Top 10 AI Tools for Network Engineers

In the ever-evolving world of technology, network engineers play a vital role in ensuring that our digital communications run smoothly. With the increasing complexity of networks and the growing demand for efficiency, artificial intelligence (AI) is becoming an indispensable tool for network professionals. In this blog post, we will explore the top 10 AI tools for network engineers, highlighting their functionalities, benefits, and how they can enhance network management. Whether you are a seasoned professional or just starting in the field, this guide will provide you with valuable insights into how AI can transform your work.

1. Cisco DNA Center

Cisco DNA Center is a comprehensive network management platform that leverages AI to automate and optimize network operations. It provides insights and analytics that empower network engineers to make informed decisions quickly.

Key Features:

• Automation: Automates routine tasks, reducing manual workload.
• Insights: Offers analytics to understand network performance and user experiences.
• Policy Management: Simplifies the application of network policies across devices.

Benefits:

• Reduces the time spent on network management tasks.
• Enhances decision-making with data-driven insights.
• Improves overall network performance and user satisfaction.

2. Juniper Mist AI

Juniper Mist AI is designed to provide proactive insights and automation across the network. It enhances user experiences and operational efficiency through its AI-driven capabilities.

Key Features:

• Proactive Insights: Offers real-time analytics on network performance.
• Automation: Automates troubleshooting processes to minimize downtime.
• User Experience: Monitors user experiences to optimize connectivity.

Benefits:

• Helps identify and resolve issues before they impact users.
• Increases network reliability and performance.
• Streamlines operations with automated processes.

3. Darktrace

Darktrace is an AI-driven cybersecurity tool that detects and responds to cyber threats in real-time. It learns the normal behavior of network devices to identify anomalies and potential security breaches.

Key Features:

• Anomaly Detection: Recognizes unusual patterns in network behavior.
• Self-Learning: Adapts to new threats using machine learning.
• Real-time Response: Provides immediate alerts and response options for security incidents.

Benefits:

• Enhances network security by identifying threats early.
• Reduces the risk of data breaches and cyberattacks.
• Provides peace of mind with continuous monitoring.

4. Trellix

Trellix combines security and performance management, utilizing AI to provide insights into network traffic and potential vulnerabilities. It is designed to give network engineers a comprehensive view of their network’s health.

Key Features:

• Traffic Analysis: Monitors network traffic to identify patterns and potential issues.
• Vulnerability Assessment: Scans for vulnerabilities in real-time.
• Integrated Security: Combines security features with performance management.

Benefits:

• Improves network performance by identifying bottlenecks.
• Strengthens security posture through continuous monitoring.
• Offers a holistic view of network operations.

5. LangChain

LangChain is a tool for building complex workflows and integrating various services, particularly useful for automating network management tasks. It allows engineers to create custom solutions that fit their specific needs.

Key Features:

• Workflow Automation: Simplifies the creation of automated workflows.
• Service Integration: Connects multiple services for seamless operations.
• Custom Solutions: Allows for tailored workflows based on unique requirements.

Benefits:

• Enhances efficiency by reducing manual processes.
• Increases flexibility in network management.
• Facilitates collaboration between different tools and services.

6. Spinach

Spinach is an AI tool that helps engineers streamline their workflows, focusing on automation and efficiency in engineering tasks. It is particularly beneficial for network engineers looking to optimize their processes.

Key Features:

• Workflow Optimization: Analyzes and improves existing workflows.
• Task Automation: Automates repetitive engineering tasks.
• Performance Tracking: Monitors performance metrics for continuous improvement.

Benefits:

• Reduces time spent on mundane tasks.
• Increases overall productivity and efficiency.
• Encourages innovation by freeing up time for complex problem-solving.

7. PyTorch

PyTorch is a popular machine learning library that can be utilized by network engineers for developing AI models to enhance network performance. Its flexibility and ease of use make it a favorite among engineers.

Key Features:

• Dynamic Computation Graphs: Allows for flexible model building.
• Extensive Libraries: Offers a wide range of tools for machine learning.
• Community Support: Large community providing resources and support.

Benefits:

• Empowers engineers to create custom AI solutions.
• Facilitates experimentation with different models and approaches.
• Enhances the ability to analyze and optimize network performance.

