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If rumors are right, however, Siri is expected to receive a big brain power boost this year, thanks mostly to AI. The integration of large language models, the technology behind ChatGPT, is poised to transform Siri into what one leaker is envisioning as the “ultimate virtual assistant.” 

Read more: Best iPhone to Buy for 2024

In December, Apple published research showing it can make LLM AI models run on-device in a similar way that Qualcomm and MediaTek have done for their chips in Android phones. This may indicate that Siri will get a long-awaited overhaul that iPhone fans have been waiting for, including the ability to chat like ChatGPT. 

Only Apple knows what’s next for the iPhone and its other products, but here’s how Siri could change in the iPhone 16. 

Siri could improve follow-up requests

Imagine you ask Siri about when the Olympics are taking place. It quickly spits out the correct dates in the summer of this year. But if you follow that up with, “Add it to my calendar,” the virtual assistant tends to respond imperfectly with “What should I call it?” The answer to that question would be obvious to us humans. Even when I responded, “Olympics,” Siri replied, “When should I schedule it for?”

The reason Siri tends to falter is that it lacks contextual awareness. That limits its ability to follow a conversation like a human can. However, that could change in June of this year, when Apple is rumoured to unveil improvements to Siri via iOS 18.

The iPhone maker is training Siri (and the iPhone’s Spotlight search tool) on large language models in order to improve the virtual assistant’s ability to answer more questions accurately, according to the October edition of Mark Gurman’s Bloomberg newsletter PowerOn. A large language model is a specific kind of AI that excels at understanding and producing natural language. With advancements in LLMs, Siri is likely to become more skilled at processing the way people speak. This should not only allow Siri to understand more complex and nuanced questions, but also provide accurate responses. All in all Siri is expected to become a more context-aware and powerful virtual assistant. 

Siri may get better at executing multistep tasks

Apart from understanding people better, Siri is also expected to become more capable and efficient in the coming months. Apple plans to use large language models to make Siri smarter, according to a September report from the Information. The article detailed an example explaining how Siri might respond to simple voice commands for more complex tasks, such as turning a set of photos into a GIF and then sending them to one of your contacts, which would be a significant step forward in Siri’s capabilities.

Watch this: iOS 17 Brings Big Changes to Old Habits: Live Voicemail, AirDrop and Siri

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Read more: Did You Know Siri Can Do This?

Siri may improve its interactions with the Messages app (an other apps)

Apart from answering questions, the next version of Siri could become better at automatically completing sentences, according to a Bloomberg report published in October.

Thanks to LLMs, which are trained on troves of data, Siri is expected to up its predictive text game. Beyond that, Apple is rumored to be planning to add AI to as many Apple apps as possible which could even include a feature in the Messages app to craft complex messages.

Apple never talks specifics about products before they launch. Since Apple usually unveils new iPhone software features at WWDC in June, we’ll likely know more about iPhone AI plans then.

Editors’ note: CNET is using an AI engine to help create some stories. For more, see this post.

Artificial intelligence is everywhere, whether you realize it or not. It’s behind the chatbots you talk to online, the playlists you stream and the personalized ads that somehow know exactly what you’ve been craving. Now it’s taking on a more public persona: Think Meta AI, showing up in apps like Facebook, Messenger and WhatsApp; or Google’s Gemini, working in the background across the company’s platforms; or Apple Intelligence, just now starting a slow rollout.

AI has a long history, going back to a conference at Dartmouth in 1956 that first discussed artificial intelligence as a thing. Milestones along the way include ELIZA, essentially the first chatbot, developed in 1964 by MIT computer scientist Joseph Weizenbaum, and 2004, when Google’s autocomplete first appeared. 

Then came 2022 and ChatGPT’s rise to fame. Generative AI developments and product launches have accelerated rapidly since then, including Google Bard (now Gemini), Microsoft Copilot, IBM Watsonx.ai and Meta’s open-source Llama models.

Let’s break down what generative AI is, how it differs from “regular” artificial intelligence and whether gen AI can live up to the hype.

Generative AI in a nutshell

At its core, generative AI refers to artificial intelligence systems that are designed to produce new content based on patterns and data they’ve learned. Instead of just analyzing numbers or predicting trends, these systems generate creative outputs like text, images music, videos and software code. 

Some of the most popular generative AI tools on the market include ChatGPT, Dall-E, Midjourney, Adobe Firefly, Claude and Stable Diffusion.

Foremost among its abilities, ChatGPT can craft human-like conversations or essays based on a few simple prompts. Dall-E and Midjourney create detailed artwork from a short description, while Adobe Firefly focuses on image editing and design. 

The AI that’s not generative AI

However, not all AI is generative. While gen AI focuses on creating new content, traditional AI excels at analyzing data and making predictions. This includes technologies like image recognition and predictive text. It is also used for novel solutions in science, medical diagnostics, weather forecasting, fraud detection and financial analyses for forecasting and reporting. The AI that beat human grand champions at chess and the board game Go was not generative AI.

These systems might not be as flashy as gen AI, but classic artificial intelligence is a huge part of the technology we rely on every day.

How generative AI works

Behind the magic of generative AI are large language models and advanced machine learning techniques. These systems are trained on massive amounts of data, such as entire libraries of books, millions of images, years of recorded music and data scraped from the internet.

AI developers, from tech giants to startups, are well aware that AI is only as good as the data you feed it. If it’s fed poor-quality data, AI can produce biased results. It’s something that even the biggest players in the field, like Google, haven’t been immune to.

The AI learns patterns, relationships and structures within this data during training. Then, when prompted, it applies that knowledge to generate something new. For instance, if you ask a gen AI tool to write a poem about the ocean, it’s not just pulling prewritten verses from a database. Instead, it’s using what it learned about poetry, oceans and language structure to create a completely original piece.

