> For the complete documentation index, see [llms.txt](https://akm5630.gitbook.io/understanding-causal-inference/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://akm5630.gitbook.io/understanding-causal-inference/why-we-need-causality/leaders-in-the-industry.md).

# Leaders in the Industry

Opinion by the leaders/ People to follow

## [**Yoshua Bengio**](https://yoshuabengio.org/) *\*\**

**(**A **\*\*co-recipient of the 2018 ACM A.M. Turing Award for his work in deep learning**)\*\*

![](https://1457788420-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-MFIP0MKlNdCTGB9r7S-%2Fsync%2F1cf3e1a0ec04be2d7ca8135a9c7293de28e959f4.png?generation=1598053527437707\&alt=media)

* "I think we need to consider the hard challenges of AI and not be satisfied with short-term, incremental advances. I’m not saying I want to forget deep learning. On the contrary, I want to build on it. But we need to be able to extend it to do things like reasoning, **learning causality**, and exploring the world in order to learn and acquire information." -[MIT Review](https://www.technologyreview.com/2018/11/17/66372/one-of-the-fathers-of-ai-is-worried-about-its-future/)
* "If you have a good **causal model of the world** you are dealing with, you can generalize even in unfamiliar situations. That’s crucial. We humans are able to project ourselves into situations that are very different from our day-to-day experience. Machines are not, because they don’t have these causal models." - [MIT Review](https://www.technologyreview.com/2018/11/17/66372/one-of-the-fathers-of-ai-is-worried-about-its-future/)

## [**Judea Pearl** ](http://bayes.cs.ucla.edu/jp_home.html)

(Awarded with the Turing Award in 2011)

![](https://1457788420-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-MFIP0MKlNdCTGB9r7S-%2Fsync%2Fb9a924ea8e3cb4d198066cf4f2170821e124cca4.png?generation=1598053528505640\&alt=media)

Judea Pearl says AI can’t be truly intelligent until it has a rich understanding of cause and effect, which would enable the introspection that is at the core of cognition.

## [**Elias Bareinboim**](https://causalai.net/) *\*\**

(Director, Causal Artificial Intelligence Lab CausalAI Laboratory)

![](https://1457788420-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-MFIP0MKlNdCTGB9r7S-%2Fsync%2Fe4d1935433fd4aec4b40b2ffa3962dfe0875f8d2.png?generation=1598053526879239\&alt=media)

## [**Fei-Fei Li** ](https://profiles.stanford.edu/fei-fei-li)

**(**Director of the Stanford Artificial Intelligence Lab)

I believe that today’s machine-learning and AI tools won’t be enough to bring about real AI. “It’s not just going to be data-rich deep learning,” she says. Li believes AI researchers will need to think about things like emotional and social intelligence. -

## **\*\*\[**&#x47;ary Marcu&#x73;**]\(**<http://garymarcus.com/index.html>**) \*\***

**(**&#x53;cientist, Author, Entrepreneu&#x72;**)**

"In particular, we need to stop building computer systems that merely get better and better at detecting statistical patterns in data sets — often using an approach known as deep learning — and start building computer systems that from the moment of their assembly innately grasp three basic concepts: time, space and causality"

## [Miguel Hernan](https://www.hsph.harvard.edu/miguel-hernan/)

### **"Causal Inference is Hard"**