Simple PyTorch Example:

Here’s a basic example of using PyTorch to create a simple linear regression model:

import argparse
import gym
import numpy as np
from itertools import count
from collections import namedtuple

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.distributions import Categorical

# Cart Pole

parser = argparse.ArgumentParser(description='PyTorch actor-critic example')
parser.add_argument('--gamma', type=float, default=0.99, metavar='G',
                    help='discount factor (default: 0.99)')
parser.add_argument('--seed', type=int, default=543, metavar='N',
                    help='random seed (default: 543)')
parser.add_argument('--render', action='store_true',
                    help='render the environment')
parser.add_argument('--log-interval', type=int, default=10, metavar='N',
                    help='interval between training status logs (default: 10)')
args = parser.parse_args()

env = gym.make('CartPole-v1')
env.reset(seed=args.seed)
torch.manual_seed(args.seed)

SavedAction = namedtuple('SavedAction', ['log_prob', 'value'])

class Policy(nn.Module):
    """
    implements both actor and critic in one model
    """
    def __init__(self):
        super(Policy, self).__init__()
        self.affine1 = nn.Linear(4, 128)

        # actor's layer
        self.action_head = nn.Linear(128, 2)

        # critic's layer
        self.value_head = nn.Linear(128, 1)

        # action & reward buffer
        self.saved_actions = []
        self.rewards = []

    def forward(self, x):
        """
        forward of both actor and critic
        """
        x = F.relu(self.affine1(x))

        # actor: choses action to take from state s_t
        # by returning probability of each action
        action_prob = F.softmax(self.action_head(x), dim=-1)

        # critic: evaluates being in the state s_t
        state_values = self.value_head(x)

        # return values for both actor and critic as a tuple of 2 values:
        # 1. a list with the probability of each action over the action space
        # 2. the value from state s_t
        return action_prob, state_values

model = Policy()
optimizer = optim.Adam(model.parameters(), lr=3e-2)
eps = np.finfo(np.float32).eps.item()

def select_action(state):
    state = torch.from_numpy(state).float()
    probs, state_value = model(state)

    # create a categorical distribution over the list of probabilities of actions
    m = Categorical(probs)

    # and sample an action using the distribution
    action = m.sample()

    # save to action buffer
    model.saved_actions.append(SavedAction(m.log_prob(action), state_value))

    # the action to take (left or right)
    return action.item()

def finish_episode():
    """
    Training code. Calculates actor and critic loss and performs backprop.
    """
    R = 0
    saved_actions = model.saved_actions
    policy_losses = [] # list to save actor (policy) loss
    value_losses = [] # list to save critic (value) loss
    returns = [] # list to save the true values

    # calculate the true value using rewards returned from the environment
    for r in model.rewards[::-1]:
        # calculate the discounted value
        R = r + args.gamma * R
        returns.insert(0, R)

    returns = torch.tensor(returns)
    returns = (returns - returns.mean()) / (returns.std() + eps)

    for (log_prob, value), R in zip(saved_actions, returns):
        advantage = R - value.item()

        # calculate actor (policy) loss
        policy_losses.append(-log_prob * advantage)

        # calculate critic (value) loss using L1 smooth loss
        value_losses.append(F.smooth_l1_loss(value, torch.tensor([R])))

    # reset gradients
    optimizer.zero_grad()

    # sum up all the values of policy_losses and value_losses
    loss = torch.stack(policy_losses).sum() + torch.stack(value_losses).sum()

    # perform backprop
    loss.backward()
    optimizer.step()

    # reset rewards and action buffer
    del model.rewards[:]
    del model.saved_actions[:]

def main():
    running_reward = 10

    # run infinitely many episodes
    for i_episode in count(1):

        # reset environment and episode reward
        state, _ = env.reset()
        ep_reward = 0

        # for each episode, only run 9999 steps so that we don't
        # infinite loop while learning
        for t in range(1, 10000):

            # select action from policy
            action = select_action(state)

            # take the action
            state, reward, done, _, _ = env.step(action)

            if args.render:
                env.render()

            model.rewards.append(reward)
            ep_reward += reward
            if done:
                break

        # update cumulative reward
        running_reward = 0.05 * ep_reward + (1 - 0.05) * running_reward

        # perform backprop
        finish_episode()

        # log results
        if i_episode % args.log_interval == 0:
            print('Episode {}\tLast reward: {:.2f}\tAverage reward: {:.2f}'.format(
                  i_episode, ep_reward, running_reward))

