AI News, Computer Vision and Machine Learning Public Group

Mitsubishi Electric | Changes for the Better MERL - Mitsubishi Electric Research Laboratories

The Control and Dynamical Systems (CD) group at MERL is seeking highly motivated interns at different levels of expertise to conduct research on planning and control for autonomous vehicles.

For algorithm development and analysis it is highly desirable to have deep background in one or more among: sampling-based planning methods, particle filtering, model predictive control, reachability methods, formal methods and abstractions of dynamical systems, and experience with their implementation in Matlab/Python/C++.

Eye on A.I. | Newsletters

The team, which included researchers from Nvidia Research, the Vector Institute, the University of Toronto, and Aalto University, say the work may point towards systems that will be able to render whole scenes for video games or animated movies in a much faster and less expensive way.

The winning teams, which hailed from South Korea, a combined Oxford/Zurich team and a team from the University of Toronto,  all used neural networks and found that these deep learning systems outperformed previous forecasting methods, including other kinds of machine learning.

systems that have bested human competitors at complex strategy games such as Go and the video games Starcraft 2, the MineRL competitors had access to relatively little data and computing power from which to train their bots: just 1,000 hours of pre-recorded game play and and a single Nvidia GPU, as well as a maximum of four days training time.  An algorithm that can both maximize revenue and guarantee fairness?

But a team of researchers from the University of Massachusetts Amherst and Stanford University created an algorithm, which they called Robinhood, that could be used to design systems that both maximizes a reward, such as revenue, while also guaranteeing fairness, within certain user-specified limits.

In order to work, the user has to be able to specify the fairness constraint — for instance that men and women have equal opportunity to be approved for a bank loan—in a formal, mathematical way.

The authors tested their algorithm on datasets for loan applications, an educational tutoring system and criminal recidivism and found in each case Robinhood could maximize outcomes while guaranteeing fairness.

In vision, attention mechanisms have already been used to augment convolutional neural networks, but the Google Brain team proposes that attention alone, without any convolutions at all, might produce equally good results while at the same time reducing the amount of computer power needed to train and run these vision models.  Weights weights, don't tell me.

Adam Gaier and David Ha, both researchers at Google Brain, presented their finding that it was possible to design networks that would perform well on image classification tasks, even with no training, when each node of the network was assigned the exact same random weight.

In 2016, it purchased Nervana Systems, a startup developing A.I.-specific chips.  Emotion-Recognition Systems Under Fire. The AI Now Institute, a center at New York University dedicated to understanding the societal impact of A.I., has called for a ban on the use of emotion-recognition software in important decisions in its latest annual report.

AI Now says that the scientific basis of the technology is 'contested' and it 'should not be allowed to play a role in important decisions about human lives.'  Facebook Ads Still Discriminate Against Women, Older Workers, ProPublica Finds. The investigative journalism organization ProPublica has found Facebook ad targeting can discriminate by gender, race and age even when advertisers use a new service that doesn't allow targeting by those specific characteristics.

chip companies faced off: •Graphcore (which recently signed a major deal to make its Inference Processing Units available through Microsoft’s Azure cloud) •Cerebrus (which has built the world’s largest computer chip, designed to handle A.I.

training loads) •Intel (which entered A.I.-specific hardware through its acquisition of Nervana Systems in 2016 and which has now acquired Habana) •Habana itself •Google (which has its Tensor Processing Units available in its cloud) While trumpeting their own chips’ performance, each of these companies bashed Nvidia, whose graphics processing units (GPUs), chips originally designed to handle the heavy computing workloads of video games, have until now been the standard equipment for A.I.

chips with companies' existing software and workflows is not seamless and most businesses don’t want to take the time—or run the risk—of retooling their projects to run on the new hardware, despite the promise of a training speedup.

And, if the push for more human-like learning efficiency (see above) does bear results, such massive models and the equally massive compute (and electricity) they require may ultimately wind up seeming like a weird, brief eddy in the flow of A.I.’s development.

Conference—by Jeremy Kahn In the past 18 months, a succession of ever-more-capable language models (algorithms that can predict the next word in a sentence) have debuted, finding their way into search engines, chat bots, question answering systems and writing generators.

AI for the Common Good.

Open Source NLP Platform Design, evaluate, and contribute new models on our open-source PyTorch-backed NLP platfom, where you can also find state-of-the-art implementations of several important NLP models and tools.


However, the requirements of much larger file sizes, different storage formats, and immersive viewing conditions pose significant challenges to the goals of acquiring, transmitting, compressing and displaying high quality VR content.

Meeting these challenges requires basic tools in the form of large, representative subjective VR quality databases on which VR quality models can be developed and which can be used to benchmark VR quality prediction algorithms.

How AI Startups Must Compete with Google | Dr Fei-Fei Li (Google Cloud) & Mike Abbott (KPCB)


Intro to Deep Learning and Computer Vision | Kaggle

The 1st video in the deep learning series at SUBSCRIBE: ...

How computers learn to recognize objects instantly | Joseph Redmon

Ten years ago, researchers thought that getting a computer to tell the difference between a cat and a dog would be almost impossible. Today, computer vision ...

Using Artificial Intelligence to Enhance Your Game (1 of 2)

Machine learning has revolutionized many important fields, ranging from computer vision and natural language processing to healthcare and robotics. In two ...

Computer Vision and Machine Learning, by Nick Wong

A basic introduction to some fundamental concepts in machine learning using Tensorflow, coupled with an introduction to OpenCV2, a computer vision project.

How A.I. Will Impact Marketing and Growth in 2020 | And Where to Start

In 2017 we put together a graph to showcase the must-haves, the should-haves and the nice-to-haves of different AI applications for marketing and growth.

Deep Learning State of the Art (2019) - MIT

New lecture on recent developments in deep learning that are defining the state of the art in our field (algorithms, applications, and tools). This is not a complete ...

YOLO Object Detection (TensorFlow tutorial)

You Only Look Once - this object detection algorithm is currently the state of the art, outperforming R-CNN and it's variants. I'll go into some different object ...

PAI: Platform of A.I. in Alibaba (TF Dev Summit '19)

The Ali Baba Group's Wei Lin, Senior Director of PAI Platform of Artificial Intelligence gives a quick overview of how PAI operates at Ali Baba. Speaker: Wei Lin ...

TensorFlow Object Detection | Realtime Object Detection with TensorFlow | TensorFlow Python |Edureka

AI & Deep Learning Using TensorFlow - ** This Edureka video will provide you with a detailed and ..