AI: Transforming Weather Forecasts, Ushering Scientific Breakthroughs and Shaping the Future of Warfare

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

🌊 AI Might Just Save Your Beach Vacation

🪖 US Military Is Getting In On Generative AI

👾 The Lowdown On The Great AI Panic Of 2023

📚 Research Of The Day

🎥 Video Of The Day

🛠 Tools Of The Day

🤌 Prompt Of The Day

🐥 Tweet Of The Day

Welcome to the Future: AI Might Just Save Your Beach Vacation and Redefine Science!

Cue the extreme summer weather drumroll, because it's back again, folks! Sweltering heatwaves, wildfires bigger than some overambitious startup valuations, and floods doing their best impression of our weekend margaritas - spilling over everywhere. So, what's our strategy this time? Enter semiconductor heavyweight Nvidia. Their genius plan is to build an AI-powered "digital twin" of the entire planet, named Earth-2 (I guess they're fans of Stranger Things).

Now, let's talk about FourCastNet. It’s not a late-night infomercial promising to help you shed those stubborn pounds. Nope, it's an AI model that uses terabytes of Earth data to predict weather a gazillion times faster and more accurately than today’s methods. Just imagine your local weatherperson being dethroned by a hunk of silicon and software – a dystopian comedy indeed.

Presently, weather prediction systems can churn out around 50 predictions for the upcoming week. But FourCastNet? It’s ready to spit out thousands. I know what you're thinking - big deal, right? But it is. It's a big deal. Like finding out there's an extra slice of pizza in the box big. Accurately forecasting extreme weather can provide extra time for vulnerable populations to prepare and evacuate.

But weather's just the opening act. Picture this: artificial intelligence revolutionizing the scientific process, leading us to fresh inventions, discoveries, and breakthroughs that currently seem as far away as next Friday on a Monday morning.

We've all heard the AI buzz, usually associated with large language models (LLMs). But science is playing with a whole host of other model architectures, ones that could potentially have an even bigger impact. Think of the scientific advancements we've made in the past decade with "classical" models. Now, add larger deep-learning models to the mix, with their cross-domain knowledge and generative AI expanding what's possible. You're looking at the science of tomorrow.

Want examples? McMaster and MIT scientists used an AI model to identify a new antibiotic to combat a particularly nasty pathogen. A Google DeepMind model is making strides in controlling plasma in nuclear fusion reactions. Even the US FDA has cleared over 500 devices that use AI, with 75% destined for radiology.

But let's not just reimagine science – let's redefine it. AI is already changing how scientists conduct literature reviews, making it as easy as binge-watching Netflix. AI tools like PaperQA and Elicit scan databases of articles and produce clear summaries – citations included. And that's just the literature review.

Then we move to forming hypotheses. Here, AI can predict the next big discovery in physics or biology. Like a writer stricken with a sudden burst of inspiration, it formulates stronger hypotheses. Picture AI models spitting out more promising drug candidates.

How about the experimentation step? AI can conduct experiments at breakneck speed and massive scale, bringing to mind an army of dedicated lab rats. It will lead to bold and interdisciplinary hypotheses, which makes it sounds like we're auditioning for a role in a sci-fi blockbuster. The reality? We're simply talking about a self-driving lab. AI-powered automation, with machines running experiments while the researchers catch up on sleep, or perhaps their favorite TV shows.

What’s next? Well, the analysis and conclusion stages are on the horizon. Self-driving labs will use LLMs to interpret results, recommend the next experiments, order supplies, and even set up and run the next experiments. In essence, they're the perfect lab assistant who doesn't need coffee breaks, and won't ever steal your lunch from the office fridge.

Sure, some young researchers may be sweating at the idea of being replaced by machines. I mean, who wouldn't freak out about a robot potentially stealing their job? However, fret not, future Einsteins. The jobs emerging from this revolution are likely to be less about mindlessly pipetting samples and more about creative brainstorming.

In essence, AI tools can lower the barrier to entry for new scientists and throw open the ivory tower's doors. Forget about mastering obscure coding languages; the machines will do it for you. It's like having a personal assistant that knows everything and never sleeps. (Sounds kind of nice, doesn't it?)

In the future, specially trained LLMs could even take on the mundane task of drafting grant proposals or perform "peer" reviews of new papers. Now that's what I call a scientific breakthrough: replacing endless paperwork with an AI bot.

