We deployed 100 reinforcement learning (RL)-controlled cars into rush-hour highway traffic to smooth congestion and reduce fuel consumption for everyone. Our goal is to tackle "stop-and-go" waves, those frustrating slowdowns and speedups that usually have no clear cause but lead to congestion and significant energy waste. To train efficient flow-smoothing controllers, we built fast, data-driven simulations that RL agents interact with, learning to maximize energy efficiency while maintaining throughput and operating safely around human drivers.
Overall, a small proportion of well-controlled autonomous vehicles (AVs) is enough to significantly improve traffic flow and fuel efficiency for all drivers on the road. Moreover, the trained controllers are designed to be deployable on most modern vehicles, operating in a decentralized manner and relying on standard radar sensors. In our latest paper, we explore the challenges of deploying RL controllers on a large-scale, from simulation to the field, during this 100-car experiment.
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New research from Stanford University reveals that ChatGPT and similar AI writers are surprisingly popular among those with less formal education.
Essentially, researchers found that regions in the U.S. featuring more tradespeople, artisans, craftsmen and similar are using AI writing more than people living in areas where college degrees are more prevalent.
The telling stats: 19.9% of people living in ‘less educated’ areas of the U.S. have adopted AI writing tools like ChatGPT – as compared to 17.4% in regions with higher education profiles.
Even more dramatic: Adoption in the state of Arkansas, where college degrees are less prevalent: A full 30% of people in Arkansas are using ChatGPT and similar AI to auto-write letters to businesses and government organizations.
In other news and analysis on AI writing:
*Microsoft’s ChatGPT Competitor – Copilot – Gets an Upgrade: Microsoft has rolled-out a new version of its AI writer/chatbot Copilot, which it says is now more deeply embedded into its Windows software.
In part, the change was made in response to user complaints over previous versions of Copilot, which they say operated more like a ‘wrapper’ or outside app that ‘felt’ only weakly linked to Windows software.
With the upgrade, Microsoft is promising users will see marked performance gains from Copilot.
*ChatGPT Competitor Claude: Great for Auto-Writing Pre-Meeting Reports: Mike Krieger, chief product officer, Anthropic is pushing a new use case for the company’s ChatGPT-competitor, Claude.
Essentially, the AI tech can be used to scan calendars and company data to auto-write detailed client reports before a meeting, according to Krieger.
Observes writer Muslim Farooque: “With this move, Anthropic is taking on big players like Microsoft, OpenAI, and Google — all racing to dominate AI-powered business tools.
*One Writer’s Take: Google Has the Best AI Writing Editor: Count writer Amanda Caswell is among those who strongly prefer Google’s new editor for AI writing – Canvas – over ChatGPT’s online editor that carries the same name.
Observes Caswell: “Gemini Canvas is far more thorough and detailed in its critique than ChatGPT Canvas. It’s essentially a real editor. ChatGPT made me feel like my mom was editing the story and was sparing my feelings.
“In a word: Wow.”
*College Rolling-out New Certificate in AI Writing: Beginning Fall 2025, students at Boise State College can obtain a certificate in AI writing after completing three courses on the discipline.
Those are:
~Writing For/With AI
~Applications of AI (with a strong focus on content production)
~Style and the Future of AI Writing
*AI Tech Titans Want to Use Copyrighted Writing for Free: ChatGPT-maker OpenAI – and Google – are looking for clearance from the U.S. government to train their AI on newspaper, magazine and other copyrighted text on the Web for free.
The reason: Given China’s recent major gains in tightening-up the AI race, U.S. AI purveyors need every advantage to stay ahead of China.
Currently, many content creators – including The New York Times – are suing OpenAI for using their content to train ChatGPT without permission.
*On the Research Bench: Text-To-Data-Driven Slides: Adobe is currently experimenting with new AI tech that promises to convert data-heavy research into vibrant slide presentations in Powerpoint.
Dubbed ‘Project Slide Wow,’ the experimental tech is aimed at marketers and business analysts looking to quickly build data-backed presentations without being forced to manually structure content or design slides.
Observes Jane Hoffswell, research scientist, Adobe: “It’s analyzing all the charts in this project, generating captions for them, organizing them into a narrative and creating the presentation slides.”
Currently, Adobe has no firm release date for the experimental slide-maker.
*ChatGPT-Maker’s AI Agents: The Complete Rundown: Writer Siddhese Bawker offers an excellent overview in this piece on the tiers of AI agents currently available from OpenAI.
Such agents are able to work independently on a task for you, which might include clicking-and-pointing with your browser to research, analyze and then auto-write on what it found.
Even better: Extremely advanced AI agents are able to perform such tasks with PhD-level intelligence.
