GPT-4o, where the “o” stands for “omni,” is designed to cater to a broader spectrum of input and output modalities. “It accepts as input any combination of text, audio, and image and generates any combination of text, audio, and image outputs,” OpenAI announced.

Users can expect a response time as quick as 232 milliseconds, mirroring human conversational speed, with an impressive average response time of 320 milliseconds.

Pioneering capabilities

The introduction of GPT-4o marks a leap from its predecessors by processing all inputs and outputs through a single neural network. This approach enables the model to retain critical information and context that were previously lost in the separate model pipeline used in earlier versions.

Prior to GPT-4o, ‘Voice Mode’ could handle audio interactions with latencies of 2.8 seconds for GPT-3.5 and 5.4 seconds for GPT-4. The previous setup involved three distinct models: one for transcribing audio to text, another for textual responses, and a third for converting text back to audio. This segmentation led to loss of nuances such as tone, multiple speakers, and background noise.

As an integrated solution, GPT-4o boasts notable improvements in vision and audio understanding. It can perform more complex tasks such as harmonising songs, providing real-time translations, and even generating outputs with expressive elements like laughter and singing. Examples of its broad capabilities include preparing for interviews, translating languages on the fly, and generating customer service responses.

Nathaniel Whittemore, Founder and CEO of Superintelligent, commented: “Product announcements are going to inherently be more divisive than technology announcements because it’s harder to tell if a product is going to be truly different until you actually interact with it. And especially when it comes to a different mode of human-computer interaction, there is even more room for diverse beliefs about how useful it’s going to be.

“That said, the fact that there wasn’t a GPT-4.5 or GPT-5 announced is also distracting people from the technological advancement that this is a natively multimodal model. It’s not a text model with a voice or image addition; it is a multimodal token in, multimodal token out. This opens up a huge array of use cases that are going to take some time to filter into the consciousness.”

Performance and safety

GPT-4o matches GPT-4 Turbo performance levels in English text and coding tasks but outshines significantly in non-English languages, making it a more inclusive and versatile model. It sets a new benchmark in reasoning with a high score of 88.7% on 0-shot COT MMLU (general knowledge questions) and 87.2% on the 5-shot no-CoT MMLU.

The model also excels in audio and translation benchmarks, surpassing previous state-of-the-art models like Whisper-v3. In multilingual and vision evaluations, it demonstrates superior performance, enhancing OpenAI’s multilingual, audio, and vision capabilities.

OpenAI has incorporated robust safety measures into GPT-4o by design, incorporating techniques to filter training data and refining behaviour through post-training safeguards. The model has been assessed through a Preparedness Framework and complies with OpenAI’s voluntary commitments. Evaluations in areas like cybersecurity, persuasion, and model autonomy indicate that GPT-4o does not exceed a ‘Medium’ risk level across any category.

Further safety assessments involved extensive external red teaming with over 70 experts in various domains, including social psychology, bias, fairness, and misinformation. This comprehensive scrutiny aims to mitigate risks introduced by the new modalities of GPT-4o.

Availability and future integration

Starting today, GPT-4o’s text and image capabilities are available in ChatGPT—including a free tier and extended features for Plus users. A new Voice Mode powered by GPT-4o will enter alpha testing within ChatGPT Plus in the coming weeks.

Developers can access GPT-4o through the API for text and vision tasks, benefiting from its doubled speed, halved price, and enhanced rate limits compared to GPT-4 Turbo.

OpenAI plans to expand GPT-4o’s audio and video functionalities to a select group of trusted partners via the API, with broader rollout expected in the near future. This phased release strategy aims to ensure thorough safety and usability testing before making the full range of capabilities publicly available.

“It’s hugely significant that they’ve made this model available for free to everyone, as well as making the API 50% cheaper. That is a massive increase in accessibility,” explained Whittemore.

OpenAI invites community feedback to continuously refine GPT-4o, emphasising the importance of user input in identifying and closing gaps where GPT-4 Turbo might still outperform.

(Image Credit: OpenAI)

See also: OpenAI takes steps to boost AI-generated content transparency

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The launch of GPT-4 Turbo with Vision on the API follows the initial release of GPT-4’s vision and audio upload features last September and the unveiling of the turbocharged GPT-4 Turbo model at OpenAI’s developer conference in November.

