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EP 326: How Data Will Be AI’s Bottleneck

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Sisällön tarjoaa Everyday AI. Everyday AI tai sen podcast-alustan kumppani lataa ja toimittaa kaiken podcast-sisällön, mukaan lukien jaksot, grafiikat ja podcast-kuvaukset. Jos uskot jonkun käyttävän tekijänoikeudella suojattua teostasi ilman lupaasi, voit seurata tässä https://fi.player.fm/legal kuvattua prosessia.

Send Everyday AI and Jordan a text message

Win a free year of ChatGPT or other prizes! Find out out.
Yeah, AI is cool. But have you tried AI WITH good data?! If you're running into AI implementation bottlenecks, it could be your data to blame. Matthijs de Vries, Founder & CEO of Nuklai, joins us to tackle AI and data.
Newsletter: Sign up for our free daily newsletter
More on this Episode: Episode Page
Join the discussion: Ask Jordan and Matthijs questions on AI and data
Related Episodes: Ep 268: AI’s Data-Driven Decision Paradox
Ep 145: NVIDIA Leader Talks GenAI + Data: Unlocking new ways to interact with our world
Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineup
Website: YourEverydayAI.com
Email The Show: info@youreverydayai.com
Connect with Jordan on LinkedIn
Topics Covered in This Episode:
1. Data and Large Language Models (LLMs)
2. Practical Data Strategies
3. Data Quality Issues
Timestamps:
01:35 Daily AI news
05:00 About Matthijs and Nuklai
06:48 Data bottleneck hinders implementation of generative AI.
10:26 Start with a goal, leverage data effectively.
13:20 Collaborating on data is costly, causing limitations.
15:46 Standardize data access to improve overall efficiency.
18:46 Discussion on the use of synthetic data.
23:13 Challenges for small AI projects due to funding.
27:33 Crowdsourcing data important for future developments.
28:38 Data used to improve bread quality. Multiple purposes.
Keywords:
Everyday AI, Jordan Wilson, generative AI, data bottleneck, OpenAI, GPT 4, SAM 2, video segmentation, Meta, AI Studio, chatbot creation, llama 3.1 model, Matt deFries, Nuclei, structured data, unstructured data, Large Language Models (LLMs), AI implementation, data in silos, data consortiums, data pipelines, data collection, memory efficiency, synthetic data, crowdsourcing data, data quality, human-generated data, collaboration, data science, philosophy in data.
Learn how work is changing on WorkLab, available wherever you get your podcasts.

  continue reading

405 jaksoa

Artwork
iconJaa
 
Manage episode 431664235 series 3470198
Sisällön tarjoaa Everyday AI. Everyday AI tai sen podcast-alustan kumppani lataa ja toimittaa kaiken podcast-sisällön, mukaan lukien jaksot, grafiikat ja podcast-kuvaukset. Jos uskot jonkun käyttävän tekijänoikeudella suojattua teostasi ilman lupaasi, voit seurata tässä https://fi.player.fm/legal kuvattua prosessia.

Send Everyday AI and Jordan a text message

Win a free year of ChatGPT or other prizes! Find out out.
Yeah, AI is cool. But have you tried AI WITH good data?! If you're running into AI implementation bottlenecks, it could be your data to blame. Matthijs de Vries, Founder & CEO of Nuklai, joins us to tackle AI and data.
Newsletter: Sign up for our free daily newsletter
More on this Episode: Episode Page
Join the discussion: Ask Jordan and Matthijs questions on AI and data
Related Episodes: Ep 268: AI’s Data-Driven Decision Paradox
Ep 145: NVIDIA Leader Talks GenAI + Data: Unlocking new ways to interact with our world
Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineup
Website: YourEverydayAI.com
Email The Show: info@youreverydayai.com
Connect with Jordan on LinkedIn
Topics Covered in This Episode:
1. Data and Large Language Models (LLMs)
2. Practical Data Strategies
3. Data Quality Issues
Timestamps:
01:35 Daily AI news
05:00 About Matthijs and Nuklai
06:48 Data bottleneck hinders implementation of generative AI.
10:26 Start with a goal, leverage data effectively.
13:20 Collaborating on data is costly, causing limitations.
15:46 Standardize data access to improve overall efficiency.
18:46 Discussion on the use of synthetic data.
23:13 Challenges for small AI projects due to funding.
27:33 Crowdsourcing data important for future developments.
28:38 Data used to improve bread quality. Multiple purposes.
Keywords:
Everyday AI, Jordan Wilson, generative AI, data bottleneck, OpenAI, GPT 4, SAM 2, video segmentation, Meta, AI Studio, chatbot creation, llama 3.1 model, Matt deFries, Nuclei, structured data, unstructured data, Large Language Models (LLMs), AI implementation, data in silos, data consortiums, data pipelines, data collection, memory efficiency, synthetic data, crowdsourcing data, data quality, human-generated data, collaboration, data science, philosophy in data.
Learn how work is changing on WorkLab, available wherever you get your podcasts.

  continue reading

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