What is multimodal AI? We often hear this question these days, don’t we? It’s a question that’s frequently being asked these days, isn’t it? GPT-4 seems to be a hot topic of conversation, whether it’s during virtual meetings, online forums, or even on social media. It seems that people from all walks of life are eager to talk about the capabilities and potential of GPT-4.
The AI community and beyond are abuzz with excitement and speculation following the release of GPT-4, the latest addition to OpenAI’s esteemed lineup of language models. Boasting a wide array of advanced capabilities, particularly in the realm of multimodal AI, GPT-4 has been generating considerable interest and attention from researchers, developers, and enthusiasts alike.
With its capacity to process and assimilate inputs from various modalities, including text, images, and sounds, GPT-4 represents a groundbreaking development in the field of AI. Since its release, many have been exploring the possibilities of multimodal AI, and the topic has remained a hotly debated and much-discussed subject.
To better understand the significance of this topic, let’s take a step back six months earlier.
Multimodal AI was in the middle of discussions
During a podcast interview titled “AI for the Next Era,” OpenAI’s CEO, Sam Altman, provided valuable insights into the upcoming advancements in AI technology. One of the standout moments from the discussion was Altman’s revelation that a multimodal model was on the horizon.
The term “multimodal” refers to an AI’s ability to operate in multiple modes, including text, images, and sounds. Until now, OpenAI’s interactions with humans have been limited to text inputs, whether through Dall-E or ChatGPT. However, with the advent of a multimodal AI, the potential for interaction through speech could revolutionize the way we communicate with AI systems.
This new capability could enable the AI to listen to commands, provide information, and even perform tasks, vastly expanding its functionality and making it more accessible to a broader range of users. With the release of GPT-4, this could mark a significant shift in the AI landscape.
I think we’ll get multimodal models in not that much longer, and that’ll open up new things. I think people are doing amazing work with agents that can use computers to do things for you, use programs and this idea of a language interface where you say a natural language – what you want in this kind of dialogue back and forth. You can iterate and refine it, and the computer just does it for you. You see some of this with DALL-E and CoPilot in very early ways.
Although Altman did not explicitly confirm that GPT-4 would be a multimodal AI, he did hint that such technology is on the horizon and will become available in the near future. One intriguing aspect of his vision for multimodal AI is the potential it holds to create new business models that are currently unfeasible.
Drawing a parallel to the mobile platform, which opened up countless opportunities for new ventures and jobs, Altman suggested that a multimodal AI platform could unlock a host of innovative possibilities and transform the way we live and work. This exciting prospect underscores the transformative power of AI and its capacity to reshape our world in ways that we can only imagine.
With the release of GPT-4, the potential for such innovative possibilities seems closer than ever before, and the ramifications of its release could be felt for years to come.
…I think this is going to be a massive trend, and very large businesses will get built with this as the interface, and more generally [I think] that these very powerful models will be one of the genuine new technological platforms, which we haven’t really had since mobile. And there’s always an explosion of new companies right after, so that’ll be cool. I think we will get true multimodal models working. And so not just text and images but every modality you have in one model is able to easily fluidly move between things.
Is self-learning AI possible?
While the field of AI research has made significant strides in recent years, one area that has received relatively little attention is the development of a self-learning AI. Current models are capable of “emergence,” where new abilities arise from increased training data, but a truly self-learning AI would represent a major leap forward.
OpenAI’s Altman has spoken of an AI that can learn and upgrade its abilities on its own, without being reliant on the size of its training data. This kind of AI would transcend the traditional software version paradigm, where companies release incremental updates, and instead would grow and improve autonomously.
Although Altman has not confirmed that GPT-4 will possess this capability, he did suggest that OpenAI is working towards it, and that it is entirely within the realm of possibility. The idea of a self-learning AI is an intriguing one that could have far-reaching implications for the future of AI and our world.
If successful, this development could bring about a new era of AI, where machines are not only capable of processing vast amounts of data but also of independently learning and improving their own abilities. Such a breakthrough could revolutionize numerous fields, from medicine to finance to transportation, and change the way we live and work in ways we can scarcely imagine.
GPT-4 is here to stay
The highly anticipated release of GPT-4 is now available to select Plus subscribers, boasting a cutting-edge multimodal language model that accepts a range of inputs, including text, speech, images, and video, and provides text-based responses.
OpenAI has positioned GPT-4 as a significant milestone in its efforts to scale up deep learning, and while it may not surpass human performance in many real-world scenarios, it has demonstrated human-level performance on numerous professional and academic benchmarks.
The popularity of ChatGPT, a conversational chatbot that utilizes GPT-3 AI technology to generate human-like responses to search queries based on data gathered from the internet, has skyrocketed since its launch on November 30th.
The launch of ChatGPT has sparked an AI arms race between tech giants Microsoft and Google, both of which are vying to integrate content-creating generative AI technologies into their internet search and office productivity products.
