#ai development course

[101blockchainss]

Improve your AI skills with the 101 Blockchains AI Development Course. Our experts will develop all the required skills and show you how to implement AI solutions in real-world case studies. This course provides you with practical knowledge so you can learn things fast.

[ladyjmontilyet]

I keep thinking about how absolutely godawful Megalopolis was so here's some of my favourite tweets that I found the day I saw it:

It really was just 2.5 hours of Coppola saying "look how stupid I am and how I misunderstood the entire philosophy of stoicism and how the lessons of Rome apply to modern day". Not to mention the rampant misogyny and Islamophobia and orientalism. I've never regretted paying to see a movie in my entire 30 years of life before this - and I once saw an experimental movie that featured six minutes of sitting in pitch black and later on had someone getting fisted out of nowhere.

the one highlight: Wow Platinum. i love a "crazy bitch" with all my heart. truly unhinged. i think Aubrey Plaza knew exactly what movie she was in.

[seraheart]

I kinda wish that the DetCo canon would do more with the fact that the relationship (I don't mean this in the shipping sense) between Conan and Haibara has been, or at least logically should have been, really strained for a long time.

Originally, they had this development where Haibara was really messed up, overly cautious and trying to force her maladaptive survival psychological issues onto other people, and generally not good at positive interactions. Then, slowly, Conan and the others started gaining her trust (not entirely though), and also her general mental health improved (never completely though).

But then it turned around, and started getting significantly worse. Haibara isn't really in a healing arc anymore. If Aoyama still took her seriously as a character (which, to be fair, I don't think is the case), she would be in a retraumatization arc. Conan and his allies are limiting Haibara's agency, invading her privacy, dismissing her concerns about all this, and pretending that this isn't happening while she can obviously tell that it is happening.

I think the really obvious turning point was the Mystery Train arc. Haibara even called Conan and Agasa out on it and stated that she wouldn't forgive if she were to be treated like that again. Instead of taking her hurt seriously, they just told her she should "be thankful", and dismissed her hurt as "tsundere", refusing to take her trauma seriously after using it and using her as a mere chess piece in their plans (and Akai even triggering her more by gloating about it).

Since then, Haibara has been trapped under the constant, violating supervision of these people who have demonstrated that they don't respect her, and also refuse to acknowledge that the problem even exists. It's not a situation where her recover arc could realistically continue. By all logic, she should be spiraling, getting worse again.

And maybe this is on purpose; Aoyama definitely didn't plan the manga to get this long when he introduced Haibara, and originally the slow-burn of her recovery was a good way to postpone a scenario where Haibara could actually trust Conan enough to give him the kind of information that would lead to the finale arc. But... eventually, even with the extreme slow burn of DetCo, Haibara's recovery arc and relationship development with Conan would have gotten to the point where her continued withholding of crucial information would no longer make sense... and, I guess, rather than start concluding the story at a humanly reasonable rate, Aoyama just opted to nuke Haibara's whole recovery arc and character and relationship development (not only with Conan but also Agasa).

Which could have been tragic but realistic (albeit kind of a major downer out of tune with the manga's usual tone, and upsetting to anyone who had been invested in the slow-burn mental health improvement arc), but then Aoyama can't even be arsed to take it seriously, and is now just pretending that the situation with Haibara and her relationships with Conan and Agasa are still "normal" instead of FUBAR.

It's regrettable and really shows how much everyone dragging out this franchise to milk it for more money just... doesn't actually care about the story anymore, hasn't in a long time. They'll eventually just kill central, fan-favorite, long-running story arcs rather than actually letting them conclude or evolve in a satisfying manner if that would mean risking their precious status quo (which has warped beyond all recognition anyway, so I'm not sure why they bother).

[bigtiddygandalf]

i just had to take this ai personality test to submit a job application (to be a bartender).

[elfroot-and-laurels-moved]

low key i do wanna try to do OC-tober this year maybe with my dragon age OCs.......

