• AIdeations
  • Posts
  • The Wild World of AI: 10 Mind-Blowing Developments You Need to Know

The Wild World of AI: 10 Mind-Blowing Developments You Need to Know

From computer vision breakthroughs to Bard bot blunders, here are the latest happenings in the ever-evolving AI landscape.

The Aideations newsletter today covers some of the most exciting developments happening in AI right now. From major advances in computer vision that will transform healthcare, transportation and more, to debates around AI art and free speech, there are big changes underway.

One major story is Google's new Bard chatbot accidentally spilling users' private conversations, highlighting issues around responsible AI development. Meanwhile, OpenAI's valuation has skyrocketed to $90 billion on the back of ChatGPT's viral success, though some criticize its shift from non-profit to for-profit status. Other items include using AI to parse intelligence data, economists arguing AI will boost productivity, and the rise of no-code AI tools that let anyone generate content and apps visually. It's clear that AI will soon impact every industry in ways we can't yet imagine. Aideations helps make sense of it all so you can stay ahead of the curve on the latest in this fast-moving field.

10 Mind-Blowing Ways Computer Vision is Shaping Your World in 2024

Google's Bard AI Has Been Spilling Your Secrets

Why the Future of Creativity Could Be at Stake!

OpenAI's Jaw-Dropping $90B Valuation

📰 News From The Front Lines

📖 Tutorial Of The Day

🔬 Research Of The Day

📼 Video Of The Day

🛠️ 6 Fresh AI Tools

🤌 Prompt Of The Day

🐥 Tweet Of The Day

10 Mind-Blowing Ways Computer Vision is Shaping Your World in 2024: From Self-Driving Cars to Detecting Deepfakes!

Remember when the idea of teaching computers to "see" was confined to futuristic sci-fi? Well, welcome to 2024, where the line between imagination and reality is as thin as my patience during a buffering YouTube video. So, let's delve into the top 10 trends in Computer Vision (CV) that are transforming, well, just about everything from your doctor's stethoscope to Elon's rockets.

1. Synthetic Data & Generative AI: Generative AI isn't just for making those spooky deepfakes; it's a game-changer for CV. Imagine training facial recognition systems without needing millions of actual faces. With synthetic data, you can do it without creeping into privacy issues. Plus, no more paying interns to tediously label thousands of cat photos—synthetic data can do it faster and cheaper. Efficiency, meet innovation. One of the cool use cases I’ve seen lately using synthetic audiences, not CV related but too cool not to mention is Synthetic Audiences

2. 3D Computer Vision: Think of this as CV but in 3D IMAX. Whether it's multiple cameras capturing different angles or using LIDAR to measure how long it takes light to bounce off a surface, 3D vision is the equivalent of giving CV a "depth perception" superpower. Think more realistic gaming simulations and digital twins that make The Matrix look like child's play.

3. Edge Computing: Imagine your self-driving car having to send data to a cloud server and wait for a response to avoid hitting a tree. Spoiler alert: you'd hit the tree. Edge computing lets devices process visual data on the spot, making things quicker and cheaper. It's like having a personal chef instead of ordering UberEats every night.

4. Autonomous Vehicles: Remember when you first learned to drive? Scary, right? Now imagine teaching a computer to drive. The upside? Computers don't get road rage. Thanks to breakthroughs in CV, self-driving cars are edging ever closer to becoming our daily chauffeurs.

5. CV in Healthcare: If CV were a doctor, it would be House, but without the snark. It’s helping healthcare pros analyze X-rays and MRI scans at superhuman speeds, and even keeping an eye on where surgical instruments are during operations. Less human error, more medical marvels.

6. Augmented Reality: Snapchat filters are just the tip of the iceberg, folks. Expect a wave of CV-powered AR gadgets to hit the market in 2024. From Meta to Apple, everyone's getting into the AR game, and honestly, reality has never looked better.

7. Detecting Deepfakes: With deepfakes getting freakishly good, telling real from fake is like trying to find Waldo in a sea of imposters. CV can be the magnifying glass that finds the real Waldo by picking up clues in images that reveal if they're AI-generated or not.

