Generative AI
Wall Street Banks Are Cracking Down on AI-Powered ChatGPT
Bank of America Corp., Citigroup Inc., Deutsche Bank AG, Goldman Sachs Group Inc. and Wells Fargo & Co. are among lenders that have recently banned usage of the new tool, with Bank of America telling employees that ChatGPT and openAI are prohibited from business use, according to people with knowledge
Companies scramble to incorporate generative AI in products
Whether these new AI technologies are ready for prime time remains a wide-open question. Even assuming these early bugs are worked out, a host of thorny business and legal challenges remain, as we've been writing about. Whether these new AI technologies are ready for prime time remains a wide-open question. Even assuming these early bugs are worked out, a host of thorny business and legal challenges remain, as we've been writing about.
Use ChatGPT -- With Superpowers!. Hi guys, in my last Medium story, Iโฆ
Notes for ChatGPT by Zoho: Say hello to Zoho Notebook extension for ChatGPT! Without switching tabs, you can use this amazing tool to save all of your ChatGPT conversations as notes in the Notebook app. To use it, all you need to do is just ask all of your questions in ChatGPT and save the entire conversation or each individual chat as a note in Notebook. YouTube Summarizer with ChatGPT: This free Chrome extension lets you quickly access the summary of the YouTube videos you are currently watching with OpenAI's ChatGPT AI technology. All you can do with this tool is get transcripts in many languages, summarize the video with ChatGPT, scroll into the currently playing timestamp, and copy-n-paste all the transcripts.
Elon Musk reportedly building team to develop ChatGPT alternative
Amid concerns about the neutrality of OpenAI's text-based artificial intelligence (AI) platform ChatGPT, Tesla (NASDAQ: TSLA) CEO Elon Musk seems to be busy working on creating an alternative to the high-profile chatbot as he has reportedly approached AI researchers in recent weeks. Indeed, Musk has allegedly been recruiting Igor Babuschkin, a researcher who has recently left Alphabet's (NASDAQ: GOOGL) DeepMind AI unit and specialized in the machine-learning models used by the likes of ChatGPT, according to a report by The Information published on February 27. Specifically, the report referred to the media outlet's communication with two unnamed people that are said to have direct knowledge of the team-assembling efforts, as well as a third person who was briefed on the conversations between Elon and Babuschkin. As the report recalls, Musk has been critical of OpenAI, which he co-founded in 2015 but has since cut ties with, for installing safeguards that prevent ChatGPT from producing text that might offend specific groups of users, suggesting in 2022 that the technology was an example of "training AI to be woke." More recently, he joked that "what we need is TruthGPT," which led to the appearance of an eponymous project that stated it was already developing such a bot using the technology underlying the cryptocurrency industry and asking for Musk's assistance. Despite criticism, crypto trading platform Binance has praised ChatGPT over its potential to be used in crypto adoption, expansion, and education as it is able to explain complicated concepts, such as proof-of-work (PoW), Bitcoin mining, and others, in a conversational and often fun way, like through a rap song or imitating a 1920s mobster.
How AI Could Transform Email
What if your inbox were jam-packed with AI-generated emails? You may already be on the receiving end of emails written by artificial intelligence, with the help of a human prompter. Austin Distel, a senior director of marketing at Jasper, is one of those humans. Austin smiles as he demonstrates Japer's knack for email composition. "These are tools in my tool belt that helped me perform faster, but also better," he says before sharing that he often uses generative AI to rewrite work emails so they sound like Jerry Seinfeld.
Council Post: Five Artificial Intelligence Predictions For The Near Future
When it comes to artificial intelligence (AI), the advances we saw in 2021 pale in comparison to those that occurred last year, and AI shows no signs of slowing down. Industries from financial services to healthcare to manufacturing are adopting AI-enabled solutions to restructure how they operate as well as to solve previously intractable problems. Building on my AI predictions from last year, in this article I will explore five ways in which AI is poised to transform our society in the near future. Generative AI had an explosive year in 2022. Popular systems like DALL-E 2, Stable Diffusion and Midjourney can produce incredibly detailed images from a text prompt in a matter of seconds, irrevocably altering the landscape of graphic design.
'Prompt engineering' is one of the hottest jobs in generative AI. Here's how it works.
Knowing how to talk to chatbots may get you hired as a prompt engineer for generative AI. Prompt engineers are experts in asking AI chatbots -- which run on large language models -- questions that can produce desired responses. Unlike traditional computer engineers who code, prompt engineers write prose to test AI systems for quirks; experts in generative AI told The Washington Post that this is required to develop and improve human-machine interaction models. Alex Shoop, an expert in AI systems design, told Venture Beat that as AI tools evolve, prompt engineers help ensure that chatbots are rigorously tested, that their responses are reproducible, and that safety protocols are followed. The rise of the prompt engineer comes as chatbots like OpenAI's ChatGPT have taken the world by storm.
OpenAI Is Now Everything It Promised Not to Be: Corporate, Closed-Source, and For-Profit
By March 2019, OpenAI shed its non-profit status and set up a "capped profit" sector, in which the company could now receive investments and would provide investors with profit capped at 100 times their investment. The company's decision was likely a result of its desire to compete with Big Tech rivals like Google and ended up receiving a $1 billion investment shortly after from Microsoft. In the blog post announcing the formation of a for-profit company, OpenAI continued to use the same language we see today, declaring its mission to "ensure that artificial general intelligence (AGI) benefits all of humanity." As Motherboard wrote when the news was first announced, it's incredibly difficult to believe that venture capitalists can save humanity when their main goal is profit.
Imitating Creators: Prospective governance and mechanisms for identifying AI-generated content
We are slowly seeing the emerging trend of organisations considering the use of generative technologies across all areas of business. Collectively known as'generative AI', these technologies (such as the popular Chat-GPT and Dall-E) are capable of taking a prompt from its user and creating entirely new content, such as blog posts, letters to clients, or internal policies. In a previous article, we examined several points that organisations should consider, such as potential for IP infringement and inadvertent PR issues. The article goes on to consider several steps organisations can take to mitigate these risks throughout the process, such as regular testing and ensuring appropriate safeguards are put in place. As will be clear for those who have already interacted with these technologies, while there is certainly value in implementing them within certain processes, these safeguards are clearly a necessary step to ensure that the AI is behaving accurately and, in the case of written works, in a way that is not misleading.
Active learning is the future of generative AI: Here's how to leverage it
During the past six months, we have witnessed some incredible developments in AI. The release of Stable Diffusion forever changed the artworld, and ChatGPT-3 shook up the internet with its ability to write songs, mimic research papers and provide thorough and seemingly intelligent answers to commonly Googled questions. However, most of these generative AI models are foundational models: high-capacity, unsupervised learning systems that train on vast amounts of data and take millions of dollars of processing power to do it. Currently, only well-funded institutions with access to a massive amount of GPU power are capable of building these models. The majority of companies developing the application-layer AI that's driving the widespread adoption of the technology still rely on supervised learning, using large swaths of labeled training data.