[jcmarchi]

Starting reading the AI Snake Oil book online today

New Post has been published on https://thedigitalinsider.com/starting-reading-the-ai-snake-oil-book-online-today/

Starting reading the AI Snake Oil book online today

The first chapter of the AI snake oil book is now available online. It is 30 pages long and summarizes the book’s main arguments. If you start reading now, you won’t have to wait long for the rest of the book — it will be published on the 24th of September. If you haven’t pre-ordered it yet, we hope that reading the introductory chapter will convince you to get yourself a copy.

We were fortunate to receive positive early reviews by The New Yorker, Publishers’ Weekly (featured in the Top 10 science books for Fall 2024), and many other outlets. We’re hosting virtual book events (City Lights, Princeton Public Library, Princeton alumni events), and have appeared on many podcasts to talk about the book (including Machine Learning Street Talk, 20VC, Scaling Theory).

Our book is about demystifying AI, so right out of the gate we address what we think is the single most confusing thing about it: 

AI is an umbrella term for a set of loosely related technologies

Because AI is an umbrella term, we treat each type of AI differently. We have chapters on predictive AI, generative AI, as well as AI used for social media content moderation. We also have a chapter on whether AI is an existential risk. We conclude with a discussion of why AI snake oil persists and what the future might hold. By AI snake oil we mean AI applications that do not (and perhaps cannot) work. Our book is a guide to identifying AI snake oil and AI hype. We also look at AI that is harmful even if it works well — such as face recognition used for mass surveillance. 

While the book is meant for a broad audience, it does not simply rehash the arguments we have made in our papers or on this newsletter. We make scholarly contributions and we wrote the book to be suitable for adoption in courses. We will soon release exercises and class discussion questions to accompany the book.

Chapter 1: Introduction. We begin with a summary of our main arguments in the book. We discuss the definition of AI (and more importantly, why it is hard to come up with one), how AI is an umbrella term, what we mean by AI Snake Oil, and who the book is for. 

Generative AI has made huge strides in the last decade. On the other hand, predictive AI is used for predicting outcomes to make consequential decisions in hiring, banking, insurance, education, and more. While predictive AI can find broad statistical patterns in data, it is marketed as far more than that, leading to major real-world misfires. Finally, we discuss the benefits and limitations of AI for content moderation on social media.

We also tell the story of what led the two of us to write the book. The entire first chapter is now available online.

Chapter 2: How predictive AI goes wrong. Predictive AI is used to make predictions about people—will a defendant fail to show up for trial? Is a patient at high risk of negative health outcomes? Will a student drop out of college? These predictions are then used to make consequential decisions. Developers claim predictive AI is groundbreaking, but in reality it suffers from a number of shortcomings that are hard to fix. 

We have discussed the failures of predictive AI in this blog. But in the book, we go much deeper through case studies to show how predictive AI fails to live up to the promises made by its developers.

Chapter 3: Can AI predict the future? Are the shortcomings of predictive AI inherent, or can they be resolved? In this chapter, we look at why predicting the future is hard — with or without AI. While we have made consistent progress in some domains such as weather prediction, we argue that this progress cannot translate to other settings, such as individuals’ life outcomes, the success of cultural products like books and movies, or pandemics. 

Since much of our newsletter is focused on topics of current interest, this is a topic that we have never written about here. Yet, it is foundational knowledge that can help you build intuition around when we should expect predictions to be accurate.

Chapter 4: The long road to generative AI. Recent advances in generative AI can seem sudden, but they build on a series of improvements over seven decades. In this chapter, we retrace the history of computing advances that led to generative AI. While we have written a lot about current trends in generative AI, in the book, we look at its past. This is crucial for understanding what to expect in the future. 

Chapter 5: Is advanced AI an existential threat? Claims about AI wiping out humanity are common. Here, we critically evaluate claims about AI’s existential risk and find several shortcomings and fallacies in popular discussion of x-risk. We discuss approaches to defending against AI risks that improve societal resilience regardless of the threat of advanced AI.

