Generative AI, ChatGPT, and Google Bard: Evaluating the Impact and Opportunities for Scholarly Publishing

In short, any organization that needs to produce clear written materials potentially stands to benefit. Organizations can also use generative AI to create more technical materials, such as higher-resolution versions of medical images. And with the time and resources saved here, organizations can pursue new business opportunities and the chance to create more value. With Vertex AI Search and Vertex AI Conversation, developers can ingest data and add customization genrative ai to build a search engine, chatbot or “voicebot” that can interact with customers and answer questions grounded in a company’s data. Google envisions the tools being used to build apps for use cases like food ordering, banking assistance and semi-automated customer service. During its annual Cloud Next conference, Google announced updates to Vertex AI, its cloud-based platform that provides workflows for building, training and deploying machine learning models.

FTC chair Lina Khan and fellow commissioners warned House representatives of the potential for modern AI technologies, like ChatGPT, to be used to “turbocharge” fraud in a congressional hearing. The Browsing feature can be enabled by heading to the New Features section of the app settings, selecting “GPT-4” in the model switcher and choosing “Browse with Bing” from the drop-down list. This kind of investigation doesn’t just appear out of thin air — the FTC doesn’t look around and say “That looks suspicious.” Generally a lawsuit or formal complaint is brought to their attention and the practices described by it imply that regulations are being ignored.

China lets Baidu, others launch ChatGPT-like bots to public, tech shares jump

As well as hidden and emerging capabilities, there are hidden and emerging threats. How, for example, will colleges adapt to the proliferation of AI-written essays? Is machine learning going to create a tsunami of spam that will ruin the web forever? And what about the inability of AI language models to distinguish fact from fiction or the proven biases of AI image generators that sexualize women and people of color?

In addition to generative AI for infrastructure-as-code like Project Wisdom, observability could see LLMs play an increased role in the future. It’s also clear that some software engineering tasks, such as test generation, will soon be taken over by AI, according to one analyst. Additionally, since clerical jobs have traditionally been an important source of women’s employment as economies develop, wider use of Generative AI could mean that certain clerical jobs may never emerge in lower-income countries. The study documents notable differences in the effects on countries at different levels of development, linked to current economic structures and existing technological gaps. Everyday examples include programing, scripts, email replies, listicles, blog ideas, summarization, etc.

How do text-based machine learning models work? How are they trained?

ChatGPT generally produced a better and more fluent summary than Bard, but was also much more expensive, in commercial applications, and generated less detail. ChatGPT was able to successfully identify the title, authors, author affiliation, and contact information, but it could not recognize or disambiguate author and institution, nor could it link text to publicly available databases such as ORCID. The most serious problem was that when we asked ChatGPT to find the authors of a given paper, it instead generated and returned fake names. The content we used in the experiments described below include content created by the team, OA content, or publicly accessible titles, abstracts, or other metadata. We are excited to introduce ChatGPT to get users’ feedback and learn about its strengths and weaknesses.

Plus, generative AI draws from a vast amount of information without feeling overwhelmed or stifled by capacity limitations. “As a long-time artificial intelligence enthusiast, I’ve been watching the development of ChatGPT with great interest. For those unfamiliar with it, ChatGPT is a powerful new artificial intelligence system developed by OpenAI that is capable of engaging in conversations with humans. ChatGPT and future AI conversation engines like it can be used for education, research, and other uses. But it’s also a glimpse at the future of business communication, marketing, and media.

Adobe’s triumph over the doomsters illustrates a wider point about the contest for dominance in the fast-developing market for AI tools. The supersize models powering the latest wave of so-called “generative” AI rely on oodles of data. Having already helped themselves to much of the internet, often without permission, AI firms are now seeking out new data sources to sustain the feeding frenzy.

Several tools claim to detect ChatGPT-generated text, but in our tests, they’re inconsistent at best. Several major school systems and colleges, including New York City Public Schools, have banned ChatGPT from their networks and devices. They claim that the AI impedes the learning process by promoting plagiarism and misinformation, a claim that not every educator agrees with. An Australian mayor has publicly announced he may sue OpenAI for defamation due to ChatGPT’s false claims that he had served time in prison for bribery.

