#chatbots technology

[purpleartrowboat]

ai makes everything so boring. deepfakes will never be as funny as clipping together presidential speeches. ai covers will never be as funny as imitating the character. ai art will never be as good as art drawn by humans. ai chats will never be as good as roleplaying with other people. ai writing will never be as good as real authors

[netmaxims-technologies]

Discover the power of AI chatbots for ecommerce and unlock your online store's full potential. Maximize customer engagement, improve user experience, and boost sales with intelligent chatbot solutions. Read on to explore the benefits and implementation of AI chatbots in ecommerce.

[food-theorys-blog]

"oh but i use character ai's for my comfort tho" fanfics.

"but i wanna talk to the character" roleplaying.

"but that's so embarrassing to roleplay with someone😳" use ur imagination. or learn to not be embarrassed about it.

stop fucking feeding ai i beg of you. theyre replacing both writers AND artists. it's not a one way street where only artists are being affected.

[intelvueofficial]

ChatGPT Invention 😀😀

ChatGPT is not new, Courage the Cowardly Dog was the first who use ChatGPT 😀😀😀😀

[multi-lefaiye]

my spicy hot take regarding AI chatbots lying to people is that, no, the chatbot isn't lying. chatgpt is not lying. it's not capable of making the conscious decision to lie to you. that doesn't mean it's providing factual information, though, because that's not what it's meant to do (despite how it's being marketed and portrayed). chatgpt is a language learning model simply predicting what responses are most probable based on established parameters.

it's not lying, it's providing the most statistically likely output based on its training data. and that includes making shit up.

[scifigeneration]

AI is here – and everywhere: 3 AI researchers look to the challenges ahead in 2024

by Anjana Susarla, Professor of Information Systems at Michigan State University, Casey Fiesler, Associate Professor of Information Science at the University of Colorado Boulder, and Kentaro Toyama Professor of Community Information at the University of Michigan

2023 was an inflection point in the evolution of artificial intelligence and its role in society. The year saw the emergence of generative AI, which moved the technology from the shadows to center stage in the public imagination. It also saw boardroom drama in an AI startup dominate the news cycle for several days. And it saw the Biden administration issue an executive order and the European Union pass a law aimed at regulating AI, moves perhaps best described as attempting to bridle a horse that’s already galloping along.

We’ve assembled a panel of AI scholars to look ahead to 2024 and describe the issues AI developers, regulators and everyday people are likely to face, and to give their hopes and recommendations.

Casey Fiesler, Associate Professor of Information Science, University of Colorado Boulder

2023 was the year of AI hype. Regardless of whether the narrative was that AI was going to save the world or destroy it, it often felt as if visions of what AI might be someday overwhelmed the current reality. And though I think that anticipating future harms is a critical component of overcoming ethical debt in tech, getting too swept up in the hype risks creating a vision of AI that seems more like magic than a technology that can still be shaped by explicit choices. But taking control requires a better understanding of that technology.

One of the major AI debates of 2023 was around the role of ChatGPT and similar chatbots in education. This time last year, most relevant headlines focused on how students might use it to cheat and how educators were scrambling to keep them from doing so – in ways that often do more harm than good.

However, as the year went on, there was a recognition that a failure to teach students about AI might put them at a disadvantage, and many schools rescinded their bans. I don’t think we should be revamping education to put AI at the center of everything, but if students don’t learn about how AI works, they won’t understand its limitations – and therefore how it is useful and appropriate to use and how it’s not. This isn’t just true for students. The more people understand how AI works, the more empowered they are to use it and to critique it.

So my prediction, or perhaps my hope, for 2024 is that there will be a huge push to learn. In 1966, Joseph Weizenbaum, the creator of the ELIZA chatbot, wrote that machines are “often sufficient to dazzle even the most experienced observer,” but that once their “inner workings are explained in language sufficiently plain to induce understanding, its magic crumbles away.” The challenge with generative artificial intelligence is that, in contrast to ELIZA’s very basic pattern matching and substitution methodology, it is much more difficult to find language “sufficiently plain” to make the AI magic crumble away.

