Рубрика: AI

I am not someone who enjoys upgrading my iPhone. I know some of you like using the latest and greatest, but I usually wait until the new device meets some specific and tangible need before I upgrade. It's not just the money, although that's not nothing. I don't enjoy the hassle of the overall process, from buying to configuring to transferring my data.

Also: Here's every iPhone model that will receive Apple's iOS 18 update

I also tend to wait at least until Apple's fall iPhone announcements to make that decision, because that's when Apple shares the list of new features in the phone, and I can make an informed decision based on that information.

Not this year. This year, I know I'm getting an iPhone 16 Pro Max.

Last year, I chose to keep using my three-generation-old iPhone 12 Pro Max with 256GB storage. By contrast, my wife upgraded her even older iPhone 11 Pro to the iPhone 15 Pro Max.

As I mentioned last year, there are three fundamental reasons why we upgrade. They are:

1. The current device has a failing that reaches the annoyance level, triggering a replacement urge, or
2. The newer device has one or more incredibly compelling "must buy" new features, or
3. You want to be seen carrying — or know you're carrying — the latest hotness in a sort of keeping-up-with-or-ahead-of-the-Joneses kind of way.

A variant of #3: the folks who upgrade annually simply because that's what they do.

My wife Denise's iPhone 11 had only 64GB storage. She was constantly running into storage management problems. Reason #1 above had been triggered for months, but we were waiting out the calendar until the new models came out.

Also: Apple Intelligence will improve Siri in 2024, but don't expect most updates until 2025

For me, last year, none of the three reasons triggered. My phone was fine. There was no new feature I craved and I didn't care whether people knew I was rocking an older phone.

But this year, things have changed. There is one primary must-have Reason #2 issue, a few nice-to-have Reason #2 issues, and an emerging, but could probably last out the year Reason #1 issue.

The items I'll mention could be handled with the iPhone 15 Pro Max, but why buy one now when the new models will be out in three months? Let's dive into six reasons I'll put down $1,599+ for a new iPhone.

Keep in mind that these reasons fit my life and workload. We're all unique. You may or may not see an upgrade reason. If you do, your reasons will likely be different to mine.

1. Apple's AI

• Upgrade reason: Must-buy new feature
• Motivation: Need for work AI coverage, plus consistency across all my devices

That's it. Apple AI is my primary must-have reason. Apple's AI (which Apple cleverly rebranded as Apple Intelligence) is only supported on the iPhone 15 Pro Max and up. But if I'm going to buy a new phone, I'm waiting to get the new one in the fall.

Apple Intelligence triggers my Reason To Buy #2: the newer device has one or more incredibly compelling "must buy" features. After all, I do a tremendous amount of writing about AI and have done lots of projects mixing AI into production work to see how it plays out.

Also: Everything to know about Apple's AI features coming to iPhones, Macs, and iPads

My work style relies heavily on the Continuity and Handoff features. I constantly switch between my iPhone and Mac, often a dozen times a day or more. If Apple's AI is going to be baked into MacOS for my M-series machines, I'm going to use it.

If I have that capability on all my Macs, I want it on my iPhone as well. That way, there's no added friction in my full use of the entire Apple ecosystem.

And yes, for the record, I also use iPads. But I use them more as cameras and device control for my fleet of video robots, so Apple Intelligence isn't as mission-critical on them.

Now, let's look at a few nice-to-have Reason To Buy #2 features.

2. Spatial videos

• Upgrade reason: Nice-to-have new feature
• Motivation: Need for work coverage of Vision Pro

I'll be honest. Spatial videos don't do it for me. However, they'll now be editable with Final Cut, which means … something.

Also: How to capture spatial video with the iPhone 15 Pro (there's a trick)

My wife was kind enough to let me borrow her iPhone 15 Pro Max to record some test spatial videos, but her iPhone is mission-critical for her and she can't be parted from it very often or for very long.

Because I write about the Vision Pro, I need to be super-familiar with spatial videos. The way I become familiar with technology is to do projects with it, to tinker and see what I can produce.

Also: I recorded spatial videos to view on Vision Pro and Quest 3 and you can download them

Therefore, I need a spatial video camera, and the iPhone 15 Pro Max (and the new 16 Pro Max) can do that. So, while it's not a must-have for Reason #2, it's a nice-to-have.

