CyberGuy shows you how to create and customize events in the calendar app. When the pandemic hit and so many aspects of our lives went digital, older adults had to get accustomed to using more technology like Facetime, Zoom and more. Now, older adults have become a lot more tech-savvy and even have their favorite devices that they enjoy using. Here are five tech obsessions that older adults have adopted over the last few years. Perhaps the most popular devices among older adults are ones like Apple Watches, FitBits and other products that help people keep track of their health.

In recent years, pretrained models have been widely used in various fields, including natural language understanding, computer vision, and natural language generation. However, the performance of these language generation models is highly dependent on the model size and the dataset size. While larger models excel in some aspects, they cannot learn up-to-date knowledge and are relatively difficult to relearn. In this paper, we introduce EvoText, a novel training method that enhances the performance of any natural language generation model without requiring additional datasets during the entire training process (although a prior dataset is necessary for pretraining). EvoText employs two models: $G$, a text generation model, and $D$, a model that can determine whether the data generated by $G$ is legitimate. Initially, the fine-tuned $D$ model serves as the knowledge base. The text generated by $G$ is then input to $D$ to determine whether it is legitimate. Finally, $G$ is fine-tuned based on $D$'s output. EvoText enables the model to learn up-to-date knowledge through a self-escalation process that builds on a priori knowledge. When EvoText needs to learn something new, it simply fine-tunes the $D$ model. Our approach applies to autoregressive language modeling for all Transformer classes. With EvoText, eight models achieved stable improvements in seven natural language processing tasks without any changes to the model structure.

In this chapter we provide an overview of computational modeling for semantic change using large and semi-large textual corpora. We aim to provide a key for the interpretation of relevant methods and evaluation techniques, and also provide insights into important aspects of the computational study of semantic change. We discuss the pros and cons of different classes of models with respect to the properties of the data from which one wishes to model semantic change, and which avenues are available to evaluate the results.

Artificial intelligence (AI) researchers have been developing and refining large language models (LLMs) that exhibit remarkable capabilities across a variety of domains and tasks, challenging our understanding of learning and cognition. The latest model developed by OpenAI, GPT-4, was trained using an unprecedented scale of compute and data. In this paper, we report on our investigation of an early version of GPT-4, when it was still in active development by OpenAI. We contend that (this early version of) GPT-4 is part of a new cohort of LLMs (along with ChatGPT and Google's PaLM for example) that exhibit more general intelligence than previous AI models. We discuss the rising capabilities and implications of these models. We demonstrate that, beyond its mastery of language, GPT-4 can solve novel and difficult tasks that span mathematics, coding, vision, medicine, law, psychology and more, without needing any special prompting. Moreover, in all of these tasks, GPT-4's performance is strikingly close to human-level performance, and often vastly surpasses prior models such as ChatGPT. Given the breadth and depth of GPT-4's capabilities, we believe that it could reasonably be viewed as an early (yet still incomplete) version of an artificial general intelligence (AGI) system. In our exploration of GPT-4, we put special emphasis on discovering its limitations, and we discuss the challenges ahead for advancing towards deeper and more comprehensive versions of AGI, including the possible need for pursuing a new paradigm that moves beyond next-word prediction. We conclude with reflections on societal influences of the recent technological leap and future research directions.

Machine learning (ML) enabled classification models are becoming increasingly popular for tackling the sheer volume and speed of online misinformation and other content that could be identified as harmful. In building these models, data scientists need to take a stance on the legitimacy, authoritativeness and objectivity of the sources of ``truth" used for model training and testing. This has political, ethical and epistemic implications which are rarely addressed in technical papers. Despite (and due to) their reported high accuracy and performance, ML-driven moderation systems have the potential to shape online public debate and create downstream negative impacts such as undue censorship and the reinforcing of false beliefs. Using collaborative ethnography and theoretical insights from social studies of science and expertise, we offer a critical analysis of the process of building ML models for (mis)information classification: we identify a series of algorithmic contingencies--key moments during model development that could lead to different future outcomes, uncertainty and harmful effects as these tools are deployed by social media platforms. We conclude by offering a tentative path toward reflexive and responsible development of ML tools for moderating misinformation and other harmful content online.