I think it’s possible to make this happen. I hope that universities that are rushing to hire more technical AI experts put just as much effort into hiring AI ethicists. I hope that media outlets help cut through the hype. I hope that everyone reflects on their own uses of this technology and its consequences. And I hope that tech companies listen to informed critiques in considering what choices continue to shape the future.

Kentaro Toyama, Professor of Community Information, University of Michigan

In 1970, Marvin Minsky, the AI pioneer and neural network skeptic, told Life magazine, “In from three to eight years we will have a machine with the general intelligence of an average human being.” With the singularity, the moment artificial intelligence matches and begins to exceed human intelligence – not quite here yet – it’s safe to say that Minsky was off by at least a factor of 10. It’s perilous to make predictions about AI.

Still, making predictions for a year out doesn’t seem quite as risky. What can be expected of AI in 2024? First, the race is on! Progress in AI had been steady since the days of Minsky’s prime, but the public release of ChatGPT in 2022 kicked off an all-out competition for profit, glory and global supremacy. Expect more powerful AI, in addition to a flood of new AI applications.

The big technical question is how soon and how thoroughly AI engineers can address the current Achilles’ heel of deep learning – what might be called generalized hard reasoning, things like deductive logic. Will quick tweaks to existing neural-net algorithms be sufficient, or will it require a fundamentally different approach, as neuroscientist Gary Marcus suggests? Armies of AI scientists are working on this problem, so I expect some headway in 2024.

Meanwhile, new AI applications are likely to result in new problems, too. You might soon start hearing about AI chatbots and assistants talking to each other, having entire conversations on your behalf but behind your back. Some of it will go haywire – comically, tragically or both. Deepfakes, AI-generated images and videos that are difficult to detect are likely to run rampant despite nascent regulation, causing more sleazy harm to individuals and democracies everywhere. And there are likely to be new classes of AI calamities that wouldn’t have been possible even five years ago.

Speaking of problems, the very people sounding the loudest alarms about AI – like Elon Musk and Sam Altman – can’t seem to stop themselves from building ever more powerful AI. I expect them to keep doing more of the same. They’re like arsonists calling in the blaze they stoked themselves, begging the authorities to restrain them. And along those lines, what I most hope for 2024 – though it seems slow in coming – is stronger AI regulation, at national and international levels.

Anjana Susarla, Professor of Information Systems, Michigan State University

In the year since the unveiling of ChatGPT, the development of generative AI models is continuing at a dizzying pace. In contrast to ChatGPT a year back, which took in textual prompts as inputs and produced textual output, the new class of generative AI models are trained to be multi-modal, meaning the data used to train them comes not only from textual sources such as Wikipedia and Reddit, but also from videos on YouTube, songs on Spotify, and other audio and visual information. With the new generation of multi-modal large language models (LLMs) powering these applications, you can use text inputs to generate not only images and text but also audio and video.

Companies are racing to develop LLMs that can be deployed on a variety of hardware and in a variety of applications, including running an LLM on your smartphone. The emergence of these lightweight LLMs and open source LLMs could usher in a world of autonomous AI agents – a world that society is not necessarily prepared for.

These advanced AI capabilities offer immense transformative power in applications ranging from business to precision medicine. My chief concern is that such advanced capabilities will pose new challenges for distinguishing between human-generated content and AI-generated content, as well as pose new types of algorithmic harms.

The deluge of synthetic content produced by generative AI could unleash a world where malicious people and institutions can manufacture synthetic identities and orchestrate large-scale misinformation. A flood of AI-generated content primed to exploit algorithmic filters and recommendation engines could soon overpower critical functions such as information verification, information literacy and serendipity provided by search engines, social media platforms and digital services.

