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When you think of innovative organizations, the U.S. Postal Service is probably not at the top of your list. In fact, the post office has sort of become a metaphor for an old, inefficient way of doing things -- think of the term "snail mail." Do people even buy stamps anymore? And when was the last time you, dear reader, stepped foot in a post office? Well, many people still do.

I've managed to get the model running generation on my PC. One thing needed to point out is that the checkpoint can NOT be loaded exactly as the GPT-2 model checkpoint from Huggingface pytorch-transformer repository. You'll also need to manually define the config, e.g. The generation works just fine by a nucleus sampling approach, and once in a while an E-O-T will be given to indicate end of one post. Bot: they're having an open gym soon in June... dont think they'll be up there this time though Bot: So what's this gym called?

In the age of information technology, more and more people--young and old alike–don't have problems sharing their personal information online, sometimes, even private ones. And it seems like there's no big deal when it comes to biometric identity, either. Every day, people from all over the world use facial or fingerprint recognition to unlock phones and log in to apps and games. They also want to be tagged in friends' photos, enabled by identification algorithms used by Facebook and Google. From employee IDs to national IDs, to digital and airport security, biometric identification and authentication are proliferating. The biometric signatures of a person characterize their physiological or behavioral characteristics.

Five years ago today, Amazon surprised the tech press by unveiling Alexa and the Echo, its first smart speaker. "Well this one came out of nowhere," was the reaction of Verge reporter Chris Welch. Five years later, Alexa is a household name -- literally. If you have children and an Alexa device, they probably know how to ask the robot to play their favorite songs at breakfast. You might have Alexa in your kitchen, on your bedside table, or even in your car. But half a decade after Alexa launched, there's a lingering question: has Amazon's assistant actually become more useful?

This 5-hour course is created to take you by hand and teach you how to tackle the most fundamental building blocks of practical data science: data wrangling and visualization. It will equip you to use some of the most important Python data wrangling and visualization packages such as Seaborn. You will also be able to decide which wrangling and visualization techniques are best suited to answer your research questions and applicable to your data and interpret the results. Minerva Singh is a PhD graduate from Cambridge University where she specialized in Tropical Ecology. She is also a Data Scientist on the side.

One industry that has thrived on its smart use of "machine learning" is the ride-hailing platform Grab. Its backend AI technology has enabled it to tweak and develop a more reliable platform for drivers and passengers. Thanks to the vast amount of GPS trails the platform have collected, the company has developed a more accurate map on the app for drivers and users, giving them better estimates on travel time and routes. In the future, Grab is also looking into developing a new feature that will enable the platform to turn photos sent by users into accurate addresses, helping drivers to locate passengers even better. Again, it's a technology that benefits all parties on the platform -- passengers can get home faster, while drivers can earn more from completing more trip requests.

Faces are far from the only thing artificial intelligence researchers want computers to recognize. One Japanese tech company has created an AI-powered camera system, called BakeryScan, that is able to recognize different types of breads and pastries. Developed by Japanese tech and media firm Brain Co., the scanning tools are currently installed in more than 400 retail locations across Japan. One of the technology's biggest clients is the Andersen Group, a chain of high-end Danish-inspired bakeries that operate across Japan, according to a report from The Asahi Shimbun. BakeryScan works through a camera that's mounted above a backlit checkout tray.

This will be an important topic discussed at POWERGEN International only two weeks away in New Orleans. Click here to learn more! Today, utility engineers spend a significant portion of their time completing repetitive administration tasks. Some organizations estimate that upwards of 40 percent of the time of highly trained engineers is spent on these mundane tasks. The maturation of artificial intelligence (AI) techniques such as machine learning and natural language processing (NLP) has made them increasingly viable for use in automating more complex and higher impact tasks.

DarwinAI's Generative Synthesis technology – the byproduct of years of scholarship from the University of Waterloo – uses AI itself to observe a neural network as it is being trained and then generates a number of new and highly optimized versions of said network. The result are new and entirely unique networks that are not only significantly smaller than the original, but infer faster without sacrificing functional accuracy. Moreover, the understanding garnered by the engine enables explainable deep learning, whereby the platform can illuminate how a network is reaching its conclusions across an entire dataset or a singular inference. The benefits of DarwinAI's Generative Synthesis platform are threefold: In addition, the Darwin platform is complementary to low-level hardware optimization techniques found in toolkits such as Movidius, making it is essential for deploying deep learning applications to Intel chipsets. DarwinAI's proprietary IP is the product of years of scholarship from the University of Waterloo.