OpenAI’s entry-level agent is included in a ChatGPT Pro subscription ($200/month.)
Higher level agents are OpenAI’s Knowledge Worker Agent ($200/month), Developer Agent ($10,000/month) and Research Agent ($20,000/month).
*ChatGPT Wants to be the Interface for Your Data: Businesses hoping to integrate their databases with ChatGPT — so they can use the AI to analyze and auto-write reports about that data and more — may not have to wait long.
Writer Kyle Wiggers reports that OpenAI is currently testing in-house developed ‘connectors’ that will ideally make such fusions possible.
So far, development of connectors to Google Drive and Slack is already underway.
Observes Wiggers: “ChatGPT Connectors will allow ChatGPT Team subscribers to link workspace Google Drive and Slack accounts to ChatGPT so the chatbot can answer questions informed by files, presentations, spreadsheets and Slack conversations.”
*AI BIG PICTURE: New Hyper-Realistic Voice AI Goes Viral: A new AI voice sensation – Sesame AI – appears ready to dethrone Eleven Labs as the industry standard in realistic voice AI.
Essentially, the Web has blown-up with praise for Sesame AI, which apparently generates AI voices that are so real and human, their sheer intimacy disturbs some people.
Even so: AI Uncovered – producer of this 11-minute video – does note that Eleven Labs still beats Sesame AI when it comes to auto-generating spoken word from a script.
Share a Link: Please consider sharing a link to https://RobotWritersAI.com from your blog, social media post, publication or emails. More links leading to RobotWritersAI.com helps everyone interested in AI-generated writing.
–Joe Dysart is editor of RobotWritersAI.com and a tech journalist with 20+ years experience. His work has appeared in 150+ publications, including The New York Times and the Financial Times of London.
The post ChatGPT: The Great Equalizer appeared first on Robot Writers AI.
Large Language Models (LLMs) have come a long way since their early days of mimicking autocomplete on steroids. But generating fluent text isn’t enough – true intelligence demands reasoning. That means solving math problems, debugging code, drawing logical conclusions, and even reflecting on errors. Yet modern LLMs are trained to predict the next word, not to think. So how are they suddenly getting better at reasoning?
The answer lies in a constellation of new techniques – from prompt engineering to agentic tool use – that nudge, coach, or transform LLMs into more methodical thinkers. Here’s a look at five of the most influential strategies pushing reasoning LLMs into new territory.
One of the earliest and most enduring techniques to improve reasoning in LLMs is surprisingly simple: ask the model to explain itself.
Known as Chain-of-Thought (CoT) prompting, this method involves guiding the model to produce intermediate reasoning steps before giving a final answer. For instance, instead of asking “What’s 17 times 24?”, you prompt the model with “Let’s think step by step,” leading it to break down the problem: 17 × 24 = (20 × 17) + (4 × 17), and so on.
This idea, first formalized in 2022, remains foundational. OpenAI’s o1 model was trained to “think longer before answering” – essentially internalizing CoT-like reasoning chains. Its successor, o3, takes this further with simulated reasoning, pausing mid-inference to reflect and refine responses.
The principle is simple: by forcing intermediate steps, models avoid jumping to conclusions and better handle multi-step logic.
If a question is hard, spend more time thinking – humans do this, and now LLMs can too.
Inference-time compute scaling boosts reasoning by allocating more compute during generation. Instead of a single output pass, models might generate multiple reasoning paths, then vote on the best one. This “self-consistency” method has become standard across reasoning benchmarks.
OpenAI’s o3-mini uses three reasoning effort options (low, medium, high) that determine how long the model reasons internally before answering. At high reasoning levels, o3-mini outperforms even the full o1 model on math and coding tasks.
A related technique, budget forcing, introduced in the 2025 paper s1: Simple Test-Time Scaling, uses special tokens to control reasoning depth. By appending repeated “Wait” tokens, the model is nudged to generate longer responses, self-verify, and correct itself. An end-of-thinking token like “Final Answer:” signals when to stop. This method improves accuracy by extending inference without modifying model weights – a modern upgrade to classic “think step by step” prompting.
The tradeoff is latency for accuracy, and for tough tasks, it’s often worth it.
Another game-changer: don’t just predict words – reward correct logic.
Models like OpenAI’s o1 and DeepSeek-R1 are trained with reinforcement learning (RL) to encourage sound reasoning patterns. Instead of just imitating data, these models get rewards for producing logical multi-step answers. DeepSeek-R1’s first iteration, R1-Zero, used only RL – no supervised fine-tuning – and developed surprisingly powerful reasoning behaviors.
