On Saturday, a YouTube creator called ChromaLock published a video detailing how he modified a Texas Instruments TI-84 graphing calculator to connect to the internet and access OpenAI's ChatGPT, potentially enabling students to cheat on tests. The video, titled "I Made the Ultimate Cheating Device," demonstrates a custom hardware modification that allows users of the graphing calculator to type in problems sent to ChatGPT using the keypad and receive live responses on the screen. ChromaLock began by exploring the calculator's link port, typically used for transferring educational programs between devices. He then designed a custom circuit board he calls "TI-32" that incorporates a tiny Wi-Fi-enabled microcontroller, the Seed Studio ESP32-C3 (which costs about 5), along with other components to interface with the calculator's systems. This story originally appeared on Ars Technica, a trusted source for technology news, tech policy analysis, reviews, and more.

Most previous research on image categorization has focused on medium-scale data sets, while large-scale image categorization with millions of images from thousands of categories remains a challenge. With the emergence of structured large-scale dataset such as the ImageNet, rich information about the conceptual relationships between images, such as a tree hierarchy among various image categories, become available. As human cognition of complex visual world benefits from underlying semantic relationships between object classes, we believe a machine learning system can and should leverage such information as well for better performance. In this paper, we employ such semantic relatedness among image categories for large-scale image categorization. Specifically, a category hierarchy is utilized to properly define loss function and select common set of features for related categories. An efficient optimization method based on proximal approximation and accelerated parallel gradient method is introduced. Experimental results on a subset of ImageNet containing 1.2 million images from 1000 categories demonstrate the effectiveness and promise of our proposed approach.

Automated reasoning in geometry is available in various software tools for several years, mostly in prover packages. In this paper we pay our attention to a non-trivial presence of a geometry prover in the software tool GeoGebra Discovery [4, 5] that aims at reaching secondary schools with its intuitive user interface. Most importantly, we give a report on a STEM/STEAM project that was discussed in a group of prospective mathematics teachers at the Private University College of Education of the Diocese of Linz in Upper Austria during the winter semester 2022/23, in the frame of a course that focuses on exploiting technology in mathematics education (36 students in 2 working groups). This project consisted of several other experiments that were already communicated by the second author. The discussed activity, a detailed study of the movement of a rocking camel, is however, completely new. Also, some major improvements in the underlying software tool (implemented by the second author with a substantial help of the students' feedback), makes it much easier to model similar project setups and conclude mathematical knowledge in an automated way.

Autoscaling is a vital mechanism in cloud computing that supports the autonomous adjustment of computing resources under dynamic workloads. A primary goal of autoscaling is to stabilize resource utilization at a desirable level, thus reconciling the need for resource-saving with the satisfaction of Service Level Objectives (SLOs). Existing proactive autoscaling methods anticipate the future workload and scale the resources in advance, whereas the reliability may suffer from prediction deviations arising from the frequent fluctuations and noise of cloud workloads; reactive methods rely on real-time system feedback, while the hysteretic nature of reactive methods could cause violations of the rigorous SLOs. To this end, this paper presents OptScaler, a hybrid autoscaling framework that integrates the power of both proactive and reactive methods for regulating CPU utilization. Specifically, the proactive module of OptScaler consists of a sophisticated workload prediction model and an optimization model, where the former provides reliable inputs to the latter for making optimal scaling decisions. The reactive module provides a self-tuning estimator of CPU utilization to the optimization model. We embed Model Predictive Control (MPC) mechanism and robust optimization techniques into the optimization model to further enhance its reliability. Numerical results have demonstrated the superiority of both the workload prediction model and the hybrid framework of OptScaler in the scenario of online services compared to prevalent reactive, proactive, or hybrid autoscalers. OptScaler has been successfully deployed at Alipay, supporting the autoscaling of applets in the world-leading payment platform.

It can be tiring calling on Amazon's virtual assistant for every command to turn on a variety of smart home devices in succession. But did you know you can set up Alexa routines and create a single voice command that triggers a series of different actions? To design routines, you need to use the Alexa app. Once you make one, you can use an Alexa speaker, smart display, or another smart home device with built-in Alexa to trigger a routine. You can also schedule routines, or trigger them via third-party smart home devices, by dismissing alarms, or with Echo buttons.

Yesterday's hearing with the CEOs of Google, Facebook and Twitter was as messy as you might have predicted -- and we'll get into that below. But I want to start today with voice assistants. Alexa, Siri, Google Assistant and the rest know a lot. But they barely remember anything, which curtails exactly how much they can truly assist with your life beyond music controls, timers and reminding you exactly what shows you've seen Joel Kinnaman in. A new feature spotted for Google Assistant on Android called Memory could transform it into the assistant we need, however.

One way to build out a smart home is to buy lots of components--sensors, smart bulbs, security cameras, speakers, and whatnot--and connect them all to a hub that helps them communicate with each other and with you, via your smartphone. But let's be real: That can involve spending a lot of money and investing a lot of time. If your wants and needs are simpler, just a few relatively inexpensive products will deliver most of the conveniences a high-end smart home can deliver, and on a much more modest budget. And if you make sure those smart home products are compatible with each other, you'll build a solid foundation that you can expand over time. The key is knowing which smart home products don't depend on a smart home hub to operate.

IFTTT has brought us plenty of cool tricks -- from allowing your Roomba or TomTom GPS to talk to your smart home devices to integrating with Domino's pizza tracker -- and of course, users have been able to build their own "if this then that" applets. Now, IFTTT is ready to move beyond "if this then that," so it's introducing IFTTT Pro, which will allow users to create applets with queries, conditional logic and multiple actions. For instance, IFTTT says Pro can create an applet that, in the evenings, queries both your Google Calendar and Slack before turning on your Philips Hue lights and playing your evening Spotify playlist. While this is a useful service, IFTTT is making a "big and potentially controversial change." It will charge $9.99 per month for Pro, and subscribers will be able to create unlimited Applets. But its regular, free plan will now limit users to creating just three Applets.

The days of creating as many free IFTTT applets as you want are over, with IFTTT announcing that--like so many other formerly free services--it's going the subscription route. The IFTTT Pro plan, which will allow subscribers to create an unlimited number of applets with new and more advanced features, will ultimately cost $10 a month, although if you sign up before October 7, you'll be able to pay what you want for a year. Meanwhile, IFTTT's "free forever" plans will now cap the number of applets you can create to just three, and if you've already created more than three applets, you'll need to pick which three you want to keep if you're planning on sticking with a free account. IFTTT built a loyal following over the years, with its users building and sharing millions of free applets that connect a wide variety of online services and gadgets, ranging from Google Calendar and Twitter to Roombas and Philips Hue smart bulbs. Free users will still be able to use as many IFTTT community applets as they like, but the new three-applet creation cap poses a dilemma for those who've built a number of IFTTT applets of their own.

Rapid advances in AI technologies mean marketing automation is no longer optional. Not all content marketing tasks can or should be automated. No matter how far AI advances go, software will never replace a human for crafting an insightful and meaningful blog post. But as you go through your daily content tasks, it's worth asking, "Can this be automated?" Chances are, many of them can.