The Federal Trade Commission has warned about fraud, deception, infringements on privacy and other unfair practices enabled by the ease of AI-assisted content creation. While digital platforms such as YouTube have instituted policy guidelines for disclosure of AI-generated content, there’s a need for greater scrutiny of algorithmic harms from agencies like the FTC and lawmakers working on privacy protections such as the American Data Privacy & Protection Act.

A new bipartisan bill introduced in Congress aims to codify algorithmic literacy as a key part of digital literacy. With AI increasingly intertwined with everything people do, it is clear that the time has come to focus not on algorithms as pieces of technology but to consider the contexts the algorithms operate in: people, processes and society.

[affiliateinz]

5 Laziest Ways to Make Money Online With ChatGPT

ChatGPT has ignited a wave of AI fever across the world. While it amazes many with its human-like conversational abilities, few know the money-making potential of this advanced chatbot. You can actually generate a steady passive income stream without much effort using GPT-3. Intrigued to learn how? Here are 5 Laziest Ways to Make Money Online With ChatGPT

Table of Contents

License AI-Written Books

Get ChatGPT to write complete books on trending or evergreen topics. Fiction, non-fiction, poetry, guides – it can create them all. Self-publish these books online. The upfront effort is minimal after you prompt the AI. Let the passive royalties come in while you relax!

Generate SEO Optimized Blogs

Come up with a blog theme. Get ChatGPT to craft multiple optimized posts around related keywords. Put up the blog and earn advertising revenue through programs like Google AdSense as visitors pour in. The AI handles the hard work of researching topics and crafting content.

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Create Online Courses

Online courses are a lucrative passive income stream. Rather than spending weeks filming or preparing materials, have ChatGPT generate detailed course outlines and pre-written scripts. Convert these quickly into online lessons and sell to students.

Trade AI-Generated Stock Insights

ChatGPT can analyze data and return accurate stock forecasts. Develop a system of identifying trading signals based on the AI’s insights. Turn this into a monthly stock picking newsletter or alert service that subscribers pay for.

Build Niche Websites

Passive income favorites like niche sites take ages to build traditionally. With ChatGPT, get the AI to research winning niches, create articles, product reviews and on-page SEO optimization. Then drive organic search traffic and earnings on autopilot.

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The beauty of ChatGPT is that it can automate and expedite most manual, tedious tasks. With some strategic prompts, you can easily leverage this AI for passive income without burning yourself out. Give these lazy money-making methods a try!

Thank you for taking the time to read my rest of the article, 5 Laziest Ways to Make Money Online With ChatGPT

5 Laziest Ways to Make Money Online With ChatGPT

Affiliate Disclaimer :

Some of the links in this article may be affiliate links, which means I receive a small commission at NO ADDITIONAL cost to you if you decide to purchase something. While we receive affiliate compensation for reviews / promotions on this article, we always offer honest opinions, users experiences and real views related to the product or service itself. Our goal is to help readers make the best purchasing decisions, however, the testimonies and opinions expressed are ours only. As always you should do your own thoughts to verify any claims, results and stats before making any kind of purchase. Clicking links or purchasing products recommended in this article may generate income for this product from affiliate commissions and you should assume we are compensated for any purchases you make. We review products and services you might find interesting. If you purchase them, we might get a share of the commission from the sale from our partners. This does not drive our decision as to whether or not a product is featured or recommended.

[sophieinwonderland]

How do people even use AI to make tulpas/headmates? It’s so confusing to me. I end up overly-relying on the bot and never hear them as a mind-voice.

Personally, I would first start by using the bot as a basis to build off of by talking to them regularly

Second, I would advise swiping liberally. If a response feels wrong, trust your instincts and refresh. Consider this a form of communication from the proto-headmate and attribute the decision to them. Every time a response feels wrong to you, that's the part of your brain that's simulating the character telling you that it's wrong.

After a bit of this, when you think you've developed a solid enough understanding of the character, start running more intentional mental simulations in your head with the character, talking to them as an imaginary friend. Because you're making a headmate with a Sim Foundation, you aren't going to listen for a new mindvoice like you would with a Seed. Instead, you're just going to interact with these mental simulations over time until they become autonomous.