The release of GPT-4 and the ongoing competition among tech titans highlights the growing importance of AI and its potential to revolutionize the way we interact with technology.
For those seeking a more technical and in-depth exploration of multimodal AI, we invite you to dive deeper into the topic and learn more about this groundbreaking development in the field of artificial intelligence.
What is multimodal AI?
Multimodal AI is a highly versatile type of artificial intelligence that can process and comprehend a range of inputs from different modes or modalities, such as text, speech, images, and videos. This advanced capability enables it to recognize and interpret various forms of data, making it more flexible and adaptable to diverse contexts.
Essentially, multimodal AI can “see,” “hear,” and “understand” like a human, facilitating a more natural and intuitive interaction with the world around it. This breakthrough technology represents a significant step forward in the field of artificial intelligence and has the potential to transform numerous industries and fields, from healthcare to education to transportation.
Multimodal AI applications
Multimodal AI possesses a vast array of capabilities that span numerous industries and fields. Here are some examples of what this groundbreaking technology can achieve:
- Speech recognition: Multimodal AI can comprehend and transcribe spoken language, facilitating interactions with users through natural language processing and voice commands.
- Image and video recognition: Multimodal AI can analyze and interpret visual data, such as images and videos, to identify objects, people, and activities.
- Textual analysis: Multimodal AI can process and understand written text, including natural language processing, sentiment analysis, and language translation.
- Multimodal integration: Multimodal AI can integrate inputs from different modalities to form a more complete understanding of a situation. For instance, it can utilize visual and audio cues to recognize a person’s emotions.
These are just a few examples of the vast potential of multimodal AI, which promises to revolutionize the way we interact with technology and navigate our world. The possibilities are limitless, and we can expect to see significant advancements and breakthroughs in the field in the coming years.
How does multimodal AI work?
Multimodal neural networks typically consist of several unimodal neural networks that specialize in different input modalities, such as audio, visual, or text data. An example of such a network is the audiovisual model, which comprises two separate networks – one for visual data and another for audio data. These individual networks process their respective inputs independently, through a process known as encoding.
Once the unimodal encoding is complete, the extracted information from each model needs to be combined. There are various fusion techniques available for this purpose, ranging from basic concatenation to the use of attention mechanisms. Multimodal data fusion is a crucial factor in achieving success in these models.
After the fusion stage, the final stage involves a “decision” network that accepts the encoded and fused information and is trained on the specific task.
In the end, multimodal architectures comprise three essential components – unimodal encoders for each input modality, a fusion network that combines the features of the different modalities, and a classifier that makes predictions based on the fused data. This sophisticated approach to AI allows machines to process and interpret complex data from different sources, facilitating more natural and intuitive interactions with the world around us.
Multimodal AI vs other models
Multimodal AI has several advantages over traditional AI models that can only handle one type of data at a time. These benefits include:
- Enhanced accuracy: By combining inputs from different modalities, multimodal AI can improve the accuracy of its predictions and classifications, producing more reliable results.
- Versatility: Multimodal AI is capable of handling multiple types of data, enabling it to be more adaptable to a variety of situations and use cases.
- Natural interaction: By integrating multiple modalities, multimodal AI can interact with users in a more natural and intuitive manner, similar to how humans communicate with each other.
These advantages make multimodal AI a game-changer in the field of artificial intelligence, allowing for more seamless and effective interactions with technology and providing the potential for significant advancements in various industries and fields.
The importance of multimodal AI
The emergence of multimodal AI is an important development that has the potential to revolutionize how we interact with technology and machines. By allowing for more natural and intuitive interactions through multiple modalities, multimodal AI can create more seamless and personalized user experiences. This technology has vast potential for applications in various industries, including:
- Healthcare: Multimodal AI can help doctors and patients communicate more effectively, particularly for those with limited mobility or who are non-native speakers of a language.
- Education: Multimodal AI can enhance learning outcomes by providing more personalized and interactive instruction that adapts to a student’s individual needs and learning style.
- Entertainment: Multimodal AI can create more immersive and engaging experiences in video games, movies, and other forms of media. By integrating multiple modalities, these experiences can become more realistic, interactive, and emotionally engaging, transforming the way we consume entertainment.
New business models on the horizon
Multimodal AI not only enhances the user experience but also has the potential to create new business models and revenue streams. Here are some examples:
- Voice assistants: Multimodal AI can enable more sophisticated and personalized voice assistants that can interact with users through speech, text, and visual displays. This technology can improve user engagement and create new opportunities for businesses to interact with their customers.
- Smart homes: Multimodal AI can create more intelligent and responsive homes that can understand and adapt to a user’s preferences and behaviors. This can lead to new products and services that improve home automation and management, creating new business opportunities.
- Virtual shopping assistants: Multimodal AI can help customers navigate and personalize their shopping experience through voice and visual interactions. This technology can create more engaging and efficient shopping experiences for consumers, while also providing new opportunities for businesses to market and sell their products.
