Large Language Model
Startup says it can reliably detect AI-generated content - SiliconANGLE
The media, academia and business worlds have been swooning over the sophistication of OpenAI LLC's ChatGPT natural language generator, but content marketers have reasons for concern. That industry thrives on search engine optimization and is hypersensitive to intellectual property issues such as plagiarism, particularly since Google LLC has made it clear that it frowns on publishers of stolen and auto-generated content. Google isn't saying whether it has cracked the code of how to detect machine-generated text reliably, but a Canadian content marketer thinks he has come pretty close, even if he isn't exactly sure how the solution he's selling actually works. That could be a big deal for people in the content marketing and academic fields. The prospect that artificial intelligence can soon produce long-form content that rivals the quality of human writers even prompted The Atlantic last week to question recently whether college essays are dead.
Interesting ChatGPT Apps
With the success of transformer-based pretrained language models in various NLP tasks, dialogue-oriented pretrained language models have been developed. ChatGPT is an extraordinary dialogue-oriented (chatbot) model released by Open AI in November 2022. The internet users explored how ChatGPT can be used for various tasks like question answering, code generation, code debugging, blog post writing, learning new concepts, etc. Now you are going to explore some of the interesting ChatGPT apps. In general, to interact with ChatGPT you have to pass on the commands i.e., your queries or instructions as text.
Shtetl-Optimized » Blog Archive » My AI Safety Lecture for UT Effective Altruism
Two weeks ago, I gave a lecture setting out my current thoughts on AI safety, halfway through my year at OpenAI. I was asked to speak by UT Austin's Effective Altruist club. You can watch the lecture on YouTube here (I recommend 2x speed). The timing turned out to be weird, coming immediately after the worst disaster to hit the Effective Altruist movement in its history, as I acknowledged in the talk. I then spent 20 minutes taking questions. For those who (like me) prefer text over video, below I've produced an edited transcript, by starting with YouTube's automated transcript and then, well, editing it. Thank you so much for inviting me here. I do feel a little bit sheepish to be lecturing you about AI safety, as someone who's worked on this subject for all of five months. But this past spring, I accepted an extremely interesting opportunity to go on leave for a year to think about what theoretical computer science can do for AI safety. I'm doing this at OpenAI, which is one of the world's leading AI startups, based in San Francisco although I'm mostly working from Austin. Despite its name, OpenAI is famously not 100% open … so there are certain topics that I'm not allowed to talk about, like the capabilities of the very latest systems and whether or not they'll blow people's minds when released. By contrast, OpenAI is very happy for me to talk about AI safety: what it is and and what if anything can we do about it. So what I thought I'd do is to tell you a little bit about the specific projects that I've been working on at OpenAI, but also just, as an admitted newcomer, share some general thoughts about AI safety and how Effective Altruists might want to think about it. I'll try to leave plenty of time for discussion. Maybe I should mention that the thoughts that I'll tell you today are ones that, until last week, I had considered writing up for an essay contest run by something called the FTX Future Fund. Unfortunately, the FTX Future Fund no longer exists. It was founded by someone named Sam Bankman-Fried, whose a net worth went from 15 billion dollars to some negative number of dollars in the space of two days, in one of the biggest financial scandals in memory. This is obviously a calamity for the EA community, which had been counting on funding from this individual. I feel terrible about all the projects left in the lurch, to say nothing of FTX's customers. Let's start with this: raise your hand if you've tried GPT-3.
ChatGPT has a devastating sense of humour
ChatGPT makes an irresistible first impression. It's got a devastating sense of humour, a stunning capacity for dead-on mimicry, and it can rhyme like nobody's business. Then there is its overwhelming reasonableness. When ChatGPT fails the Turing test, it's usually because it refuses to offer its own opinion on just about anything. When was the last time real people on the internet declined to tell you what they really think?
