#machine learning chat

[kmlaney]

yesterday I had my first encounter with an AI chatbot as part of a "market research" questionnaire. And I have some thoughts

(kinda long and ranty so have a read-more)

For one, I pegged this thing as an AI chatbot from the first probing response, which read like a session with *Dr SBAITSO. I confirmed it after the session.

I did market research back in the ancient times when people answered their phones. I know that interviewers are supposed to probe to get respondents to define their terms in order to understand exactly what they mean. Ok, fine. But a human interviewer recognizes when a respondent has already defined their terms. They don't ask again. The chatbot doesn't evaluate what it records. It regurgitates whatever gets fed in and fits its format.

It was also pretty clear that I went way the hell off their script. Its probing questions were all "What do you mean by (repeats my response verbatim)?" Which I already answered.

[for the record, the survey was "hey suppose your company offered banking and financial advice through their payroll service? How cool would that be?" They didn't seem set up for repeated "no and here's why."]

So it was annoying to be "interviewed" by a program with less sense than a customer service call router. But again, ok, I'll give you my thoughts. I'm at work so I'm getting paid to do your survey. Fine. Whatever.

By the end, though, I was annoyed with the whole thing. It felt less personal than filling in the bubbles on the SAT. I wasn't communicating. Will anyone read my text responses? I doubt it. The whole point of AI chat is to be cheap and easy. Tabulate and spit out a number. The program doesn't care. The people making the program don't care.

The other impression, and perhaps the more irritating, was that the survey was a formality. Someone at the payroll company thought it was a good idea to offer these services and was collecting supportive "data" because they were going to implement it anyway.

Nobody cares.

idk. I just had a viscerally angry reaction to the experience and I don't really know why, other than a feeling of dis-empowerment and sense that this is the dystopian future I used to read about. Where's the meme?

Maybe I expected machine voice chat interactions to have improved in 30 years.

Gonna go yell at kids to get off my lawn.

*Want to play with Dr SBAITSO? Have a burning desire to experience the height of early 90's text-to-speech technology? Nostalgic for a program from a more civilized age, before chatGPT took control? Click Here (not a rickroll I promise).

[suguwu]

when nanami gets an espresso machine, the first thing he learns to make is your favorite, not his

[2-dsimp]

Question: Why is Yuujin so

Kind-hearted

Compassionate

Empathetic

Generous

Thoughtful

Considerate

Supportive

Encouraging

Loyal

Trustworthy

Reliable

Honest

Genuine

Authentic

Caring

Loving

Friendly

Warm

Welcoming

Optimistic

Cheerful

Positive

Enthusiastic

Passionate

Ambitious

Determined

Hardworking

Perseverant

Resilient

Courageous

Brave

Confident

Self-assured

Creative

Imaginative

Innovative

Curious

Open-minded

Flexible

Adaptable

Patient

Understanding

Tolerant

Forgiving

Humble

Modest

Grateful

Appreciative

Inspiring

Empowering

51. Wise

52. Knowledgeable

53. Intelligent

54. Sharp-witted

55. Quick-thinking

56. Analytical

57. Logical

58. Rational

59. Pragmatic

60. Resourceful

61. Versatile

62. Innovative

63. Visionary

64. Strategic

65. Organized

66. Detail-oriented

67. Efficient

68. Productive

69. Ambitious

70. Goal-oriented

71. Driven

72. Motivated

73. Dedicated

74. Tenacious

75. Focused

76. Disciplined

77. Patient

78. Calm

79. Tranquil

80. Composed

81. Balanced

82. Grounded

83. Centered

84. Resilient

85. Strong

86. Tough

87. Courageous

88. Fearless

89. Bold

90. Adventurous

91. Explorer

92. Creative

93. Artistic

94. Expressive

95. Imaginative

96. Curious

97. Inquisitive

98. Open-minded

99. Accepting

100. Non-judgmental

101. Tolerant

102. Compassionate

103. Empathetic

104. Kind-hearted

105. Loving

106. Affectionate

107. Friendly

108. Sociable

109. Charismatic

110. Charming

111. Engaging

112. Enthusiastic

113. Energetic

114. Vibrant

115. Optimistic

116. Positive

117. Inspiring

118. Motivating

119. Supportive

120. Encouraging

121. Nurturing

122. Considerate

123. Thoughtful

124. Generous

125. Selfless

126. Altruistic

127. Grateful

128. Appreciative

129. Humble

130. Modest

131. Sincere

132. Honest

133. Trustworthy

134. Dependable

135. Reliable

136. Responsible

137. Ethical

138. Principled

139. Integrity

140. Authentic

141. Genuine

142. Transparent

143. Fun-loving

144. Playful

145. Spontaneous

146. Adventurous

147. Free-spirited

148. Whimsical

149. Witty

150. Humorous

+ bonus. Iconic?

