Monthly Archives: December 2019
Global AI Survey: AI proves its worth, but few scale impact
Most companies report measurable benefits from AI where it has been deployed; however, much work remains to scale impact, manage risks, and retrain the workforce. A group of high performers shows the way.
Adoption of artificial intelligence (AI) continues to increase, and the technology is generating returns. 1 The findings of the latest McKinsey Global Survey on the subject show a nearly 25 percent year-over-year increase in the use of AI 2 in standard business processes, with a sizable jump from the past year in companies using AI across multiple areas of their business. 3 A majority of executives whose companies have adopted AI report that it has provided an uptick in revenue in the business areas where it is used, and 44 percent say AI has reduced costs. Read More
8 biggest AI trends of 2020, according to experts
Artificial intelligence is one of the fastest moving and least predictable industries. Just think about all the things that were inconceivable a few years back: deepfakes, AI-powered machine translation, bots that can master the most complicated games, etc.
But it never hurts to try our chances at predicting the future of AI. We asked scientists and AI thought leaders about what they think will happen in the AI space in the year to come. Here’s what you need to know. Read More
Rule of thumb: Which AI / ML algorithms to apply to business problems
How to know which AI/ ML algorithm to apply to which business problem?
This is a common question
Ajit Jaokar found a good reference for it – Executive’s guide to AI by Mc Kinsey
He summarizes the insights in this post: Read More
Can we build artificial brain networks using nanoscale magnets?
Artificial intelligence software has increasingly begun to imitate the brain. Algorithms such as Google’s automatic image-classification and language-learning programs use networks of artificial neurons to perform complex tasks. However, because conventional computer hardware was not designed to run brain-like algorithms, these machine-learning tasks require orders of magnitude more computing power than the human brain does. The brain, and biological systems in general, are able to perform high-performance calculations much more efficiently than computers, and they do it quickly and with minimal energy consumption
Building artificial neural networks is an emerging field of research in bio-inspired computing. Read More
LUKE Arm — a prosthetic arm controlled with your mind
Breakthrough Research In Reinforcement Learning From 2019
Reinforcement learning (RL) continues to be less valuable for business applications than supervised learning, and even unsupervised learning. It is successfully applied only in areas where huge amounts of simulated data can be generated, like robotics and games.
However, many experts recognize RL as a promising path towards Artificial General Intelligence (AGI), or true intelligence. Thus, research teams from top institutions and tech leaders are seeking ways to make RL algorithms more sample-efficient and stable.
We’ve selected and summarized 10 research papers that we think are representative of the latest research trends in reinforcement learning. Read More
A Beginner’s Guide to Generative Adversarial Networks (GANs)
Generative adversarial networks (GANs) are deep neural net architectures comprised of two nets, pitting one against the other (thus the “adversarial”).
GANs were introduced in a paper by Ian Goodfellow and other researchers at the University of Montreal, including Yoshua Bengio, in 2014. Referring to GANs, Facebook’s AI research director Yann LeCun called adversarial training “the most interesting idea in the last 10 years in ML.”
GANs’ potential is huge, because they can learn to mimic any distribution of data. That is, GANs can be taught to create worlds eerily similar to our own in any domain: images, music, speech, prose. They are robot artists in a sense, and their output is impressive – poignant even. Read More
Going Beyond GAN? New DeepMind VAE Model Generates High Fidelity Human
Generative adversarial networks (GANs) have become AI researchers’ “go-to” technique for generating photo-realistic synthetic images. Now, DeepMind researchers say that there may be a better option.
In a new paper, the Google-owned research company introduces its VQ-VAE 2 model for large scale image generation. The model is said to yield results competitive with state-of-the-art generative model BigGAN in synthesizing high-resolution images while delivering broader diversity and overcoming some native shortcomings of GANs. Read More
The Worldwide Web of Chinese and Russian Information Controls
The global diffusion of Chinese and Russian information control technology and techniques has featured prominently in the headlines of major international newspapers.1 Few stories, however, have provided a systematic analysis of both the drivers and outcomes of such diffusion. This paper does so – and finds that these information controls are spreading more efficiently to countries with hybrid or authoritarian regimes, particularly those that have ties to China or Russia. Chinese information controls spread more easily to countries along the Belt and Road Initiative; Russian controls spread to countries within the Commonwealth of Independent States. In arriving at these findings, this working paper first defines the Russian and Chinese models of information control and then traces their diffusion to the 110 countries within the countries’ respective technological spheres, which are geographical areas and spheres of influence to which Russian and Chinese information control technology, techniques of handling information, and law have diffused. Read More
#china, #russia, #surveillance
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