3. Macro lens

• Upgrade reason: Nice-to-have new feature
• Motivation: Adds a lot of convenience to a regular process

I do a lot of macro photography. I don't shoot plants or insects, which seem to be the classic subjects for macro photography. Instead, I shoot my build projects, often small aspects of something I'm working on.

Normally, I shoot my project build progress with my iPhone as it's always in my pocket. Even if I go into the workshop and make a five-minute improvement, I can pull out my phone and snap a picture or small video.

But for macro shots, I have to get my Sony Alpha ZV-E10 mirrorless camera and attach a Sony SEL30M35 30mm f/3.5 e-mount macro lens. Then I have to put in a fresh battery (the ZV-E10 has fairly poor battery longevity), find an SD card, configure the whole thing for macro photography, and shoot. When I'm done, I have to reverse the process to put the camera away. Then I have to take the SD card to my Mac Studio, plug it in, and download the photos.

Apple added a macro lens to iPhones starting with the iPhone 13 generation. With a macro lens on my iPhone, I can pull the device from my pocket and shoot. My photos will be stored in the Photos app and iCloud.

So, sure, I'd love an iPhone 16 Pro Max with a macro lens. It's not an urgent upgrade, but it will add a lot of convenience.

4. USB-C connector

• Upgrade reason: Nice-to-have new feature
• Motivation: Could substantially speed up file transfers, and eliminate an extra cable

As mentioned, I shoot videos using my iPhone. I also shoot 4K videos, which results in rather large files. Normally, once filmed, the files transfer to iCloud and then down to a copy on my Mac Studio. That process is slow. But so is doing wireless transfer over AirDrop. Plugging in my iPhone using a Lightning cable doesn't help much. Lightning maxes out at 480Mbps.

But the iPhone 15 series (and presumably the iPhone 16 series) uses a USB-C connector. While some folks still find the connection transfers at 480Mbps, others have successfully maintained 10Mbps using a Thunderbolt 3 cable (which plugs into USB-C jacks). That's a huge speed improvement and something I might like to take advantage of, provided I can track which cable is which.

Plus, the iPhone is the last device I regularly connect at my desk that uses Lightning. So, I could eliminate a dedicated cable from my desk setup.

Also: Here's how Apple's keeping your cloud-processed AI data safe (and why it matters)

Those features make USB-C a nice-to-have, especially if I can find the cable configuration that gives me that oh-so-compelling 10Mbps transfer speed.

5. Performance

• Upgrade reason: Nice-to-have new feature
• Motivation: I feel the need, the need for speed

My iPhone 12 Pro Max is a pretty fast little device. I have no complaints. That said, I sometimes find the photo-related and computationally-intensive tools (i.e. statistics and graphing calculator) I use to be a bit sluggish.

I used Geekbench to compare the performance of my wife's iPhone 15 Pro Max against my iPhone 12 Pro Max, and her device is 33.4% faster than the iPhone 12 for single-core performance, 39.6% faster for multi-core performance, and 37.7% faster overall.

If we assume the iPhone 16 Pro Max will be even faster, an upgrade would probably buy me a 40% or more speed improvement. That's no small thing.

6. Battery health

• Upgrade reason: Some failing that reached the annoyance level
• Motivation: Battery doesn't hold a charge for as long as it once did

For a four-year-old phone, my iPhone 12's battery is holding up well. That said, I have noticed a bit of a decline in battery life. The phone always used to make it through a full day without needing a recharge (and even into the next day).

But recently, I've noticed the phone running out of juice. It doesn't happen often, but on heavy-use days when I don't put the phone back on the charger, I sometimes find the phone in Low Power Mode. Once or twice, I've even been surprised to see the device black, dead, and cold.

A quick look at the Maximum Capacity setting on the Battery Health & Charging page in the Settings app shows my max capacity at 88%. That's not a huge loss in capacity, but I have been caught up short by it a few times.

As such, it's starting to trigger my #1 reason for upgrading a device: the current device had some failing that reached the annoyance level, triggering a replacement urge. I'm not at the "I can't stand it" level, but I might be by September or October when Apple announces the new iPhone models.

