For over a decade, MIT Associate Professor Rafael Gómez-Bombarelli has harnessed artificial intelligence (AI) to innovate new materials. With the evolution of technology, his vision has expanded to revolutionize scientific research. He emphasizes that we are experiencing a significant shift in scientific intelligence, merging language processing and various modalities to transform how we understand material structures and synthesis recipes.

Gómez-Bombarelli's research intersects physics-based simulations with machine learning and generative AI, leading to groundbreaking applications such as advanced materials for batteries, catalysts, plastics, and organic light-emitting diodes (OLEDs). He has co-founded several companies that leverage AI for drug discovery and robotics, including his latest venture, Lila Sciences, focused on a scientific superintelligence platform for the life sciences.

The integration of AI in scientific methodologies enhances research efficiency. By automating molecular simulations and high-throughput experiments, Gómez-Bombarelli's lab has accelerated material discovery, identifying hundreds of promising candidates for practical applications. His commitment to aligning research with industry needs ensures that AI-driven innovations are not only theoretical but also commercially viable and impactful.

As AI continues to mature, major tech companies and governmental initiatives are recognizing its potential to accelerate scientific discovery. Gómez-Bombarelli believes this is an exciting time for AI in science, fostering collaboration and diverse perspectives within his computational research group. His mission focuses on maximizing the benefits of AI to pave the way for a future where scientific advancements are both achievable and sustainable.

Happy Safer Internet Day 2026! This year's theme, “Together for a Better Internet,” emphasizes not just traditional online safety, but also the transformative potential of generative artificial intelligence (gen AI) in education. For today’s students, gen AI is integral to their digital interactions and creative projects. The pressing question is no longer whether to incorporate AI in schools but how to effectively integrate it into curricula to empower learners.

Incorporating AI education allows students to understand the tools they use, diminishing the "black box" effect—where the inner workings of AI remain mysterious. By teaching students how large language models (LLMs) predict and generate text, they learn to critically assess output, reducing misplaced trust and overreliance.

An AI curriculum equips students with vital tools to navigate risks and develop critical thinking skills necessary for everyday AI use. This not only enhances individual well-being but also prepares the future American workforce. Google provides valuable resources for educators, including an AI Literacy Guide for children and activities that stress responsible AI usage. These resources impart essential principles:

1. AI as Tool, Not Replacement: Students learn to leverage AI to enhance their creativity, rather than allowing it to do their work.

2. Critical Evaluation: Given that generative AI can make errors or "hallucinate," students are taught to verify information and question outputs critically.

3. Privacy Awareness: Educational emphasis on safeguarding personal information encourages responsible interactions with AI.

Through structured engagement with AI, students evolve from passive users to informed navigators, ready for an AI-driven future. Prioritizing AI education fosters positive digital citizenship and enhances creativity, ensuring students feel empowered in an increasingly tech-centric world.

Artificial intelligence (AI) and large language models are transforming work organization and the nature of entry-level positions across the economy. While public concern centers around job loss, the real challenge for higher education is adapting to the changing landscape of early-career roles. Many traditional entry-level tasks are becoming automated, which alters how new professionals acquire experience and skills necessary for career advancement.

Instead of panicking, community colleges should embrace this shift and focus on ensuring entry-level work serves as a viable pathway into secure, long-term careers in an AI-driven economy. Recent studies indicate an 11% decline in entry-level hiring due to automation, highlighting an increased demand for AI-related skills. However, rather than completely eliminating jobs, employers are restructuring workflows in anticipation of AI’s expanding capabilities. This has resulted in automation of routine tasks like basic coding and data processing, allowing employees more time for higher-value activities such as analysis and relationship-building.

To address these changes, community colleges should integrate AI literacy into their curricula, helping students understand how AI tools impact everyday tasks and decision-making. Expanding experiential learning opportunities is also crucial, as employers look for graduates who can problem-solve in real-world scenarios. Furthermore, close partnerships with local employers will enable colleges to align educational programs with evolving job requirements.

As career pathways become less linear, institutions must prepare students for career agility, emphasizing transferable skills and continuous learning. Investing in faculty development ensures instructors effectively teach students how to engage with AI in ethical and meaningful ways. By adapting to these shifts, community colleges can maintain the relevance of entry-level education, enabling graduates to navigate an AI-enabled future successfully.

AI holds the promise of enhancing workplace efficiency, allowing employees to engage in more valuable tasks. However, research indicates that the deployment of AI tools often leads to increased workloads rather than alleviation. In an eight-month study at a U.S.-based tech company, employees were observed using generative AI tools, which prompted them to work faster and take on additional responsibilities. While these tools were not mandated, employees embraced them, leading to significant task expansion. For instance, product managers began coding, and researchers incorporated engineering tasks into their roles, which AI made more accessible.

However, this enthusiasm can result in unsustainable work patterns. Employees increasingly blurred the line between work and personal time, utilizing AI during breaks or at odd hours, which diminished recovery periods. Task management became challenging as workers juggled multiple AI-generated outputs, raising expectations for speed.

To navigate these issues, organizations should establish an “AI practice,” comprising intentional norms for AI use. Key strategies could include implementing intentional pauses for reflection to prevent cognitive overload, sequencing work phases to reduce interruptions, and fostering human connections to stimulate creativity. By thoughtfully integrating AI into workflows, companies can maintain productivity without sacrificing employee well-being.

Ultimately, while AI offers substantial potential for enhancing work processes, failure to manage its integration can lead to increased strain and diminished quality of output. Leaders must take proactive steps to shape how AI impacts the workplace, ensuring it serves as a tool for positive change rather than a source of stress.