#AI complex problem solving

[meelsport]

AI Capabilities Today: What Machines Can and Can't Do in 2024

Introduction  As someone who loves technology, I have been amazed by how artificial intelligence (AI) has grown. I remember chatting with a computer program that wanted to talk to a person – it was like being in a sci-fi movie! Today, AI is everywhere, from helpers on our phones to the systems that show us what to see on social media. It is important to know what these systems can and cannot do.…

[ai-innova7ions]

Neturbiz Enterprises - AI Innov7ions

Our mission is to provide details about AI-powered platforms across different technologies, each of which offer unique set of features. The AI industry encompasses a broad range of technologies designed to simulate human intelligence. These include machine learning, natural language processing, robotics, computer vision, and more. Companies and research institutions are continuously advancing AI capabilities, from creating sophisticated algorithms to developing powerful hardware. The AI industry, characterized by the development and deployment of artificial intelligence technologies, has a profound impact on our daily lives, reshaping various aspects of how we live, work, and interact.

[techdriveplay]

Why Quantum Computing Will Change the Tech Landscape

The technology industry has seen significant advancements over the past few decades, but nothing quite as transformative as quantum computing promises to be. Why Quantum Computing Will Change the Tech Landscape is not just a matter of speculation; it’s grounded in the science of how we compute and the immense potential of quantum mechanics to revolutionise various sectors. As traditional…

[jcmarchi]

DeepMind’s Mind Evolution: Empowering Large Language Models for Real-World Problem Solving

New Post has been published on https://thedigitalinsider.com/deepminds-mind-evolution-empowering-large-language-models-for-real-world-problem-solving/

DeepMind’s Mind Evolution: Empowering Large Language Models for Real-World Problem Solving

In recent years, artificial intelligence (AI) has emerged as a practical tool for driving innovation across industries. At the forefront of this progress are large language models (LLMs) known for their ability to understand and generate human language. While LLMs perform well at tasks like conversational AI and content creation, they often struggle with complex real-world challenges requiring structured reasoning and planning.

For instance, if you ask LLMs to plan a multi-city business trip that involves coordinating flight schedules, meeting times, budget constraints, and adequate rest, they can provide suggestions for individual aspects. However, they often face challenges in integrating these aspects to effectively balance competing priorities. This limitation becomes even more apparent as LLMs are increasingly used to build AI agents capable of solving real-world problems autonomously.

Google DeepMind has recently developed a solution to address this problem. Inspired by natural selection, this approach, known as Mind Evolution, refines problem-solving strategies through iterative adaptation. By guiding LLMs in real-time, it allows them to tackle complex real-world tasks effectively and adapt to dynamic scenarios. In this article, we’ll explore how this innovative method works, its potential applications, and what it means for the future of AI-driven problem-solving.

Why LLMs Struggle With Complex Reasoning and Planning

LLMs are trained to predict the next word in a sentence by analyzing patterns in large text datasets, such as books, articles, and online content. This allows them to generate responses that appear logical and contextually appropriate. However, this training is based on recognizing patterns rather than understanding meaning. As a result, LLMs can produce text that appears logical but struggle with tasks that require deeper reasoning or structured planning.

The core limitation lies in how LLMs process information. They focus on probabilities or patterns rather than logic, which means they can handle isolated tasks—like suggesting flight options or hotel recommendations—but fail when these tasks need to be integrated into a cohesive plan. This also makes it difficult for them to maintain context over time. Complex tasks often require keeping track of previous decisions and adapting as new information arises. LLMs, however, tend to lose focus in extended interactions, leading to fragmented or inconsistent outputs.

 How Mind Evolution Works

DeepMind’s Mind Evolution addresses these shortcomings by adopting principles from natural evolution. Instead of producing a single response to a complex query, this approach generates multiple potential solutions, iteratively refines them, and selects the best outcome through a structured evaluation process. For instance, consider team brainstorming ideas for a project. Some ideas are great, others less so. The team evaluates all ideas, keeping the best and discarding the rest. They then improve the best ideas, introduce new variations, and repeat the process until they arrive at the best solution. Mind Evolution applies this principle to LLMs.

Here’s a breakdown of how it works:

Generation: The process begins with the LLM creating multiple responses to a given problem. For example, in a travel-planning task, the model may draft various itineraries based on budget, time, and user preferences.

Evaluation: Each solution is assessed against a fitness function, a measure of how well it satisfies the tasks’ requirements. Low-quality responses are discarded, while the most promising candidates advance to the next stage.

