The head of Amazon Web Services has big plans to offer AI tools to businesses, but says that replacing coders with AI is "a non-starter for anyone who's trying to build a long-term company." Amid the breathless coverage and relentless AI hype of recent years, one of the world's biggest tech companies--Amazon--has been notably absent. Matt Garman, the CEO of Amazon Web Services, is looking to change that. At the recent AWS re:Invent conference, Garman announced a bunch of frontier AI models, as well as a tool designed to let AWS customers build models of their own. That tool, Nova Forge, allows companies to engage in what's known as custom pretraining--adding their data in the process of building a base model--which should allow for vastly more customized models that suit a given company's needs. Sure, it doesn't quite have the sexiness of a Sora 2 announcement, but that's not Garman's goal: He's less interested in mass consumer use of AI and more interested in enterprise solutions that'll integrate AI into all of AWS's offerings--and have a material impact on a corporate P&L. For this week's episode of, I caught up with Garman after AWS re:Invent to talk about what the company announced, whether he feels behind in the AI race, how he thinks about managing huge teams (and managing internal dissent), and why he's not convinced that AI is (or should be) the great job thief of our era. We always start these conversations with some very quick questions, like a warmup. If AWS had a mascot, what would it be? We have a big S3 bucket sometimes that goes around, so we'll call it that. Sorry, what is an S3 bucket? An S3 bucket is like a thing that you store your S3 objects in, but we actually have a large foam big bucket that walks around and actually looks like a paint bucket. So you do have a mascot. Well, S3 has a bucket, it has a mascot. It's probably the closest we have, and I like it. What's the most expensive mistake you've ever made? Personally, the most expensive mistake I ever made was playing basketball too long and I tore my Achilles. So that cost me about nine months of being able to walk. I probably should have known that into my thirties I was well past basketball-playing age.

Ben & Jerry's row deepens as three board members removed Three members of Ben & Jerry's independent board will no longer be eligible to serve in their roles, after the ice cream company introduced a new set of governance practices. These include a nine-year limit set on board members' terms. Chair Anuradha Mittal, who earlier said she had no plans to resign under pressure, is among those affected. The move was criticised by the company's co-founder Ben Cohen, who called it a blatant power grab designed to strip the board of legal authority and independence. His remarks are the latest in a long-running row between Ben and Jerry's and its owner over the Cherry Garcia maker's social activism and the continued independence of its board.

What is in the 28-point US plan for Ukraine? 'Ukraine is running out of men, money and time' Can the US get all sides to end the war? Why is Europe opposing Trump's peace plan? A Russian drone attack killed a 62-year-old Ukrainian man as he was riding a bicycle in the Velyka Pysarivka community of Ukraine's Sumy region, Governor Oleh Hryhorov said in a post on the Telegram messaging app. Russian forces launched 850 attacks on Ukraine's Zaporizhia region in a single day, injuring 14 people and damaging houses, cars and infrastructure, Governor Ivan Fedorov said on Telegram.

UK launches taskforce to'break down barriers' for women in technology The government has launched a new taskforce it says will help women enter, stay and lead in the UK tech sector. Led by technology secretary Liz Kendall, it will see female leaders from tech companies and organisations advise the government on how to boost diversity and economic growth in the industry. BCS, the Chartered Institute for IT, recently suggested women accounted for only 22% of those working in IT specialist roles in the UK. Ms Kendall said the Women in Tech group would break down the barriers that still hold too many people back. When women are inspired to take on a role in tech and have a seat at the table, the sector can make more representative decisions, build products that serve everyone, she said.

While large margin classifiers are originally an outcome of an optimization framework, support vectors (SVs) can be obtained from geometric approaches. This article presents advances in the use of Gabriel graphs (GGs) in binary and multiclass classification problems. For Chipclass, a hyperparameter-less and optimization-less GG-based binary classifier, we discuss how activation functions and support edge (SE)-centered neurons affect the classification, proposing smoother functions and structural SV (SSV)-centered neurons to achieve margins with low probabilities and smoother classification contours. We extend the neural network architecture, which can be trained with backpropagation with a softmax function and a cross-entropy loss, or by solving a system of linear equations. A new subgraph-/distance-based membership function for graph regularization is also proposed, along with a new GG recomputation algorithm that is less computationally expensive than the standard approach. Experimental results with the Friedman test show that our method was better than previous GG-based classifiers and statistically equivalent to tree-based models.