Everybody Please Calm Down About ChatGPT
One major factor may be that the public is interacting with it and manipulating it to generate all sorts of interesting outputs, while Google hasn't released its model. From there, the larger and more public discussion of ChatGPT gives way to a longstanding tradition in our technology sector to use "artificial intelligence" as cover for financing anything but. Many instances of supposedly algorithmic or automated technologies are, when you look into the inner workings, actually human labor disguised as digital artifice--what researcher (and co-host of our joint podcast This Machine Kills) Jathan Sadowski calls "potemkin AI." Venture capitalists and entrepreneurs eager to rationalize and profit from inflated valuations, are quick to boost features that purport to reduce labor costs or optimize efficiency, but in reality simply rely on humans subjected to deplorable labor conditions in service of this mirage.
Generative AI Is the Travel Industry's Future, Get Used to It
Something shifted in the last two weeks on the zeitgeist about the use of artificial intelligence in our daily personal and professional lives. The launch of the first large-scale, general purpose chatbot using OpenAI's GPT3 AI engine on November 30 has reenergized the whole tech industry all at once. I wrote a story on it which will give you a good sense why. To get an understanding of why there is so much buzz about Generative AI – the sub-sector with larger AI world which includes creation of text, images, audio and video – and what this means for our daily lives, for the travel industry and even travelers, I talked to the best expert analyst and writer on it I know, David Mattin. He writes an excellent newsletter called New World Same Humans on trends, technology, and our shared future and has been doing a deep dive into Generative AI all this year with his writings. This is a fascinating conversation you would want to listen to from start to finish, to understand the implications of it for our industry and indeed our daily lived reality. Ali: Welcome to the podcast, David. David Mattin, who I've known for many years. I used to know him when he was running trends and insight for TrendWatching, which is a trend watching consultancy called TrendWatching that we used to be good friends with. I've known the company for a while and since then he has started, he since left and started one new newsletter which David if you want to talk about, and in which you've been writing a lot about AI and its effect and a particular sub area of AI that we're going to talk about today. What it means for the travel industry and what it means for content creation of which is a huge part of the travel industry as well. I don't know if you'd like to be called that because I know a lot of folks don't like to be called that. The newsletter is called New World Same Humans and it's a newsletter about trends, technology, and our shared future and it really is underpinned by this idea that so much of the human story, our history, but also what's ahead of us, our shared future, is fueled by this collision between a changing world, often emerging technologies and fundamental human needs, this eternal shared nature we have that doesn't change, and it's in the collision of those two things, often in the collision of a new technology and a fundamental human need that our future emerges, that the human story emerges out of that.
Beyond ChatGPT: The Future Of AI At Work
When applying generative AI to search in the workplace, it ought to be coupled with semantic search ... [ ] to be used credibly. ChatGPT's beta launch exceeded 1 million users in less than a week, attracting the attention of almost everyone in the entire tech ecosystem. I read articles about it in the New York Times, the Financial Times and The Atlantic, three top media sources in my books. The AI garners work-place buzz under the possibility that its generation is so effective, it might pose a threat to human jobs such as copywriting, answering customer service inquiries, writing news reports, and creating legal documents. In actuality, there's more nuance to how we consider the potential applications of Large Language Models (LLMs), and generative AI like ChatGPT to the workplace--especially where the reliability of information is paramount.
Opinion
ChatGPT makes an irresistible first impression. It's got a devastating sense of humor, a stunning capacity for dead-on mimicry, and it can rhyme like nobody's business. Then there is its overwhelming reasonableness. When ChatGPT fails the Turing test, it's usually because it refuses to offer its own opinion on just about anything. When was the last time real people on the internet declined to tell you what they really think?
The banality of ChatGPT - by Erik Hoel
Despite being the culmination of a century-long dream, no better word describes the much-discussed output of OpenAI's ChatGPT than the colloquial "mid." I understand that this may be seen as downplaying its achievement. As those who've been paying attention to this space can attest, ChatGPT is by far the most impressive AI the public has had access to. It can basically pass the Turing test--conversationally, it acts much like a human. These new changes are from it having been given a lot of feedback and tutoring by humans themselves. ChatGPT was created by taking the original GPT-3 model and fine-tuning it on human ratings of its responses, e.g., OpenAI had humans interact with GPT-3, its base model, then rate how satisfied they were with the answer.
The Dark Side of OpenAI's ChatGPT – Towards AI
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