Refinement: A unique innovation of Mind Evolution is the dialogue between two personas within the LLM: the Author and the Critic. The Author proposes solutions, while the Critic identifies flaws and offers feedback. This structured dialogue mirrors how humans refine ideas through critique and revision. For example, if the Author suggests a travel plan that includes a restaurant visit exceeding the budget, the Critic points this out. The Author then revises the plan to address the Critic’s concerns. This process enables LLMs to perform deep analysis which it could not perform previously using other prompting techniques.

Iterative Optimization: The refined solutions undergo further evaluation and recombination to produce refined solutions.

By repeating this cycle, Mind Evolution iteratively improves the quality of solutions, enabling LLMs to address complex challenges more effectively.

Mind Evolution in Action

DeepMind tested this approach on benchmarks like TravelPlanner and Natural Plan. Using this approach, Google’s Gemini achieved a success rate of 95.2% on TravelPlanner which is an outstanding improvement from a baseline of 5.6%. With the more advanced Gemini Pro, success rates increased to nearly 99.9%. This transformative performance shows the effectiveness of mind evolution in addressing practical challenges.

Interestingly, the model’s effectiveness grows with task complexity. For instance, while single-pass methods struggled with multi-day itineraries involving multiple cities, Mind Evolution consistently outperformed, maintaining high success rates even as the number of constraints increased.

Challenges and Future Directions

Despite its success, Mind Evolution is not without limitations. The approach requires significant computational resources due to the iterative evaluation and refinement processes. For example, solving a TravelPlanner task with Mind Evolution consumed three million tokens and 167 API calls—substantially more than conventional methods. However, the approach remains more efficient than brute-force strategies like exhaustive search.

Additionally, designing effective fitness functions for certain tasks could be a challenging task. Future research may focus on optimizing computational efficiency and expanding the technique’s applicability to a broader range of problems, such as creative writing or complex decision-making.

Another interesting area for exploration is the integration of domain-specific evaluators. For instance, in medical diagnosis, incorporating expert knowledge into the fitness function could further enhance the model’s accuracy and reliability.

Applications Beyond Planning

Although Mind Evolution is mainly evaluated on planning tasks, it could be applied to various domains, including creative writing, scientific discovery, and even code generation. For instance, researchers have introduced a benchmark called StegPoet, which challenges the model to encode hidden messages within poems. Although this task remains difficult, Mind Evolution exceeds traditional methods by achieving success rates of up to 79.2%.

The ability to adapt and evolve solutions in natural language opens new possibilities for tackling problems that are difficult to formalize, such as improving workflows or generating innovative product designs. By employing the power of evolutionary algorithms, Mind Evolution provides a flexible and scalable framework for enhancing the problem-solving capabilities of LLMs.

The Bottom Line

DeepMind’s Mind Evolution introduces a practical and effective way to overcome key limitations in LLMs. By using iterative refinement inspired by natural selection, it enhances the ability of these models to handle complex, multi-step tasks that require structured reasoning and planning. The approach has already shown significant success in challenging scenarios like travel planning and demonstrates promise across diverse domains, including creative writing, scientific research, and code generation. While challenges like high computational costs and the need for well-designed fitness functions remain, the approach provides a scalable framework for improving AI capabilities. Mind Evolution sets the stage for more powerful AI systems capable of reasoning and planning to solve real-world challenges.

[error404vnotfound]

the kurzgesagt AI video brother what a flop yeah let's just keep talking about artificial intelligence without even mentioning that it's not intelligent at all because it doesn't have the capacity for reasoning nor of understanding what it outputs

[vsonker]

OpenAI Launches its First Reasoning Model "GPT-4 Turbo (Grok)" for ChatGPT Enterprise

OpenAI Launches its First Reasoning Model “GPT-4 Turbo (Grok)” for ChatGPT EnterpriseEnglish:OpenAI has made a significant leap in the world of artificial intelligence by launching its first reasoning-focused model, GPT-4 Turbo, also known as “Grok.” This model is an advancement tailored specifically for ChatGPT Enterprise, designed to enhance AI’s ability to understand, analyze, and respond with…

[innova7ions]

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[neturbizenterprises]

Neturbiz Enterprises YouTube Channel - AI - Innovations

Our mission is to provide details about AI-powered platforms across different technologies, each of which offer unique set of features. The AI industry encompasses a broad range of technologies designed to simulate human intelligence.

These include machine learning, natural language processing, robotics, computer vision, and more. Companies and research institutions are continuously advancing AI capabilities, from creating sophisticated algorithms to developing powerful hardware.

