Hyatt Hotels Corp. unveiled Hyatt Select, a new upper-midscale brand designed for shorter stays in secondary and tertiary markets.
According to Jim Chu, chief growth officer at Hyatt, the brand aims to offer “a cost-effective, conversion-friendly option for owners, while delivering an experience for guests who want reliability, comfort and thoughtful design.”
The Hyatt Select model will accommodate both newbuilds and conversions, with properties ranging in size from 70 to 200 guestrooms. The brand has what Hyatt describes as a “lean operating model,” designed with efficient staffing to reduce labor costs while maintaining service standards.
Hyatt Select will offer complimentary breakfast, a 24/7 self-serve market operated by third-party providers and guestrooms equipped with free high-speed internet and workspaces.
The brand initially will be focused on the Americas before expanding to other regions.
The new concept complements Hyatt Studios, a new upper-midscale, extended-stay brand. The first Hyatt Studios opened in Mobile, Alabama, on Feb. 18.
Both Hyatt Select and Hyatt Studios are part of Hyatt’s Essentials portfolio, alongside Caption by Hyatt, Hyatt Place, Hyatt House and UrCove by Hyatt.
When the restaurant’s phone number is entered into an artificial intelligence (AI) agent with the command, “Call restaurant A and check if it is possible to make a reservation for 4 people at 1 p.m. on Monday, and there is a vegetarian option on the menu,” the agent calls the restaurant directly in a virtual voice and completes the reservation according to the request. After giving the AI agent a cable TV customer center phone number and requesting cancellation, he calls the customer center and performs ARS guidance and waiting for the counselor’s call, and then calls with the counselor.
Leaving a phone call to an AI agent that’s annoying and sometimes scary? It’s not a fanciful future, it’s something that’s happening now. AI call agent technology, such as a service in which AI calls instead to reserve restaurants and cancel subscriptions, is penetrating into daily life one after another.
U.S. AI search startup Jens Park officially launched its “AI Call Secretary” service in Korea in June. Jens Park’s AI call assistant service is characterized by AI calling instead when a user links his or her number starting with “+82 (Korea’s international phone country code)” with the Jens Park platform and enters the name, phone number, and purpose of the call. It is also possible to make calls from Korea to overseas. If you are preparing to travel to Japan, you can use your AI phone to call the Japanese restaurant you want and ask, “I’m going to visit next Tuesday evening, can I make a reservation for 2 people?”
AI phone agents have emerged that can be easily used by general users in daily life, going beyond corporate services such as “AI counselors” applied to call centers and Naver’s “Clova Care Call” to ask how they are doing.
FineAI, a U.S. start-up that started its service this year, said, “It is an AI agent that helps customer services such as billing and cancellation,” and came up with a service that performs tasks that customers have to call and solve. From canceling a subscription to a carrier pricing plan or cable TV, to calling an airline to demand compensation for flight delays, FineAI uses agents to replace them.
The fact that the U.S. still has to call and apply for the cancellation of its subscription is considered to be the background of the emergence of such a call agent. Diddy Das, a partner at Silicon Valley Venture Capital (VC) Menlo Ventures, mentioned FineAI and said, “Fine automates long calls that you couldn’t make due to lack of time. “It’s the first AI agent product that has allowed me to save so much time,” he said.
In order to implement such a voice-based call agent, latency must be minimized to enable natural conversation. Previously, in order for AI to listen to and answer people, it was necessary to first convert the voice inputted with STT (Speech-to-Text) technology into text, then understand the text with a language model to generate an appropriate answer, and output the answer as voice through text-to-speech (TTS) technology. However, as the performance of the ‘multi-modal’ model that can process voice continues to be advanced, it is now possible for AI to understand voice immediately and to generate and answer the result directly with voice. For GPT-4o, the minimum response time is 232 milliseconds (0.232 seconds), which can respond at a similar rate to humans. AI’s voice tone is also becoming more natural like humans beyond mechanical sounds.
Last month, Google also introduced an AI business phone function in which AI calls companies on behalf of users and collects information such as reservation status and price. There is no AI call agent available to individuals in Korea yet. The AI call app “Excio,” operated by LG U+, is considering developing a function to make calls instead of users. Exio provides a “receive instead of a phone” function in which AI picks up the phone instead and takes notes on the contents when it is difficult for users to answer the phone.
Artificial Intelligence is no longer the future, it’s the present. From deep learning and data science to neural networks and natural language processing, AI is everywhere. And if you’re diving into AI development, machine learning, or research, one thing is absolutely essential…
A high-performance laptop that can handle intensive tasks, large datasets, and GPU acceleration without breaking a sweat.
So, in today’s blogpost, I’m breaking down the Top 5 Best Laptops for Artificial Intelligence from budget options to professional-grade machines. Whether you’re a student, researcher, or engineer, there’s something here for you. Let’s get started!”
When choosing a laptop for AI tasks, you need to focus on three major components:
Processor (CPU): AI workloads need serious computing power. An Intel i7 or Ryzen 9 CPU from the latest generation is the baseline.