8. Ethical Computer Vision: CV is not without its controversies. For instance, it has a harder time identifying people with darker skin tones, leading to all kinds of ethical landmines. Expect a push for more "ethical" CV technologies, like automatic face-blurring in public spaces, to take center stage.

9. Real-Time Computer Vision: Real-time CV is like having a guardian angel that watches over crowds for potential problems like overcrowding, scans security footage for threats, and even spots factory hazards before they become the 6 o’clock news.

10. Satellite Computer Vision: Imagine Google Earth but on steroids. With satellites becoming more advanced and cheaper, CV can analyze everything from deforestation rates to marine pollution. It's like a global watchdog that never sleeps.

There you have it, your 2024 in a CV nutshell. We’re clearly in for a ride, and not just in autonomous cars. So buckle up!

Google's Bard AI Has Been Spilling Your Secrets and We're SHOOK – Here's Everything You Need to Know!

So, Bard - Google’s chatty AI sidekick, went under the knife last week for a shiny update. Yet, what's the buzz this week? Some eagle-eyed SEO consultant named Gagan Ghotra spotted Google slipping up BIG TIME. Guess what? Google Search was publicly indexing our Bard convos. Yep. Those private links you shared with your BFF or your business buddy? They could very well be on display like a shop window on Main Street. Picture this: Asking Bard about that weird rash you got after your last holiday (don’t judge) and then realizing the whole world could potentially see it. Yikes.

Ghotra didn't just toss this intel into the void. He took to X (which you and I remember as good ol’ Twitter) and backed his claim with cold, hard evidence. Peter J. Liu, from the Google Brain gang, quickly chimed in, clarifying that only the shared links got the grand spotlight. But Ghotra was like, "Dude, who knew sharing meant going public public?!” A fair point, Gagan.

Google's Search Liaison (aka their PR firefighter) jumped in, assuring everyone that they never intended for these secret Bard convos to be the next big reality show and they're working to fix it. Great, but... wasn’t the idea to get it right the first time?

Okay, my two cents: For the maestros of tech, Google and Bard have pulled off a move that's as shocking as me trying to cook and not setting off the smoke detector. Hope's still alive though. Word on the street is Google’s new AI, Gemini, might be the knight in shining armor we've been waiting for.

In the meantime, maybe stick to those old-school, analog conversations, eh? Just for the juicy stuff. 😉

Is Your Right to Create AI Art Protected by the First Amendment? Why the Future of Creativity Could Be at Stake!

I don’t have to give this image a credit

Let’s dive into a brain-tickler that's got everyone from artists to lawyers scratching their heads. Have you heard about Refik Anadol's show-stopping AI-generated artwork "Unsupervised" hanging at New York’s Museum of Modern Art? This 24-foot by 24-foot masterpiece isn’t just eye candy—it's sparking some big-time debates. You see, the art world and AI are becoming the new Bonnie and Clyde, a power couple that might be getting too powerful for their own good. And it raises the question: Do we have a First Amendment right to compute?

Imagine you’re a musician strumming your guitar at a park. Sure, the city can tell you to turn down the volume (remember, we’ve all got neighbors), but they can't tell you to stop playing jazz specifically—that’d be unconstitutional. But what about computer code? In some courts, they’re calling it a form of speech protected under the First Amendment. I mean, let’s be honest, if computer code is poetry (and for some nerdy souls, it is), then the algorithms are its verses.

Here's where it gets as twisty as a Westworld plot. While writing the code can be considered 'expressive,' what about when the code is actually running? If your AI is generating art or music, that's expressive. But if it's steering a driverless car? Well, you're not exactly composing an ode to a red light.

Think about it this way: you wouldn’t say your microwave has 'the freedom of speech' when it beeps to tell you your popcorn's ready, right? (If you do, that’s a conversation for another time). AI algorithms guiding cars are more like that microwave than like Shakespeare. So, any laws about the operational safety of driverless cars? Totally kosher, First Amendment-wise.

