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From Narrow AI to Multimodal Systems
When Kevin Engelke first started working in AI, most solutions were what we now call “narrow AI” – systems tailored to excel at specific tasks but limited in adaptability. Classic examples include sentiment analysis, spam filters, and image classification. Today, we are moving into the era of generative AI, which has opened new possibilities for content creation, such as text generation, image upscaling, and audio extension. This paved the way for the next level: multimodal AI.
Multimodal AI is an integrated approach that uses various data formats in combination, e.g. text, images, video, and audio, enabling richer, more versatile outputs. For example, a multimodal AI system could interpret an image and modify it based on text input, adding new elements or altering colors based on written descriptions. This synergy between data types brings us closer to artificial general intelligence (AGI) – an AI that can perform with human-like adaptability.
Cross-Platform Integration: The Key to Flexibility
One of the most exciting aspects of AI today is its adaptability across platforms and hardware. AI models, which are essentially complex mathematical equations, can now run on various hardware setups. While GPUs are common for AI training, more specialized hardware like IBM’s Telum processor and Google’s TPU have been developed for advanced calculations. At Beta Systems, we utilize a mix of GPUs and specialized processors, enabling the team to train models on one system and deploy them seamlessly on another.
The rise of libraries like ONNX (Open Neural Network Exchange) facilitates this interoperability by enabling models to switch between different frameworks, such as TensorFlow and PyTorch. This versatility is critical for transferring AI models between training and production environments, offering scalability and flexibility in AI deployment.
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A Look Ahead: Actionable AI, Integration, and Customization
When Kevin Engelke thinks about AI’s future, he envisions systems that go beyond data processing and content creation to provide actionable intelligence. Imagine planning a vacation: not only could AI generate a checklist, but it could also book flights and accommodations autonomously. In technical settings, such as system management, AI could follow a step-by-step setup guide or execute tasks directly.
The next wave of innovation will also focus on deeper integration into our daily lives. From smartphones to VR glasses, AI is gradually being embedded in various devices, making it a part of our everyday experiences. Moreover, as we move forward, the emphasis on privacy and offline functionality will be essential for broader AI acceptance. And, of course, AI customization is a top priority. An adaptable AI that tailors responses based on a user’s level of expertise or familiarity with a topic will enhance usability and relevance.
The Takeaway
We’re only scratching the surface of AI’s potential. As Sam Altman, CEO of OpenAI, put it: “Generative AI is still in its early stage, and we have only scratched the surface of what it can do.” With more personalization, interactivity, and integration into real-world settings, the future of AI holds immense promise.
Kevin Engelke is looking forward to continuing this journey and exploring the potential of AI to transform how we interact, learn, and grow.
If you’re interested in Beta Systems’ work or have any questions, feel free to reach out. Together, we’re shaping the future of technology.
Conclusion
AI is still in its early stages, but its impact is growing rapidly. Organizations that embrace multimodal, integrated, and adaptive AI will lay the foundation for future innovation and lasting competitive advantage.
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