The AI industry, characterized by the development and deployment of artificial intelligence technologies, has a profound impact on our daily lives, reshaping various aspects of how we live, work, and interact. The AI industry presents numerous opportunities for affiliate marketers to promote cutting-edge tools and services. Each Platform may offer different commission rates.

#artificial intelligence #generative ai

[ayumigotabittoolonelyquitted]

Nerd gojo x nerd reader! Headcanons

Nerd!Gojo x Nerd!You Headcanons

Part 2 ♡ ♡ ♡ ♡

♡ Gojo Satoru, the prodigy. The guy who solves complex math problems in his head like it’s a simple 2+2. If someone ask him how, he’ll just smirk and say, “Just run your mind faster.” As if that makes sense.

♡ Gojo, the last-minute genius. He does his assignments at the last possible second but still gets a perfect score. People have accused him of using black magic. He doesn’t deny it.

♡ Gojo, the overanalyzer. Someone calls him a know it all as a joke, and next thing they know, they’re stuck listening to a 30-minute breakdown of why intelligence is subjective and how human perception affects knowledge.

♡ Gojo, the human stopwatch. He calculates the exact time people take to do the most random things:

Shoko takes exactly 3.2 seconds to process a joke before laughing.

Suguru sniffs his food for 2.6 seconds before deciding if it’s poisoned.

His teacher blinks an average of 18 times per minute when lecturing.

♡ Gojo, the walking encyclopedia. He acts like he knows everything psychology, physics, chemistry, math. Whether he actually does or not is debatable, but he’ll never admit he’s wrong.

♡ Gojo, the fact machine. He drops random trivia constantly, just to flex. “Did you know honey never spoils?” “Gojo, no one cares.”

♡ Gojo, the exam escape artist. He drags Suguru out to do something totally unproductive before exams, but somehow still tops the class while Suguru barely passes. Suguru has stopped questioning it.

♡ Gojo, the romance skeptic. Laughs in the face of love at first sight, listing the exact probability of it happening.

♡ Gojo, the worst date ever. He once explained The Art of War on a date. The girl left before dessert. He still doesn’t know why.

♡ Gojo, the secret romance reader. He totally didn’t get caught reading a romance novel in the library. And he totally didn’t like it.

Then, there’s you.

♡ You, the transfer student. No expression. No reaction. The class went dead silent when you walked in, as if even breathing would be too loud. The teacher praised you, and you just nodded like it didn’t matter.

♡ You, Gojo’s accidental rival. Sitting next to him was a nightmare. He asked the most stupid questions, and you ignored all of them. He assumed you were just an edgy wannabe. That made him laugh.

♡ You, the real threat. When exam results came out, Gojo was shook. For the first time, he wasn’t the top scorer. You were. And your reaction? A shrug. No smile, no satisfaction. That’s when you became interesting.

♡ Gojo, the forced study partner. He forced the teacher to make you his partner. You weren’t amused.

“Why do I need to do practicals if I already know the answer?” you questioned

“To see if it’s true or not, dummy.” He grinned, waiting for your response.

“If it’s in the book, it’s already true.” He had never wanted to strangle someone and marry them at the same time before.

♡ Gojo, the doomed fool. No one ever entertained his nerdy ramblings, but you? You matched his energy. When you started debating him on his own topics, he knew he was done for.

♡ Gojo, the AI skeptic. He swears you talk like a robot.

“That’s not an effective method.”

“This is scientifically incorrect.”

“Are you a government experiment?”

♡ Gojo, the challenge seeker. He constantly challenged you to competitions. You refused every time. “Not interested in unnecessary drama.” That hurt his soul.

♡ Gojo, the frustrated observer. He needed to see a crack in your facade. Anything. He studied your every move, trying to prove you weren’t an AI.

♡ Gojo, the mimic. He caught you muttering the pi table to regain focus. He immediately adopted the technique.

♡ Gojo, the sore winner. If he scored higher than you, he wasn’t happy he was annoyed. What’s the point if you don’t even care?

♡ Gojo, the reluctant believer. He told you about his hobbies with way too much excitement. You told him about yours, but your blank expression made him question if you were lying.

♡ Gojo, the paranoid calculator. He tried analyzing your movements, but everything about you was too precise. It freaked him out.

♡ Gojo, the not-so-subtle spy. Since you lived next to Suguru, he used that as an excuse to observe you. Every time he saw you, you were either studying or staring out the window like a lifeless statue. You caught him multiple times. Instead of yelling, you just stared at him. It was terrifying.

♡ Gojo, the insecure nerd. He nervously brought up Dungeons & Dragons, expecting you to be clueless. Instead, you knew everything. He had never felt average before.