When did ChatGPT get released?

Specialised information sets are also prized, as they allow models to be “fine-tuned” for more niche applications. Microsoft’s purchase of GitHub, a repository for software code, for $7.5bn in 2018 helped it develop a code-writing AI tool. You’ve probably seen that generative AI tools (toys?) like ChatGPT can generate endless hours of entertainment. Generative AI tools can produce a wide variety of credible writing in seconds, then respond to criticism to make the writing more fit for purpose. This has implications for a wide variety of industries, from IT and software organizations that can benefit from the instantaneous, largely correct code generated by AI models to organizations in need of marketing copy.

• Walmart is expanding AI efforts in its workplace with a new AI “assistant.” It’s one of many generative AI tools the company has already employed across to its 50,000 corporate employees.
• Leveraging a dataset containing 570 billion individual words, OpenAI’s ChatGPT can compose convincing cover letters on demand, or synthesise a few career details into a competent, bullet-pointed CV.
• Checking readability and writing quality and suggesting improvements are common applications of LLMs.
• Multiple enterprises utilize ChatGPT, although others may limit the use of the AI-powered tool.
• For those unfamiliar with it, ChatGPT is a powerful new artificial intelligence system developed by OpenAI that is capable of engaging in conversations with humans.

The disruption has offered an opportunity for change, so perhaps this beast is a blessing in disguise. The way AI has been portrayed in the HE landscape over the past few months emphasises the perceived usefulness and ease of use of generative AI, such as ChatGPT, predominantly in the context of assessment creation. Most debates are anchored around the issues of academic integrity and are therefore projecting fears of quality assurance. However, AI in the context of assessment and academic integrity is just one small piece in a big puzzle. From a T&L perspective, the purpose of HE is traditionally viewed as supporting students’ development of higher-order thinking skills through knowledge acquisition, knowledge-sharing and, most importantly, knowledge creation. And so earlier this year, Google rushed to release its chatbot Bard amid fears Microsoft’s revamped generative-AI-based Bing web search might eat into its own search engine business.

Introducing ChatGPT

Suddenly, AI becomes a partner for learning, a co-creator that might accelerate insight. For example, we could encourage students to use ChatGPT to create a business model for a new entrepreneurial venture. The business model is evaluated and analysed using a range of tools explored throughout the module. Students apply the outcomes of the evaluation and analysis and consider, for example, the application of strategies relating to sustainable business.

I wrote last month about a prediction that 90% of all online content may be synthetic media within four years. Google has found out the hard way that no matter how much interesting stuff you come up with in a lab, for your own systems or for research, if you don’t openly and obnoxiously productize and market it early on, you’ll be seen as a chaser rather than a leader by pundits. From engineers troubleshooting bugs, to data analysts clustering free-form data, to finance analysts writing tricky spreadsheet formulas—the use cases for ChatGPT Enterprise are plenty. It’s become a true enabler of productivity, with the dependable security and data privacy controls we need.

New use cases are being tested monthly, and new models are likely to be developed in the coming years. As generative AI becomes increasingly, and seamlessly, incorporated into business, society, and our personal lives, we can also expect a new regulatory climate to take shape. As organizations begin experimenting—and creating value—with these tools, leaders will do well to keep a finger on the pulse of regulation and risk. Generative AI outputs are carefully calibrated combinations of the data used to train the algorithms. Because the amount of data used to train these algorithms is so incredibly massive—as noted, GPT-3 was trained on 45 terabytes of text data—the models can appear to be “creative” when producing outputs.

ChatGPT can produce what one commentator called a “solid A-” essay comparing theories of nationalism from Benedict Anderson and Ernest Gellner—in ten seconds. It also produced an already famous passage describing how to remove a peanut butter sandwich from a VCR in the style of the King James Bible. AI-generated art models like DALL-E (its name a mash-up of the surrealist artist Salvador Dalí and the lovable Pixar robot WALL-E) can create strange, beautiful images on demand, like a Raphael painting of a Madonna and child, eating pizza.