Simplifying and integrating enterprise technology can help companies move faster, respond to disruptions, and build a stronger foundation for AI, says Harish Manohar, SAP IT Director at Jabil. As companies scale, the technology supporting operations can become a liability just as quickly as it becomes an asset. Disconnected systems, site-specific tools, spreadsheets, and manual workarounds can create data silos that make it harder to spot problems early, coordinate responses, and make decisions with confidence. For Jabil, a global manufacturing company with more than 100 sites across more than 30 countries, the answer has been to make integration and simplification a priority. The company adopted a "simplify-first, then-innovate mindset," says Harish Manohar, SAP IT director at Jabil, recognizing that adding new technologies without first reducing complexity risks creating more risk. The goal is to standardize processes, consolidate where possible, and establish a more consistent data backbone across the organization. "Any innovation without simplification is going to add more complexity," Manohar says. That philosophy also changes how Jabil approaches modernization. "Any modernization or transformation should add measurable business value," Manohar says. The company is focused on connecting processes end-to-end across its supply chain and creating a foundation that can scale consistently across regions.

What happens when AI runs out of pictures? A hospital may only ever collect a few dozen scans of a rare condition - for example, an unusual tumour. The radiology department wants software to flag this on a scan - not to replace the specialist, but so a hospital without one still gets their scan checked the same way. The clinicians know what they're looking for. Over a decade, the hospital might gather 40 confirmed cases.

When you purchase through links in our articles, we may earn a small commission. Instead of a set number of chats and AI-generated podcasts each day, Google Notebook users will get "compute-specific" limits along with five-hour usage windows. Fans of Google Notebook's AI-generated podcasts may have to settle for fewer of them once a new usage-tracking method goes into effect next week. Starting September 2, Google Notebook will switch over to "compute-specific" usage limits that track the "complexity" of your Google Notebook prompts and requests. Also coming with the "compute-specific" usage limits change are five-hour usage windows, along with weekly usage limits, according to a Google support page, replacing the current daily and monthly limits.

AIhub monthly digest: August 2026 - IJCAI-ECAI in Bremen, the mathematics of simplicity, and does AI change the way we think? Welcome to our monthly digest, where you can catch up with any AIhub stories you may have missed, peruse the latest news, recap recent events, and more. This month, we report on events at IJCAI-ECAI 2026, learn about the mathematics of simplicity, investigate the accountability vacuum, and find out how AI changes the way we think. On Rashomon sets, the mathematics of simplicity, and why we don't need black boxes: an interview with Cynthia Rudin In the latest in our series of interviews with AI pioneers, we hear from Cynthia Rudin about interpretability, noise, and the case against complexity for complexity's sake. The 35th International Joint Conference on Artificial Intelligence and the 29th European Conference on Artificial Intelligence (IJACI-ECAI 2026) was held from 15-21 August, in Bremen, Germany.

Robots walking down the street, surrounded by astounded onlookers, is an increasingly common sight. But these machines aren't yet the do-it-all assistants you'd want working in a kitchen or factory, and a major bottleneck is data. Much like humans, robots learn best by experience. The challenge is that it's labor-intensive and time-consuming to physically teach these machines so many actions across different settings. "One natural idea is to use simulation as a training ground. While there has been significant progress over the last few years in the physics engines that power robotics simulators, one of the remaining challenges has been creating sufficiently rich and diverse simulation content to capture the complexity of the real world," says Russ Tedrake, the Toyota Professor of Electrical Engineering and Computer Science (EECS), Aeronautics and Astronautics, and Mechanical Engineering at MIT, and a principal investigator at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL).

RoboCup 2026 saw history made, as two teams of 11 humanoids took to the soccer field, the first time a full complement of robots has competed. The game saw (Bremen, Germany) take on (Leipzig, Germany), with both sides using machines designed by Booster Robotics. Back in 1997, RoboCup's founders set the lofty goal of developing a team of autonomous robots that could beat the human World Cup champions by 2050. There has been significant progress since those early days, and this match saw another step towards that ambition. "This match shows how far humanoid robotics has come," said Ubbo Visser, President of the RoboCup Federation.