GPT-4 Turbo promises significant speed improvements, larger input context windows of up to 128,000 tokens (equivalent to about 300 pages), and increased affordability for developers.

A key enhancement is the ability for API requests to utilise the model’s vision recognition and analysis capabilities through text format JSON and function calling. This allows developers to generate JSON code snippets that can automate actions within connected apps, such as sending emails, making purchases, or posting online. However, OpenAI strongly recommends building user confirmation flows before taking actions that impact the real world.

Several startups are already leveraging GPT-4 Turbo with Vision, including Cognition, whose AI coding agent Devin relies on the model to automatically generate full code:

Devin, built by @cognition_labs, is an AI software engineering assistant powered by GPT-4 Turbo that uses vision for a variety of coding tasks. pic.twitter.com/E1Svxe5fBu

— OpenAI Developers (@OpenAIDevs) April 9, 2024

Healthify, a health and fitness app, uses the model to provide nutritional analysis and recommendations based on photos of meals:

The @healthifyme team built Snap using GPT-4 Turbo with Vision to give users nutrition insights through photo recognition of foods from around the world. pic.twitter.com/jWFLuBgEoA

— OpenAI Developers (@OpenAIDevs) April 9, 2024

TLDraw, a UK-based startup, employs GPT-4 Turbo with Vision to power its virtual whiteboard and convert user drawings into functional websites:

Make Real, built by @tldraw, lets users draw UI on a whiteboard and uses GPT-4 Turbo with Vision to generate a working website powered by real code. pic.twitter.com/RYlbmfeNRZ

— OpenAI Developers (@OpenAIDevs) April 9, 2024

Despite facing stiff competition from newer models such as Anthropic’s Claude 3 Opus and Google’s Gemini Advanced, the API launch should help solidify OpenAI’s position in the enterprise market as developers await the company’s next large language model.

(Photo by v2osk)

See also: Stability AI unveils 12B parameter Stable LM 2 model and updated 1.6B variant

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The model is now available alongside Anthropic’s Sonnet and Opus models in the Claude API and on Claude.ai for Claude Pro subscribers.

“Speed is essential for our enterprise users who need to quickly analyse large datasets and generate timely output for tasks like customer support,” an Anthropic spokesperson said.

“Claude 3 Haiku is three times faster than its peers for the vast majority of workloads, processing 21K tokens (~30 pages) per second for prompts under 32K tokens.”

Haiku is designed to generate swift output, enabling responsive, engaging chat experiences, and the execution of many small tasks simultaneously.

With state-of-the-art vision capabilities and strong performance on industry benchmarks across reasoning, math, and coding, Haiku is a versatile solution for a wide range of enterprise applications. pic.twitter.com/ssMa7L1bgJ

— Anthropic (@AnthropicAI) March 13, 2024

Anthropic’s pricing model for Haiku has an input-to-output token ratio of 1:5, designed explicitly for enterprise workloads which often involve longer prompts. The company says businesses can rely on Haiku to quickly analyse large volumes of documents, such as quarterly filings, contracts, or legal cases, for half the cost of other models in its performance tier.

As an example, Claude 3 Haiku can process and analyse 400 Supreme Court cases or 2,500 images for just one US dollar.

Alongside its speed and affordability, Anthropic says Claude 3 Haiku prioritises enterprise-grade security and robustness. The company conducts rigorous testing to reduce the likelihood of harmful outputs and jailbreaks. Additional security layers include continuous systems monitoring, endpoint hardening, secure coding practices, strong data encryption protocols, and stringent access controls.

Anthropic also conducts regular security audits and works with experienced penetration testers to proactively identify and address vulnerabilities.

From today, customers can use Claude 3 Haiku through Anthropic’s API or with a Claude Pro subscription. Haiku is available on Amazon Bedrock and will be coming soon to Google Cloud Vertex AI.