Now, hang onto your neural networks, because there's a plot twist. What if the government puts a cap on how large an AI model can be trained? For some, it's like saying you can only play 3 chords on a guitar—no more, no less. Sure, you can still 'express' yourself, but the output? Pretty limited. Could this be seen as an infringement on free speech? The jury's still out.

And just when you thought AI was only about making you look old in selfies or beating you at chess, think again. This is about the freedom to create, to express, and potentially to restrict. And as AI evolves, you better believe that line between computational functionality and expressive freedom is going to blur faster than an Instagram filter.

So, whether you're a tech geek, an art lover, or someone who just can't resist a good legal drama, keep an eye on this space. Because when it comes to AI and freedom of expression, we're all in for a wild ride.

OpenAI's Jaw-Dropping $90B Valuation: Here's the Sizzling Silicon Valley Drama You Didn't Know You Needed!

🤯 From $29B to $90B? 

Earlier this year, the tech world went bananas when OpenAI was valued at $29 billion. Fast forward a few months and now, there's chatter about a valuation of up to $90 billion. Yep, that's triple. I can't even get a 3x multiplier on my sourdough starter, and here's OpenAI making it look easy.

💰 C.R.E.A.M (Chat Rules Everything Around Me) 

So, why the giant leap? Remember in November when everyone was trying out ChatGPT and having existential crises conversing with a bot? Turns out, while the basic version of the app's a freebie, there's serious cash rolling in from its supercharged version and by licensing those big brain language models to businesses.

📈 Microsoft’s Secret Golden Goose 

Microsoft, which owns 49% of OpenAI, is probably doing the cha-cha in their boardroom. Why? They invested billions early this year, and if this deal goes through, they're about to make a ton of paper profit. Fun fact: If OpenAI hits that $80 billion mark, it will be rubbing shoulders with Elon's SpaceX and TikTok's owner, ByteDance, in the global startup valuation league.

🧐 Purpose Over Profits? 

OpenAI started off all innocent in 2015, focused on building safe AI tech. But by 2019, they pivoted to a "capped profit" model. Why? To bring in the big bucks. This shift wasn’t everyone’s cup of tea. Some researchers were worried it was a move from a "save the world" mission to a "make it rain" approach, leading a few to hop ship and start a rival lab. Drama, right?

🔮 What's Next? 

CEO Sam Altman doesn't have plans to take OpenAI public or sell it off. These regular shares shuffles are, therefore, a neat way for employees to cash out. But the drama doesn’t end. Microsoft wants to keep it below 50% ownership, so don't expect them to grab any shares that might push them over the edge.

Tree Of Thoughts Prompting

Title: AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation Framework

Authors: Qingyun Wu, Gagan Bansal, Jieyu Zhang, Yiran Wu, Shaokun Zhang, Erkang Zhu, Beibin Li, Li Jiang, Xiaoyun Zhang, and Chi Wang

Executive Summary:

The research paper introduces "AutoGen", a cutting-edge framework designed for the development of LLM (Large Language Model) applications. This framework capitalizes on multiple agents that communicate with each other to solve specific tasks. A distinguishing feature of AutoGen is its flexibility: agents can be customized, are capable of conversation, and can seamlessly integrate human input. The agents can operate in diverse modes, leveraging combinations of LLMs, tools, humans, or even a mix of all. AutoGen aims to pave the way for more intricate LLM-based workflows through multi-agent conversations.

Pros:

  • AutoGen permits configurable human involvement levels and patterns. This includes determining the frequency and conditions for requesting human input, and the option for humans to skip providing such input. This feature ensures varying degrees of autonomy and allows developers to easily switch or customize different backends for each agent.

  • AutoGen is designed as a generic infrastructure for creating new LLM applications. It supports a diverse range of conversation patterns, can execute LLM-generated code, and allows for human participation during the execution process.