However, RL-only training led to issues like language instability. The final DeepSeek-R1 used multi-stage training: RL for reasoning and supervised fine-tuning for better readability. Similarly, Alibaba’s QwQ-32B combined a strong base model with continuous RL scaling to achieve elite performance in math and code.
The result? Models that not only get answers right, but do so for the right reasons – and can even learn to self-correct.
What happens when the model makes a mistake? Can it catch itself?
Until recently, LLMs struggled with self-correction. In 2023, researchers found that simply asking a model to “try again” rarely improved the answer – and sometimes made it worse. But new work in 2025 introduces backtracking – a classic AI strategy now adapted to LLMs.
Wang et al. from Tencent AI Lab identified an “underthinking” issue in o1-style models: they jump between ideas instead of sticking with a line of reasoning. Their decoding strategy penalized thought-switching, encouraging deeper exploration of each idea.
Meanwhile, Yang et al. proposed self-backtracking – letting the model rewind when stuck, then explore alternate paths. This led to >40% accuracy improvements compared to approaches that solely relies on the optimal reasoning solutions.
These innovations effectively add search and planning capabilities at inference time, echoing classical AI methods like depth-first search, layered atop the flexible power of LLMs.
Sometimes, reasoning means knowing when to ask for help.
Modern LLMs increasingly invoke external tools – calculators, code interpreters, APIs, even web search – to handle complex queries.
Alibaba’s QwQ-32B incorporates agent capabilities directly, letting it call functions or access APIs during inference. Google’s Gemini 2.0 (Flash Thinking) supports similar features – for example, it can enable code execution during inference, allowing the model to run and evaluate code as part of its reasoning process.
Why does this matter? Some tasks – like verifying real-time data, performing symbolic math, or executing code – are beyond the model’s internal capabilities. Offloading these subtasks lets the LLM focus on higher-order logic, dramatically improving accuracy and reliability.
In essence, tools let LLMs punch above their weight – like a digital Swiss Army knife, extending reasoning with precision instruments.
LLMs don’t just “learn to reason” in one step – they acquire it through a layered set of techniques that span training, prompting, inference, and interaction with the world. CoT prompting adds structure. Inference-time scaling adds depth. RL adds alignment. Backtracking adds self-awareness. Tool use adds reach.
Top-performing models like OpenAI’s o1 and o3, DeepSeek’s R1, Google’s Gemini 2.0 Flash Thinking, and Alibaba’s QwQ combine several of these strategies – a hybrid playbook blending clever engineering with cognitive scaffolding.
As the field evolves, expect even tighter coupling between internal reasoning processes and external decision-making tools. We’re inching closer to LLMs that don’t just guess the next word – but genuinely think.
We’ll let you know when we release more summary articles like this one.
The post How Do LLMs Reason? 5 Approaches Powering the Next Generation of AI appeared first on TOPBOTS.
Claire chatted to Josie Gotz from the Manufacturing Technology Centre about robotics for material recovery, reuse and recycling.
Josie Gotz is a Senior Research Engineer in the Intelligent Robotics Team at the Manufacturing Technology Centre. She works as the technical lead on a variety of robotics and automation projects from research and development through to integration across a wide variety of manufacturing sectors. She specialises in creating innovative solutions for these industries, combining vision systems and artificial intelligence to build flexible automation systems. Josie has a particular interest in automated disassembly for material recovery, reuse and recycling.
Over the past few years, there has been a rapid transformation in the recruitment landscape due to growing expectations for accuracy and diversity in hiring processes. Traditional recruiting methods are sluggish to handle the intricate hiring needs of modern businesses. Thus, companies are using artificial intelligence technology-powered tools to streamline the employment process and bring innovation to the process.
Artificial Intelligence (AI) can help organizations hire the right candidates more quickly and precisely by automating the process from resume screening to profile matching. But creating an AI-driven hiring platform is not that easy. It needs a deep understanding of both conventional recruiting models and AI potentialities in the recruiting landscape.
This article will guide you through the fundamental processes of developing an AI-powered hiring platform. We will also share insights and strategies for success in handling recruiting challenges with AI applications.
Developing an AI-powered recruitment platform requires understanding key features that would make it truly effective and exceptional. Such features should be designed in a manner to automate, smoothen, and upgrade various recruitment processes at work, through which one can assure better hiring decisions that result in enhanced workforce efficiency.
One of the trickiest and most time-consuming processes of hiring is reviewing hundreds of resumes to shortlist a quality one. By independently scanning and sorting resumes based on predefined criteria like education, talents, and experience, AI can significantly reduce the workload.
Only the top candidates advance to the next round of the hiring process because these algorithms may be trained to recognize patterns in successful candidates and apply them to the evaluation of new candidates.
