During the twentieth century, discoveries in mathematical logic revolutionized our understanding of the very foundations of mathematics. In 1931, the logician Kurt Gödel showed that, in any system of axioms that is expressive enough to model arithmetic, some true statements will be unprovable1. And in the following decades, it was demonstrated that the continuum hypothesis -- which states that no set of distinct objects has a size larger than that of the integers but smaller than that of the real numbers -- can be neither proved nor refuted using the standard axioms of mathematics2–4. They identify a machine-learning problem whose fate depends on the continuum hypothesis, leaving its resolution forever beyond reach. Machine learning is concerned with the design and analysis of algorithms that can learn and improve their performance as they are exposed to data.
Two scientists at the University of Washington School of Medicine have developed a software program that represents the first use of deep artificial neural networks in squeak detection. University of Washington (UW) School of Medicine researchers have developed a software program to identify and decode rodent vocalizations. The DeepSqueak deep neural network converts audio signals into an image, or sonogram, which could be further refined with machine-vision algorithms developed for self-driving cars. Said the UW School of Medicine's Russell Marx, "DeepSqueak uses biomimetic algorithms that learn to isolate vocalizations by being given labeled examples of vocalizations and noise." According to co-developer Kevin Coffey, the program could distinguish between about 20 kinds of rodent calls.
The evolution of artificial intelligence (AI) -- from artificial narrow intelligence (ANI), through artificial general intelligence (AGI), to artificial super intelligence (ASI) -- is on its way to changing everything. It's expected that soon, artificial intelligence will combine the intricacy and pattern recognition strength of human intelligence with the speed, memory and knowledge sharing of machine intelligence. As the rise of AI continues, AI is challenging and changing not only the way humans live, learn and work, but also how entities across nations: its government, industries, organizations and academia (NGIOA) construct their commercial and economic industries and markets. With this technology driven growth of artificial intelligence, the need to do most manual, mathematical and mundane work is already in decline and will likely be greatly diminished in the coming years. Moreover, with all these new digital assistants and decision-making algorithms assisting and directing humans, more complex day-to-day work for humans is being greatly lessened.
A coming milestone in the automobile world is the widespread rollout of Level 4 autonomy, where the car drives itself without supervision. Waymo, the company spun out of Google's self-driving car research, said it would start a commercial Level 4 taxi service by late 2018, although that hadn't happened as of press time. And GM Cruise, in San Francisco, is committed to do the same in 2019, using a Chevrolet Bolt that has neither a steering wheel nor pedals. These cars wouldn't work in all conditions and regions--that's why they're on rung 4 and not rung 5 of the autonomy ladder. But within some limited operational domain, they'll have the look and feel of a fully robotized car.
When the Montour School District launched America's first Artificial Intelligence Middle School program in the fall of 2018, many questions arose. How? (Just to name a few). But, as a student-centered and future-focused district, the thought process was not if we should teach AI, but what if we don't teach AI? Also, why isn't everyone teaching AI? Through a series of courses developed and implemented by Montour team members and partners, the AI program officially launched in October 2018. To date, hundreds of class have already been taught to students in areas of AI Ethics, AI Autonomous Robotics, AI Computer Science, and AI Music. The goal for the program is to make an all-inclusive AI program for all middle school students that is relevant and meaningful in a world where children live and prepare them for a future where they will thrive.
In one of the iconic scenes from the teen movie "Fast Times at Ridgemont High," sun-baked stoner Jeff Spicoli has a double cheese and sausage pizza delivered to his classroom, boldly interrupting his uncompromising instructor mid-lecture. Spicoli was considered a mischievous airhead for flouting early-1980s dining etiquette, but he may actually have been way ahead of his time. More than three decades later, a California campus is embracing a kind of food delivery -- via robot. On Wednesday, students at University of the Pacific in Stockton, Calif., will be able to order snacks and beverages for the first time from a bright-colored roving robot on wheels known as the "Snackbot." Its stout body perched atop six small wheels, the electric Snackbot resembles some combination of an Igloo cooler and a Volkswagen Microbus.
When Google announced that it would absorb DeepMind's health division, it sparked a major controversy over data privacy. Though DeepMind confirmed that the move wouldn't actually hand raw patient data to Google, just the idea of giving a tech giant intimate, identifying medical records made people queasy. This problem with obtaining lots of high-quality data has become the biggest obstacle to applying machine learning in medicine. To get around the issue, AI researchers have been advancing new techniques for training machine-learning models while keeping the data confidential. The latest method, out of MIT, is called a split neural network: it allows one person to start training a deep-learning model and another person to finish.
Amazon SageMaker, Microsoft Azure ML Services, Google Cloud ML Engine, IBM Watson Knowledge Studio are examples of ML PaaS in the cloud. If your business wants to bring agility into machine learning model development and deployment, consider ML PaaS. It combines the proven technique of CI/CD with ML model management.
At least 21 such attacks have been leveled at Waymo vans in Chandler, as first reported by The Arizona Republic. Some analysts say they expect more such behavior as the nation moves into a broader discussion about the potential for driverless cars to unleash colossal changes in American society. "People are lashing out justifiably," said Douglas Rushkoff, a media theorist at City University of New York and author of the book "Throwing Rocks at the Google Bus." He likened driverless cars to robotic incarnations of scabs -- workers who refuse to join strikes or who take the place of those on strike. "There's a growing sense that the giant corporations honing driverless technologies do not have our best interests at heart," Mr. Rushkoff said.