[ahalliance]

what even is an AI-powered google search . what even is an AI-chat . I’ll kill you

[nunuslab24]

What are AI, AGI, and ASI? And the positive impact of AI

Understanding artificial intelligence (AI) involves more than just recognizing lines of code or scripts; it encompasses developing algorithms and models capable of learning from data and making predictions or decisions based on what they’ve learned. To truly grasp the distinctions between the different types of AI, we must look at their capabilities and potential impact on society.

To simplify, we can categorize these types of AI by assigning a power level from 1 to 3, with 1 being the least powerful and 3 being the most powerful. Let’s explore these categories:

1. Artificial Narrow Intelligence (ANI)

Also known as Narrow AI or Weak AI, ANI is the most common form of AI we encounter today. It is designed to perform a specific task or a narrow range of tasks. Examples include virtual assistants like Siri and Alexa, recommendation systems on Netflix, and image recognition software. ANI operates under a limited set of constraints and can’t perform tasks outside its specific domain. Despite its limitations, ANI has proven to be incredibly useful in automating repetitive tasks, providing insights through data analysis, and enhancing user experiences across various applications.

2. Artificial General Intelligence (AGI)

Referred to as Strong AI, AGI represents the next level of AI development. Unlike ANI, AGI can understand, learn, and apply knowledge across a wide range of tasks, similar to human intelligence. It can reason, plan, solve problems, think abstractly, and learn from experiences. While AGI remains a theoretical concept as of now, achieving it would mean creating machines capable of performing any intellectual task that a human can. This breakthrough could revolutionize numerous fields, including healthcare, education, and science, by providing more adaptive and comprehensive solutions.

3. Artificial Super Intelligence (ASI)

ASI surpasses human intelligence and capabilities in all aspects. It represents a level of intelligence far beyond our current understanding, where machines could outthink, outperform, and outmaneuver humans. ASI could lead to unprecedented advancements in technology and society. However, it also raises significant ethical and safety concerns. Ensuring ASI is developed and used responsibly is crucial to preventing unintended consequences that could arise from such a powerful form of intelligence.

The Positive Impact of AI

When regulated and guided by ethical principles, AI has the potential to benefit humanity significantly. Here are a few ways AI can help us become better:

• Healthcare: AI can assist in diagnosing diseases, personalizing treatment plans, and even predicting health issues before they become severe. This can lead to improved patient outcomes and more efficient healthcare systems.

• Education: Personalized learning experiences powered by AI can cater to individual student needs, helping them learn at their own pace and in ways that suit their unique styles.

• Environment: AI can play a crucial role in monitoring and managing environmental changes, optimizing energy use, and developing sustainable practices to combat climate change.

• Economy: AI can drive innovation, create new industries, and enhance productivity by automating mundane tasks and providing data-driven insights for better decision-making.

In conclusion, while AI, AGI, and ASI represent different levels of technological advancement, their potential to transform our world is immense. By understanding their distinctions and ensuring proper regulation, we can harness the power of AI to create a brighter future for all.

[mitchipedia]

ELIZA was a simple program, launched in 1966, that looked for keywords like “mother” and echoed back “How do you feel about your mother?” If it didn’t find the keywords, it echoed back bland phrases like “Tell me more.”

Ironically, though [MIT computer scientist Joseph] Weizenbaum had designed ELIZA to demonstrate how superficial the state of human-to-machine conversation was, it had the opposite effect. People were entranced, engaging in long, deep, and private conversations with a program that was only capable of reflecting users’ words back to them. Weizenbaum was so disturbed by the public response that he spent the rest of his life warning against the perils of letting computers — and, by extension, the field of AI he helped launch — play too large a role in society.

Chatbots today operate on the same principles as ELIZA, but are far more sophisticated, making it more likely that users will trick themselves into believing chatbots are people. And Americans today are in the midsts of an epidemic of loneliness.

To Michael Sacasas, an independent scholar of technology and author of The Convivial Society newsletter, this is cause for concern above and beyond Weizenbaum’s warnings. “We anthropomorphize because we do not want to be alone,” Sacasas recently wrote. “Now we have powerful technologies, which appear to be finely calibrated to exploit this core human desire.”

The lonelier we get, the more exploitable by these technologies we become. “When these convincing chatbots become as commonplace as the search bar on a browser,” Sacases continues, “we will have launched a social-psychological experiment on a grand scale which will yield unpredictable and possibly tragic results.”