The 35th International Joint Conference on Artificial Intelligence and the 29th European Conference on Artificial Intelligence (IJACI-ECAI 2026) distinguished paper awards recognise some of the best papers presented at the conference each year. This year, three articles were named as distinguished papers . In approval-based budget division, the task is to allocate a divisible resource to the candidates based on the voters' approval preferences over the candidates. For this setting, Brandl et al. [2021] have shown that no distribution rule can be strategyproof, efficient, and fair at the same time. In this paper, we aim to circumvent this impossibility theorem by focusing on approximate strategyproofness.

Hosted by Ben Byford, The Machine Ethics Podcast brings together interviews with academics, authors, business leaders, designers and engineers on the subject of autonomous algorithms, artificial intelligence, machine learning, and technology's impact on society. This month we speak with Demetrios for the second time about: what ML and MLops are, narrow machine learning being still relevant, vibe coding, working with agents, talking to your computer, unknown productivity gains of LLMs, the chat interface as a bad interface for all knowledge, AIs that know when they're wrong, the lack of ground truth, and more Demetrios founded the largest community dealing with producitonizing AI and ML models. The MLOps Community is now where tens of thousands of practitioners come to learn from one another. In his free time he can be found building stone stackings in the woods with his daughters. This podcast was created and is run by Ben Byford and collaborators.

Machine learning algorithms and their training datasets have grown substantially in both size and complexity over the past decade. This increased model complexity has made it challenging to interpret and predict their behavior in unobserved scenarios. Hence, many applications that involve societal decisions still rely on simple, interpretable models like linear regression, often after feature engineering. Examples of such applications include predicting national housing prices, estimating wages across industries, forecasting loan amounts across banks, predicting life insurance premiums across groups, and projecting energy consumption across communities [CGKMN24]. A shared safety and sometimes legal concern across the above applications is the potential for wildly different model qualities for different distributions, i.e., outputting a notably worse model for some source data distributions [Dat14; BS16; HPS16; VVB18; SBFVV19; BHJKR21; CGNSG23; Cho16; KLMR18; ADW19; CGKMN24; SVWZ24].

Abstractitations, recent work explores online RLHF that iterates between generating on-policy responses and collecting preferences [Lee et al., 2024, Park et al., 2022]. Among online The Self-Improving Alignment (SAIL) algorithmapproaches, SAIL reduces a bilevel alignment formulation addresses distribution shift by reducing a bilevelto a computationally efficient single-level surrogate and formulation of the problem to an efficient, single-reports strong empirical gains [Ding et al., 2024]. Empirically, SAIL has demonstratedisting online pipelines are largely heuristic and do not anastrong performance on this task. However, a for-lytically control the distributional shift induced by iterative mal analysis of its convergence properties has beendata collection [Chakraborty et al., 2024, Shen et al., 2024], lacking. We identify a key theoretical challenge: which has been linked to suboptimal performance in practice the standard SAIL objective function is not guar- [Sharma et al., 2024]. To address this limita-A growing line of work argues that the coupling between tion, we propose a regularized objective, SAILreward learning and policy updates is fundamentally bilevel and should be modeled as such [Chakraborty et al., 2024].RevKL, which incorporates a reverse KullbackAs a follow-up, Ding et al. [2024] reduces the bilevel align-Leibler (KL) divergence penalty to improve the optimization landscape. Our central theoretical con-ment objective to a tractable single-level surrogate and retribution is to prove that this regularized objectiveports strong empirical gains, yet it lacks formal convergence satisfies the Polyak-Lojasiewicz (PL) conditionguarantees. Related theoretical analyses in bilevel/RLHFstyle problems exist [e.g., Yang et al., 2025, Chakrabortywithin a bounded parameter space. We establish et al., 2024, Gaur et al., 2025], yet they either focus onglobal convergence guarantees, achieving a nearlinear sample complexity.