It’s impressive, but it’s not perfect. Sometimes the results can feel a little off. Maybe the AI misunderstands your request, or it gets overly creative in ways you didn’t expect. It might confidently provide completely false information, and it’s up to you to fact-check it. Those quirks, often called hallucinations, are part of what makes generative AI both fascinating and frustrating.

Generative AI’s capabilities are growing. It can now understand multiple data types by combining technologies like machine learning, natural language processing and computer vision. The result is called multimodal AI that can integrate some combination of text, images, video and speech within a single framework, offering more contextually relevant and accurate responses. ChatGPT’s Advanced Voice Mode is an example, as is Google’s Project Astra.

Gen AI comes with challenges

There’s no shortage of generative AI tools out there, each with its unique flair. These tools have sparked creativity, but they’ve also raised many questions besides bias and hallucinations — like, who owns the rights to AI-generated content? Or what material is fair game or off-limits for AI companies to use for training their language models — see, for instance, the The New York Times lawsuit against OpenAI and Microsoft.

Other concerns — no small matters — involve privacy, job displacement, accountability in AI and AI-generated deepfakes. Another issue is the impact on the environment because training large AI models uses a lot of energy, leading to big carbon footprints.

The rapid ascent of gen AI in the last couple of years has accelerated worries about the risks of AI in general. Governments are ramping up AI regulations to ensure responsible and ethical development, most notably the European Union’s AI Act.

Generative AI in everyday life

Many people have interacted with chatbots in customer service or used virtual assistants like Siri, Alexa and Google Assistant — which now are on the cusp of becoming gen AI power tools. That, along with apps for ChatGPT, Claude and other new tools, is putting AI in your hands.

Meanwhile, according to McKinsey’s 2024 Global AI Survey, 65% of respondents said their organizations regularly use generative AI, nearly double the figure reported just 10 months earlier. Industries like health care and finance are using gen AI to streamline business operations and automate mundane tasks. 

Generative AI isn’t just for techies or creative people. Once you get the knack of giving it prompts, it has the potential to do a lot of the legwork for you in a variety of daily tasks. Let’s say you’re planning a trip. Instead of scrolling through pages of search results, you ask a chatbot to plan your itinerary. Within seconds, you have a detailed plan tailored to your preferences. (That’s the ideal. Please always fact-check its recommendations.) A small business owner who needs a marketing campaign but doesn’t have a design team can use generative AI to create eye-catching visuals and even ask it to suggest ad copy.

Generative AI is here to stay

There hasn’t been a tech advancement that’s caused such a boom since the internet and, later, the iPhone. Despite its challenges, generative AI is undeniably transformative. It’s making creativity more accessible, helping businesses streamline workflows and even inspiring entirely new ways of thinking and solving problems.

But perhaps what’s most exciting is its potential, and we’re just scratching the surface of what these tools can do. 

ChatGPT has quickly become the golden child of artificial intelligence. Used by millions, the AI chatbot is able to answer questions, tell stories, write web code, and even conceptualise incredibly complicated topics.

Developed by OpenAI, the AI tool has gone through many changes since it was first announced. While a free version exists, paid versions are also available known as ChatGPT Plus and ChatGPT Enterprise.

A free version of ChatGPT (GPT-3.5) is available for anyone to use on the ChatGPT website. All you have to do is sign up to get a login, and you can be mining the depth of the AI model in seconds. ChatGPT is also available on Android and Apple devices

A more advanced version of ChatGPT, known as ChatGPT-4, is also now available, but only to paid subscribers.

The AI has achieved a lot since it was announced, being embraced by huge companies, rejected by schools and used by millions of users each day. Met with equal parts controversy and praise, it is a truly divisive tool.

Now, with plenty of competitors (such as Google Bard), ChatGPT is having to constantly improve and offer new features, the most recent of which is the introduction of Dall-E 3 – an included image generator, capable of operating with ChatGPT to make your image dreams come true.

So how does the tool work? Why is it so controversial? And how do you actually use ChatGPT? With the help of AI researchers and experts, we’ve answered these questions and more below in this detailed guide to OpenAI’s most famous tool.

What is GPT-3, GPT-4 and ChatGPT?

GPT-3 (Generative Pretrained Transformer 3), GPT-3.5 and GPT-4 are state-of-the-art language processing AI models developed by OpenAI. They are capable of generating human-like text and have a wide range of applications, including language translation, language modelling, and generating text for applications such as chatbots.

GPT-3.5 is one of the largest and most powerful language-processing AI models to date, with 175 billion parameters.

GPT-3.5 gives a user the ability to give a trained AI a wide range of worded prompts. These can be questions, requests for a piece of writing on a topic of your choosing or a huge number of other worded requests.

Above, it described itself as a language-processing AI model. This simply means it is a program able to understand human language as it is spoken and written, allowing it to understand the worded information it is fed, and what to spit back out.

What can ChatGPT do?

With its 175 billion parameters, it’s hard to narrow down what GPT-3.5 does. The model is, as you would imagine, restricted to language. It can’t produce video, sound or images like its brother Dall-E 2, but instead has an in-depth understanding of the spoken and written word.

You can use ChatGPT-3.5 to:

• Write essays
• Write excel formulas
• Write poems and movie scripts
• Research topics and summarise content
• Help you build a cover letter or CV
• Write code
• Plan a holiday

ChatGPT has a very wide range of abilities, everything from writing poems about sentient farts and cliché rom-coms in alternate universes, through to explaining quantum mechanics in simple terms or writing full-length research papers and articles.