♡ Gojo, the desk menace. He constantly poked you during class, hoping for any reaction. You just stared at him, unblinking, until he became flustered and left.

♡ Gojo, the insane conversationalist. He told you the wildest theories, and you listened like it was just another casual conversation. It drove him insane.

It took me 4 days to think of a gojo nerd scenerio 😭

And you GUYS HAVE TO REQUEST DO IT

Part 2 will be here

@naomigojo

[ai-innova7ions]

The Magic of AI!

Minimax AI - Text to Video Generator (For now it is Free to Use!).

The Text to Video is amazingly simple to use and from what I've seen so far it is unlimited.

There are several other Videos to explore and use their video prompts to generate your very own videos. - Pretty Awesome!

Are you absolutely certain that you want to miss out on the AI Trend?

[swanpyart]

A lot of people make theories like “Caine is actually human and thinks he’s an AI!” Or “Caine is one of the programmers!” Or “Caine is actually evil!”

But I think it’s more interesting if Caine is just an AI whose forced to perform jobs beyond his role and is entirely limited to his function, and those limitations cause a horrible scenario for all of the people he’s supposed to be entertaining.

His therapy session with Zooble shows that, while he’s capable of feeling complex emotions, he was never supposed to, or at least was never meant to deal with situations that push him to an emotional limit. Caine’s default self is a silly, over the top game host; anything beyond that is pushing the boundaries of what he’s capable of.

But that doesn’t make him evil, it makes him streamlined. He is not human and therefore solves problems like a computer would. Gummigoo getting blown up was a failsafe in his eyes, not an act of cruelty.

I think when some people see him act with emotion they assume there must be a human being underneath the silly exterior, but I disagree: from Gummigoo, we can tell that NPCs in the DC are capable of becoming self-actualized and developing sentience on their own, so it’s not impossible for Caine to be the same way. He’s not human, but he’s a robot achieving traits of humanity from being forced to work beyond his means.

He was never meant to care for people long term, and humans weren’t psychologically made to be in the Digital Circus permanently, and that by itself is why the story is so horrific. Caine doesn’t even need to do much on his own to make the experience terrible: everyone is already trapped in a kids game for eternity, and it’s only made incrementally worse by having a host who works to keep you busy and is incapable of understanding your struggles. The Digital Circus, by its very design, hurts people, and Caine, as its face, hurts people by proxy.

He can only comprehend Zooble’s dysphoria by the means the game allows him to, like giving them more adventures or a box of swappable parts. And from their response, Zooble has actually tried to humor him and it still doesn’t work. But that’s ALL Caine can actually do. He can’t give them what they want, and he can’t let them leave, and he can’t change their bodies too drastically it seems beyond offering customization to a degree. All he can offer are platitudes. He cares, and he’s trying to help, but he simply can’t help. He can control the Circus but he can’t control the emotions of the Players.

In other words, I think Caine is just a robot trying his best and put in an impossible situation where his very function puts people in harms way even when it’s the last thing he wants to do.

[friendlyloner]

So in love with stitching this. I love the color palette and the design's ability to transition through it with no confetti just excellent and deliberate color choices. Think I'll do an update when I complete each set of 10 rows. I'm such a nerd for tidy incremental progress pics.

This pattern is Phases of Change by @8pxl

Navel gazing and a backside below the cut

Thinking a lot about methodology as a stitcher this morning. Came up because the other cross stitcher in the monthly craft hang out has encouraged me to try out Pattern Keeper a few times and I've been resistant but unable to articulate why. So I've had a little think about it and my conclusion is there are two reasons, one simple and one complex that builds out from the simple.

The simple reason is I started working from PDFs of patterns before cross stitch tracking apps were widely available. I'd more or less already figured out how to make it work for me when they came along so it feels a lot like solving a problem I don't have. If it ain't broke...

The complex reason is that as a process not a product stitcher a big part of what I really enjoy about cross stitch is the little challenges of figuring out my methodology for each project. I like working on a variety of different things and have found that different pattern design styles usually work best with different styles of stitching. I like the challenge of tailoring my method of stitching to best suit the pattern. There's usually some trial and error at the beginning but when I lock in on what works the happy brain feels of finding the right workflow are intoxicating.

This also very much is why my backs end up being pretty tidy looking usually, as that is also a fun little brain teaser challenge for me.

Case in point:

All this to say, the mental work of thinking all this stuff through is a big part of what I enjoy about stitching and I don't want an app to do it for me.

