- June 28,2014 : Google’s Ray Kurzweil is working on embedding AI into search.
- Good stuff. It is high time, we add intelligence to search.
- June 25, 2014 : Dueling Definitions : Interesting take on the definition, use & context of AI at O’Reilly Radar !
- June 11, 2014
- Interesting paper on counterintuitive properties of ANN, we have a long way to go before achieving real intelligence !
- OTOH, a computer passed the Turing Test, a historical milestone for AI
- On the security front, DARPA is looking for machines to defend the cyberspace – I think AI has a big role in security
- Untapped opportunities in AI – Some of AI’s viable approaches lie outside the organizational boundaries of Google and other large Internet companies.
- [May 17,’XIV] Yann/LeCun / NYU/Facebook @reddit – lots of interesting insights on the state of AI
- Most probably I will summarize the discussion in a blog
- [May 17,’XIV] Prof.Andrew Ng moving to Baidu as Chief Scientist
- [Jan 19,2014] An excellent article on Wired about Hinton, who s the undoubted pioneer in Deep Learning – “Meet the Man Google hired to make AI a reality”
- [October 13,2013] Good post by Derrick Harris of GigaOm on work at Stanford on Sentiment Analysis and Deep learning
Back to the main feature …
An interesting blog in GigaOm by Derrick Harris on Deep Learning for the masses. What interested me most was Jeremy Howard from Kaggle.
- “…It’s going to enable whole new classes of products that have never existed before …”
- Yep, I agree. I had written earlier about Deep Learning being the next frontier
- The evolution of modern AI from “work like humans” to “think like humans” is exciting
- But there’s a catch: deep learning is really hard. So far, only a handful of teams in hundreds of Kaggle competitions have used it. Most of them have included Geoffrey Hinton or have been associated with him.
- Yep, it is hard. We are trying to bootstrap an application system and haven’t even scratched the surface – so it seems
- If data scientists in places outside Google could simply (a relative term if ever there was one) input their multidimensional data and train models to learn it, that could make other approaches to predictive modeling all but obsolete.
- Yep. Deel Learning is being applied in image recognition, translation et al. It would be interesting to see how the technologies can be applied to retail, banking, manufacturing et al
I also think the broader architecture of the three amigos viz Interface,Inference & Intelligence needs to come together
Smarter Models = Smarter Apps – Yep, definitely !