If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Akbar Solo Researchers in Moscow and America have discovered how to use machine learning to grow artificial organs, especially to tackle blindness Researchers from the Moscow Institute of Physics and Technology, Ivannikov Institute for System Programming, and the Harvard Medical School-affiliated Schepens Eye Research Institute have developed a neural network capable of recognizing retinal tissues during the process of their differentiation in a dish. Unlike humans, the algorithm achieves this without the need to modify cells, making the method suitable for growing retinal tissue for developing cell replacement therapies to treat blindness and conducting research into new drugs. The study was published in Frontiers in Cellular Neuroscience. How would this enable easier organ growth? This would allow to expand the applications of the technology for multiple fields including the drug discovery and development of cell replacement therapies to treat blindnessIn multicellular organisms, the cells making up different organs and tissues are not the same.
On a family trip a few months back, I was flipping through an airline magazine and landed on the puzzles page. There were three puzzles, "Easy", "Medium", and "Hard". At the top of the page a word that would become my obsession over the next couple of months: "Sudoku". I had heard about Sudoku puzzles, but I had never really considered trying one. I grabbed a pencil from one of the kids and started with the "Easy" puzzle. It took me quite some time (and I tore the paper in one spot after erasing too many times) but I eventually completed the puzzle.
Robot-related services powered by cloud computing will reach US$157.8 billion in annual revenue by 2030, according to new figures released by ABI Research. The analyst firm says despite being only in its nascent stages, the value of cloud infrastructure to robots is key for both deployment (encompassing development, configuration, and instalment) and operation (maintenance, analytics, and control). With the popularisation of mobile robotics in a wide range of verticals, it will become necessary to utilise the computing power of cloud infrastructure to store and manage the vast troves of collected data as well as to train more advanced algorithms used to power robot cognition, it says. "Since 1961, most commercial robots have been wired or tied to external infrastructure for movement. The next generation of robot deployments will be increasingly mobile, tied to cellular and WIFI connectivity, will consume vast troves of data in order to operate autonomously, and will need effective management through real-time measurements for performance, status and operability," explains Rian Whitton, senior analyst at ABI Research.
This time, Dilated Convolution, from Princeton University and Intel Lab, is briefly reviewed. The idea of Dilated Convolution is come from the wavelet decomposition. Thus, any ideas from the past are still useful if we can turn them into the deep learning framework. And this dilated convolution has been published in 2016 ICLR with more than 1000 citations when I was writing this story.
Google today announced that it has signed up Verizon as the newest customer of its Google Cloud Contact Center AI service, which aims to bring natural language recognition to the often inscrutable phone menus that many companies still use today (disclaimer: TechCrunch is part of the Verizon Media Group). For Google, that's a major win, but it's also a chance for the Google Cloud team to highlight some of the work it has done in this area. It's also worth noting that the Contact Center AI product is a good example of Google Cloud's strategy of packaging up many of its disparate technologies into products that solve specific problems. "A big part of our approach is that machine learning has enormous power but it's hard for people," Google Cloud CEO Thomas Kurian told me in an interview ahead of today's announcement. "Instead of telling people, 'well, here's our natural language processing tools, here is speech recognition, here is text-to-speech and speech-to-text -- and why don't you just write a big neural network of your own to process all that?' Very few companies can do that well. We thought that we can take the collection of these things and bring that as a solution to people to solve a business problem. And it's much easier for them when we do that and […] that it's a big part of our strategy to take our expertise in machine intelligence and artificial intelligence and build domain-specific solutions for a number of customers."
Power Virtual Agents is gaining traction around the world and the market has responded with a strong desire for us to support more languages. Today we're excited to announce that we are bringing to public preview support for an extended set of languages! This enables our partners and customers to build even more engaging and locally relevant experiences for their users. When you create a new bot, you select the language you want the bot to understand when interacting with your users. You'll see that your new bot is prepopulated with content in the target language and you can easily create more topics with trigger phrases and bot responses in the language you've selected.
Microsoft is shedding its empathetic chatbot Xiaoice into an independent entity, the U.S. software behemoth said (in Chinese) Monday, confirming an earlier report by the Chinese news site Chuhaipost in June. The announcement came several months after Microsoft announced late last year it would close down its voice assistant app Cortana in China among other countries. Xiaoice has over the years enlisted some of the best minds in artificial intelligence and ventured beyond China into countries like Japan and Indonesia. Microsoft said it called the shots to accelerate Xiaoice's "localized innovation" and buildout of the chatbot's "commercial ecosystem." The spin-off will see the new entity license technologies from Microsoft for subsequent research and development in Xiaoice and continue to use the Xiaoice brand (and Rinna in Japanese), while Microsoft will retain its stakes in the new company.
In this paper, we study the volatility forecasts in the Bitcoin market, which has become popular in the global market in recent years. Since the volatility forecasts help trading decisions of traders who want a profit, the volatility forecasting is an important task in the market. For the improvement of the forecasting accuracy of Bitcoin’s volatility, we develop the hybrid forecasting models combining the GARCH family models with the machine learning (ML) approach. Specifically, we adopt Artificial Neural Network (ANN) and Higher Order Neural Network (HONN) for the ML approach and construct the hybrid models using the outputs of the GARCH models and several relevant variables as input variables. We carry out many experiments based on the proposed models and compare the forecasting accuracy of the models. In addition, we provide the Model Confidence Set (MCS) test to find statistically the best model. The results show that the hybrid models based on HONN provide more accurate forecasts than the other models.
Were thrilled to announce ODSCs new virtual event, ODSC Applied AI, on July 15, 2020. ODSC Applied AI is a one-day, free, virtual event featuring 90-minute hands-on workshops & tutorials that will help you strengthen existingor build newskills using real-world data. The event will be organized into four tracks based on job roles. And, each track is designed to teach you how to best utilize machine learning & AI in that role to meet your personal or business goals. There are only a limited number of spots available for the hands-on workshops in each track.
Mammoth quantities of pristine data are one of the most valuable resources in these times, which is the potential source of huge revenue thanks to advances in deep learning and associated hardware that's needed to speed up those innumerable matrix multiplications. What if we don't have a lot of data and procuring more isn't feasible? Or we lack the expensive hardware that's imperative for training very deep networks? Both can be solved by using the concept of transfer learning, which we'll soon find out is something we are unconsciously familiar with. Transfer learning is a supervised learning method that aids construction of new models using pre-trained weights of previously constructed and fine-tuned models.