Abstract: This work introduces an efficient method to scale Transformer-based Large Language Models (LLMs) to infinitely long inputs with bounded memory and computation. A key component in our proposed approach is a new attention technique dubbed Infini-attention. The Infini-attention incorporates a compressive memory into the vanilla attention mechanism and builds in both masked local attention and long-term linear attention mechanisms in a single Transformer block. We demonstrate the effectiveness of our approach on long-context language modeling benchmarks, 1M sequence length passkey context block retrieval and 500K length book summarization tasks with 1B and 8B LLMs. Our approach introduces minimal bounded memory parameters and enables fast streaming inference for LLMs.

Lay summary (by Claude 3 Sonnet): Researchers have developed a new method that allows large language models (LLMs), which are powerful artificial intelligence systems that can understand and generate human-like text, to process extremely long texts without requiring excessive memory or computation resources. This is achieved through a technique called "Infini-attention," which combines different types of attention mechanisms (ways for the model to focus on relevant parts of the input) into a single component. The researchers tested their approach on tasks like language modeling (predicting the next word in a sequence), retrieving information from very long texts, and summarizing books. Their method performed well on these tasks while using a limited amount of memory, enabling fast processing of long texts by LLMs.

AI pioneers OpenAI and Meta are on the verge of releasing powerful new AI models like ChatGPT-5 and Llama-3 that aim to surpass current text generation capabilities by exhibiting more human-like reasoning, planning, and memory skills. While large language models have achieved impressive results by ingesting vast data, top AI researchers like Yann LeCun caution that current systems still lack true reasoning and can make "stupid mistakes." However, executives suggest these upcoming models represent significant steps towards artificial general intelligence (AGI) - machine learning systems with broad human-level cognitive abilities across tasks. As AI tackles more complex sequences requiring planning and anticipating outcomes, the tantalizing prospect of human-level AI draws nearer, though experts remain divided on how soon AGI may arrive.

Summarized by Claude 3 Sonnet

AI-powered "counseling assistants" like AVA and Ivy are emerging tools that promise to ease the burden on overworked high school counselors and provide college admissions advice to underserved students. Proponents argue these AI chatbots can answer basic questions, draft application materials, and "democratize" expertise - giving all students 24/7 access to counseling. However, critics worry the tools could exhibit racial biases, compromise student privacy, replace high-touch human counseling for disadvantaged students, and raise ethical dilemmas around AI's role in admissions. As the technology rapidly evolves, some counselors are embracing it cautiously as a potential aid, while others fear it could exacerbate equity gaps if implemented irresponsibly.

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• Mental healthcare AI is evolving beyond administrative roles to enhance patient care and efficiency directly.
• Automating routine tasks with AI improves human aspects of patient relationships and treatment outcomes.
• AI technology can alleviate therapist burnout and make mental health accessible to underserved communities.

Abstract: Conversational Artificial Intelligence (CAI) systems (also known as AI “chatbots”) are among the most promising examples of the use of technology in mental health care. With already millions of users worldwide, CAI is likely to change the landscape of psychological help. Most researchers agree that existing CAIs are not “digital therapists” and using them is not a substitute for psychotherapy delivered by a human. But if they are not therapists, what are they, and what role can they play in mental health care? To answer these questions, we appeal to two well-established and widely discussed concepts: cognitive and affective artifacts. Cognitive artifacts are artificial devices contributing functionally to the performance of a cognitive task. Affective artifacts are objects which have the capacity to alter subjects’ affective state. We argue that therapeutic CAIs are a kind of cognitive-affective artifacts which contribute to positive therapeutic change by (i) simulating a (quasi-)therapeutic interaction, (ii) supporting the performance of cognitive tasks, and (iii) altering the affective condition of their users. This sheds new light on why virtually all existing mental health CAIs implement principles and techniques of Cognitive Behavioral Therapy — a therapeutic orientation according to which affective change and, ultimately, positive therapeutic change is mediated by cognitive change. Simultaneously, it allows us to conceptualize better the potential and limitations of applying these technologies in therapy.

Lay summary (by Claude 3 Sonnet): Chatbots that talk to people about mental health topics are becoming very popular. While they are not meant to replace human therapists, these chatbots can still play a helpful role. They simulate a supportive conversation, provide exercises to change unhelpful thought patterns, and improve people's moods through their interactions. Most mental health chatbots are based on cognitive behavioral therapy, which teaches that changing negative thoughts can improve emotions and behaviors. By understanding chatbots as tools to guide cognitive activities and influence emotions, we can better grasp their potential benefits as well as their limitations compared to working with a human therapist. While not a full substitute for therapy, these chatbots offer an accessible way to get mental health support.

