Venture towns researching new how to#

Venture towns researching new how to#

“I don’t think there’s anybody that knows how to throw up more stores, more quickly, as these guys,” said Craig Johnson, founder of Customer Growth Partners, a retail consulting firm.īETTING ON BRICKS-AND-MORTAR No other retailer is opening more stores, faster, than Dollar General. It’s highly profitable, too, with enviable margins that top big-box chains like Walmart and Target as well as smaller rival Dollar Tree. Sales have risen every year since its IPO in 2009, during the depths of the financial crisis, with revenue growing six-fold to $34 billion. New stores deliver robust, after-tax returns of as much as 22%. The company’s stock price has nearly tripled in the last five years. Investors are on board with bricks-and-mortar, at least when done right. “This customer has no place else to go.”įorget the e-commerce era. Cohen, director of retail studies at Columbia University’s business school. The QAT API was previewed during a recorded session at the conference.“This is the corner grocery store on steroids,” said Mark A. The formal launch of the QAT API comes after the unveiling of TensorFlow Quantum, a machine learning framework for training quantum models, at the TensorFlow Dev Summit. “We are very excited to see how the QAT API further enables TensorFlow users to push the boundaries of efficient execution in their TensorFlow Lite-powered products as well as how it opens the door to researching new quantization algorithms and further developing new hardware platforms with different levels of precision,” wrote Google in a blog post.

Google says that by default, QAT API - which is a part of the TensorFlow Model Optimization Toolkit - is configured to work with the quantized execution support available in TensorFlow Lite, Google’s toolset designed to adapt models architected on its TensorFlow machine learning framework to mobile, embedded, and internet of things devices. This enables developers to convert a model trained with the API to a quantized integer-only TensorFlow Lite model, for example, or to experiment with various quantization strategies while simulating how quantization affects accuracy for different hardware backends. MetaBeat will bring together thought leaders to give guidance on how metaverse technology will transform the way all industries communicate and do business on October 4 in San Francisco, CA.Īside from emulating the reduced precision computation, QAT API is responsible for recording the statistics necessary to quantize a trained model or parts of it. (For example, all values in range might be represented in a single bucket.) Problematically, when the lossy numbers are used in several computations, the losses accumulate and need to be rescaled for the next computation. That’s because quantization squeezes a small range of floating-point values into a fixed number of information buckets, leading to information loss similar to rounding errors when fractional values are represented as integers. Often, the process of going from a higher to lower precision is noisy. The goal is to support the development of smaller, faster, and more efficient machine learning models well-suited to run on off-the-shelf machines, such as those in medium- and small-business environments where computation resources are at a premium. Google today released Quantization Aware Training (QAT) API, which enables developers to train and deploy models with the performance benefits of quantization - the process of mapping input values from a large set to output values in a smaller set - while retaining close to their original accuracy. Interested in learning what's next for the gaming industry? Join gaming executives to discuss emerging parts of the industry this October at GamesBeat Summit Next.