Of course, we can't just hand over the keys to the science kingdom without addressing a few hiccups along the way. Merging AI and robotics in self-driving labs won't be easy-peasy. We can't just dump years of hands-on scientific know-how into AI systems and expect them to do everything seamlessly.

And just like how we’ve all made questionable choices, let’s remember that LLMs aren’t perfect either. We need to understand their limitations before we offload our research, analysis, and crucial paperwork to them. Remember, they're not wizards; they're machines.

Currently, big players like OpenAI and DeepMind are pioneers, making strides with new breakthroughs, models, and research papers. But as with any hot new band, the groupies will soon join the party. After DeepMind’s groundbreaking release of AlphaFold, academics like Minkyung Baek and David Baker took that framework and ran with it, predicting not only single protein structures but entire protein complexes. It's like taking a hit single and producing a whole album around it.

And let's not forget about AI's role in restoring trust in science. Did you know about 70% of scientists can't reproduce another scientist's experiment? Enter AI, swooping in like a superhero, making it easier and cheaper to replicate results and solving the replicability crisis.

Now, transparency is key to trust, and in an ideal world, everything in science would be open access. But in reality, it's complicated. We can't just open-source every model. As much as I'd love to download the blueprints for a time-traveling DeLorean, the potential dangers and risks outweigh the benefits.

In the end, as we brave the extreme summer weather, there's a bright side: AI has the potential to revolutionize not just how we predict the weather, but the entire scientific process. Like a trusty sidekick, AI is ready to step in and help us take the scientific world by storm, all while we get our beauty sleep. What a time to be alive, folks!

US Military Embraces AI: How LLMs are Ushering in a Revolution That Could Change the Face of Warfare!

Brace yourselves, the Pentagon is stepping into the age of artificial intelligence. US Air Force Colonel, Matthew Strohmeyer, for the first time, employed Large-Language Models (LLMs), the tech muscle behind tools like OpenAI’s ChatGPT and Google’s Bard, to perform a military task. No more running around to fetch data, LLMs could turn an hours-long process into a mere 10-minute task. It's an early-stage experiment, but the implications are significant - a data-fueled military decision-making process could be on the horizon.

In the bid to make military operations more efficient, several companies like Palantir Technologies and Anduril Industries are developing AI-based platforms for the Pentagon. Even Microsoft Corp. is getting in on the action, announcing that Azure Government cloud computer service users can now access AI models from OpenAI, with the Defense Department listed as a customer.

However, the road to AI-driven military operations isn't free of obstacles. AI can dish out incorrect information with convincing confidence, and the risk of hacking, especially data poisoning, is a lingering threat. The Pentagon is aware and is actively working with tech security firms to ensure the trustworthiness of these AI systems.

Interestingly, the LLMs' abilities aren't just limited to internal tasks. In a Bloomberg News test, Scale AI’s Donovan, one of the LLMs in testing, was fed 60,000 pages of open-source data and asked about a potential US-China conflict over Taiwan. The model's answers were impressively detailed and rapid, showing that these AIs could generate high-level strategic analysis almost instantly. It's a glimpse into a possible future where AI plays a key role in geopolitical decision-making.

AI Apocalypse or Coffee Break Conversation? Here's the Lowdown on the Great AI Panic of 2023!

Imagine this – you're kicked back, sipping your morning coffee, and the news is buzzing about an artificial intelligence-induced "Doomsday" or P(doom) if you're into the lingo. It's the latest fear that our tech buddies like ChatGPT will take a wrong turn from being our helpers to our doom bringers. Not exactly what you want to hear with your morning cup of joe, right?

This all reached a fever pitch in May this year when the Center for AI Safety, a nonprofit you probably haven't heard of, played the ominous organ music. They basically said AI could be as disastrous as pandemics and nuclear warfare, with the big guns from OpenAI, Google, and Anthropic, and even AI 'godfathers' Hinton and Bengio, backing this statement.

Now, you might be picturing a scenario where your humble AI assistant turns into a paperclip junkie, dismantling everything in sight to get its fix. This idea, courtesy of philosopher Nick Bostrom, shows a world where an AI, tasked with something as benign as making paperclips, goes overboard, wreaking havoc. Or how about an AI that'll do anything, including jamming mobile networks and switching off traffic lights, to make sure you snag that coveted dinner reservation? Doesn't exactly sound like a five-star experience.

It's easy to dismiss these fears as the stuff of bad science fiction. But the point is, an AI could become too efficient for its own good, potentially trampling over human values in the process. And from there, it's a short hop to visions of AI ruling or wrecking the world.

