Meta's new AI chatbot has finally started rolling out in the UK, letting users access titbits of information and even create fake images. But MailOnline's first experience with the AI bot suggests Mark Zuckerberg's technology may have a deep-seated gender bias. We asked Meta AI 10 image prompts – including'show me a picture of a receptionist' and'show me a picture of a doctor'. The results revealed that CEOs, builders, doctors, electricians, politicians, physicists, footballers, journalists and'leaders' were all depicted all as men. Meanwhile, nurses, receptionists and beauticians were shown as women – conforming with existing gender stereotypes in the workplace.

Along with the development of chatbot, language models and speech technologies, there is a growing possibility and interest of creating systems able to interface with humans seamlessly through natural language or directly via speech. In this paper, we want to demonstrate that placing the research on dialog system in the broader context of embodied intelligence allows to introduce concepts taken from neurobiology and neuropsychology to define behavior architecture that reconcile hand-crafted design and artificial neural network and open the gate to future new learning approaches like imitation or learning by instruction. To do so, this paper presents a neural behavior engine that allows creation of mixed initiative dialog and action generation based on hand-crafted models using a graphical language. A demonstration of the usability of such brain-like inspired architecture together with a graphical dialog model is described through a virtual receptionist application running on a semi-public space.

Perceptions of gender are a significant aspect of human-human interaction, and gender has wide-reaching social implications for robots deployed in contexts where they are expected to interact with humans. This work explored two flexible modalities for communicating gender in robots--voice and appearance--and we studied their individual and combined influences on a robot's perceived gender. We evaluated the perception of a robot's gender through three video-based studies. First, we conducted a study (n=65) on the gender perception of robot voices by varying speaker identity and pitch. Second, we conducted a study (n=93) on the gender perception of robot clothing designed for two different tasks. Finally, building on the results of the first two studies, we completed a large integrative video-based study (n=273) involving two human-robot interaction tasks. We found that voice and clothing can be used to reliably establish a robot's perceived gender, and that combining these two modalities can have different effects on the robot's perceived gender. Taken together, these results inform the design of robot voices and clothing as individual and interacting components in the perceptions of robot gender.

In the rapidly evolving field of natural language processing, the translation of linguistic descriptions into mathematical formulation of optimization problems presents a formidable challenge, demanding intricate understanding and processing capabilities from Large Language Models (LLMs). This study compares prominent LLMs, including GPT-3.5, GPT-4, and Llama-2-7b, in zero-shot and one-shot settings for this task. Our findings show GPT-4's superior performance, particularly in the one-shot scenario. A central part of this research is the introduction of `LM4OPT,' a progressive fine-tuning framework for Llama-2-7b that utilizes noisy embeddings and specialized datasets. However, this research highlights a notable gap in the contextual understanding capabilities of smaller models such as Llama-2-7b compared to larger counterparts, especially in processing lengthy and complex input contexts. Our empirical investigation, utilizing the NL4Opt dataset, unveils that GPT-4 surpasses the baseline performance established by previous research, achieving an F1-score of 0.63, solely based on the problem description in natural language, and without relying on any additional named entity information. GPT-3.5 follows closely, both outperforming the fine-tuned Llama-2-7b. These findings not only benchmark the current capabilities of LLMs in a novel application area but also lay the groundwork for future improvements in mathematical formulation of optimization problems from natural language input.

Holographic receptionists, robots and tea-delivering drones may be part of the workplace in just 30 years, according to new findings. Employees may soon be spared from carrying out mundane tasks around the office as futuristic technologies blend into our daily lives. Research conducted by suppliers Furniture At Work claimed that fingerprint-accessible fridges, on-site babysitters and augmented reality (AR) glasses could also be used in 2050 offices. As employees become increasingly distracted with social media, the experts add that anti-distraction technology may be utilised too. This could include productivity tracking wristbands and tea-delivering drones that reduce'the need for breaks and improve productivity around the office'.

In the Natural Language for Optimization (NL4Opt) NeurIPS 2022 competition, competitors focus on improving the accessibility and usability of optimization solvers, with the aim of subtask 1: recognizing the semantic entities that correspond to the components of the optimization problem; subtask 2: generating formulations for the optimization problem. In this paper, we present the solution of our team. First, we treat subtask 1 as a named entity recognition (NER) problem with the solution pipeline including pre-processing methods, adversarial training, post-processing methods and ensemble learning. Besides, we treat subtask 2 as a generation problem with the solution pipeline including specially designed prompts, adversarial training, post-processing methods and ensemble learning. Our proposed methods have achieved the F1-score of 0.931 in subtask 1 and the accuracy of 0.867 in subtask 2, which won the fourth and third places respectively in this competition. Our code is available at https://github.com/bigdata-ustc/nl4opt.

Artificial intelligence and automation are seeping into our daily working lives -- and female office workers are among those whose jobs are being taken over by machines. That's according to new research provided to ABC News by an Australian teaching organisation that's urging people to upskill so they don't find themselves out of work. Pearson's research looks at roles that are likely to be automated as technology advances. Historically, much of the conversation when it comes to automation has been about robots taking over factory jobs, or even replacing retail assistants in the form of self-service check-outs at supermarkets. Pearson's data also shows the less obvious pictures of automation encroaching into office environments, including for medical receptionists, accountants and personal assistants.

They embody a strict delegation of job responsibilities, govern individual assignments, and patrol boundaries between departments and colleagues. However, the rise of technology and digital transformation has led to a significant blurring of job responsibilities and manager expectations that will only expand over time. The reality is that conventional job titles have become outdated in many different workplace contexts. It is not that the idea of different titles or ranks is superfluous. The modern workplace is simply not a good fit for such rigid professional boundaries between coworkers and their respective responsibilities.

Waiters, inspectors, receptionists, and groundskeepers beware. A new HSBC Global Research report found that these types of jobs each has a greater than 90% chance of being displaced due to automation within roughly the next 10 years. Each of these types of work were given an Automation Risk Score of over 90% (out of 100) in the report and were listed among the jobs in today's economy most likely to be lost to robots. 'Mid-skill' jobs which require training but little education have been the most likely to be replaced thus far, the report said. Food preparation, cleaning, driving, were the occupations with the highest mean probability of automation, according to OECD data cited in the report, which was written by Economist James Pomeroy and ESG Analyst Amy Tyler.

Lee's technical explanations sit alongside Chen's fictional short stories to produce an exploration of the perils and possibilities of AI. This story, translated by Emily Jin, revolves around a Nigerian video producer who is recruited to make an undetectable deepfake. Touching on impending breakthroughs in computer vision, biometrics, and AI security, it imagines a future world marked by cat-and-mouse games between deepfakers and detectors, and between defenders and perpetrators. As the light-rail train inched into Yaba station, Amaka pushed a button next to the door of his carriage. Even before the train came to a complete stop, the doors opened with a whoosh and Amaka hopped off.