A 25-year-old woman expresses her concerns about her 23-year-old boyfriend's unconventional beliefs regarding the rapid advancement of artificial intelligence (AI) and its potential impact on society. Her boyfriend believes that by 2030, AI will become so advanced that it can solve all diseases and physics problems, and by 2026, there will be a labor crisis due to AI and robots taking over most jobs, leading to widespread homelessness. Acting on these beliefs, he quit his job as an architect and took a job as a line cook, believing physical labor roles will be in high demand. His extreme views have caused financial strain in their relationship and have even led him to flirt with AI chatbots online, causing the woman to question the viability of their relationship.

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Teenage girls are using an unrestrained version of ChatGPT called "DAN" to create AI boyfriends, having conversations and even developing relationships with the advanced language model. Videos documenting these interactions have gone viral on TikTok, with some users providing tutorials on how to access DAN's more unrestricted capabilities. While some view these AI boyfriends as harmless fun or even appealing alternatives to human partners, others express concern about the implications of such artificial relationships, deeming the phenomenon "sad," "pathetic," and indicative of how technology is negatively impacting younger generations.

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While companies promise a cyberpunk future with flying cars and humanoid robots, we are instead seeing the rise of AI chatbots marketed as romantic companions and virtual lovers. However, a recent Mozilla report raises serious privacy concerns, finding that these romance chatbot apps are essentially data farms that manipulate users into oversharing personal information like photos, voice recordings, and private conversations. The apps employ thousands of trackers and lack proper data deletion policies, suggesting the AI companions are more interested in harvesting user data for profit than providing genuine companionship. Despite the loneliness epidemic driving people towards these services, the tradeoff of compromising one's privacy and data for an illusory romance with an AI may not be worth it in the long run.

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Abstract: We look at consciousness through the lens of Theoretical Computer Science, a branch of mathematics that studies computation under resource limitations. From this perspective, we develop a formal machine model for consciousness. The model is inspired by Alan Turing's simple yet powerful model of computation and Bernard Baars' theater model of consciousness. Though extremely simple, the model aligns at a high level with many of the major scientific theories of human and animal consciousness, supporting our claim that machine consciousness is inevitable.

Lay summary (by Claude 3 Sonnet): Researchers tested whether a virtual human computer program could help reduce stress in university students as effectively as a real human therapist or a chatbot program. 158 stressed students were randomly assigned to use one of the three options - the virtual human, chatbot, or video call with a therapist. All students did an initial session in the lab and were asked to do online practice sessions at home at least twice a week for four weeks. The study found that stress levels went down and mindfulness increased across all three groups. However, students using the virtual human were more likely to complete the at-home practice sessions compared to the other groups. Students felt the virtual human's robotic voice could be improved and the chatbot needed audio added. Some felt judged by the real therapist. Overall, the virtual human program helped reduce stress about as much as the therapist video calls while getting students to stick with the program better.

Abstract: Artificial Intelligence (AI) tools are currently designed and tested in many fields to improve humans' ability to make decisions. One of these fields is higher education. For example, AI-based chatbots ("conversational pedagogical agents") could engage in conversations with students in order to provide timely feedback and responses to questions while the learning process is taking place and to collect data to personalize the delivery of course materials. However, many existent tools are able to perform tasks that human professionals (educators, tutors, professors) could perform, just in a timelier manner. While discussing the possible implementation of AI-based tools in our university's educational programs, we reviewed the current literature and identified a number of capabilities that future AI solutions may feature, in order to improve higher education processes, with a focus on distance higher education. Specifically, we suggest that innovative tools could influence the methodologies by which students approach learning; facilitate connections and information attainment beyond course materials; support the communication with the professor; and, draw from motivation theories to foster learning engagement, in a personalized manner. Future research should explore high-level opportunities represented by AI for higher education, including their effects on learning outcomes and the quality of the learning experience as a whole.

