Surprisingly, not all alleles behave the same way depending on which parent they came from. Parent-of-origin effects (POEs), where the phenotypic impact of an allele differs based on maternal versus paternal inheritance, are a well-established but less characterized layer of the genome. The canonical mechanism is genomic imprinting, in which only one parental copy of a gene is expressed. Imprinted genes play critical roles in placentation, growth, and neurodevelopment, and their dysregulation underlies disorders such as Prader–Willi and Beckwith–Wiedemann syndromes.

POEs have also been proposed as one explanation for the “missing heritability” problem, the gap between phenotypic variance explained by standard GWAS and total estimated heritability. The trouble is that detecting POEs directly requires parental genotypes or trio data, which are expensive to collect and poorly represented in modern biobank cohorts. In the first UK Biobank release, for example, fewer than 5,000 of over 400,000 genotyped participants had directly available parental genotypes.

Variance-based methods offer a workaround. The key insight is that a POE inflates the phenotypic variance of heterozygotes relative to homozygotes: because heterozygous individuals carry either the maternal or paternal copy but we cannot observe which, their phenotypes are drawn from a mixture of two shifted distributions, producing excess variance. Hoggart et al. introduced a univariate test for this signal, and POIROT extended it to the multivariate setting. But POIROT has practical gaps: it returns a single p-value per variant with no per-trait effect estimates, no uncertainty quantification, and critically it cannot distinguish a true POE from a variance QTL (vQTL) that happens to inflate heterozygote variance through a different mechanism.

The Approach: Spectral Estimation of the POE Vector

POISE (Parent of Origin Inference via Spectral Estimation) addresses these limitations by modeling the problem as a latent mixture and using spectral decomposition to recover the POE signal directly.

The core observation is that when a POE exists, the covariance matrix of the heterozygous population gains a rank-one perturbation along the direction of the POE vector Δ = βM − βP. This “bump” stretches the covariance ellipse in exactly one direction separating the two hidden inheritance groups. In a setting with uncorrelated phenotypes, this means the leading eigenvalue of the heterozygous covariance matrix exceeds 1 by an amount that encodes the POE magnitude, while all other eigenvalues remain at 1. POISE recovers Δ by extracting the leading eigenvalue and eigenvector and inverting the eigenvalue relation.

In practice, phenotypes are correlated. Metabolic traits co-vary, expression levels within pathways move together, so a naive application of the above would conflate pre-existing correlations with the POE signal. POISE handles this via a whitening step: we first estimate the baseline covariance from homozygotes (who carry no POE signal), use it to decorrelate the heterozygous observations, apply the spectral estimator in the whitened space, and then transform back to the original phenotype coordinates. The result is a per-trait vector of POE coefficients that is directly interpretable: large entries identify phenotypes strongly affected by parental origin; entries near zero indicate phenotypes that are not.

Three Additional Guarantees

Beyond the point estimate, POISE provides three features that are absent from existing GWAS-based POE methods.

Per-trait confidence intervals. POISE computes bias-corrected and accelerated (BCa) bootstrap confidence intervals for each phenotype’s POE coefficient. These account for both the bias and skewness of the bootstrap distribution, providing reliable uncertainty quantification even when spectral inflation (the tendency of leading sample eigenvalues to exceed their population counterparts at finite sample sizes) would otherwise distort naive interval estimates.

An exact permutation test. Under the null hypothesis (no POE), the genotype group labels are exchangeable after mean-centering. POISE exploits this exchangeability to construct a permutation-based p-value that has exact Type I error control without distributional assumptions and that remains valid even when point estimates are biased in magnitude, because the same bias affects the observed and permuted test statistics identically.

A minimum detectable effect size filter. Detecting a POE from unlabeled heterozygotes is fundamentally harder than a standard mean-shift test: the signal enters only through the covariance perturbation, so the sample size requirement scales with the fourth power of the effect size rather than the second. POISE derives a closed-form information-theoretic detectability floor from the Chernoff information between the null and alternative distributions, and only reports a POE when the lower bootstrap confidence interval endpoint exceeds this threshold. This simultaneously guards against practically insignificant effects and against false positives from vQTLs that inflate heterozygote variance through non-additive mechanisms.

Simulation Results: Better Calibration, Higher Power, Zero vQTL False Positives

We evaluated POISE across three simulation scenarios.

