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) …
Nintendo Co. has added Sharp Corp. as an assembler of its Switch console, according to people directly involved in the matter, as it works to stabilize production and hedge against U.S.-China trade tensions. The video game giant has struggled to produce enough units for most of this year as the hit game Animal Crossing: New Horizons and stuck-at-home consumers fueled demand. While the coronavirus outbreak hurt production early on, Nintendo President Shuntaro Furukawa said this month that output has returned to normal and the Switch is now made in Malaysia, in addition to existing China and Vietnam locations. That Malaysia factory is owned by Sharp, said the people, who asked not to be identified because the information isn't public. Nintendo's main assembly partner Foxconn Technology Co., a key unit of Foxconn Technology Group, owns a Sharp stake and helped connect the two Japanese companies, they added.
This is some Kodachrome level color voodoo – color grading and shot matching powered by machine-learning. And it comes from a collaboration with some friends of ours from the artist and live visual side, so it's doubly worth mentioning. What if the current techniques called AI turned out to be really important to creative artists – just not for the reason the general public expected? That's sure what Colourlab Ai looks like. It harnesses the powers of massive data crunching of pixels, the thing "AI" in the current generation was designed to do, and then applies it to making your video look amazing.
Multivariate Adaptive Regression Splines, or MARS, is an algorithm for complex non-linear regression problems. The algorithm involves finding a set of simple linear functions that in aggregate result in the best predictive performance. In this way, MARS is a type of ensemble of simple linear functions and can achieve good performance on challenging regression problems with many input variables and complex non-linear relationships. In this tutorial, you will discover how to develop Multivariate Adaptive Regression Spline models in Python. Multivariate Adaptive Regression Splines (MARS) in Python Photo by Sei F, some rights reserved.
One of the best things about this paper is that their code is completely available on GitHub and they even created two google colab notebooks for you to try on your own pictures! But first, let's just quickly see how they achieved that and more amazing results! Image-to-image translation is a super interesting task recently mostly involving GANs and frequently style transfer as well. They are powerful in such an application since the goal here is to transform a picture into another while conserving its property and only change the overall "style" of the image. The role of the GAN architecture coupled with style transfer is to learn a way to properly generate a new image based on an original image by training two networks simultaneously.
What this means is that information is organized in subjects. You might be studying math, finance, computer science, programming… every subject is organized into hierarchical sets of subcomponents. At the latest level, we have what I can define as information: the smallest atomic component of knowledge of non-fixed length that is enough to constitute a defined partition of a subject. For example, in statistics (subject), the normal distribution can be considered an argument. The same for derivatives, limits, integrals, functions… Each subject has a myriad of arguments located at different levels of the knowledge tree.
Researchers from Boston University and University of Virginia published a new paper in the Journal of Marketing that examines how consumers respond to AI recommenders when focused on the functional and practical aspects of a product (its utilitarian value) versus the experiential and sensory aspects of a product (its hedonic value). The study, forthcoming in the the Journal of Marketing, is titled "Artificial Intelligence in Utilitarian vs. Hedonic Contexts: The'Word-of-Machine' Effect" and is authored by Chiara Longoni and Luca Cian. More and more companies are leveraging technological advances in AI, machine learning, and natural language processing to provide recommendations to consumers. As these companies evaluate AI-based assistance, one critical question must be asked: When do consumers trust the "word of machine," and when do they resist it? A new Journal of Marketing study explores reasons behind the preference of recommendation source (AI vs. human).
The Joint Artificial Intelligence Center began in 2018 to accelerate the DOD's adoption and integration of artificial intelligence. From the start, it was meant to serve as an AI center of excellence and to provide resources, tools and expertise to the department. The JAIC's new director said that while the center's early efforts bore fruit, the overall effort was not transformational enough and a more aggressive approach is needed. "In JAIC 1.0, we helped jumpstart AI in the DOD through Pathfinder projects we called mission initiatives," said Marine Corps Lt. Gen. Michael S. Groen, during a briefing today at the Pentagon. "We learned a great deal and brought onboard some of the brightest talent in the business. When we took stock, however, we realized that this was not transformational enough. We weren't going to be in a position to transform the department through the delivery of use cases."
Neural networks are great at learning trends in both large and small data sets. However, data scientists have to be aware of the dangers of overfitting, which are more evident in projects where small data sets are used. Overfitting is when an algorithm is trained and modeled to fit a set of data points too closely so that it does not generalize well to new data points. Often, overfitting machine learning models have very high accuracy on the data sets they are trained on, but as a data scientist, the goal is usually to predict new data points as precisely as possible. To make sure that the model is evaluated based on how good it is to predict new data points, and not how well it is modeled to the current ones, it is common to split the datasets into one training set and one test set (and sometimes a validation set).
AMP Robotics, the recycling robotics technology developer backed by investors including Sequoia Capital and Sidewalk Infrastructure Partners, is close to closing on as much as $70 million in new financing, according to multiple sources with knowledge of the company's plans. The new financing speaks to AMP Robotics' continued success in pilot projects and with new partnerships that are exponentially expanding the company's deployments. Earlier this month the company announced a new deal that represented its largest purchase order for its trash sorting and recycling robots. That order, for 24 machine learning-enabled robotic recycling systems with the waste handling company Waste Connections, was a showcase for the efficacy of the company's recycling technology. That comes on the back of a pilot program earlier in the year with one Toronto apartment complex, where the complex's tenants were able to opt into a program that would share recycling habits monitored by AMP Robotics with the building's renters in an effort to improve their recycling behavior.
Concordia University in Irvine will discontinue its use of antigen testing for asymptomatic students and employees, after more than 50 false positives prompted unwarranted concern about a possible major coronavirus outbreak. As of Wednesday, university officials said there were six active cases -- four students and two employees -- on campus as opposed to the more than 60 infections reported two days ago. Testing in another six cases has not been confirmed, and 55 students and employees have been confirmed as negative for the virus, they said. Campus officials had canceled athletic practices and urged against out-of-state travel for Thanksgiving because of the erroneous test results, which were preliminary pending confirmation from an outside lab. The university previously had been posting only confirmed test results on its COVID-19 dashboard, but made an exception for the unconfirmed numbers because of the indication of a "potential outbreak."