- Drive.ai never set out to build the best self-driving car.
- Instead of striking early deals with car manufacturers to see who can roll out the most robust working product—though Drive.ai says it has some secret deals in place already—the company has focused on creating the best possible, fully autonomous self-driving AI “brain” in the world.
- The company’s test vehicles have shown promising results driving in cloudy and rainy conditions that continue to stump other self-driving cars, bringing the world that much closer to the true prize in self-driving tech: the ability to eliminate the human altogether.
- For now, drivers are expected to maintain awareness at all times, ready to take control of mostly self-driving cars if conditions make the automated system screw up.
- And by lowering the price tag on self-driving from the cost of a new futuristic vehicle to that of a retrofit kit that can hook up with your old Toyota Tercel, Drive.ai could create a mass market overnight.
Using a simple kit users can make their cars self-driving if Drive.Ai is able to make these work for all car models on the road.
Continue reading “The Startup Rushing to Usher in the Self-Driving Era Even Faster”
- That may be true, but according to a Valve spokesperson writing in the thread, it wouldn’t be the best approach.
- “Instead, you’d want to take a machine-learning approach, training (and continuously retraining) a classifier that can detect the differences between cheaters and normal/highly-skilled players.”
- “The process of parsing, training, and classifying player data places serious demands on hardware, which means you want a machine other than the server doing the work.
- And because you don’t know ahead of time who might be using this kind of cheat, you’d have to monitor matches as they take place, from all ten players’ perspectives.”
- The spokesperson continued: “There are over a million CS:GO matches played every day, so to avoid falling behind you’d need a system capable of parsing and processing every demo of every match from every player’s perspective, which currently means you’d need a datacenter capable of powering thousands of CPU cores.”
Anti-cheat software has a lot of weight to pull in the modern age, with few major games going to market without some form of online competitive mode. Detecting and smiting cheaters is a thankless task too, with most folk ignoring anti-cheat technology unless it stops working effectively. Typically enough, Valve has a new approach in mind.During a discussion on the Counter-Strike: Global Offensive Reddit page, one user asked why Valve doesn’t implement auto-detection for spinbots – bots that literally spin on the spot, auto-killing every player in range. Other users posit quite reasonably that it wouldn’t be hard to detect this supernaturally quick and effective player behavior. That may be true, but according to a Valve spokesperson writing in the thread, it wouldn’t be the best approach.”So some bad news: any hard-coded detection of spin-botting leads to an arms race with cheat developers – if they can find the edges of the heuristic you’re using t
Continue reading “Valve wants to take a ‘machine learning’ approach to Counter-Strike anti-cheat”
- In a nutshell, InfoGAN is able to generate representations that contain information about the dataset in an unsupervised way.
- This model achieves state of the art results in all but POS tagging (where it came out in second place).
- It goes a step further, training a single model that is able to translate between multiple pairs of languages.
- Called Generative Adversarial Networks , it has enabled models to tackle unsupervised learning.
- The model is trained separately for each pair of languages like Chinese-English.
Deep Learning has been the core topic in the Machine Learning community the last couple of years and 2016 was not the exception. In this article, we will go through the advancements we think have contributed the most (or have the potential) to move the field forward and how organizations and the community are making sure that these powerful technologies are going to be used in a way that is beneficial for all.
Continue reading “The major advancements in Deep Learning in 2016”
- Google Machine Learning Models for Image Captioning Ported to TensorFlow and Open-Sourced
- The model comes up with a caption that hadn’t previously existed.
- The model appears to address this problem by introducing a fine-tuning phase that allows the model to extract information useful for describing details of objects, exclusive of the classification phase.
- Google chronicled their journey over the past few years with their announcement around open-sourcing a TensorFlow model for image captioning, and some of the testing for comparing accuracy and performance benchmarks between the new approach and existing implementations.
- It splits the image classification phase for identifying objects from another phase that adds adjectives and prepositional phrases, and from a phase in which the model gives the caption structure to make it more syntactically correct and humanlike.
As TensorFlow becomes more widely adopted in the machine learning and data science domains, existing machine learning models and engines are being ported from existing frameworks to TensorFlow for improved performance, furthering the adoption and success of the open-sourced project.
Continue reading “Google Machine Learning Models for Image Captioning Ported to TensorFlow and Open-Sourced”