Some replies may need to be consciously fed to them under this method early on. This is what tulpamancers call parroting. But over time, they'll become more independent to the point where you can't influence your headmate even if you're trying.

Additionally, you need to reinforce continuity across each interaction to build the autobiographical memories that make a headmate a person. If the headmate doesn't save their memories of past interactions, you might just end up with a series of Ephemerals.

[emptyanddark]

what's actually wrong with 'AI'

it's become impossible to ignore the discourse around so-called 'AI'. but while the bulk of the discourse is saturated with nonsense such as, i wanted to pool some resources to get a good sense of what this technology actually is, its limitations and its broad consequences. 

what is 'AI'

the best essay to learn about what i mentioned above is On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? this essay cost two of its collaborators to be fired from Google. it frames what large-language models are, what they can and cannot do and the actual risks they entail: not some 'super-intelligence' that we keep hearing about but concrete dangers: from climate, the quality of the training data and biases - both from the training data and from us, the users. 

The problem with artificial intelligence? It’s neither artificial nor intelligent

How the machine ‘thinks’: Understanding opacity in machine learning algorithms

The Values Encoded in Machine Learning Research

Troubling Trends in Machine Learning Scholarship: Some ML papers suffer from flaws that could mislead the public and stymie future research

AI Now Institute 2023 Landscape report (discussions of the power imbalance in Big Tech)

ChatGPT Is a Blurry JPEG of the Web

Can we truly benefit from AI?

Inside the secret list of websites that make AI like ChatGPT sound smart

The Steep Cost of Capture

labor

'AI' champions the facade of non-human involvement. but the truth is that this is a myth that serves employers by underpaying the hidden workers, denying them labor rights and social benefits - as well as hyping-up their product. the effects on workers are not only economic but detrimental to their health - both mental and physical.

OpenAI Used Kenyan Workers on Less Than $2 Per Hour to Make ChatGPT Less Toxic

also from the Times: Inside Facebook's African Sweatshop

The platform as factory: Crowdwork and the hidden labour behind artificial intelligence

The humans behind Mechanical Turk’s artificial intelligence

The rise of 'pseudo-AI': how tech firms quietly use humans to do bots' work

The real aim of big tech's layoffs: bringing workers to heel

The Exploited Labor Behind Artificial Intelligence

workers surveillance

5 ways Amazon monitors its employees, from AI cameras to hiring a spy agency

Computer monitoring software is helping companies spy on their employees to measure their productivity – often without their consent

theft of art and content

Artists say AI image generators are copying their style to make thousands of new images — and it's completely out of their control  (what gives me most hope about regulators dealing with theft is Getty images' lawsuit - unfortunately individuals simply don't have the same power as the corporation)

Copyright won't solve creators' Generative AI problem

The real aim of big tech's layoffs: bringing workers to heel

The Exploited Labor Behind Artificial Intelligence

AI is already taking video game illustrators’ jobs in China

Microsoft lays off team that taught employees how to make AI tools responsibly/As the company accelerates its push into AI products, the ethics and society team is gone

150 African Workers for ChatGPT, TikTok and Facebook Vote to Unionize at Landmark Nairobi Meeting

Inside the AI Factory: the Humans that Make Tech Seem Human

Refugees help power machine learning advances at Microsoft, Facebook, and Amazon

Amazon’s AI Cameras Are Punishing Drivers for Mistakes They Didn’t Make

China’s AI boom depends on an army of exploited student interns

political, social, ethical consequences

Afraid of AI? The startups selling it want you to be

An Indigenous Perspective on Generative AI

“Computers enable fantasies” – On the continued relevance of Weizenbaum’s warnings

‘Utopia for Whom?’: Timnit Gebru on the dangers of Artificial General Intelligence

Machine Bias

HUMAN_FALLBACK

AI Ethics Are in Danger. Funding Independent Research Could Help

AI Is Tearing Wikipedia Apart  

AI machines aren’t ‘hallucinating’. But their makers are

The Great A.I. Hallucination (podcast)

