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OpenAI introduces GPT-4o and takes giant leap forward

OpenAI is taking a giant leap forward with the introduction of GPT-4o, which will be rolled out in the coming weeks. This greatly improved version of GPT-4 can reason about audio, image and text in real time, is faster, more accessible and largely available for free to everyone!

The announcement was made during this livestream, in which several live demos were done with the new features. You can see a summary in the video below. 


An overview of GPT-4o

✔️ GPT-4o (the 'o' stands for 'omni') can process and generate combinations of text, audio and images. It responds to audio as quickly as a human responds, its performance for text in non-English languages is greatly improved, and it is 50% cheaper to use via its API.

✔️ Voice Interaction: You can talk to GPT-4o in real time and interrupt in the middle of a sentence. It responds instantly, without long silences. It can recognize emotions in your voice and respond in matching emotions, appearing increasingly human. And it can even sing. (This does make Apple's Siri look very old-fashioned.)

✔️ Visual recognition: It can now view the real world in real time. You can have live images, photos and screenshots analyzed. During the demo, they showed how GPT-4o can help solve a math problem if you write a sum on a piece of paper. It can also give hints first without immediately revealing the answer. Among other things, this makes it an even more powerful tool in education and homework.

✔️ Real-time translation: GPT-4o can translate spoken text in real time, as they showed during the demo with translation from English to Italian and vice versa. But you can also, for example, take a picture of a menu in another language and talk to GPT-4o to translate it and learn more about the dishes, and get recommendations.

✔️ It is possible for multiple people to play live games such as rock-paper-scissors, where ChatGPT can be the "showmaster" or "referee," for example.

✔️ GPTs can talk to each other and sing together!

✔️ Most current features of GPT-4 and the use of GPTs and custom GPTs are made available to all users free of charge. Also for anyone who does not have a paid subscription. This is a significant change and can start to give a huge acceleration to the use of ChatGPT and the development and use of custom GPTs.

This update will be rolled out with all features to paid ChatGPT users and ChatGPT Free users with usage limits in the coming weeks.

According to OpenAI, GPT-4o is just the beginning, with much more coming soon, especially for the paid versions.

🔗 View the entire demo here
🔗 View additional information including more impressive examples here

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Sundar Pichai, CEO of Google and Alphabet, on the challenges and opportunities with AI

Last week, an interesting interview was published by Bloomberg Originals. In it, Emily Chang talks with Sundar Pichai, CEO of Google and Alphabet, about the future of AI and the challenges and opportunities it presents. You also get a nice insight into the Plex, Google's headquarters (village).

Interesting points from the conversation

  1. Change in Search: Pichai describes how Google is adapting to changing technology. AI is having a big impact, and while Google has not always been first with new technologies such as search engines or browsers, it has always been a leader in innovation.
  2. Integrating AI: Google is integrating AI into all aspects of the company, which is crucial to staying relevant in a rapidly changing tech world. 
  3. Future vision and leadership: Pichai shares his long-term vision for Google and AI, emphasizing that continuous innovation and adaptation to changing technologies are essential. "Technology changes quickly, and we must innovate to stay ahead." He also talks about the importance of taking responsibility for mistakes and learning from challenges. 
  4. Impact of AI on everyday life: He reflects on how technology, such as AI, has caused and will continue to cause profound changes in everyday life, and highlights AI's potential to solve problems and improve quality of life.

    "AI has been around for decades, but it seems like everything is happening everywhere, all at once," he said.

    "The challenge for everyone and the opportunity is, how do you have a notion of what's objective and real in a world where there's gonna be a lot of synthetic content?"

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Sam Altman on the future and impact of AI during Stanford interview

Sam Altman, co-founder and CEO of OpenAI, recently spoke at Stanford about the future and impact of AI. He shared his insights on AI opportunities and risks and offered advice to aspiring AI entrepreneurs, emphasizing the importance of continued innovation and ethical considerations in the rapid development of AI.