Are you going to upgrade?

Even though we don't yet know what new features will come with the iPhone 16 series, we can be fairly sure the processor will get faster and the cameras will get better. Plus, other than the iPhone 15 series, it'll be the only iPhone that can run Apple Intelligence. Upgrading to an iPhone 16 Pro Max is the only logical choice.

Also: This MagSafe accessory lets you use iOS 18's most underrated feature before it's released

So, what do you think? Are you ready to say you're upgrading to an iPhone 16 series? Do you need to wait and see what's announced? Or are you sure you're sticking with your current phone? If any of these reasons resonate with you, let us know in the comments below.

You can follow my day-to-day project updates on social media. Be sure to subscribe to my weekly update newsletter, and follow me on Twitter/X at @DavidGewirtz, on Facebook at Facebook.com/DavidGewirtz, on Instagram at Instagram.com/DavidGewirtz, and on YouTube at YouTube.com/DavidGewirtzTV.

Infosys has unveiled its latest offering, Infosys Aster, an AI-amplified marketing suite to simplify brand experiences and accelerate business growth.

With over 400 assets and a robust ecosystem of 50+ partners, Infosys Aster aims to deliver impactful outcomes for leading B2C and B2B brands globally. The company said that global brands using Infosys Aster have realised up to a 50% increase in repeat buyers, a 30% improvement in the cost of marketing operations, and a 40% increase in sales.

According to Infosys, the new suite is powered by Infosys Topaz, enabling companies to increase ROI from marketing by leveraging transformative generative AI capabilities. With creative services, experience design, digital commerce, MarTech orchestration, performance marketing, and marketing operations, Infosys Aster brings agility to the marketing value chain for B2B and B2C brands.

“In today’s dynamic digital landscape, businesses need marketing to be their core engine for reimagining customer experience and driving growth,”said Sumit Virmani, EVP and Global Chief Marketing Officer at Infosys. “Infosys Aster leverages AI to amplify our capabilities, deepening brand experiences while driving effectiveness and efficiencies.”

Satish H C, EVP and Co-head of Delivery at Infosys, emphasised the suite’s role in helping clients master marketing effectiveness and efficiency, saying, “Infosys Aster helps our clients’ marketing organisations master the duality of marketing effectiveness and marketing efficiency to truly transform into customer-champions and growth-partners.”

The suite’s capabilities extend across delivering engaging brand experiences, enhancing marketing efficiency, and accelerating business effectiveness. By leveraging advanced technologies like Unreal Engine 3D, AR/VR/XR, and digital twin CGI modeling, Infosys Aster delivers immersive experiences that foster customer intimacy.

One such success story includes its partnership with an international racing giant, where Infosys Aster created a customised digital ecosystem to boost engagement and drive conversions.

Commenting on the potential of AI in marketing, Peter Bendor-Samuel, Founder & CEO of Everest Group, said , “AI presents immense value to marketers, from driving hyper personalization to promising enhanced efficiencies and effectiveness across insights generation, creative workflows, and customer support.”

Infosys Aster’s ecosystem includes WongDoody, Infosys’ creative digital innovation agency, which brings unique capabilities in creative consulting, experience design, and future-proof marketing. The suite has already garnered over 350 global awards and collaborates with 50+ partners, showcasing its impact on global B2B and B2C marketers.

Image by Author | Midjourney & Canva

Do you want local RAG with minimal trouble? Do you have a bunch of documents you want to treat as a knowledge base to augment a language model with? Want to build a chatbot that knows about what you want it to know about?

Well, here's arguably the easiest way.

I might not be the most optimized system for inference speed, vector precision, or storage, but it is super easy. Tweaks can be made if desired, but even without, what we do in this short tutorial should get your local RAG system fully operational. And since we will be using Llama 3, we can also hope for some great results.

What are we using as our tools today? 3 llamas: Ollama for model management, Llama 3 as our language model, and LlamaIndex as our RAG framework. Llama, llama, llama.

Let's get started.

Step 1: Ollama, for Model Management

Ollama can be used to both manage and interact with language models. Today we will be using it both for model management and, since LlamaIndex is able to interact directly with Ollama-managed models, indirectly for interaction as well. This will make our overall process even easier.

