Support for Rich Communication Services (RCS) is one of the lesser-known features in iOS 18, which is now available for everyone. RCS is an enhanced version of SMS for text messaging, and its implementation will enable Apple users to communicate with Android users in a similar way to how they do with other Apple users.

In doing so, you will be able to use read receipts with your friends and family on Android. You can also send high-quality images and videos, view typing indicators, and enjoy enhanced group messaging capabilities.

Getting started with RCS as an iPhone user is a pain-free process. Let’s take a look.

How to enable RCS messaging in iOS 18

Luckily, RCS should be activated on your iPhone with iOS 18 installed automatically. To be sure, follow these directions.

Step 1: Open the Settings app on your phone.

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Step 2: Scroll down and choose Apps.

Step 3: Select Messages.

Step 4: Scroll down, under Text messaging and select RCS messaging.

Step 5: Toggle RCS messaging to the on position if it is not already enabled. You can also disable RCS messaging by toggling off the feature.

How to tell you are using RCS

There are a few ways you can tell that you are sending a message in iMessage via RCS.

Step 1: First, even without sending a message, you’ll see “RCS” at the top of the message box as soon as you type in the number of someone not using an Apple device.

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Step 2: You can also recognize the use of RCS when someone sends you something beyond regular text. For example, there’s congratulations confetti, as seen in the example below. That wouldn’t be possible in iOS 17 or older.

What to do if RCS isn’t working on your iPhone

So again, if you don’t see the RCS messaging option after completing the steps above on your iPhone with iOS 18 installed, then it’s because the carrier needs to get on board with supporting it. The fix for that is unfortunately just waiting it out. You can check Apple’s wireless carrier support page to check if your carrier supports RCS.

It’s only been a few days since Apple unveiled the iPhone 16 series. The company just began shipping the units to the early buyers yesterday. However, it hasn’t stopped the rumors around the iPhone 17 series from pouring in. Ross Young of Display Supply Chain Consultants has already shared some details regarding the displays of 2017 iPhones. Young predicts the iPhone 17 and the slimmer model, presumably called iPhone 17 Slim or iPhone 17 Pro, will have 120Hz displays.

It’s worth mentioning that most of the information about the iPhones from Young before launch has been true in the past. Notably, he was the first to reveal that the iPhone 16 Pro and 16 Pro Max would offer 6.3-inch and 6.9-inch screens, respectively.

iPhone 17 and iPhone 17 Air (or iPhone 17 Slim) will finally get a 120Hz refresh rate

In a post on X, Young said that the iPhone 17 and iPhone 17 Air, or whatever Apple ends up calling will finally get a 120Hz refresh rate. Both iPhones will flaunt the ProMotion technology, which will allow them to deliver a 120Hz screen refresh rate. The newly launched standard iPhone 16 and 16 Plus can still only offer up to a 60Hz screen refresh rate.

Thanks to the higher refresh rate, the iPhone 17 and iPhone 17 Air will be able to provide smoother scrolling and videos. They will also be able to switch to a more power-efficient refresh rate, thanks to the ProMotion tech. This will allow for the always-on display functionality. It will allow the next year’s iPhones to provide various Lock Screen elements, even when they are locked.

The iPhone 13 Pro can ramp down to 10Hz, while the iPhone 14 Pro through 16 Pro can reduce to 1Hz. But, it’s not clear if it would ramp down to 10Hz or 1Hz on the iPhone 17 series.

iPhone 17 Pro will not offer under-display Face ID tech

In the now-deleted X post, Young was also replying to some queries regarding the iPhone 17 series via comments. He also responded to questions regarding the presence of the under-display Face ID tech on the regular iPhone 17 Pro models. Some misinterpreted the information and reported that the standard iPhone 17 Pro would also have under-display Face ID. However, in a comment later, Young clarified that this tech is not expected on the iPhone 17 Pro.

You can save big today on the Elgato Stream Deck+ with $30 off the control panel on Amazon. Great for streamers or anyone who wants tactile shortcuts and dials for their workflow, the Stream Deck+ drops from its usual $200 to $170 with a discount and a clickable coupon.