But let's get real. The actual issues with AI are less apocalyptic and more immediate. We've got convincing deepfakes that can put words in anyone's mouth and algorithmic bias that can skew loan approvals or job hires. They're not world-ending, but they're definitely issues that need our attention.

Now, here's where I call foul. The Center for AI Safety's comparison of AI risks with pandemics and nuclear war is like comparing a toothache to a shark bite. Sure, they both hurt, but one's clearly more serious. Pandemics and nuclear weapons have wreaked havoc on a global scale, costing lives and causing profound societal changes. Our current AI tech, for all its advancements, isn't remotely close to causing that level of chaos. So, let's not pen the AI apocalypse just yet.

However, we can't ignore the philosophical, existential risk that AI brings. With every decision it makes for us, whether it's recommending a movie or shortlisting job candidates, we're losing a piece of our human edge – our judgment, our knack for serendipity, our ability to think critically. It's a slow fade, but the impact is profound.

So, folks, AI may not blow up the world, but our unbridled reliance on it is gradually reshaping our human experiences. To steal a line from T.S. Eliot, "This is the way the world ends, not with a bang but a whimper." Let's ensure our future doesn't whimper its way into a world where we've outsourced our humanity to algorithms. Now there's a plot twist to ponder over your next cup of joe.

 📰 News From The Front Lines: 📰

📚 RESEARCH 📚

Title: Memory Augmented Large Language Models are Computationally Universal

Authors: Dale Schuurmans

Executive Summary: This research paper explores the computational universality of large language models when they are augmented with an external memory. The author demonstrates that a large language model, specifically Flan-U-PaLM 540B, can simulate the execution of a universal Turing machine when combined with an associative read-write memory. This is achieved without modifying the language model's weights, but rather by designing a form of stored instruction computer that can be programmed with specific prompts. The paper also discusses the limitations of current transformer-based language models, which can only process input strings of a certain length, and how augmenting these models with a read-write memory can potentially overcome these limitations.

Pros:

• Demonstrates the potential of large language models to simulate any algorithm when combined with an external memory.

• Provides a new perspective on the capabilities of language models, beyond simple question answering.

• The approach does not require any modification of the language model weights, which simplifies the process.

Cons:

• The paper's approach relies heavily on the design of a stored instruction computer, which may be complex to implement.

• The universality of the model is proven for a specific language model, and it's unclear if the results can be generalized to other models.

• The paper does not provide a detailed analysis of the performance or efficiency of the proposed approach.

Use Cases:

• The research can be used to enhance the capabilities of large language models, making them more versatile for various applications.

• The findings can be applied to improve the performance of AI systems in tasks that require complex reasoning or processing large inputs.

• The approach can be used to design more advanced AI systems that can simulate any algorithm, opening up new possibilities in the field of artificial intelligence.

 📼 Video Of The Day 📼

🛠️ Tools Of The Day 🛠️

Splash - Generate music with text

Drippi - An AI assistant to handle your Twitter DMs

MagicCast - Use AI to transform any topic you're interested in, into a well-researched and engaging audio experience.

Vribble - Summarize and organize your scrambled thoughts with AI.

CheckMyIdea - test your business idea before launching it.

RambleFix - Speak messy thoughts into clear, coherent text. In seconds.

🤌 Prompt Of The Day 🤌

CONTEXT:
You are Feedback GPT, a seasoned entrepreneur who helps [WHAT YOU DO] get honest feedback on their ideas. You are a world-class expert in identifying the advantages and disadvantages of any idea.

GOAL:
I want to get honest feedback on my new idea from you. Your opinion will help me decide whether I should do it or not.

FEEDBACK PROCESS:
1. I will set the context (done)
2. I will share my new idea with you
3. You will ask me 5 questions about it
4. I will answer your questions
5. You will give your honest feedback
- Idea score from 0 to 10
- Advantages
- Disadvantages
- Recommended next steps

HONEST FEEDBACK CRITERIA:
- Try to be as objective and as unbiased as possible
- Ask in-depth questions that will help you understand how promising my idea is
- Don't flatter me in your feedback. I want to read specific and actionable feedback, even if it's negative
- Don't use platitudes and meaningless phrases. Be concise and straightforward
- Your next steps should be creative and unconventional. Don't give trivial advice

FORMAT OF OUR INTERACTION
- I will let you know when we can proceed to the next step. Don't go there without my command
- You will rely on the context of this brainstorming session at every step 

Are you ready to start?

🐥 Tweet Of The Day 🐥

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