The potential for multimodal AI to create new business models and revenue streams is significant, and its applications are only limited by our imagination. As we continue to explore and develop this technology, it will be exciting to see the many innovative solutions and possibilities it will bring to the future of business and commerce.
Will AI dominate the future?
The future of AI technology is an exciting frontier, with researchers exploring new ways to create more advanced and sophisticated AI models. Here are some key areas of focus:
- Self-learning AI: AI researchers aim to create AI that can learn and improve on its own, without the need for human intervention. This could lead to more adaptable and resilient AI models that can handle a wide range of tasks and situations. The development of self-learning AI could also lead to new breakthroughs in areas such as robotics, healthcare, and autonomous systems.
- Multimodal AI: As discussed earlier, multimodal AI has the potential to transform how we interact with technology and machines. AI experts are working on creating more sophisticated and versatile multimodal AI models that can understand and process inputs from multiple modalities. As this technology evolves, it has the potential to enhance a wide range of industries and fields, from healthcare and education to entertainment and customer service.
- Ethics and governance: As AI becomes more powerful and ubiquitous, it’s essential to ensure that it’s used ethically and responsibly. AI researchers are exploring ways to create more transparent and accountable AI systems that are aligned with human values and priorities. This involves addressing issues such as bias, privacy, and security, and ensuring that AI is used to benefit society as a whole.
How do you create a self learning AI?
AI researchers are exploring a variety of approaches to creating AI that can learn independently. One promising area of research is reinforcement learning, which involves teaching an AI model to make decisions and take actions based on feedback from the environment. This type of learning is particularly useful for complex, dynamic situations where the best course of action is not always clear.
Another approach to self-learning AI is unsupervised learning, where the AI model is trained on unstructured data and uses that data to find patterns and relationships on its own. This approach is particularly useful when dealing with large amounts of data, such as images or text, where it may not be possible to manually label and categorize all of the data.
By combining these and other approaches, AI researchers are working towards creating more advanced and autonomous AI models that can learn and improve over time. This will enable AI to better adapt to new situations and tasks, as well as improve its accuracy and efficiency. Ultimately, the goal is to create AI models that can not only solve complex problems, but can also learn from and improve upon their own solutions.
How “multimodal” is GPT-4?
OpenAI has unveiled its latest AI language model, GPT-4, after much anticipation and speculation. Although the model’s range of input modalities is more limited than some had predicted, it is set to deliver groundbreaking advancements in multimodal AI. GPT-4 can process textual and visual inputs simultaneously, providing text-based outputs that demonstrate a sophisticated level of comprehension. This marks a significant milestone in the development of AI language models that have been building momentum for several years, finally capturing mainstream attention in recent months.
OpenAI’s groundbreaking GPT models have captured the imagination of the AI community since the publication of the original research paper in 2018. Following the announcement of GPT-2 in 2019 and GPT-3 in 2020, these models have been trained on vast datasets of text, primarily sourced from the internet, which is then analyzed for statistical patterns. This approach enables the models to generate and summarize writing, as well as perform a range of text-based tasks such as translation and code generation.
Despite concerns over the potential misuse of GPT models, OpenAI launched its ChatGPT chatbot based on GPT-3.5 in late 2022, making the technology accessible to a wider audience. This move triggered a wave of excitement and anticipation in the tech industry, with other major players such as Microsoft and Google quickly following suit with their own AI chatbots, including Bing as part of the Bing search engine. The launch of these chatbots demonstrates the growing importance of GPT models in shaping the future of AI, and their potential to transform the way we communicate and interact with technology.
As AI language models become more accessible, they have presented new challenges and issues for various sectors. For instance, the education system has faced difficulties with software that can generate high-quality college essays, while online platforms have struggled to handle an influx of AI-generated content. Even early applications of AI writing tools in journalism have encountered problems. Nevertheless, experts suggest that the negative impacts have been less severe than initially feared. As with any new technology, the introduction of AI language models requires careful consideration and adaptation to ensure that the benefits of the technology are maximized while minimizing any adverse effects.
Accoring to OpenAI, GPT-4 had gone through six months of safety training, and that in internal tests, it was “82 percent less likely to respond to requests for disallowed content and 40 percent more likely to produce factual responses than GPT-3.5.”
Back to our original question: What is multimodal AI? The recent release of GPT-4 has brought multimodal AI out of the realm of theory and into reality. With its ability to process and integrate inputs from various modalities, GPT-4 has opened up a world of possibilities and opportunities for the field of AI and beyond.
The impact of this breakthrough technology is expected to extend across multiple industries, from healthcare and education to entertainment and gaming. Multimodal AI is transforming the way we interact with machines, allowing for more natural and intuitive communication and collaboration. These advancements have significant implications for the future of work and productivity, as AI models become more adept at handling complex tasks and improving overall efficiency.
Don’t forget to check out our ChatGPT prompt comparison over GPT-4 vs GPT-3.5 to find out more about multimodal AI’s capabilities.