(Image Credit: Anthropic)

See also: EU approves controversial AI Act to mixed reactions

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The San Francisco-based AI lab said its new text-embedding-3-small and text-embedding-3-large models offer upgraded performance over previous generations. For example, text-embedding-3-large achieves average scores of 54.9 percent on the MIRACL benchmark and 64.6 percent on the MTEB benchmark, up from 31.4 percent and 61 percent respectively for the older text-embedding-ada-002 model. 

Additionally, OpenAI revealed the price per 1,000 tokens has dropped 5x for text-embedding-3-small compared to text-embedding-ada-002, from $0.0001 to $0.00002. The company said developers can also shorten embeddings to reduce costs without significantly impacting accuracy.

Next week, OpenAI plans to release an updated GPT-3.5 Turbo model and cut its pricing by 50 percent for input tokens and 25 percent for output tokens. This will mark the third price reduction for GPT-3.5 Turbo in the past year as OpenAI aims to drive more adoption.

OpenAI has additionally updated its GPT-4 Turbo preview to version gpt-4-0125-preview, noting over 70 percent of requests have transitioned to the model since its debut. Improvements include more thorough completion of code generation and other tasks. 

To support developers building safe AI apps, OpenAI has also rolled out its most advanced content moderation model yet in text-moderation-007. The company said this identifies potentially harmful text more accurately than previous versions.

Finally, developers now have more control over API keys and visibility into usage metrics. OpenAI says developers can assign permissions to keys and view consumption on a per-key level to better track individual products or projects.

OpenAI says that more platform improvements are planned over the coming months to cater for larger development teams.

(Photo by Jonathan Kemper on Unsplash)

See also: OpenAI suspends developer of politician-impersonating chatbot

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While the company has not yet disclosed specific details about the source of these attacks, OpenAI acknowledged that they are dealing with “periodic outages due to an abnormal traffic pattern reflective of a DDoS attack.”

Users affected by these incidents reported encountering errors such as “something seems to have gone wrong” and “There was an error generating a response” when accessing ChatGPT.

This recent wave of attacks follows a major outage that impacted ChatGPT and its API on Wednesday, along with partial ChatGPT outages on Tuesday, and elevated error rates in Dall-E on Monday.

OpenAI displayed a banner across ChatGPT’s interface, attributing the disruptions to “exceptionally high demand” and reassuring users that efforts were underway to scale their systems.

Threat actor group Anonymous Sudan has claimed responsibility for the DDoS attacks on OpenAI. According to the group, the attacks are in response to OpenAI’s perceived bias towards Israel and against Palestine.

The attackers utilised the SkyNet botnet, which recently incorporated support for application layer attacks or Layer 7 (L7) DDoS attacks. In Layer 7 attacks, threat actors overwhelm services at the application level with a massive volume of requests to strain the targets’ server and network resources.

Brad Freeman, Director of Technology at SenseOn, commented:

“Distributed denial of service attacks are internet vandalism. Low effort, complexity, and in most cases more of a nuisance than a long-term threat to a business. Often DDOS attacks target services with high volumes of traffic which can be ’off-ramped, by their cloud or Internet service provider.

However, as the attacks are on Layer 7 they will be targeting the application itself, therefore OpenAI will need to make some changes to mitigate the attack. It’s likely the threat actor is sending complex queries to OpenAI to overload it, I wonder if they are using AI-generated content to attack AI content generation.”

However, the attribution of these attacks to Anonymous Sudan has raised suspicions among cybersecurity researchers. Some experts suggest that this could be a false flag operation and the group might have connections to Russia instead which, along with Iran, is suspected of stoking the bloodshed and international outrage to benefit its domestic interests.

The situation once again highlights the ongoing challenges faced by organisations dealing with DDoS attacks and the complexities of accurately identifying the perpetrators.

