Throughout history, people have developed tools and systems to augment and amplify their own capabilities. Whether the printing press or the assembly line, these innovations have allowed us to do more than we ever could alone. Jobs changed, new professions emerged, and people adapted. 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 is becoming more culturally aware. Large Language Models (LLMs) trained on culturally diverse data will gain a more nuanced understanding of human experiences and complex societal challenges. This cultural embedding will make generative AI more accessible to users worldwide.
Culture influences all aspects of our daily lives – from how we eat to how we dress – and forms the basis of our existence in a particular community. However, cultural differences can lead to confusion. Just as people adapt to different cultures, technologies must do so too. This is particularly crucial for LLMs to better ‘speak a cultural language.’ Unfortunately, most LLMs lack cultural nuances, resulting in inappropriate responses due to biases in training data.
Emerging non-Western LLMs, like Jais and Yi-34B, indicate a future with models that better represent cultural diversity in educational and medical domains. Two research areas play a crucial role in this cultural exchange. Reinforcement Learning from AI Feedback (RLAIF) allows models to interact and update their understanding of cultural concepts based on feedback. Multi-agent debate involves different models in generating answers, leading to a single unambiguous response.
These research areas enable models to learn from each other, gaining a more nuanced understanding of societal challenges by looking at issues through a cultural lens. This progress will lead to more robust and technically accurate responses across various themes.
FemTech is finally taking off
Better healthcare for women is becoming more accessible as investments in FemTech increase, healthcare becomes more hybrid, and a wealth of data enables better diagnoses. The rise of FemTech will not only benefit women but also give the entire healthcare system a positive boost.
Women’s healthcare is crucial, especially considering that women spend over $500 billion each year and make eight out of ten health decisions. Despite this, modern medicine has historically focused mainly on men. However, investments in FemTech have recently increased significantly, by as much as 197 percent. This development is driven by cloud technology and improved data access.
Companies like Tia, Elvie, and Embr Labs use data and predictive analytics to provide personalized care, while hybrid care models, affordable diagnostic devices, and online platforms increase access to healthcare. This evolution will democratize healthcare, particularly for women in rural areas.
Technological innovations, such as smart menstrual aids and wearables, will enable women to create personal health profiles and share data with healthcare providers. Better education, widespread data availability, and non-invasive solutions will also drastically improve the treatment of menopause-related issues. In sports, where women have traditionally trained based on male models, such unique health data can prevent unnecessary injuries and improve female athletes’ health.
AI assistants redefine productivity for developers. AI assistants will evolve from simple code generators to educational tools that support the entire software development lifecycle. They will explain complex systems in simple language, suggest targeted improvements, and take over repetitive tasks, allowing developers to focus on the most impactful parts of their work.
In 2021, I predicted the rise of generative AI in software development, which is now becoming a reality. Tools generate entire code blocks based on natural language instructions. In the 2023 Stack Overflow Developer Survey, 70 percent of respondents reported using or planning to use such AI tools. Future AI assistants will not only write code but also understand and clearly explain concepts. They will provide contextual understanding of systems, personalize advice for individuals, teams, or companies, and serve as educational tools for junior developers and valuable assets for senior engineers.
These assistants are already reducing many complex aspects of software development, such as writing tests and boilerplate codes. They can even restructure and migrate entire applications, allowing developers to fully utilize their creativity and focus on innovation. In the coming years, engineering teams will become more productive, develop higher quality systems, and shorten the software release lifecycle.
Education is evolving to match the pace of technological innovations
Higher education alone cannot keep up with the speed of technological change. Led by the business world, skill-based training programs will emerge. This shift towards lifelong learning will benefit both individuals and companies.
Previously, software development took years; now, thanks to cloud computing, continuous innovations, and a ‘minimum viable product’ approach, these cycles are much shorter. Higher education is still lagging, creating a growing gap between education and the workplace. However, technical education seems to be undergoing a shift similar to the evolution in software development. Companies are increasingly investing in skill-based education on a large scale. A prime example is Amazon’s training of 21 million learners worldwide in new technological skills. This work-based learning model opens up numerous new possibilities. This doesn’t mean that the traditional
Dr. Werner Vogels is CTO hij Amazon