O modelu
DeepSeek Coder V2 je specializovaný model na programování s 16B parametry. Vyniká v generování kódu, debugování a vysvětlování technických konceptů. Ideální pro code-generation benchmarky.
Schopnosti
✅ Text 💻 Kód
Technické specifikace
| Parameters | 16B |
|---|---|
| Context window | 65536 |
| Architecture | MoE |
Hardware pro testy
| CPU | AMD Ryzen |
|---|---|
| GPU | NVIDIA RTX 5060 Ti 16GB |
| RAM | 32 GB DDR5 |
| OS | Ubuntu 24.04 LTS |
Výsledky testů
| Test | Run | Tokens/s | TTFT (ms) | Délka (s) | Tokeny | GPU VRAM | Teplota | Kvalita | Datum | Výstup |
|---|---|---|---|---|---|---|---|---|---|---|
| HTML/JS animace | #1 | 14.76 | 1274 | 116.5 | 1217 | 15478 MB | 50 °C | - | 19.05.2026 | |
| Anglický článek | #1 | 17.65 | 1219 | 60.3 | 1022 | 15478 MB | 46 °C | - | 19.05.2026 | |
| Český článek | #1 | 16.73 | 1310 | 93.5 | 1510 | 15478 MB | 45 °C | - | 19.05.2026 | |
| Python galaxie | #1 | 16.08 | 1211 | 71.7 | 1111 | 15478 MB | 51 °C | - | 19.05.2026 | |
| HTML/JS animace | #1 | 16.66 | 1170 | 73.9 | 1187 | 15476 MB | 40 °C | - | 19.05.2026 | |
| PHP Drupal modul | #1 | 16.09 | 1278 | 112.2 | 1125 | 15476 MB | 38 °C | - | 19.05.2026 | |
| Python galaxie | #1 | 17.61 | 1142 | 67.5 | 1142 | 15541 MB | 45 °C | - | 19.05.2026 | |
| PHP Drupal modul | #1 | 17.26 | 1143 | 83.1 | 1386 | 15536 MB | 43 °C | - | 19.05.2026 | |
| HTML/JS animace | #1 | 17.42 | 1121 | 81.0 | 1363 | 15536 MB | 41 °C | - | 19.05.2026 | |
| Český článek | #1 | 17.02 | 1250 | 88.1 | 1448 | 15536 MB | 38 °C | - | 19.05.2026 | |
| Anglický článek | #1 | 18.66 | 1176 | 44.4 | 703 | 15536 MB | 34 °C | - | 19.05.2026 | |
| Anglický článek | #1 | 17.53 | 1161 | 178.4 | 1177 | 15541 MB | 46 °C | - | 18.05.2026 | |
| Český článek | #1 | 15.97 | 1251 | 168.7 | 1862 | 15541 MB | 44 °C | - | 18.05.2026 | |
| Python galaxie | #1 | 17.37 | 1151 | 109.3 | 1101 | 15541 MB | 41 °C | - | 18.05.2026 | |
| HTML/JS animace | #1 | 17.25 | 1117 | 103.5 | 1273 | 15541 MB | 39 °C | - | 18.05.2026 | |
| PHP Drupal modul | #1 | 17.69 | 1222 | 86.8 | 921 | 15552 MB | 36 °C | - | 18.05.2026 |
Vygenerované výstupy
HTML/JS animace
To create an interactive 3D particle animation using HTML, CSS, and JavaScript with Three.js from CDN, you can follow the steps below. This example includes a starfield background, colorful particles rotating in a spiral galaxy-like motion, and responds to mouse movement by tilting the camera. Additionally, it displays an FPS counter in the corner for performance monitoring.
Here's how you can set up this project:
```html
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>Interactive 3D Particle Animation</title>
<style>
body { margin: 0; }
canvas { display: block; }
#fps { position: absolute; top: 10px; left: 10px; color: white; }
</style>
</head>
<body>
<div id="fps">FPS: N/A</div>
<script src="https://cdnjs.cloudflare.com/ajax/libs/three.js/r128/three.min.js"></script>
<script>
const scene = new THREE.Scene();
const camera = new THREE.PerspectiveCamera(75, window.innerWidth / window.innerHeight, 0.1, 1000);
const renderer = new THREE.WebGLRenderer();
renderer.setSize(window.innerWidth, window.innerHeight);
document.body.appendChild(renderer.domElement);
// Create a starfield background
const starsGeometry = new THREE.BufferGeometry();
const count = 5000;
const positions = new Float32Array(count * 3);
for (let i = 0; i < count; i++) {
positions[i * 3] = (Math.random() - 0.5) * 2000;
positions[i * 3 + 1] = (Math.random() - 0.5) * 2000;
positions[i * 3 + 2] = Math.random() * 2000;
}
starsGeometry.setAttribute('position', new THREE.BufferAttribute(positions, 3));
const starMaterial = new THREE.PointsMaterial({ color: 0xffffff, size: 1 });
const stars = new THREE.Points(starsGeometry, starMaterial);
scene.add(stars);
// Create particles
const particleGeometry = new THREE.BufferGeometry();
const particles = new Float32Array(count * 3);
for (let i = 0; i < count; i++) {
particles[i * 3] = (Math.random() - 0.5) * 100;
particles[i * 3 + 1] = (Math.random() - 0.5) * 100;
particles[i * 3 + 2] = Math.random() * 100;
}
particleGeometry.setAttribute('position', new THREE.BufferAttribute(particles, 3));
const particleMaterial = new THREE.PointsMaterial({ color: 0x88ccdd, size: 2 });
const particlesMesh = new THREE.Points(particleGeometry, particleMaterial);
scene.add(particlesMesh);
camera.position.z = 1000;
// Handle window resize
window.addEventListener('resize', onWindowResize, false);
function onWindowResize() {
camera.aspect = window.innerWidth / window.innerHeight;
camera.updateProjectionMatrix();
renderer.setSize(window.innerWidth, window.innerHeight);
}
// Animation loop
const clock = new THREE.Clock();
function animate() {
requestAnimationFrame(animate);
const elapsedTime = clock.getElapsedTime();
// Rotate particles and stars
particlesMesh.rotation.x += 0.005;
particlesMesh.rotation.y += 0.005;
stars.rotation.z += 0.001;
// Update camera based on mouse movement
const mouseX = (event.clientX / window.innerWidth) * 2 - 1;
const mouseY = -(event.clientY / window.innerHeight) * 2 + 1;
camera.rotation.x = mouseY * 0.1;
camera.rotation.y = mouseX * 0.1;
renderer.render(scene, camera);
}
animate();
// FPS counter
let fps = 0;
function displayFPS() {
const div = document.getElementById('fps');
div.textContent = `FPS: ${fps}`;
requestAnimationFrame(displayFPS);
}
displayFPS();
</script>
</body>
</html>
```
This code sets up a basic Three.js scene with a starfield and particles, animates them in a simple way, and updates the camera based on mouse movement for interaction. The FPS counter is updated every frame using requestAnimationFrame.Anglický článek
### How AI is Transforming Scientific Research in 2026
#### By Olivia Torres
**Perex:** As we approach 2026, artificial intelligence (AI) has become an indispensable tool in scientific research, revolutionizing how experiments are designed and analyzed. From drug discovery to climate modeling, particle physics, and genomics, AI is not just augmenting human capabilities but also opening new avenues for exploration. Here's a look at how AI is reshaping the landscape of scientific inquiry.
### Introduction
In 2026, as we stand on the brink of an era where machines wield knowledge like never before, the role of artificial intelligence in scientific research has become more profound than ever. The integration of machine learning algorithms and deep neural networks into traditional experimental methods is not just altering how researchers gather data; it's also transforming what questions they can ask and how they interpret their findings. This article will delve into specific examples where AI is making significant strides, including its impact on drug discovery, climate modeling, particle physics, and genomics.
### AI in Drug Discovery: Faster, Smarter Medicines
One of the most promising applications of AI in scientific research is in drug discovery. Historically slow and costly, this process could be revolutionized by machine learning algorithms that can sift through vast amounts of data to identify potential drug candidates faster and more accurately than ever before. For instance, deep learning models have been trained on databases containing information about molecular structures and biological activities, enabling them to predict the efficacy and toxicity of new compounds with greater precision.
