Category Archives: AI

What are His Academic Credentials? Perhaps MIT, Stanford?

Source: LinkedIn, date indeterminate
<answer is at the source>

China Plans to have a US$150 billion AI industry by 2030

Source: NYTimes, Jul 2017

The country laid out a development plan on Thursday to become the world leader in A.I. by 2030, aiming to surpass its rivals technologically and build a domestic industry worth almost $150 billion.

The plan comes with China preparing a multibillion-dollar national investment initiative to support “moonshot” projects, start-ups and academic research in A.I., according to two professors who consulted with the government about the effort.

Related Resources: (from the Chinese government:

1. “State Council policy briefing on July 21″

Li Meng said the guideline puts forward the goal and tasks for AI development by 2030. The major goal is broken into three stages: catch up with the advanced global levels in AI technology and application by 2020, make major breakthroughs in basic theories by 2025, and become a global innovation center in this field by 2030.

2. “”China issues guideline on artificial intelligence development“”

The State Council has issued a guideline on developing artificial intelligence (AI), setting a goal of becoming a global innovation center in this field by 2030.

The total output value of artificial intelligence industries should surpass 1 trillion yuan ($147.80 billion). A mature theory and technology system should be formed.


A Visual Introduction to Machine Learning

Source: R2D3 website, Apr 2017

Machine Learning

Source: CMU, 2015



Related Resource: Bradford Cross’s article, Sep 2016

Our constant push to invent new formalisms driven by improved performance on practical problems yields the increasingly creative composition of the different learning paradigms that Jordan outlines. Taken together, these motivating factors are driving a boom in novel machine learning research.

These forces are leading to a boom in both exploring new approaches and refining older approaches that haven’t been practicable until more recently. For example, neural networks have been around since the the 1950s, and deep neural networks have been around since the 1980s. Despite their long history, deep nets have only taken off more recently as the result of a confluence of multiple trends — large available data sets with new hardware (e.g., NVIDIA and Nervana) and approaches to train the networks faster.

Deep learning papers represented only ~0.15% of computer science papers in arXiv published in early 2012 but grew ~10X to ~1.4% by the end of 2014. In 2016, 80% of papers at many top NLP conferences are deep learning papers. Deep nets are now demonstrating state of the art results across applications in computer vision, speech, NLP, bioinformatics, and a growing list of other domains.


Generative Adversarial Networks (GAN)

Source: KDNuggets, Jan 2017

a GAN as a new architecture for an unsupervised neural network able to achieve far better performance compared to traditional nets. 

To be more precise GANs are a new way of training a neural net. GANs contain not one but two independent nets that work separately and act as adversaries (see the diagram below). 

The first neural net is called the Discriminator (D) and is the net that has to undergo training. D is the classifier that will do the heavy lifting during the normal operation once the training is complete. The second network is called the Generator (G) and is tasked to generate random samples that resemble real samples with a twist rendering them as fake samples.

Sam Altman on AI, and Impact upon “Assembly Line” Jobs

Source: Business Insider, Mar 2017

Sam Altman, the president of Silicon Valley startup incubator Y Combinator, said in an on-stage interview on Monday that he sees the future of the human race as a “merge” with machines, not a conflict with them.

“I think we’re going to need something like that so that we’re one thing and it’s not us vs AI,” Altman suggested.

I don’t think people working on an assembly line have found their highest, most fulfilling calling,” Altman said. “I take the mindset of we owe it to society as a whole to retrain people whose jobs we displace but if we can make them happier and have a better job and put their efforts, their talents, to better use, that’s still a net win.”

AI (Robots) will Erode Employment

Source: NY Times, Mar 2017

The industry most affected by automation is manufacturing. For every robot per thousand workers, up to six workers lost their jobs and wages fell by as much as three-fourths of a percent, according to a new paper by the economists, Daron Acemoglu of M.I.T. and Pascual Restrepo of Boston University. It appears to be the first study to quantify large, direct, negative effects of robots.

“The conclusion is that even if overall employment and wages recover, there will be losers in the process, and it’s going to take a very long time for these communities to recover,” Mr. Acemoglu said.

The paper adds to the evidence that automation, more than other factors like trade and offshoring that President Trump campaigned on, has been the bigger long-term threat to blue-collar jobs. The researchers said the findings — “large and robust negative effects of robots on employment and wages” — remained strong even after controlling for imports, offshoring, software that displaces jobs, worker demographics and the type of industry.

Robots affected both men’s and women’s jobs, the researchers found, but the effect on male employment was up to twice as big. The data doesn’t explain why, but Mr. Acemoglu had a guess: Women are more willing than men to take a pay cut to work in a lower-status field.

In an isolated area, each robot per thousand workers decreased employment by 6.2 workers and wages by 0.7 percent. But nationally, the effects were smaller, because jobs were created in other places.