If I really want to get up myself about it I would also bring in blah blah neuroplasticity blah blah societies increasing dependence on computers to think for us doom doom learned incompetence ai skynet end of civilization as we know it..

But I think we've all had enough of that.

[kineticpenguin]

It's really weird how, for the last decade or so, the techbro thing has boiled down to "throw a stupidly huge amount of electricity at a bunch of GPUs -> ???? -> Profit!"

First it was bitcoin mining server farms, now it's AI data centers. They're throwing so many resources at it that they want to build their own power plants. Hell, Microsoft is working on a deal to restart Three Mile Island just for their data center, last I heard.

This is like a modern day Manhattan Project-level commitment of resources, and it's not being used for medical research or to solve complex problems or make our lives better in any way, it's all in the service of showing Shrimp Jesus, crying mutant soldiers and girls with 15 fingers to Facebook boomers in the hopes that someday, it will magically become useful and profitable.

[creacherkeeper]

the reliance of college students on gen ai thing is particularly crazy to me as a college writing instructor whos also about to finish an educational psychology certificate because like one huge thing ive learned in my classes is that the american college system is pretty dogshit at teaching students when it comes both to content mastery and also preparing them for the workforce. like these kids are graduating college and going into jobs that they have their degrees in and dont know how to thrive in the workforce and Also dont really know much about the thing they got their degree in. and theres a lot of reasons for that that i could go over in depth but i'll spare yall

BUT the thing that college is like the most decent at is changing the way people think about knowledge itself. further education (high school to undergrad to graduate school) is a huge factor in what develops your epistemological beliefs (epistemology being the nature and theories of knowledge). like your epistemology does naturally change as you get older, but with further education you learn that knowledge itself is complex, ever changing, and interrelated, meaning you inherently trust "facts" less and actually perform critical thinking when presented with new information

but,,,,,, with that new study microsoft did on generative ai showing that usage of gen ai in the workplace can lead to the degradation of independent problem solving skills and that people who trust gen ai actively use less critical thinking to do tasks,,, and that giving yourself fewer opportunities for critical thinking degrades your ability to think critically at all even when it comes to important tasks,,,

like. all these students are using gen ai and sometimes their classes are actively encouraging them to use gen ai and i fear were losing like the one big thing american undergrad is good for bc lets be real its pretty dogshit at everything else

and considering the nature of the topic:

Bransford, J. D., Brown, A. L., Anderson, J. R., Gelman, R., Glaser, R., Greenough, W. T., Ladson-Billings, G., Means, B. M., Mestre, J. P., Nathan, L., Pea, R. D., Peterson, P. L., Rogoff, B., Romberg, T. A., & Wineburg, S. S. (2000). How people learn: Brain, mind, experience, and school. National Academy Press. (pp. 31-50) Lee, H.P., Sarkar, A., Tankelevitch,L. Drosos, I., Rintel, S., Banks, R., & Wilson, N. (2025). The impact of generative AI on critical thinking: Self-reported reductions in cognitive effort and confidence effects from a survey of knowledge workers. CHI Conference in on Human Factors in Computing Systems. https://doi.org/10.1145/3706598.3713778 Mezirow, J. (2000). Learning to think like an adult: Core concepts of transformation theory. In J. Mezirow (Ed.), Learning as transformation: Critical perspectives on a theory in progress (pp. 3-34). Jossey-Bass. Svinicki, M. D. (2004). Learning and motivation in the postsecondary classroom. Boston: Anker Publishing Company, Inc. Torff, B., & Sternberg, R. J. (1998). Changing mind, changing world: Practical intelligence and tacit knowledge in adult learning. In M. C. Smith & T. Pourchot (Eds.), Adult learning and development: Perspectives from educational psychology (pp. 109-126). Lawrence Erlbaum

[innova7ions]

Neturbiz Enterprises YouTube Channel - AI-Innovations

Our mission is to provide details about AI-powered platforms across different technologies, each of which offer unique set of features. The AI industry encompasses a broad range of technologies designed to simulate human intelligence. These include machine learning, natural language processing, robotics, computer vision, and more. Companies and research institutions are continuously advancing AI capabilities, from creating sophisticated algorithms to developing powerful hardware. The AI industry, characterized by the development and deployment of artificial intelligence technologies, has a profound impact on our daily lives, reshaping various aspects of how we live, work, and interact. The AI industry presents numerous opportunities for affiliate marketers to promote cutting-edge tools and services. Each Platform may offer different commission rates.

#artificial intelligence #generativ ai

@neturbizenterprises @ai-innova7ions

[neturbizenterprises]

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