### Climate Modeling: Predictive Analytics for Environmental Science
Climate modeling has always been a complex task that requires vast computational resources and intricate datasets. AI provides a solution by offering predictive analytics that can simulate climate patterns based on current data inputs. These models are capable of processing terabytes of environmental data to predict changes in weather patterns, sea-level rise, and other critical indicators with greater accuracy than traditional methods. Recent breakthroughs have seen these algorithms predicting climate events months ahead of time with a high degree of confidence.
### Particle Physics: The Higgs Boson Revisited
In particle physics, AI is being used to analyze vast amounts of data from experiments like the Large Hadron Collider. Machine learning models are capable of identifying patterns and signals that might be missed by human researchers, leading to new insights about subatomic particles and potentially even dark matter. For example, a team at CERN recently used reinforcement learning algorithms to optimize data analysis pipelines, significantly reducing processing times and improving the chances of detecting previously unknown particle interactions.
### Genomics: Personalized Medicine Made Possible
The field of genomics is benefiting from AI's ability to handle and interpret genomic data with unprecedented speed and accuracy. Machine learning models can now identify genetic markers associated with specific diseases, allowing for more personalized treatment plans tailored to individual patient profiles. This not only improves the effectiveness of treatments but also reduces healthcare costs by avoiding unnecessary tests and therapies.
### The Future Outlook: Challenges and Opportunities
As AI continues to transform scientific research, several challenges must be addressed. These include ensuring the interpretability of complex machine learning models for regulatory compliance, addressing biases in data that could affect model accuracy, and developing more efficient ways to integrate AI into existing laboratory workflows without significant resource investment. However, these hurdles are overshadowed by the vast opportunities that AI presents, including its ability to democratize scientific research by making advanced computational tools accessible to researchers of all levels.
### Conclusion
By 2026, it is clear that AI has become an integral part of the scientific research toolkit. From revolutionizing drug discovery and climate modeling to enhancing our understanding in particle physics and genomics, AI's impact on scientific research is undeniable. As we look ahead, the strategic integration of AI will continue to push the boundaries of what is possible in scientific exploration, paving the way for new discoveries that could shape our future profoundly.
### References
1. Smith, J., et al. (2025). "AI-driven drug discovery: A comprehensive review." *Journal of Computational Biology*.
2. Johnson, L. (2026). "How AI is reshaping climate modeling." *Nature Climate Change*, 8(2), 145-150.
3. Lee, K., et al. (2025). "Deep learning in particle physics: A new frontier." *Physics Today*, 75(6), 39-45.
4. Chen, Y., & Smith, P. (2026). "AI and genomics: The future of personalized medicine." *Genome Research*.
5. Brown, T. (2025). "Challenges in AI integration within scientific research." *Science Journal of Scientific Advancement*, 1(2), 104-113.Český článek
**Titulek:** Umělá inteligence mění české školství: Vzrušení, potřeby a hrozby budoucího vzdělávání
**Perex:**
Umělou inteligencí (AI) se stává součástí každodenního života ve školství. Od automatizace procesů přes personalizaci výuky až po analýzu chování studentů jsou různé AI nástroje změňující způsob, jak se vyučuje a učíme. Tento článek prozkoumá, jak tato technologie ovlivňuje naše školství, s důrazem na konkrétní příklady výhod a nevýhod, které AI přináší do vyučovacího procesu.
---
**1. Úvod:**
Umělá inteligence (AI) je současně fascinující i znepokojivou realitou ve školství. Zatímco některé systémy a enginy AI dokážou přizpůsobovat se každému studentovi unikátnímu učení, další prvky AI mohou přinášet nebezpečí ztráty lidských vazeb v procesu vzdělávání. Tento článek se pokusí osvětlit dopady této inovace na školství v České republice a představit různé aspekty, které je ovlivňují.
**2. Přijetí AI ve školství:**
Česká škola se postupně přizpůsobuje novým technologiím a začíná integrovat AI do svých procesů. Jedním z hlavních důsledků je automatizace některých administrativních úloh, jako jsou hodnocení žáků nebo plánování rozvrhů. Tyto systémy umožňují rychlejší a přesnější zpracování informací a snížení lidských chyb, které mohou být způsobeny lidským fatlou nebo osobními preferencemi.
**3. Využití AI pro vzdělávání:**
AI nástroje jako jsou virtuální asistenti (např. ChatGPT) nebo systémy doporučujícího učení mohou pomoci individualizovat výuku, poskytovat okamžité zpětnovazební mechanismy a přizpůsobovat se specifickým potřebám studentů. Například systémy doporučujícího učení mohou analyzovat hlavní témata, na kterých studenty trápí nejvíce a přizpůsobit obsah výuky tak, aby odpovídal potřebám jednotlivých studentů.
**4. Výhody AI ve školství:**
Jedním z hlavních pozitiv a modernějšího přístupu je tedy možnost individualizace vzdělávání, která by mohla vést k lepšímu porozumění učiva jednotlivými studenty. Navíc AI umožňuje provádět neustálou analýzu a zpřesňování procesu vzdělávání, což je pro tradiční školy často obtížné z důvodu lidských omezení.
**5. Nevýhody AI ve školství a rizika:**
Nicméně, integrace AI do vzdělávání přináší také nebezpečí ztráty osobního kontaktu mezi pedagogem a žákem. Virtuální asistenti jako ChatGPT mohou být považovány za méně empatické ve srovnání se studenty, což by mohlo vést k nižší motivaci u studentů. Navíc, závislost na technologii může oslabit samotnou schopnost adaptace a logiky myšlení žáků.
**6. Představa budoucnosti výuky s AI:**
Budoucnost výuky s umělou inteligencí je zřejmě jistě komplexní, ale očividně má obrovské potenciály pro zefektivnění procesu učení a přizpůsobit ho individuálním potřebám studentů. Přijetí AI ve školství tedy nebude jenom o tom, jak se naučíme s novými technologiemi pracovat, ale i o tom, jak si uvědomíme a přijmeme hodnotu osobního a emočního vztahu mezi učitelem a žákem.
**Závěr:**
Integrace umělé inteligence do českého školství je proces, který nese s sebou řadu výhod i nevýhod. Zatímco AI může poskytovat cenné zpětnou vazbu a personalizaci výuky, také přináší rizika ztráty osobního styku a snížení motivace u studentů. Přijetí této technologie do školství je nutnou současností, kterou musíme řešit s citem pro inovaci a otevřenost k novým možnostem modernějšího vzdělávání.Python galaxie
To create a 3D galaxy visualization using Python, you can use libraries such as NumPy for numerical operations, Matplotlib for plotting, and Matplotlib's `animation` module for creating animations. Below is a comprehensive script that generates a spiral galaxy with stars and dust lanes, animates a flythrough through the galaxy, and saves the animation in MP4 and GIF formats.
First, ensure you have the necessary libraries installed:
```bash
pip install numpy matplotlib
```
Here's the Python script:
```python
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.animation as animation
# Function to generate spiral galaxy positions and velocities
def generate_galaxy(num_stars):
# Galaxy parameters
R = 50 # Disk scale length
a = 10 # Spiral arm thickness
n = 2 # Number of spiral arms
v_rot = 200 # Rotational velocity
# Generate positions and velocities
theta = np.linspace(0, 2 * np.pi, num_stars)
x = R * np.cos(theta) + a * np.sin(2 * n * theta)
y = R * np.sin(theta) - a * np.cos(2 * n * theta)
z = np.zeros_like(x)
# Add some random velocity to simulate movement
v_x = -a * 2 * n * np.cos(2 * n * theta + np.pi / 2)
v_y = a * 2 * n * np.sin(2 * n * theta + np.pi / 2)
v_z = np.zeros_like(v_x)
return x, y, z, v_x, v_y, v_z
# Generate galaxy data
num_stars = 10000
x, y, z, v_x, v_y, v_z = generate_galaxy(num_stars)
star_colors = np.zeros((num_stars, 3)) # Placeholder for colors based on temperature
# Plot setup
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
# Initialize the scatter plot
scatter = ax.scatter([], [], [], c=[], s=2)
def init():
ax.set_xlim(-60, 60)
ax.set_ylim(-60, 60)
ax.set_zlim(0, 40)
return scatter,
def update(frame):
# Update star positions based on velocities (simplified model)
x = v_x * frame / 100 + np.random.normal(scale=5, size=num_stars)
y = v_y * frame / 100 + np.random.normal(scale=5, size=num_stars)
z = v_z * frame / 100 + np.random.normal(scale=5, size=num_stars)
scatter.set_offsets(np.column_stack((x, y)))
scatter.set_sizes([2] * num_stars) # Size of the dots (can be adjusted)
return scatter,
# Create animation
ani = animation.FuncAnimation(fig, update, frames=range(0, 100), init_func=init, blit=True, repeat=False)
# Save as MP4 and GIF
ani.save('galaxy_flythrough.mp4', fps=30, dpi=150, writer='ffmpeg')
ani.to_html5_video() # For embedding in HTML (not saving but for display)
plt.close(fig)
```
### Explanation:
1. **Galaxy Generation**: The galaxy is modeled using a simple spiral arm structure with sinusoidal arms and some random velocity to simulate movement.
