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AI Data Tool Suite for Annotation, Calibration & Validation w/ Mohammad Musa

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Manage episode 390737232 series 3289599
Contenuto fornito da AVL, North America, AVL, and North America. Tutti i contenuti dei podcast, inclusi episodi, grafica e descrizioni dei podcast, vengono caricati e forniti direttamente da AVL, North America, AVL, and North America o dal partner della piattaforma podcast. Se ritieni che qualcuno stia utilizzando la tua opera protetta da copyright senza la tua autorizzazione, puoi seguire la procedura descritta qui https://it.player.fm/legal.

In this episode Stephan is joined by Mohammad Musa Founder and CEO of Deepen. Deepen provides the only safety-first full AI data tool suite for annotation, calibration & validation.

Data adaptation is a crucial aspect of transforming sensor suites, enabling the interpretation and validation of data from various camera types and thermal sensors. The precision of this data adaptation is achieved through fine-grained pixel-level accuracy, allowing for multi-modal ability in light, camera, and radar over time.

Balancing automation and human involvement is crucial for ensuring accurate results and minimizing mistakes. Considering economics while balancing automation and human input is essential to avoid potential errors.

Automated quality checks and validation processes analyze AI and human actions across multiple runs, using heuristics and signals to create a workflow for productive labeling operations.

To ensure return on investment, customers should ensure solid data collection and calibration, avoid garbage-in and garbage-out scenarios, and collect data that aligns with the vehicle's operation.

Mohammad Musa started Deepen AI in January 2017 focusing on AI tools and infrastructure for the Autonomous Development & Robotics industries. Prior to Deepen, Mohammad was the head of Launch & Readiness at Google Apps for Work where he led a cross functional team managing product launches, product roadmap, trusted tester and launch communications. Before Google, Mohammad worked in software engineering and technical sales positions in the video games and semiconductor industries in multiple startups.

Deepen

Multi-sensor data labelling and calibration tools and services to accelerate computer vision training for autonomous vehicles, robotics and more.

- Labelling accuracy, delivered fast with our in-house team

- Special multi-sensor calibration bundle

- Customizable solutions to suit your needs

The only safety-first full AI data tool suite for annotation, calibration & validation.

If you would like to be a guest on the show contact: namarketing@avl.com

  continue reading

Capitoli

1. AI Data Tool Suite for Annotation, Calibration & Validation w/ Mohammad Musa (00:00:00)

2. Enabling Safety in ADAS/AD Development (00:03:05)

3. AI Algorithms and ADAS Annotation (00:10:38)

4. Differentiators in Quality Annotations (00:18:32)

128 episodi

Artwork
iconCondividi
 
Manage episode 390737232 series 3289599
Contenuto fornito da AVL, North America, AVL, and North America. Tutti i contenuti dei podcast, inclusi episodi, grafica e descrizioni dei podcast, vengono caricati e forniti direttamente da AVL, North America, AVL, and North America o dal partner della piattaforma podcast. Se ritieni che qualcuno stia utilizzando la tua opera protetta da copyright senza la tua autorizzazione, puoi seguire la procedura descritta qui https://it.player.fm/legal.

In this episode Stephan is joined by Mohammad Musa Founder and CEO of Deepen. Deepen provides the only safety-first full AI data tool suite for annotation, calibration & validation.

Data adaptation is a crucial aspect of transforming sensor suites, enabling the interpretation and validation of data from various camera types and thermal sensors. The precision of this data adaptation is achieved through fine-grained pixel-level accuracy, allowing for multi-modal ability in light, camera, and radar over time.

Balancing automation and human involvement is crucial for ensuring accurate results and minimizing mistakes. Considering economics while balancing automation and human input is essential to avoid potential errors.

Automated quality checks and validation processes analyze AI and human actions across multiple runs, using heuristics and signals to create a workflow for productive labeling operations.

To ensure return on investment, customers should ensure solid data collection and calibration, avoid garbage-in and garbage-out scenarios, and collect data that aligns with the vehicle's operation.

Mohammad Musa started Deepen AI in January 2017 focusing on AI tools and infrastructure for the Autonomous Development & Robotics industries. Prior to Deepen, Mohammad was the head of Launch & Readiness at Google Apps for Work where he led a cross functional team managing product launches, product roadmap, trusted tester and launch communications. Before Google, Mohammad worked in software engineering and technical sales positions in the video games and semiconductor industries in multiple startups.

Deepen

Multi-sensor data labelling and calibration tools and services to accelerate computer vision training for autonomous vehicles, robotics and more.

- Labelling accuracy, delivered fast with our in-house team

- Special multi-sensor calibration bundle

- Customizable solutions to suit your needs

The only safety-first full AI data tool suite for annotation, calibration & validation.

If you would like to be a guest on the show contact: namarketing@avl.com

  continue reading

Capitoli

1. AI Data Tool Suite for Annotation, Calibration & Validation w/ Mohammad Musa (00:00:00)

2. Enabling Safety in ADAS/AD Development (00:03:05)

3. AI Algorithms and ADAS Annotation (00:10:38)

4. Differentiators in Quality Annotations (00:18:32)

128 episodi

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