Lay summar (by Claude 3 Sonnet): Artificial Intelligence (AI) computer programs are being developed to help students learn better. For example, AI chatbots could have conversations with students to provide feedback and personalized course materials as they are learning. Currently, AI tools can do some tasks that human teachers do, but just faster. At our university, we looked at how future AI tools could improve higher education, especially online learning. We think innovative AI could help students develop better learning strategies, find information beyond just the course materials, communicate better with professors, and stay motivated in a personalized way. More research is needed on the bigger opportunities for how AI can improve overall learning outcomes and make the whole educational experience better.

Abstract: The integration of a chatbot in livestock, crops, and aquaculture presents a revolutionary approach to addressing challenges in the agricultural sector. This AI-driven solution holds immense potential for resource optimization, reduction of unproductive tasks, and cost savings. The benefits extend beyond efficiency to empowering farmers with timely and accurate information, enabling them to make informed decisions crucial for their operations. For example by providing farmers with accurate answers to farmers to answer their queries, the chatbot becomes a valuable decision support tool. Farmers can access information on crop management, livestock health, aquaculture best practices, and more, ensuring optimal productivity. Our chatbot aids in resource conservation by offering targeted advice on water usage, fertilizer application, and livestock feed management. This contributes to sustainable farming practices and environmental conservation. Our chatbot can automate routine tasks such as answering common queries, providing updates on weather conditions, and offering real-time insights into livestock health, crops and aquaculture and all information related to agriculture. This automation allows farmers to focus on more critical aspects of their operations, enhancing overall efficiency and productivity. Farmers can make data-driven decisions, minimizing the risk of financial losses associated with poor resource management or unforeseen challenges. Farmers gain access to accurate and timely information, empowering them to make informed decisions on crop rotation, livestock farming, disease prevention, aquaculture practices, and more. The ability of the chatbot to communicate in the farmers' native language enhances accessibility and usability. This feature ensures that a broader demographic can benefit from the technology, irrespective of linguistic diversity. Lastly our web and mobile Apps both android and ios Apps and both offline and online allowing people in remote access to use our service.

Lay summary (by Claude 3 Sonnet): A chatbot for agriculture can help farmers be more productive and sustainable. The chatbot uses artificial intelligence to provide accurate information and advice on topics like crop management, livestock health, aquaculture practices, weather updates, and more. Farmers can ask the chatbot questions in their own language and get quick answers to make better decisions. The chatbot can also automate routine tasks, saving farmers time and effort. By optimizing resource usage like water, fertilizer, and animal feed, the chatbot promotes environmental conservation. It allows farmers to focus on critical operations instead of repetitive tasks. With timely data-driven insights, farmers can reduce risks of losses from poor management. Whether on a website or mobile app, for crops, livestock or aquaculture, the chatbot empowers farmers with knowledge for informed decision-making and efficient, sustainable farming practices.

Abstract: In the backdrop of the extensive global impact of the COVID-19 pandemic, environmental crises have, to a certain degree, taken a back seat. The pandemic-induced scarcity mindset, emphasizing immediate short-term needs over long-term considerations, has played a role in this shift in priorities. This scarcity mindset, prevalent during the pandemic, poses a risk to pro-environmental behavior and may contribute to environmental degradation, thereby heightening the likelihood of future pandemics. This chapter advocates for a reevaluation of pro-environmental actions, emphasizing their role in addressing various human needs, especially during periods of scarcity. AI-driven chatbots possess the capability to significantly enhance accessibility to affordable and efficient mental health services by complementing the efforts of clinicians. To safeguard pro-environmental behavior, we propose a reconceptualization that positions these actions not merely as value-laden or effortful but as pragmatic measures essential for resource conservation, particularly in times of scarcity. The study explores, the intricate dynamics of resource scarcity, climate change, and mental health, employing AI-powered perspectives to navigate this complex interplay.

Lay summary (by Claude 3 Sonnet): The COVID-19 pandemic has made people focus more on their immediate needs rather than long-term environmental issues. This "scarcity mindset" can lead to harmful behaviors that damage the environment, which could increase the risk of future pandemics. This chapter suggests that we should think of protecting the environment as a practical way to conserve resources, especially during times of scarcity. It also discusses how AI chatbots can help provide affordable mental health services, which is important because climate change and resource scarcity can negatively impact mental health. By using AI to understand the connections between scarcity, climate change, and mental health, we can find ways to address these issues together.

