Building AI applications with agentic workflows introduces significant challenges, especially when these workflows rely on large language models (LLMs). The problem is that LLMs aren’t deterministic-they don’t always produce the same output given the same input. This unpredictability becomes even more problematic when we try to layer additional complexity, like autonomous agents, on top of an already unstable system. In many enterprise settings, where reliability and consistency are key, these complex agentic workflows can cause more harm than good. Most tasks can be handled without the need for this extra layer of abstraction. By using LLMs in a more controlled and straightforward way, you can get the job done without introducing unnecessary risks. Instead of over-complicating the architecture, focusing on simpler, tightly managed LLM-based solutions can lead to more stable and reliable outcomes-exactly what’s needed for critical enterprise operations.
Wow, what an introduction to the actual presentation. I can only imagine how motivating it must be for the employees who are lucky enough to experience this level of insight on a regular basis. :)
With its creative processes, SmythOS is leading the way in advancing AI. For those who are serious about utilizing AI, it is an essential tool. #TechTrends #AI #Innovation #SmythOS
🎯 Key points for quick navigation: 00:00:14 *🌟 The event focuses on bringing together the builder community, irrespective of whether they are Snowflake customers or not, aiming to foster idea exchange and inspiration.* 00:00:54 *📸 The speaker humorously shares excitement about meeting industry luminary Andrew Ng, highlighting the enthusiasm for technology development.* 00:01:36 *🚢 Snowflake encourages developers to innovate through examples like building applications without the need for deploying servers.* 00:02:18 *🏗️ Snowflake is increasing its openness by transitioning to a platform with more community-led development and open-source contributions.* 00:02:43 *🎉 The company recently concluded its first international AI hackathon, showcasing its commitment to fostering innovation and supporting developers.* 00:03:25 *🤝 Snowflake collaborates closely with startups to help them scale applications, highlighting partnerships with startups earning millions on the Snowflake Marketplace.* 00:04:18 *🏆 The startup challenge hosted by Snowflake awarded significant investment, with Signal Flare emerging as a winner.* 00:05:02 *🚀 Snowflake's accelerator programs launched with VC firms invest up to $100 million in early-stage startups.* 00:05:45 *🌍 They've launched the NSTAR education program to provide free training resources globally, advancing skills related to data and AI.* 00:06:09 *🌟 Andrew Ng is introduced as a luminary speaker, hailed for his extensive contributions to the AI field, prompting reflections on AI's transformative potential.* 00:07:05 *📈 Andrew Ng emphasizes the potential impact of AI agents and agentic workflows, encouraging exploration of scalable and efficient AI systems.* 00:07:59 *🌐 Ng appreciates initiatives like open-sourcing models, promoting broad technological access to prevent monopolization.* 00:09:09 *⚖️ Ng expresses concern about regulatory proposals in California which might stifle open-source innovation in AI.* 00:11:12 *🚦 Ng advocates for thoughtful regulation focusing on harmful applications rather than stifling technological advancement.* 00:11:55 *🔍 The conversation explores the transformative potential of AI agents in enhancing the capabilities and benefits of large language models.* 00:13:52 *🤖 Ng introduces the concept of agentic workflows, comparing them to traditional zero-shot prompting, highlighting iterative improvements in AI performance.* 00:16:12 *💡 Agentic workflows showcase significant improvements in results, suggesting their potential to surpass advancements between different model versions.* 00:17:07 *🏄 Ng’s team demonstrates the use of vision agents to automate tasks using visual datasets, exemplified by tracking distances in shark-surfer videos.* 00:18:02 *🔬 The vision agent automatically generates code to perform complex visual tasks, demonstrating its efficiency and utility.* 00:19:41 *⚙️ The innovative use of vision agents can streamline computer vision tasks, making application development more efficient for users.* 00:21:12 *🧪 Testing mechanisms enable vision agents to correct and enhance code generation, improving accuracy and efficiency in application outputs.* 00:23:00 *📄 Automatic code revision and testing through vision agents demonstrate significant potential for automating complex visual analyses.* 24:50 *🚗 The speaker expresses excitement about agentic AI for various applications, although current systems have limitations such as common failures in object detection.* 25:36 *🧠 The current AI system struggles with complex reasoning, often requiring precise prompt tuning to achieve accurate results.* 26:17 *🛠️ The visual agent software is in beta and sometimes yields impressive results, though it remains sensitive to prompt phrasing.* 27:00 *🌐 The vision agent team released the core engine as open source, aiming to contribute to the advancement of AI agents and improve developer capabilities.* 28:16 *⚖️ AI agents are being utilized across various applications, including legal work and complex document analysis.* 29:22 *📚 AI research agents engaging in tasks such as web searches and document synthesis are gaining traction and becoming more practical.* 30:19 *🚀 Recent advancements have transformed many AI applications from novelties into functional tools, highlighting a shift towards practical utility.* Made with HARPA AI
Wish Andrew had talked about more of activities in the field but ran out of time. If the first dude have just introduced Andrew and let him have a stage - but he needed to waste everyone's time.
