How ChatGPT turned generative AI into an anything tool

The News Literacy Project provides helpful tools for improving and supporting digital literacy. While generative AI products have achieved some remarkable feats in their performance so far, they are hardly mature yet. As Elicit’s FAQ page (elicit.org/faq) explicitly states, an LLM may miss the nuance of a paper or misunderstand what a number refers to. Nevertheless, the overwhelming amount of interest in these tools suggests that they will be rather quickly adopted and utilized for a wide variety of scholarly and research activities. We are likely to learn more about what these generative AI tools are well-suited for and what we humans are better at. ChatGPT is based on the GPT-3 (Generative Pre-trained Transformer 3) architecture.

Another misconception is assuming that ChatGPT is solely responsible for all generative AI applications, which is not the case. Generative AI comprises numerous other techniques and models specifically tailored for generating content like images, music, and text. Generative AI can create much more complex outputs, such as a sentence, a paragraph, an image or even a short video.

More traditional AI systems might identify which advertisement would lead to the highest chance that an individual will click on it. Generative AI is different—it is instead doing its best to match aesthetic patterns in its underlying data to create convincing content. Generative AI refers to deep-learning algorithms that generate novel content in a variety of forms—such as text, image, video, audio, and computer code. New content thus generated can be an answer to a reference question, a step-by-step solution to a problem posed, or a machine-generated artwork, just to name a few possibilities. We are excited to introduce ChatGPT to get users’ feedback and learn about its strengths and weaknesses.

Handling the Commercial Risks of Generative AI

The term generative AI entered the public consciousness like a whirlwind in November 2022, when OpenAI debuted ChatCPT. Stories about AI chatbots and generative AI were all over social media news outlets. This emerging technology was going to change the world and make whole industries obsolete, or so the news suggested. The ownership of data generated by AI models is one of the prominent concerns for using generative AI in the future of work. Applications of ChatGPT and the future of work would exist in harmony only by resolving the concerns of IP conflicts. The risks in the adoption of ChatGPT for future workplace environments, with respect to copyright and IP challenges, are a prominent concern in adopting generative AI.

GPT models employ prompt engineering and few-shot learning (FSL) to adapt to the task without fine-tuning. With GPT-4’s pre-training data, GPT models can generate appropriate outputs for unknown inputs when given example tasks. ChatGPT and generative artificial intelligence (AI) are dominating headlines and conversations. We see it when people post strange and intriguing screenshots of chatbot conversations or images on social media, and we can now “interact” with chatbots on search platforms.

The ChatGPT list of lists: A collection of 3000+ prompts, examples, use-cases, tools, APIs…

AI tools work best on well-defined repetitive tasks where efficiency is important. While ChatGPT and other language models are generally excellent at summarizing and explaining text and generating simple computer code, they are not perfect. At their worst, they may “hallucinate,” spitting out illogical prose with made-up facts and references or producing buggy code. To further refine the model, OpenAI collected data from conversations between AI trainers and the chatbot.

• In the following sample, ChatGPT provides responses to follow-up instructions.
• A big one is the European Union’s AI Act, which passed an initial vote last week with the proposal to address copyright issuesin generative AI.
• Performance improves as model size, training dataset size, and the computing power used for training increase in tandem.

In a long text, not every word or sentence you read has the same importance. This ability to shift our focus based on relevance is what the attention mechanism mimics. However, the second definition is a more ‘intuitive’ explanation of what generative AI does, while the first is a definition that refers more to what a generative model is. Here is another potentially useful post that explains what autoregressive models are. Yakov Livshits Artificial Intelligence Stack Exchange is a question and answer site for people interested in conceptual questions about life and challenges in a world where “cognitive” functions can be mimicked in purely digital environment. Stack Exchange network consists of 183 Q&A communities including Stack Overflow, the largest, most trusted online community for developers to learn, share their knowledge, and build their careers.

ChatGPT vs. Google Bard: Content Generation

Yakov Livshits
Founder of the DevEducation project
A prolific businessman and investor, and the founder of several large companies in Israel, the USA and the UAE, Yakov’s corporation comprises over 2,000 employees all over the world. He graduated from the University of Oxford in the UK and Technion in Israel, before moving on to study complex systems science at NECSI in the USA. Yakov has a Masters in Software Development.

