Skip to main content

Research Intelligence

ScienceView
Artificial Intelligence

Keep AI-augmented science contestable

| Source: Science

Artificial intelligence (AI) is moving upstream in science. Systems such as AlphaFold have already transformed protein-structure prediction, while newer agentic systems can search literatures, generate hypotheses, write and execute code, interpret results, and automate much of the path from idea to paper. Recent debates have asked how such systems may change scientific progress, surprise, and human understanding. Yet they also change something more fundamental: who—or what—participates in decidi

Artificial intelligence (AI) is moving upstream in science. Systems such as AlphaFold have already transformed protein-structure prediction, while newer agentic systems can search literatures, generate hypotheses, write and execute code, interpret results, and automate much of the path from idea to paper. Recent debates have asked how such systems may change scientific progress, surprise, and human understanding. Yet they also change something more fundamental: who—or what—participates in deciding which questions are asked, which evidence is surfaced, and which alternatives survive. As machine intelligence becomes part of science’s epistemic infrastructure, steps must be taken to ensure that researchers who did not build, own, or operate the system can still contest how ideas and evidence became a claim.

Read the original source →

Related Topics

Related Stories

Artificial Intelligence

Deep learning of fossil pollen morphology reveals 25,000 y of ecological change in eastern African grasslands.

Grass (Poaceae) pollen is largely overlooked in investigations of grassland evolution because the pollen of most species cannot be differentiated using traditional optical microscopy. However, the combination of superresolution microscopy and deep learning enables the capture and quantification of distinct morphological variation across the pollen of grass species. Using a semisupervised deep-learning strategy, we trained convolutional neural networks (CNNs) on superresolution images of known mo

Continue reading
Artificial Intelligence

Large-scale semidefinite programming with graphics processing units.

Semidefinite programming (SDP) provides a powerful framework in applied mathematics with applications spanning optimization, machine learning, quantum computing, and beyond. However, the computational cost of solving large-scale SDP problems remains a significant practical limitation. We break this long-standing computational bottleneck through a synergistic codesign of low-rank algorithms and graphics processing unit (GPU) architectures, developing accelerated first-order methods that leverage

Continue reading
Artificial Intelligence

Genome stability disrupted by the circRBM39(4,5,6)-RPA2 axis represses breast tumorigenesis.

Genome instability (GI) is a hallmark of cancer. GI is associated with accumulative DNA damage and tumor-specific defects in DNA repair. Multiple antitumor drugs have been developed to promote persistent GI and to drive DNA damage beyond a threshold that tumor cells can survive. However, regulatory roles of circular RNAs (circRNAs) in GI and DNA damage remain elusive. Through circRNA profiling and a small interfering RNA (siRNA)-mediated screen, we have identified a circRNA termed circRBM39(4,5,

Continue reading
Artificial Intelligence

α-aminobutyric acid promotes tumor progression by polarizing macrophages through the ASNS-mTORC2 axis.

Tumor-associated macrophages (TAMs), the most abundant immune cell subset in the tumor microenvironment (TME), exhibit phenotypic plasticity and exert critical roles in tumor progression and antitumor immunity. Targeting TAM polarization has emerged as a promising strategy for cancer immunotherapy, yet the key regulators governing this process remain incompletely defined. Here, we identified α-aminobutyric acid (AABA) as a pro-tumor metabolite that drives M2-like polarization of TAMs to pr

Continue reading
Artificial Intelligence

Plant fiber exploitation contributed to the emergence of ground-edged cutting tools in North China.

Ground-edged cutting tools are widely regarded as technological hallmarks of early agriculture in North China, commonly hypothesized to have been developed primarily for cereal harvesting. Yet the functional foundations of this innovation remain insufficiently tested. This study integrates use-wear and microfossil analyses of 140 stone tools from the Peiligang site, with experimental data, to reassess their roles within long-term trajectories of technological change from the Late Paleolithic to

Continue reading