        # check if we have "solved" the cart pole problem
        if running_reward > env.spec.reward_threshold:
            print("Solved! Running reward is now {} and "
                  "the last episode runs to {} time steps!".format(running_reward, t))
            break

if __name__ == '__main__':
    main()

these are 2 files running pytorch

import argparse
import gym
import numpy as np
from itertools import count
from collections import deque
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.distributions import Categorical

parser = argparse.ArgumentParser(description='PyTorch REINFORCE example')
parser.add_argument('--gamma', type=float, default=0.99, metavar='G',
                    help='discount factor (default: 0.99)')
parser.add_argument('--seed', type=int, default=543, metavar='N',
                    help='random seed (default: 543)')
parser.add_argument('--render', action='store_true',
                    help='render the environment')
parser.add_argument('--log-interval', type=int, default=10, metavar='N',
                    help='interval between training status logs (default: 10)')
args = parser.parse_args()

env = gym.make('CartPole-v1')
env.reset(seed=args.seed)
torch.manual_seed(args.seed)

class Policy(nn.Module):
    def __init__(self):
        super(Policy, self).__init__()
        self.affine1 = nn.Linear(4, 128)
        self.dropout = nn.Dropout(p=0.6)
        self.affine2 = nn.Linear(128, 2)

        self.saved_log_probs = []
        self.rewards = []

    def forward(self, x):
        x = self.affine1(x)
        x = self.dropout(x)
        x = F.relu(x)
        action_scores = self.affine2(x)
        return F.softmax(action_scores, dim=1)

policy = Policy()
optimizer = optim.Adam(policy.parameters(), lr=1e-2)
eps = np.finfo(np.float32).eps.item()

def select_action(state):
    state = torch.from_numpy(state).float().unsqueeze(0)
    probs = policy(state)
    m = Categorical(probs)
    action = m.sample()
    policy.saved_log_probs.append(m.log_prob(action))
    return action.item()

def finish_episode():
    R = 0
    policy_loss = []
    returns = deque()
    for r in policy.rewards[::-1]:
        R = r + args.gamma * R
        returns.appendleft(R)
    returns = torch.tensor(returns)
    returns = (returns - returns.mean()) / (returns.std() + eps)
    for log_prob, R in zip(policy.saved_log_probs, returns):
        policy_loss.append(-log_prob * R)
    optimizer.zero_grad()
    policy_loss = torch.cat(policy_loss).sum()
    policy_loss.backward()
    optimizer.step()
    del policy.rewards[:]
    del policy.saved_log_probs[:]

def main():
    running_reward = 10
    for i_episode in count(1):
        state, _ = env.reset()
        ep_reward = 0
        for t in range(1, 10000):  # Don't infinite loop while learning
            action = select_action(state)
            state, reward, done, _, _ = env.step(action)
            if args.render:
                env.render()
            policy.rewards.append(reward)
            ep_reward += reward
            if done:
                break

        running_reward = 0.05 * ep_reward + (1 - 0.05) * running_reward
        finish_episode()
        if i_episode % args.log_interval == 0:
            print('Episode {}\tLast reward: {:.2f}\tAverage reward: {:.2f}'.format(
                  i_episode, ep_reward, running_reward))
        if running_reward > env.spec.reward_threshold:
            print("Solved! Running reward is now {} and "
                  "the last episode runs to {} time steps!".format(running_reward, t))
            break

if __name__ == '__main__':
    main()

Breakdown of the Code:

1. Data Generation: We create synthetic data that follows a linear trend.
2. Model Definition: A simple linear model with one input and one output.
3. Loss Function and Optimizer: We use Mean Squared Error as the loss function and Stochastic Gradient Descent for optimization.
4. Training Loop: We train the model for 100 epochs, updating the weights based on the loss.

8. TensorFlow

TensorFlow is another widely used framework for machine learning, useful for building complex predictive models that can analyze network traffic patterns. Its scalability and robustness make it suitable for large-scale applications.

Key Features:

• Scalability: Designed to handle large datasets and complex models.
• Versatility: Supports various machine learning and deep learning tasks.
• Community and Documentation: Strong community support with extensive documentation.

Benefits:

• Enables the development of sophisticated AI solutions.
• Improves the ability to predict and analyze network traffic.
• Facilitates collaboration and sharing of models across teams.

9. Cisco’s AI-Reinforcement Learning Course

Cisco offers a specialized course focusing on using AI and reinforcement learning for managing networks. This course is ideal for network engineers looking to enhance their skills and knowledge in AI applications.

Key Features:

• Comprehensive Curriculum: Covers foundational and advanced topics.
• Hands-on Learning: Provides practical exercises and projects.
• Expert Instructors: Learn from industry experts and experienced instructors.