Graphics (GPU): If you’re training models in TensorFlow, PyTorch, or using GPU acceleration, a dedicated NVIDIA GPU RTX 2070 or above is a must.
RAM & Storage: Aim for at least 16GB of RAM and 512GB–1TB SSD storage for smooth data processing, caching, and managing large datasets.
Battery Life: You’ll also want a solid battery (at least 7–8 hours), a good display, and a build that can handle constant work or travel.
If you’re looking for raw AI performance without paying a premium price, the ASUS Zephyrus G14 is an unbeatable value.
This machine features the powerful AMD Ryzen 9 processor, paired with a dedicated NVIDIA GPU and 16GB of RAM making it perfect for machine learning, AI model training, and multitasking across data-intensive applications.
The 1TB SSD ensures you have more than enough space for large datasets, code libraries, and even virtual environments.
What makes this laptop shine is its balanced build it’s lightweight and portable, yet doesn’t compromise on performance.
Whether you’re working with TensorFlow, PyTorch, or diving into deep learning frameworks, the Zephyrus G14 delivers stable performance with great thermals.
Its design is sleek and minimal, appealing to both students and professionals alike. You get workstation-level specs packed into a 14-inch chassis a powerful combo for AI on the move.
The MSI P65 Creator is purpose-built for creative professionals but its specs make it an absolute dream for AI developers and researchers.
Powered by a high-end Intel Core i7 processor and a massive 32GB of RAM, this laptop can breeze through AI workloads with ease. Its 1TB SSD ensures lightning-fast file access and plenty of storage for massive datasets and project files.
Plus, it comes with a dedicated GPU that accelerates deep learning tasks significantly.
One of its standout features is the color-accurate, anti-glare display ideal for visualizing data, building AI interfaces, or multitasking across several windows.
This laptop is also well-known for its quiet cooling system, so you can train models overnight or work in silence.
Built with a slim aluminum chassis, it’s surprisingly portable for a workstation-class device.
If you want seamless, stutter-free performance across your AI workflows, the P65 Creator is a smart and reliable choice.
The Razer Blade 15 is an excellent all-rounder capable of handling AI workloads, everyday computing, and even some light gaming during downtime.
Under the hood, you’ll find a 10th Gen Intel Core i7 processor and a GTX 1650 Ti GPU not the highest-end graphics card, but still solid enough for training mid-size models and running frameworks like Keras, Scikit-learn, or JupyterLab with GPU acceleration.
While it only includes 8GB of RAM and 256GB of storage out of the box, both are upgradeable, which means you can scale up your system as your needs grow.
The build quality is premium, with a durable aluminum chassis and a beautiful 15.6-inch display.
Whether you’re studying AI, doing part-time freelance work, or transitioning into full-time development, the Blade 15 provides the right blend of portability, upgradability, and raw computing muscle to support your journey.
If you’re stepping into the AI space and want something capable yet budget-friendly, the ASUS VivoBook K571 hits a sweet spot.
It’s powered by an Intel Core i7 processor and NVIDIA GTX 1650 graphics, which gives you enough power to train smaller models, run simulations, or experiment with various machine learning frameworks.
With 1TB of storage, you also won’t run out of space when storing datasets, scripts, or libraries.
Although it comes with 8GB RAM, the system performs well for beginner to intermediate-level AI workloads and multitasking.
The display is sharp and bright, and the keyboard is comfortable for long coding sessions.
While it may not handle extremely heavy GPU training jobs, it’s more than capable for learners, students, or entry-level professionals exploring neural networks, AI ethics, or NLP models.
For under budget, this VivoBook delivers far more than expected ideal for those starting out.
The Microsoft Surface Book 2 blends power, portability, and premium build quality making it a top-tier option for AI developers who value versatility.
It’s equipped with a capable Core i7 processor and a dedicated NVIDIA GTX 1050 GPU, which allows it to perform well in AI development environments like TensorFlow and Azure ML Studio.
The 13.5-inch display is crisp and touch-enabled, perfect for interactive visualizations, quick sketches, or note-taking with a stylus.
While the 8GB RAM and 1.9GHz base speed might seem modest, its seamless integration with Microsoft’s ecosystem and incredible portability make it a productivity beast.
It also doubles as a tablet useful for presentations, whiteboarding, or reading research papers.
Surface Book 2 is best suited for professionals and educators who need a dependable, multifunctional machine for AI development, teaching, and mobile workspaces without compromising on quality or battery life.
Conclusion
There you have it the top 5 best laptops for AI and machine learning. Whether you’re working with TensorFlow, Keras, Scikit-learn, or simply experimenting with AI algorithms, these machines will support your journey.
If you’re just starting out, the ASUS VivoBook is a great budget option. Need professional smoothness? Go for the MSI P65. But for the best all-round performance and value, I highly recommend the Zephyrus G14.
Got any questions or still can’t decide which one’s right for you? Drop your thoughts in the comments I respond to every single one!
