#Smart AI

[halopedia]

Lore Thursday — Black-Box

Created from the brain of a researcher who had committed suicide for his part in the Spartan-II program, Black-Box is a 4th gen "smart" AI who was the personal assistant of former CINCONI Margaret Parangosky before being reassigned to Serin Osman.

[dougielombax]

Look.

I adore Caine.

But he REALLY isn’t beating the rampancy allegations!

[john-halo]

Not sure if this has been asked before, but do you have spare AI Crystal chips in case the main one somehow gets damaged/destroyed? And if you do, where do you keep such highly sensitive and valuable items on your armor?

A suit has secure armored pods for small items, like data crystal chips. But I never carried a spare AI chip.

The main was already slotted into my neural implant. If physical damage to the chip became a factor, I'd already be too dead to do anything about it.

[jakebennet98]

Why Understanding the AI Maturity Model is Key to Your AI Success

Artificial intelligence is revolutionizing businesses across the globe, but not every organization is ready to unlock its full potential. That’s where the AI maturity model becomes an essential tool.

Unlike simple checklists or quick assessments, AI maturity models dive deep into the critical elements that determine how well a company can adopt, manage, and scale AI initiatives. They evaluate areas like data infrastructure, team skills, governance, strategy, and culture — all crucial for sustainable AI growth.

Why should you care?

Many companies rush to implement AI with great excitement but without a clear plan. This often leads to fragmented efforts, wasted resources, or underwhelming results. The AI maturity model helps by providing a structured framework to understand exactly where your organization stands and what needs to improve.

Here’s what an AI maturity model offers:

Defined stages of AI adoption, from initial experimentation to full enterprise integration

Insight into gaps and risks that could hinder progress

Guidance for smarter investments in technology, people, and processes

A clear roadmap for scaling AI responsibly and effectively

By leveraging these insights, business leaders can avoid common pitfalls and ensure their AI efforts deliver real value.

In a fast-evolving AI landscape, sometimes the best move is to pause and honestly assess your readiness. An AI maturity model gives you that clarity, helping you build a strong foundation for long-term success.

[tudip123]

Choose AI/ML Algorithms Very Efficiently

The world is getting smarter every day, to keep up to date and satisfy consumer expectation tech companies adapting machine learning algorithms to make things easy but choosing a machine learning algorithm is always a tedious job for techies, there are lots of algorithms present for different kind of problems and we can use this for tackling things in different ways.

The machine learning algorithm’s main goal is to inspect the data and find similar patterns between them, and with that, make detailed predictions. As the name implies, ML algorithms are basically calculations prepared in different ways.

We are creating data every day; we are just surrounded by data in different formats. It comes from a variety of sources: business data, personal social media activity, sensors in the IoT, etc. Machine learning algorithms are used to extract data and turn it into something useful that can serve to automate processes, personalize experiences, and make difficult forecasts that human brains cannot do on their own.

Choosing algorithms solely depends on your project requirements. Given the type of tasks that ML algorithms answer, each type trains in absolute tasks, taking into consideration the limitations of the knowledge that you have and the necessities of your project.

Types of AI/ML Algorithms

Different types of machine learning algorithms are:

Supervised learning

Unsupervised learning

Semi-Supervised learning

Reinforcement learning

Supervised ML algorithm:

This is the most popular ML algorithm because of its flexibility and comprehensiveness, and it is mostly used to do the most common ML tasks. It requires labeled data.

Supervised knowledge depends on supervision; we train the machines utilizing the branded dataset and establish the training; bureaucracy thinks about the output. It allows you to collect data from previous experiences. Helps you improve performance tests using occurrence.

Unsupervised ML algorithm:

Unsupervised learning is typically achieved by using unsupervised machine learning techniques. Using unsupervised algorithms, you can handle problems differently than with supervised algorithms and operate in more complicated ways. Unsupervised learning, however, could be more irregular than the subsequent deep learning and support learning patterns based on natural input.

There are three main tasks in Unsupervised learning, such as:

Clustering: It is a data mining technique used for grouping unlabeled data based on similarities between them.

Association: It uses different rules to find relationships between variables in a given dataset. These plans are frequently secondhand for advertising basket studies and recommendation transformers.

Dimensionality Reduction: It is used when the number of features in a dataset is too high. It reduces the number of data inputs to a controllable size while more maintaining the dossier honor. Often, this technique is secondhand in the preprocessing dossier stage, in the way that when autoencoders erase noise from being able to be seen with eyes dossier to boost picture quality.

Semi-Supervised ML algorithm:

When you are using a training dataset with both labeled and unlabeled data or you can’t decide on whether to use supervised or unsupervised algorithms, Semi-Supervised is the best choice in that case.

Reinforcement ML algorithm:

Reinforcement knowledge algorithms are mostly based on dynamic compute methods. The idea behind this type of ML treasure is to compare investigation and exploitation. Other machine learning algorithms used mapping middle from two points of recommendation and productivity, Unlike directed supervised placement, where the feedback supported by the power is correct set of conduct for performing a task, support education uses rewards and penalties as signals for helpful and negative behavior.

Conclusion

Choosing an ML algorithm is apparently a complex task, particularly if you don’t have a far-reaching background in this field. However, knowledge about the types of algorithms and the tasks that they were created to resolve and solving a set of questions might help you resolve this complication. Learning more about machine learning algorithms, their types, and answering these questions might lead you to an algorithm that’ll be a perfect match for your goal.

Click the link below to learn more about the blog Choose AI/ML Algorithms Very Efficiently:

[curveai]

Our Smart AI Predictive Models deliver precise forecasts by employing advanced artificial intelligence and machine learning techniques to analyze and interpret diverse data sources and market trends. We offer a sophisticated approach to generating accurate projections, enabling businesses to navigate complex market environments with confidence and optimize their strategic initiatives for long-term success.