Type I error. Under 5,000 null replicates with both Gaussian and heavy-tailed (t9) noise, the permutation p-values were well-calibrated against a Uniform(0,1) distribution (Kolmogorov–Smirnov p = 0.33 and 0.09, respectively). Proposition 1 in the paper guarantees that this calibration extends to any non-singular baseline covariance structure.

Power. Under Gaussian errors, POISE showed uniformly higher power than POIROT across signal strengths. The advantage was most pronounced at moderate SNR: at SNR = 0.19, POISE achieved 70.4% power versus 46.4% for POIROT—a difference of 24 percentage points. POISE reached 80% power at SNR ≈ 0.24, while POIROT required SNR ≈ 0.29. Under heavy-tailed (t9) errors, the two methods performed comparably, confirming that the spectral approach does not sacrifice robustness under distributional misspecification.

Robustness to vQTLs. We simulated a pure variance-shift null—no true POE, but the heterozygote covariance scaled uniformly relative to homozygotes—and varied the scaling factor κ from 0.1 to 1.1. POIROT rejected the null consistently whenever κ deviated from 1, regardless of whether the deviation was compatible with any additive POE. POISE produced zero false positives for all κ < 1. This follows directly from the eigenvalue truncation in the estimator: when the heterozygote covariance is deflated relative to the homozygote baseline, the leading eigenvalue of the whitened covariance falls below 1 and the POE estimate is forced to zero.

UK Biobank Application: A Strict Superset of POIROT

We applied POISE to 602,000 UK Biobank participants, estimating POEs jointly for BMI, LDL cholesterol, and HDL cholesterol. These three traits have established or plausible links to parent-of-origin biology. Variants were prescreened using Bonferroni-corrected POIROT p-values, and all phenotypes were inverse-normal transformed prior to analysis.

At a Bonferroni threshold of 1.5 × 10⁻⁷, POIROT identified 338 variants, of which 186 passed the effect size criterion. POISE identified 893 variants, of which 320 passed the same criterion. Critically, the POISE-significant set was a strict superset of the POIROT-significant set: every one of the 186 POIROT variants was recovered. POISE additionally identified 134 variants that POIROT did not detect.

The concordant set (variants detected by both methods) was dominated by the extended MHC class I region on chromosome 6, which accounted for 152 of 186 variants. Outside chromosome 6, the concordant set encompassed several canonical lipid-metabolism loci with established POE evidence, including variants at APOB (chromosome 2), the APOA5/APOC3/BUD13/ZPR1 apolipoprotein cluster (chromosome 11), and loci at APOE/TOMM40, CETP, and LIPC. The concordant variants were enriched for coding variants (10.8% exonic) and carried larger absolute effect sizes on BMI than the POISE-exclusive set (mean |β| = 0.312 vs. 0.208).

The 134 POISE-exclusive variants spanned 19 chromosomes and had higher average allele frequencies (mean gnomAD global AF = 0.218 vs. 0.098 in the concordant set), consistent with POISE capturing common-variant signals that lack sufficient heterozygote-variance signature for POIROT to detect. Biologically, the exclusive set included rs12243326 in TCF7L2—the most strongly replicated type 2 diabetes susceptibility gene and a genome-wide significant BMI locus—as well as variants in ABCG8 (sterol transporter), SCARB1 (HDL scavenger receptor SR-BI), and the classic CETP TaqIB polymorphism (rs708272).

Limitations and Next Steps

POISE is complementary to, not a replacement for, phasing-based methods that directly assign parental origin. Phased methods provide directional evidence (maternal vs. paternal) and are not susceptible to non-POE variance heterogeneity. POISE, operating on the full unrelated cohort, trades directional resolution for scale: it can screen hundreds of thousands of variants across the entire biobank population rather than the fraction for which phasing or relatedness inference is feasible.

Two limitations are worth noting. First, like all variance-based methods, POISE cannot fully distinguish a true POE from an inflation-type vQTL arising from gene–environment interaction or epistasis (the eigenvalue truncation protects against deflation-type vQTLs but not inflation). Second, the direction of the parental effect is fundamentally unidentifiable from unlabeled heterozygotes. Hits identified exclusively by POISE will therefore require follow-up with phasing-based or family-based methods to determine whether the effect is maternal or paternal in origin.

Code and Paper

POISE is implemented in Python and openly available at the Bystro GitHub repository. The preprint describes the full method, proofs, and UK Biobank results in detail.

