Owing to the high cost and time associated with developing and validating anti-cancer drugs in clinical trials which is further exacerbated by the 96% failure rate, the development of preclinical computational models that can accurately predict whether a cell line is sensitive or resistant to a particular drug is imperative. The availability of large-scale pharmacogenomics datasets collected via high-throughput screening technologies offers feasible resources to develop robust drug response models and identify the important biomarkers predictive of drug sensitivity. Large language models (LLM), such as the Generative Pre-trained Transformer (GPT-3) from OpenAI, are "taskagnostic models" pre-trained on large textual corpora crawled from the Web that have exhibited unprecedented capabilities on a broad array of NLP tasks.

People around the world are turning to machines as a new religion. Six-foot robot priests are delivering sermons and conducting funerals, AI is writing Bible verses and ChatGPT is being consulted as if it was an oracle. Some religious organizations, like the Turing Church founded in 2011, are based on the notion that AI will put human beings on a par with God-like aliens by giving them super intelligence. An expert in human-computer interaction told DailyMail.com The personalized, intelligent-seeming responses offered by bots, such as ChatGPT, are also luring people to seek meaning from the technology, Lars Holmquist, a professor of design and innovation at Nottingham Trent University, told DailyMail.com.

OpenAI, the maker of ChatGPT, has quietly changed its rules and removed a ban on using the chatbot and its other AI tools for military purposes - and revealed that it is already working with the Department of Defense. Experts have previously voiced fears that AI could escalate conflicts around the world thanks to'slaughterbots' which can kill without any human intervention. The rule change, which occurred after Wednesday last week, removed a sentence which said that the company would not permit usage of models for'activity that has high risk of physical harm, including: weapons development, military and warfare.' The spokesman said: 'Our policy does not allow our tools to be used to harm people, develop weapons, for communications surveillance, or to injure others or destroy property. 'There are, however, national security use cases that align with our mission.

Will artificial intelligence take our jobs? If you listen to Silicon Valley executives talking about the capabilities of today's cutting edge AI systems, you might think the answer is "yes, and soon." But a new paper published by MIT researchers suggests automation in the workforce might happen slower than you think. The researchers at MIT's computer science and artificial intelligence laboratory studied not only whether AI was able to perform a task, but also whether it made economic sense for firms to replace humans performing those tasks in the wider context of the labor market. They found that while computer vision AI is today capable of automating tasks that account for 1.6% of worker wages in the U.S. economy (excluding agriculture), only 23% of those wages (0.4% of the economy as a whole) would, at today's costs, be cheaper for firms to automate instead of paying human workers.

OpenAI has removed the account of the developer behind an artificial intelligence-powered bot impersonating the US presidential candidate Dean Phillips, saying it violated company policy. Phillips, who is challenging Joe Biden for the Democratic party candidacy, was impersonated by a ChatGPT-powered bot on the dean.bot The bot was backed by Silicon Valley entrepreneurs Matt Krisiloff and Jed Somers, who have started a Super Pac – a body that funds and supports political candidates – named We Deserve Better, supporting Phillips. San Francisco-based OpenAI said it had removed a developer account that violated its policies on political campaigning and impersonation. "We recently removed a developer account that was knowingly violating our API usage policies which disallow political campaigning, or impersonating an individual without consent," said the company.

Hosted by Eleanor Drage and Kerry Mackereth, The Good Robot is a podcast which explores the many complex intersections between gender, feminism and technology. In this episode, we talked to Rebecca Woods, a Senior Lecturer in Language and Cognition at Newcastle University. We have an amazing chat about language learning in AI, and she tells us how language is crucial to how GPT functions. She's also an expert in how children learn languages, and she compares this to teaching AI how to process language. Rebecca is a Lecturer in Language and Cognition in the School of English Language, Literature and Linguistics at Newcastle University.

Detecting text generated by modern large language models is thought to be hard, as both LLMs and humans can exhibit a wide range of complex behaviors. However, we find that a score based on contrasting two closely related language models is highly accurate at separating human-generated and machine-generated text. Based on this mechanism, we propose a novel LLM detector that only requires simple calculations using a pair of pre-trained LLMs. The method, called Binoculars, achieves state-of-the-art accuracy without any training data. It is capable of spotting machine text from a range of modern LLMs without any model-specific modifications. We comprehensively evaluate Binoculars on a number of text sources and in varied situations. Over a wide range of document types, Binoculars detects over 90% of generated samples from ChatGPT (and other LLMs) at a false positive rate of 0.01%, despite not being trained on any ChatGPT data.

Out-of-context (OOC) detection is a challenging task involving identifying images and texts that are irrelevant to the context in which they are presented. Large vision-language models (LVLMs) are effective at various tasks, including image classification and text generation. However, the extent of their proficiency in multimodal OOC detection tasks is unclear. In this paper, we investigate the ability of LVLMs to detect multimodal OOC and show that these models cannot achieve high accuracy on OOC detection tasks without fine-tuning. However, we demonstrate that fine-tuning LVLMs on multimodal OOC datasets can further improve their OOC detection accuracy. To evaluate the performance of LVLMs on OOC detection tasks, we fine-tune MiniGPT-4 on the NewsCLIPpings dataset, a large dataset of multimodal OOC. Our results show that fine-tuning MiniGPT-4 on the NewsCLIPpings dataset significantly improves the OOC detection accuracy in this dataset. This suggests that fine-tuning can significantly improve the performance of LVLMs on OOC detection tasks.

When we design and deploy an Reinforcement Learning (RL) agent, reward functions motivates agents to achieve an objective. An incorrect or incomplete specification of the objective can result in behavior that does not align with human values - failing to adhere with social and moral norms that are ambiguous and context dependent, and cause undesired outcomes such as negative side effects and exploration that is unsafe. Previous work have manually defined reward functions to avoid negative side effects, use human oversight for safe exploration, or use foundation models as planning tools. This work studies the ability of leveraging Large Language Models (LLM)' understanding of morality and social norms on safe exploration augmented RL methods. This work evaluates language model's result against human feedbacks and demonstrates language model's capability as direct reward signals.

Zero-shot translation is an open problem, aiming to translate between language pairs unseen during training in Multilingual Machine Translation (MMT). A common, albeit resource-consuming, solution is to mine as many translation directions as possible to add to the parallel corpus. In this paper, we show that the zero-shot capability of an English-centric model can be easily enhanced by fine-tuning with a very small amount of multi-parallel data. For example, on the EC30 dataset, we show that up to +21.7 ChrF non-English overall improvements (870 directions) can be achieved by using only 100 multi-parallel samples, meanwhile preserving capability in English-centric directions. We further study the size effect of fine-tuning data and its transfer capabilities. Surprisingly, our empirical analysis shows that comparable overall improvements can be achieved even through fine-tuning in a small, randomly sampled direction set (10\%). Also, the resulting non-English performance is quite close to the upper bound (complete translation). Due to its high efficiency and practicality, we encourage the community 1) to consider the use of the fine-tuning method as a strong baseline for zero-shot translation and 2) to construct more comprehensive and high-quality multi-parallel data to cover real-world demand.