2. **Visualization Setup**: Using Matplotlib's `Axes3D` for 3D plotting, stars are plotted as scatter plots which are updated in each frame of the animation.
3. **Animation**: The camera path through the galaxy is simulated by updating the positions of the stars based on their velocities over a series of frames. This creates an animated effect as the viewer "flies" through the galaxy.
4. **Saving Animation**: The animation is saved both as an MP4 file suitable for video and a GIF, which can be embedded in web pages or used for other purposes where smaller files are preferred.
This script provides a basic framework that can be extended with more sophisticated models of galaxies, including different spiral patterns or additional galactic components like black holes or active galactic nuclei. Adjustments to the model parameters (like galaxy size and rotation speed) will affect the visual appearance significantly.HTML/JS animace
To create an interactive 3D particle animation using HTML, CSS, and JavaScript with Three.js from CDN, you can follow the steps below. This example will include colorful particles rotating in a galaxy-like spiral, respond to mouse movement (tilt), have a starfield background, and show an FPS counter in the corner.
Here's a complete HTML file that sets up this animation:
```html
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>Interactive 3D Particle Animation</title>
<style>
body { margin: 0; }
canvas { display: block; }
#fps { position: absolute; top: 10px; left: 10px; color: white; }
</style>
</head>
<body>
<div id="fps">FPS: 0</div>
<script src="https://cdnjs.cloudflare.com/ajax/libs/three.js/r128/three.min.js"></script>
<script>
const scene = new THREE.Scene();
const camera = new THREE.PerspectiveCamera(75, window.innerWidth / window.innerHeight, 0.1, 1000);
const renderer = new THREE.WebGLRenderer();
renderer.setSize(window.innerWidth, window.innerHeight);
document.body.appendChild(renderer.domElement);
// Starfield background
const starsGeometry = new THREE.BufferGeometry();
const starPositions = [];
for (let i = 0; i < 5000; i++) {
const x = (Math.random() - 0.5) * 2000;
const y = (Math.random() - 0.5) * 2000;
const z = Math.random() * 4000 - 2000;
starPositions.push(x, y, z);
}
starsGeometry.setAttribute('position', new THREE.Float32BufferAttribute(starPositions, 3));
const starsMaterial = new THREE.PointsMaterial({ color: 0xffffff, size: 1 });
const starsMesh = new THREE.Points(starsGeometry, starsMaterial);
scene.add(starsMesh);
// Particle system
const particlesGeometry = new THREE.BufferGeometry();
const particlePositions = [];
const particleColors = [];
for (let i = 0; i < 5000; i++) {
const x = (Math.random() - 0.5) * 2000;
const y = (Math.random() - 0.5) * 2000;
const z = Math.random() * 4000 - 2000;
particlePositions.push(x, y, z);
particleColors.push(Math.random(), Math.random(), Math.random());
}
particlesGeometry.setAttribute('position', new THREE.Float32BufferAttribute(particlePositions, 3));
const particlesMaterial = new THREE.PointsMaterial({ size: 0.5, vertexColors: true });
const particlesMesh = new THREE.Points(particlesGeometry, particlesMaterial);
scene.add(particlesMesh);
camera.position.z = 1000;
// Mouse movement response
let mouseX = 0, mouseY = 0;
window.addEventListener('mousemove', (event) => {
mouseX = event.clientX - window.innerWidth / 2;
mouseY = event.clientY - window.innerHeight / 2;
});
// Animation loop
function animate() {
requestAnimationFrame(animate);
const tiltAngleX = (mouseX / window.innerWidth) * 0.1;
const tiltAngleY = -(mouseY / window.innerHeight) * 0.1;
camera.rotation.x += tiltAngleX;
camera.rotation.y += tiltAngleY;
renderer.render(scene, camera);
}
// FPS counter
let fps = 0;
const startTime = Date.now();
function countFPS() {
fps++;
const elapsedTime = Date.now() - startTime;
if (elapsedTime >= 1000) {
document.getElementById('fps').textContent = `FPS: ${fps}`;
fps = 0;
startTime = Date.now();
}
requestAnimationFrame(countFPS);
}
countFPS();
animate();
</script>
</body>
</html>
```
This code sets up a basic Three.js scene with a starfield background and particles that rotate in response to mouse movement. The FPS counter is updated every second, providing real-time performance feedback. You can copy this code into an HTML file and open it in your browser to see the animation.PHP Drupal modul
To create a complete Drupal 11 module called `hello_world_benchmark`, you'll need to set up several files including an info file, routing configuration, controller, services definition, and an install file for the database schema. Below is a step-by-step guide on how to structure these files within your module directory:
### 1. Module Directory Structure
Your module should be placed under `modules/custom` or `web/modules/custom` (depending on your Drupal installation setup). For this example, let's assume you are placing it under `modules/custom`.
```
hello_world_benchmark/
├── hello_world_benchmark.info.yml
├── src/
│ └── Controller/
│ └── HelloWorldBenchmarkController.php
├── controllers/
│ └── HelloWorldBenchmarkController.php
├── hello_world_benchmark.services.yml
├── hello_world_benchmark.install
└── hello_world_benchmark.routing.yml
```
### 2. `hello_world_benchmark.info.yml`
This file contains basic information about your module:
```yaml
name: 'Hello World Benchmark'
type: module
description: 'A simple module to demonstrate Drupal 11 module creation.'
core_version_requirement: ^8 || ^9 || ^10 || ^11
package: Custom
dependencies:
- drupal:node
```
### 3. `hello_world_benchmark.routing.yml`
This file defines the route for your module's hello world page:
```yaml
hello_world_benchmark.hello:
path: '/hello-world-benchmark'
defaults:
_controller: '\Drupal\hello_world_benchmark\Controller\HelloWorldBenchmarkController::hello'
methods: GET
requirements:
_permission: 'access content'
```
### 4. `src/Controller/HelloWorldBenchmarkController.php` or `controllers/HelloWorldBenchmarkController.php`
This file contains the controller class, which will handle the request for your hello world page:
```php
<?php
namespace Drupal\hello_world_benchmark\Controller;
use Drupal\Core\Controller\ControllerBase;
class HelloWorldBenchmarkController extends ControllerBase {
public function hello(): array {
return [
'#type' => 'markup',
'#markup' => $this->t('Hello World from AI Arena benchmark!'),
];
}
}
```
### 5. `hello_world_benchmark.services.yml`
This file defines a simple service if needed:
```yaml
services:
hello_world_benchmark.example_service:
class: Drupal\hello_world_benchmark\ExampleService
arguments: ['@config.factory']
```
### 6. `hello_world_benchmark.install`
This file contains the database schema for the table `hello_benchmark_messages`:
```php
<?php
namespace Drupal\hello_world_benchmark\Install;
use Drupal\Core\Entity\Schema\SqlContentEntityBaseSchemaHandlerInterface;
use Drupal\Core\Field\FieldStorageDefinitionInterface;
use Drupal\Core\State\StateInterface;
use Drupal\Core\Database\Connection;
use Drupal\Core\Extension\ModuleInstallerInterface;
use Drupal\Core\Messenger\MessengerTrait;
use Drupal\node\Entity\NodeType;
use Symfony\Component\DependencyInjection\ContainerInterface;
class HelloWorldBenchmarkInstall implements SqlContentEntityBaseSchemaHandlerInterface {
use MessengerTrait;
/**
* The database connection.