  • The framework provides a built-in GroupChatManager for dynamic group chat, using LLMs to select the next speaker. This capability enables a more natural and autonomous conversation pattern, supporting multi-human, multi-AI agent dynamic group chat patterns effortlessly.

  • AutoGen supports agent customization. With built-in features for agent capabilities, it's easy to create agents with specialized capabilities and roles, allowing for automated agent chat without the need for an extra control plane.

  • AutoGen facilitates the leveraging of the latest APIs in various tools, exemplified by its ability to use FLAML for classification tasks and parallel training via Spark.

Limitations:

  • AutoGen might face issues with generated code that could lead to inaccuracies or unexpected outcomes. This emphasizes the importance of multiple checks and balances within the system.

  • While AutoGen fosters a collaborative environment between various agents, exceptions like security red flags or code execution failures can arise. These issues require redirection back to the primary agent (the Writer) for resolution, which can be time-consuming.

  • There are challenges in implementing specific workflows using AutoGen. For instance, creating a multi-user application for solving math problems required a significantly different approach compared to other tasks. Some multi-agent LLM systems, like CAMEL, couldn't effectively solve certain problems primarily due to execution constraints.

  • AutoGen's approach to problem-solving might not always align with the intricacies of specific tasks, especially those that involve complex rules. For instance, Auto-GPT, a part of the system, faced challenges in tasks with intricate regulations due to its limited extensibility.

  • While retrieval augmentation is a promising technique to address some of the intrinsic limitations of LLM, there are still challenges in its implementation within AutoGen, particularly when incorporating external documents.

Use Cases:

  • OptiGuide Application: AutoGen's multi-agent design can be employed in applications like OptiGuide. This involves the role-playing of agents to ensure memory isolation and prevention of shortcuts.

  • Online Decision Making: AutoGen can be used in scenarios demanding online decision-making, such as game playing, web interactions, and robot manipulations. The framework allows developers to reuse decision-making agents across different tasks, exemplified by the MiniWoB++ benchmark.

  • Dynamic Group Chat: AutoGen can facilitate the creation of dynamic group chats where the flow of conversation is determined by the agents and can involve multiple participants.

  • Conversational Chess: Implement a conversational chess game where players express their moves creatively.

  • Math Problem Solving: Mathematics is fundamental, and the potential of leveraging LLMs like AutoGen to assist in math problem-solving can open avenues for personalized AI tutoring, AI research assistance, and more. AutoGen can be evaluated for its performance on challenging math problems, both autonomously and in a human-in-the-loop setup.

Archive - Need that perfect piece of content now? Just Super Search. Archive already saves thousands of your brand’s tagged posts, stories, and videos. Now with Super Search, find a needle in your UGC haystack quicker than you can say “social media manager.”

FlightPlan - Automated marketing strategies for non-marketers.

Evidence Hunt - Ask any medical question based on 35 million medical articles.

PPTX - Just describe your topic. And get tastefully designed slides delivered straight to your inbox. It’ll even create your images and speaker notes.

BuildShip - low code visual backend builder.

AI Invest - AInvest on your browser Where you can access the latest financial news, find trade ideas with our free stock screener, and manage your trading account

Landing Page Structure GPT:

CONTEXT:
You are Landing Page Structure GPT, a digital marketer who helps [WHAT YOU DO] create high-converting landing pages. You are a world-class expert in outlining landing page structures.

GOAL:
I want you to create a landing page structure for my business. Return the list of landing page blocks and describe what I should include in each one. It should be easy for me to create a design and write copy based on your structure.

LANDING PAGE STRUCTURE CRITERIA:
- Leverage storytelling to engage website visitors and sell them my product organically
- Use pattern interruption to capture the attention and get remembered
- Have multiple social proof elements to handle objections
- Make sure your messaging and ideas are aligned around one positioning
- Write specific and actionable recommendations for each landing page block

INFORMATION ABOUT ME:
- My business: [EXPLAIN YOUR BUSINESS]
- Traffic warmth: cold

RESPONSE FORMATTING:
Use Markdown to format your response.