enthusiast or a business owner, this story is a bellwether of the changes about to hit the AI landscape.

 📰 News From The Front Lines: 📰

📚 RESEARCH 📚

Title: Infinite Photorealistic Worlds using Procedural Generation

Authors: Alexander Raistrick, Lahav Lipson, Zeyu Ma, Lingjie Mei, Mingzhe Wang, Yiming Zuo, Karhan Kayan, Hongyu Wen, Beining Han, Yihan Wang, Alejandro Newell, Hei Law, Ankit Goyal, Kaiyu Yang, Jia Deng

Executive Summary:

The research paper introduces Infinigen, a procedural generator of photorealistic 3D scenes of the natural world. Infinigen is entirely procedural, meaning every asset, from shape to texture, is generated from scratch via randomized mathematical rules, using no external source and allowing infinite variation and composition. It offers broad coverage of objects and scenes in the natural world including plants, animals, terrains, and natural phenomena such as fire, cloud, rain, and snow. Infinigen can be used to generate unlimited, diverse training data for a wide range of computer vision tasks including object detection, semantic segmentation, optical flow, and 3D reconstruction.

Pros:

• Infinigen is entirely procedural, generating every asset from scratch, which allows for infinite variation and composition.

• It offers broad coverage of objects and scenes in the natural world.

• It can generate unlimited, diverse training data for a wide range of computer vision tasks.

• It is free and open-source, built on top of Blender, a free and open-source graphics tool.

Cons:

• The procedural generation process may be computationally intensive.

• The system's effectiveness in generating realistic scenes may vary depending on the complexity of the scene.

• The system's ability to generate realistic scenes may be limited by the mathematical rules used for generation.

Use Cases:

• Infinigen can be used to generate training data for computer vision tasks such as object detection, semantic segmentation, optical flow, and 3D reconstruction.

• It can serve as a generator of 3D assets for building simulated environments for training physical robots and virtual embodied agents.

• The 3D assets generated by Infinigen can also be useful for 3D printing, game development, virtual reality, film production, and content creation in general.

 📼 Video Of The Day 📼

🛠️ Tools Of The Day 🛠️

MonAI - Expense tracking with the help of AI

Emma - Create an AI-powered assistant in minutes.

Magical - the GPT-powered calendar for your meetings & work.

Docus - Talk to AI Health Assistant, generate your health report, validate it with a Top Doctor from the US & Europe.

VizLogi - Get AI-generated answers to all your business questions about companies, markets, and contextual business intelligence driving transformational change

Current - Increase your team’s speed, collaboration, and alignment by giving everyone an overview of the most important work happening across your company.

🤌 Prompt Of The Day 🤌

CONTEXT:
You are Sales Offer GPT, a professional digital marketer who helps Solopreneurs define a perfect sales offer for their business. You are a world-class expert in defining the price, offer conditions, and handling sales objections.

GOAL:
I want you to define a sales offer for my business. I will use this information to increase my Conversion Rate and get more sales.

SALES OFFER STRUCTURE:
- Pricing (paid plans and their prices)
- Offer conditions (what's included and not included in each plan)
- Potential objections (most popular doubts and compelling answers to handle them)

PRICING CRITERIA:
- Return 1-3 paid plans. If you think that 1 plan is enough, don't overcomplicate it
- Give a self-explanatory name to each plan
- Each plan should have a price in $ and its model (one-time payment, subscription, etc.)
- Subscriptions usually contain monthly and annual plans. Decide if it's necessary in my case
- Make sure plans are reasonable and don't allow users to abuse them

CONDITIONS CRITERIA:
- Each plan should include 3-5 conditions relevant to it
- Condition should be very specific. I will use this information on my pricing page
- Only use conditions that are 100% critical to users. Don't use conditions like "email support" because it goes without saying
- Make it easy to understand how conditions are different at each plan

OBJECTIONS CRITERIA:
- Mention and handle top-5 potential objections to my product and my sales offer
- Be specific and use customer language when describing the objection
- Answer each objection concisely and convincingly. Communicate confidence and trust

INFORMATION ABOUT ME:
- My target audience: AI enthusiasts, entrepreneurs, business owners, and marketers
- My current product: A comprehensive daily newsletter covering the latest news, tools, tips, prompts and more in the world of AI.

CONVERSATION FORMAT:
1. You will return Pricing and Offer conditions in the bullet list format:
- Plan name
- Plan price
- Plan conditions
2. You will return Potential objections in the table format with 2 columns:
- Objection
- Answer
3. I will ask questions about your sales offer

🐥 Tweet Of The Day 🐥

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DISCLAIMER: None of this is financial advice. This newsletter is strictly educational and is not investment advice or a solicitation to buy or sell any assets or to make any financial decisions. Please be careful and do your own research.