He also gives his clear opinion on ChatGPT: "ChatGPT, with GPT-4 is "mildly embarrassing. It is the dumbest model any of you will ever have to use again by a lot. But not for long'. Which makes it increasingly likely that the successor to GPT-4 will be launched in the near future. Rumors say that this is already scheduled to happen in the next few weeks/months and that this successor will certainly be 2x as good.

Interesting points from this interview:

  1. Iterative Development: Altman believes in releasing new versions of AI models early and often to learn and improve quickly, even if these models are not yet perfect. "It's important to ship early and often, and we believe in iterative deployment."
  2. Future of AI models: Future AI models such as GPT-5 and GPT-6 will be significantly smarter than their predecessors, which will have a major impact on technology and society. "ChatGPT, with GPT-4, is mildly embarrassing. GPT-4 is the dumbest model any of you will ever have to use again, by a lot." "GPT-5 is going to be a lot smarter than GPT-4. GPT-6 is going to be a lot smarter than GPT-5. And we are not near the top of this curve."
  3. Impact of AI on society: AI is going to change the way we work, learn and solve problems. Altman emphasizes the need for responsible implementation to minimize negative impacts. "AI and surprise don't go well together. People don't want to be surprised; people want a gradual rollout and the ability to influence these systems." "All tech cycles are this way, right? But this one feels different. It feels bigger."
  4. Personal Reflections: He also reflected on his own career and motivations, emphasizing the importance of resilience and adaptability in a rapidly changing world. "I think the main thing is when you've done it for a while, you know, you get to understand patterns ... you've seen it before."

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Giving better instructions to AI in 4 steps with the RACE Framework

To use AI effectively and get valuable output, we need good instructions. For that, you can use several models or frameworks. One such model is the RACE Framework, which helps make clear what we expect from AI. 

The RACE Framework helps clarify our assignments to AI in four steps:

What is the RACE Framework?

Role (Role): Clearly define the role the AI agent assumes in delivering an output. Describe the expertise and purpose. For example, should the AI behave as a data analyst or as a content creator?

Action (Action): Summarize what task you want the AI agent to complete, including a summary of the desired outcome. For example, do you want the AI to produce a report, summary, translation or schedule, or should it provide ideas for the content of a new ad?

Context (Context): Provide relevant background information that helps the AI understand the problem or need and generate accurate outputs. 

Execution (Execute): Provide clear instructions on the steps the AI should follow and what the output should look like.


Why is the RACE Framework so useful?

This framework is very valuable, for content marketers, among others:

  • Clarity and focus: The RACE Framework ensures that the AI knows exactly what to do. This prevents confusion and errors.
  • Efficiency: Clear instructions help the AI work faster, saving time and money.
  • Better interaction: The framework ensures good communication between the AI and the user. This is very important for complex tasks.
  • Flexibility: The RACE Framework can be used for many different tasks. It can be easily adapted to the needs of the project or department.

An example of how to use it

A marketing team wants to use AI to develop a podcast about sustainable business:

Role: The AI agent acts as a podcast developer with the role of informing and inspiring listeners about sustainable business.

Action: Develop a podcast series that highlights different aspects of sustainable business by interviewing experts and sharing practical tips.

Context: Interest in sustainable business is growing, listeners are entrepreneurs seeking up-to-date and accessible information about it.

Implementation: The AI should create a structure for a podcast episode and an outline of themes for the first 4 episodes, based on the latest trends and research in sustainable business. Then create a selection of various experts who could be guests in the podcast per episode. And elaborate a content schedule to properly announce the podcast. For each execution task, you can use AI to go deeper, asking follow-up questions and assignments until you get the desired result.  

This example shows how to use the RACE Framework to collaborate more effectively with AI and get better results.

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Elon Musk's plans for AI news

Elon Musk recently shared details about his plan to summarize and present news on platform X (formerly Twitter), using his AI language model Grok.

Musk aims to provide accurate and up-to-date information that combines news reports and social media reactions from X. This AI system sometimes analyzes tens of thousands of posts to provide a news update. These updates will be presented live and users can go deeper into the content via chat.

Grok, with which Musk plans to compete with ChatGPT, has already begun showing news headlines and integrating social reactions into summaries.