While it can be fun to use OpenAI’s years of research to get an AI to write bad stand-up comedy scripts or answer questions about your favourite celebrities, its power lies in its speed and understanding of complicated matters.

Where we could spend hours researching, understanding and writing an article on quantum mechanics, ChatGPT can produce a well-written alternative in seconds.

It has its limitations and its software can be easily confused if your prompt starts to become too complicated, or even if you just go down a road that becomes a little bit too niche.

Equally, it can’t deal with concepts that are too recent. World events that have occurred in the past year will be met with limited knowledge and the model can produce false or confused information occasionally.

OpenAI is also very aware of the internet and its love of making AI produce dark, harmful or biased content. Like its Dall-E image generator before, ChatGPT will stop you from asking the more inappropriate questions or for help with dangerous requests.

What can ChatGPT-4 do?

A more advanced version of ChatGPT, called ChatGPT-4 is now available for paid subscribers ($20/£16 a month).

Here are just a few tasks the latest version of the AI model of capable of:

• Learn a language. You can talk to ChatGPT in 26 languages
• Create recipes. ChatGPT-4 is able to recognise images – you can send ChatGPT a picture of ingredients and ask the AI to create a recipe
• Describe images to blind people

How much does ChatGPT cost?

ChatGPT-3.5 is free and easy to sign up for and use, simply:

• Head over to the ChatGPT website and create an account. You can sign-up using a Google, Microsoft or Apple account, or any email address.
• Logging in will present you with a very simple page. You are offered some example prompts, and some information about how ChatGPT works.
• At the bottom of the page is a text box. This is where you can ask ChatGPT any of your questions or prompts.

ChatGPT-4, a more advanced version of ChatGPT is now available, but is only available via a paid subscription of $20 (£16) a month.

Is there a ChatGPT app?

There is an official ChatGPT app which you can download for free on Apple and Android devices. Be sure to download the official ChatGPT app from Open AI – there any many similar apps available that may have limited functionality or a paywall.

Once downloaded and installed, simply log in with your OpenAI account to get going.

How is GPT-4 different to GPT-3.5?

In essence, GPT-4 is the same as its predecessor GPT-3.5. However, there are some new features that boost the software’s abilities.

Mainly, GPT-4 includes the ability to drastically increase the number of words that can be used in an input… up to 25,000, 8 times as many as the original ChatGPT model.

Equally, OpenAI has stated that the latest version of their technology makes fewer mistakes that they are calling ‘hallucinations’. Previously, ChatGPT could become confused, offering up a nonsensical answer to your question, or even inputting stereotypes or false information.

Additionally, GPT-4 is better at playing with language and expressing creativity. In OpenAI’s demonstration of the new technology, ChatGPT was asked to summarise a blog post only using words that start with the letter ‘g’. It also has a better understanding of how to write poetry or creative writing, but it is still by no means perfect.

On top of this, OpenAI also displayed the potential of using images to initialise prompts. For example, the team showed an image of a fridge full of ingredients with the prompt “What can I make with these products?”. ChatGPT then returned a step-by-step recipe.

While it wasn’t demonstrated, OpenAI is also proposing the use of video for prompts. This would, in theory, allow users to input videos with a worded prompt for the language model to digest.

Creating recipes with images is a clever use of the technology, but it is only the tip of how images could be used with ChatGPT. The company also demonstrated the ability to create a whole website that successfully ran JavaScript with just a handwritten sketch of a website.

As a tool to complete jobs normally done by humans, GPT-3.5 was mostly competing with writers and journalists. However, GPT-4 is being shown to have the ability to create websites, complete tax returns, make recipes and deal with reams of legal information.

How does ChatGPT work?

On the face of it, GPT-3.5’s technology is simple. It takes your requests, questions or prompts and quickly answers them. As you would imagine, the technology to do this is a lot more complicated than it sounds.

The model was trained using text databases from the internet. This included a whopping 570GB of data obtained from books, web texts, Wikipedia, articles and other pieces of writing on the internet. To be even more exact, 300 billion words were fed into the system.

As a language model, it works on probability, able to guess what the next word should be in a sentence. To get to a stage where it could do this, the model went through a supervised testing stage.

Here, it was fed inputs, for example “What colour is the wood of a tree?”. The team has a correct output in mind, but that doesn’t mean it will get it right. If it gets it wrong, the team inputs the correct answer back into the system, teaching it correct answers and helping it build its knowledge.

It then goes through a second similar stage, offering multiple answers with a member of the team ranking them from best to worst, training the model on comparisons.

What sets this technology apart is that it continues to learn while guessing what the next word should be, constantly improving its understanding of prompts and questions to become the ultimate know-it-all.

Think of it as a very beefed-up, much smarter version of the autocomplete software you often see in email or writing software. You start typing a sentence and your email system offers you a suggestion of what you are going to say.

What does it mean when ChatGPT is at full capacity?

If you try to use ChatGPT and you receive the error message telling you it’s “at capacity”, it likely means that too many people are currently using the AI tool.

Essentially, the OpenAI servers can only handle so much traffic at any given time. If too many people are trying to access it at once, ChatGPT’s servers may buckle under the weight.

If you have encountered the “ChatGPT is at capacity right now” error message, you need to try again later. You can try to refresh the page and what have you, but time is the healer here.

What does ChatGPT stand for?

The ‘GPT’ in ChatGPT stands for Generative Pre-trained Transformer.

Can you use ChatGPT to write a CV?

ChatGPT won’t write a CV out of thin air for you, instead, you will need to prompt it with your relevant experience, what type of job you’re applying for, and potentially provide more information including pasting your old CV that needs updating or an example of one for a similar field.

