Elon Musk's so-called Department of Government Efficiency has deployed a proprietary chatbot called GSAi to 1,500 federal workers at the General Services Administration, WIRED has confirmed. The move to automate tasks previously done by humans comes as DOGE continues its purge of the federal workforce. GSAi is meant to support "general" tasks, similar to commercial tools like ChatGPT or Anthropic's Claude. It is tailored in a way that makes it safe for government use, a GSA worker tells WIRED. The DOGE team hopes to eventually use it to analyze contract and procurement data, WIRED previously reported.

OYSTER BAY, NY--Advances in machine learning technology will help propel sales of smart cameras for machine vision applications to 197 million units and a total value of $35 billion by 2027, according to global technology intelligence firm ABI Research. "The shift from machines that can automate simple tasks to autonomous machines that can'see' to optimize elements for extended periods will drive new levels of industrial innovation. This is the innovation that machine learning offers to machine vision. Machine learning can augment classic machine vision algorithms by employing the range and reach of neural network models, thus expanding machine vision far beyond visual inspection and quality control," explains David Lobina, artificial intelligence and machine learning analyst at ABI Research. Smart cameras, embedded sensors and powerful computers can bring machine learning analyses to every process step.

Deployed on quality content platforms, the world's first knowledge dashboard leverages innovative technology to show users how much they know about the topics they care about CRUX, the developer of Knowledge Quantification technology used, is launching Knowledge Hub for deployment on quality content platforms and publisher sites. Knowledge Hub provides each user with a complete view of all the topics they are reading about – and how much of each topic they have already covered. The Knowledge Hub shows users in real time the impact of important content they have not yet read – and provides a personalized knowledge journey of the best articles to increase their knowledge. Knowledge Hub is the latest user experience based on CRUX's innovative knowledge quantification technology that measures each user's knowledge based on the content they consume. Deployed on quality publishers like NIKKEI, Sifted and The American Prospect, the technology is revolutionizing user engagement, retention and conversion.

The latest KDnuggets poll reconfirms today's dire industry buzz: Very few machine learning models actually get deployed. In this article, I'll summarize the poll results and argue that this pervasive failure of ML projects comes from a lack of prudent leadership. I'll also argue that MLops is not the fundamental missing ingredient – instead, an effective ML leadership practice must be the dog that wags the model-integration tail. Considering the growing chatter about ML's failure to launch, there's been relatively little concrete industry research – especially when it comes to surveys on model deployment in particular rather than ROI in general – so I proposed this poll to Gregory Piatetsky and Matthew Mayo of KDnuggets. They helped me formulate and wordsmith it.

The code below is a boilerplate of image classification models seen elsewhere and has been retooled specifically for this dataset. For the dataset, I have built a web scraper using Beautiful Soup to download the images of top 50 dog breeds as reported in American Kennel Club. In total, there were 5000 images, 100 images per breed, allowing us to maintain the same distrubtion of training and validation dataset between classes. I have chosen ResNet34 over ResNet101, ResNet50 and ResNet18 as the model architecture here because of its optimal performance metrics (speed and accuracy). To faciltate model generalization, default data augmentation was applied to the training dataset using a batch size of 8. I have used a batch size of 8 here because of an'out of memory' error when 32 or 64 were used in AWS SageMaker notebook instance.

Much has been said about GPT-3 already. Traditionally, we start with data for a problem and develop the model based on the data. The model is specific to the problem. If you want to train a model to predict traffic patterns in New York, you build a model of New York traffic patterns. If you want to model air pollution in New York, that's a different model With GPT-3 you start with the model instead of the data.

The technological revolution of recent decades has made a deep impression on all kinds of businesses. Affecting everything from contact centre productivity to the delivery of healthcare, there's scarcely a sector of the economy that hasn't been radically transformed. Now comes the next phase of this technological revolution – artificial intelligence (AI). Having been a staple of science fiction since the 1950s, AI is now a real-life phenomenon. As a result, we stand on the precipice of a far-reaching digital transformation. One area where AI is already having seismic effects is that of the retention and use of data.

Deployed for AI, e-prop would require only 20 watts, approximately one-millionth the energy a supercomputer uses. Artificial intelligence models continue to grow in sophistication and complexity, adding to the need for more data, computation, and energy. To help combat increasing energy costs, researchers at TU Graz's Institute of Theoretical Computer Science have developed a new algorithm, called e-propagation (e-prop for short). E-prop mimics how neurons send electrical impulses to other neurons in our brain, which massively reduces the amount of energy human brains use, in comparison to machine learning. Deployed for AI, e-prop would require only 20 watts, approximately one-millionth the energy a supercomputer uses.

A major new effort is underway to use modern technology to fight human trafficking. It's a tool that may help clamp down on a growing problem that crisscrosses the globe. Human trafficking is an estimated $150 billion business with as many as 40 million victims worldwide. It's a big, evil business with human trafficking victims described as modern-day slaves. "They don't get to make the most basic decisions about their lives," said John Richmond, US ambassador-at-large to monitor and combat human trafficking.

The problem with merchandising today is obvious – no two stores are alike, and treating them alike requires an acceptance of the notion of acceptable financial loss,