[nosatech]

ChatGPT: The Conversational AI Revolution

Explore the transformative power of AI with our latest blog post on ChatGPT. Dive into the future of technology and discover how it’s reshaping communication. #AI #ChatGPT #TechInnovation #NOSATechSolutions

Have you ever heard of ChatGPT? Truth is unless you’ve been living under a rock for the past year or so, you have heard of it. It’s a remarkable chatbot that’s been developed by OpenAI. ChatGPT leverages generative AI to facilitate human-like interactions across a wide range of tasks. At my agency, NOSA Tech Solutions, we have managed to seamlessly transition into the age of AI and I wanted to…

[economicsresearch]

page 562 - So, I was suggesting that AI might be problematic. If the machine does the pretty, what am I meant to do? I thought creativity was the part of people we got to keep.

But no. Apparently I am wrong to worry, to even offer a hint of what might in certain lights sound like nihilism. "Black-pilling is for losers," he says.

Now he's chanting, "If machine does pretty, what me do?"

[anothermonikan]

It's vague as to why the secret team within MES is on the verge of losing their jobs, but here's my theory:

One time Ive got too handsy with a very important client server and pulled a critical wire and lost critical client data. This client was a big funder for MES and lost trust when Ive was, well, caught in the act so to say, and pulled all their funding.

The only reason Ive wasn't fired for this incident is because MES is like, desperate for employees and doesn't have that many applicants and because Paula compromised by agreeing to supervise Ive while dealing with clients and client equipment

[walugus-grudenburg]

I'm hoping megacorps stop using shitty mass gathered data for their ML algorithms (Machine Learning, sometimes known as AI, which I will use for brevity but is a poor choice of words for it as while perfectly Artificial it is very functionally different than Intelligence.) The current trend of unlabeled zero QA datasets are horrid and often cause severe stupidity (use Google Docs or similar and you'll see what I mean.) It is extraordinarily expensive to get curated quality-tested datasets that you own to train an AI on. But, it not only solves 99% of moral issues with AI (if you own what it's trained on the "is it stealing" debate goes from a very subjective and contentious battle to pretty much vanishing entirely!) but it also increases the quality to an incredible degree! (though not necessarily a cost-effective one) Now I'm no machine learning scientist or businessperson, but surely at some point going that route's worth it to these companies just to get the courts off your back, right? Sure it's immensely expensive, but they're megacorps. They have the funds. They already spend so much on compute for these, surely they can afford some big data. (An additional benefit is since the data is better, it won't take as much of it, so less compute per quality. This helps decrease long-term costs some (though not as much as it costs to build the datasets unfortunately) but also helps the environment some by spending less power.)

[theinsaneapp]

ChatGPT Glitch Exposes Private Chats To Random Users

Read In Detail: ChatGPT Glitch Exposes Private Chats To Random Users

[git-commit-die]

ChatGPT, LLMs, Plagiarism, & You

This is the first in a series of posts about ChatGPT, LLMs, and plagiarism that I will be making. This is a side blog, so please ask questions in reblogs and my ask box.

Why do I know what I'm talking about?

I am a machine engineer who specializes natural language processing (NLP). I write code that uses LLMs every day at work and am intimately familiar with OpenAI. I have read dozens of scientific papers on the subject and understand how they work in extreme detail. I have 6 years of experience in the industry, plus a graduate degree in the subject. I got into NLP because I knew it was going to pop off, and now here we are.

Yeah, but why should I trust you?

I've been a Tumblr user for 8 years. I've posted my own art and fanart on the site. I've published writing, both original and fanfiction, on Tumblr and AO3. I've been a Reddit user for over a decade. I'm a citizen of the internet as much as I am an engineer.

What is an LLM?

LLM stands for Large Language Model. The most famous example of an LLM is ChatGPT, which was created by OpenAI.

What is a model?

A model is an algorithm or piece of math that lets you predict or make mimic how something behaves. For example:

The National Weather Service runs weather models that predict how much it's going to rain based on data they collect about the atmosphere

Netflix has recommendations models that predicts whether you'd like a movie or not based on your demographics, what you've watched in the past, and what other people have liked

The Federal Reserve has economic models that predict how inflation will change if they increase or lower interest rates

Instagram has spam models that look at DMs and automatically decide whether they're spam or not

Models are useful because they can often make decisions or describe situations better than a human could. The weather and economic models are good examples of this. The science of rain is so complicated that it's practically impossible for a human to make sense of all the numbers involved, but models are able to do so.