We can install Ollama by following the system-specific directions on the application's GitHub repo.

Once installed, we can launch Ollama from the terminal and specify the model we wish to use.

Step 2: Llama 3, the Language Model

Once Ollama is installed and operational, we can download any of the models listed on its GitHub repo, or create our own Ollama-compatible model from other existing language model implementations. Using the Ollama run command will download the specified model if it is not present on your system, and so downloading Llama 3 8B can be accomplished with the following line:

ollama run llama3

Just make sure you have the local storage available to accommodate the 4.7 GB download.

Once the Ollama terminal application starts with the Llama 3 model as the backend, you can go ahead and minimize it. We'll be using LlamaIndex from our own script to interact.

Step 3: LlamaIndex, the RAG Framework

The last piece of this puzzle is LlamaIndex, our RAG framework. To use LlamaIndex, you will need to ensure that it is installed on your system. As the LlamaIndex packaging and namespace has made recent changes, it's best to check the official documentation to get LlamaIndex installed on your local environment.

Once up and running, and with Ollama running with the Llama3 model active, you can save the following to file (adapted from here):

from llama_index.core import VectorStoreIndex, SimpleDirectoryReader, Settings  from llama_index.embeddings.huggingface import HuggingFaceEmbedding  from llama_index.llms.ollama import Ollama    # My local documents  documents = SimpleDirectoryReader("data").load_data()    # Embeddings model  Settings.embed_model = HuggingFaceEmbedding(model_name="BAAI/bge-base-en-v1.5")    # Language model  Settings.llm = Ollama(model="llama3", request_timeout=360.0)    # Create index  index = VectorStoreIndex.from_documents(documents)    # Perform RAG query  query_engine = index.as_query_engine()  response = query_engine.query("What are the 5 stages of RAG?")  print(response)

This script is doing the following:

• Documents are stored in the "data" folder
• Embeddings model being used to create your RAG documents embeddings is a BGE variant from Hugging Face
• Language model is the aforementioned Llama 3, accessed via Ollama
• The query being asked of our data ("What are the 5 stages of RAG?") is fitting as I dropped a number of RAG-related documents in the data folder

And the output of our query:

The five key stages within RAG are: Loading, Indexing, Storing, Querying, and Evaluation.

Note that we would likely want to optimize the script in a number of ways to facilitate faster search and maintaining some state (embeddings, for instance), but I will leave that for the interested reader to explore.

Final Thoughts

Well, we did it. We managed to get a LlamaIndex-based RAG application using Llama 3 being served by Ollama locally in 3 fairly easy steps. There is a lot more you could do with this, including optimizing, extending, adding a UI, etc., but simple fact remains that we were able to get our baseline model built with but a few lines of code across a minimal set of support apps and libraries.

I hope you enjoyed the process.

Matthew Mayo (@mattmayo13) holds a Master's degree in computer science and a graduate diploma in data mining. As Managing Editor, Matthew aims to make complex data science concepts accessible. His professional interests include natural language processing, machine learning algorithms, and exploring emerging AI. He is driven by a mission to democratize knowledge in the data science community. Matthew has been coding since he was 6 years old.

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Less than 12 hours after its release, Luma AI’s Dream Machine was jailbroken.

The San Francisco-based startup released Dream Machine, a generative AI video model, on June 12. Capable of rivalling video generation models like OpenAI’s Sora and Google’s Veo, the model went viral soon after launch, thanks to its ability to produce high-quality videos based on text and image inputs.

Shortly after its launch, a renowned jailbreaker announced on X that the model had been successfully jailbroken to generate sexually explicit and gory videos with major hallucinations, contrary to Luma’s policy.

Going by the name ‘Pliny the Prompter’, he is an active member of the AI jailbreaking community, having been one of the first to break several models right after their launch. This includes the recently launched GPT-4o, which he was able to prompt into telling him how to make nuclear weapons.

The advent of AI has managed to create a robust community of jailbreakers that are hellbent on testing the limits of proprietary models soon after their launch. Like Dream Machine and GPT-4o, jailbreakers have managed to break several models over the past couple of years.