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Following yesterday’s leak of a custom GPT-4 chatbot creator, OpenAI unveiled several other key features during its DevDay that promise a transformative impact on the landscape of AI applications:

• GPT-4 Turbo: OpenAI introduced the preview of GPT-4 Turbo, the next generation of its renowned language model. This new iteration boasts enhanced capabilities and an extensive knowledge base encompassing world events up until April 2023.
  • One of GPT-4 Turbo’s standout features is the impressive 128K context window, allowing it to process the equivalent of more than 300 pages of text in a single prompt.
  • Notably, OpenAI has optimised the pricing structure, making GPT-4 Turbo 3x cheaper for input tokens and 2x cheaper for output tokens compared to its predecessor.
• Assistants API: OpenAI also unveiled the Assistants API, a tool designed to simplify the process of building agent-like experiences within applications.
  • The API equips developers with the ability to create purpose-built AIs with specific instructions, leveraging additional knowledge and calling models and tools to perform tasks.
• Multimodal capabilities: OpenAI’s platform now supports a range of multimodal capabilities, including vision, image creation (DALL·E 3), and text-to-speech (TTS).
  • GPT-4 Turbo can process images, opening up possibilities such as generating captions, detailed image analysis, and reading documents with figures.
  • Additionally, DALL·E 3 integration allows developers to create images and designs programmatically, while the text-to-speech API enables the generation of human-quality speech from text.
• Pricing overhaul: OpenAI has significantly reduced prices across its platform, making it more accessible to developers.
  • GPT-4 Turbo input tokens are now 3x cheaper than its predecessor at $0.01, and output tokens are 2x cheaper at $0.03. Similar reductions apply to GPT-3.5 Turbo, catering to various user requirements and ensuring affordability.
• Copyright Shield: To bolster customer protection, OpenAI has introduced Copyright Shield.
  • This initiative sees OpenAI stepping in to defend customers and cover the associated legal costs if they face copyright infringement claims related to the generally available features of ChatGPT Enterprise and the developer platform.

OpenAI’s latest announcements mark a significant stride in the company’s mission to democratise AI technology, empowering developers to create innovative and intelligent applications across various domains.

See also: OpenAI set to unveil custom GPT-4 chatbot creator

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The last-minute event was largely seen as a response to Microsoft’s integration of OpenAI’s models into its products. Just yesterday, Microsoft held an even more impromptu event where it announced that a new version of OpenAI’s ChatGPT chatbot – based on GPT-4 – will be integrated into the Edge browser and Bing search engine.

Google was expected to make a large number of AI announcements at its I/O developer conference in May. The event this week felt like a rushed and unpolished attempt by Google to remind the world (or, more likely, investors) that it’s also an AI leader and hasn’t been left behind.

OpenAI reportedly set off alarm bells at Google with ChatGPT. At the invite of Google CEO Sundar Pichai, the company’s founders – Larry Page and Sergey Brin – returned for a series of meetings to review Google’s AI product strategy.

In the wake of those meetings, it was allegedly decided that Google will speed up its AI review process so it can deploy solutions more quickly. Amid those reports, and Google’s firing of high-profile ethics researchers, many are concerned that the company will rush unsafe products to market.

Prabhakar Raghavan, SVP at Google, led proceedings. In his opening remarks, he stated that Google’s goal is to “significantly improve the lives of as many people as possible”. Throughout the event, various speakers appeared to really want to push the narrative that Google won’t take risks.

“When it comes to AI, it’s critical that we bring models to the world responsibly,” said Raghavan.

Google Search

Search is Google’s bread-and-butter. The threat that a ChatGPT-enhanced Bing could pose to Google appears to have been what caused such alarm within the company.

“Search is still our biggest moonshot,” said Raghavan. Adding, “the moon keeps moving.”

Google used this section to highlight some of the advancements it’s been making in the background that most won’t be aware of. This has included the use of zero-shot machine translation to add two dozen new languages to Google Translate over the past year.

Another product that continues to be enhanced by AI is Google Lens, which is now used more than 10 billion times per month.

“The camera is the next keyboard,” explains Raghavan. “The age of visual search is here.”

Liz Reid, VP of Engineering at Google, took the stage to provide an update on what the company is doing in this area.

Google Lens is being expanded to support video content. A user can activate Lens, touch something they want to learn more about in a video clip (such as a landmark), and Google will bring up more information about it.

“If you can see it, you can search it,” says Reid.

Multi-search is another impressive visual search enhancement that Google showed off. The feature allows users to search with both an image and text so, for example, you could try and find a specific chair or item of clothing in a different colour.