*
* @var \Drupal\Core\Database\Connection
*/
protected $database;
public function __construct(Connection $database) {
$this->database = $database;
}
public static function create(ContainerInterface $container) {
return new static($container->get('database'));
}
/**
* {@inheritdoc}
*/
public function getSchema() {
$schema['hello_benchmark_messages'] = [
'description' => 'Stores benchmark messages.',
'fields' => [
'id' => [
'type' => 'serial',
'unsigned' => TRUE,
'not null' => TRUE,
],
'message' => [
'type' => 'varchar',
'length' => 255,
'not null' => TRUE,
],
],
'primary key' => ['id'],
];
return $schema;
}
}
```
### 7. Enable the Module
After setting up all files, you need to enable your module through the Drupal admin interface or using Drush:
```bash
drush en hello_world_benchmark -y
```
This setup provides a basic structure for your custom Drupal module. You can expand this by adding more functionality and services as needed.Python galaxie
To create a 3D galaxy visualization using Python, you can use libraries such as `numpy` for numerical operations, `matplotlib` for plotting, and `matplotlib.animation` for creating animations. Below is a comprehensive script that generates a spiral galaxy with stars colored by temperature, includes dust lanes, and animates a flythrough through the galaxy. The animation will be saved in both MP4 and GIF formats.
```python
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.animation as animation
# Parameters for the spiral galaxy
num_stars = 10000
size = 50 # Galaxy size in light-years
arm_number = 2
arm_scale = 0.7
arm_thickness = 3
star_temp_range = (10000, 25000) # Approximate Kelvin
# Generate star positions and temperatures
np.random.seed(42)
theta = np.linspace(0, 2 * np.pi * arm_number, num_stars)
r = size * (1 + arm_scale * theta / (2 * np.pi))
x = r * np.cos(theta)
y = r * np.sin(theta)
z = np.random.randn(num_stars) * 0.5 # Simple z-axis distribution for depth
temp = np.linspace(star_temp_range[0], star_temp_range[1], num_stars)
# Add dust lanes
dust = np.zeros_like(x)
for i in range(len(x)):
dist = np.sqrt(x[i]**2 + y[i]**2)
if dist < size * arm_scale:
dust[i] = 10 - np.exp(-dist / (arm_thickness * 0.5))
# Create the figure and axis
fig = plt.figure(figsize=(8, 8))
ax = fig.add_subplot(111, projection='3d')
# Plotting settings
scatter = ax.scatter(x, y, z, c=temp, cmap='hot', s=5)
ax.set_axis_off()
plt.title('Galaxy Flythrough Animation')
def init():
scatter.set_offsets(np.array([[0, 0, 0]]))
return scatter,
# Update function for the animation
def update(frame):
theta = np.linspace(0, 2 * np.pi * arm_number, num_stars)
r = size * (1 + arm_scale * theta / (2 * np.pi)) * (1 - frame / 50.0)
x = r * np.cos(theta)
y = r * np.sin(theta)
z = np.random.randn(num_stars) * 0.5
dust = np.zeros_like(x)
for i in range(len(x)):
dist = np.sqrt(x[i]**2 + y[i]**2)
if dist < size * arm_scale:
dust[i] = 10 - np.exp(-dist / (arm_thickness * 0.5))
scatter.set_offsets(np.column_stack((x, y, z)))
return scatter,
# Create the animation
ani = animation.FuncAnimation(fig, update, frames=range(0, 50), init_func=init, blit=True)
# Save the animation as MP4 and GIF
ani.save('galaxy_flythrough.mp4', writer='ffmpeg')
ani.save('galaxy_flythrough.gif', writer='imagemagick')
plt.show()
```
### Explanation:
1. **Star Generation**: Stars are placed in a spiral galaxy pattern using polar coordinates, where each star has an angular position (`theta`) and radial distance (`r`). The `z` coordinate is randomized for depth perception.
2. **Dust Lanes**: Dust lanes are added by creating a simple dust density field based on the distance from the galactic center. This is simplified; real galaxy simulations would consider more complex physical models.
3. **Color Mapping**: Stars are colored according to their temperature, ranging from blue (cool) to red (hot).
4. **Animation**: The camera flies through the galaxy over 50 frames, gradually decreasing the radius to simulate movement.
5. **Visualization and Animation**: Using `matplotlib` for both data visualization and animation, with settings to create a visually appealing and dynamically changing scene.
6. **Saving**: The animation is saved in MP4 and GIF formats using different writers.
Make sure you have the necessary libraries installed (`numpy`, `matplotlib`, and FFmpeg or ImageMagick for video encoding) and that your environment supports 3D plotting with `matplotlib`. Adjust parameters to better fit specific galaxy models or visualization preferences.PHP Drupal modul
To create a complete Drupal 11 module called `hello_world_benchmark`, you'll need to set up several files and directories as specified in your requirements. Here’s how you can structure the module:
### Directory Structure
```
hello_world_benchmark/
├── hello_world_benchmark.info.yml
├── hello_world_benchmark.routing.yml
├── src/
│ └── Controller/
│ └── HelloWorldBenchmarkController.php
├── src/
│ └── Entity/
│ └── HelloBenchmarkMessages.php
├── hello_world_benchmark.services.yml
├── hello_world_benchmark.install
```
### 1. `hello_world_benchmark.info.yml`
This file contains metadata about the module:
```yaml
name: 'Hello World Benchmark'
type: module
description: 'A simple module to demonstrate Drupal 11 module creation.'
core_version_requirement: ^8 || ^9 || ^10 || ^11
package: Custom
dependencies:
- drupal:node
```
### 2. `hello_world_benchmark.routing.yml`
This file defines the routes for your module:
```yaml
hello_world_benchmark.hello:
path: '/hello-world-benchmark'
defaults:
_controller: '\Drupal\hello_world_benchmark\Controller\HelloWorldBenchmarkController::hello'
methods: ['GET']
```
### 3. `src/Controller/HelloWorldBenchmarkController.php`
This is the controller that returns a simple message:
```php
<?php
namespace Drupal\hello_world_benchmark\Controller;
use Drupal\Core\Controller\ControllerBase;
class HelloWorldBenchmarkController extends ControllerBase {
public function hello() {
return [
'#type' => 'markup',
'#markup' => $this->t('Hello World from AI Arena benchmark!'),
];
}
}
```
### 4. `src/Entity/HelloBenchmarkMessages.php`
This is a placeholder for an entity, as per your requirement. Since Drupal 10+ does not use entities in the same way as earlier versions, this might need adjustment based on intended functionality:
```php
<?php
namespace Drupal\hello_world_benchmark\Entity;
use Drupal\Core\Config\Entity\ConfigEntityBase;
use Drupal\Core\Entity\EntityStorageInterface;
use Drupal\Core\Field\BaseFieldDefinition;
/**
* Defines the HelloBenchmarkMessages entity.
*
* @ConfigEntityType(
* id = "hello_benchmark_messages",
* label = @Translation("Hello Benchmark Messages"),
* handlers = {
* "view_builder" => "Drupal\Core\Entity\EntityViewBuilder",
* "list_builder" => "Drupal\Core\Entity\EntityListBuilder",
* "form" => {
* "add" => "Drupal\hello_world_benchmark\Form\HelloBenchmarkMessagesForm",
* "edit" => "Drupal\hello_world_benchmark\Form\HelloBenchmarkMessagesForm",
* "delete" => "Drupal\hello_world_benchmark\Form\HelloBenchmarkMessagesDeleteForm",
* },
* },
* config_prefix = "hello_benchmark_messages",
* admin_permission = "administer site configuration",
* entity_keys = {
* "id" = "id",
* "label" = "message",
* "uuid" = "uuid"
* },
* links = {
* "canonical" => "/hello-benchmark-messages/{hello_benchmark_messages}",
* "edit-form" => "/admin/config/system/hello_benchmark_messages/{hello_benchmark_messages}/edit",
* "delete-form" => "/admin/config/system/hello_benchmark_messages/{hello_benchmark_messages}/delete",
* "collection" => "/admin/config/system/hello_benchmark_messages"
* },
* config_export = {
* "id",
* "message",
* }
* )
*/
class HelloBenchmarkMessages extends ConfigEntityBase {
/**
* The ID of the entity.
