Holistic AI’s Post

The recent developments in the field of Artificial Intelligence have generated a lot of excitement and uncertainty. Generative AI and, in particular, large language model (LLM) chatbots that can be used to easily create original content are widely debated. Here, we take a closer look at LLMs and the ethical considerations of AI in academia. Find out more: https://brnw.ch/21wGMRT #largelanguagemodel #openscience #openaccess

Large language models have transformed AI, but are they really "people"? A recent MIT Technology Review article challenges the way we test and evaluate these models. Let's explore why it's time to rethink our approach to AI assessment. #AI #LanguageModels #Ethics #ArtificialIntelligence #TechTrends

Large language models have transformed AI, but are they really "people"? A recent MIT Technology Review article challenges the way we test and evaluate these models. Let's explore why it's time to rethink our approach to AI assessment. #AI #LanguageModels #Ethics #ArtificialIntelligence #TechTrends

Excited to share a recent ArXiv paper titled "CHAIN-OF-VERIFICATION REDUCES HALLUCINATION IN LARGE LANGUAGE MODELS." This innovative research offers a new approach to improve factual accuracy in AI-generated content. Check it out here: https://lnkd.in/dqnfw6GU #AI #Research #FactualAccuracy #llms

Demystifying AI Ethics: Detecting and Mitigating Bias in Language Models. Explore intricate methodologies for responsible AI, navigating bias detection complexities. https://lnkd.in/gHJC8n7Z Discover statistical fairness metrics, adversarial learning, and human-in-the-loop strategies shaping ethical AI. Embrace continual monitoring frameworks and fairness-aware training for robust mitigation. Uncover the importance of explainable AI in ensuring transparency and accountability. Join the journey towards fair and inclusive language models, crucial for equitable AI. #generativeai #largelanguagemodels #artificialintelligence

"Hallucinations in AI: The Silent Threat to Reliability" Just read a fascinating article in Nature about detecting hallucinations in large language models. As AI becomes more integrated into our daily lives, the risk of these systems generating false or unsubstantiated information is a growing concern. The researchers have developed a groundbreaking method using semantic entropy to identify when an AI might be "confabulating" or making things up. This could be a game-changer for industries relying on AI-generated content, from legal to medical fields. What are your thoughts on AI reliability? How can we balance innovation with the need for accuracy? Read more: https://lnkd.in/dCb-YXmz #AI #TechInnovation #DataScience #FutureOfTechnology <a href="https://lnkd.in/dCb-YXmz">Read the full article here</a>

I never liked the term "hallucinations" to describe instances where AI provides incorrect information yet sounds convincing. The term anthropomorphizes AI, making it sound human and the mistakes less serious. If I used a handheld calculator to perform a calculation and it provided incorrect information, I wouldn't say my calculator is hallucinating; I would say it's broken. A new term, "confabulations," seems to be a better descriptor for the errors AI programs generate. However, this term implies that AI makes mistakes by filling in gaps in its knowledge with false information. While this may sometimes be true (see the article below), it's not always the case. Despite this, I prefer "confabulations" over "hallucinations." What do you think? https://lnkd.in/g9pDQ8Ms

Should we delegate important tasks to AI? A new benchmark study reveals behavior gaps in large language models. https://lnkd.in/gxHCSu5i

Large language models have transformed AI, but are they really "people"? A recent MIT Technology Review article challenges the way we test and evaluate these models. Let's explore why it's time to rethink our approach to AI assessment. #AI #LanguageModels #Ethics #ArtificialIntelligence #TechTrends

With a background in Trust & Safety, I feel compelled to offer some insight/criticism into the framing of this research 💡 Within the industry, we manage technology through what are known as 'product policies'. Our aim is to make these policies as applicable, comprehensive, and benign as possible. I believe that to conduct meaningful research, the focus should extend beyond the mere politicization of products. Instead, a profound understanding of the underlying product policies is essential. Why? Because the fabric of our policies is often intertwined with inherently political issues. Human rights are political. Gender equality is political. In truth, no technology product can exist in a non-political vacuum. Nonetheless, the research is quite interesting: Recent research from the University of Washington, Carnegie Mellon University, and Xi’an Jiaotong University has unveiled not just the varying political biases in AI language models, but also the depth and implications of these biases. Some fascinating findings: 🔹 While OpenAI's ChatGPT and GPT-4 lean left-wing libertarian, Meta's LLaMA stands out as right-wing authoritarian. 🔹 When probed on topics like feminism and democracy, the responses given by these AIs were plotted on a political compass, revealing their underlying stances. 🔹 Intriguingly, Google’s BERT models, which were trained on traditional books, exhibited more social conservatism compared to OpenAI’s GPT models that leaned on liberal internet texts. 🔹 Models are malleable: Reinforcing training data deepens existing biases. For instance, GPT-2 showed support for “taxing the rich,” but its successor GPT-3 didn’t. The consequences? Potential misinformation, skewed viewpoints, and genuine harm. As AI impacts our daily lives more, ensuring responsible development and deployment is no longer optional but imperative. Tech giants, it's time to step up and ensure transparent, unbiased AI! Let's innovate responsibly. 💡🌐 #AI #AccountabilityInTech #OpenAI #Meta #InnovationResponsibly