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Block Introduces Dessa, A Voice AI Model For More Natural Conversations

Block has introduced Dessa, a speech AI model designed to make conversations with AI sound more natural by capturing emotion, pauses and human speech patterns. It can listen and respond simultaneously and is designed to run locally on consumer hardware.

According to Block’s research team, Dessa was trained entirely on human speech using a single NVIDIA GPU rack. Unlike conventional systems that convert speech into text before generating a response, it processes spoken interactions directly.

Real-Time Speech And Emotional Expression

Dessa uses a full-duplex architecture, allowing it to listen and speak at the same time rather than waiting for a person to finish. Block says this approach helps preserve vocal cues that can be lost when speech is converted into text.

The company also says Dessa draws knowledge from conversational data within the model itself, rather than relying on a separate large language model. Its full capabilities and limitations have not yet been disclosed.

“We believe voice is the most natural way to interact with intelligence,” Block CEO Jack Dorsey wrote in a post announcing Dessa, highlighting its ability to convey emotion, nuance and natural cadence.

Demonstrations shared by Block researchers show the model adapting its tone to different situations. However, these examples do not yet establish how consistently it can interpret emotion in real-world conversations.

Local Processing And Safety

Block says Dessa can run on consumer hardware, including a MacBook with an M4 Max chip or better. Local processing could reduce latency, cloud computing costs and reliance on external infrastructure.

The model is not yet publicly available. Block has invited safety researchers to test scenarios in which a convincing AI voice could mislead someone into believing they are speaking with a human.

Potential Applications

Block’s ecosystem includes products for consumers and small businesses, creating potential applications in commerce and customer service. Its Square platform already supports AI-powered voice ordering.

Dessa could eventually support additional voice-enabled services while reducing reliance on cloud processing. However, its commercial potential will depend on reliability, safety and performance on everyday devices.

Blood Proteins May Signal Prostate Cancer Risk More Than 15 Years Before Diagnosis

Distinctive proteins in men’s blood may offer clues to prostate cancer risk more than 15 years before a diagnosis, according to research from the University of Oxford. The findings could help scientists better understand why some prostate cancers become aggressive while others grow slowly and never cause serious harm.

Researchers analyzed blood samples from 2,555 men collected between 1992 and 2000. Of those participants, 982 were later diagnosed with prostate cancer; the remaining 1,573 served as a comparison group. The team measured more than 6,000 proteins in the samples, which had been collected years—and, in some cases, more than 15 years—before diagnosis.

Protein Patterns Were Linked To Different Risks

The analysis identified proteins associated with prostate cancer risk overall, as well as proteins linked specifically to more aggressive or fast-growing forms of the disease. Higher levels of certain immune-system proteins, including GZMK and ANXA2, were associated with a lower long-term risk.

“Finding proteins linked to known prostate cancer driver genes in blood collected years before diagnosis gives us new insights into the earliest stages of the disease,” said Karl Smith-Byrne, the study’s corresponding author at Oxford.

The results, published in European Urology, are significant because prostate cancer is not a single, uniform disease. Many tumors grow slowly and may never threaten a patient’s health, while others progress rapidly and can be life-threatening. A clearer way to distinguish between these outcomes could eventually help improve how clinicians assess risk.

Promising Findings, Not A Screening Test

The researchers stressed that the protein patterns are not ready to be used as a screening test. The associations need further validation before they can inform clinical decisions, and the study does not establish that the proteins cause cancer or that measuring them can predict an individual’s outcome.

“Ultimately, we hope this will help us understand the biology of aggressive prostate cancer and identify markers that could improve how we assess risk in the future,” said first author Zhe Huang, a postdoctoral epidemiologist at Oxford Population Health.

Research Could Point To New Prevention Strategies

Prostate cancer is the most commonly diagnosed cancer among men in the European Union and the second most common worldwide, after lung cancer. In the EU, approximately 330,000 cases are diagnosed and 75,000 men die from the disease each year.

By identifying biological signals that appear long before diagnosis, the research may help scientists investigate the pathways involved in aggressive disease. Smith-Byrne said that, alongside existing and future genetic evidence, the findings could help reveal pathways that might ultimately be targeted to prevent aggressive prostate cancer.

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