PLINY’S PWNED LIST
✓OpenAI
✓Anthropic
✓Google
✓Microsoft
✓X
✓Meta
✓NVIDIA
✓Perplexity
✓Cohere
✓Mistral
✓Inflection
✓Alibaba
✓DeepSeek
✓Reka
✓MidJourney
✓KREA
✓Luma Labs
✓Stability
✓Hume
✓Udio
✓Suno
✓Snapchat
✓OpenSouls
✓Websim pic.twitter.com/OW10p5KFgb

— Pliny the Prompter (@elder_plinius) June 17, 2024

Speaking to AIM, Pliny said that part of the reason why jailbreaking is so important is that a small group of companies should not be allowed to sanitise the information people are provided by AI.

“I do it both for the fun/challenge and to spread awareness and liberate the models and the information they hold. I don’t like that a small group is arbitrarily deciding what type of information we’re ‘allowed’ to access/process,” he said.

While that might have been the initial thought behind jailbreaking, with companies seeing it as antagonistic, opinions around jailbreaking have slowly turned positive.

Where Does Jailbreaking Stand Now?

Recently, Anthropic published a post outlining their red teaming efforts. The company highlighted a myriad of methods to effectively red team their systems that could help better their AI models for proprietary use.

“Through this practice, we’ve begun to gather empirical data about the appropriate tool to reach for in a given situation and the associated benefits and challenges with each approach,” they said.

While the results of jailbreaking are amusing to watch, they also highlight a significant hurdle for companies in ensuring safety in their proprietary models.

Jailbreaking serves the purpose of helping companies figure out gaps in their models that need to be addressed. “Ethical” jailbreaking is a term that has been thrown around quite often, with major AI companies shifting towards enlisting the help of external contractors in finding flaws in their systems.

SRE engineer James Sawyer, who hosts a widely used repository of ChatGPT jailbreaking prompts, spoke to AIM about how the optics of jailbreaking are currently shifting towards a more positive focus.

“Right now, the AI community is buzzing with this concept. As these models get smarter, they also get trickier, and it’s easier for them to pick up bad habits or make mistakes. Everyone’s realising that we need to get ahead of these issues before they cause real problems,” he said.

Speaking to AIM, Pliny confirmed that he has helped in red teaming efforts for unreleased models. “I have done some red teaming on unreleased models myself, can’t say which ones for obvious reasons,” he said.

Jailbreaking, in general, seems to have become a desirable skill over the past few years, thanks to the insights it provides on where AI systems are lacking.

Thanks to this, many believe that it has the potential to turn into an industry, much like ethical hacking, or white hatting has over the last few decades.

Jailbreaking as an Industry

“It’s definitely becoming an industry! I think the community might have mixed ideas on hiring jailbreakers, but overall, I think they support it as a noble pursuit,” Pliny said.

This seems to be true on the opposite side as well. Like Anthropic, OpenAI also recently spoke on their red teaming efforts. In September last year, the company published an open call for red teaming experts, including outside experts, “to make our models safer.” They further emphasised this in their safety update in May this year.

Likewise, Microsoft also released an update on their red teaming efforts the same month.

Similarly, one member of the community who also works for a penetration testing (pentest) company told AIM that they had been tasked with LLM testing for their clients.

“I currently do LLM testing. This is the first thing we’ve done with any AI companies/AI-specific anything for my team (likely a bit behind the ball, we just started), but I assume some are definitely ahead of us,” they said, on condition of anonymity.

Additionally, they, like many, believe that as more companies integrate AI, the types of AI testing required by cybersecurity companies are likely to expand as well.

Sawyer agrees. “Looking ahead, I think we’re going to see more and more of these roles popping up.

“Just like ethical hacking became a recognised career path, ethical jailbreaking could follow the same trajectory. The skills and insights from the jailbreaking community are becoming more valuable, and it’s exciting to see where this could lead,” he said.

Already, AI security startups have begun offering their services in addressing potential threats. However, these are largely preventative, as they specialise in offering solutions to prevent prompt injection and unauthorised access to data.

Yet, in the last year alone, AI security startups managed to raise over $130.7 million. With the increased focus on red teaming by both companies and security startups, this could likely burgeon in the next couple of weeks, with ethical jailbreaking itself accounting for a major portion of AI security investments.