[familythings]

The Latest Improvements in AI: Robots Getting Smarter (And Funnier)

The Latest Improvements in AI: Robots Getting Smarter (And Funnier) If you thought AI was just about Siri answering your questions or a robot driving your car, think again! The latest improvements in artificial intelligence are seriously making things more interesting. It’s not just about the serious “let’s automate everything” way. Nope, AI is getting way smarter, and dare we say, a little bit…

[adambrooksg]

How AI Works in Mobile: Unraveling the Mysteries in Smartphone Devices

Our smartphones have become incredibly sophisticated tools, seemingly anticipating our needs and responding with an almost human-like intelligence. But how does this magic happen? This article delves into the fascinating world of how AI works in mobile, unveiling the Smartphone AI technologies powering our devices and transforming the landscape of mobile technology. From Simple Tools to Smart…

[inkskinned]

i have chronic pain. i am neurodivergent. i understand - deeply - the allure of a "quick fix" like AI. i also just grew up in a different time. we have been warned about this.

15 entire years ago i heard about this. in my forensics class in high school, we watched a documentary about how AI-based "crime solving" software was inevitably biased against people of color.

my teacher stressed that AI is like a book: when someone writes it, some part of the author will remain within the result. the internet existed but not as loudly at that point - we didn't know that AI would be able to teach itself off already-biased Reddit threads. i googled it: yes, this bias is still happening. yes, it's just as bad if not worse.

i can't actually stop you. if you wanna use ChatGPT to slide through your classes, that's on you. it's your money and it's your time. you will spend none of it thinking, you will learn nothing, and, in college, you will piss away hundreds of thousands of dollars. you will stand at the podium having done nothing, accomplished nothing. a cold and bitter pyrrhic victory.

i'm not even sure students actually read the essays or summaries or emails they have ChatGPT pump out. i think it just flows over them and they use the first answer they get. my brother teaches engineering - he recently got fifty-three copies of almost-the-exact-same lab reports. no one had even changed the wording.

and yes: AI itself (as a concept and practice) isn't always evil. there's AI that can help detect cancer, for example. and yet: when i ask my students if they'd be okay with a doctor that learned from AI, many of them balk. it is one thing if they don't read their engineering textbook or if they don't write the critical-thinking essay. it's another when it starts to affect them. they know it's wrong for AI to broad-spectrum deny insurance claims, but they swear their use of AI is different.

there's a strange desire to sort of divorce real-world AI malpractice over "personal use". for example, is it moral to use AI to write your cover letters? cover letters are essentially only templates, and besides: AI is going to be reading your job app, so isn't it kind of fair?

i recently found out that people use AI as a romantic or sexual partner. it seems like teenagers particularly enjoy this connection, and this is one of those "sticky" moments as a teacher. honestly - you can roast me for this - but if it was an actually-safe AI, i think teenagers exploring their sexuality with a fake partner is amazing. it prevents them from making permanent mistakes, it can teach them about their bodies and their desires, and it can help their confidence. but the problem is that it's not safe. there isn't a well-educated, sensitive AI specifically to help teens explore their hormones. it's just internet-fed cycle. who knows what they're learning. who knows what misinformation they're getting.

the most common pushback i get involves therapy. none of us have access to the therapist of our dreams - it's expensive, elusive, and involves an annoying amount of insurance claims. someone once asked me: are you going to be mad when AI saves someone's life?

therapists are not just trained on the book, they're trained on patient management and helping you see things you don't see yourself. part of it will involve discomfort. i don't know that AI is ever going to be able to analyze the words you feed it and answer with a mind towards the "whole person" writing those words. but also - if it keeps/kept you alive, i'm not a purist. i've done terrible things to myself when i was at rock bottom. in an emergency, we kind of forgive the seatbelt for leaving bruises. it's just that chat shouldn't be your only form of self-care and recovery.

and i worry that the influence chat has is expanding. more and more i see people use chat for the smallest, most easily-navigated situations. and i can't like, make you worry about that in your own life. i often think about how easy it was for social media to take over all my time - how i can't have a tiktok because i spend hours on it. i don't want that to happen with chat. i want to enjoy thinking. i want to enjoy writing. i want to be here. i've already really been struggling to put the phone down. this feels like another way to get you to pick the phone up.

the other day, i was frustrated by a book i was reading. it's far in the series and is about a character i resent. i googled if i had to read it, or if it was one of those "in between" books that don't actually affect the plot (you know, one of those ".5" books). someone said something that really stuck with me - theoretically you're reading this series for enjoyment, so while you don't actually have to read it, one would assume you want to read it.

i am watching a generation of people learn they don't have to read the thing in their hand. and it is kind of a strange sort of doom that comes over me: i read because it's genuinely fun. i learn because even though it's hard, it feels good. i try because it makes me happy to try. and i'm watching a generation of people all lay down and say: but i don't want to try.

[cozylittleartblog]

"content creator" is a corporate word.

we are artists.

[dougielombax]

*points a gun at Black Box’s data crystal chip*

“TELL ME WHAT HAPPENED TO ADMIRAL COLE YOU SHITFUCK!!!!!!”

[ralfmaximus]

Breville Smart Oven vs Ottoman empire.

Found on Mastodon.

[peanutalergy]

he would be offended by this btw

[remixingreality]

[cfserkgk]

I had a thought --- You know how Conan and Haibara are also the "same age" as Anya and the Eden kids in spy x family? So hence here's Conan and Haibara in the Eden uniforms since I can allow myself to fantasise.

I don't know if this has been done before, but I like coai a very lot, they're my childhood. There's just something about their camaraderie and mutual trust that makes me so happy.