Although the Stream Deck+ sacrifices some buttons compared to the cheaper Stream Deck MK.2, this model makes up for it with four dials and a touch strip. Each dial is customizable and clickable, allowing you to layer different dial shortcuts with each press inward. You can twist them to adjust things like volume, smart lights and in-game settings.

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Save $30 on Elgato’s Stream Deck model that adds dials and a touch strip.

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$170 at Amazon

Its eight buttons are backlit and fully customizable. Streamers can use the Stream Deck desktop app to assign functions for things like muting mics, activating effects or triggering transitions. But you don’t need to be a YouTuber or Twitch streamer for it to be helpful. For example, I’m neither and use a Stream Deck daily to toggle preset macOS window arrangements through the third-party app Moom. It’s also handy for text expansion shortcuts or emojis.

The 4.2 x 0.5-inch touch strip displays labels and levels for each knob, giving you a clear visual cue about what you’re controlling with each twist. The touch-sensitive bar also supports custom long presses and page swipes.

Amazon’s sale covers both the black and white Stream Deck+ models. Make sure you click on the $10 coupon box on the product page to bring it down to $170.

Follow @EngadgetDeals on Twitter for the latest tech deals and buying advice in the lead up to October Prime Day 2024.

In a world that is rapidly embracing large language models (LLMs), prompt engineering has emerged as a new skill to unlocking their full potential. Think of it as the language to speak with these intelligent AI systems, enabling us to tap into their vast capabilities and reshape how we create, work, solve problems and do much more. It can allow anyone — including your grandma — to program a complex multi-billion parameter AI system in the cloud.

LLMs are fundamentally built on deep learning algorithms and architectures. They are trained on massive datasets of text. Like a human who has devoured countless books, LLMs learn patterns, grammar, relationships and reasoning abilities from data. Internal settings can be tuned to change how the model processes information and adjusted to improve accuracy. When given a prompt at the inferencing stage, the LLMs use their learned knowledge and parameters to generate the most probable and contextually relevant output. It is because of these prompts that the LLMs can generate human-quality text, hold conversations, translate languages, write different kinds of creative content and answer questions in an informative way.

Many free (open source) LLMs and paid (closed source) hosted LLM services are available today. LLMs are transforming every industry as well as every aspect of our lives. Here’s how:

• Customer service: Powerful AI chatbots provide instant support and answer customer queries.
• Education: Personalized learning experiences and AI tutors are available.
• Healthcare: LLMs are being used to analyze medical issues, accelerate drug discovery and personalize treatment plans.
• Marketing and content creation: LLMs can generate engaging marketing copy, website content and scripts for videos.
• Software development: LLMs are assisting developers with code generation, debugging and documentation.

Important prompt types and techniques

Prompts act as a guiding light for LLMs. A well-crafted prompt can significantly impact the quality and relevance of the output of LLMs. Imagine asking a personal assistant to “make a reservation for dinner.” Depending on how much information you provide, such as preferred cuisine or time, you will get a more accurate result. Prompt engineering is the art and science of crafting prompts to elicit desired outputs from AI systems. It involves designing and refining prompts to generate accurate, relevant and creative outputs that align with the user’s intent.

Let us delve deeper by looking at prompt engineering techniques that can help a user guide LLMs toward desired outcomes.

From practice, prompts could be broadly classified as falling into one of the following categories:

• Direct prompts: These are small direct instructions, such as “Translate ‘hello’ into Spanish.”
• Contextual prompts: A bit more context is added to small direct instructions. Such as, “I am writing a blog post about the benefits of AI. Write a catchy title.”
• Instruction-based prompts: These are elaborate instructions with specific details of what to do and what not to do. For instance, “Write a short story about a talking cat. The cat should be grumpy and sarcastic.”
• Examples-based prompts: Prompters might say, “Here’s an example of a haiku: An old silent pond / A frog jumps into the pond— / Splash! Silence again. Now, write your own haiku.”

