Former US treasury secretary Larry Summers is stepping down from the board at OpenAI, a week after a tranche of emails between him and late convicted sex offender Jeffrey Epstein was released. Summers said in a statement to the BBC that he was grateful for the opportunity to have served, excited about the potential of the company, and look forward to following their progress. Summers, who was also once the president of Harvard University, said on Monday that he would be stepping back from public commitments over his ties to Epstein. The recently released emails showed Summers communicated with Epstein until the day before Epstein's 2019 arrest for the alleged sex trafficking of minors. In a statement, the artificial intelligence company said it respected Summers' decision to resign.

A group of quantum physicists at Spanish firm Multiverse Computing claims to have created a version of the powerful reasoning AI model DeepSeek R1 that strips out the censorship built into the original by its Chinese creators. In China, AI companies are subject to rules and regulations meant to ensure that content output aligns with laws and "socialist values." As a result, companies build in layers of censorship when training the AI systems. When asked questions that are deemed "politically sensitive," the models often refuse to answer or provide talking points straight from state propaganda. Multiverse Computing specializes in quantum-inspired AI techniques, which it used to create DeepSeek R1 Slim, a model that is 55% smaller but performs almost as well as the original model. It allowed them to identify and remove Chinese censorship so that the model answered sensitive questions in much the same way as Western models.

The Relay app allows users to track their porn-free streaks and get group support. Its creators say they're taking a stand against porn and AI erotica. Jamie would meticulously schedule his days around finding time alone to watch porn and masturbate--often up to five times a day. The 32-year-old Michigan engineer, who did not want to use his real name due to privacy concerns, first watched porn at the impressionable age of 12, but never realized he had a problem until just after his father's funeral three years ago. "I didn't shed a single tear," he says.

A group of quantum physicists claims to have created a version of the powerful reasoning AI model DeepSeek R1 that strips out the censorship built into the original by its Chinese creators. The scientists at Multiverse Computing, a Spanish firm specializing in quantum-inspired AI techniques, created DeepSeek R1 Slim, a model that is 55% smaller but performs almost as well as the original model. Crucially, they also claim to have eliminated official Chinese censorship from the model. In China, AI companies are subject to rules and regulations meant to ensure that content output aligns with laws and "socialist values." As a result, companies build in layers of censorship when training the AI systems.

The increasing popularity of online two-sided markets such as ride-sharing, accommodation and freelance labor platforms, goes hand in hand with new socioeconomic challenges. One major issue remains the existence of bias and discrimination against certain social groups.

President Donald Trump criticizes 'Woke AI' and excessive state regulation while House Republican leaders consider including AI preemption language in defense legislation.

Small and medium-sized enterprises (SMEs) represent 99.9% of U.S. businesses yet remain systematically excluded from AI due to a mismatch between their operational scale and modern machine learning's data requirements. This paper introduces SmallML, a Bayesian transfer learning framework achieving enterprise-level prediction accuracy with datasets as small as 50-200 observations. We develop a three-layer architecture integrating transfer learning, hierarchical Bayesian modeling, and conformal prediction. Layer 1 extracts informative priors from 22,673 public records using a SHAP-based procedure transferring knowledge from gradient boosting to logistic regression. Layer 2 implements hierarchical pooling across J=5-50 SMEs with adaptive shrinkage, balancing population patterns with entity-specific characteristics. Layer 3 provides conformal sets with finite-sample coverage guarantees P(y in C(x)) >= 1-alpha for distribution-free uncertainty quantification. Validation on customer churn data demonstrates 96.7% +/- 4.2% AUC with 100 observations per business -- a +24.2 point improvement over independent logistic regression (72.5% +/- 8.1%), with p < 0.000001. Conformal prediction achieves 92% empirical coverage at 90% target. Training completes in 33 minutes on standard CPU hardware. By enabling enterprise-grade predictions for 33 million U.S. SMEs previously excluded from machine learning, SmallML addresses a critical gap in AI democratization. Keywords: Bayesian transfer learning, hierarchical models, conformal prediction, small-data analytics, SME machine learning

Contract management involves reviewing and negotiating provisions, individual clauses that define rights, obligations, and terms of agreement. During this process, revisions to provisions are proposed and iteratively refined, some of which may be problematic or unacceptable. Automating this workflow is challenging due to the scarcity of labeled data and the abundance of unstructured legacy contracts. In this paper, we present a modular framework designed to streamline contract management through a retrieval-augmented generation (RAG) pipeline. Our system integrates synthetic data generation, semantic clause retrieval, acceptability classification, and reward-based alignment to flag problematic revisions and generate improved alternatives. Developed and evaluated in collaboration with an industry partner, our system achieves over 80% accuracy in both identifying and optimizing problematic revisions, demonstrating strong performance under real-world, low-resource conditions and offering a practical means of accelerating contract revision workflows.

Recent advances in speech-enabled AI, including Google's NotebookLM and OpenAI's speech-to-speech API, are driving widespread interest in voice interfaces globally. Despite this momentum, there exists no publicly available application-specific model evaluation that caters to Africa's linguistic diversity. We present AfriSpeech-MultiBench, the first domain-specific evaluation suite for over 100 African English accents across 10+ countries and seven application domains: Finance, Legal, Medical, General dialogue, Call Center, Named Entities and Hallucination Robustness. We benchmark a diverse range of open, closed, unimodal ASR and multimodal LLM-based speech recognition systems using both spontaneous and non-spontaneous speech conversation drawn from various open African accented English speech datasets. Our empirical analysis reveals systematic variation: open-source ASR models excels in spontaneous speech contexts but degrades on noisy, non-native dialogue; multimodal LLMs are more accent-robust yet struggle with domain-specific named entities; proprietary models deliver high accuracy on clean speech but vary significantly by country and domain. Models fine-tuned on African English achieve competitive accuracy with lower latency, a practical advantage for deployment, hallucinations still remain a big problem for most SOTA models. By releasing this comprehensive benchmark, we empower practitioners and researchers to select voice technologies suited to African use-cases, fostering inclusive voice applications for underserved communities.