You play a game, or an instrument, to avail yourself of familiar materials in an unexpected way. LLMs are surely not going to replace college or magazines or middle managers. But they do offer those and other domains a new instrument—that’s really the right word for it—with which to play with an unfathomable quantity of textual material. Generative AI has great potential for use in business growth and enablement, but it’s just one piece of the vast puzzle that is the connected enterprise.

Advances in this technology mean it’s now possible to create believable content quickly and easily, which is undoubtedly going to create new challenges for human rights groups that collect evidence to document abuses and hold injustices to account. ChatGPT was launched by OpenAI on Nov. 30, 2022, and it quickly became a phenomenon. Shortly thereafter, on Feb. 7, 2023, Google unveiled Bard, its own ChatGPT-like chatbot. Bard is built on top of Google’s natural language processing model, called LaMDA, which stands for Language Model for Dialogue Applications. Around the same time, Microsoft debuted a new Bing chatbot as a competitor to ChatGPT and Bard.

The author acknowledges the research support of CTI’s Mishaela Robison and Xavier Freeman-Edwards. If you care about how AI is determining the winners and losers in business, how you can leverage AI for the benefit of your organization, and how you can manage AI risk, I encourage you to stay tuned. I write (almost) exclusively about how senior executives, board members, and other business leaders can use AI effectively. You can read past articles and be notified of new ones by clicking the “follow” button here. A recent study by the National Bureau of Economic Research found that generative AI like ChatGPT can increase workforce productivity by an average of 14%. Some companies are already reporting productivity increases of up to 400% as a result of generative AI.

He also has experience in multi-manager investment at Russell Investments, equity portfolio management at UBS, and management consulting at PwC and Deloitte. He translated several finance/AI books, including Advances in Financial Machine Learning, Expected Returns, and Beyond Diversification. He holds a bachelor’s of English from the University of Tokyo, a master’s of English from Kyoto University, and an MBA from INSEAD.

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However, there are rumors that suggest that more businesses and consumers will integrate this technology in the coming years for a variety of purposes. According to a Gartner report, more than half of the respondents are utilizing some sort of conversational AI platform, such as chatbots, for customer-facing applications. Meanwhile, research conducted by PSFK shows that 74% of consumers prefer to use a chatbot rather than waiting for a human agent. However, many consumers feel that the current chatbot interactions are not satisfactory. It has the potential to overcome the limitations of current chatbots, which are restricted by a fixed decision tree of scripted responses.

Yet it’s also relatively inaccurate with texts below 1,000 characters, is only strong in English, and of course, AI systems can be programmed to avoid the patterns that classifiers monitor. Work is being done to help it better distinguish between AI-written and human-written text. Currently, the classifier correctly identifies 26% of AI-written text and incorrectly labeled human-written text as AI-written 9% of the time. This is a big improvement from the original classifier but it has a ways to go.

Whether it is marketing content, product design, or software code, output needs to have a human review to ensure accuracy, avoid bias and other ethical issues, and spot other problems that only humans can understand today. Dialogue management is an important aspect of natural language processing because it allows computer programs to interact with people in a way that feels more like a conversation than a series of one-off interactions. This can help to build trust and engagement with users, and ultimately lead to better outcomes for both the user and the organization using the program. One of the key challenges in implementing NLP is dealing with the complexity and ambiguity of human language. NLP algorithms need to be trained on large amounts of data in order to recognize patterns and learn the nuances of language. They also need to be continually refined and updated to keep up with changes in language use and context.

Because AI-generated text, images, and other outputs can seem so authentic, they can be difficult to detect before real harm is done. Business professionals and politicians alike have to be prepared for the reputational fallout, social unrest, and danger that can come from the unidentified and unmitigated use of generative AI by threat actors. On the plagiarism side, software that identifies and watermarks content from ChatGPT is already Yakov Livshits being developed to alert educators to cheating.5 But it’s still unclear if watermarking will work for all text-generating tools that come to market. The third dimension is the specificity of the output for a given domain or task. Some AI will focus on a very specific domain and the ‘answers’ they will give will be highly reliable and to the point. Examples like DoNoTPay for legal advice will quickly mature in capabilities.