We Asked Coffee Pros to Blind Test Coffee Machines. For our latest WIRED Blind Test, we sat coffee industry professionals down to rank leading do-it-all coffee machines--and the winner wasn't what anyone expected. What do you love about coffee? Is it the caffeine boost in the morning, the creamy sweetness of a cappuccino or latte, the bucket of filter coffee you can sip on all day, or the quick kick of a good espresso? Or is it the zen-like ritual of it all, the measuring of beans and the precision of the perfect extraction? Good thing it's much better for you than science previously realized.

Sunday Robotics has a new way to train robots to do common household tasks. The startup plans to put its fully autonomous robots in homes next year. Memo may not be the world's fastest barista, but it is impressive--for a robot. I recently watched as Memo, a new home robot from a company called Sunday Robotics, made coffee in an open-plan kitchen in Mountain View, California. Memo looks like something out of Wall-E, with a gleaming white body, two arms, a friendly cartoonish face, and a red baseball cap.

Since 2004, the number of American adults who've enjoyed a daily cup of joe has increased 37 percent. Breakthroughs, discoveries, and DIY tips sent every weekday. Whether you're an early bird or a night owl, coffee is probably part of your daily routine. Since 2004, the number of American adults who've enjoyed a daily cup of java has jumped up 37 percent, the highest level in more than 20 years, according to the National Coffee Association . But coffee is hardly a new invention.

This work presents an optimization method for the synthesis of finite state machines. The focus is on the reduction in the on-chip area and the cost of the circuit. A list of finite state machines from MCNC91 benchmark circuits have been evolved using Cartesian Genetic Programming. On the average, almost 30% of reduction in the total number of gates has been achieved. The effects of some parameters on the evolutionary process have also been discussed in the paper.

Large Language Models (LLMs) are showcasing impressive ability in handling complex reasoning tasks. In real-world situations, problems often span a spectrum of complexities. Humans inherently adjust their problem-solving approaches based on task complexity. However, most methodologies that leverage LLMs tend to adopt a uniform approach: utilizing consistent models, prompting methods, and degrees of problem decomposition, regardless of the problem complexity. Inflexibility of them can bring unnecessary computational overhead or sub-optimal performance. To address this problem, we introduce an Adaptive-Solver framework. It strategically modulates solving strategies based on the difficulties of the problems. Given an initial solution, the framework functions with two primary modules. The initial evaluation module assesses the adequacy of the current solution. If improvements are needed, the subsequent adaptation module comes into play. Within this module, three key adaptation strategies are employed: (1) Model Adaptation: Switching to a stronger LLM when a weaker variant is inadequate. (2) Prompting Method Adaptation: Alternating between different prompting techniques to suit the problem's nuances. (3) Decomposition Granularity Adaptation: Breaking down a complex problem into more fine-grained sub-questions to enhance solvability. Through such dynamic adaptations, our framework not only enhances computational efficiency but also elevates the overall performance. This dual-benefit ensures both the efficiency of the system for simpler tasks and the precision required for more complex questions. Experimental results from complex reasoning tasks reveal that the prompting method adaptation and decomposition granularity adaptation enhance performance across all tasks. Furthermore, the model adaptation approach significantly reduces API costs (up to 50%) while maintaining superior performance.

Earlier this month, facing an increasingly precarious situation, the US Food and Drug Administration (FDA) and the Drug Enforcement Administration (DEA) joined forces to address the ongoing Adderall shortage. Technically, neither organization has the power to compel pharmaceutical companies to produce mixed amphetamine salts, but in the face of skyrocketing diagnoses for attention deficit hyperactivity disorder (ADHD) in the pandemic era of telemedicine, they wanted to reassure the public that they were looking into potential alternatives to stimulant medications. In a joint statement, the agencies acknowledged that while they were actively working with the pharmaceutical industry to address the shortages, the FDA did approve a "game based digital therapeutic" to address ADHD symptoms in children back in 2020. While it's unclear whether digital therapeutics can replace stimulants entirely (they probably can't), it is clear that people want options beyond amphetamines. And this summer, digital medicine company Akili Interactive dropped the first "over-the-counter" digital therapeutic for managing ADHD symptoms in adults, using the same technology underlying their previously FDA-approved prescription video game for kids.

In the early hours of the morning, the news was breaking. Specialty coffee was being recognized as a vital part of the global coffee trade, and the industry was seeing huge growth. This was great news for coffee growers and roasters all over the world, and it was especially exciting for those in the know, who could look forward to even more opportunities to make a name for themselves. So, how did Sprudge uncover this scoop and break the news to the world? Thanks to the miracle of artificial intelligence, which some are calling AI.

Extracting informative and meaningful temporal segments from high-dimensional wearable sensor data, smart devices, or IoT data is a vital preprocessing step in applications such as Human Activity Recognition (HAR), trajectory prediction, gesture recognition, and lifelogging. In this paper, we propose ESPRESSO (Entropy and ShaPe awaRe timE-Series SegmentatiOn), a hybrid segmentation model for multi-dimensional time-series that is formulated to exploit the entropy and temporal shape properties of time-series. ESPRESSO differs from existing methods that focus upon particular statistical or temporal properties of time-series exclusively. As part of model development, a novel temporal representation of time-series $WCAC$ was introduced along with a greedy search approach that estimate segments based upon the entropy metric. ESPRESSO was shown to offer superior performance to four state-of-the-art methods across seven public datasets of wearable and wear-free sensing. In addition, we undertake a deeper investigation of these datasets to understand how ESPRESSO and its constituent methods perform with respect to different dataset characteristics. Finally, we provide two interesting case-studies to show how applying ESPRESSO can assist in inferring daily activity routines and the emotional state of humans.

Editor-at-Large based in London, United Kingdom, United Kingdom. Expertise in Internet, Music, Unemployment, Sexual health, Stocks & trading, see all» Education, General business news & info, Careers & workplace, Pharmaceuticals, Government, Environment & green living, Concerts, Small business, Celebrities, Books, Drinks, Video games, Science & space, Automotive, Sports, Technology, Movies, Board games, Charity & volunteer work, Jobs, Social media, Politics, Food, dining & restaurants, Travel, Pets, Health, Men's health, Food, recipes, Women's health, Entertainment Over the next decade, businesses will face maturing cybercrime and renewed nation-state cyberattacks. Both of these threats are key areas for which businesses need to be aware, as well as for governments to take action. Electric scooters are growing in popularity in many parts of the world. While the safety risks have been well-publicised, such as data posted by the U.S. CDC, the cybersecurity risks are not as well known – and yet these could be equally serious.

Starbucks Corp. is getting personal with its customers, enlisting help from advanced technologies that the company says will kick its digital capabilities up a notch. Kevin Johnson, Starbucks' SBUX, 0.44% chief executive, talked on the post-earnings conference call with analysts about Deep Brew, an artificial intelligence program, saying it has been percolating for the past year. The company has been ramping up its investment in artificial intelligence during the course of the program. "Deep Brew will increasingly power our personalization engine, optimize store labor allocations and drive inventory management in stores," said Johnson, according to a FactSet transcript. With more customers signing up for the company's loyalty program, the opportunity for personalization is increasing.