Imagine you spend months running experiments to identify which proteins in the brain drive Alzheimer’s disease progression. You build a model, it predicts outcomes beautifully, and you use it to pick targets for intervention. Then the intervention fails, not because the biology was wrong, but because the model’s predictions and its internal network representation were quietly disagreeing with each other the whole time. This is not a hypothetical. It is a structural flaw in one of the most widely used classes of machine learning models in science today. A new paper from researchers at Emory, Stanford, Duke, and UC Davis introduces a method called generative principal component regression (gPCR) that addresses this flaw head-on.

The problem: two goals, one model

Scientists working with high-dimensional data, thousands of proteins, hundreds of neural frequency bands, tens of thousands of gene expression measurements, face a fundamental tension. On one hand, they want interpretability. Factor models like PCA compress data into a small number of latent “networks,” each corresponding to some underlying biological process. These networks are scientifically meaningful: they can be measured, targeted, and manipulated. On the other hand, they want predictive accuracy. When trying to predict whether a patient has Alzheimer’s, or whether a mouse is under stress, pure interpretability is not enough, you need a model that actually gets the right answer. The standard approach to combining these is principal component regression (PCR): first run PCA to find the latent networks, then regress the outcome on those networks. The problem is that PCA finds the directions of maximum variance in the data, not the directions most predictive of the outcome. If the biologically relevant signal happens to be low-variance, which it often is, PCR misses it entirely.

Supervised Variational Autoencoders: A silver bullet?

Supervised variational autoencoders (SVAEs) try to solve this by adding a predictive objective on top of the generative model. The idea is appealing: train the model to both reconstruct the data and predict the outcome, and you should get representations that are both interpretable and predictive. But SVAEs have a critical structural problem. They use two separate components to do their work: an encoder (which maps observations to the latent space) and a decoder (which reconstructs observations from the latent space). The supervision loss acts only on the encoder, so the encoder learns to represent the outcome well, but the decoder, which defines the generative model and its scientifically interpretable loadings, does not. In other words, the encoder and decoder end up describing two different latent spaces. The encoder says “this brain state is associated with stress.” The decoder says something different. When you use the decoder’s loadings to design a stimulation experiment — as neuroscientists actually do — you are acting on a representation that does not match the model’s own predictions.

We demonstrate this vividly with MNIST digits. They train all three models (logistic regression, SVAE, and gPCR) to distinguish 0s from 1s, then ask each model to “convert” a 1 into a 0 by shifting the image in the direction the model says matters. The SVAE encoder produces a manipulation that its own decoder says is still a 1. gPCR produces a clean, recognizable 0.

The gPCR solution

The key insight behind gPCR is simple: instead of using a flexible encoder that can drift away from the generative model, constrain the encoder to always equal the generative posterior. This means the latent space the model uses for prediction is the same latent space defined by the generative model’s loadings. Mathematically, gPCR maximizes a weighted objective: the standard generative log-likelihood plus a predictive term, where the expectation in the predictive term is taken under the generative posterior rather than a separate encoder distribution. This forces any predictive information to be reflected in the loadings themselves. For linear Gaussian models the generative posterior has a closed form, making this tractable and computationally efficient. The per-iteration cost matches that of standard penalized regression. There is also a supervision strength parameter, but unlike in standard regression, gPCR is relatively insensitive to its precise value. Below a critical threshold, the model behaves like PCA. Above it, it locks on to the predictive subspace. The plateau of good performance is wide, which makes tuning straightforward.

Results: neuroscience and Alzheimer’s

The authors validate gPCR on three real datasets.

Electrophysiology: predicting brain region activity. Using recordings from mice in a stress task, gPCR dramatically outperformed standard PCR and matched the performance of Elastic Net regression — a dedicated predictive model — while retaining the network interpretation PCR provides.

Electrophysiology: predicting social behavior. In a harder task (distinguishing social from non-social interaction), gPCR outperformed LASSO regression and matched Ridge and Elastic Net, despite also having to reconstruct the data. Perhaps more importantly, the coefficients it learned aligned with natural neural frequency bands — unlike LASSO, which arbitrarily zeroed out neighboring frequencies.

