"Our generative AI policy prohibits submissions based on third-party content -- including text prompts referring to people, places, property, or an artist's style -- without proper authorization," Sarah Casillas, the senior director of content for Adobe Stock, said in a statement. All AI-generated artwork has to be uploaded and submitted as illustrations. The updated policy cautions users to not submit multiple versions of pictures based on the same prompt. It also warns creators not to describe AI generated content as depicting real people or places. The content has to be titled and tagged with the keywords generative AI, to make sure there is no confusion in considering the work as AI-generated, even if it looks like a photograph.

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.

Deep learning has ignited the AI renaissance over the past decade. DL has become the mainstream of technological innovation and digital transformation. Over time, due to different algorithms and use cases, it has established two well-known branches, CNN and RNN. CNN (Convolutional Neural Network) is a DL model designed to process and analyze data with a grid-like structure, such as images. It uses convolutional layers to extract features from data and is often used for image classification, object detection, and segmentation.

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.

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.

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.

Originally published on Towards AI the World's Leading AI and Technology News and Media Company. If you are building an AI-related product or service, we invite you to consider becoming an AI sponsor. At Towards AI, we help scale AI and technology startups. Let us help you unleash your technology to the masses. It's free, we don't spam, and we never share your email address.

While hiring freezes at Big Tech firms might be hurting certain AI investments, it's clear that there remains a strong appetite throughout the enterprise for AI technologies -- whether developed in-house or outsourced to third parties. According to a McKinsey survey from early December, AI adoption at companies has more than doubled since 2017, with 63% of businesses expecting spending on AI to increase over the next three years. In February, IDC forecast that companies would increase their spend on AI solutions by 19.6% in 2022, reaching $432.8 billion by the end of the year and over $500 billion in 2023. Generative AI is driving much of the recent corporate interest, with text-to-image tools such as OpenAI's DALL-E 2 and Stable Diffusion seeing swift uptake despite the risks. Adobe just this month announced that it would open its stock image service, Adobe Stock, to creations made with the help of generative AI programs, following in the footsteps of Shutterstock (but not rival Getty Images).

Recently, the field has been greatly impressed and inspired by OpenAI's ChatGPT. It is undoubtedly clever, capable, and very fun to talk to. Its multi-faceted abilities are significantly beyond many NLP researchers' and practitioners' expectations based on the impression of (not-that-strong) original GPT-3. The natural question is how ChatGPT gets there, and where these fantastic abilities come from. In this post, we try to dissect the emergent abilities and trace them to their sources, hoping to give a comprehensive roadmap about how the GPT-3.5 model family, along with related large language models, evolved to their current forms.

The Daily is made by Lisa Tobin, Rachel Quester, Lynsea Garrison, Clare Toeniskoetter, Paige Cowett, Michael Simon Johnson, Brad Fisher, Chris Wood, Jessica Cheung, Stella Tan, Alexandra Leigh Young, Lisa Chow, Eric Krupke, Marc Georges, Luke Vander Ploeg, M.J. Davis Lin, Dan Powell, Dave Shaw, Sydney Harper, Robert Jimison, Mike Benoist, Liz O. Baylen, Asthaa Chaturvedi, Rachelle Bonja, Diana Nguyen, Marion Lozano, Corey Schreppel, Anita Badejo, Rob Szypko, Elisheba Ittoop, Chelsea Daniel, Mooj Zadie, Patricia Willens, Rowan Niemisto, Jody Becker, Rikki Novetsky, John Ketchum, Nina Feldman, Will Reid, Carlos Prieto, Sofia Milan, Ben Calhoun and Susan Lee. Our theme music is by Jim Brunberg and Ben Landsverk of Wonderly.