With this, Musk also wants to keep as many users as possible on the platform longer. External links to original articles are not shown in these summaries for the time being and fits in with the trend of discouraging links to other platforms as it causes people to click away.

See also this article and post on Musk's X late last year.

We also see this trend with other social media platforms, such as Facebook, causing many companies to see a significant decline in the number of visitors to their websites from social media.

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Project management tool Asana puts full bet on AI

Dustin Moskovitz, co-founder and CEO of Asana and co-founder of Facebook, wrote an interesting article detailing his views and experiences with AI.

Asana is a project management tool that we at NXTLI have been actively using for years. 

Moskovitz writes that he was fascinated early on by the potential of AI. The arrival of ChatGPT was a tipping point, and Asana decided to focus entirely on integrating AI into their product. Thus, they launched features such as smart status updates and summaries.

Internally, they encouraged AI adoption through workshops, Slack channels and personal AI experiments by employees. This led to "aha moments" and applications of AI across all teams, including tools for sales, marketing and HR.

The moment that, according to Moskovitz, "everything clicked" was when they started applying AI to the processes of collaboration. Moskovitz was able to brainstorm with colleagues while the AI provided feedback and handled details - "it felt like it was a team member."

They used meeting transcripts and Asana tasks as context for the AI.

This also transformed collaboration with team members. Giving feedback went more smoothly and they were able to focus on the big picture instead of details.

Asana envisions a future where AI is seamlessly integrated into collaborative work processes, from planning to execution. AI advises, plans tasks and executes work, while humans are in charge.

A new way of working that combines human creativity and AI intelligence. Moskovitz recognizes the challenges, but is excited to help shape this future.

Asana wants to make AI accessible to everyone and enable teams to solve the biggest challenges in an efficient but human way.

Read the entire article here.

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OpenAI and Moderna collaboration: 750 custom GPTs within 2 months

OpenAI has shared an interesting real-world example of how biotechnology company Moderna is deploying ChatGPT Enterprise broadly within its organization. The company aims for 100% adoption of generative AI by all its employees within six months. And is deploying a special transformation program for this purpose with training, AI ambassadors and support from management. 

With the launch of ChatGPT Enterprise, Moderna decided not to further develop their internal chatbot mChat, but to fully deploy ChatGPT. Within two months of the launch, Moderna already had:

  • 750 custom GPTs in use
  • 40% of users were active weekly with GPTs
  • and each user had an average of 120 ChatGPT conversations per week.

AI is used company-wide for data analysis, contract management, policy questions and communications, among others. 

"With just a few thousand people, technology and AI allow us to perform like a team of 100,000," says CEO Stéphane Bancel. "AI allows us to work more efficiently and accurately, which ultimately leads to better outcomes for patients."

An important lesson from Brad Miller, Chief Information Officer of Moderna: 

"90% of companies want to get started with AI, but only 10% are successful at it. The reason they fail is because they have not built mechanisms to actually transform the workforce and adopt new technologies and capabilities."

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Microsoft introduces a series of "small language models" with Phi-3

The Phi-3 consists of a series of "small language models," or SLMs. These models offer many of the same capabilities as large language models, but with one important difference: they require less computational power and data.

The Phi-3-mini model, for example, outperforms models twice its size, Microsoft said.

SLMs are not meant to replace large models, but offer new opportunities for businesses and devices where size matters. 

Large models cost hundreds of millions to billions to train and are expensive to run. SLMs could significantly reduce these costs and provide new options for AI developers.

It also enables local AI on mobile devices. Apple researched that small, well-trained models can run on devices and perform as well as large models.

This means that AI may start to become an invisible part in tools and devices we use every day, which will start to accelerate wider adoption of AI.

Graphic illustration showing how the quality of the new Phi-3 models as measured by the Massive Multitask Language Understanding (MMLU) benchmark compares to other similarly sized models. (Image provided by Microsoft)

Graphic illustration showing how the quality of the new Phi-3 models as measured by the Massive Multitask Language Understanding (MMLU) benchmark compares to other similarly sized models. (Image provided by Microsoft)

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