Throughout history, people have developed tools and systems to augment and amplify their own capabilities. In the past year, the rate of change has rapidly accelerated. Cloud technologies, machine learning, and generative AI have become more accessible, impacting nearly every aspect of our lives from writing emails to developing software, even detecting cancer at an early stage. The coming years will be filled with innovation in areas designed to democratize access to technology and help us keep up with the increasing pace of every-day life—and it starts with Generative AI.
Generative AI becomes culturally aware
Large language models (LLMs) trained on culturally diverse data will gain a more nuanced understanding of human experience and complex societal challenges. This cultural fluency promises to make generative AI more accessible to users worldwide.
Culture influences everything. It is the foundation for how each one of us exists within a community. Culture provides rules and guidelines that inform and govern our behaviors and beliefs—and this contract changes depending on where we are and who we are with. At the same time, these differences can sometimes result in confusion and misinterpretation. In the coming years, culture will play a crucial role in how technologies are designed, deployed, and consumed; its effects will be most evident in generative AI.
For LLM-based systems to reach a world-wide audience, they need to achieve the type of cultural fluency that comes instinctively to humans.. A lot of this has to do with the training data that’s available. Common Crawl, which has been used to train many LLMs, is roughly 46% English, and an even greater percentage of the content available—regardless of language—is culturally Western (skewing significantly towards the United States). Using the same prompt with a model pre-trained on Arabic texts, specifically for Arabic language generation, culturally appropriate responses were generated.
In the past few months, non-Western LLMs have started to emerge: Jais, trained on Arabic and English data, Yi-34B, a bilingual Chinese/English model, and Japanese-large-lm, trained on an extensive Japanese web corpus. These are signs that culturally accurate non-Western models will open up generative AI to hundreds of millions of people with impacts ranging far and wide, from education to medical care.
Keep in mind, language and culture are not the same. Even being able to do perfect translation does not give a model cultural awareness. As a myriad of histories and experiences are embedded into these models, we will see LLMs begin to develop a broader, worldwide range of perspectives.
FemTech finally takes off
Women’s healthcare reaches an inflection point as FemTech investment surges, care goes hybrid, and an abundance of data unlocks improved diagnoses and patient outcomes. The rise of FemTech will not only benefit women, but lift the entire healthcare system.
At AWS, we’ve been working closely with women-led start-ups and have seen first-hand the growth in FemTech. In the last year alone, funding has increased 197%. With increased access to capital, technologies like machine learning, and connected devices designed specifically for women, we are at the precipice of an unprecedented shift, not only in the way women’s care is perceived, but how it’s administered. Companies like Tia, Elvie, and Embr Labs are showing the immense potential of leveraging data and predictive analytics to provide individualized care and meet patients where they’re comfortable—at home and on-the-go.
AI assistants redefine developer productivity
AI assistants will evolve from basic code generators into teachers and tireless collaborators that provide support throughout the software development lifecycle. They will explain complex systems in simple language, suggest targeted improvements, and take on repetitive tasks, allowing developers to focus on the parts of their work that have the most impact.
The AI assistants on the horizon will not only understand and write code, they will be tireless collaborators and teachers. No task will exhaust their energy, and they’ll never grow impatient explaining a concept or redoing work—no matter how many times you ask. With infinite time and unlimited patience, they will support everyone on the team and contribute to everything from code reviews to product strategy.
Education evolves to match the speed of tech innovation
Higher education alone cannot keep up with the rate of technological change. Industry-led skills-based training programs will emerge that more closely resemble the journeys of skilled tradespeople. This shift to continuous learning will benefit individuals and businesses alike.
Education is radically different across the world, but it’s been widely accepted that to hire the best people—and to land the best job yourself—a college degree is table stakes. This has been especially true in technology. But we’re beginning to see this model break down, both for individuals and for companies. For students, costs are rising and many are questioning the value of a traditional college degree when practical training is available. For companies, fresh hires still require on-the-job-training. As more and more industries call for specialization from their employees, the gap is widening between what’s taught in school and what employers need. Similar to the software development processes of decades past, we have reached a pivotal point with tech education, and we will see what was once bespoke on-the-job-training for a few evolve into industry-led skills-based education for many.