<span class="vcard">Adam Zewe | MIT News</span>
Adam Zewe | MIT News

Explained: Generative AI’s environmental impact

Rapid development and deployment of powerful generative AI models comes with environmental consequences, including increased electricity demand and water consumption.

MIT researchers introduce Boltz-1, a fully open-source model for predicting biomolecular structures

With models like AlphaFold3 limited to academic research, the team built an equivalent alternative, to encourage innovation more broadly.

Researchers reduce bias in AI models while preserving or improving accuracy

A new technique identifies and removes the training examples that contribute most to a machine-learning model’s failures.

Enabling AI to explain its predictions in plain language

Using LLMs to convert machine-learning explanations into readable narratives could help users make better decisions about when to trust a model.

A new way to create realistic 3D shapes using generative AI

Researchers propose a simple fix to an existing technique that could help artists, designers, and engineers create better 3D models.

Photonic processor could enable ultrafast AI computations with extreme energy efficiency

This new device uses light to perform the key operations of a deep neural network on a chip, opening the door to high-speed processors that can learn in real-time.

MIT researchers develop an efficient way to train more reliable AI agents

The technique could make AI systems better at complex tasks that involve variability.

A causal theory for studying the cause-and-effect relationships of genes

By sidestepping the need for costly interventions, a new method could potentially reveal gene regulatory programs, paving the way for targeted treatments.

Despite its impressive output, generative AI doesn’t have a coherent understanding of the world

Researchers show that even the best-performing large language models don’t form a true model of the world and its rules, and can thus fail unexpectedly on similar tasks.

Nanoscale transistors could enable more efficient electronics

Researchers are leveraging quantum mechanical properties to overcome the limits of silicon semiconductor technology.