Robohub.org
 

“Deep learning is the most fundamental advance in AI research since AI started in 1956”


by
14 July 2016



share this:
Screenshot from presentation about deep learning we may use everyday. Source: Frank Chen, with Andreessen Horowitz

Screenshot from presentation about deep learning we may use everyday. Source: Frank Chen, with Andreessen Horowitz

Frank Chen, a partner at Andreessen Horowitz, the Silicon Valley venture capital and private equity firm, said, “It is absolutely non-controversial that deep learning is the most fundamental advance in AI research since the start [of A.I.] in 1956.”

In the 45-minute podcast primer shown below – a worthwhile investment of your time – he went on to say:

“We [Andreessen Horowitz] think AI and deep learning can be a fundamental and technology platform shift as mobile and cloud have been in the last 5-10 years … All the serious applications from here on out need to have deep learning and AI inside in exactly the same way that all serious computing systems needed to have Intel chips inside of them. I think now about the startups that we see, that deep learning needs to be inside these new systems as a fundamental technique that we expect to see in all serious applications moving forward.”

Enjoy. It’s an informative primer.



tags: , ,


Frank Tobe is the owner and publisher of The Robot Report, and is also a panel member for Robohub's Robotics by Invitation series.
Frank Tobe is the owner and publisher of The Robot Report, and is also a panel member for Robohub's Robotics by Invitation series.





Related posts :



Researchers are teaching robots to walk on Mars from the sand of New Mexico

  02 Sep 2025
Researchers are closer to equipping a dog-like robot to conduct science on the surface of Mars

Engineering fantasy into reality

  26 Aug 2025
PhD student Erik Ballesteros is building “Doc Ock” arms for future astronauts.

RoboCup@Work League: Interview with Christoph Steup

and   22 Aug 2025
Find out more about the RoboCup League focussed on industrial production systems.

Interview with Haimin Hu: Game-theoretic integration of safety, interaction and learning for human-centered autonomy

and   21 Aug 2025
Hear from Haimin in the latest in our series featuring the 2025 AAAI / ACM SIGAI Doctoral Consortium participants.

AIhub coffee corner: Agentic AI

  15 Aug 2025
The AIhub coffee corner captures the musings of AI experts over a short conversation.

Interview with Kate Candon: Leveraging explicit and implicit feedback in human-robot interactions

and   25 Jul 2025
Hear from PhD student Kate about her work on human-robot interactions.

#RoboCup2025: social media round-up part 2

  24 Jul 2025
Find out what participants got up to during the second half of RoboCup2025 in Salvador, Brazil.

#RoboCup2025: social media round-up 1

  21 Jul 2025
Find out what participants got up to during the opening days of RoboCup2025 in Salvador, Brazil.



 

Robohub is supported by:




Would you like to learn how to tell impactful stories about your robot or AI system?


scicomm
training the next generation of science communicators in robotics & AI


 












©2025.05 - Association for the Understanding of Artificial Intelligence