Chapter 6: Why can’t AI fix social media? One area where AI is heavily used is content moderation on social media platforms. We discuss the current state of AI use on social media, and highlight seven reasons why improvements in AI alone are unlikely to solve platforms’ content moderation woes. We haven’t written about content moderation in this newsletter.

Chapter 7: Why do myths about AI persist? Companies, researchers, and journalists all contribute to AI hype. We discuss how myths about AI are created and how they persist. In the process, we hope to give you the tools to read AI news with the appropriate skepticism and identify attempts to sell you snake oil.

Chapter 8: Where do we go from here? While the previous chapter focuses on the supply of snake oil, in the last chapter, we look at where the demand for AI snake oil comes from. We also look at the impact of AI on the future of work, the role and limitations of regulation, and conclude with vignettes of the many possible futures ahead of us. We have the agency to determine which path we end up on, and each of us can play a role.

We hope you will find the book useful and look forward to hearing what you think. 

The New Yorker: “In AI Snake Oil, Arvind Narayanan and Sayash Kapoor urge skepticism and argue that the blanket term AI can serve as a smokescreen for underperforming technologies.”

Kirkus: “Highly useful advice for those who work with or are affected by AI—i.e., nearly everyone.”

Publishers’ Weekly: Featured in the Fall 2024 list of top science books.

Jean Gazis: “The authors admirably differentiate fact from opinion, draw from personal experience, give sensible reasons for their views (including copious references), and don’t hesitate to call for action. . . . If you’re curious about AI or deciding how to implement it, AI Snake Oil offers clear writing and level-headed thinking.”

Elizabeth Quill: “A worthwhile read whether you make policy decisions, use AI in the workplace or just spend time searching online. It’s a powerful reminder of how AI has already infiltrated our lives — and a convincing plea to take care in how we interact with it.”

We’ve been on many other podcasts that will air around the time of the book’s release, and we will keep this list updated.

The book is available to preorder internationally on Amazon.

[theskoomacat]

M*uthwashing was good, the only things i didn't enjoy lie in the area of writing (but so do some things i enjoyed the most!) - but I expected more from it? kinda?

[demonstars]

he’s on the right path for the wrong reason?

more like the wrong path for the right logic

[bcneheaded]

Yall I've had this idea in my head for an AI entity character thing since forever and?? It's driving me absolutely bonkers, this consciousness wants OUT but I have no idea how to write it, it is simply too intricate and confusing

[softwaredevelopmentindia-one]

India is a hub for technology and innovation and the field of machine learning Solution Development in India — Microlent Systems

India is a hub for technology and innovation, and the field of machine learning is no exception. With a growing number of companies specializing in this field, it can be challenging to know which one to choose. In this article, we will discuss the best machine learning companies in India, with a special focus on Microlent Systems.

Microlent Systems: Microlent Systems is a software development company located in Jodhpur, Rajasthan, that specializes in machine learning. They offer a range of services, including data analysis, predictive modeling, and natural language processing. They have worked with clients across various industries, from healthcare to finance.

Fractal Analytics: Fractal Analytics is a data analytics company that specializes in machine learning. They offer a range of services, including data engineering, data visualization, and artificial intelligence. They have worked with clients such as Microsoft, Coca-Cola, and PepsiCo.

Wipro: Wipro is a global technology company that offers machine learning solutions, including predictive maintenance, fraud detection, and chatbot development. They have worked with clients across various industries, including banking, healthcare, and retail.

Analytics India Magazine: Analytics India Magazine is a media company that covers the latest trends and developments in the field of machine learning. They offer training programs, research reports, and industry events to help businesses stay up-to-date with the latest developments in machine learning.

Tiger Analytics: Tiger Analytics is a consulting firm that offers machine learning solutions, including predictive modeling, optimization, and data visualization. They have worked with clients such as Adidas, Samsung, and Amazon.

LatentView Analytics: LatentView Analytics is a data analytics company that offers machine learning solutions, including customer segmentation, price optimization, and demand forecasting. They have worked with clients such as Microsoft, Coca-Cola, and Johnson & Johnson.

