Artificial Intelligence

Segment Geospatial Package

December 1, 2025
Artificial Intelligence

The segment-geospatial package draws its inspiration from segment-anything-eo repository authored by Aliaksandr Hancharenka. To facilitate the use of the Segment Anything Model (SAM) for geospatial data, I have developed the segment-anything-py and segment-geospatial Python packages, which are now available on PyPI and conda-forge. My primary objective is to simplify the process of leveraging SAM for geospatial data analysis by enabling users to achieve this with minimal coding effort. I have adapted the source code of segment-geospatial from the segment-anything-eo repository, and credit for its original version goes to Aliaksandr Hancharenka.

Verbalized Sampling: How to Mitigate Mode Collapse and Unlock LLM Diversity

November 27, 2025
Artificial Intelligence

alt stanford prompting technique

Stanford researchers built a new prompting technique!

By adding ~20 words to a prompt, it:

  • boosts LLM’s creativity by 1.6-2x
  • raises human-rated diversity by 25.7%
  • beats fine-tuned model without any retraining
  • restores 66.8% of LLM’s lost creativity after alignment

Post-training alignment methods, such as RLHF, are designed to make LLMs helpful and safe.

However, these methods unintentionally cause a significant drop in output diversity (called mode collapse).

Agents, robots, and us: Skill partnerships in the age of AI

November 25, 2025
Artificial Intelligence

AI is expanding the productivity frontier. Realizing its benefits requires new skills and rethinking how people work together with intelligent machines.

At a glance #

Work in the future will be a partnership between people, agents, and robots—all powered by AI. Today’s technologies could theoretically automate more than half of current US work hours. This reflects how profoundly work may change, but it is not a forecast of job losses. Adoption will take time. As it unfolds, some roles will shrink, others grow or shift, while new ones emerge—with work increasingly centered on collaboration between humans and intelligent machines.

Image Segmentation With YOLOv11

November 22, 2025
Artificial Intelligence

Short example of doing image segmentation with YOLOv11 (Ultralytics)

from ultralytics import YOLO
import random
import cv2
import numpy as np

model = YOLO("yolo11x-seg.pt") 

img = cv2.imread("YourImagePath")

# if you want all classes
yolo_classes = list(model.names.values())
classes_ids = [yolo_classes.index(clas) for clas in yolo_classes]

conf = 0.2

results = model.predict(img, conf=conf)
colors = [random.choices(range(256), k=3) for _ in classes_ids]

for result in results:
    for mask, box in zip(result.masks.xy, result.boxes):
        points = np.int32([mask])
        color_number = classes_ids.index(int(box.cls[0]))
        cv2.fillPoly(img, points, colors[color_number])

cv2.imshow("Image", img)
cv2.waitKey(0)

cv2.imwrite("YourSavePath", img)

Sumber:

The State of Artificial Intelligence in 2025

November 10, 2025
Artificial Intelligence

alt The State of AI 2025

The State of AI 2025

  1. Everyone’s testing, few are scaling. 88% of companies now use AI somewhere. Only 33% have scaled it beyond pilots.
  2. The profit gap is huge. Just 6% see real EBIT impact. Most are still stuck in “experiments,” not execution.
  3. The winners think bigger. Top performers aren’t cutting costs. They’re redesigning workflows and creating new products.
  4. AI agents are emerging. 23% are testing agents. Only 10% have scaled them (mostly in IT and R&D).
  5. The jobs shift is starting. 30% of companies expect workforce reductions next year, mostly in junior or support roles.

TL;DR: #

AI adoption is nearly universal. Impact isn’t. The gap between pilots and profit is where the next unicorns will be built.

Tongyi DeepResearch: A New Era of Open-Source AI Researchers

September 16, 2025
Machine Learning, Artificial Intelligence

alt the smol training playbook

From Chatbot to Autonomous Agent #

We are proud to present Tongyi DeepResearch, the first fully open-source Web Agent to achieve performance on par with OpenAI’s DeepResearch across a comprehensive suite of benchmarks. Tongyi DeepResearch demonstrates state-of-the-art results, scoring 32.9 on the academic reasoning task Humanity’s Last Exam (HLE), 43.4 on BrowseComp and 46.7 on BrowseComp-ZH in extremely complex information‑seeking tasks, and achieving a score of 75 on the user-centric xbench-DeepSearch benchmark, systematically outperforming all existing proprietary and open-source Deep Research agents.