Deep reinforcement learning(DRL) has been at the center of some of the biggest breakthroughs of artificial intelligence(AI) in the last few years. However, despite all its progress, DRL methods remain incredibly difficult to apply in mainstream solutions given the lack of tooling and libraries. Consequently, DRL remains mostly a research activity that hasn’t seen a lot of adoption into real world machine learning solutions. Addressing that problem requires better tools and frameworks. Among the current generation of artificial intelligence(AI) leaders, DeepMind stands alone as the company that has done the most to advance DRL research and development. Recently, the Alphabet subsidiary has been releasing a series of new open source technologies that can help to streamline the adoption of DRL methods. Read More
Monthly Archives: September 2019
Russia’s National AI Center Is Taking Shape
A famed Russian technical university is helping to lead the government’s push for public-private efforts to develop AI technologies and applications — including a joint project with China’s Huawei — and to stop top talent from flowing to the West.
In December 2017, three months after Vladimir Putin predicted that artificial intelligence could produce “global domination,” the Russian government picked the Moscow Institute of Physics and Technologies to host a new Center for Artificial Intelligence. Today, this center aims to foster partnerships among the nation’s leading state-run and private companies and universities. Read More
Amazon and Leading Technology Companies Announce the Voice Interoperability Initiative
Today, Amazon (NASDAQ: AMZN) and leading technology companies announced the Voice Interoperability Initiative, a new program to ensure voice-enabled products provide customers with choice and flexibility through multiple, interoperable voice services. The initiative is built around a shared belief that voice services should work seamlessly alongside one another on a single device, and that voice-enabled products should be designed to support multiple simultaneous wake words.More than 30 companies are supporting the effort, including global brands like Amazon, Baidu, BMW, Bose, Cerence, ecobee, Harman, Logitech, Microsoft, Salesforce, Sonos, Sound United, Sony Audio Group, Spotify and Tencent; telecommunications operators like Free, Orange, SFR and Verizon; hardware solutions providers like Amlogic, InnoMedia, Intel, MediaTek, NXP Semiconductors, Qualcomm Technologies, Inc., SGW Global and Tonly; and systems integrators like CommScope, DiscVision, Libre, Linkplay, MyBox, Sagemcom, StreamUnlimited and Sugr. Read More
Will robots really steal our jobs?
A new PricewaterhouseCoopers report analyzes the long-term impacts of AI and automation, dividing the future of automation into three “waves:” the algorithm wave, extending into the early 2020s; the augmentation wave, into the late 2020s, and the autonomy wave, extending into the mid-2030s as described in Table 1.1 below.
Green AI
The computations required for deep learning research have been doubling every few months, resulting in an estimated 300,000x increase from 2012 to 2018 [2]. These computations have a surprisingly large carbon footprint[40]. Ironically, deep learning was inspired by the human brain, which is remarkably energy efficient. Moreover, the financial cost of the computations can make it difficult for academics, students, and researchers, in particular those from emerging economies, to engage in deep learning research.
This position paper advocates a practical solution by making efficiency an evaluation criterion for research along-side accuracy and related measures. In addition, we propose reporting the financial cost or “price tag” of developing,training, and running models to provide baselines for the investigation of increasingly efficient methods. Our goal isto make AI both greener and more inclusive—enabling any inspired undergraduate with a laptop to write high-quality research papers. Green AI is an emerging focus at the Allen Institute for AI. Read More
5 New Generative Adversarial Network (GAN) Architectures For Image Synthesis
AI image synthesis has made impressive progress since Generative Adversarial Networks (GANs) were introduced in 2014. GANs were originally only capable of generating small, blurry, black-and-white pictures, but now we can generate high-resolution, realistic and colorful pictures that you can hardly distinguish from real photographs.
Here we have summarized for you 5 recently introduced GAN architectures that are used for image synthesis. Read More
At Tech’s Leading Edge, Worry About a Concentration of Power
Each big step of progress in computing — from mainframe to personal computer to internet to smartphone — has opened opportunities for more people to invent on the digital frontier.
But there is growing concern that trend is being reversed at tech’s new leading edge, artificial intelligence.
Computer scientists say A.I. research is becoming increasingly expensive, requiring complex calculations done by giant data centers, leaving fewer people with easy access to the computing firepower necessary to develop the technology behind futuristic products like self-driving cars or digital assistants that can see, talk and reason.
The danger, they say, is that pioneering artificial intelligence research will be a field of haves and have-nots. Read More
Every Part of the Supply Chain Can Be Attacked
When it comes to 5G technology, we have to build a trustworthy system out of untrustworthy parts.
The United States government’s continuing disagreement with the Chinese company Huawei underscores a much larger problem with computer technologies in general: We have no choice but to trust them completely, and it’s impossible to verify that they’re trustworthy. Solving this problem — which is increasingly a national security issue — will require us to both make major policy changes and invent new technologies. Read More
Predicting Failures from Sensor Data using AI/ML — Part 2
This is Part 2 of the blog post series and continuation of the original post, Predicting Failures from Sensor Data using AI/ML — Part 1.
Sensor data takes time-based maintenance to the next level. Part 1 explored hard-disk failure detection with H2O.ai’s Driverless AI. Read More
Predicting Failures from Sensor Data using AI/ML— Part 1
Whether it’s healthcare, manufacturing or anything that we depend on either personal or in business, Prevention of a problem is always known to be better than cure!
Classic prevention techniques involve time-based checks to see how things are progressing, positively or negatively. Time-based checks are based on statistical measures like ‘how likely’ things would go wrong based on historical data. It works really well for the most part:
— Taking your vehicle to service on regular intervals (X miles or Ymonths) as recommended by your manufacturer, can reduce the odds of something failing unexpectedly.
— Doing yearly physical checkups with your Doctor can prevent any developing adverse condition.
— <name your favorite use case>
The total cost of disruption in business continuity or personal health care, is generally orders more than what is paid for periodic checks! Read More
Rick's Cafe AI 