Benefits:

• Enhances understanding of AI in network management.
• Provides practical skills that can be applied immediately.
• Increases career opportunities in the growing field of AI.

10. Apache MXNet

Apache MXNet is a flexible and efficient deep learning framework that can be applied in network engineering for building scalable AI applications. It is particularly suited for tasks requiring high performance and scalability.

Key Features:

• Efficiency: Optimized for speed and resource management.
• Flexibility: Supports multiple programming languages and APIs.
• Scalability: Can scale across multiple GPUs and machines.

Benefits:

• Enables the development of high-performance AI applications.
• Supports a wide range of deep learning tasks in network engineering.
• Facilitates collaboration across different programming environments.

Conclusion

The integration of AI tools in network engineering represents a significant shift in how network management is approached. These tools not only enhance network performance but also improve security and operational efficiency. As networks become more complex, the need for automated and intelligent solutions will continue to grow. By incorporating these AI tools into their workflows, network engineers can streamline processes, make better decisions, and ultimately provide a better experience for users.

In summary, the top 10 AI tools for network engineers—Cisco DNA Center, Juniper Mist AI, Darktrace, Trellix, LangChain, Spinach, PyTorch, TensorFlow, Cisco’s AI-Reinforcement Learning Course, and Apache MXNet—offer various functionalities that cater to the diverse needs of network professionals. Embracing these technologies is essential for staying competitive in the field and ensuring the security and efficiency of network operations.

As the landscape of networking continues to evolve, so too will the tools and techniques available to engineers. Staying informed about these advancements and continuously seeking out new knowledge will be key to success in this dynamic field.

References

1. Top 10 AI-Powered Tools Every Network Engineer Should Know Top 10 AI-Powered Tools Every Network Engineer Sho…
2. Top 10 AI tools used for Network Engineers – YouTube networkingjobs #networkengineer #ccna #ccnp #firewall #cyberscuri…
3. AI and network administration : r/networking – Reddit Prompt Engineering/LangChain – LangChain is a great tool that allo…
4. 10 Must-Have AI Tools for Engineers – Spinach AI 10 AI tools for engineers to explore · 1. Spinach · 2. PyTorch · …
5. What is artificial intelligence (AI) for networking? AI for networking enhances both end user and IT operator experiences by sim…
6. AI for Network Engineers – Udemy AI for Network Engineers. AI-Reinforcement learning for creating P…
7. What are the 10 AI tools? – Quora Some popular AI tools include TensorFlow, Microsoft Cognitive Toolkit (CNTK), …
8. 70 Best Networking AI tools – 2024 – TopAI.tools Discover the best 70 paid and free AI Networking AI, and find their featur…
9. 11 Best Generative AI Tools and Platforms in 2024 – Turing The top 11 generative AI tools and platforms that empo…

10. Top 10 Most Popular Network Simulation Tools – I-MEDITA Tools like Cisco Packet Tracer and Netkit are popular choices for teaching net…

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Top 10 AI Tools for Video Editors: Enhancing Creativity and Efficiency

In the world of video editing, technology is evolving at an unprecedented pace. Artificial Intelligence (AI) tools are becoming essential for video editors, helping them streamline their workflows and enhance the quality of their work. Whether you are a professional filmmaker or a hobbyist looking to create engaging content, the right AI tools can make a significant difference. In this comprehensive guide, we will explore the top 10 AI tools for video editors, detailing their key features, unique capabilities, and how they can enhance your editing process.

1. Adobe Premiere Pro

• Overview: Adobe Premiere Pro is a leading video editing software that integrates AI capabilities through Adobe Sensei. This powerful tool is favored by professionals for its advanced features.

Key Features:

• Automated Editing: Premiere Pro can analyze your footage and suggest edits, saving you time (Adobe, 2023).
• Color Correction: AI-driven tools help to balance colors and enhance visuals automatically (Adobe, 2023).
• Smart Reframing: Automatically reframes your video to fit different aspect ratios, ensuring it looks great on any platform (Adobe, 2023).

Use Case:

Imagine editing a wedding video where you have hours of footage. Adobe Premiere Pro can help you quickly find the best moments and adjust the colors to make the video pop, all while you focus on storytelling.

2. Wondershare Filmora

• Overview: Filmora is known for its user-friendly interface, making it an excellent choice for both beginners and experienced editors.