Proteomics: Alzheimer’s phenotypes. On a cohort of 300 individuals (140 healthy controls, 160 with Alzheimer’s), gPCR predicted tau tangle and beta amyloid levels as well as the best regression methods, while standard PCR failed. More strikingly, the networks gPCR identified, such as redox homeostasis, oxidant detoxification, mitochondrial function, ubiquitination, align closely with established Alzheimer’s biology. The regression methods did not show the same alignment.

Why this matters for experimental design

The practical stakes here are high. In neuroscience, stimulation experiments are designed based on model loadings: you identify which features (frequency bands, brain regions) are most associated with a latent factor, then apply a targeted stimulus to those features. If the model’s loadings do not actually represent the predictive subspace, which we show is the case for SVAEs, then the stimulation protocol is fundamentally skewed. In the synthetic experiments, gPCR-based stimulation protocols produced an average phenotype shift of 0.89, compared to 0.41 for SVAE and 0.18 for PCR. In a real experiment, that difference could be the gap between a successful intervention and a null result.

Limitations and what comes next

gPCR currently requires the generative posterior to have a closed form, which restricts it to linear Gaussian models. This covers a wide range of applications, but nonlinear relationships between latent variables and observations are left for future work. The authors suggest MCMC-based sampling from the posterior as a potential path forward, at the cost of increased computational complexity. The method also depends on covariance estimation, which can become unstable under extreme data sparsity. This is a common limitation of any second-order statistical approach. Code is available in the Bystro GitHub repository.

The bottom line

gPCR is a principled solution to a problem that has quietly undermined scientific inference in latent variable modeling for years. It matches the predictive performance of regression while preserving the network interpretation of generative models, and unlike SVAEs, it ensures those two things are actually consistent with each other. For researchers who use their models not just to predict but to understand and intervene, that consistency is not a nice-to-have. It is the whole point.

Written by Austin Talbot, Head of ML at bystro.

Read the full publication:

A. Talbot et al., “Generative Principal Component Regression via Variational Inference,” in IEEE Transactions on Signal Processing, vol. 74, pp. 1656-1670, 2026, doi: 10.1109/TSP.2026.3682931.

AI tools have become indispensable for knowledge workers. They draft documents, summarize reports, answer questions, and increasingly, help researchers survey scientific literature. These tasks used to consume hours of focused effort. But there is a well-known shadow hanging over all of this productivity: hallucinations.

An AI hallucination is not a small rounding error. It is when an AI assistant fabricates something such as a paper that never existed, an author who never wrote it, or a journal that never published it and then presents it with the same confident fluency as something completely accurate. In most professional settings, this is an inconvenience. In academic research, pharmaceutical development, regulatory writing, or government policy work, it can be far more damaging. A fabricated citation used in a drug submission, a legal brief, or a published paper is not just embarrassing, it can mislead downstream decisions that affect real outcomes.

We recognized a specific and practical gap: existing AI benchmarks tend to measure things like math reasoning, coding ability, or trivia recall. Very few evaluations test whether an AI can be trusted to produce a factually grounded, comprehensive, and verifiable literature search – a task professionals actually need done well. This evaluation was designed to examine this gap.

What Was Done

Each AI assistant was given the exact same prompts, asking it to generate literature searches across four technically demanding domains:

• Genetics - specifically, admixture models in population genetics

• Electrical Engineering - Kalman filtering and state-space models

• Machine Learning - Bayesian optimization

• Statistics - statistical estimation under shape constraints

The prompts varied in style, ranging from a simple one-sentence request to a full LaTeX-formatted methods section introduction, mimicking the kinds of real inputs a researcher or engineer might actually provide. When an AI assistant’s output was unclear about format, we requested a BibTeX file. Gemini, which initially returned very few references, was given a minimal follow-up prompt requesting more sources. Every single returned reference was then manually evaluated in two stages:

• Does this paper exist? A citation was considered real only if a paper precisely matching the title could be verified to exist.

• Are the details correct? For each real paper, the researchers checked the journal name, authors, publication year, page numbers, and volume/issue numbers for accuracy. Omissions were excluded from the analysis.

This is a rigorous, no-shortcuts evaluation that is an open-ended, expert-verified, assessment of factual reliability in a domain that matters to researchers.

Outcomes

AI assistant testing reveals a wide performance gap in their ability to gather and display the results of literature searches.