I tested his models and every use case I tried produced erroneous results. Even the built-in demos on the site did not work correctly. It just doesn't work. This is a perfect example of the AI hype and bubble we are in. By the way, I have been working with AI for 17 years.
![The Near Future of AI [Entire Talk] - Andrew Ng (AI Fund)](http://i.ytimg.com/vi/KDBq0GqKpqA/mqdefault.jpg)
13:39 is when Andrew's actual presentation starts.
you're the true hero!
Kind of disprespectful to do an intro without andrew that long
The Snowflake CEO wasted half the time babbling endlessly leaving very little time for the actual guest.. ugh
Thank you. I didn’t come to waste my time listening to that CEO
Thank you, I did not waste listening to oooh, and wow, its developer conference lets keep it to business.
Building AI applications with agentic workflows introduces significant challenges, especially when these workflows rely on large language models (LLMs). The problem is that LLMs aren’t deterministic-they don’t always produce the same output given the same input. This unpredictability becomes even more problematic when we try to layer additional complexity, like autonomous agents, on top of an already unstable system.
In many enterprise settings, where reliability and consistency are key, these complex agentic workflows can cause more harm than good. Most tasks can be handled without the need for this extra layer of abstraction. By using LLMs in a more controlled and straightforward way, you can get the job done without introducing unnecessary risks. Instead of over-complicating the architecture, focusing on simpler, tightly managed LLM-based solutions can lead to more stable and reliable outcomes-exactly what’s needed for critical enterprise operations.
Andrew is such a humble person I always loves to hear him ❤
Wow, what an introduction to the actual presentation. I can only imagine how motivating it must be for the employees who are lucky enough to experience this level of insight on a regular basis. :)
What a fantastic presenter, thank you Andrew!
Thank you Andrew!