Key Features:

• Auto Scene Detection: Filmora’s AI can identify different scenes in your footage, making it easier to edit (Wondershare, 2023).
• Motion Tracking: This feature allows you to track moving objects in your videos and add effects accordingly (Wondershare, 2023).
• Audio Synchronization: Automatically sync audio with video clips, ensuring perfect timing (Wondershare, 2023).

Use Case:

For a YouTube vlogger, Filmora can simplify the editing process by automatically detecting different scenes in their travel videos, allowing them to create engaging content with minimal effort.

3. Runway

• Overview: Runway is designed for creative professionals, offering innovative tools for video editing.

Key Features:

• AI-Powered Editing: Use AI to edit videos quickly and creatively (Runway, 2023).
• Background Removal: Easily remove backgrounds from videos, perfect for creating unique content (Runway, 2023).
• Real-Time Collaboration: Work with team members in real-time, enhancing productivity (Runway, 2023).

Use Case:

A creative agency can use Runway to produce promotional videos that require quick edits and unique styles, allowing for collaboration across different teams.

4. Synthesia

• Overview: Synthesia allows users to create AI-driven videos from text, making it an excellent tool for marketing and education.

Key Features:

• Text-to-Video: Generate videos from written content, making it easy to create engaging presentations (Synthesia, 2023).
• Custom Avatars: Choose or create avatars to deliver your message in a personalized manner (Synthesia, 2023).
• Multilingual Support: Create videos in multiple languages without the need for voice actors (Synthesia, 2023).

Use Case:

An online educator can quickly create tutorial videos by inputting their script into Synthesia, allowing them to focus on content quality rather than production logistics.

5. TimeBolt

• Overview: TimeBolt automates the editing process by removing silences and pauses, significantly speeding up the workflow.

Key Features:

• Silence Removal: Automatically detects and removes silences in your videos (TimeBolt, 2023).
• Speed Optimization: Allows creators to quickly edit long recordings without tedious manual work (TimeBolt, 2023).
• Customizable Settings: Adjust settings to determine how much silence to remove (TimeBolt, 2023).

Use Case:

For a podcaster, TimeBolt can help edit lengthy interviews by cutting out dead air, enabling quicker turnaround times for publishing episodes.

6. Vidyo.ai

• Overview: Vidyo.ai uses AI to create short video clips from longer content, optimizing videos for social media.

Key Features:

• Clip Generation: Automatically generates short clips from longer videos, perfect for social media promotion (Vidyo.ai, 2023).
• Highlight Detection: Identifies the most engaging parts of a video to create highlights (Vidyo.ai, 2023).
• Easy Sharing: Simplifies the process of sharing clips on various platforms (Vidyo.ai, 2023).

Use Case:

A content creator can use Vidyo.ai to take an hour-long webinar and generate several short clips to share on Instagram and TikTok, maximizing engagement.

7. Descript

• Overview: Descript is particularly useful for podcasters, offering transcription features alongside video editing capabilities.

Key Features:

• Transcription: Automatically transcribe audio and video content into text (Descript, 2023).
• Text-Based Editing: Edit video content by editing the text, making it intuitive and user-friendly (Descript, 2023).
• Overdub: Create voiceovers by typing text, mimicking the original speaker’s voice (Descript, 2023).

Use Case:

For a video podcast, Descript allows the creator to edit their content by simply adjusting the written transcript, making the process faster and more efficient.

8. Veed.io

• Overview: Veed.io offers a range of AI tools for video creation and editing, perfect for quick edits.

Key Features:

• Subtitles Generation: Automatically generate subtitles for your videos, improving accessibility (Veed.io, 2023).
• Audio Enhancement: Improve audio quality with AI-driven tools (Veed.io, 2023).
• Templates: Access a variety of templates for different types of videos (Veed.io, 2023).

Use Case:

A social media manager can use Veed.io to quickly create engaging videos for campaigns, complete with subtitles and enhanced audio, all in a matter of minutes.

9. DeepBrain

• Overview: DeepBrain specializes in AI-generated videos and voiceovers, allowing users to create professional-quality videos without extensive editing skills.

Key Features:

• AI Video Creation: Generate videos from scripts with AI avatars delivering the content (DeepBrain, 2023).
• Voiceover Generation: Create high-quality voiceovers in various languages (DeepBrain, 2023).
• User-Friendly Interface: Simple tools make it accessible for all skill levels (DeepBrain, 2023).

Use Case:

A small business can use DeepBrain to create promotional videos quickly, without needing to hire a video production team.

10. DaVinci Resolve

• Overview: DaVinci Resolve is renowned for its color grading capabilities and incorporates AI tools for enhanced editing.