Self-supervision methods learn representations by solving pretext tasks that do not require human-generated labels, alleviating the need for time-consuming annotations. These methods have been applied in computer vision, natural language processing, environmental sound analysis, and recently in music information retrieval, e.g. for pitch estimation. Particularly in the context of music, there are few insights about the fragility of these models regarding different distributions of data, and how they could be mitigated. In this paper, we explore these questions by dissecting a self-supervised model for pitch estimation adapted for tempo estimation via rigorous experimentation with synthetic data. Specifically, we study the relationship between the input representation and data distribution for self-supervised tempo estimation.

At a recent White House press conference, a Fox News correspondent asked the Biden administration's press secretary about AI safety researcher Eliezer Yudkowsky's highly publicized claim that if we don't pause or halt the development of artificial intelligence, then "literally everyone on earth will die." The question was met with some laughter from the White House press corps. But as someone with a technical background who covers AI and talks regularly to researchers, developers, and investors in the field, I saw nothing to chuckle at. Rather, I and other more optimistic AI watchers worry that overly dire warnings of imminent AI-driven destruction may cause us to pause or halt the development of a powerful technology with immense potential for improving our lives. Insiders hold a truly wide range of opinions on the best way to approach AI--from Yudkowsky's insistence that we immediately abandon all research in the area, to my own more moderate concern about large-scale industrial accidents arising from misuse of the technology, to an extreme optimism in some quarters about AI's potential to turn humanity into an immortal, star-spanning species.

In February, BuzzFeed's leadership announced that the company's storied quiz operation was pivoting to A.I. OpenAI's generative language tool ChatGPT has proven to be effective at regurgitating hackneyed cultural motifs back at its users, which makes it perfect for the platitudinal terrain of BuzzFeed quizzes. The company has gone all-in on the new revolution by adopting a text synthesis program modeled on ChatGPT's technology, tiling the website with uncanny questionnaires--all scented with the trademark unspecificity of machine learning--and published under the byline "Buzzy the Robot." Buzzy is listed on the masthead as an A.I. Creative Assistant, and I suspect that he's not a member of the union. "What If You Were A Disney Princess? This Quiz Will Answer That Question," reads one of the characteristically mangled headlines written by Buzzy.

Climate change, clean energy and sustainability, building low emissions technologies and developing ethical artificial intelligence are some of the challenges being tackled by CSIRO, Australia's national science agency, and the United States National Science Foundation (NSF) under a multi-million-dollar partnership. The recently established partnership between the two leading science organisations is aiming to accelerate joint research and initiatives in areas of mutual priority between Australia and the United States. CSIRO Chief Executive Larry Marshall said the two leading science organisations have already enabled a number of opportunities across the two countries in only a year, launching this month an AUD$100 million Global Centers initiative, partnering in the areas of responsible and ethical Artificial Intelligence (AI) and developing sustainable materials for global challenges. "As national science agencies, CSIRO and the NSF are working together to build international bridges for national benefit, strengthening our science and innovation to improve lives around the world," Dr Marshall said. "As the world races towards new applications for technologies like AI, it will take global collaboration to champion responsible and ethical applications that embrace the full potential of technological advances and drive healthy competitive advantages.

Viral Video: It has been a long time since we started availing the services of robots, the electronic humans, perhaps the first ever non-official definition of the wonder machine. Now, robotics is very much in vogue and has mass use across industries. One of the key reasons to deploy these programmable machines is their high efficiency and the ability to work for longer hours than humans without getting tired. However, a video has surfaced showing a robot placing plastic containers on a conveyor belt. The video is in a time-lapse, suggesting that the robot has been on the job for hours and the last few frames show the real-time where the machine picks up a container and as soon as it lifts it, it collapses.