We've all heard them: 24 carat gold, 100% Florida orange juice, 100% all natural, 100% made in the USA. Much of what we consume--from food to data--is qualified in some way to help us gain insights into what we're consuming. Sometimes, it's directly related to things like ingredients and other times, it's more about the social and political implications. But the rise of machine learning and natural language processing has led to the development of advanced language models such as GPT (Generative Pre-trained Transformer) and raised important questions about creativity and ownership, to name just a few. These models are amazing and are capable of generating human-like text, making it difficult to distinguish between text written by a human and text generated by a machine.

This article is from Big Technology, a newsletter by Alex Kantrowitz. Last weekend, a new Substack called the Rationalist lifted analysis and writing directly from my own newsletter on the platform, Big Technology. Its plagiarized post on the "Creator Economy"--which I'd covered only days prior--went viral, hitting the front page of Hacker News and sparking a conversation with more than 80 comments. It would've been a terrific debut for any publication, if it was authentic. What made the case of the Rationalist particularly striking, though, was its author--an avatar by the name of "Petra"--admitted they'd used A.I. tools to produce the story, including those from OpenAI, Jasper, and Hugging Face.

Since the release of OpenAI's ChatGPT, large language models (LLM), neural networks trained on enormous text corpora, and other sorts of data have gained much attention in the artificial intelligence industry. On the one hand, huge language models are capable of amazing feats, producing lengthy texts that are mostly coherent and giving the appearance that they have mastered both human language and its fundamental abilities. On the other hand, several experiments demonstrate that LLMs are simply repeating their training data and only displaying impressive results due to their extensive text exposure. They fail as soon as they are given tasks or problems that call for reasoning, common sense, or implicitly learned skills. ChatGPT frequently needs help to figure out straightforward math issues.

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Since its launch in November last year, ChatGPT has become an extraordinary hit. Essentially a souped-up chatbot, the AI program can churn out answers to the biggest and smallest questions in life, and draw up college essays, fictional stories, haikus, and even job application letters. It does this by drawing on what it has gleaned from a staggering amount of text on the internet, with careful guidance from human experts. Ask ChatGPT a question, as millions have in recent weeks, and it will do its best to respond – unless it knows it cannot. The answers are confident and fluently written, even if they are sometimes spectacularly wrong.

OpenAI is led by Sam Altman, who became well known in Silicon Valley as the head the start-up builder Y Combinator. Mr. Altman, 37, and his co-founders created OpenAI in 2015 as a nonprofit. But he soon remade the venture as a for-profit company that could more aggressively pursue financing. A year later, Microsoft invested $1 billion in the company and committed to building the supercomputer technologies OpenAI's enormous models would demand while becoming its "preferred partner for commercializing" its technologies. OpenAI later officially licensed its technologies to Microsoft, allowing the company to directly add them to Microsoft products and services.

Blockchain, also coined as decentralized AI, has the potential to empower AI to be more trustworthy by creating a decentralized trust of privacy, security, and audibility. However, systematic studies on the design principle of blockchain as a trust engine for an integrated society of cyber-physical-social-system (CPSS) are still absent. In this article, we provide an initiative for seeking the design principle of blockchain for a better digital world. Using a hybrid method of qualitative and quantitative studies, we examine the past origin, the current development, and the future directions of blockchain design principles. We have three findings. First, the answer to whether blockchain lives up to its original design principle as a distributed database is controversial. Second, the current development of the blockchain community reveals a taxonomy of 7 categories, namely, privacy and security, scalability, decentralization, applicability, governance and regulation, system design, and cross-chain interoperability. Both research and practice are more centered around the first category of privacy and security and the fourth category of applicability. Future scholars, practitioners, and policy-makers have vast opportunities in other, much less exploited facets and the synthesis at the interface of multiple aspects. Finally, in counter-examples, we conclude that a synthetic solution that crosses discipline boundaries is necessary to close the gaps between the current design of blockchain and the design principle of a trust engine for a truly intelligent world.

Multilingual pre-trained models are able to zero-shot transfer knowledge from rich-resource to low-resource languages in machine reading comprehension (MRC). However, inherent linguistic discrepancies in different languages could make answer spans predicted by zero-shot transfer violate syntactic constraints of the target language. In this paper, we propose a novel multilingual MRC framework equipped with a Siamese Semantic Disentanglement Model (SSDM) to disassociate semantics from syntax in representations learned by multilingual pre-trained models. To explicitly transfer only semantic knowledge to the target language, we propose two groups of losses tailored for semantic and syntactic encoding and disentanglement. Experimental results on three multilingual MRC datasets (i.e., XQuAD, MLQA, and TyDi QA) demonstrate the effectiveness of our proposed approach over models based on mBERT and XLM-100. Code is available at:https://github.com/wulinjuan/SSDM_MRC.

Check out all the on-demand sessions from the Intelligent Security Summit here. Got It AI said it has developed AI to identify and address ChatGPT "hallucinations" for enterprise applications. ChatGPT has taken the tech world by storm by showing the capabilities of generative AI, which can enable ordinary folks to prompt AI to generate a wide variety of things, from computer programs to original songs. Some of those creations are remarkable. But the bad thing about ChatGPT is its error rate.

Chinchilla AI is yet another example of AI language model, claimed to outperform GPT-3. The engine behind the ChatGPT is outperformed by DeepMind's new language model. The news spread rapidly, and soon everyone wondered: "What is Chinchilla AI?" Are you one of them? You came to the right place. As always, we continue to share with you the latest trends in the AI world.