The three panels below quantify important features of AI assistant performance:

• Panel 1 - The number of references returned by each AI assistant summed across all four prompts. As relevance is difficult to quantify objectively and not the subject of this analysis, it was not measured for the results returned by each AI assistant. bystro THiNK returned 1.7 times as many results as the runner-up and 5.5 times as many results as the poorest performer.

  

• Panel 2 - The percent of references returned that were hallucinated. The best performers, bystro THiNK and ChatGPT, returned no completely hallucinated references, but for THiNK, the fraction was 0 out of 177, while for ChatGPT it was 0 out of 58. Claude Sonnet and Gemini Pro produced 3-6% hallucinated references.

  

• Panel 3 - The percent of references with fully accurate information across AI assistants. Omissions were not included in the analysis since missing data is not as damaging as incorrect data. bystro THiNK output contained no incorrect data across all 177 references returned. The next best performer, ChatGPT, returned 5 out of 58 references with inaccuracies.

Final Thoughts

All of the models characterized in this work are good AI assistants for many applications, but it is important to select the right tool for the job: it is not effective to bash on a nail with a pencil since a hammer works better, but try writing with a hammer…

This benchmarking study makes a clear, practical case: when it comes to generating scientific literature searches, not all AI tools perform equally, and the differences are large enough to matter in practice. bystro THiNK demonstrated both the broadest coverage and the highest factual precision with 177 real references, zero hallucinations, and zero errors. Its closest competitor returned fewer than one-third as many references and still had citation errors that a human reviewer would need to catch.

The broader implication is equally important: standard AI benchmarks that dominate headlines about GPT and Gemini performance are largely measuring the wrong things for researchers, scientists, and technical writers. Getting the right answer on a standardized reasoning test is not the same as reliably synthesizing and accurately citing real scientific evidence.

The task-grounded, expert-verified, benchmarking evaluation presented here represents an important complement to the standard evaluation landscape. For anyone using AI in technical or scientific work, it is this kind of evaluation that should matter most.

Written by Austin Talbot, Head of ML, and Martin Zillmann, Head of BD at bystro.

A recent study looking at a technology to improve the performance of 4 LLMs (GPT-3.5, GPT-4, Vicuna-13B, and ChatGLM-6B) measured their performance on 4 multiple-choice medical question datasets: MedQA (US Medical Licensing Examination), PubMedQA (original and revised versions), MedMCQA, and EYE500 (Wang, et al. 2025). The authors found that accuracy across the data set, without augmentation using their technology, varied between 28-68% across the models and hallucinations varied between 35-68% across the models. Among hallucinations, the main types were factual inconsistencies (42%) and logical inconsistencies (24%).

With this backdrop, it is important to demonstrate that AI models can provide accurate results when given complex tasks. bystro THiNK, while containing elements of LLMs, utilizes four core agents in its workflow:

bystro THiNK was purpose-built to perform complex scientific tasks and should inherently have better performance. One of the key features of THiNK that should increase reliability is the cross-verification for the results. The actual performance of the THiNK tool will be explored below.

What Was Done

This evaluation was performed using a publication exploring the miRNA transcriptome and proteome in a Pompe disease mouse model (Merberg D, et al 2024) and the associated dataset available from Zenodo. This manuscript was selected because understanding miRNA and protein expression patterns in the Pompe disease mouse model involves the analysis of very large data sets using multiple tools to align and clean the raw data and multiple statistical tools to derive meaning from the data.

The initial prompt was:

bystro responded with the clarifying questions below rather than jumping into analyses as many similar systems do.

Here are a few questions that could be helpful to address:

Q1. Do you have the raw or processed data files (e.g., miRNA expression counts, proteomics abundance tables) from this study that you’d like me to analyze, or should I focus on reviewing the paper’s methods and findings and attempt to replicate them using any publicly deposited data (e.g., from GEO or PRIDE/ProteomeXchange)?

Q2. When you say “replicate the findings,” do you mean: (a) a full computational replication using the original or deposited data (re-running the statistical analyses from scratch), (b) a critical review of their methods and results to assess reproducibility and validity, or (c) applying their analytical approach to your own dataset? This will significantly affect the scope of the work.

Q3. If you do have your own data, could you share or describe the file formats and contents (e.g., normalized miRNA read counts matrix, TMT/label-free proteomics abundance file, sample metadata with disease vs. control labels)? Knowing the exact format will help me set up the correct analysis pipeline.

The follow up prompt addressed some of these questions:

With this clarification regarding the user’s intentions, the bystro agents began the analysis and construction of a response, which was completed in roughly 32 minutes.

