Hello readers,

Welcome to the AI For All newsletter! Today, we’re talking about AI systems that can understand and design biological code, how LLMs could revolutionize the IIoT, and this week’s biggest news!

A new open-source “genome foundation model” called Evo 2 marks a major step toward AI systems that can understand and design biological code at scale. Trained on over 9 trillion DNA bases spanning bacteria, archaea, and eukaryotes, the model learns the statistical patterns that govern how genomes work—from gene coding regions to regulatory DNA and RNA splice sites. By analyzing enormous evolutionary datasets, Evo 2 can identify functional genetic elements and predict how mutations will affect biological systems without needing task-specific training, as detailed in Nature.

Practically, this capability could dramatically accelerate genome analysis and medical research. The model can evaluate whether mutations are likely to disrupt genes, help annotate newly sequenced genomes, and improve the interpretation of variants linked to diseases like cancer. In tests, Evo 2 successfully predicted the functional impact of many human genetic variants and identified key features such as exon–intron boundaries and protein structures directly from DNA sequences. This suggests AI models could soon serve as general-purpose assistants for genomics, helping researchers rapidly interpret the vast and growing volume of genetic data.

Beyond prediction, Evo 2 also demonstrates early capabilities in biological design. Researchers used the model to generate DNA sequences resembling real genomes, including mitochondrial genomes and bacterial chromosomes with realistic gene structures. The system can also propose regulatory DNA that shapes how genes are turned on or off in different cell types, pointing toward future applications in synthetic biology, therapeutics, and engineered organisms. 

Because the model and training dataset are fully open source, the creators hope it will become a shared platform for exploring biological complexity—similar to how large language models transformed natural language processing. With further development and experimental validation, genome-scale AI systems like Evo 2 could enable faster discovery of gene functions, improved disease diagnostics, and new ways to design biological systems for medicine, agriculture, and biotechnology.

The best AI outputs come from detailed prompts. But typing long, context-rich prompts is slow - so most people don't bother.

Wispr Flow turns your voice into clean, ready-to-paste text. Speak naturally into ChatGPT, Claude, Cursor, or any AI tool and get polished output without editing. Describe edge cases, explain context, walk through your thinking - all at the speed you talk.

Millions of people use Flow to give AI tools 10x more context in half the time. 89% of messages sent with zero edits.

Works system-wide on Mac, Windows, iPhone, and now Android (free and unlimited on Android during launch).

Try Wispr Flow free →

IIoT architecture can be thought of in layers: at the lower level — sensors, edge devices, and gateways — specialized ML algorithms handle analytics; at the higher level, where human interaction happens, LLMs interpret and communicate insights.

Read more at IoT For All