BRIDGEi2i: BRIDGEi2i is a consulting firm that offers machine learning solutions, including sales forecasting, customer segmentation, and supply chain optimization. They have worked with clients across various industries, including banking, retail, and healthcare.

Amazon Web Services: Amazon Web Services (AWS) offers a range of machine learning solutions, including image and speech recognition, chatbot development, and predictive analytics. They have worked with clients such as Netflix, Airbnb, and Samsung.

In conclusion, there are several excellent machine learning companies in India, each offering unique solutions and services. Microlent Systems stands out among them, with its expertise in data analysis, predictive modeling, and natural language processing. Located in Jodhpur, Rajasthan, they are well-positioned to provide high-quality machine learning solutions to clients across various industries.

Read More :

https://microlent.com/blog/why-you-should-focus-on-improving-best-machine-learning-companies-in-india.html

[101blockchainss]

Get enrolled in 101 Blockchain's latest Generative AI Course. Learn the principles of how genealogical AI works and how to use it in real-world applications. This course will cover all basic to advanced concepts.

[chipped-chimera]

THIS. DEAR LORD THIS. THIS IS WHAT HAS BEEN DRIVING ME INSANE ABOUT AI.

Look I have contemplated writing an AI Analysis post coming from an actual artist's perspective SEVERAL times with the knowledge I've accumulated but rarely have the spoons to do it but I'll just do a short bit of it now.

So when something really upsets me that is happening and I have little control, I habitually do this thing where I will actively go out there and research the shit out of it. Because I've spent enough time in therapy to know the thing that scares us the most is the unknown. Make the unknown known? It becomes significantly less scary.

And I am backing it up when they say 'AI is a buzzword'. It 120% is. What the AI labelling is hiding under the world's biggest and perhaps most obfuscated umbrella-term is machine learning.

So it would probably shock you to know, by that metric we have been using AI for YEARS. Your autocomplete keyboard on your phone that remembers your words according to usage? Machine learning. Facial recognition on mobile phone cameras and facebook? Machine learning. The ALGORITHMS that have been driving a lot of my most beloathed social medias for years? MACHINE. LEARNING. Auto-generated captions on videos, reverse image searching, targeted advertising, analysis of weather systems, handwriting recognition, DNA sequencing, search engines, and of course your dynamic enemy 'AI' in videogames that has to react to your actions as a player - these are ALL products of machine learning and by that metric? You have technically been using AI for years but we just didn't call it that yet.

In my great search of understanding all things AI, what an Australian tech journalist commentator said was - we're basically calling anything 'new' in machine learning that we don't quite understand yet collectively 'AI'. And I agree 100%. The reality is AI has been with us since about the 1960s.

Hang on Chimera/Kery I hear you say, on the Wikipedia page of machine learning it says machine learning is a result of trying to build AI, not AI! Yes, but you literally cannot have the 'Intelligence' part without the machine learning part. You take out the learning and you've just got a brick of data that you can't do shit with. The intelligence part comes in when based on the data it's been fed and the responses it has gotten back from it's environment, whether that is a researcher saying yes or no, or literal environmental feedback in a robot that is learning optimal locomotion through a space - it executes actions. So again, by that metric when you whip out your phone to take a selfie and your phone starts to track where your face is? It is executing an action based on its data-set of 'what is a face'. That. Is. AI.

So everything is AI now? Yeah it's an umbrella term, that's what I said. The disparity between knowing what machine learning and AI is to the point we call specific things AI (image generation, large language models, voice generation) and other things 'not AI' (see my long list again) is down to MARKETING.