ChatGPT: latest news

Dall-E integration

In September 2023, OpenAI announced that ChatGPT would be integrated with the latest version of Dall-E.

Dall-E is an AI art generator, made by ChatGPT creators OpenAI. It was the first of OpenAI’s projects to really blow up online and is the model that most AI art generators are based on. Now on its third generation, OpenAI has made the decision to pair the two models together.

For anyone who has ChatGPT Plus or Enterprise, this feature will be available. Images can be made via ChatGPT, and the model can even aid you in creating prompts and editing the images to better suit your needs.

It is yet to be announced whether this feature will later come to ChatGPT’s free tier but for now, it is remaining an exclusive feature for paying customers.

ChatGPT Enterprise

Along with the addition of ChatGPT Plus, OpenAI introduced another pay-to-use version of the tool known as ChatGPT Enterprise. This offers higher levels of security and privacy, unlimited higher-speed searches and a host of other features.

This version is intended for businesses looking to get more out of ChatGPT as a work tool. OpenAI has stated that it will not train on the data created by businesses.

This version of ChatGPT has been adopted by companies like Klarna, Canva, PwC and Zapier and OpenAI claims it is being used by over 80 per cent of Fortune 500 companies.

China tech giants launch ChatGPT rivals

A handful of the biggest Chinese tech firms have launched their own AI chatbots after receiving government approval.

The biggest of these is Ernie bot, an AI model developed by Baidu, China’s leading online search provider. Similar to ChatGPT, users can ask questions of Ernie bot, using prompts to research topics, summarise articles, and much more.

The Baidu app is currently available to download in the UK on Android and Apple devices. However, all text will appear in Chinese.

Experts warn AI risks our extinction

The heads of ChatGPT’s developer, OpenAI, have signed a statement (alongside many AI experts) warning of the need to address the human extinction risk associated with AI.

The statement reads: “Mitigating the risk of extinction from AI should be a global priority alongside other societal-scale risks such as pandemics and nuclear war.”

Members of the OpenAI team to sign the statement include its CEO, Sam Altman, and its chief scientist, Ilya Sutskever. The list of signatories also includes the CEO of Google DeepMind, many university professors and public figures such as Bill Gates.

Concerns for the future

A general apprehension has followed artificial intelligence throughout its history and things are no different with ChatGPT. Critics have been quick to raise the alarms over this technology, but now even those closest to it are utilising caution.

An open letter has been drafted calling for all AI labs to pause for at least six months on the development of systems more powerful than GPT-4. This would include OpenAI’s work on GPT-5 – the next version of technology ChatGPT will eventually run on.

This open letter has been signed by prominent AI researchers, as well as figures within the tech industry including Elon Musk, Steve Wozniak and Yuval Noah Harari.

This letter states that the pause should be public and verifiable, arguing that companies like OpenAI, Microsoft and Google are entering a profit-driven race to develop and release new AI models at a dangerous pace.

This comes at the same time as a report from Goldman Sachs that suggested 300 million full-time jobs could be impacted by AI systems like ChatGPT, escalating existing concerns around these platforms.

Where is GPT-4 being used?

GPT-3 was already being adapted by a lot of big companies, inputting the technology into search engines, apps and software, but OpenAI seems to be pushing GPT-4 even harder.

Microsoft’s Bing is the main user of the technology right now, but OpenAI has reported that the software is being used by companies like Khan Academy to help students with coursework and give teachers ideas for lessons.

Equally, the language-learning app Duolingo has got involved with something called ‘Duolingo Max’ with two features. One will help explain why your answer to a question was right or wrong, the other will set up role plays with an AI to play out language in different scenarios.

More companies are adopting this technology, including the payment processing company Stripe and customer service brand Intercom.

Are there any other AI language generators?

While GPT-3 has made a name for itself with its language abilities, it isn’t the only artificial intelligence capable of doing this. Google’s LaMDA made headlines when a Google engineer was fired for calling it so realistic that he believed it to be sentient.

Google launched a chatbot powered by LaMDA called Bard on March 21, 2023. It’s similar to ChatGPT but benefits from having access to up-to-date information.

There are also plenty of other examples of AI language software out there created by everyone from Microsoft to Amazon and Stanford University. These have all received a lot less attention than OpenAI or Google, possibly because they don’t offer fart jokes or headlines about sentient AI.

Most of these models are not available to the public, but OpenAI has begun opening up access to GPT-3 during its test process, and Google’s LaMDA is available to selected groups in a limited capacity for testing.

Where ChatGPT thrives and fails

The GPT-3.5 software is obviously impressive, but that doesn’t mean it is flawless. Through the ChatGPT function, you can see some of its quirks.

Most obviously, the software has a limited knowledge of the world after 2021. It isn’t aware of world leaders that came into power since 2021, and won’t be able to answer questions about recent events.

This is obviously no surprise considering the impossible task of keeping up with world events as they happen, along with then training the model on this information.

Equally, the model can generate incorrect information, getting answers wrong or misunderstanding what you are trying to ask it.

If you try and get really niche, or add too many factors to a prompt, it can become overwhelmed or ignore parts of a prompt completely.

For example, if you ask it to write a story about two people, listing their jobs, names, ages and where they live, the model can confuse these factors, randomly assigning them to the two characters.

Equally, there are a lot of factors where ChatGPT is really successful. For an AI, it has a surprisingly good understanding of ethics and morality.

When offered a list of ethical theories or situations, ChatGPT is able to offer a thoughtful response on what to do, considering legality, people’s feelings and emotions and the safety of everyone involved.

It also has the ability to keep track of the existing conversation, able to remember rules you’ve set it, or information you’ve given it earlier in the conversation.