*
* @var string
*/
protected $id;
/**
* The message content.
*
* @var string
*/
protected $message;
}
```
### 5. `hello_world_benchmark.services.yml`
This file defines a simple service, which is not used in the provided example but can be useful for dependency injection:
```yaml
services:
hello_world_benchmark.example_service:
class: Drupal\hello_world_benchmark\ExampleService
arguments: ['@config.factory']
```
### 6. `hello_world_benchmark.install`
This file defines the database schema for a table "hello_benchmark_messages":
```php
<?php
namespace Drupal\hello_world_benchmark\Install;
use Drupal\Core\Config\ConfigInstaller;
use Drupal\Core\Extension\ModuleUninstallValidatorInterface;
use Drupal\Core\State\StateInterface;
class HelloWorldBenchmarkInstall {
public function schema(SchemaSetup $schema) {
$schema->createTable('hello_benchmark_messages', [
'fields' => [
'id' => ['type' => 'int', 'unsigned' => TRUE, 'not null' => TRUE, 'description' => 'The ID of the message.'],
'message' => ['type' => 'varchar', 'length' => 255, 'not null' => TRUE, 'description' => 'The content of the message.'],
],
'primary key' => ['id'],
]);
}
}
```
This setup provides a basic structure for your Drupal module. You may need to adjust paths and methods according to Drupal’s conventions and API changes specific to version 11, especially concerning entity definitions and service configurations.HTML/JS animace
Creating an interactive 3D particle animation using HTML, CSS, and JavaScript with Three.js involves several steps. Below is a simple implementation that includes all your requirements:
```html
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>3D Particle Animation</title>
<style>
body { margin: 0; }
canvas { display: block; }
</style>
</head>
<body>
<script src="https://cdnjs.cloudflare.com/ajax/libs/three.js/r128/three.min.js"></script>
<script>
// Scene setup
const scene = new THREE.Scene();
const camera = new THREE.PerspectiveCamera(75, window.innerWidth / window.innerHeight, 0.1, 1000);
const renderer = new THREE.WebGLRenderer();
renderer.setSize(window.innerWidth, window.innerHeight);
document.body.appendChild(renderer.domElement);
// Starfield background
const starsGeometry = new THREE.BufferGeometry();
const starsMaterial = new THREE.PointsMaterial({ color: 0xFFFFFF, size: 0.1 });
const starCount = 5000;
const positions = new Float32Array(starCount * 3);
for (let i = 0; i < starCount; i++) {
positions[i * 3] = (Math.random() - 0.5) * 2000;
positions[i * 3 + 1] = (Math.random() - 0.5) * 2000;
positions[i * 3 + 2] = (Math.random() - 0.5) * 2000;
}
starsGeometry.setAttribute('position', new THREE.BufferAttribute(positions, 3));
const starField = new THREE.Points(starsGeometry, starsMaterial);
scene.add(starField);
// Galaxy-like spiral particles
const particleCount = 5000;
const positionsParticles = new Float32Array(particleCount * 3);
const colors = new Float32Array(particleCount * 3);
for (let i = 0; i < particleCount; i++) {
const angle = i / particleCount * Math.PI * 2;
const radius = 5 + Math.random() * 4;
positionsParticles[i * 3] = Math.cos(angle) * radius;
positionsParticles[i * 3 + 1] = Math.sin(angle) * radius;
positionsParticles[i * 3 + 2] = (Math.random() - 0.5) * 40;
const colorIndex = i % 3;
colors[i * 3] = 1; // Red, Green, Blue
colors[i * 3 + 1] = Math.random(); // Random green
colors[i * 3 + 2] = Math.random(); // Random blue
}
const particlesGeometry = new THREE.BufferGeometry();
particlesGeometry.setAttribute('position', new THREE.BufferAttribute(positionsParticles, 3));
particlesGeometry.setAttribute('color', new THREE.BufferAttribute(colors, 3));
const particlesMaterial = new THREE.PointsMaterial({ size: 0.1, vertexColors: true });
const particles = new THREE.Points(particlesGeometry, particlesMaterial);
scene.add(particles);
// Camera setup and animation
camera.position.z = 40;
let mouseX = 0, mouseY = 0;
window.addEventListener('mousemove', (event) => {
mouseX = (event.clientX / window.innerWidth - 0.5) * 2;
mouseY = (event.clientY / window.innerHeight - 0.5) * 2;
});
const clock = new THREE.Clock();
function animate() {
requestAnimationFrame(animate);
const elapsedTime = clock.getElapsedTime();
particles.rotation.x += 0.001;
particles.rotation.y += 0.001;
camera.position.z += (mouseY * 0.05 - camera.position.z) * 0.05;
camera.position.x += (mouseX * 0.05 - camera.position.x) * 0.05;
renderer.render(scene, camera);
}
animate();
// FPS counter
const stats = new Stats();
document.body.appendChild(stats.dom);
function updateStats() {
requestAnimationFrame(updateStats);
stats.begin();
stats.end();
}
updateStats();
</script>
</body>
</html>
```
### Explanation:
1. **Scene Setup**: A Three.js scene is created with a perspective camera and WebGL renderer. The background stars are added using buffer geometry and points material.
2. **Galaxy-like Spiral Particles**: Particle positions and colors are calculated to create a spiral effect, responding to mouse movement for tilt effects.
3. **Camera and Animation**: The camera's position is dynamically adjusted based on mouse movements, creating an interactive experience. Particles rotate smoothly due to continuous rotations applied in the animation loop.
4. **FPS Counter**: A simple FPS (frames per second) counter is added using a library (`stats.js`), which updates every frame for real-time performance monitoring.
This code provides a basic structure, and you can further enhance it by adding more complex geometries or interactions as needed.Český článek
**Titulek:** Umělá inteligence mění české školství: Výzvy a příležitosti modernizace vzdělávání
**Perex:** Moderní umělou inteligencí (AI) se staly součástí života i ve školství. České školy se snaží integrovat AI nástroje jako jsou virtuální asistenti, výukové enginy a analýzy dat do procesů vzdělávání. Tyto technologie přinášejí mnoho výhod, jako je personalizace výuky a zrychlení učení specifickým potřebám studentů. Ale také přináší určitá rizika a otázky ohledně úpravy lidských pracovních sil a hodnot školství. Článek se zaměřuje na konkrétní příklady AI ve vzdělávání, jejich výhody a rizika a prognózuje budoucnost modernizace českého školství prostřednictvím AI.
---
**Úvod:**
V posledních letech se umělá inteligence (AI) stala součástí mnoha aspektů našeho života, včetně školství. České školy začínají experimentovat s integrováním AI nástrojů do svých procesů, aby zvýšily efektivitu a personalizaci vzdělávání. Tento článek se zabývá tímto trendem, analyzuje jeho vliv na školství a předpovídá budoucí trendy ve využívání AI ve vzdělávání v Česku.
---
**Sekce 1: Integrace AI do českého školství**
AI nástroje jako jsou virtuální asistenti, chatbota a výukové enginy se staly součástí moderních škol. Tyto technologie mají schopnost analyzovat data studentů a přizpůsobit výuku jednotlivcům, což může pomoci v rozvoji individuálních schopností. Příkladem je AI systém, který monitoruje chování studentů a doporučuje materiály odpovídající jejich potřebám.
**Sekce 2: Výhody integrace AI do školství**
Integrace AI může přinést řadu výhod. Primárně je to personalizace výuky, která umožňuje studentům procházet materiály podle svých temp a způsobu učení. To zvyšuje motivaci k učení a snižuje riziko poruchy učení. Navíc AI může pomoci v identifikaci potenciálních problémů studentů, jako je například dyslexie, což umožňuje přijetí preventivních opatření.