Let me take you back to the tail 'end' of the pandemic. You're OpenAI and through scraping a lot of publically available data of just people chatting or writing various things - with dubious consent - you have made a really good chat-bot. Yeah you heard me, CHAT-BOT. If you're old like me, you remember chat-bots - they're those goofy things you played with as a teenager and laughed at because it'd say silly things and it'd be funny to put two together trying to talk to each other because they'd begin spouting nonsense and getting stuck in a loop. Or they're the widely hated artificial help systems on government websites embedded in a chatbox that does jack shit. Or the annoying pop up on some website you're just trying to buy shit from and stock-image-sandra is here in a text box 'ready to help you'. Chat-bots have an image problem. You can't release ChatGPT, your fancy chat-bot as a 'chat-bot', how the hell are you supposed to get investors? You've got some really good projects on the go (with dubiously sourced data) but you're running out of money. You need to do something fast.

So you take out the AI umbrella term, and right before everyone is just about ready to leave their hermit-chronically-online-pandemic-induced lifestyles - you drop the metaphorical bomb. You hand over your tech, now with the shiny new AI label, to the public. The AI label hides the fact from the public that you're basically rebranding shit we've had forever and by keeping it purposefully murky you can (hopefully) get people to ignore the fact that you've basically pulled vast swathes of data with dubious consent because - but it's AI! It's such a superior piece of technology! We can't un-invent the wheel because the ends didn't justify the means! It could change the world!

Despite the fact it's been 'changing the world' since 1960 and the only difference here is you linked enough computers together to make it better than what was currently available. But you now have to pay electricity costs for all that tech so, out into the wild it goes!

And now you've triggered a technological arms race and the use of AI (and your bottom line) is skyrocketing! AI that was previously the domain of government and massive corporate use is now in the hands of people to play with - their personal tech literacy be dammed (no literally be dammed, the less they understand the better). And they won't want to have it taken off them - in fact they'll fight each other over the value of your chat-bot and image generator in spite of the fact you stole data to train it. So your profits keep rolling in and next minute, despite your ethos being 'open source to all' - you're getting approached by Microsoft for a partial buy in and now you're 'semi-private', whatever the hell that means. Who cares! Money!

I have so, so much more to say on all this but I'll leave it for a proper post. But the lesson of this very tl;dr history of OpenAI is this: AI is machine learning. Machine learning is a TOOL. AI is a TOOL.

And a tool is only as ethical as the hand that chooses to wield it. Artificial intelligence is neutral. It is not good. It is not bad. It is just like the knife on your kitchen bench, with all the potential of doing good and useful things like help you make dinner and also horrendous, horrible things like commit a violent crime. And who made the knife in your kitchen? Is it artisan? Handcrafted by someone well paid in their profession? Or was it mass produced in third world conditions? Now is your knife itself bad? Should we ban all kitchen knives?

AI is a marketing buzzword for shit we've had for years - this is just the shiny version that went public to get money and we all fell for it hook, line and sinker.

So I challenge you, the next time something wizz-bang-shiny-tech-whatever is placed in front of you, and maybe it's a bit scary - to do what I do. Instead of filing it into a box of good or bad, start arguments online with someone with only limited information over whether someone is 'good' or 'bad' for participating or not participating in use of this technology because it's now emotionally loaded for you - do what I do. RESEARCH IT. Understand it, deeply. Listen to commentary on it from both sides, learn about the intent of why it was handed to you and for the love of god USE SOME CRITICAL THINKING SKILLS.

Because I guarantee you once you do that? Stuff will quickly become a lot less murky. You'll be able to see where your own blindspots are, and prevent them from being exploited - in this case, being taken advantage of by big corporations who are trying to pull and 'oopsie-woopsie' on unethical datasets for profit. You'll be able to hold them accountable. You'll also be less likely to get pulled into stupid arguments online about shit because you know it is way more nuanced than tech-bro putting out his big titty waifu image soup - he's small game here. Who cares about him. Go for the people at the top of this who are hoping to keep sliding on by with their rolling profits because you're too busy having fights among yourselves. Go for them and go for the fucking throat.

Any technology can be used for weal or woe, and it is entirely about the hand who wields it. Or in this case, the hand who programmed it.

If we want to continue to use AI or Machine Learning in an ethical, revolutionary manner we need to stop falling for the marketing, and hold each other accountable to uses that will continue to benefit humanity. Not pull it apart.

