Why hasn’t #MachineLearning conquered SARS-CoV-2 that causes COVID-19 (P.S., SARS-CoV-2 is the name of the #virus, while COVID-19 is the name of the disease)? One of the possible answers is that the virus “learns” faster than machines through “mutations”.
That causes us thinking: If mutation is such an efficient weapon (for virus), can we learn something from it and then apply our understanding to #DeepLearning to create “fast-mutating” #DeepLearning models capable of helping us to fight intractable crisis like a #pandemic?
Augmented reality has been the next big thing for a while, but we haven’t seen many practical applications. Here’s a tool that looks useful, though: using AR and AI to copy and paste objects from the real world to your computer using just your phone.
Mr. Schmidt is pressing forward with a Silicon Valley worldview where advances in software and A.I. are the keys to figuring out almost any issue. While that philosophy has led to social networks that spread disinformation and other unintended consequences, Mr. Schmidt said he was convinced that applying new and relatively untested technology to complex situations — including deadly ones — would make service members more efficient and bolster the United States in its competition with China.
The former Google C.E.O. has reinvented himself as the prime liaison between Silicon Valley and the military-industrial complex.
The U.S. military has been “stuck in software in the 1980s,” said Eric Schmidt, Google’s former chief executive. Credit… Winni Wintermeyer/Redux.
The drone-maker won the international award for its autonomous drones which have permitted companies to operate efficiently and flexibly despite the absence of workers around the world. The award was given to Percepto by the US-based company Frost and Sullivan, a business consulting firm involved in market research and analysis, for its ‘technological leadership’ in developing unique docking stations that operate independently without the need for a human operator in close proximity.
Imagine being able to know when a stock is heading up or going down in the next week and then with the remaining cash you have, you would put all of your money to invest or short that stock. After playing the stock market with the knowledge of whether or not the stock will increase or decrease in value, you might end up a millionaire!
Unfortunately, this is impossible because no one can know the future. However, we can make estimated guesses and informed forecasts based on the information we have in the present and the past regarding any stock. An estimated guess from past movements and patterns in stock price is called Technical Analysis. We can use Technical Analysis (TA)to predict a stock’s price direction, however, this is not 100% accurate. In fact, some traders criticize TA and have said that it is just as effective in predicting the future as Astrology. But there are other traders out there who swear by it and have established long successful trading careers.
In our case, the Neural Network we will be using will utilize TA to help it make informed predictions. The specific Neural Network we will implement is called a Recurrent Neural Network — LSTM. Previously we utilized an RNN to predict Bitcoin prices (see article below):
Some big M&A is afoot in Israel in the world of smart transportation. According to multiplereports and sources that have contacted TechCrunch, chip giant Intel is in the final stages of a deal to acquire Moovit, a startup that applies AI and big data analytics to track traffic and provide transit recommendations to some 800 million people globally. The deal is expected to close in the coming days at a price believed to be in the region of $1 billion.
We have contacted Nir Erez, the founder and CEO of Moovit, as well as Intel spokespeople for a comment on the reports and will update this story as we learn more. For now, Moovit’s spokesperson has not denied the reports and what we have been told directly.
“At this time we have no comment, but if anything changes I’ll definitely let you know,” Moovit’s spokesperson.
UV light kills viruses in air-borne droplets and of the three types of ultraviolet light – UV-A, UV-B and UV-C – UV-C is the most damaging.
UV-C can damage the nucleic acids within an organism and prevent it from replicating. Its use as a disinfectant is fairly common in hospital and laboratory settings.
In Israel, IAI engineers have been working on a system that can work autonomously and automatically in a plane, once given a plan of the aircraft or any other large space.
The triumph of Google’s AlphaGo in 2016 against Go world champion Lee Sedol by 4:1 caused quite the stir that reached far beyond the Go community, with over a hundred million people watching while the match was taking place. It was a milestone in the development of AI: Go had withstood the attempts of computer scientists to build algorithms that could play at a human level for a long time. And now an artificial mind had been built, dominating someone that had dedicated thousands of hours of practice to hone his craft with relative ease.
This was already quite the achievement, but then AlphaGoZero came along, and fed AlphaGo some of its own medicine: it won against AlphaGo with a margin of 100:0 only a year after Lee Sedol’s defeat. This was even more spectacular, and for more than the obvious reasons. AlphaGoZero was not only an improved version of AlphaGo. Where AlphaGo had trained with the help of expert games played by the best human Go players, AlphaGoZero had started literally from zero, working the intricacies of the game out without any supervision.
Given nothing more than the rules of the game and how to win, it had locked itself in its virtual room and played against itself for only 34 hours. It didn’t combine historically humanity’s built up an understanding of the principles and aesthetics of the game with the unquestionably superior numerical power of computers, but it emerged, just by itself, as the dominant Go force of the known universe.
The world’s largest meat processing company begins experimenting with machine learning in their plants. Developing and implementing these smart machines, capable of performing skilled and dexterous tasks, is pushing the current boundaries of automation.
JBS is the world’s largest meat processing company. With revenues of over $51 billion, it operates over 300 production units worldwide specializing in the processing of pork, beef, poultry, and lamb [1]. As meat and protein remain a mostly commoditized industry, JBS continually strives to maximize efficiency in all aspects of the value chain. To increase its processing efficiencies and worker safety, JBS bought a controlling share of New Zealand based Scott Technology, an automation and robotics company in late 2015 [2]. This move accelerated the implementation of machine learning in meat processing plants.