Key Features:

• Facial Recognition: Automatically tags and organizes footage based on faces (Blackmagic Design, 2023).
• Auto Color Correction: AI tools adjust colors to ensure consistency throughout the video (Blackmagic Design, 2023).
• Robust Editing Tools: Comprehensive suite of editing features for professional use (Blackmagic Design, 2023).

Use Case:

A film editor can utilize DaVinci Resolve’s advanced color grading tools to ensure that every shot in their film maintains a consistent aesthetic, enhancing the overall viewing experience.

11. Vmaker AI

Overview: Vmaker AI is an award-winning AI video editor that turns your raw video footage into a publish-ready video using AI in minutes.

Key Features:

• AI Video Editing: Upload your rough-cut video file to Vmaker AI, and it will automatically edit and add b-rolls, background music, transitions, effects, subtitles, intros, outros, and more, making your video publish-ready.
• AI Avatar: Vmaker AI generates videos from a simple prompt, effortlessly turning your ideas into reality. It offers over 100 AI avatars with 99% accuracy and more than 150 voices in various languages.
• AI Subtitle Generator: Automatically generate subtitles in 35+ languages using AI and translate them into over 100 languages within minutes.
• AI Clip Maker: Repurpose a long video into multiple short videos automatically.
• AI Highlights Video Maker: Create striking highlights for your videos to be used for promotions or inserted as an intro.

Use Case:

YouTubers can grow their channels quickly by editing videos in a 3X shorter time frame. L\&D teams can create AI human avatar videos for onboarding, training, and more.

Conclusion

The integration of AI tools into video editing workflows is revolutionizing the industry. By leveraging these technologies, video editors can focus more on creativity and storytelling rather than getting bogged down by tedious tasks. Each of the tools mentioned above offers unique features that cater to different needs, whether you’re a beginner or a seasoned professional.

As you explore these AI tools, consider your specific editing needs and how these technologies can enhance your productivity and creativity. Embrace the future of video editing and take your projects to the next level with the power of AI.

Final Thoughts

Incorporating AI tools into your video editing process not only enhances efficiency but also opens up new creative possibilities. Whether you’re creating content for social media, educational purposes, or professional filmmaking, these tools can help you produce high-quality videos that engage your audience and tell your story effectively. Embrace these advancements, and watch your editing process transform!

References

• Adobe. (2023). Adobe Premiere Pro. Retrieved from Adobe Premiere Pro
• Wondershare. (2023). Filmora. Retrieved from Wondershare Filmora
• Runway. (2023). Runway. Retrieved from Runway
• Synthesia. (2023). Synthesia. Retrieved from Synthesia
• TimeBolt. (2023). TimeBolt. Retrieved from TimeBolt
• Vidyo.ai. (2023). Vidyo.ai. Retrieved from Vidyo.ai
• Descript. (2023). Descript. Retrieved from Descript
• Veed.io. (2023). Veed.io. Retrieved from Veed.io
• DeepBrain. (2023). DeepBrain. Retrieved from DeepBrain

• Blackmagic Design. (2023). DaVinci Resolve. Retrieved from DaVinci Resolve

  References

  1. The 7 Best AI Video Tools for Creators and Marketers, Tried … – Buffer Best AI video tool for podcasters and interviewers​​ Descrip…
  2. The best AI video generators | Zapier We tested dozens of AI video generators, and these…
  3. Best AI Tools for Video Editing: 10 Picks for 2024 – Upwork Best AI Tools for Video Editing: 10 Picks for 2024 · 1. Adobe Premiere Pro …
  4. Which video editing AI tool is the best? : r/ArtificialInteligence – Reddit When it comes to video editing, Adobe Premiere Pro with Adobe Sensei stands…
  5. The best AI video editors – Creative Bloq The best AI video editing tools enable you to remove backgroun…
  6. 18 Mind-Blowing Best AI Video Generator And Editing Tools Veed.io is one of the best video creation and editing tools, offering usefu…
  7. 10 Best AI Video Editing Tools of 2023 (and how to use them) Get unlimited video and audio downloads with Envato: https://1.envato.market/cre…
  8. Top 10 Free AI Tools for Video Editing – Analytics Vidhya Top 10 Free AI Tools for Video Editing · 1. OpenShot · 2. Shotcut · 3. VEE…
  9. What are the top 10 AI tools for video editing (www.aidurshal.com)? For AI-enhanced video editing, tools like Adobe Premier…

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