The following are important techniques that have been proven to be very effective in prompt engineering:

• Iterative refinement: This involves continuously refining prompts based on the AI’s responses. It can lead to better results. Example: You might start with “Write a poem about a sunset.” After seeing the output, refine it to “Write a melancholic poem about a sunset at the beach.”
• Chain of thought prompting: Encouraging step-by-step reasoning can help solve complex problems. Example: Instead of just a complex prompt like “A farmer has 14 tractors, eight cows and 10 chickens. If he sells half his birds and buys 3 more cows, how many animals would give him milk?”, adding “Think step by step” or “Explain your reasoning” is likely to give much better results and even clearly point out any intermediate errors that the model could have made.
• Role-playing: This means assigning a role or persona to the AI before handing it the task. Example: “You are a museum guide. Explain the painting Vista from a Grotto by David Teniers the Younger.”
• Multi-turn prompting: This involves breaking down a complex task into a series of prompts. This technique involves using a series of prompts to guide the AI to the required answer. Example: “Create a detailed outline,” followed by “Use the outline to expand each point into a paragraph,” followed by “The 2nd paragraph is missing X. Rewrite it to focus on…” and then finally completing the piece.

Challenges and opportunities in prompt engineering

There are some challenges and opportunities in prompt engineering. Although they have improved exponentially, LLMs may still struggle with abstract concepts, humor, complex reasoning and other tasks, which often requires carefully crafted prompts. AI models also can reflect biases present in their training data.

Prompt engineers need to understand this and address and mitigate potential biases in their final solutions. Additionally, different models may naturally interpret and respond to prompts in different ways, which poses challenges for generalization across models. Most LLM creators usually have good documentation along with prompt templates and other guidelines that work well for that model. It is always useful to familiarize yourself to efficiently use models. Finally, although inferencing speeds are continuously improving, effective prompting also presents an opportunity to program LLMs precisely at inference time to save compute and energy resources.

As AI becomes increasingly intertwined with our lives, prompt engineering is playing a crucial role in shaping how we interact with and benefit from its power. When done right, it holds immense potential to unleash possibilities that we have not imagined yet.

Deven Panchal is with AT&T Labs.  

Lenovo has released the ThinkPad T14s Gen 6, a new notebook that comes equipped with Zen 5 core and RDNA 3.5 chips.

This thin and lightweight device measures just 16.9mm in thickness and weighs only 1.3kg, making it an excellent choice for business users on the move.

An AI-generated recreation of the classic computer game Doom can be played normally despite having no computer code or graphics. Researchers behind the project say similar AI models could be used to create games from scratch in the future, just as they create text and images today.

The model, called GameNGen, was made by Dani Valevski at Google Research and his colleagues, who declined to speak to New Scientist. According to their paper on the research, the AI can be played for up to 20 seconds while retaining all the features of the original, such as scores, ammunition levels and map layouts. Players can attack enemies, open doors and interact with the environment as usual.

After this period, the model begins to run out of memory and the illusion falls apart.

The original Doom was released in 1993 and has become a popular subject for computer science projects in the years since, including attempts to get it running on unusual and limited hardware such as toasters, treadmills and espresso machines.

But in all those cases, the hardware is simply running the original game’s code. What GameNGen does is fundamentally different: a type of AI called a neural network has learned by observation how to recreate the game without seeing any of its code.

The researchers first created an AI model that learned to interact with Doom as a human would. That model was then tasked with playing the game over and over again while a second AI model, based on the Stable Diffusion image generator, learned how hundreds of millions of inputs resulted in changes in the game state.

That second model essentially then became a copy of the game, with all of the knowledge, rules and instructions from the original code encoded in the mysterious network of artificial neurons in its own architecture. In tests, human players were only slightly better than random chance at distinguishing short clips of the game from clips of the AI simulation.

GameNGen’s creators claim in their paper that it is a proof-of-concept for games being created by a neural network rather than lines of code. They suggest that games could be generated from text descriptions or concept art, which would make production less costly than using human programmers.

Andrew Rogoyski at the University of Surrey, UK, says the idea of getting a neural network to hallucinate a game environment, and the interactions a human has with it, is an interesting step forward, but not one that will replace human game designers.

“I don’t think it’s the end of those game studios. I think what the game studios have is the imagination, the skills, to actually create these worlds, to understand gameplay, to understand engagement, understand how to draw us into a story. It’s not just the nuts and bolts, the bits and bytes,” he says. “There’s something very human about creating engaging experiences that we as human beings enjoy that, at the moment, and for the foreseeable future, will largely come from other human beings.”