Over the past year, cloud, machine Xi, and generative AI have become more ubiquitous, impacting nearly every aspect of human life, from writing emails to developing software and even early cancer screening. Innovation will be an important theme in every field in the coming years, aiming to democratize technology and help us keep up with the ever-accelerating pace of life, and it all starts with generative AI.
Generative AI will become culturally aware
Large language models (LLMs) trained with cultural diversity data will provide a more nuanced understanding of human experience and complex societal challenges. This cultural fluency is expected to make generative AI more accessible to users around the world.
Culture influences everything about us, from the stories we tell, the food we eat, and the way we dress, to our values, etiquette, prejudices, the way we handle problems and make decisions. It's the foundation of our community, it's our rules of conduct, our beliefs, it's a contract that depends on where we are and who we're with.
At the same time, cultural differences can sometimes lead to confusion and misunderstanding. Japanese culture considers it a pleasure to suck the soup out loud while eating noodles, but other cultures consider it rude;Traditional Indian wedding Xi requires the bride to wear a delicate and colorful lehenga, while Western tradition is to have the bride wear a white wedding dress, and in Greece there is even a Xi of spitting on the wedding dress for good luck. As human beings, we are Xi to cross-cultural environments, so we can synthesize various cultural information, adjust our interpretations, and respond appropriately.
So why not expect the technology we rely on to do the same?In the coming years, culture will play a crucial role in how technology is designed, deployed, and used, and its impact will be felt in generative AI.
For large language models to reach global users, they must achieve the same cultural fluency as humans themselves. Researchers at the Georgia Institute of Technology demonstrated earlier this year that even when a large language model was provided with an Arabic prompt that explicitly mentions Islamic prayers, the resulting response was still a suggestion to drink with friends, which is inappropriate in Islamic culture. Much of this has to do with the available training data. Approximately 46% of the content of the Common Crawl dataset currently used to train many large language models is in English, and a larger percentage of the available content is based on Western culture (with a clear bias towards American culture), regardless of language. If you feed the same prompt into a model that is pre-trained in Arabic text and specifically generates Arabic responses, you'll get a more culturally appropriate response, such as a suggestion for tea or coffee. In recent months, a number of large language models for non-Western contexts have begun to emerge: JAIS trained with Arabic and English data, the Chinese-English bilingual model YI-34B, and Japan-Large-LM trained with a large number of Japanese web corpora. These are indications that culturally accurate non-Western models will deliver generative AI to hundreds of millions of people, with implications ranging from education to healthcare.
Keep in mind that cultures and languages are not identical, and even if a model gives the most perfect translation, it may not be culturally aware. As countless histories and experiences are incorporated into the model, we will see large language models begin to develop a broader global perspective. Just as humans learn from debates** and the exchange Xi of ideas, large language models need similar opportunities to expand their horizons and understand cultures. Two areas of research will play a key role in this cultural exchange: one is reinforcement Xi based on AI feedback (rlaif), in which one model absorbs feedback from another, allowing different models to influence each other and update their understanding of different cultural concepts based on these influences;The second is collaboration through multi-agent debate, in which multiple instances of a model generate responses, then debate the correctness and rationale of each response, and finally arrive at a consistent response through this debate process. Both areas of research reduce the labor costs required to train and fine-tune models.
Women's tech is finally taking off
The surge in femtech investment, the development of hybrid healthcare, and the abundance of data have led to improvements in diagnosis and outcomes, leading to an inflection point in women's healthcare. The rise of women's technology will not only benefit women, but will also advance the entire healthcare system.
Women's healthcare is not a niche market. In the United States alone, women spend more than $500 billion a year on health care. They make up 50 per cent of the population and 80 per cent of health care consumption decision-makers. However, modern medicine has always been based on men by default, and it was not until 1993, when the National Institutes of Health (NIH) enacted the Revitalization Act, that clinical research in the United States included female subjects. Menstrual care and menopause** have historically been seen as taboo topics, and because women are excluded from clinical trials and research, their results tend to be much worse than men's.
On average, women are diagnosed later than men with a variety of conditions, and women have a 50% higher risk of being misdiagnosed after a heart attack. Prescription drugs are perhaps the best example of inequality: women use prescription drugs at significantly higher rates than men to cause malpractice. While this data may seem worrying on the surface, investment in women's healthcare, also known as women's tech, is on the rise, aided by cloud technology and big data.
Amazon Web Services has been working closely with women-led startups and has seen firsthand the growth of women in technology. In the last year alone, investment has increased by 197%. With the proliferation of funding, technology such as machine Xi and connected devices designed specifically for women, we are facing unprecedented change, not only in terms of how people think about women's care, but also in terms of how it is managed. Companies such as TIA, ELVIE, and EMBR Labs have demonstrated great potential to leverage data and analytics to deliver personalized care, whether at home or on the go.
As the bias around women's health needs fades and more money flows into the space, women's tech companies will continue to respond to a variety of medical conditions and needs that were previously overlooked. At the same time, the development of a hybrid healthcare model that utilizes the best medical platforms, convenient and available low-cost diagnostic equipment, and on-demand access to medical specialties will greatly increase women's access to health care.
Clients such as M**en have proven themselves to be leaders in their field, and these companies have broken the boundaries between mental health and physical well-being, providing users with a variety of services such as emotional counseling, menopause care, and more. The continued maturity and popularity of these platforms will democratize health care, making it easier for women in rural areas and historically underserved areas to connect with obstetricians and gynecologists, mental health professionals, and other specialists through apps and telehealth platforms.
Smart tampon systems such as NextGen Jane are being developed that will allow women to build uterine health profiles, identify potential disease genomic markers and seamlessly share that information with clinicians. Wearables, on the other hand, provide women and their physicians with a wealth of longitudinal health data that can be analyzed. Currently, more than 70% of women do not have access to menopausal symptoms, and enhanced education, data and the adoption of non-invasive solutions will greatly improve outcomes far beyond obstetrics and gynaecology care.
For example, on the eve of the Women's World Cup, about 30 athletes suffered anterior cruciate ligament injuries as a result of preparing for the tournament. Like traditional medicine, the female training model is also modeled after the male training model, and does not take much into account physiological factors. As a result, women are six times more likely than men to retire due to an anterior cruciate ligament injury, and are 25% less likely to fully return to play than men. This is another area where studying health data on women's characteristics will have a significant impact, not only to prevent injuries in female athletes, but also to improve their health across the board.
We are at an inflection point in women's healthcare. Access to large amounts of diverse data, combined with cloud technologies such as computer vision and deep Xi, will reduce misdiagnoses and help minimize the impact of medications*** on women today, disproportionately. Endometriosis and postpartum depression will also receive due attention. We will finally see women's healthcare move from the fringe to the forefront. Because female-led teams are more willing to solve numerous health problems than male-led teams, women's technology will not only benefit women, but will also improve the healthcare system as a whole.
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