Character.AI is bringing to life the science-fiction dream of open-ended conversations and collaborations with computers. We are building the next generation of dialog agents—with a long-tail of applications spanning entertainment, education, general question-answering and others. Our dialog agents are powered by our own proprietary technology based on large language models, built and trained from the ground up with conversation in mind. How does the Character.AI beta work? The Character.AI beta is based on neural language models. A supercomputer reads huge amounts of text and learns to hallucinate what words might come next in any given situation. Models like these have many uses including auto-complete and machine translation. At Character.AI, you collaborate with the computer to write a dialog - you write one character's lines, and the computer creates the other character's lines, giving you the illusion that you are talking with the other character.

Eight archetypes of gamification emerged from the analysis of health-related mobile apps: (1) competition and collaboration, (2) pursuing self-set goals without rewards, (3) episodical compliance tracking, (4) inherent gamification for external goals, (5) internal rewards for self-set goals, (6) continuous assistance through positive reinforcement, (7) positive and negative reinforcement without rewards, and (8) progressive gamification for health professionals. The results indicate a close relationship between the identified archetypes and the actual health behavior that is being targeted.

Digital media are omnipresent in modern life, but the science on the impact of digital media on behavior is still in its infancy. There is an emerging evidence base of how to use digital media for behavior change. Strategies to change behavior implemented using digital technology have included a variety of platforms and program strategies, all of which are potentially more effective with increased frequency, intensity, interactivity, and feedback. It is critical to accelerate the pace of research on digital platforms, including social media, to understand and address its effects on human behavior. The purpose of the current paper is to provide an overview and describe methods in this emerging field, present use cases, describe a future agenda, and raise central questions to be addressed in future digital health research for behavior change. Digital media for behavior change employs three main methods: (1) digital media interventions, (2) formative research using digital media, and (3) digital media used to conduct evaluations. We examine use cases across several content areas including healthy weight management, tobacco control, and vaccination uptake, to describe and illustrate the methods and potential impact of this emerging field of study. In the discussion, we note that digital media interventions need to explore the full range of functionality of digital devices and their near-constant role in personal self-management and day-to-day living to maximize opportunities for behavior change. Future experimental research should rigorously examine the effects of variable levels of engagement with, and frequency and intensity of exposure to, multiple forms of digital media for behavior change.

“Brave New World“and Utopia are movies about technology and crazy people like Elon Musk

The best way for increasing the usage and value of the product is crafting the product from a behavioral perspective instead of feature perspective. The best way for changing this mindset is asking simple questions about your users and what behaviors you want to create for bringing them value. Sankey Diagrams are awesome tools for measuring these behaviorals funnels. Once we have detected and optimized the different behaviors, the impact of adding new features will be much higher than before.

Designing Health & Fitness Apps with the Mind in Mind - Massimo Ingegno (and other speakers)

Webbinarium om att skaffa elbil och laddbox.

This is why augmented reality will inherit the earth. It will not only overshadow virtual reality as our primary gateway to the metaverse but will also replace the current ecosystem of phones and desktops as our primary interface to digital content. After all, walking down the street with your neck bent, staring at a phone in your hand is not the most natural way to experience content to the human perceptual system. Augmented reality is, which is why I firmly believe that within 10 years, AR hardware and software will become dominant, overshadowing phones and desktops in our lives.

Syndromic surveillance systems monitor disease indicators to detect emergence of diseases and track their progression. Here, we report on a rapidly deployed active syndromic surveillance system for tracking COVID‑19 in Israel. The system was a novel combination of active and passive components: Ads were shown to people searching for COVID‑19 symptoms on the Google search engine. Those who clicked on the ads were referred to a chat bot which helped them decide whether they needed urgent medical care. Through its conversion optimization mechanism, the ad system was guided to focus on those people who required such care. Over 6 months, the ads were shown approximately 214,000 times and clicked on 12,000 times, and 722 people were informed they needed urgent care. Click rates on ads and the fraction of people deemed to require urgent care were correlated with the hospitalization rate ( R2=0.54 and R2=0.50 , respectively) with a lead time of 9 days. Males and younger people were more likely to use the system, and younger people were more likely to be determined to require urgent care (slope: −0.009 , P=0.01 ). Thus, the system can assist in predicting case numbers and hospital load at a significant lead time and, simultaneously, help people determine if they need medical care.

Leveraging artificial intelligence (AI)–driven apps for health education and promotion can help in the accomplishment of several United Nations sustainable development goals. SnehAI, developed by the Population Foundation of India, is the first Hinglish (Hindi + English) AI chatbot, deliberately designed for social and behavioral changes in India. It provides a private, nonjudgmental, and safe space to spur conversations about taboo topics (such as safe sex and family planning) and offers accurate, relatable, and trustworthy information and resources.

Emotions and worries can reduce individuals’ available attention and affect economic decisions. In a four-week experiment with 2,384 US adults, offering free access to a popular mindfulness meditation app (Headspace) that costs $13 per month improves mental health, productivity and decisionmaking. First, it causes a 0.44 standard deviation reduction in symptoms of stress, anxiety, and depression, comparable to the impacts of expensive in-person therapy, with improvements even among participants with minimal or mild symptoms at baseline. Second, it increases earnings on a proofreading task by 1.9 percent. Third, it makes decision-making more stable across emotional states, reducing the interference of personal worries with risk choices. Overall, our results demonstrate the potential of affordable mindfulness meditation apps to improve mental health, productivity, and the impact of emotions on economic decisions.

This is the story of how the web2 internet broke the business model of media, and how the advent of web3 signals a disruption to that business model that tilts the scales in favor of creators. Without native monetization methods built into the web2 internet, the predominant business models were opaque, advertising-based, and dependent on closed-garden networks, which gave an outsized advantage to platforms. On the horizon, new business models and technologies hold promise to unlock the kind of economic opportunity and control that will lead to a true creative Golden Age for artists and creators.

Why are “click here” and “by this link” poor choices? And is it acceptable to use “read more”? In this article, I’ll explain popular wording and formatting mistakes and will show more accessible and informative alternatives.

For starters, every interaction a person has with a digital product follows the same pattern: Information — User filters the information Significance — User looks for its meaning Time — User takes an action within a time frame Memory — User stores fragments of the interaction in their memory For each of these stages of interaction, I’ve compiled a list of the most relevant design principles and cognitive biases that will help you to build habit-forming products.

The higher the sensitivity of data breached, the more significant was the impact on stock prices

Social engineering is free for hackers but fraught with danger for regular users

In March 2020, more than one-third of Dark web websites data was stolen

You may be wondering: If users want personalization, then what’s the problem? The problem is that personalization is a bit like walking a tightrope. A very thin line separates the “good” kind of personalization from the creepy kind. “I like it because it’s so similar to me” can easily become “I don’t like it because it’s eerily similar to me.” “This is relevant to me and saves me time and effort” can easily become “The algorithm is stereotyping me and that’s not cool.” This switch from good to bad is where user psychology comes in. Understanding the real reason why personalization works can help us understand why it does not work sometimes.