Traditional machine learning met hods, such as support vector machines (SVM), artificial neural networks (ANN), and logistic regression, have been employed to classify dates based on morphological features like color, t exture, and shape. While effective, these approaches often lack the flexibility and comprehensive quality control needed in modern agricultural practices. To address these limitations, the SmartDate system represents a significant technological advancement by integrat ing deep learning with genetic algorithms and reinforcement learning. This AI-driven system not only excels in date fruit classification but also predicts expiration dates, filling a cruci al gap in existing solutions. SmartDate leverages multispectral and hyperspectral imaging, coupled with Visible-Near-Infrared (VisNIR) spectral sensors, to assess key quality indicators such as moisture c ontent, sugar levels, firmness, and internal defects. This allows for a more thorough evaluation of fruit quality compared to co nventional methods. Moreover, the inclusion of reinforcement learning e nables SmartDate to adapt in real-time to production envir onment changes, optimizing sorting accuracy and ensuring t hat only premium quality dates reach the market.

Like many of you, I receive a variety of notifications by various means. Postal letters, email reminders, pop-ups on my laptop, audio signals on my mobile, highlighted chat application entries, text messages, phone calls, taps on the shoulder--the list is long! Thinking a bit more about this, one of the purposes of notification is to resynchronize otherwise asynchronous processes. You tell Google Assistant to set a timer for 15 minutes and go off to do something else. After 15 minutes, you get an audible reminder that the 15 minutes are up, and you should turn off the spaghetti before it turns to mush.

Our research addresses the critical challenge of managing blood transfusions and optimizing allocation in resource-constrained regions. We present heuristic matching algorithms for donor-patient and blood bank selection, alongside machine learning methods to analyze blood transfusion acceptance data and predict potential shortages. We developed simulations to optimize blood bank operations, progressing from random allocation to a system incorporating proximity-based selection, blood type compatibility, expiration prioritization, and rarity scores. Moving from blind matching to a heuristic-based approach yielded a 28.6% marginal improvement in blood request acceptance, while a multi-level heuristic matching resulted in a 47.6% improvement. For shortage prediction, we compared Long Short-Term Memory (LSTM) networks, Linear Regression, and AutoRegressive Integrated Moving Average (ARIMA) models, trained on 170 days of historical data. Linear Regression slightly outperformed others with a 1.40% average absolute percentage difference in predictions. Our solution leverages a Cassandra NoSQL database, integrating heuristic optimization and shortage prediction to proactively manage blood resources. This scalable approach, designed for resource-constrained environments, considers factors such as proximity, blood type compatibility, inventory expiration, and rarity. Future developments will incorporate real-world data and additional variables to improve prediction accuracy and optimization performance.

Over the past few months, some of the most prominent people in AI have fashioned themselves as modern messiahs and their products as deities. Top executives and respected researchers at the world's biggest tech companies, including a recent Nobel laureate, are all at once insisting that superintelligent software is just around the corner, going so far as to provide timelines: They will build it within six years, or four years, or maybe just two. Although AI executives commonly speak of the coming AGI revolution--referring to artificial "general" intelligence that rivals or exceeds human capability--they notably have all at this moment coalesced around real, albeit loose, deadlines. Many of their prophecies also have an undeniable utopian slant. First, Demis Hassabis, the head of Google DeepMind, repeated in August his suggestion from earlier this year that AGI could arrive by 2030, adding that "we could cure most diseases within the next decade or two."

This article was originally published by The Conversation. Have you ever bitten into a nut or a piece of chocolate expecting a smooth, rich taste only to encounter an unexpected and unpleasant chalky or sour flavor? That taste is rancidity in action, and it affects pretty much every product in your pantry. Now artificial intelligence can help scientists tackle this issue more precisely and efficiently. We're a group of chemists who study ways to extend the life of food products, including those that go rancid.

Google's Chromebook has become ubiquitous in classrooms across the United States, often considered the go-to option for digital learning given its relative affordability and web-based programs -- a combination that proved even more valuable for distance learning during the height of the COVID-19 pandemic. Since Chromebooks' launch more than a decade ago as a cheaper alternative to tablets, their use has expanded exponentially in schools nationwide, providing more students a personal computer device -- including in many low-income districts. And although issues of internet connection and at-home access to devices persist, new improvements to the Chromebook could help stretch its lifetime and scope. Google recently announced plans to expand Chromebooks' automatic updates up to 10 years, maximizing the potential lifespan of the devices that have become key for both in-school lessons and after-school studies. Beginning next year, the change will automatically apply to all Chromebooks launched in 2021 or later, and for devices released before 2021 there will be an option to extend the updates to 10 years from the platform's original release, Google officials said.

On May 14, the AI Facebook Research Team has published an article regarding Expire-Span, a human-brain like algorithm which learns to forget the irrelevant information. Expire-Span, a method that learns to retain the most important information and expire the irrelevant information. In practice, Expire-Span is a deep learning algorithm, which first predicts the most relevant information for a given task, and then it equips every information with an expiration date, namely a deadline. When the date expires, the associated information is forgotten. This aspect permits the Expire-Span algorithm to be very scalable in terms of memory.

Today's AI technology, much like humans, learns from examples. AI systems are developed on datasets containing text, images, audio, and other information that serve as a ground truth. By figuring out the relationships between these examples, AI systems gradually "learn" to make predictions, like which word is likely to come next in a sentence or whether objects in a picture are inanimate. The technique holds up remarkably well in the language domain, for example, where systems like OpenAI's GPT-3 can write content from essays to advertisements in human-like ways. But similar in character to humans, AI that isn't supplied fresh, new data eventually grows stale in its predictions -- a phenomenon known as model drift.

Unlike human memory, most neural networks typically process information indiscriminately. On a small scale, this is functional. But current AI mechanisms used to selectively focus on certain parts of their input struggle with ever-larger quantities of information, incurring unsustainable computational costs. For this reason, Facebook researchers want to help future AIs to pay more attention to important data by assigning an expiration date. They announced the development of a novel method in deep learning, called Expire-Span, a first-of-its-kind operation that equips neural networks with the ability to forget at scale.

Computers are just too good at remembering all the stuff we teach them. Normally, that's fine; you wouldn't want the systems that maintain your medical or financial records to start randomly dropping 1s and 0s (OK, well maybe the one that tracks your credit card debt, but other than that). However, these systems generally do not discriminate between information sources, meaning every bit of data processed with equal vigor. But as the amount of information available increases, AI systems must expend more and more finite computing resources to handle it. Facebook researchers hope to help future AIs pay better attention by giving data an expiration date.