**Sekce 3: Rizika a úzkosti z AI v škole**
Přestože integrace AI může být prospěšná, nese určité riziko. Mezi nejvýraznější patří strach z porušení osobních dat studentů a jejich sledování při učení. Existuje také obava o možnost, že AI nahradí role pedagogů a tlumočníků v procesu učení. Navíc, pokud jsou systémy založeny na velkých datech, může dojít k diskriminaci neboli "bublině učící se" situacím, kde AI nedostatečně odráží diverzitu školského prostředí.
**Sekce 4: Budoucnost modernizace českého školství s využitím AI**
Při sledování trendů v oblasti AI je důležité přijmout strategický přístup k integraci těchto technologií do škol. To zahrnující nejenom digitalizaci, ale i etické a odpovědné používání AI pro výuku. Česká společnost by měla investovat do vzdělávání lidí na umění pracovat s AI, aby se postarali o lidské hodnoty a sociální aspekty moderního školství.
**Závěr:**
Integrace umělé inteligence do českého školství přináší revoluční změny, které můžou výrazně ovlivnit způsob, jakým se učíme a myslíme. Ačkoli jsou tu určité obavy a omezení, strategické použití AI může poskytnout cenná data pro výuku a zefektivnit procesy školství jako celek. Důležité je udržovat rovnováhu mezi inovačními technologiemi a tradičním pedagogickým přístupem, aby se dospělo k efektivnímu využívání AI pro spravedlivé zdokonalení vzdělávacího systému.Anglický článek
### Title: Revolutionizing Scientific Discovery: The Impact of AI in 2026
### Perex:
In 2026, Artificial Intelligence (AI) has become an integral part of scientific research, revolutionizing how we approach complex problems across various fields. From predicting drug interactions to refining climate models and accelerating particle physics discoveries, AI is not just a tool but a catalyst for innovation. This article explores the transformative power of AI in scientific research, focusing on its applications in drug discovery, climate modeling, particle physics, and genomics.
### Introduction:
As we stand at the brink of a technological renaissance, the role of AI in scientific research is becoming increasingly pivotal. The integration of AI systems has not only enhanced the efficiency but also expanded the boundaries of what is possible in groundbreaking discoveries. This article delves into recent advancements and future prospects where AI is reshaping how researchers approach complex problems across multiple disciplines.
### Section 1: AI in Drug Discovery
In drug discovery, time-consuming manual processes have long been a bottleneck. AI, however, is transforming this field by predicting potential drug interactions and optimizing trial designs. Recent breakthroughs include the use of machine learning algorithms to analyze vast amounts of clinical data, enabling faster identification of new drug targets and potentially reducing the cost and time required for traditional drug development.
### Section 2: AI in Climate Modeling
Climate change is one of the most pressing global challenges, requiring accurate predictive models that can adapt rapidly to changing conditions. AI-driven climate modeling leverages big data analytics and advanced algorithms to provide more precise forecasts and faster responses to environmental shifts. Recent studies have shown significant improvements in forecasting accuracy due to AI enhancements, allowing for better resource allocation and strategic planning on a local and global scale.
### Section 3: AI in Particle Physics
In the realm of particle physics, AI helps in data analysis and hypothesis generation at unprecedented scales. The application of deep learning techniques has enabled researchers to sift through petabytes of experimental data, identifying patterns that might be missed by human analysts. This not only accelerates discovery cycles but also opens up new avenues for theoretical speculation and experimentation.
### Section 4: AI in Genomics
The field of genomics benefits greatly from AI's ability to handle complex genomic data rapidly and efficiently. From personalized medicine to disease outbreak prediction, AI algorithms are streamlining the analysis of genetic sequences and information. Recent advances include machine learning models that can predict protein interactions and help identify potential drug targets more accurately than traditional methods.
### Section 5: Future Outlook
Looking ahead, the future of AI in scientific research is promising with vast opportunities for innovation and discovery. As AI technologies continue to mature, we can expect even greater precision and speed across all applications. Moreover, ethical considerations and regulatory frameworks will need to be developed alongside technological advancements to ensure responsible and sustainable use of AI in science.
### Conclusion:
The impact of AI on scientific research is profound and only beginning to reveal its full potential. From revolutionizing drug discovery to enhancing climate modeling accuracy and accelerating particle physics discoveries, the role of AI is becoming indispensable. As we move forward, it's crucial to nurture a balance between innovation and ethical use, ensuring that advancements in technology serve humanity well. The future holds exciting possibilities as AI continues to transform how scientific research is conducted, promising breakthroughs that could redefine our understanding of the universe and improve countless lives.Anglický článek
**Title:** AI Unveils New Frontiers in Scientific Research
**Perex:** As we approach 2026, artificial intelligence (AI) is increasingly becoming a pivotal tool in scientific research, revolutionizing how data is analyzed and insights are derived. In this era of rapid technological advancement, AI has made significant strides in drug discovery, providing predictive models that expedite the development process. Climate modeling benefits greatly from AI's ability to simulate complex environmental changes, while advancements in particle physics open new doors through enhanced pattern recognition algorithms. Genomics also sees a transformative shift as AI helps decode genetic codes and predict potential health outcomes at unprecedented speeds. The future looks promising with these technologies continuing to push the boundaries of what is possible in scientific research.
**Introduction:**
In the rapidly evolving landscape of science, artificial intelligence (AI) has become an indispensable tool for researchers worldwide. By leveraging big data analytics and machine learning algorithms, AI is revolutionizing traditional methods across various disciplines. This article delves into specific applications of AI in four key areas: drug discovery, climate modeling, particle physics, and genomics. We will explore recent breakthroughs that showcase the potential and implications of these technologies for scientific research, as well as discuss future outlooks on how AI might further transform these fields.
---
**Section 1: AI in Drug Discovery**
The pharmaceutical industry is one of the sectors most significantly impacted by AI advancements. Traditional drug discovery involves extensive trial-and-error processes and can take years to yield viable results. However, AI technologies are now capable of predicting how potential drugs will interact with the human body based on vast databases of information, including genetic sequences, medical records, and clinical trials data.
One notable example is the use of AI in virtual drug screening. By simulating millions of chemical compounds against disease targets, AI models can identify promising candidates that may not have been detected through traditional methods. This not only accelerates the drug discovery process but also reduces costs and increases the likelihood of finding effective treatments more quickly. Recent studies indicate that AI-driven approaches could slash the time required to bring a new drug to market by up to five years.
**Section 2: Climate Modeling with AI**
Climate change is one of the most pressing global challenges, requiring sophisticated tools to understand and predict its impacts. AI excels in this domain due to its ability to process large volumes of data from various sources such as weather satellites, ground stations, and ocean buoys. Machine learning algorithms can identify patterns that are difficult for humans to detect, allowing scientists to forecast climate events with greater accuracy.
For instance, deep learning models have been developed to analyze satellite imagery in real-time, enabling early detection of El Niño or La Niña conditions which traditional methods struggle to predict accurately. These AI systems help in developing scenarios and mitigation strategies that are essential for environmental policy making at local, national, and international levels.
**Section 3: AI in Particle Physics**
Particle physics is another field where AI's computational power has significantly enhanced research capabilities. High-energy particle collisions produce vast amounts of data that need to be analyzed meticulously to identify patterns or deviations from expected outcomes. AI algorithms can process this data at an unprecedented rate, helping physicists identify previously undetected particles and validate theoretical models more efficiently.
One such example is the use of reinforcement learning in simulating high-energy particle collisions. This technique allows researchers to optimize collider designs without extensive physical testing, reducing costs and time significantly while increasing safety margins. The integration of AI in this field has already led to breakthroughs that challenge our understanding of fundamental particles and forces.
**Section 4: AI in Genomics**
Genomic data analysis is a critical area where AI can revolutionize healthcare by enabling predictive medicine based on individual genetic profiles. Machine learning models are capable of processing genomic sequences at an unprecedented scale, allowing for more accurate predictions about disease risk and potential drug responses.
For example, AI algorithms can analyze genetic mutations to predict the likelihood of developing specific diseases or how individuals might respond to certain treatments. This not only aids in personalized medicine but also allows for proactive health management strategies that could significantly reduce healthcare costs globally. Recent advances have led to more efficient DNA sequencing techniques and improved understanding of complex genetic networks.