Proper quantification of predictive uncertainty is essential for the use of machine learning in safety-critical applications. V arious uncertainty measures have been proposed for this purpose, typically claiming superiority over other measures. In this paper, we argue that there is no single best measure. Instead, uncertainty quantification should be tailored to the specific application. To this end, we use a flexible family of uncertainty measures that distinguishes between total, aleatoric, and epistemic uncertainty of second-order distributions. These measures can be instantiated with specific loss functions, so-called proper scoring rules, to control their characteristics, and we show that different characteristics are useful for different tasks. In particular, we show that, for the task of selective prediction, the scoring rule should ideally match the task loss. On the other hand, for out-of-distribution detection, our results confirm that mutual information, a widely used measure of epistemic uncertainty, performs best. Furthermore, in an active learning setting, epistemic uncertainty based on zero-one loss is shown to consistently outperform other uncertainty measures.

Understanding and distinguishing temporal patterns in time series data is essential for scientific discovery and decision-making. For example, in biomedical research, uncovering meaningful patterns in physiological signals can improve diagnosis, risk assessment, and patient outcomes. However, existing methods for time series pattern discovery face major challenges, including high computational complexity, limited interpretability, and difficulty in capturing meaningful temporal structures. To address these gaps, we introduce a novel learning framework that jointly trains two Transformer models using complementary time series representations: shapelet-based representations to capture localized temporal structures and traditional feature engineering to encode statistical properties. The learned shapelets serve as interpretable signatures that differentiate time series across classification labels. Additionally, we develop a visual analytics system -- SigTIme -- with coordinated views to facilitate exploration of time series signatures from multiple perspectives, aiding in useful insights generation. We quantitatively evaluate our learning framework on eight publicly available datasets and one proprietary clinical dataset. Additionally, we demonstrate the effectiveness of our system through two usage scenarios along with the domain experts: one involving public ECG data and the other focused on preterm labor analysis.

In Data Science, entities are typically represented by single valued measurements. Symbolic Data Analysis extends this framework to more complex structures, such as intervals and histograms, that express internal variability. We propose an extension of multiclass Fisher's Discriminant Analysis to interval-valued data, using Moore's interval arithmetic and the Mallows' distance. Fisher's objective function is generalised to consider simultaneously the contributions of the centres and the ranges of intervals and is numerically maximised. The resulting discriminant directions are then used to classify interval-valued observations.To support visual assessment, we adapt the class map, originally introduced for conventional data, to classifiers that assign labels through minimum distance rules. We also extend the silhouette plot to this setting and use stacked mosaic plots to complement the visual display of class assignments. Together, these graphical tools provide insight into classifier performance and the strength of class membership. Applications to real datasets illustrate the proposed methodology and demonstrate its value in interpreting classification results for interval-valued data.

More UK families will know what sacrifice for our nation means as the nation seeks to deter a potential confrontation with Russia, the head of the military has said. Sir Richard Knighton said the country's security cannot be outsourced to the armed forces and requires a whole-of-society response, including harnessing UK universities and manufacturing. While the chief of the defence staff suggested there was only a remote chance of Russia directly attacking the UK, he told an event at the Royal United Services Institute that so-called hybrid attacks showed the threat was worsening . He referenced a Russian spy ship that was recently suspected of mapping undersea cables near UK waters. Every day the UK is subject to an onslaught of cyber-attacks from Russia and we know that Russian agents are seeking to conduct sabotage and have killed on our shores, he added.

How the US left Ukraine exposed to Russia's winter war Will Europe use frozen Russian assets to fund war? How can Ukraine rebuild China ties? Ukraine has carried out a successful underwater drone strike on a Russian submarine in the port of Novorossiysk, causing critical damage to the vessel, its domestic security service says. In a statement on Monday, the Security Service of Ukraine (SBU) said the Kilo-class submarine was knocked out of operation in the first such attack by Sea Baby drones. The SBU said the submarine "carried four Kalibr cruise missile launchers" used to strike Ukrainian territory.