PCWorld explores strategic AI delegation, emphasizing three key questions to determine which tasks are appropriate for AI assistance versus human decision-making. The guide matters for productivity-focused users seeking to leverage AI chatbots like ChatGPT, Claude, and Gemini for tedious data compilation and repetitive tasks while avoiding over-reliance. Key recommendations include delegating boring, repeatable tasks like email management and voice memo transcription to AI, but retaining all final decision-making authority for yourself. You know what's harder than making decisions?

Let's start with a game. Open up your chatbot of choice--Claude, ChatGPT, Gemini--and type "Give me a random number between 1 and 10." You're going to get 7. Almost always. Now type "Another" and you'll get 3 or 4. Type "Another" again and you'll get 8 or 9. That won't work every time--but if it did for you, you may wonder if I have superpowers.

An essential catalyst for the remarkable breakthroughs in AI that led to the modern large language models (LLMs) such as ChatGPT and Gemini has been the algorithms used to train these models on massive datasets. While the LLM architectures have gotten progressively more complex, the training algorithms have stayed relatively simple, and in fact, they have all been based on the decades-old paradigm of stochastic gradient descent (SGD). The key idea behind SGD is that in order to minimize a certain objective function (such as an LLM's error on the training data), it suffices to access only a noisy estimate of that objective at any given time (e.g., based on a small sample of the data) while making incremental progress towards the solution. This is essential for LLM training, as the datasets have become so massive one could not hope to perform computations on everything all at once. Commonly attributed to a 1951 paper by Robbins and Monro [34], SGD has seen a resurgence of interest over the last 20 years by AI researchers and computer scientists striving to understand its effectiveness, leading to numerous variants and extensions used in modern LLMs [12, 9], most notably the Adam algorithm [25]. As a result, we have gained a robust mathematical understanding of the computational complexity of SGD algorithms in a wide range of settings (e.g., see [11, 15, 5, 17]). Yet, despite this progress there is a surprising gap in the understanding of SGD: The complexity of an algorithm proposed by Stefan Kaczmarz in 1937 [24] for solving a system of linear equations - the oldest published example of an SGD algorithm, which predates Robbins and Monro's paper by over a decade - has not been settled.

Here's how to roll back to Google Assistant on your Android phone Here's how to roll back to Google Assistant on your Android phone Go back to a (slightly) simpler time. So, you're here because Gemini is driving you nuts. It's a pretty common sentiment about the AI assistant, especially for those who use their phones to control their smart home devices. Even if you don't use your Android phone to control your smart home, you may still want to get rid of Gemini because it takes too long to respond or it can't even understand your commands in the first place. Maybe it gives you inaccurate weather forecasts and information about nearest establishments.

Google's first new smart speaker in six years is here and once again leads its competitors--now with paywalled features. Sounds a little more human than competitors. Gemini is helpful and smart. Some assistant features are hidden behind paywalls. Works best if you buy or have bought several Google devices for your home.

The question is whether Gemini is a good enough smart home assistant. Somehow, it's been almost nine months since Google first revealed its latest smart speaker, the boringly-named Google Home Speaker. I don't understand why it took the company so long to get it to market, as there's nothing that particularly changes the game here hardware-wise. The $99 orb looks a lot like Apple's HomePod mini, with a speaker that fires audio in 360 degrees and microphones so you can chat with Google Assistant Gemini for Home. It's the definition of an expected, iterative piece of hardware -- but on the other hand, it's been almost six years since Google released the Nest Audio and seven since the Nest Home Mini.

Upgrades include 360-degree audio and deeper integration with Gemini. Last fall Google teased that it was working on an all-new Google Home speaker due out sometime in mid 2026. And while it took a tiny bit longer than expected, today the company began taking pre-orders for its latest speaker ahead of its official on-sale date of June 25. While the new Google Home serves a similar role to the existing Nest Audio, including the ability to function as linked stereo speakers when paired with a second unit, there are a number of other design changes and upgrades. Instead of relying on directional sound, the Google Home was created to deliver clear 360-degree audio in an even more compact chassis.

Arriving six years after Google's last smart speaker, the new HomePod-style device was redesigned to play host to Gemini's chatbot. The last time Google released a smart speaker, the world was in the throes of a pandemic . Yes, it's been six years since the company trotted out a dedicated speaker. However, this newest Google Home Speaker brings a big change with it: The device has been redesigned to showcase the new Gemini assistant instead of the Google Assistant that powered all previous speakers and smart displays. Google announced the speaker last fall alongside new Nest smart home cameras and video doorbells, promising a spring 2026 launch.

A.1 Comparison of Results for Gemini with Different Format Transformations Gemini attained optimal performance metrics for sensitive category and format transformation scenarios tasks, surpassing all comparator models in maximum achievable performance. The focus was then placed on Gemini's ability to recognize and restore both original and transformed data. The experimental results are shown in Table 1. In the main text section Experiments, due to space constraints, only four key observations were analyzed, as follows: i) The LRAcc and DRAcc of total format transformed data is less than original data, which indicates that it is more difficult to recognize and restore data after format transformed. These transformations only affect numbers, and only the IMEI and IMSI (purely numeric) sensitive categories support such transformations. Due to the lack of contextual information in the sample data, large language models may confuse these with personal identifiers, mobile numbers, and MEID.

The remarkable progress in text-to-video diffusion models enables the generation of photorealistic videos, although the content of these generated videos often includes unnatural movement or deformation, reverse playback, and motionless scenes. Recently, an alignment problem has attracted huge attention, where we steer the output of diffusion models based on some measure of the content's goodness. Because there is a large room for improvement of perceptual quality along the frame direction, we should address which metrics we should optimize and how we can optimize them in the video generation. In this paper, we propose diffusion latent beam search with lookahead estimator, which can select a better diffusion latent to maximize a given alignment reward at inference time. We then point out that improving perceptual video quality with respect to alignment to prompts requires reward calibration by weighting existing metrics. This is because when humans or vision language models evaluate outputs, many previous metrics to quantify the naturalness of video do not always correlate with the evaluation. We demonstrate that our method improves the perceptual quality evaluated on the calibrated reward, VLMs, and human assessment, without model parameter update, and outputs the best generation compared to greedy search and best-of-N sampling under much more efficient computational cost.