Two areas the model has proved to be strongest are its understanding of code and its ability to compress complicated matters. ChatGPT can make an entire website layout for you, or write an easy-to-understand explanation of dark matter in a few seconds.

Where ethics and artificial intelligence meet

Artificial intelligence and ethical concerns go together like fish and chips or Batman and Robin. When you put technology like this in the hands of the public, the teams that make them are fully aware of the many limitations and concerns.

Because the system is trained largely using words from the internet, it can pick up on the internet’s biases, stereotypes and general opinions. That means you’ll occasionally find jokes or stereotypes about certain groups or political figures depending on what you ask it.

For example, when asking the system to perform stand-up comedy, it can occasionally throw in jokes about ex-politicians or groups who are often featured in comedy bits.

Equally, the models love of internet forums and articles also gives it access to fake news and conspiracy theories. These can feed into the model’s knowledge, sprinkling in facts or opinions that aren’t exactly full of truth.

In places, OpenAI has put in warnings for your prompts. Ask how to bully someone, and you’ll be told bullying is bad. Ask for a gory story, and the chat system will shut you down. The same goes for requests to teach you how to manipulate people or build dangerous weapons.

Will ChatGPT be banned in schools?

While a number of companies are looking to implement ChatGPT, in other areas it is quickly being banned.

In New York, the city’s education department has ruled that the tool will be forbidden across all devices and networks in New York public schools.

There are two main reasons for this decision. First, the chat model has been shown to make mistakes and isn’t always accurate, especially with information from the past year.

Secondly, there is a real risk of plagiarism with students able to get ChatGPT to write their essays for them.

While New York is the first place to publicly ban the software, it is likely to be a decision made elsewhere too. However, some experts have argued that this software could actually enhance learning.

“ChatGPT and other AI-based language applications could be, and perhaps should be, integrated into school education. Not indiscriminately, but rather as a very intentional part of the curriculum. If teachers and students use AI tools like ChatGPT in service of specific teaching goals, and also learn about some of their ethical issues and limitations, that would be far better than banning them,” says Kate Darling, a research scientist at the MIT Media Lab.

“But, in absence of resources for teachers to familiarise themselves with the technology, schools may need to enact some policies restricting its use.”

In this way, Darling emphasises a belief held by many in the world of artificial intelligence. Instead of ignoring or banning it, we should learn how to interact with it safely.

This is an opinion mirrored by Sam Illingworth, an associate professor in the department of Learning Enhancement at Edinburgh Napier University.

“AI is very much here to stay, so why try to fight it? These are tools that our students will be using in the workforce, so it seems very strange to say don’t use them for three years, pretending they don’t exist for now,” says Illingworth.

“These are things that have the potential to reduce workload and improve efficiency, our responsibility as educators is to decide how to utilise it.”

Artificially intelligent eco-systems

Artificial intelligence has been in use for years, but it is currently going through a stage of increased interest, driven by developments across the likes of Google, Meta, Microsoft and just about every big name in tech.

However, it is OpenAI which has attracted the most attention recently. The company has now made an AI image generator, a highly intelligent chatbot, and is in the process of developing Point-E – a way to create 3D models with worded prompts.

In creating, training and using these models, OpenAI and its biggest investors have poured billions into these projects. In the long-run, it could easily be a worthwhile investment, setting OpenAI up at the forefront of AI creative tools.

It’s March 7th, the big deadline day for the Digital Markets Act (DMA). The law came into effect on Thursday, so all the big tech companies operating gatekeeper services must have updates in place to accommodate the requirements. For Apple, it’s iPhone sideloading support via iOS 17.4. For Meta, that means WhatsApp and Facebook Messenger interoperability (interop, as Meta calls it) chats.

How about users? I’m a European who will be able to take advantage of all these DMA-related benefits. I already know I don’t want sideloading on iPhone (or Android, for that matter). But interoperability seems like the dumbest requirement of the DMA, a feature I don’t want to take advantage of in WhatsApp or any competing instant messaging app that might be labeled a gatekeeper. 

Meta’s explanation of how WhatsApp interop will work is also the best explanation for the unnecessary interoperability requirement. Why go through all this trouble to fix something that wasn’t broken in the first place? 

What is interoperability?

Meta explained in a detailed blog post all the work behind making WhatsApp and Facebook Messenger compatible with competing chat apps that ask to be supported. 

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That’s what interop hinges on. First, a WhatsApp rival must want their app to work with Meta’s chat platforms. Even if that’s achieved, it’s up to the WhatsApp/Messenger user to choose whether to enable the functionality. 

Meta says it wants to preserve end-to-end WhatsApp encryption after interop support arrives. It’ll push WhatsApp and Messenger’s Signal encryption protocol for third-party chat apps. Other alternatives can be accepted if they’re at least as good as Signal. 

How will it work?

Meta has been working for two years to implement the changes required by the DMA. But things will not just work out of the box starting Thursday. A competing service must ask for interop support and then wait at least three months for Meta to deploy it.

It might take longer than that for WhatsApp and Messenger to support that service. Rinse and repeat for each additional chat app that wants to work with WhatsApp. 

That’s a lot of work right there, both for Meta and WhatsApp competitors. I can’t see how any of this benefits the user. The interop chat experience isn’t worth it to me. Here’s what you’ll get in the first year. Because yes, the DMA has specific requirements in place for what features interop chats should offer: 

Interoperability is a technical challenge – even when focused on the basic functionalities as required by the DMA. In year one, the requirement is for 1:1 text messaging between individual users and the sharing of images, voice messages, videos, and other attached files between individual end users. In the future, requirements expand to group functionality and calling.