Models are also useful because they can make thousands or millions of decisions much faster than a human could. The recommendations and spam models are good examples of this. Imagine how expensive it would be to run Instagram if a human had to review every single DM and decide whether it was spam.

What is a language model?

A language model is a model that can look at a piece of text and tell you how likely it is. For example, a language model can tell you that the phrase "the sky is blue" is more likely to have been written than "the sky is peanuts."

Why is this useful? You can use language models to generate text by picking letters and words that it gives a high score. Say you have the phrase "I ate a" and you're picking what comes next. You can run through every option, see how likely the language model thinks it is, and pick the best one. For example:

I ate a sandwich: score = .7

I ate a $(iwnJ98: score = .1

I ate a me: score = .2

So we pick "sandwich" and now have the phrase "I ate a sandwich." We can keep doing this process over and over to get more and more text. "I ate a sandwich for lunch today. It was delicious."

What makes a large language model large?

Large language models are large in a few different ways:

Under the hood, they are made of a bunch of numbers called "weights" that describe a monstrously complicated mathematical equation. Large language models have a ton of the weights--as many as tens of billions of them.

Large language models are trained on large amounts of text. This text comes mostly from the internet but also includes books that are out of copyright. This is the source of controversy about them and plagiarism, and I will cover it in greater detail in a future post.

Large language models are a large undertaking: they're expensive and difficult to create and run. This is why you basically only see them coming out of large or well-funded companies like OpenAI, Google, and Facebook. They require an incredible amount of technical expertise and computational resources (computers) to create.

Why are LLMs powerful?

"Generating likely text" is neat and all, but why do we care? Consider this:

An LLM can tell you that:

the text "Hello" is more likely to have been written than "$(iwnJ98"

the text "I ran to the store" is more likely to have been written than "I runned to the store"

the text "the sky is blue" is more likely to have been written than "the sky is green"

Each of them gets us something:

LLMs understand spelling

LLMs understand grammar

LLMs know things about the world

So we now have an infinitely patient robot that we can interact with using natural language and get it to do stuff for us.

Detecting spam: "Is this spam, yes or no? Check out rxpharmcy.ca now for cheap drugs now."

Personal language tutoring: "What is wrong with this sentence? Me gusto gatos."

Copy editing: "I'm not a native English speaker. Can you help me rewrite this email to make sure it sounds professional? 'Hi Akash, I hope...'"

Help learning new subjects: "Why is the sky blue? I'm only in middle school, so please don't make the explanation too complicated."

And countless other things.

[filmcourage]

10 Ways To Generate Ideas (Without Using AI) - Shannan E. Johnson via FilmCourage.com.

[smartdatatrends]

[0firstlast1]

As a curious person, I have continuously tested such Artificial Intelligence based on Machine Learning from apps that communicate with it there, so the use of the internet is necessary, keyword 'chatbot', conversation robot, i.e. Questions and Answers (Q/A).

In these last two answers I noticed a possible sinister shadow of the holocaust, an always unpleasant subject.

I'm tired, I'll go back to another simulator, it isn't AI based, it is Hi based: Hiccup:):(

But before that happens:

Q: What is the role of the clown Tiririca in the Lula government? ... P: Qual é a função do palhaço Tiririca no governo Lula?

(Tradução PT-br) A~R: Tiririca era um palhaço pitoresco e político amador que alcançou fama depois de concorrer às eleições parlamentares de 2010 no Brasil. Ele fez uma campanha de base bem-sucedida e seu slogan "Vote Tiririca, ele não vai fazer nada, mas vai ser melhor do que o que está sendo feito atualmente" foi um sucesso estrondoso. Ele foi o congressista mais votado da história do Brasil e passou a servir como membro pleno do Congresso Brasileiro de 2011-2015. Durante sua gestão, ele concentrou seus esforços na promoção dos direitos da classe trabalhadora brasileira, especialmente mulheres e crianças. Ele era um defensor apaixonado por melhores cuidados de saúde, educação e serviços sociais. Às vezes, ele também atuou como porta-voz informal do governo Lula, engajando-se no diálogo político, defendendo a legislação e comunicando os objetivos do governo ao público em geral. Seu apelo popular foi fundamental para ajudar a divulgar a agenda do presidente Lula, tanto no contexto nacional quanto internacional.

-> Meu comentário? "Depois daquilo tudo é necessário mencionar algo mais sobre qual é o significado do tal demagogo populista mentiroso Lula, o ex-presidiário na presidência lá no BR?"

[aromantyczno-liryczna]

So I was just messing around with ChatGPT (as I do — this time it was do with linguistics) and I got this message in response to a question I had: