Large language models (LLMs) have revolutionized the field of artificial intelligence, enabling natural language processing tasks that were previously thought to be exclusive to humans. In this work, we introduce Qwen, the first installment of our large language model series. Qwen is a comprehensive language model series that encompasses distinct models with varying parameter counts. It includes Qwen, the base pretrained language models, and Qwen-Chat, the chat models finetuned with human alignment techniques. The base language models consistently demonstrate superior performance across a multitude of downstream tasks, and the chat models, particularly those trained using Reinforcement Learning from Human Feedback (RLHF), are highly competitive. The chat models possess advanced tool-use and planning capabilities for creating agent applications, showcasing impressive performance even when compared to bigger models on complex tasks like utilizing a code interpreter. Furthermore, we have developed coding-specialized models, Code-Qwen and Code-Qwen-Chat, as well as mathematics-focused models, Math-Qwen-Chat, which are built upon base language models. These models demonstrate significantly improved performance in comparison with open-source models, and slightly fall behind the proprietary models.

The collaboration between humans and artificial intelligence (AI) is a significant feature in this digital age. However, humans and AI may have observation, interpretation, and action conflicts when working synchronously. This phenomenon is often masked by faults and, unfortunately, overlooked. This paper systematically introduces the human-AI conflict concept, causes, measurement methods, and risk assessment. The results highlight that there is a potential second decision-maker besides the human, which is the AI; the human-AI conflict is a unique and emerging risk in digitalized process systems; and this is an interdisciplinary field that needs to be distinguished from traditional fault and failure analysis; the conflict risk is significant and cannot be ignored. Keywords: human-AI conflict, risk, digitization, automation. 1. Introduction Automation, digitization, and artificial intelligence (AI) have become the trends in the development of industrial history (Pistikopoulos et al., 2021).

A robotic drone that can travel through air and water, and also attach itself to larger objects with a suction cup, could be useful for tagging wild animals, say its creators. The suction cup is inspired by the remora fish, which attaches itself to larger marine creatures in a symbiotic relationship where the remora eats parasites that would irritate its host and also gets a ride in return. "My original thought was'let's find a point where we can beat nature'," says Li Wen at Beihang University in Beijing. "Let's do a robot that can not only swim and stick underwater, but also can fly into the air and stick in the air. I don't think there are any animals that can do this."

Wen Li, a Shanghai marketer in the hospitality industry, first suspected that an algorithm was messing with her when she and a friend used the same ride-hailing app one evening. Wen's friend, who less frequently ordered rides in luxury cars, saw a lower price for the same ride. Wen blamed the company's algorithms, saying they wanted to squeeze more money from her. Chinese ride-hailing companies say prices vary because of fluctuations in traffic. But some studies and news reports claim the apps may offer different prices based on factors including ride history and the phone a person is using. "I mean, come on--just admit you are an internet company and this is what you do to make extra profit," Wen says.

Covariate shift is a fundamental problem for learning in non-stationary environments where the conditional distribution p(y x) is the same between training and test data while their marginal distributions ptr(x) and pte(x) are different. Although many covariate shift correction techniques remain effective for real world problems, most do not scale well in practice. In this paper, using inspiration from recent optimization techniques, we apply the Frank-Wolfe algorithm to two well-known covariate shift correction techniques, Kernel Mean Matching (KMM) and Kullback-Leibler Importance Estimation Procedure (KLIEP), and identify an important connection between kernel herding and KMM. Our complexity analysis shows the benefits of the Frank-Wolfe approach over projected gradient methods in solving KMM and KLIEP. An empirical study then demonstrates the effectiveness and efficiency of the Frank-Wolfe algorithm for correcting covariate shift in practice.

Artificial intelligence has drawn scrutiny for perpetuating the biases of the mostly white tech guys developing it. Much of the criticism has swirled around the facial recognition algorithms used in surveillance technology, shown to have higher error rates for women and BIPOC, per the ACLU, increasing their risk of wrongful arrest and police violence. Now, a new analysis reveals an insidious way that AI can widen racial health disparities, too. Researchers found that the datasets used to train AI programs to detect skin cancer includes hardly any images of dark skin, according to a National Cancer Research Institute press release. Simply put, this technology is being optimized for light skin.

In a low-rise building overlooking a busy intersection in Beijing, Ji Rong Wen, a middle-aged scientist with thin-rimmed glasses and a mop of black hair, excitedly describes a project that could advance one of the hottest areas of artificial intelligence. Wen leads a team at the Beijing Academy of Artificial Intelligence (BAAI), a government-sponsored research lab that's testing a powerful new language algorithm--something similar to GPT-3, a program revealed in June by researchers at OpenAI that digests large amounts of text and can generate remarkably coherent, free-flowing language. "This is a big project," Wen says with a big grin. "It takes a lot of computing infrastructure and money." Wen, a professor at Renmin University in Beijing recruited to work part-time at BAAI, hopes to create an algorithm that is even cleverer than GPT-3. He plans to combine machine learning with databases of facts, and to feed the algorithm images and video as well as text, in hope of creating a richer understanding of the physical world--that the words cat and fur don't just often appear in the same sentence, but are associated with one another visually.

"In this environment, it's more important than ever that we have as many methods as possible to reach people where they are for health care and education," said Dr. Leana Wen, president of Planned Parenthood Federation of America. "The chatbot is one more critical tool we're piloting to provide that information to people." For years, conservative lawmakers have sought to defund Planned Parenthood, which works to offer low-cost or free health services to women through grants and government-supported health plans like Medicare and Medicaid. Roo, as the chatbot is called, is meant to extend the organization's reach by providing information to young people who don't have access to sex education or only have access to programs that teach abstinence, said Dr. Wen. Planned Parenthood funded the initiative through a private grant, as well as a sizable investment from Work & Co., a design and technology company that has worked with the organization for more than 14 months to plan and develop the product.

Horizons Ventures, the VC firm founded by Hong Kong's richest man Li Ka-Shing, has made a rare early-stage investment after it backed AI startup Fano Labs. Horizons has invested in the likes of Facebook, Razer, Slack, Improbable, Spotify and more, and now it is putting undisclosed money into Fano Labs, which recently graduated AI accelerator program Zeroth. This deal also marks the firm's first investment in a Hong Kong-based company. Founded by academics, Fano Labs uses speech recognition and natural language processing to help out at call centers. No, the robots are taking those jobs (yet) but they are helping call centers themselves to run more efficiently.

WASHINGTON: Scientists have developed a new artificial intelligence system that can decode the human mind, and interpret what a person is seeing by analysing brain scans. The advance could aid efforts to improve artificial intelligence (AI) and lead to new insights into brain function. Critical to the research is a type of algorithm called a convolutional neural network, which has been instrumental in enabling computers and smartphones to recognise faces and objects. "That type of network has made an enormous impact in the field of computer vision in recent years," said Zhongming Liu, an assistant professor at Purdue University in the US. "Our technique uses the neural network to understand what you are seeing," Liu said.