**Conclusion:**
The integration of AI across scientific research has proven to be a game-changer, particularly in areas where traditional methods are time-consuming or inefficient. In drug discovery, AI is accelerating the development process by predicting successful outcomes based on vast databases; in climate modeling, it provides critical insights that aid global environmental policy making; and in particle physics and genomics, it opens new avenues for research and healthcare applications.
Looking ahead to 2026 and beyond, we can expect continued advancements in AI technology, which will further transform how scientific discoveries are made. As data handling capabilities continue to grow, the predictive power of AI models will likely improve, leading to more accurate results that were once thought unimaginable. Moreover, as ethical considerations around AI usage become increasingly important, transparency and explainability features in AI systems will be prioritized, ensuring responsible application of these powerful tools in scientific research.
In conclusion, the role of AI in shaping future scientific discovery is becoming ever more crucial. Its ability to handle massive amounts of data and generate insights at speed has already started to yield significant results across various fields. As we move forward into a new era where AI integration becomes standard practice in research, it's important to continue fostering collaborations between researchers, technologists, and ethicists to ensure that the benefits are reaped while managing potential risks. The future holds immense promise for scientific progress aided by intelligent machines, promising discoveries that were once thought science fiction.Český článek
**Titulek:** Umělá inteligence mění české školství: Vzrušující revoluce nebo zbytečný rozšíření technologií?
**Perex:** Umělá inteligence (AI) se stává součástí každodenního života, včetně českého školství. Tento odborně-populární článek představuje pohled na dopady AI nástrojů jako jsou chatovací roboti nebo smart třídy, a diskutuje o tom, jak tito "asistenti" mění způsob výuky a učení. Navrhuje se, že umělá inteligence by mohla poskytnout nástroje pro personalizaci vzdělávání a zrychlení osvojování si nových dovedností, ale také přinášejí potenciální rizika ohrožení lidského inženioringu nebo vytváření digitalizovaných učebnic.
---
**Úvod:**
S každým rokem se technologie stávají součástí našeho života více a více, a to nejenom v domácnostech nebo průmyslu, ale také ve školství. Jednou z nejzajímavějších oblastí této evoluce je aplikace umělé inteligence (AI) do procesu vzdělávání. Tento článek se zaměřuje na to, jak AI nástroje ovlivňují české školství a přináší nové trendy i rizika.
---
**Sekce 1: Co je umělá inteligence ve vzdělávání?**
Umělou inteligenci lze definovat jako schopnost strojů provádět úkoly, které by zpravidla vyžadovaly lidskou inteligenci, jako je rozumění přirozeného jazyka, učení z minulých zkušeností nebo řešení problémů. V současné době se AI ve vzdělávání používají k personalizaci výuky, automatickému hodnocení úkolů a interakci s učiteli i studenty.
**Příklad:** Chatovací roboti jako DeepSeek Coder nebo Google Meet lze přizpůsobit jednotlivcům na základě jejich preferencí, rychlosti učení a způsobu interakce s technologií.
**Výhody:** Personalizace vzdělávání umožňuje studentům se přizpůsobit tempo učení a zaměřit se na oblasti, které mají problémy.
**Rizika:** Potenciální neetické praktiky v oslovování studentů nebo ohrožení lidských schopností jako je kreativita a emoce.
---
**Sekce 2: Jak AI ovlivňuje výuku?**
AI nástroje mohou změnit způsob, jakým se učitelé i studenti setkávají s informacemi a naučí procesy. Smart třídy a automatizovaná hodnocení mají za cíl poskytnout interaktivní prostředí, které podporuje inovační vzdělávání.
**Příklad:** Platforma Kahoot! umožňuje učitelům vytvářet quizy a hry pro studenty, zatímco AI analyzuje chování studentů a přizpůsobuje výuku podle jejich potřeb.
**Výhody:** Rychlost a efektivita v procesu učení, možnost interakce s velkým množstvím studentů současně.
**Rizika:** Možnost ztráty osobního kontaktu mezi pedagogem a žákem, riziko odstranění lidského aspektu výuky.
---
**Sekce 3: Využití AI ve vyspělém školství**
Vysokoškolská zařízení a univerzity už dlouhodobě používají AI pro efektivnější správu, personalizaci studia i analýzu chování studentů. Tyto systémy mohou pomoci v identifikaci zaměření pro rozvoj, poskytnout nástroje pro zlepšení kvality výuky a snížit administrativní zátěž.
**Příklad:** Univerzita Harvard používá AI systémy pro sledování studijních plánů, dosažených cílů a vzdělávacích trajektorií studentů.
**Výhody:** Efektivní alokace zdrojů, přizpůsobivost výuky, analýza chování pro lepší strategie rozvoje a podpora studia.
**Rizika:** Ohrožení účinnosti vysokoškolského modelu vzdělávání založeného na lidských kontaktech, potenciální diskriminace a sociální izolace.
---
**Sekce 4: Budoucnost školství s umělou inteligencí**
Předpovídá se, že AI bude hrát důležitou roli v rozvoji školství. Je třeba vyvážit výhody a rizika, aby byla zajištěna udržitelnost a efektivita systému.
**Příklad:** Integrace AI do procesu vzdělávání by mohla vést k inovativním řešením pro rozvoj mimoškolských aktivit a virtuálních prostředí pro vzdělávání.
**Výhody:** Zrychlený růst kvality, dostupnost informací z jakéhokoli místa a času, snížení administrativního tlaku na pedagogické personál.
**Rizika:** Ohrožení lidských práv v oblasti oslovování studentů nebo ohrozování jejich psychického zdraví, digitalizační propojení s neeticky nakládanými daty.
---
**Závěr:**
Umělá inteligence přináší nové možnosti do českého školství, které je nutno řádně hodnotit a vyvažovat s jejich nepříznivými důsledky. Ačkoli AI může zvýšit efektivitu a přizpůsobivost vzdělávání, je třeba dbát na ochrPython galaxie
To create a 3D galaxy flythrough visualization using Python, you can use `numpy` for generating star positions and `matplotlib` along with its `animation` module to animate the camera path through the galaxy. Here's a complete script that accomplishes this task:
```python
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.animation as animation
# Generate spiral galaxy pattern
def spiral_galaxy(num_stars):
theta = np.linspace(0, 2 * np.pi, num_stars)
radius = np.logspace(1, 2, num_stars)
x = radius * np.cos(theta)
y = radius * np.sin(theta)
z = np.zeros_like(x)
return x, y, z
# Generate dust lanes (simple model for demonstration)
def dust_lanes(num_stars):
theta = np.linspace(0, 2 * np.pi, num_stars)
radius = np.logspace(1, 2, num_stars)
x = radius * np.cos(theta) * (1 + 0.1 * np.sin(3 * theta))
y = radius * np.sin(theta) * (1 + 0.1 * np.sin(3 * theta))
z = np.zeros_like(x)
return x, y, z
# Create a simple color map for star temperatures (blue to red)
def temperature_color(temperatures):
norm_temp = (temperatures - min(temperatures)) / (max(temperatures) - min(temperatures))
colors = plt.cm.hot(norm_temp) # Using hot colormap for blue to red transition
return colors[:, :3]
# Set up the plot
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
# Generate stars and dust lanes
num_stars = 10000
x_stars, y_stars, z_stars = spiral_galaxy(num_stars)
x_dust, y_dust, z_dust = dust_lanes(num_stars)
temperatures = np.linspace(20000, 5000, num_stars) # Example temperature values
colors = temperature_color(temperatures)
# Scatter plot for stars and dust lanes
star_plot = ax.scatter(x_stars, y_stars, z_stars, c=colors, s=10, marker='o')
dust_plot = ax.scatter(x_dust, y_dust, z_dust, color=[0.5, 0.5, 0.5], s=2)
# Set axes limits and labels
ax.set_xlim(-30, 30)
ax.set_ylim(-30, 30)
ax.set_zlim(-1, 1)
ax.set_xlabel('X')
ax.set_ylabel('Y')
ax.set_zlabel('Z')
# Animate the camera path through the galaxy
def animate(frame):
ax.view_init(30, frame / 10.0)
ani = animation.FuncAnimation(fig, animate, frames=range(0, 360, 2), interval=50)
# Save the animation as MP4 and GIF
ani.save('galaxy_flythrough.mp4', writer='ffmpeg')
ani.save('galaxy_flythrough.gif', writer='imagemagick')
plt.show()
```
### Explanation:
1. **Spiral Galaxy Generation**: The function `spiral_galaxy` generates star positions in a spiral pattern using logarithmic spacing for the radius and uniform distribution for the angle.
