In Major League Baseball, strategy and planning are major factors in determining the outcome of a game. Previous studies have aided this by building machine learning models for predicting the winning team of any given game. We extend this work by training a comprehensive set of machine learning models using a common dataset. In addition, we relate the win probabilities produced by these models to win strength as measured by score differential. In doing so we show that the most common machine learning models do indeed demonstrate a relationship between predicted win probability and the strength of the win. Finally, we analyze the results of using predicted win probabilities as a decision making mechanism on run-line betting. We demonstrate positive returns when utilizing appropriate betting strategies, and show that naive use of machine learning models for betting lead to significant loses.

The star pitcher has been studying aerospace engineering at MIT. Now his pitches will take flight in professional baseball. Like almost any MIT student, Mason Estrada wants to take what he learned on campus and apply it to the working world. Unlike any other current MIT student, Estrada's primary workplace is a pitcher's mound. Estrada, the star pitcher for MIT's baseball team, has signed a contract with the Los Angeles Dodgers, who selected him in the seventh round of the Major League Baseball draft on July 14. The right-hander, whose fastball has reached 96 miles per hour, is taking a leave of absence from the Institute and reported to the Dodgers' instructional camp in Arizona.

Statistical topic modeling is widely used in political science to study text. Researchers examine documents of varying lengths, from tweets to speeches. There is ongoing debate on how document length affects the interpretability of topic models. We investigate the effects of aggregating short documents into larger ones based on natural units that partition the corpus. In our study, we analyze one million tweets by U.S. state legislators from April 2016 to September 2020. We find that for documents aggregated at the account level, topics are more associated with individual states than when using individual tweets. This finding is replicated with Wikipedia pages aggregated by birth cities, showing how document definitions can impact topic modeling results.

If there's one thing baseball fans are averse to, it's change. Over the MLB's 149-year history, alterations to the game's rules, like lowering the pitcher's mound (1968) or introducing instant replay challenges (2014) came only after years of heated debate between reformers and purists. Maybe the most contentious issue ever to divide these two camps is whether or not to replace notoriously inaccurate human home plate umpires with less fallible machines. Though that was once largely considered out of the bounds of possibility, MLB games officiated by so-called "robot umpires" are now closer to reality than ever before. Starting this week, batters stepping up to the plate during spring training games will have the ability to challenge an umpire's pitch calls and have them immediately reviewed by a computer.

Solving complex reasoning tasks may involve visual understanding, domain knowledge retrieval, numerical calculation, and multi-step reasoning. Existing methods augment large language models (LLMs) with external tools but are restricted to specialized domains, limited tool types, or require additional training data. In this paper, we introduce OctoTools, a training-free, user-friendly, and easily extensible open-source agentic framework designed to tackle complex reasoning across diverse domains. OctoTools introduces standardized tool cards to encapsulate tool functionality, a planner for both high-level and low-level planning, and an executor to carry out tool usage. We validate OctoTools' generality across 16 diverse tasks (including MathVista, MMLU-Pro, MedQA, and GAIA-Text), achieving substantial average accuracy gains of 9.3% over GPT-4o. Furthermore, OctoTools outperforms AutoGen, GPT-Functions and LangChain by up to 10.6% when given the same set of tools. Through comprehensive analysis and ablations, OctoTools demonstrates advantages in task planning, effective tool usage, and multi-step problem solving.

Large language models (LLMs) with their strong zero-shot topic extraction capabilities offer an alternative to probabilistic topic modelling and closed-set topic classification approaches. As zero-shot topic extractors, LLMs are expected to understand human instructions to generate relevant and non-hallucinated topics based on the given documents. However, LLM-based topic modelling approaches often face difficulties in generating topics with adherence to granularity as specified in human instructions, often resulting in many near-duplicate topics. Furthermore, methods for addressing hallucinated topics generated by LLMs have not yet been investigated. In this paper, we focus on addressing the issues of topic granularity and hallucinations for better LLM-based topic modelling. To this end, we introduce a novel approach that leverages Direct Preference Optimisation (DPO) to fine-tune open-source LLMs, such as Mistral-7B. Our approach does not rely on traditional human annotation to rank preferred answers but employs a reconstruction pipeline to modify raw topics generated by LLMs, thus enabling a fast and efficient training and inference framework. Comparative experiments show that our fine-tuning approach not only significantly improves the LLM's capability to produce more coherent, relevant, and precise topics, but also reduces the number of hallucinated topics.

In this work, we propose two cost efficient methods for object identification, using a multi-fingered robotic hand equipped with proprioceptive sensing. Both methods are trained on known objects and rely on a limited set of features, obtained during a few grasps on an object. Contrary to most methods in the literature, our methods do not rely on the knowledge of the relative pose between object and hand, which greatly expands the domain of application. However, if that knowledge is available, we propose an additional active exploration step that reduces the overall number of grasps required for a good recognition of the object. One of the methods depends on the contact positions and normals and the other depends on the contact positions alone. We test the proposed methods in the GraspIt! simulator and show that haptic-based object classification is possible in pose-free conditions. We evaluate the parameters that produce the most accurate results and require the least number of grasps for classification.

In this paper, we introduce an authorship attribution method called Authorial Language Models (ALMs) that involves identifying the most likely author of a questioned document based on the perplexity of the questioned document calculated for a set of causal language models fine-tuned on the writings of a set of candidate author. We benchmarked ALMs against state-of-art-systems using the CCAT50 dataset and the Blogs50 datasets. We find that ALMs achieves a macro-average accuracy score of 83.6% on Blogs50, outperforming all other methods, and 74.9% on CCAT50, matching the performance of the best method. To assess the performance of ALMs on shorter texts, we also conducted text ablation testing. We found that to reach a macro-average accuracy of 70%, ALMs needs 40 tokens on Blogs50 and 400 tokens on CCAT50, while to reach 60% ALMs requires 20 tokens on Blogs50 and 70 tokens on CCAT50.

Political journalism suffers from a central contradiction: elections are finicky things, but the best way for a commentator to make a name for himself is to project as much confidence as he can. The collection of confidence can take many forms: journalists can position themselves as monarchs of gossip; they can embed with campaigns and provide a look from the inside; they can simply plug their ears and yell louder than the next guy. The key to staying in the game is to never allow the actual outcome of an election to change the way you go about your business. After the 2012 Presidential election, political media had a moment when it seemed like that confidence game might finally come to an end. If you worked in the news business in any capacity after Nate Silver correctly called all fifty states in 2012, you likely remember feeling desperate to catch up to the new paradigm.

A bipartisan panel of voters weighed in on the future of artificial intelligence and growing concerns surrounding the potential dangers of the emerging technology. Imagine waking up tomorrow to discover that MLB commissioner Rob Manfeld made another rule change to America's pastime: All performance-enhancing drugs are legal. Imagine the increases in strength, speed, and stamina that baseball would have to contend with as players from every team experimented with pharmaceutical cocktails to give them the ultimate advantage. What we're about to see in the 2024 election cycle, with the introduction of artificial intelligence, is the rise of "performance-enhancing digital." AI will revolutionize politics in the months ahead.