Google was going to give a live demo of multi-search but awkwardly lost the phone. Fortunately, the company says that it’s now live globally so you can give it a go yourself.

Few companies have access to the amount of information about the world and its citizens that Google does. Privacy arguments aside, it enables the company to offer powerful services that complement one another.

Reid says that users will be able to take a photo of something like a bakery item and ask Google to source a nearby place from Google Maps where the person can get their hands on an equivalent. Google says that feature is coming soon to images on mobile search results pages.

Bard

Prabhakar retook the stage to discuss Google’s response to ChatGPT.

Google’s conversational AI service is called Bard and it’s powered by LaMDA (Language Model for Dialogue Applications).

LaMDA is a model that’s built on Transformer, a neural network architecture that Google Research invented and open-sourced in 2017. Instead of relying on pre-defined responses like older chatbots, LaMDA is trained on dialogue for more open-ended natural interactions and can deliver up-to-date information from the web.

In an example of an interaction, Prabhakar asked Bard what he should consider when buying a new car. He then asked for the pros and cons of an electric car. Finally, he asked Bard to help him plan a road trip.

Bard is now available to trusted testers but Prabhakar says that Google is going to check it meets the company’s “high bar” for safety before a broader rollout.

The company says that it’s embracing NORA (No One Right Answer) for questions like, “What is the best constellation to look for when stargazing?” as it’s subjective. Generative AI will be used in such instances to bring multiple viewpoints to results—which sounds quite similar to what it’s been doing in Google News for some time to help address bias concerns.

Prabhakar goes on to highlight the potential for generative AI goes far beyond text. The SVP highlights that Google can use generative AI to create a 360-degree view of items like sneakers from just a handful of images.

Next month, Google will begin onboarding developers for its Generative Language API to help them access some powerful capabilities. Initially, the API will be powered by LaMDA. Prabhakar says that “a range of models” will follow.

Google Maps

Chris Phillips, Head of Google’s Geo Group, took to the stage to give an overview of some of the AI enhancements the company is bringing to Google Maps.

Phillips says that AI is “powering the next-generation of Google Maps”. Google is using AI to fuse billions of Street View and real-world images to evolve 2D maps into “multi-dimensional views” that will enable users to virtually soar over buildings if they’re planning a visit.

However, most impressive is how AI is enabling Google to take 2D images of indoor locations and turn them into 3D that people can explore. One provided example of where this could be useful is checking out a restaurant ahead of a date to see whether the lighting and general ambience is romantic:

Additional enhancements are being made to ‘Search with Live View’ which uses AR to help people find things nearby like ATMs.

When searching for things like coffee shops, you can see if they’re open and even how busy they typically are all from the AR view.

Google says that it’s making its largest expansion of indoor live view today. Indoor live view is expanding to 1000 new airports, train stations, and shopping centres.

Finally, Google is helping users make more sustainable transport choices. Phillips says that Google wants to “make the sustainable choice, the easy choice”.

New Google Maps features for electric vehicle owners will help with trip planning by factoring in traffic, charge level, and energy consumption. Charging stop recommendations will be improved and a “Very fast” charging filter will help EV owners pick somewhere they can get topped up quickly and be on their way.

Even more sustainable than EV driving is walking. Google is making walking directions more “glanceable” from your route overview. The company says that it’s rolling out globally on Android and iOS over the coming months.

Prabhakar retakes the stage to highlight that Google is “25 years into search” but teases that in some ways is “only just beginning.” He goes on to say that more is in the works and the “best is yet to come.”

Google I/O 2023 just got that much more exciting.

(Photo by Mitchell Luo on Unsplash)

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The company says that existing datasets of “virtually any shape and size” can be used for custom models.

A single command in the OpenAI command-line tool, alongside a user-provided file, is all that it takes to begin training. The custom GPT-3 model will then be available for use in OpenAI’s API immediately.

One customer says that it was able to increase correct outputs from 83 percent to 95 percent through fine-tuning. Another client reduced error rates by 50 percent.

Andreas Stuhlmüller, Co-Founder of Elicit, said:

“Since we started integrating fine-tuning into Elicit, for tasks with 500+ training examples, we’ve found that fine-tuning usually results in better speed and quality at a lower cost than few-shot learning.