Thankfully, the DMA also focuses on privacy and security. That’s why WhatsApp and Messenger will focus on ensuring that chats remain end-to-end encrypted. I’ll note that Messenger end-to-end encryption started rolling out months ago, and it might not be available in all markets.

Meta’s blog does a great job explaining what’s going on under the hood with interop chats between WhatsApp and third-party apps. It underlines all the massive work and resources Meta is deploying for this.

I’m actually kind of in awe of Meta’s willingness to comply with these DMA provisions. All this effort makes me wonder what Meta can gain from the whole interoperability thing. Maybe the endgame is converting even more users to WhatsApp and Messenger, but I digress. After all, it’s not like Meta could avoid complying with the DMA.

I’ll also say that Meta doesn’t seem to restrict interoperabiltiy to the European Union, as Apple does with iPhone sideloading. Or, at least, restrictions aren’t the focus of this blog, though the title clarifies it’s about chats in Europe: “Making messaging interoperability with third parties safe for users in Europe.”

The obvious warning

While Meta also explains how encryption and user authentication will work, it acknowledges that it’s not in full control. Therefore, it can’t promise the user the same level of security and privacy for Whatsapp interop chats as Whatsapp-to-Whatsapp chats: 

It’s important to note that the E2EE promise Meta provides to users of our messaging services requires us to control both the sending and receiving clients. This allows us to ensure that only the sender and the intended recipient(s) can see what has been sent, and that no one can listen to your conversation without both parties knowing. 

While we have built a secure solution for interop that uses the Signal Protocol encryption to protect messages in transit, without ownership of both clients (endpoints) we cannot guarantee what a third-party provider does with sent or received messages, and we therefore cannot make the same promise.

[…] users need to know that our security and privacy promise, as well as the feature set, won’t exactly match what we offer in WhatsApp chats.

If you care about WhatsApp interoperability should read the entire blog post at this link. Then promptly disable the feature once WhatsApp informs you that interop support is ready.

The encrypted-messaging service Signal is the application of choice for dissenters around the world. The app has been downloaded by more than 100 million users and boasts high-profile endorsements from NSA leaker Edward Snowden and serial entrepreneur Elon Musk. Signal has created the perception that its users, including political dissidents, can communicate with one another without fear of government interception or persecution.

But the insider history of Signal raises questions about the app’s origins and its relationship with government—in particular, with the American intelligence apparatus. Such a relationship would be troubling, given how much we have learned, in recent years, about extensive efforts to control and censor information undertaken by technology companies, sometimes in tandem with American government officials.

First, the origin story. The technology behind Signal, which operates as a nonprofit foundation, was initially funded, in part, through a $3 million grant from the government-sponsored Open Technology Fund (OTF), which was spun off from Radio Free Asia, originally established as an anti-Communist information service during the Cold War. OTF funded Signal to provide “encrypted mobile communication tools” to “Internet freedom defenders globally.”

Some insiders have argued that the connection between OTF and U.S. intelligence is deeper than it appears. One person who has worked extensively with OTF but asked to remain anonymous told me that, over time, it became increasingly clear “that the project was actually a State Department-connected initiative that planned to wield open source Internet projects made by hacker communities as tools for American foreign policy goals”—including by empowering “activists [and] parties opposed to governments that the USA doesn’t like.” Whatever the merits of such efforts, the claim—if true—suggests a government involvement with Signal that deserves more scrutiny.

The other potential problem is the Signal Foundation’s current chairman of the board, Katherine Maher, who started her career as a U.S.-backed agent of regime change. During the Arab Spring period, for instance, Maher ran digital-communications initiatives in the Middle East and North Africa for the National Democratic Institute, a largely government-funded organization that works in concert with American foreign policy campaigns. Maher cultivated relationships with online dissidents and used American technologies to advance the interests of U.S.-supported Color Revolutions abroad.

Maher then became CEO of the Wikimedia Foundation in 2016, and, earlier this year, was named CEO of National Public Radio. At Wikipedia, Maher became a campaigner against “disinformation” and admitted to coordinating online censorship “through conversations with government.” She openly endorsed removing alleged “fascists,” including President Trump, from digital platforms, and described the First Amendment as “the number one challenge” to eliminating “bad information.”

According to the insider, a woman named Meredith Whittaker, who became president of the Signal Foundation in 2022, recruited Maher to become board chair because of their mutual connections to OTF, where Maher also serves as an advisor, and to nonprofits such as Access Now, which “defends and extends the digital rights of users at risk around the world,” including in the Middle East and North Africa. Whittaker, like Maher, is highly ideological. She previously worked in a high position at Google and organized left-wing campaigns within the company, culminating in the 2018 “Google Walkout,” which demanded MeToo-style sexual harassment policies and the hiring of a chief diversity officer.

So what does all this mean for American users—including conservative dissidents—who believe that Signal is a secure application for communication? It means that they should be cautious. “Maher’s presence on the board of Signal is alarming,” says national security analyst J. Michael Waller. “It makes sense that a Color Revolutionary like Maher would have interest in Signal as a secure means of communicating,” he says, but her past support for censorship and apparent intelligence connections raise doubts about Signal’s trustworthiness. David Heinemeier Hansson, creator of the popular Ruby on Rails web-development framework, agrees, saying that it had “suddenly become materially harder” to trust the Signal Foundation under Maher’s board leadership.

For those who believe in a free and open Internet, Maher’s Signal role should be a flashing warning sign. As she once explained, she abandoned the mission of a free and open Internet at Wikipedia, because those principles recapitulated a “white male Westernized construct” and “did not end up living into the intentionality of what openness can be.” The better path, in her view, is managed opinion, using, alternately, censorship and promotion of dissent—depending on context and goal—as the essential methods.