So yes. AI is a buzzword. Stop falling for it.

[beesmygod]

ed zitron, a tech beat reporter, wrote an article about a recent paper that came out from goldman-sachs calling AI, in nicer terms, a grift. it is a really interesting article; hearing criticism from people who are not ignorant of the tech and have no reason to mince words is refreshing. it also brings up points and asks the right questions:

if AI is going to be a trillion dollar investment, what trillion dollar problem is it solving?

what does it mean when people say that AI will "get better"? what does that look like and how would it even be achieved? the article makes a point to debunk talking points about how all tech is misunderstood at first by pointing out that the tech it gets compared to the most, the internet and smartphones, were both created over the course of decades with roadmaps and clear goals. AI does not have this.

the american power grid straight up cannot handle the load required to run AI because it has not been meaningfully developed in decades. how are they going to overcome this hurdle (they aren't)?

people who are losing their jobs to this tech aren't being "replaced". they're just getting a taste of how little their managers care about their craft and how little they think of their consumer base. ai is not capable of replacing humans and there's no indication they ever will because...

all of these models use the same training data so now they're all giving the same wrong answers in the same voice. without massive and i mean EXPONENTIALLY MASSIVE troves of data to work with, they are pretty much as a standstill for any innovation they're imagining in their heads

[brainynbright]

Advance Your Career with AI Internship Courses - Brainy n Bright

Unlock limitless opportunities with AI Internship Courses at Brainy n Bright. Our industry-aligned programs focus on cutting-edge technologies such as artificial intelligence, machine learning, and the Internet of Things (IoT). Through hands-on projects, expert mentorship, and real-world applications, students gain in-demand technical skills essential for future tech leaders. Whether you're aspiring to build intelligent systems or create innovative AI solutions, our internships provide the knowledge and experience needed to excel. Join Brainy n Bright and take a step toward a promising tech career. Enroll now and become a future-ready AI professional!

[destkelamedia]

10 Must-Take Free Courses for Software Engineers in 2025

[jcmarchi]

Weekly News for Designers № 719

New Post has been published on https://thedigitalinsider.com/weekly-news-for-designers-%e2%84%96-719/

Weekly News for Designers № 719

Figma AI Updates to Elevate Your Design Workflow Figma has shared some prototypes for platform updates that integrate AI. This Tuts+ article covers each of them to explore their possibilities.

State of Brand Report 2023 Discover key insights into the trends, challenges and strategies of branding this year.

Internet Artifacts Explore artifacts from the early days of the internet right up until the present day.

Naming Variables In CSS Some collected thoughts from Jonathan Dallas related to naming CSS Custom Properties.

The Negative Impact of Mobile-First Web Design on Desktop Mobile-first web designs cause significant usability issues when viewed on desktop.

Free T-Shirt Mockup Templates for Photoshop These free realistic iPhone mockup templates are perfect for showcasing the UI or UX of your mobile app design or responsive website.

Introducing Learn Performance Course This is new and free course is intended for those that care about web performance, but may be just beginning to get familiar with it.

It’s 2023, Here is Why Your Web Design Sucks Heather Buchel explores the reasons why we no longer have web designers.

State of React 2023

Let’s Reinvent the Wheel

The 2023 Design Tools Survey This survey gathers usage data from thousands of designers each year and many of the design companies you know use this data to better understand and improve the design tools industry.

CSS & JavaScript Snippets for Creating Notification UIs A collection of CSS and JavaScript code snippets for creating unique notification and alert systems and UIs.

Image Layer Animations with Clip-Path Some ideas for speedy page transition animations with layered images using clip-path.

Common Questions About Interpreting Page Speed Reports Take a closer look at how various performance tools audit and report on performance metrics, such as core web vitals.

Geist Font A new and free typeface that has been specifically designed for developers and designers.

Photoshop Action Sets for Cinema & Movie Effects A collection of one-click cinema & movie effect Photoshop action sets that will take your photos to the next level.