With its creative processes, SmythOS is leading the way in advancing AI. For those who are serious about utilizing AI, it is an essential tool. #TechTrends #AI #Innovation #SmythOS
🎯 Key points for quick navigation:
00:00:14 *🌟 The event focuses on bringing together the builder community, irrespective of whether they are Snowflake customers or not, aiming to foster idea exchange and inspiration.*
00:00:54 *📸 The speaker humorously shares excitement about meeting industry luminary Andrew Ng, highlighting the enthusiasm for technology development.*
00:01:36 *🚢 Snowflake encourages developers to innovate through examples like building applications without the need for deploying servers.*
00:02:18 *🏗️ Snowflake is increasing its openness by transitioning to a platform with more community-led development and open-source contributions.*
00:02:43 *🎉 The company recently concluded its first international AI hackathon, showcasing its commitment to fostering innovation and supporting developers.*
00:03:25 *🤝 Snowflake collaborates closely with startups to help them scale applications, highlighting partnerships with startups earning millions on the Snowflake Marketplace.*
00:04:18 *🏆 The startup challenge hosted by Snowflake awarded significant investment, with Signal Flare emerging as a winner.*
00:05:02 *🚀 Snowflake's accelerator programs launched with VC firms invest up to $100 million in early-stage startups.*
00:05:45 *🌍 They've launched the NSTAR education program to provide free training resources globally, advancing skills related to data and AI.*
00:06:09 *🌟 Andrew Ng is introduced as a luminary speaker, hailed for his extensive contributions to the AI field, prompting reflections on AI's transformative potential.*
00:07:05 *📈 Andrew Ng emphasizes the potential impact of AI agents and agentic workflows, encouraging exploration of scalable and efficient AI systems.*
00:07:59 *🌐 Ng appreciates initiatives like open-sourcing models, promoting broad technological access to prevent monopolization.*
00:09:09 *⚖️ Ng expresses concern about regulatory proposals in California which might stifle open-source innovation in AI.*
00:11:12 *🚦 Ng advocates for thoughtful regulation focusing on harmful applications rather than stifling technological advancement.*
00:11:55 *🔍 The conversation explores the transformative potential of AI agents in enhancing the capabilities and benefits of large language models.*
00:13:52 *🤖 Ng introduces the concept of agentic workflows, comparing them to traditional zero-shot prompting, highlighting iterative improvements in AI performance.*
00:16:12 *💡 Agentic workflows showcase significant improvements in results, suggesting their potential to surpass advancements between different model versions.*
00:17:07 *🏄 Ng’s team demonstrates the use of vision agents to automate tasks using visual datasets, exemplified by tracking distances in shark-surfer videos.*
00:18:02 *🔬 The vision agent automatically generates code to perform complex visual tasks, demonstrating its efficiency and utility.*
00:19:41 *⚙️ The innovative use of vision agents can streamline computer vision tasks, making application development more efficient for users.*
00:21:12 *🧪 Testing mechanisms enable vision agents to correct and enhance code generation, improving accuracy and efficiency in application outputs.*
00:23:00 *📄 Automatic code revision and testing through vision agents demonstrate significant potential for automating complex visual analyses.*
24:50 *🚗 The speaker expresses excitement about agentic AI for various applications, although current systems have limitations such as common failures in object detection.*
25:36 *🧠 The current AI system struggles with complex reasoning, often requiring precise prompt tuning to achieve accurate results.*
26:17 *🛠️ The visual agent software is in beta and sometimes yields impressive results, though it remains sensitive to prompt phrasing.*
27:00 *🌐 The vision agent team released the core engine as open source, aiming to contribute to the advancement of AI agents and improve developer capabilities.*
28:16 *⚖️ AI agents are being utilized across various applications, including legal work and complex document analysis.*
29:22 *📚 AI research agents engaging in tasks such as web searches and document synthesis are gaining traction and becoming more practical.*
30:19 *🚀 Recent advancements have transformed many AI applications from novelties into functional tools, highlighting a shift towards practical utility.*
Made with HARPA AI
Wish Andrew had talked about more of activities in the field but ran out of time. If the first dude have just introduced Andrew and let him have a stage - but he needed to waste everyone's time.
The first dude paid for the stage and venue.
@@stanleyt6003 fair point but Andrew's talk made the stage more valuable
So true 😂
The first dude…😂
Be humble and stop bashing people based on your BS bias
Thank you
ANdrew is the best
"The Level 2 of AI Adoption is arriving before AGI"
The Last AI of Humanity (New Book )
Before you know devs will be out of job before anyone first - automate what you know first - devs know coding
I tested his models and every use case I tried produced erroneous results. Even the built-in demos on the site did not work correctly. It just doesn't work. This is a perfect example of the AI hype and bubble we are in. By the way, I have been working with AI for 17 years.
12:49 “Asians Asians Asians!”
🤣🤣🤣