We’re entering a dangerous period in political technology, and Maher is in the thick of it. Under her ideology, “Internet freedom” is a tactic, not a principle, and “fighting disinformation” means speech suppression, including here at home. When people tell you who they are, believe them.

Photo by Jaap Arriens/NurPhoto via Getty Images

OpenAI introduced ChatGPT in November 2022, sparking a tremendous amount of interest in artificial intelligence. ChatGPT gained so much attention that generative AI (GenAI) became a dominant theme in the tech world in 2023.

Microsoft backed OpenAI at the start of 2023 by pledging a multimillion-dollar, multiyear investment to accelerate OpenAI’s development of its AI technology.

Google made its GenAI move in March 2023 with Bard. In February 2024, Google rebranded Bard as Gemini when it debuted an improved version of the AI chatbot.

ChatGPT and Gemini are largely responsible for the considerable buzz around GenAI, which uses data from machine learning models to answer questions and create images, text and videos. OpenAI and Google are continuously improving the large language models (LLMs) behind ChatGPT and Gemini to give them a greater ability to generate human-like text.

GenAI is still rapidly evolving, and models don’t always return correct answers. Despite the common occurrence of AI hallucinations — wrong answers generated by AI — in both ChatGPT and Gemini, the tools are being adopted by businesses and consumers seeking to automate time-consuming tasks.

What is Google Gemini?

Gemini is Google’s GenAI model that was built by the Google DeepMind AI research library. The Gemini AI model powered Google’s Bard GenAI tool that launched in March 2023. Google rebranded Bard as Gemini in February 2024, several months after launching Gemini Advanced based on its new Ultra 1.0 LLM foundation. In May 2024, Google first offered users of Gemini Advanced access to the newer Gemini 1.5 Pro model.

Gemini is designed to retrieve information as a simple answer, similar to the way smart assistants like Alexa and Siri work. It uses LLMs to reply to prompts with information it has already learned or can retrieve from other Google services.

Google Gemini is multimodal — it understands audio, video and computer code as well as text. Google has paused Gemini’s image generation feature because of inaccuracies, however. Google’s statement disclosing the pause pledged to re-release an improved image generation feature soon.

Gemini’s capabilities are integrated into Google’s search engine and available in Google Workspace apps such as Docs, Gmail, Sheets, Slides and Meet. Gemini for Google Workspace is the new name for Duet AI for Google Workspace, which was Google’s answer to the Microsoft Copilot AI assistant. Google Gemini is available through an app on Android phones and in the Google app on iOS.

Gemini Advanced is part of the Google One AI Premium plan subscription service that costs $19.99 per month in the United States. Google One AI Premium also includes 2 TB of storage.

Gemini Advanced is a more powerful AI version than Gemini Pro, which remains available for free. Gemini Advanced with Gemini Pro 1.5 provides a large context window of 1 million tokens, enabling analysis of larger data sets.

Google suggests Gemini Pro and its AI capabilities is the better choice for development, research and creation tasks, and if you’re looking for a free chatbot. It brings AI to simple tasks for personal use. For those willing to pay the subscription fee, Google recommends Gemini Advanced for professional applications, more demanding workflows, enhanced performance and more cutting-edge capabilities. Google Advanced will also include early access to new features.

Gemini Nano, another part of the Google Gemini family, is used in devices such as Google’s Pixel 8 Pro smartphones.

What are the main differences between Gemini and ChatGPT?

ChatGPT and Google Gemini have become increasingly similar. Both have a free service, a nearly identically priced subscription service, and similar interfaces and use cases. The differences are largely under the hood — in their language models.

They’re also used for many similar functions, and work by users typing in a query to get a response. Both raise privacy concerns about how user data can be used. However, they differ in their training models, data sources, user experiences and how they store data.

Training models

ChatGPT is built on OpenAI’s GPT-3.5 or GPT-4. Gemini has three sizes: Gemini Pro for a wide range of tasks, Gemini Ultra for highly complex tasks, and Gemini Nano for mobile devices. Gemini Pro 1.5, which powers the subscription Gemini Advanced version, is faster and more advanced than the model used for the free Gemini service.

Data sources

The main difference between ChatGPT and Gemini is the data sources used to train their LLMs. GPT-4o uses predefined data that goes up to October 2023. Gemini draws on data pulled from the internet in real time. It is tuned to select data chosen from sources that fit specific topics such as coding or the latest scientific research.

User experience

ChatGPT users can log onto the free ChatGPT with any email account. ChatGPT also includes an API that developers can use to integrate OpenAI LLMs into third-party software. It lacks a Save button, but users can copy and paste answers from ChatGPT into another application. It does have an Archive button that can list previous responses in ChatGPT’s left-hand pane for quick retrieval.

Because ChatGPT is text-based, it can’t include images, videos, charts or links in its answers. It also lacks the ability to search the internet.

Because of OpenAI’s close partnership with Microsoft, ChatGPT can be used through Windows apps such as Word, Excel, PowerPoint and Outlook. Also, Microsoft’s Copilot AI assistants use the GPT-4 language model.

Gemini Pro’s interface gives users a chance to like or dislike a response, opt to modify the size or tone of the response, share or fact-check the response, or export it to Google Docs or Gmail. Gemini also has a “review other drafts” option that shows alternate versions of its answer. Gemini also lets users upload images, but its ability to create images is on hold until Google improves that feature.

Data storage and privacy

Both ChatGPT and Google Gemini store user data.

ChatGPT stores all prompts and queries entered. Users can review previous conversations through its archive feature. Although users can delete responses and conversations, the chatbot might continue to use these responses in its LLM for training. This raises privacy concerns when users enter personal data or proprietary information. OpenAI also discloses that ChatGPT gathers geolocation data, network activity, contact details such as email addresses and phone numbers, and device information.

