Scaling model size, training data, and compute power have driven advances in large language models (LLMs), but these approaches are reaching saturation as human-generated text is exhausted and further gains diminish. We propose experience scaling, a framework for continuous post-deployment evolution for LLMs through autonomous interaction with the environment and collaborative sharing of accumulated experience. The framework captures raw interactions, distills them into compact, reusable knowledge, and periodically refines stored content to preserve relevance and efficiency. We validate the framework in simulated real-world scenarios involving generalization to previously unseen but related tasks, repetitive queries, and over-saturated knowledge stores. Across all settings, experience scaling improves accuracy, sustains performance over time, and maintains gains when applied to novel situations. These results demonstrate that structured post-deployment learning can extend LLM capabilities beyond the limits of static human-generated data, offering a scalable path for continued intelligence progress.

The distribution of streaming data often changes over time as conditions change, a phenomenon known as concept drift. Only a subset of previous experience, collected in similar conditions, is relevant to learning an accurate classifier for current data. Learning from irrelevant experience describing a different concept can degrade performance. A system learning from streaming data must identify which recent experience is irrelevant when conditions change and which past experience is relevant when concepts reoccur, \textit{e.g.,} when weather events or financial patterns repeat. Existing streaming approaches either do not consider experience to change in relevance over time and thus cannot handle concept drift, or only consider the recency of experience and thus cannot handle recurring concepts, or only sparsely evaluate relevance and thus fail when concept drift is missed. To enable learning in changing conditions, we propose SELeCT, a probabilistic method for continuously evaluating the relevance of past experience. SELeCT maintains a distinct internal state for each concept, representing relevant experience with a unique classifier. We propose a Bayesian algorithm for estimating state relevance, combining the likelihood of drawing recent observations from a given state with a transition pattern prior based on the system's current state.

Artificial intelligence (AI) is better at hiring staff than human bosses, but companies still don't trust it in the recruitment process, a new study finds. Researchers in London have conducted a review of previous studies that assessed the effectiveness of AI as a recruitment tool. They found AI is'equal to or better than' human recruiters when it comes to hiring people who go on to perform well at work. Although AI had limited abilities in predicting employee outcomes after they were hired, AI is'fairer' and marked a substantial improvement over humans, they reveal. AI also boosts the'fill-rate' for open positions and is'mostly better than humans' at improving diversity in the workplace.

AI is currently booming and with the kind of advancements that are happening, there is no stopping. Every industry, from manufacturing, retail, pharmaceuticals, to healthcare and finance, uses AI and machine learning (a subset of AI) tools to automate mundane tasks, sift through several GBs of data to make an accurate business decision, and improve customer service amongst many other tasks. So while this is all true and evident, everyone wants to hop on the train that is artificial intelligence. Artificial Intelligence is a unique field. As a young graduate, you might not have any solid information about the field and no relevant experience. A few modules in colleges will not be much of help, and employers have a hard time looking for candidates with relevant experience.

Industries are adopting artificial intelligence and machine learning on a grand scale. Tech companies both big and small are working on artificial intelligence projects that will shape the future of industries such as healthcare, banking, business, education and more. We're not quite at the point where everything is automated and machine-run, but we're getting there. These technologies are all around us, quietly running in the background and keeping operations chugging along. AI is silently reshaping our society by affecting how we get things done, how we vote, how we purchase goods, and the choices we make.

Formerly Statoil, we are 20,000 committed colleagues developing oil, gas, wind and solar energy in more than 30 countries worldwide. Driven by our Nordic urge to explore beyond the horizon, and our dedication to safety, equality and sustainability, we're building a global business on our values and the energy needs of the future. The role of the COO organisation is to drive consistent long term safe and efficient operational performance and value creation. The COO organisation is responsible for the corporate improvement programs and works closely with the line in continuously improving Equinor's performance. Are you interested in building great machine learning products that can help Equinor transform into a greener and more competitive energy company?

Personalization has been around for quite a while, from back in the days segmented direct-mail campaigns to the more recent focus on personalization driven by Artificial Intelligence (automated personalization at scale). It seems AI is the solution that will help organizations currently struggling to launch personalization by making everything self-tuned, self-optimized, and so forth. The theme of marketing events, of billboards in the San Francisco area, and of every sort of tech vendor is that it's the answer for everything--just push the AI button and all is magically optimized. Yet, as a consumer, it's very rare that I experience a really great personalized digital experience. In fact, many marketers and agencies are not doing AI-based personalization at all--or even simple rules-based personalization, for that matter.

As a modern marketer, you know all too well how challenging it is to satisfy today's demanding buyers. You're doing your best to meet their ever-growing expectations for personalized and relevant experiences in the moment. It's clear that marketers who shift from volume-based, one-size-fits-most communication to engaging segments of one with high-value experiences will be the ones who win over the hearts and minds of their customers. The good news is that you can pivot to engage on a personal level--even on a massive scale--with artificial intelligence (AI) built for marketers. AI helps make sense of all your customer data in the moment so you can truly deliver value-based, relevant experiences, and conversations.

Abstract--Manufacturers of safety-critical systems must make the case that their product is sufficiently safe for public deployment. Much of this case often relies upon critical event outcomes from real-world testing, requiring manufacturers to be strategic about how they allocate testing resources in order to maximize their chances of demonstrating system safety. This work frames the partially observable and belief-dependent problem of test scheduling as a Markov decision process, which can be solved efficiently to yield closed-loop manufacturer testing policies. By solving for policies over a wide range of problem formulations, we are able to provide high-level guidance for manufacturers and regulators on issues relating to the testing of safety-critical systems. This guidance spans an array of topics, including circumstances under which manufacturers should continue testing despite observed incidents, when manufacturers should test aggressively, and when regulators should increase or reduce the real-world testing requirements for an autonomous vehicle. I. INTRODUCTION Confidence must be established in safety-critical systems such as autonomous vehicles prior to their widespread release. Establishing confidence is difficult because the space of driving scenarios is vast and accidents are rare. Automotive manufacturers can build confidence by conducting test drives on public roadways and make the safety case based on the frequency of observed hazardous events like disengagements and traffic accidents. Each manufacturer must devise a testing strategy capable of providing sufficient evidence that their system is safe enough for widespread adoption. Real-world testing that is too aggressive may yield hazardous events that diminish confidence in system safety. However, a manufacturer that is reluctant to test their product may forfeit opportunities to identify and address shortcomings, and may ultimately not be able to compete in the market. The fundamental tension between the desire to thoroughly test a product and the urgency to forego further testing in favor of bringing the product to market is not unique to the automotive industry.

Leading organizations are aligning marketing around a customer journey strategy, leveraging data analytics, and embracing AI -- all part of a heavily integrated, omnichannel effort built on a solid martech backbone. Buzzwords like CX and AI are dominating marketing discussions today. With the focus pointed so heavily on emerging concepts like these -- purely concentrated in digital channels and new technologies -- how are marketers' priorities, roles and responsibilities shifting? What new marketing technologies are critical to success today, and which ones will be crucial in the next couple years? Salesforce set out to find the answers to these questions.