【雇用】東大AI人材、新興勢に就職　選ばれぬ大手に改革迫る

多くのエンジニアを輩出する大学院情報理工学系研究科や前身となる研究科の修了生の就職先をたどった。平成の時代が始まって間もない1993年、一番人気はNTT（8人）…
https://www.nikkei.com/article/DGXMZO45289880W9A520C1TJC000/

2005年度生（理系/卒業生＝165/215名）
1.理三、2.理三、3.理三、4.理三、5.理三、6.京大医、7.理三、8.理三、9.理三、10.理三、
11.理三、12.京大医、13.京大医、14.理三、15.理三、16.理三、17.京大医、18.理三、19.理三、20.京大医、
21.理三、22.理一 …（22位でようやく非医学系が顔を出す極端な医学系への集中）

http://news.mit.edu/2019/teaching-machines-to-reason-about-what-they-see-0402
With minimal training data and no explicit programming, their model could transfer concepts to larger scenes and answer increasingly tricky questions
as well as or better than its state-of-the-art peers. The team presents its results at the International Conference on Learning Representations in May.

While other models trained on the full CLEVR dataset of 70,000 images and 700,000 questions, the MIT-IBM model used 5,000 images and 100,000 questions.
As the model built on previously learned concepts, it absorbed the programs underlying each question, speeding up the training process.

https://openreview.net/pdf?id=rJgMlhRctm

The opposite of this highly defined, Narrow AI is Broad AI. Broad AI systems can execute tasks across
a variety of different fields. This is the kind of technology that would be necessary to create the robots
that are at present confined to the realm of science fiction – digital assistants who could walk your dog,
answer your email, and then make you breakfast