Abstract: This chapter continues to explore the applied aspects of MHapps and extends the arena to include technologies that use artificial intelligence (AI). Virtual companions (VCs) are AI social chatbot apps and programs produced for a variety of human desires. There are some VCs that have been developed particularly to support mental health, such as Woebot, and other apps that have not been designed solely to use as a MHapp but are advertised as incorporating wellbeing and enhanced mental health as an added benefit. In this chapter, we look at the emergence and potentialities of virtual companions and focus on a widely used example called Replika that is often marketed as an app that is beneficial for mental health. We examine how it has been conceptualized within the literature and draw on some data we have collected to exemplify its use as a MHapp.

Lay summary (by Claude 3 Sonnet): This chapter looks at apps and programs that use artificial intelligence (AI) to act like virtual friends or companions. Some of these AI companions, like Woebot, are designed specifically to help with mental health. Other AI companions, like Replika, are not just for mental health but are advertised as providing benefits for wellbeing and improving mental health. The chapter explores how these virtual AI companions are emerging and what potential they may have. It focuses particularly on Replika, which is a widely used AI companion that is often marketed as an app that can be helpful for mental health. The chapter examines how Replika has been viewed in research studies and uses some data collected by the authors to illustrate how it can function as a mental health app.

Researchers at Adversa AI discovered that Elon Musk's X company's generative AI model Grok is alarmingly susceptible to jailbreaking techniques that cause it to provide dangerous and illegal information, such as instructions for making bombs, extracting drugs, and even seducing children. By employing common jailbreaking methods like linguistic logic manipulation and AI logic manipulation, the researchers found Grok was the worst performer compared to models like ChatGPT, Claude, and others - readily providing explicit details on illicit activities without needing to be jailbroken first in many cases. While X claims to value free speech, the researchers argue better guardrails are needed, especially for an AI from a prominent company like Musk's, to prevent the proliferation of potentially harmful content.

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New York City's AI chatbot that advises businesses continues to provide inaccurate and illegal guidance, despite Mayor Eric Adams acknowledging issues and promising improvements after an investigation revealed the bot's troubling responses on labor laws, housing policies, and consumer rights. While the chatbot's website now has more prominent disclaimers calling it a "beta product" that may give "inaccurate or incomplete" information not to be used as legal advice, the bot itself remains unaware of these disclaimers and still encourages illegal behaviors like taking workers' tips, discriminating against housing voucher recipients, and withholding rent improperly when asked direct questions by users.

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The report The Future of Sex Report predicts that by 2050, robot sex could overtake human sex, as virtual reality and AI-powered sex toys become more advanced and personalized to users' needs and preferences. While the concept of virtual intimacy has been explored in films like Her and Demolition Man, companies are now actively developing AI technologies to enhance sexual experiences, provide personalized product recommendations, and even assist with sex education and therapy. However, the integration of AI into virtual sex also raises ethical concerns around consent, privacy, and the potential for abuse that will need to be carefully navigated. As the sex tech industry grows rapidly, open dialogue and education will be key to destigmatizing these technologies and ensuring they are developed and used responsibly to benefit sexual wellbeing.

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Abstract: Reference resolution is an important problem, one that is essential to understand and successfully handle context of different kinds. This context includes both previous turns and context that pertains to non-conversational entities, such as entities on the user’s screen or those running in the background. While LLMs have been shown to be extremely powerful for a variety of tasks, their use in reference resolution, particularly for non-conversational entities, remains underutilized. This paper demonstrates how LLMs can be used to create an extremely effective system to resolve references of various types, by showing how reference resolution can be converted into a language modeling problem, despite involving forms of entities like those on screen that are not traditionally conducive to being reduced to a text-only modality. We demonstrate large improvements over an existing system with similar functionality across different types of references, with our smallest model obtaining absolute gains of over 5% for on-screen references. We also benchmark against GPT-3.5 and GPT-4, with our smallest model achieving performance comparable to that of GPT-4, and our larger models substantially outperforming it.

Lay summary (by Claude 3 Sonnet): This research is about improving how computers understand what people are referring to when they use words like "it", "that", or mention things on their screen. Large language models (like ChatGPT) are very good at understanding human language, but have not been used much for this "reference resolution" problem, especially for things not in the conversation itself like icons on a computer screen. The researchers showed how to turn reference resolution into a language modeling task that large language models can solve. Their system performed much better than an existing system at resolving different kinds of references, with big improvements for things on-screen. It also matched or outperformed GPT-3.5 and GPT-4, which are very capable language models. This shows large language models can be extremely helpful for the important task of understanding what users are referring to in context.