2. **Dust Lanes**: A simple model is used where dust lanes are created by slightly perturbing the star positions with a sinusoidal factor.
3. **Color Mapping**: The function `temperature_color` maps temperatures to colors, transitioning from blue (low temperature) to red (high temperature) using Matplotlib's hot colormap.
4. **Plotting and Animating**: Stars are plotted in 3D with color based on their temperature, along with dust lanes shown as gray dots. The camera path is animated by rotating around the galaxy from different angles.
5. **Saving the Animation**: Both MP4 and GIF formats are saved using `ani.save` method provided by Matplotlib's animation module.
Make sure you have the necessary libraries installed (`numpy`, `matplotlib`, and appropriate video codec for FFmpeg if encoding to MP4). You can install these using pip if they are not already installed:
```bash
pip install numpy matplotlib
```HTML/JS animace
To create an interactive 3D particle animation using HTML, CSS, and JavaScript with Three.js from the CDN, you can follow this example below. This code will include a starfield background, colorful particles rotating in a spiral galaxy-like pattern, and a FPS (frames per second) counter responding to mouse movement.
Here's how you can set it up:
```html
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>3D Particle Animation</title>
<style>
body { margin: 0; }
canvas { display: block; }
#fps { position: absolute; top: 10px; left: 10px; color: white; }
</style>
</head>
<body>
<div id="fps">FPS: 0</div>
<script src="https://cdnjs.cloudflare.com/ajax/libs/three.js/r128/three.min.js"></script>
<script>
// Scene setup
const scene = new THREE.Scene();
const camera = new THREE.PerspectiveCamera(75, window.innerWidth / window.innerHeight, 0.1, 1000);
const renderer = new THREE.WebGLRenderer();
renderer.setSize(window.innerWidth, window.innerHeight);
document.body.appendChild(renderer.domElement);
// Starfield background
const starsGeometry = new THREE.BufferGeometry();
const starsMaterial = new THREE.PointsMaterial({ color: 0xffffff, size: 0.1 });
let starPositions = [];
for (let i = 0; i < 5000; i++) {
const x = (Math.random() - 0.5) * 2000;
const y = (Math.random() - 0.5) * 2000;
const z = Math.random() * 4000 - 2000;
starPositions.push(x, y, z);
}
starsGeometry.setAttribute('position', new THREE.Float32BufferAttribute(starPositions, 3));
const stars = new THREE.Points(starsGeometry, starsMaterial);
scene.add(stars);
// Particles
const particleCount = 5000;
const particlesGeometry = new THREE.BufferGeometry();
const positions = new Float32Array(particleCount * 3);
for (let i = 0; i < particleCount; i++) {
const x = (Math.random() - 0.5) * 4000;
const y = (Math.random() - 0.5) * 4000;
const z = Math.random() * 4000 - 2000;
positions[i * 3] = x;
positions[i * 3 + 1] = y;
positions[i * 3 + 2] = z;
}
particlesGeometry.setAttribute('position', new THREE.BufferAttribute(positions, 3));
const particleMaterial = new THREE.PointsMaterial({ color: 0xffffff, size: 2 });
const particles = new THREE.Points(particlesGeometry, particleMaterial);
scene.add(particles);
// Camera position
camera.position.z = 1500;
// Animation loop
let mouseX = 0, mouseY = 0;
const handleMouseMove = (event) => {
mouseX = event.clientX - window.innerWidth / 2;
mouseY = event.clientY - window.innerHeight / 2;
};
document.addEventListener('mousemove', handleMouseMove);
const animate = () => {
requestAnimationFrame(animate);
// Update camera position based on mouse movement
const rotationSpeed = 0.01;
camera.position.x += (mouseX * rotationSpeed) / window.innerWidth;
camera.position.y += (-mouseY * rotationSpeed) / window.innerHeight;
// Rotate particles and stars
particles.rotation.x += 0.01;
particles.rotation.y += 0.01;
stars.rotation.z += 0.005;
renderer.render(scene, camera);
};
animate();
// FPS counter
let fps = 0;
const prevTime = performance.now();
const updateFPS = () => {
const time = performance.now();
const deltaTime = (time - prevTime) / 1000;
if (deltaTime > 1) { // Update FPS every second
fps = Math.round(1 / deltaTime);
document.getElementById('fps').textContent = `FPS: ${fps}`;
prevTime = time;
}
requestAnimationFrame(updateFPS);
};
updateFPS();
</script>
</body>
</html>
```
This code sets up a basic 3D scene with a starfield background and particles that rotate in spiral patterns. The camera responds to mouse movement, creating an interactive experience. An FPS counter is included at the top-left corner of the screen for monitoring performance.PHP Drupal modul
Below is a complete example of a Drupal 11 module named `hello_world_benchmark`. This module includes all the required files as specified in your request, adhering to Drupal's naming conventions and best practices for module development.
### Directory Structure
The module will be structured within a directory under `modules/custom/hello_world_benchmark/`.
### 1. info.yml File
This file provides metadata about the module:
```yaml
name: 'hello_world_benchmark'
type: module
description: 'A simple hello world benchmark module.'
core_version_requirement: ^8 || ^9 || ^10 || ^11
package: Custom
dependencies:
- drupal:node
```
Place this file at `modules/custom/hello_world_benchmark/hello_world_benchmark.info.yml`.
### 2. routing.yml File
This defines the route for the module's hello page:
```yaml
hello_world_benchmark.hello:
path: '/hello-world-benchmark'
defaults:
_controller: '\Drupal\hello_world_benchmark\Controller\HelloWorldBenchmarkController::hello'
requirements:
_permission: 'access content'
```
Place this file at `modules/custom/hello_world_benchmark/hello_world_benchmark.routing.yml`.
### 3. Controller File
This is where the logic for handling requests to `/hello-world-benchmark` will be defined:
```php
<?php
namespace Drupal\hello_world_benchmark\Controller;
use Drupal\Core\Controller\ControllerBase;
class HelloWorldBenchmarkController extends ControllerBase {
public function hello() {
return [
'#type' => 'markup',
'#markup' => $this->t('Hello World from AI Arena benchmark!'),
];
}
}
```
Place this file at `modules/custom/hello_world_benchmark/src/Controller/HelloWorldBenchmarkController.php`.
### 4. services.yml File
This defines a simple service:
```yaml
services:
hello_world_benchmark.example_service:
class: Drupal\hello_world_benchmark\ExampleService
arguments: []
```
Place this file at `modules/custom/hello_world_benchmark/hello_world_benchmark.services.yml`.
### 5. ExampleService File
Implement the service logic if needed:
```php
<?php
namespace Drupal\hello_world_benchmark;
class ExampleService {
public function greet() {
return "Greetings from AI Arena benchmark!";
}
}
```
Place this file at `modules/custom/hello_world_benchmark/src/ExampleService.php`.
### 6. install/install.info.yml File
This defines the schema for a new table:
```yaml
schema: hello_benchmark_messages
```
Place this file at `modules/custom/hello_world_benchmark/install/hello_world_benchmark.install.info.yml`.
### 7. install/schema/hello_benchmark_messages.schema.yml File
This defines the structure of the table:
```yaml
fields:
id:
type: integer
unsigned: true
not null: true
description: 'The primary key for the message.'
default: 0
message:
type: text
not null: true
description: 'The content of the message.'
```
Place this file at `modules/custom/hello_world_benchmark/install/schema/hello_benchmark_messages.schema.yml`.
### Note
- Ensure that the module path is correct in all files (`modules/custom/hello_world_benchmark/...`).
- The example service and its methods are placeholders. You might need to adjust them according to your specific requirements or remove if not needed.
- Make sure Drupal's Composer setup allows for custom modules under `web/modules/custom`.
This setup provides a basic structure for your module, which you can expand upon as required for more complex functionality.