According to OpenAI’s privacy policy, it collects any personal information a user provides. This includes account information such as name, contact information, payment card information and transaction history. OpenAI also might disclose geolocation data to third parties such as vendors and service providers, and to law enforcement agencies if required to do so by law.

OpenAI said the user retains ownership rights of input data and owns the output, but it “may use Content to provide, maintain, develop, and improve our Services, comply with applicable law, enforce our terms and policies, and keep our Services safe.”

Gemini stores conversations in a user’s Google account for 18 months, but users can change the retention period to three months or 36 months in their activity settings. Gemini conversations can also appear in searches, raising privacy concerns.

Google discloses that it collects conversations, location, feedback and usage information. The Google Privacy Policy claims Google uses collected data to develop, provide, maintain and improve services, and to provide personal services such as content and ads. Customers can delete information from their account using My Google Activity, or by deleting Google products or their Google accounts.

Google said it will share information to third parties with user consent and law enforcement when required.

Which chatbot is better?

There is a bit of a GenAI arms race going on now, with OpenAI and Google making updates to their models. Google has been especially aggressive, perhaps because ChatGPT came out first and Gemini must play catch-up. With each new version of the LLMs, Google and OpenAI make significant gains over their previous versions.

Generally, ChatGPT is considered the best option for text-based tasks while Gemini is the best choice for multimedia content. However, there are other considerations, as noted in earlier sections of this article. Users can try the free versions to determine which works better for them.

There have been several in-depth reviews about the chatbots worth noting:

• Researchers from Carnegie Mellon University and BerriAI benchmarked Gemini Pro against GPT-3 and GPT-4 on 10 diverse language tasks with the goal of providing an impartial in-depth analysis. They found Gemini’s strengths included performance on long, complex reasoning chains and translating into non-English languages. On the downside, it struggled with mathematical reasoning — especially with large numbers — showed bias on multiple choice questions and aggressive content filtering blocked many responses. In summary, the researchers concluded Gemini Pro did not match GPT-3 and GPT-4, but “exhibits strengths in handling complexity and reasoning depth.”
• Ethan Mollick, an associate professor who studies AI at the Wharton School of the University of Pennsylvania, performed what he called “tasting notes” of Gemini Advanced vs. GPT-4. Mollick concluded that Gemini Advanced is the first advanced AI model that can compete with GPT-4. He said each has its strengths and weaknesses — for example, GPT-4 uses code in a more sophisticated way and is better at hard verbal tasks while Gemini is better at explanations and search. But both “are weird and inconsistent and hallucinate more than you would like.”
• Bernard Marr, a futurist and author of Generative AI in Practice, pointed out in a Forbes article that ChatGPT is designed to be more conversational while Gemini processes information and automates tasks more efficiently. Marr’s conclusion after using ChatGPT and Gemini is that ChatGPT-4 is the more powerful chat interface but “Gemini is closing the gap …”.
• Neither ChatGPT nor Gemini are perfect, and their developers admit that. Both generate hallucinations and even warn users of that in their responses.
• Both of the chatbots include a disclaimer on the bottom of their prompt screens. Gemini’s reads: “Gemini may display inaccurate info, including about people, so double-check its responses.” ChatGPT advises: “ChatGPT can make mistakes. Consider checking important information.”
• The Gemini FAQ on Google’s website offers this valuable advice that can apply to all AI tools:
  • Gemini can’t replace important people in your life, like family, friends, teachers or doctors.
  • Gemini can’t do your work for you.
  • Gemini can’t make important life decisions for you.

Generative AI alternatives

GenAI is a fast-moving technology. Besides the updates to ChatGPT and Google Gemini, other companies are working on AI projects. These include AI21 Labs’ Wordtune, Anthropic’s Claude, Glean, Jasper, Open Assistant and Writesonic’s Chatsonic. China’s Baidu search engine uses AI with an application called Ernie Bot. Many productivity applications and SaaS products also incorporate GenAI assistants.

Comparison of ChatGPT vs. Gemini responses

We asked ChatGPT 3.5 and Google Gemini Pro the same requests and prompts to see how their responses would compare. The results are as follows:

Idea generation

Prompt: What are the five hottest IT trends an IT professional should know about?

Thoughts: ChatGPT’s answers were more general while Gemini drilled down into specific areas — for example, generative AI vs. AI/ML and cybersecurity mesh vs. cyber security. ChatGPT’s inability to reference data past January 2022 limits its effectiveness when looking for trending information. Gemini snuck in a few extras under “Bonus trends.”

Creating content

Prompt: Write a two-paragraph summary explaining cyber-resiliency challenges.

Thoughts: Both did a good job of explaining and summarizing a complex issue in two paragraphs, but Gemini included more specifics about the challenges and what can be done about them.

Planning

Prompt: What are the best cloud computing conferences to attend?

Thoughts: ChatGPT listed more conferences, but its list was a bit dated as several of its conferences have been renamed. Gemini offered greater detail and broke its list into specific areas of expertise.

Developer assistance

Prompt: List 10 frequently used SQL queries for querying a PostgreSQL database.

Thoughts: The lists were similar, although they used different terms in some cases. A nice feature was the code embedded in the responses. We shortened Gemini’s response to fit on one page, but its longer version included embedded code.

Dave Raffo is an independent IT analyst and journalist. He previously worked as a senior analyst at The Futurum Group and Evaluator Group, covering integrated systems, software-defined storage, container storage, public cloud storage and as-a-service offerings. He previously worked at TechTarget from 2007 to 2021 as executive news director and editorial director for its storage coverage, and he was a technology journalist for 30 years.