The company would focus on introducing smart range of products with artificial intelligence (AI) and internet of things (IoT) in the domestic market here, which would be the "basic infrastructure" of its offerings. TCL, which has also forayed into the home appliances segment, plans to introduce smart range of products and would launch an air-conditioner this season with AI and IoT features. "In 2019, the sales of our Android AI TVs grew by 300 per cent. The same feat elevated our status as the fourth largest TV brand in India. Empowered by a great response from our customers, we are eyeing the number three slot in the Indian smart TV segment," TCL India Country Manager Mike Chen told .
There is nothing new about the fierce competition going out there across the globe; the fear of staying behind compels each one of us to ride the growth in your business you have to adapt to market trends. But the question is what does it mean to adapt to market trends? Over a span of years, disruptive technologies such as the Internet of Things (IoT), artificial intelligence, AR/VR seems to have created a huge impact on our lives. Everything seems changed right from the way we view, use, analyze and most important of all interact with these smart devices especially in the profit-spinning realm. Days have come where we are able to witness how internet-connected virtual assistants, appliances, security systems and more can all communicate and coordinate with each other, allowing business owners to automate as well as streamline mundane, time-consuming activities.
Newswire) VSBLTY Groupe Technologies Corp. (CSE: VSBY) (5VS.F) (VSBGF) ("VSBLTY"), a leading retail software technology company, and Energetika, an international provider of "intelligent lighting" solutions, have begun deployment of their smart city security contract which combines Energetika's smart lighting with VSBLTY's crowd analytics and facial recognition to help keep Mexico City's neighborhoods safe. Energetika CEO Rodrigo Calderon said, "We have begun phase one deployment of security kits covering up to 40,000 endpoints throughout 56 communities in Mexico City beginning in the boroughs of Miguel Hidalgo, Cuajimalpa, Benito Juarez and Cuauhtemoc. Each neighborhood security kit consists of high definition cameras equipped with VSBLTY facial recognition and analytics, wireless alarms, motion sensors and panic buttons integrated with high LED facade light fixtures. This low cost system runs off local citizens' internet service and is accessible on their mobile devices in real time. With this unique security kit deployment model perfected, we have introduced this program to other Central and South American municipalities whose needs are equally compelling and where this cost-efficient solution can be installed in three million security cameras or more."
What I find most exciting of all is the fact that it is almost impossible to imagine what kind of services will emerge out of the convergence of such technologies in 7-8 years and beyond. Since the dawn of the industrial revolution, technology has always stepped in to fill gaps in human productivity. We have consistently been able to fill the gaps of connectivity, information, communication... but there is now one gap that is bigger than them all: Trust. Never have we been able to trust each other without going through a hundred loops, and STILL we are consistently paranoid about who we deal with, what we purchase, etc. Once blockchain matures and we manage to tame key aspects such as quantum-proof security, scalability, and interoperability... we will be living in a world that looks nothing like this one.
After 3 hours of Googling, I have to ask you guys. I'm looking for an app or command-line tool that is able to increase resolution using AI. Something like Let's Enhance but free. I know about Alex J. C.'s neural-enhance but my PC is not able to run Docker. And without Docker, the installation is super complex. Also, I don't have Nvidia graphics card that supports CUDA.
I'm wondering if there has been any work done on CNNs with each filter pooler trying to match a (scalar) value fed back from the most immediate downstream neuron, where this feedback value is ultimately anchored to some cost function on the top layer. I'm imagining a simple architecture where the goal is to derive the type of "inverse graphics" transformation that Hinton speaks of. The top layer feedback values could even be some affine transformation of the input data.
A few months ago I stumbled onto an interesting idea while listening to the TWiML & AI podcast. It described a process by which one could attempt to introduce confusion into a network (starting at any arbitrary hidden layer) so that it couldn't learn from select biases in the training data. For example, if you were training an image classification network, and you wanted to forbid the network to learn anything about race, you could use this technique, to do so. The problem is that I can't for the life of me remember what this technique is called, or what episode of the podcast it was discussed in.
It was the Newton-Raphson method for finding roots of an equation. I thought this method mostly applies for minimization in machine learning as cost is always defined as a positive real valued function. To relate this update equation with the title: if we consider the update portion of the equation - g(x, y) (y * x) / (y x2); y 0 It is quite similar to adam since there is a square gradient term in the denominator and the gradient term in the numerator. WIth the equation that I have mentioned, the hypothesis is that this decay is kind of estimating the cost term itself. Please let me know what you think about this hypothesis and what it's implications are.
With its long-term accumulation in the areas of software-defined processors and heterogeneous computing, multi-core heterogeneous processor platform launched by Wuxi DSP Technologies has advanced performance power ratio with ease to the software development. The experience of multiple company partners shows that in a short period of time, a small-scale R & D team can quickly develop industry-leading innovations, dramatically shorten the product development cycle, reduce project investment risks and improve market response ability.
I like the DenseNets paper a lot. Regardless of what you think of the architecture (I've seen a great deal of seemingly irrational hatred for DenseNets around here, and while I don't think it's the end-all be-all architecture, I'll gladly argue its case) the paper is straightforward, easy to read, and an excellent modern reference for a lot of the quirks of CNN design.