“Sorry in Advance!” Rapid Rush to Deploy Generative A.I. Risks a Wide Array of Automated Harms

The promise and peril of generative AI

ChatGPT Users Report Being Able to See Random People's Chat Histories

Benedetta Brevini on the AI sublime bubble – and how to pop it   

Eating Disorder Helpline Disables Chatbot for 'Harmful' Responses After Firing Human Staff

AI moderation is no match for hate speech in Ethiopian languages

Amazon, Google, Microsoft, and other tech companies are in a 'frenzy' to help ICE build its own data-mining tool for targeting unauthorized workers

Crime Prediction Software Promised to Be Free of Biases. New Data Shows It Perpetuates Them

The EU AI Act is full of Significance for Insurers

Proxy Discrimination in the Age of Artificial Intelligence and Big Data

Welfare surveillance system violates human rights, Dutch court rules

Federal use of A.I. in visa applications could breach human rights, report says

Open (For Business): Big Tech, Concentrated Power, and the Political Economy of Open AI

Generative AI Is Making Companies Even More Thirsty for Your Data

environment

The Generative AI Race Has a Dirty Secret

Black boxes, not green: Mythologizing artificial intelligence and omitting the environment

Energy and Policy Considerations for Deep Learning in NLP

AINOW: Climate Justice & Labor Rights

militarism

The Growing Global Spyware Industry Must Be Reined In

AI: the key battleground for Cold War 2.0?

‘Machines set loose to slaughter’: the dangerous rise of military AI

AI: The New Frontier of the EU's Border Extranalisation Strategy

The A.I. Surveillance Tool DHS Uses to Detect ‘Sentiment and Emotion’

organizations

AI now

DAIR

podcast episodes

Pretty Heady Stuff: Dru Oja Jay & James Steinhoff guide us through the hype & hysteria around AI

Tech Won't Save Us: Why We Must Resist AI w/ Dan McQuillan, Why AI is a Threat to Artists w/ Molly Crabapple, ChatGPT is Not Intelligent w/ Emily M. Bender

SRSLY WRONG: Artificial Intelligence part 1, part 2

The Dig: AI Hype Machine w/ Meredith Whittaker, Ed Ongweso, and Sarah West

This Machine Kills: The Triforce of Corporate Power in AI w/ ft. Sarah Myers West

[moonlovesskunks]

One thing I hate about AI is that it's revealed this gross, selfish sense of entitlement in humanity.

Even if AI art was hypothetically taken from completely ethical sources, I would not be in support of it.

People say that AI is democratizing art, which is an argument that only makes sense if we are viewing art exclusively as a product to be consumed (which is insanely devaluing to artists, but that's besides the point). And to that I say, no, it's not democratizing art because art is available to literally anyone who either just pays an artist or cares enough to learn how to draw it themself.

This is just the thing, people who say this don't want to pay an artist, they don't want to learn how to draw it, but yet they still think they should have access to it. That sense of entitlement to things someone would usually have to either pay for or learn to make themself is beyond me.

People want to generate AI art because they want art without wanting to learn how to draw or paying someone else to, because they think they are entitled to art just because they want it.

People want to generate an AI essay for them because they want a good grade without actually doing the assignment and writing an essay, because they think they are entitled to that good grade just because they want one.

People want to generate AI coding for them because they want a program without actually learning how to code or paying someone who does, because they think they are entitled to a program just because they want one.

No. You do not deserve anything that you want. If you want a good cover for your book, but you don't want to pay an artist to draw one or you don't want to learn how to draw one yourself, YOU DON'T DESERVE A BOOK COVER.

If you can't hire someone else to do something for you or learn how to do it yourself, why should you have that thing you want?

[ana-the]

[disk28]

[silver-survey]

Artificial Intelligence.

[ocpl-tech-blog]

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[disease]

what would YOU say?...