When used correctly, they can help us unpick some of the mysteries of our biology, and our mortality. Be honest: Have you ever looked up someone from your childhood on social media with the sole intention of seeing how they've aged? One of my colleagues, who shall remain nameless, certainly has. He recently shared a photo of a former classmate. "Can you believe we're the same age?" he asked, with a hint of glee in his voice. A relative also delights in this pastime. "Wow, she looks like an old woman," she'll say when looking at a picture of someone she has known since childhood. The years certainly are kinder to some of us than others. But wrinkles and gray hairs aside, it can be difficult to know how well--or poorly--someone's body is truly aging, under the hood. A person who develops age-related diseases earlier in life, or has other biological changes associated with aging (such as elevated cholesterol or markers of inflammation), might be considered "biologically older" than a similar-age person who doesn't have those changes. Some 80-year-olds will be weak and frail, while others are fit and active. Longevity clinics offer a mix of services that largely cater to the wealthy.

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Multimodal reasoning stands as a pivotal capability for large vision-language models (LVLMs). The integration with Domain-Specific Languages (DSL), offering precise visual representations, equips these models with the opportunity to execute more accurate reasoning in complex and professional domains. However, the vanilla Chain-of-Thought (CoT) prompting method faces challenges in effectively leveraging the unique strengths of visual and DSL representations, primarily due to their differing reasoning mechanisms. Additionally, it often falls short in addressing critical steps in multi-step reasoning tasks. To mitigate these challenges, we introduce the \underline{B}i-Modal \underline{B}ehavioral \underline{A}lignment (BBA) prompting method, designed to maximize the potential of DSL in augmenting complex multi-modal reasoning tasks. This method initiates by guiding LVLMs to create separate reasoning chains for visual and DSL representations. Subsequently, it aligns these chains by addressing any inconsistencies, thus achieving a cohesive integration of behaviors from different modalities. Our experiments demonstrate that BBA substantially improves the performance of GPT-4V(ision) on geometry problem solving ($28.34\% \to 34.22\%$), chess positional advantage prediction ($42.08\% \to 46.99\%$) and molecular property prediction ($77.47\% \to 83.52\%$).

The three adjectives served as parting wisdom for a dozen William & Mary students seated in McGlothlin-Street Hall. White was wrapping up the final class of the semester for his course "Neural Networks for Machine Learning." A 2017 Ph.D. graduate of W&M's Department of Computer Science, White returned to his alma mater to teach after he heard the department wanted to offer another course on machine learning, a key subset of artificial intelligence. "I believed neural networks could serve as the perfect backdrop for a class studying what learning from data means and how to do it well," White said. "Since the fundamentals draw from calculus, probability, statistics, and linear algebra, the first part of the course is pretty intense, but I was interested in returning to teach because I had some ideas on how to manage this complexity."

Before Chris White could help disrupt Jihadi finance networks, crush weapons markets, and bust up sex-slave rings with search tools that mine the dark Web, he first had to figure out how to stop himself from plummeting through the open gun door of a banking Black Hawk helicopter. White was on his way to a forward operating base outside Kabul headquarters, as part of a secret intelligence cell to help confront the Taliban and al-Qaida, smash their encrypted online money stream, and win over the hearts and minds of the Afghanistan population. Slight and lanky and 28, White felt Dukakis-ridiculous in his unwieldy body armor and bulbous helmet with "Dr. White" scrawled in marker on duct tape across the front, and with the dust from liftoff, he was finding it hard to breathe. He was still struggling with the unfamiliar seat straps when the pilot hit the stick, sending White sliding toward the hot square of the door and the desert 200 feet below. Down there, Afghanistan was a messy, dangerous place for pretty much everybody. After nearly a decade of U.S.-led war, the American body count had hit 1,000, and civilian casualties were beyond calculation, as President Obama's 30,000-troop surge intensified the fighting that spring. Many feared the situation was only going from bad to worse. The U.S. was escalating drone strikes across the border in Pakistan. And U.S. command was under assault after Gen. Stanley McChrystal, the surge's architect, found himself without a job after he and his staff made disparaging remarks about the commander in chief in some music magazine. It is hard to imagine that only a few weeks earlier, White had been just another impossibly young-looking Harvard postdoc in flip-flops looking forward to a Cambridge summer. Helicopter gunships and war zones weren't on the radar; there were lattes in the square and rock climbing, and on the other side of campus, a prestigious fellowship in the School of Engineering and Applied Sciences, where he was working at the intersection of big data, statistics, and machine learning. He had earned academic pole position and had every expectation it would continue that way forever -- becoming a professor, building a lab, and sniping out white papers from a tenured ivory tower.

By this we mean the extraction of E CONSIDER the process we call Pattern clearly one of the most primitive sources of patterns.

I] presented a systematic account of the facts of thought and of the methods to express them. But if we do not restrict the domain, there are too many things to be considered. Brunot accurately studied various ways to express surprise, prices, anteriority in the future, a question... But concepts used only in specific domains do not appear in his book. That is why I chose a technical domain -- annotations of chess games -- and I tried to fmd what was said in that domain.