This has been essential for making Elicit responsive at the same time as increasing its accuracy at summarising complex research statements.

As far as we can tell, this wouldn’t have been doable without fine-tuning GPT-3”

Joel Hellermark, CEO of Sana Labs, commented:

“With OpenAI’s customised models, fine-tuned on our data, Sana’s question and content generation went from grammatically correct but general responses to highly accurate semantic outputs which are relevant to the key learnings.

This yielded a 60 percent improvement when compared to non-custom models, enabling fundamentally more personalised and effective experiences for our learners.”

In June, Gartner said that 80 percent of technology products and services will be built by those who are not technology professionals by 2024. OpenAI is enabling custom AI models to be easily created to unlock the full potential of such products and services.

Related: OpenAI removes GPT-3 API waitlist and opens applications for all developers

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The AI giant unveiled GPT-3 in May last year to a mixed reception. Few doubted GPT-3’s impressive ability to generate text similar to a human writer, but many expressed concerns about the societal impact.

Fake news and propaganda are already difficult to counter even when it’s being generated in relatively limited amounts by human writers. The ability for anyone to use an AI to generate misinformation at scale could have serious implications.

A paper (PDF) from the Middlebury Institute of International Studies’ Center on Terrorism, Extremism, and Counterterrorism found that GPT-3 is able to generate “influential” text that has the potential to radicalise people into far-right extremist ideologies.

OpenAI itself shared those concerns and decided against releasing GPT-3 to the public at the time. Instead, only select trusted researchers and developers were given access.

The company gradually provided access to GPT-3 to more developers through a waitlist. OpenAI says “tens of thousands” of developers are already taking advantage of powerful AI models through its platform.

However, OpenAI has also been building a number of “safeguards” that have made the company feel comfortable removing the waitlist.

“Instruct” models are designed to adhere better to human instructions, provide specialised endpoints for more truthful question-answering, and deliver a free content filter to help developers mitigate abuse.

“To ensure API-backed applications are built responsibly, we provide tools and help developers use best practices so they can bring their applications to production quickly and safely,” wrote OpenAI in a blog post.

“As our systems evolve and we work to improve the capabilities of our safeguards, we expect to continue streamlining the process for developers, refining our usage guidelines, and allowing even more use cases over time.”

OpenAI has improved ‘Playground’ to make it even simpler for researchers to prototype with its models.

The company has also added an example library with dozens of prompts to get developers started. Codex, OpenAI’s new model for translating natural language into code, also makes an appearance.

Developers in supported countries can sign up and get started experimenting with OpenAI’s API right now.

19/11 update: An earlier version of the headline said that API was “generally” available. This has now been updated to clarify that an application process is still in place and that usage will still be reviewed by OpenAI.

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ML Kit is a new suite of cross-platform APIs from Google enabling app developers to use machine learning for things such as face recognition, text scanning, reading barcodes, and even identifying objects and landmarks.

From the ML Kit documentation page:

“We want the entire device experience to be smarter, not just the OS, so we’re bringing the power of Google’s machine learning to app developers with the launch of ML Kit, a new set of cross-platform APIs available through Firebase.

ML Kit offers developers on-device APIs for text recognition, face detection, image labelling and more. So mobile developers building apps like Lose It!, a nutrition tracker, can easily deploy our text recognition model to scan nutritional information and ML Kit’s custom model APIs to automatically classify over 200 different foods with your phone’s camera.”

Many of these abilities can run offline but are more limited than when connected to Google’s cloud. For example, the on-device version of the API could detect a dog is in a photo – but when connected to the internet – it could recognise the specific breed.

Google says any data sent to its cloud is deleted after processing.

ML Kit is simplifying what used to be a complicated process and making AI more accessible. Rather than having to learn how to use complex machine learning libraries such as TensorFlow, retrieve enough data to train a model, and then make it light enough to run on a mobile device… ML Kit enables access to many common features via an API call on Google Firebase.

Developers wanting to get started with ML Kit can find it in the Firebase console.

What are your thoughts on Google’s ML Kit? Let us know in the comments.

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