🧠 3 AI Trends for 2025

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In today’s Sunday Special:

1. 🏥Healthcare Applications Come to Life

2. 📖Personalized Education Becomes More Democratized

3. 🛰️Monitoring Environments for Disaster Preparedness

Read Time: 7 minutes

🎓Key Terms

• Computer Vision (CV): Enables computers to interpret, analyze, and extract visual data.

• Large Language Models (LLMs): AI models pre-trained on vast amounts of data to generate human-like text.

• Deep Learning (DL): Mimics the human brain by creating multiple layers of “artificial” neurons to solve problems.

• Machine Learning (ML): Leverages data to recognize patterns and make predictions without explicit instructions from developers.

• Convolutional Neural Network (CNN): A network of specialized layers that detect visual patterns, such as edges, textures, and structures.

• Reinforcement Learning (RL): Mimics the “trial-and-error” process humans use to learn, where actions that lead to desired outcomes are reinforced.

• Internet of Things (IoT): A network of physical devices embedded with sensors and software to connect, collect, and exchange data over the Internet.

🩺 PULSE CHECK

📜THE PRELUDE

As we roll into the new year, AI applications will evolve beyond reactive chatbots to proactive companions with voice, personality, and memory.

In domains like healthcare, education, and disaster preparedness, AI models will increasingly function as proactive companions, capable of analyzing data, setting goals, and taking action to achieve them.

So, how will this look? Today, we’ll outline three AI trends for 2025 with use cases. Though all AI adoption carries risks (e.g., privacy, cybersecurity, and hallucinations), we’ll focus on what could go right, not wrong.

1. 🏥HEALTHCARE APPLICATIONS COME TO LIFE

AI models will evolve from a tool referenced by doctors to a collaborative partner in high-stakes situations, including surgeries. In surgical contexts, the most trivial errors can create complications. So, exchanging feedback between surgeons and AI-powered surgical robots can enhance surgical precision.

Use Case: AI-Powered Surgical Robot

Researchers at John Hopkins University (JHU) developed a method to train surgical robots by showing them videos of seasoned surgeons. This method involves leveraging Imitation Learning, which eliminates the need to program surgical robots with each individual move required during a medical procedure. In other words, it brings the field of robotic surgery closer to true autonomy, where surgical robots can perform complex surgeries without constant oversight. Imitation Learning is the process of observing an action and then repeating it. In this context, the surgical robot watches videos of seasoned surgeons to learn how to perform the same surgical procedures. The Imitation Learning is paired with the same ML architecture that underpins OpenAI’s ChatGPT. However, while ChatGPT processes words to generate text, this method speaks “robot” through Kinematics: “A language that breaks down the angles or robotic motion into math.”

This method was used to train the da Vinci Surgical System to perform three fundamental tasks required in surgical procedures:

1. Suturing (i.e., “Stiches”)

2. Lifting Body Tissue

3. Manipulating a Needle

🚨Watch the surgical robot in action here.

The AI Trend for 2025?

In 2025, AI applications like the da Vinci Surgical System will be capable of predictive analytics by leveraging real-time data from a patient’s vital signs and medical history, including symptoms, diagnoses, and medications. Based on this real-time data, surgical robots will continuously suggest more effective surgical approaches to seasoned surgeons for a patient’s specific situation.

2. 📖PERSONALIZED EDUCATION BECOMES DEMOCRATIZED

AI models will completely transform the educational landscape by providing highly personalized learning experiences tailored to each student’s learning pace. This transformation will be powered by LLMs that can adapt content, apply different teaching methods, and generate unique exams to ensure no student falls behind.

Use Case: AI-Powered K-12 Tutors

Khanmigo, a tutoring tool developed by Khan Academy, is an AI-powered study buddy, debate partner, essay reviewer, homework helper, and curriculum planner.

• For Students:

  1. Interactive Tutoring: Khanmigo serves as a personalized tutor, helping students understand concepts in subjects like math, science, and humanities without providing direct answers. Instead, it encourages critical thinking by guiding students to discover solutions themselves.

  2. Adaptive Learning: Students can ask questions and receive instant help on homework assignments and study guides, similar to having a tutor available anytime. Khanmigo adjusts the complexity of explanations based on a student’s needs, making learning more accessible and understandable.

• For Teachers:

  1. Performance Tracking: Teachers can manage student accounts and track their performance over time through a dedicated dashboard on Khanmigo that summarizes learning progress.

  2. Teaching Resource Generator: Khanmigo can create quizzes, develop discussion questions, and generate report card comments based on a student’s strengths and areas for growth.

The AI Trend for 2025?

In 2025, tutoring tools will delve into a student’s learning style and emotional state and even identify subtle signs of disengagement. They’ll be able to anticipate a student’s future learning needs, proactively suggest relevant learning resources, and adjust their learning path in real-time to prevent future roadblocks.

3. 🛰️MONITORING ENVIRONMENTS FOR DISASTER PREPAREDNESS

AI models will analyze satellite images to trach environments for disaster preparedness. Descartes Labs, a leading environmental intelligence firm, uses DL, specifically CNNs, to analyze large-scale satellite imagery for environmental monitoring. Their Geospatial Analytics Platform processes data from over 100 satellites. CNNs are applied to multispectral images to detect and classify land features like forests, water bodies, and urban areas. For example, the platform detects deforestation by analyzing changes in forest canopy over time, allowing organizations like Global Forest Watch (GFW) to track illegal logging. However, these notifications to environmental groups are often delayed, so it takes longer for those groups to mobilize local law enforcement.

Use Case: AI Detects Wildfires Before They Spread

Google Research developed “FireSat,” an AI framework that detects and tracks wildfires with high-resolution satellite imagery from the U.S. Forest Service (USFS).

“FireSat” relies on a network of high-resolution satellites to capture images of the Earth’s surface every 20 minutes. Then, it uses AI models to compare the high-resolution satellite imagery with thousands of previous pictures of the same location to highlight any alarming changes. “FireSat” can detect wildfires roughly the size of a classroom. If it detects anything, it sends real-time alerts to emergency responders outlining the wildfire’s size, location, and likelihood of spreading.

The USFS currently uses low-resolution satellite images that update a few times a day, making it difficult to “detect fires smaller than a soccer field.” So, “FireSat” helps USFS make informed decisions about resource allocation and evacuation plans to protect local communities and wildlife ecosystems.

The AI Trend for 2025?

In 2025, environmental intelligence firms like Descartes Labs will integrate RL to improve response times to illegal resource extraction in remote areas with poor telecommunications services and road infrastructure. Using RL, environmental watchdogs can act immediately, whether dispatching drones or notifying local authorities, ensuring that critical environmental events are addressed swiftly. In the age of more severe wildfires, RL can learn complex patterns in fire behavior, such as how fires spread based on factors like wind and topography, leading to more accurate predictions of fire spread and intensity. This quick detection will enable emergency responders to contain outbreaks before disaster spreads.

🔑KEY TAKEAWAY

In 2025, the core theme will be the growing ability of AI applications to deliver deeply personalized experiences, whether through AI-powered surgical robots or AI-powered tutoring tools. This level of personalization will be enabled by an AI model’s increased ability to process and analyze vast amounts of real-time data. Additionally, AI models will take on a more autonomous role by supporting tasks, actively making decisions, and executing actions.

📒FINAL NOTE

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