Another important factor of AI-powered recruitment platforms is their ability to match candidate profiles with job descriptions. Candidate profiles and job descriptions can be assessed using machine learning techniques in AI to find the right fit.
These algorithms make use of background cultural fit, growth potential, and even predictive performance data, not just simple keyword matching. This enables the individuals recommended to the customer by this platform not only to be qualified for the job but also to have every possibility of succeeding in their position.
AI-driven chatbots can improve the applicant experience by interacting with applicants around the clock, responding to their inquiries, keeping them informed about the progress of their applicants, and even doing the initial screening interviews. So, the chatbot improves overall engagement and communication between the recruiter and the candidate.
Predictive analysis helps with data-driven decision-making capabilities. Artificial intelligence can evaluate candidate profiles, hiring history, and other relevant data to determine a candidate’s likelihood of success in a given role. Predictive analysis helps recruiters make better decisions that increase the success rate and return on investment.
AI-driven platforms mitigate the risk of unconscious bias when bringing objectivity into the evaluation process. Moreover, AI algorithms can be constantly checked and readjusted to make sure that they remain bias-free, thereby achieving further fairness in the recruitment process.
Detailed market research is required before the development phase. This includes understanding the current recruitment technology, finding any gaps in the current solutions, and assessing the demands of your target market. Talk to potential users, HR specialists, and recruiters to learn more about the functionalities and features they need.
The next step is to establish the essential features and functionalities of your platform, depending on your market research. They include the core AI-driven features previously covered, such as chatbots, candidate-matching algorithms, automated resume screening, predictive analytics, etc. Include unique features that would give your recruitment platform a competitive edge. Also, map out a well-defined user experience journey for both recruiters and candidates to make the platform user-friendly.
Selecting the right technology is an essential part of the mobile app development process. Depending on your requirements, you can choose AI and machine learning tools and frameworks, including NLP libraries and data analytic tools. Besides, consider the demands of data storage and processing to make sure that the chosen technology stack will be able to safely and effectively process large volumes of data. This analysis makes your Android app development and iOS app development successful.
Recruiting app design is a stage at which you do detailed wireframing and prototype the UI to ensure it provides an effortless experience from both the recruiter and candidate sides. At this phase of mobile application development, your team will start coding the front-end and back-end features using agile methods of development. An expert mobile app development team with AI developers and UX/UI designers can help ensure all areas of the AI recruiting platform for Android and iOS are fully covered.
The integration of external supporting tools and applications into the AI hiring platform is complex and requires some proper strategizing on your part. First of all, use relevant datasets that may be resumes, job descriptions, or historical data on hiring to train AI and machine learning models. After training, these models need to be integrated with the core functionalities of the platform, such as resume screening and candidate matching.
As a part of the development process, testing makes sure your platform is dependable and highly functional. The performance, security requirements, accuracy, and dependability of AI algorithms should all be tested to ensure the quality of your AI-powered recruiting application.
Deploy with a minimal amount of downtime since this will be critical to a smooth transition for users. This phase may yield valuable benefits if rolled out by first presenting your beta version to a small number of users for feedback prior to release. This allows you to make any necessary adjustments based on real-world usage.
Monitor how your AI recruiting platform works, such as user interactions, the accuracy of AI functionalities, and satisfaction scores. Use this data to make continuous improvements by refining AI algorithms, adding new features, or improving UI. Not only will it keep your platform competitive, but with regular updates and improvements, it’s going to ensure that it remains competitive for the evolving demands of both recruiters and candidates.
The cost associated with developing an AI-powered recruitment platform varies depending on many factors. Hiring an expert AI development team and investing in an advanced technology stack are the major costs. Further, costs are impacted by data processing, acquisition, and storage, especially when huge datasets are needed to train AI models.
The average cost of AI recruiting platform development falls between $80,000 and $100,000, while a more sophisticated platform might cost between $150,000 and $250,000. Although there are significant upfront expenses associated with AI development, the long-term advantages of increased hiring effectiveness and better candidate matching can justify your AI investments.
Creating an AI-powered recruitment platform is a challenging but worthwhile project that can greatly improve the efficiency of hiring procedures. AI platforms have the potential to revolutionize recruitment processes by automating candidate matching, resume screening, and predictive analysis-like tasks.
However, careful planning is required for the development process, from conducting market research to choosing the best AI technology. Even if the initial costs seem to be high, the long-term advantages make AI development worth it.
If you are looking to integrate AI into your recruitment process or develop an AI-powered hiring application from scratch, USM Business Systems is the right AI development company to meet your AI software development needs.
Get in touch with USM Business Systems.
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