Muskan Sharma stood up to men who tried to bully her over her clothes - and went on to win hearts and a beauty pageant. The 23-year-old, who was crowned Miss Rishikesh 2025 last week in the northern Indian state of Uttarakhand, told the BBC that even though it was a small local pageant, it made me feel like Miss Universe. Sharma's win has made headlines in India as it came after a viral video that showed her spiritedly arguing with a man who barged into their rehearsals just a day before the 4 October contest. Sharma, who wanted to be a model and participate in a pageant since I was in school, said the intruders came in just as they broke for lunch. We were sitting around, chilling, having a laugh when they walked in, she said.

Cricket, with its intricate strategies and deep history, increasingly captivates a global audience. The Indian Premier League (IPL), epitomizing Twenty20 cricket, showcases talent in a format that lasts just a few hours as opposed to the longer forms of the game. Renowned for its fusion of technology and fan engagement, the IPL stands as the world's most popular cricket league. This study concentrates on Dream11, India's leading fantasy cricket league for IPL, where participants craft virtual teams based on real player performances to compete internationally. Building a winning fantasy team requires navigating various complex factors including player form and match conditions. Traditionally, this has been approached through operations research and machine learning. This research introduces the FanCric framework, an advanced multi-agent system leveraging Large Language Models (LLMs) and a robust orchestration framework to enhance fantasy team selection in cricket. FanCric employs both structured and unstructured data to surpass traditional methods by incorporating sophisticated AI technologies. The analysis involved scrutinizing approximately 12.7 million unique entries from a Dream11 contest, evaluating FanCric's efficacy against the collective wisdom of crowds and a simpler Prompt Engineering approach. Ablation studies further assessed the impact of generating varying numbers of teams. The exploratory findings are promising, indicating that further investigation into FanCric's capabilities is warranted to fully realize its potential in enhancing strategic decision-making using LLMs in fantasy sports and business in general.

Operations research (OR) uses mathematical models to enhance decision-making, but developing these models requires expert knowledge and can be time-consuming. Automated mathematical programming (AMP) has emerged to simplify this process, but existing systems have limitations. This paper introduces a novel methodology that uses recent advances in Large Language Model (LLM) to create and edit OR solutions from non-expert user queries expressed using Natural Language. This reduces the need for domain expertise and the time to formulate a problem. The paper presents an end-to-end pipeline, named NL2OR, that generates solutions to OR problems from natural language input, and shares experimental results on several important OR problems.

Two-timescale stochastic approximation (TTSA) is among the most general frameworks for iterative stochastic algorithms. This includes well-known stochastic optimization methods such as SGD variants and those designed for bilevel or minimax problems, as well as reinforcement learning like the family of gradient-based temporal difference (GTD) algorithms. In this paper, we conduct an in-depth asymptotic analysis of TTSA under controlled Markovian noise via central limit theorem (CLT), uncovering the coupled dynamics of TTSA influenced by the underlying Markov chain, which has not been addressed by previous CLT results of TTSA only with Martingale difference noise. Building upon our CLT, we expand its application horizon of efficient sampling strategies from vanilla SGD to a wider TTSA context in distributed learning, thus broadening the scope of Hu et al. (2022). In addition, we leverage our CLT result to deduce the statistical properties of GTD algorithms with nonlinear function approximation using Markovian samples and show their identical asymptotic performance, a perspective not evident from current finite-time bounds.

We revisit the Reinforce policy gradient algorithm from the literature. Note that this algorithm typically works with cost returns obtained over random length episodes obtained from either termination upon reaching a goal state (as with episodic tasks) or from instants of visit to a prescribed recurrent state (in the case of continuing tasks). We propose a major enhancement to the basic algorithm. We estimate the policy gradient using a function measurement over a perturbed parameter by appealing to a class of random search approaches. This has advantages in the case of systems with infinite state and action spaces as it relax some of the regularity requirements that would otherwise be needed for proving convergence of the Reinforce algorithm. Nonetheless, we observe that even though we estimate the gradient of the performance objective using the performance objective itself (and not via the sample gradient), the algorithm converges to a neighborhood of a local minimum. We also provide a proof of convergence for this new algorithm.

We consider the problem of finding the optimal value of n in the n-step temporal difference (TD) learning algorithm. We find the optimal n by resorting to a model-free optimization technique involving a one-simulation simultaneous perturbation stochastic approximation (SPSA) based procedure that we adopt to the discrete optimization setting by using a random projection approach. We prove the convergence of our proposed algorithm, SDPSA, using a differential inclusions approach and show that it finds the optimal value of n in n-step TD. Through experiments, we show that the optimal value of n is achieved with SDPSA for arbitrary initial values. I. INTRODUCTION Reinforcement learning (RL) algorithms are widely used for solving problems of sequential decisionmaking under uncertainty. An RL agent typically makes decisions based on data that it collects through interactions with the environment in order to maximize a certain long-term reward [1], [2].

Amazon Alexa's Create With Alexa feature uses conversational and generative AI to create unique stories after users select a theme, character, a descriptive word and a color. Generative AI, which is known for churning out fantastical art based on text prompts, is now sneaking into one of the most sacred bonding experiences for parents and children: bedtime storytelling. Amazon is hopping into the generative AI craze with a new Alexa feature that creates short, five-scene stories for kids based on a few prompts. Called'Create With Alexa,' the feature lets children and parents select from given themes like underwater, enchanted forest and space exploration and pick a character, a descriptive word and a color. Then, they sit back and wait as the AI comes up with different stories, visuals, audio dialogues and background music.

Amazon announced today the launch of "Create with Alexa," a new AI tool for kids that generates animated stories. The company first revealed the feature in September. "Create with Alexa" launched in the U.S. today, November 29, across supported Echo Show devices and is available in English. To craft a story, a child says, "Alexa, make a story," and then answers prompts, the company explains in its blog post. The child selects from three themes: "space exploration," "underwater" or "enchanted forest," and then chooses the story's hero, a color scheme and adjectives like "silly," "happy" or "mysterious."

Were you unable to attend Transform 2022? Check out all of the summit sessions in our on-demand library now! When it comes to artificial intelligence (AI) adoption, there is a growing gap between the haves and the have-nots. According to IBM, the global AI adoption rate went up by 4 percentage points in 2022, reaching nearly 35%. However, the study also found that the gap in AI adoption between larger and smaller companies also grew significantly in the past year.

The Transform Technology Summits start October 13th with Low-Code/No Code: Enabling Enterprise Agility. Involve.ai, a startup using AI to bolster customer success, today announced it has raised $16 million in a series A round led by Sapphire Ventures and other investors. The company says the capital will be put toward product development and hiring on the engineering, data science, and go-to-market sides. Customer success emerged as a top priority for companies as the pandemic disrupted processes, causing businesses to rethink how they operate. But while having a good pulse on customer behaviors, preferences, and goals is essential -- 35% of respondents in a Deloitte report ranked customer success objectives like reducing churn as leading objectives -- customer success teams often lack the right tools to support their workflows.