Task-oriented dialog systems are becoming pervasive, and many companies heavily rely on them to complement human agents for customer service in call centers. With globalization, the need for providing cross-lingual customer support becomes more urgent than ever. However, cross-lingual support poses great challenges---it requires a large amount of additional annotated data from native speakers. In order to bypass the expensive human annotation and achieve the first step towards the ultimate goal of building a universal dialog system, we set out to build a cross-lingual state tracking framework. Specifically, we assume that there exists a source language with dialog belief tracking annotations while the target languages have no annotated dialog data of any form. Then, we pre-train a state tracker for the source language as a teacher, which is able to exploit easy-to-access parallel data. We then distill and transfer its own knowledge to the student state tracker in target languages. We specifically discuss two types of common parallel resources: bilingual corpus and bilingual dictionary, and design different transfer learning strategies accordingly. Experimentally, we successfully use English state tracker as the teacher to transfer its knowledge to both Italian and German trackers and achieve promising results.

This is the 5th article of series "Coding Deep Learning for Beginners". You will be able to find here links to all articles, agenda, and general information about an estimated release date of next articles on the bottom of the 1st article. They are also available in my open source portfolio -- MyRoadToAI, along with some mini-projects, presentations, tutorials and links. In this article, I will explain the concept of training Machine Learning algorithms with Gradient Descent. Majority of supervised algorithms are taking advantage of it -- especially all Neural Networks.

Neural networks and Deep Learning, the words when witnessed, fascinate the viewers, both complement each other as they fall under the umbrella of Artificial Intelligence. This article is concentred on the discussion of above-mentioned trending and thriving technologies. You will gain some basic knowledge for commencing your learning about Neural networks and Deep Learning. It'll be also very helpful if you are looking to make the career in the field of Artificial Intelligence and Machine Learning. Basically, A Neural Network is a chain or series of algorithms that aims to recognize the relationships in a set of known data provided to us through a process that mimics the way human brain operates and analyze.

There has been much written about the potential for FPGAs to take a leadership role in accelerating deep learning but in practice, the hurdles of getting from concept to high performance hardware design are still taller than many AI shops are willing to scale, particularly when GPUs dominate in training and in a pinch, standard CPUs will do just fine for datacenter inference since they involve little developer overhead. Still, companies like Xilinx and competitor Intel with its Altera assets are working toward making deep learning on FPGAs easier with a variety of techniques that reproduce key elements of deep learning workflows in inference specifically since that is where the energy efficiency and performance story is clearest. For instance, the programmable solutions group at Intel where the Altera teams were integrated post-acquisition has just developed an FPGA overlay for deep learning inference that demonstrates some respectable results on an Arria 10 1150 FPGA. "Intel's DLA (deep learning accelerator) is a software-programmable hardware overlay on FPGAs to realize the ease of use of software programmability and the efficiency of custom hardware designs." The team explains that for the hardware side of DLA they have partitioned configurable parameters into the runtime to quickly use different neural network frameworks.

Machine learning is widely predicted to make drug discovery and patient diagnosis quicker, cheaper and more effective in the future, and signs of this can already be seen. Nearly 70 years ago, artificial intelligence researchers at New Hampshire's Dartmouth College discussed building machines that could sense, reason and think like people -- a concept known as'general AI'. But their plans were destined to remain in the land of science fiction for quite some time. However, in the last decade the rapid growth in computer-processing power, the availability of large data sets and the development of advanced algorithms have driven major improvements in machine learning. AI researcher Ben Goertzel brought to light'narrow AI' in 2010.

When I tell people that they should consider applying deep learning methods to their data, a common initial response I get is I am (1) not working with "big" enough data and (2) I do not have access to enough computational resources to to train deep learning models. I believe these assumptions come from large companies (e.g., Google) that often like to show off by conducting research on large datasets, such as ImageNet which contains over a million pictures, and by using a large amount of GPUs. That's great for these companies, but from my impression, the average deep learning practitioner is not working with such large datasets (or ever even needs to) and does not have access to such large computational resources. For example, as a graduate student my funding pretty much limits me to only use freely available resources, so I conduct all of my deep learning using Google Cloud Platform's freely available (at least for one year) K80 GPU. Yes, I do not pay a single penny to conduct deep learning and I only use 1 GPU.

No exponential is forever: But'forever' can be delayed!

Google's DeepMind AI wing has scored some major Brownie points with its parent, after Google announced that it had deployed AI-based algorithms to optimize data center cooling systems. The installation has reportedly saved Google around 30% on these cooling power bills, since they were deployed in 2016. Resource usage optimization is one of the most promising areas for AI-based systems, thanks to IoT sensors being able to generate vast amounts of data that can be crunched by these programs. Data centers share much in common with other industrial facilities, but quicker equipment refresh cycles mean that there's greater opportunity to install newer sensor-filled equipment. In Google's facilities, the DeepMind code was given control of the cooling equipment, and is apparently…

Last August at the Dota 2 International tournament in Seattle, OpenAI introduced an AI bot that upset the world's top 1v1 human player. The San Francisco-based AI research institute is now at the International 2018 in Vancouver, where their team of state-of-the-art bots is battling professional human teams in a highly anticipated best-of-three 5v5 Dota 2 showdown. Alas, the humans drew first blood: In a match that would make John Connor proud, Brazilian pro team "paiN" dispatched the "OpenAI Five" Bots yesterday in 52 minutes. OpenAI Five had more kills and a slight economy edge in the midgame, but did not push their advantage and kept losing their towers. The smart bots also made some dumb moves, such as warding in the wrong positions, bad item choice, and fewer gankings (leaving your lane to kill an enemy Hero in another lane).

HAIFA, ISRAEL (August 22, 2018) – Dr. Kira Radinsky and Shahar Harel of the Technion-Israel Institute of Technology Computer Science Department have developed a smart system for the development of new drugs. Founded on artificial intelligence and deep learning, the system is expected to dramatically shorten and reduce the costs of drug development. It will be presented this week during the KDD 2018 conference in London. Drug production is a costly and lengthy process. Costs of half a billion to 2.5 billion dollars per drug, over 10-15 years are common numbers in the world of pharmacology.