Given the very small amount of information and guidance that was given in the prompt, it surprised me that the model really approached the problem much like a research scientist might, starting with retrieval of the data set, deciding what methods to use to clean and annotate the data, and, most importantly, deciding what statistical tools to use. For statistical tools, it is often very difficult to identify the appropriate statistical tool for a problem given the sample size, the type of measurement, and the effect being sought — this is probably the heaviest lift in this task. In addition, there was a task that was performed by the agents that was not explicitly requested in the prompt, a critique of the original work — a bonus of sorts.

Some of the tasks that were performed by the agents to complete the task are listed below.

• Critical analysis of the referenced paper

• Identification of the location of the Zenodo data repository

• miRNA Pipeline

  • Adapter: QIAseq 3’ adapter (AACTGTAGGCACCATCAAT) — identified forensically from reads (not disclosed in the paper)

  • Trimming: cutadapt; 18-26 bp length filter

  • Alignment: bowtie v1 to miRBase v22.1 mouse mature miRNAs; ≤1 mismatch

  • DE analysis: edgeR GLM (Bioconductor). DGEList → filterByExpr → TMM normalization → common/trended/tagwise dispersion → GLM fit → LRT for KO-HET contrast → BH FDR. Thresholds: FDR ≤ 0.05, |FC| ≥ 2

• Proteomics Pipeline

  • Filtering: Master proteins, ≥2 unique peptides

  • Normalization: log2 → quantile normalization (11 serum samples)

  • Model: One-way ANOVA (4 groups) per protein

  • Contrast: KO − HET

  • Variance moderation: Empirical Bayes (Smyth 2004); d0 = 2.06, s02 = 0.205

  • Testing: Moderated t-statistics; BH FDR

  • Thresholds: adj. p < 0.01, |FC| > 4

• Critical comparison across results

• Listing of caveats and limitations associated with the analysis

Outcomes

Below are the main outcomes of the analysis copied directly from the Results:

I have to show the figure below that was generated during analysis because we all know what it would take to do this ourselves (see above). And, I am guessing, based on personal experience, that several person-days of effort were saved using THiNK, creating the opportunity to do other, even more important things.

Figure 1. Panels A and B show volcano plots for serum miRNAs and the serum proteome, respectively, where down-regulated species are to the left (blue) and up-regulated species are to the right (red) and the y-axis is log of the p-value for the confidence of the observation. Panel C shows the measured concordance between data published by Merberg D, et al 2024 and this analysis. Panel D is a fold-change for key miRNAs, where blue indicates down-regulation and red indicates up-regulation.

It is important to note the study limitations identified by bystro THiNK.

• Alignment pipeline differs: We aligned to miRBase mature sequences rather than the mm10 genome, likely explaining the higher DE miRNA count

• miRDB vs. TargetScan: Different target prediction databases have different coverage

• Pseudoreplication: This caveat applies to both the original and replicated analyses

• This is a scientific exploratory replication and does not constitute clinical advice

Final Thoughts

For me, writing this, it is a bit of a slide rule vs calculator moment. Yes, the slide rule will get the job done, but the calculator is so much faster and more reliable. bystro THiNK is clearly the calculator and me doing the analysis by hand using conventional tools is the slide rule. The real beauty of AI tools is that they can act as efficiency amplifiers in the hands of experts. Why experts, you ask? As Plato said, knowing what question to ask is often more important than knowing the answer. However, if the model’s responses are only correct 80% of the time, it is likely that whatever efficiency gain was achieved by shrinking the analysis time will be offset by the time needed for re-analysis to identify the 20% incorrect results.

We should always keep in mind that, by removing ourselves completely from the analysis through the use of sophisticated tools such as AI agents and LLMs, we give up both the elements of process understanding and control. A quick example to highlight the point: in this post, without any prior understanding of the complex workflow behind the required analysis, we were able to produce a very intricate and persuasive result. But, if we needed to verify the results ourselves, full process understanding and the ability to perform all of the tasks using proven tools would be required.

For an AI tool to be useful as an efficiency amplifier, one has to know that the tool can be trusted and that the model’s output will be accurate and without hallucinations. Hopefully, this posting about bystro THiNK’s capabilities to replicate known outcomes is a move in that direction.

Written by Martin Zillmann, Head of BD at bystro.