Many ChatGPT users have suspected the online tool has a left-wing bias since it was released in November. Now, a thorough scientific study confirms suspicions, revealing it has a'significant and systemic' tendency to return left-leaning responses. ChatGPT's responses favour the Labour Party in the UK, as well as Democrats in the US and Brazil President Lula da Silva of the Workers' Party, it found. Concerns regarding ChatGPT's political bias have already been raised – one professor called it a'woke parrot' after receiving PC responses about'white people'. But this new research is the first largescale study using a'consistent, evidenced-based analysis' – with serious implications for politics and the economy.

Park's team tested 14 different chatbot models by asking them a series of political questions on topics such as immigration, climate change, the role of government and same-sex marriage. The research, released earlier this summer, showed that a series of models developed by Google called Bidirectional Encoder Representations from Transformers, or BERT, were more socially conservative, potentially because they were trained more on books compared to other models that leaned more on internet data and social media comments. Facebook's LLaMA model was slightly more authoritarian and right wing, while OpenAI's GPT-4, its most up-to-date technology, tended to be more economically and socially liberal.

Opera announced today that its Aria AI assistant has made its way to iOS. The feature launched on desktop in June and stems from a partnership with ChatGPT creator OpenAI. Opera says Aria, now available on all major desktop and mobile platforms, has tallied over a million users on desktop and Android. Like Microsoft's Bing Copilot and Google's Search Generative Experience, Aria can answer questions and respond to context around active web pages. "As an expert in both web navigation and browser functions, Aria facilitates AI collaboration in tasks such as information retrieval, text or code generation, and product inquiries," Opera's Kseniia Sycheva wrote in the company's announcement post today.

Viral phenomena have been around for almost as long as the internet has. You might remember the dress that took Tumblr by storm back in 2015 – was it blue and black or white and gold? But using ChatGPT, MailOnline tries to settle the debate, which has seen Twitter users go to great lengths to prove whether there are more doors or wheels in the world. MailOnline spoke to ChatGPT – but the answer may surprise you. The bot produced an autogenerated response, admitting defeat in its first sentence: 'It's difficult to provide an exact answer to this question, as it depends on a variety of factors and can change over time' Even OpenAI's proudest invention couldn't directly solve the query that has taken the internet by storm – and puzzled Twitter since last year. The bot produced an autogenerated response, admitting defeat in its first sentence: 'It's difficult to provide an exact answer to this question, as it depends on a variety of factors and can change over time'.

Despite being publicly available for less than a year, generative AI technology can already be found all around us, helping us browse the internet, taking the drudgery out of computer coding, and even improving the dialog in popular video game franchises. On Wednesday, NCSoft, the South Korean game developer and publisher behind long-running MMORPG Guild Wars, announced that it has developed four new AI large language models, dubbed VARCO, to help streamline future game development. VARCO ("Via AI, Realize your Creativity and Originality," if you squint just right) is both the quartet of language models the company has developed, as well as all of the products and services the company plans to build atop them. Those potential products include, "digital humans, generative AI platforms, and conversational language models," per an NCSoft release. The four models are VARCO the base LLM, as well as Art, Text and Human.

We've collected some of the articles, opinion pieces, videos and resources relating to large language models (LLMs). Some of these links also cover other generative models. We will periodically update this list to add any further resources of interest. This article represents the third in the series.

Chris Winfield, founder of Understanding A.I., tells'Fox & Friends Weekend' host Will Cain about a study showing patients preferred medical answers from artificial intelligence over doctors. Artificial intelligence has been used "behind the scenes" in health care for decades, but with the growing popularity of new technologies such as ChatGPT, it's now playing a bigger role in patient care -- including during routine doctor's visits. Physicians may rely on AI to record conversations, manage documentation and create personalized treatment plans. And that raises the question of whether they must get patients' permission first to use the technology during appointments. "While regulations may vary by jurisdiction, obtaining informed consent for using AI is often considered best practice and aligns with the principles of medical ethics," Dr. Harvey Castro, a Dallas, Texas-based board-certified emergency medicine physician and national speaker on artificial intelligence in health care, told Fox News Digital.

The groundbreaking invention of ChatGPT has triggered enormous discussion among users across all fields and domains. Among celebration around its various advantages, questions have been raised with regards to its correctness and ethics of its use. Efforts are already underway towards capturing user sentiments around it. But it begs the question as to how the research community is analyzing ChatGPT with regards to various aspects of its usage. It is this sentiment of the researchers that we analyze in our work. Since Aspect-Based Sentiment Analysis has usually only been applied on a few datasets, it gives limited success and that too only on short text data. We propose a methodology that uses Explainable AI to facilitate such analysis on research data. Our technique presents valuable insights into extending the state of the art of Aspect-Based Sentiment Analysis on newer datasets, where such analysis is not hampered by the length of the text data.

This work summarizes two strategies for completing time-series (TS) tasks using today's language model (LLM): LLM-for-TS, design and train a fundamental large model for TS data; TS-for-LLM, enable the pre-trained LLM to handle TS data. Considering the insufficient data accumulation, limited resources, and semantic context requirements, this work focuses on TS-for-LLM methods, where we aim to activate LLM's ability for TS data by designing a TS embedding method suitable for LLM. The proposed method is named TEST. It first tokenizes TS, builds an encoder to embed them by instance-wise, feature-wise, and text-prototype-aligned contrast, and then creates prompts to make LLM more open to embeddings, and finally implements TS tasks. Experiments are carried out on TS classification and forecasting tasks using 8 LLMs with different structures and sizes. Although its results cannot significantly outperform the current SOTA models customized for TS tasks, by treating LLM as the pattern machine, it can endow LLM's ability to process TS data without compromising the language ability. This paper is intended to serve as a foundational work that will inspire further research.

This approach is also called autoregressive decoding because each The high computational and memory requirements of generative generated token is also used as input for generating future large language models (LLMs) make it challenging tokens. This dependency between tokens is crucial for many to serve them quickly and cheaply. This paper introduces NLP tasks that require preserving the order and context of the SpecInfer, an LLM serving system that accelerates generative generated tokens, such as text completion [53]. LLM inference with speculative inference and token tree Existing LLM systems generally use an incremental decoding verification. A key insight behind SpecInfer is to combine approach to serving a request where the system computes various collectively boost-tuned small language models to the activations for all prompt tokens in a single step and then jointly predict the LLM's outputs; the predictions are organized iteratively decodes one new token using the input prompt as a token tree, whose nodes each represent a candidate and all previously generated tokens. This approach respects token sequence. The correctness of all candidate token sequences data dependencies between tokens, but achieves suboptimal represented by a token tree is verified against the runtime performance and limited GPU utilization, since the LLM in parallel using a novel tree-based parallel decoding degree of parallelism within each request is greatly limited in mechanism. SpecInfer uses an LLM as a token tree verifier the incremental phase. In addition, the attention mechanism of instead of an incremental decoder, which significantly Transformer [46] requires accessing the keys and values of all reduces the end-to-end latency and computational requirement previous tokens to compute the attention output of a new token.