Industry executives and experts share their predictions for 2024. Read them in this 16th annual VMblog.com series exclusive.
2024: The Year of Embodied AI
By Daniel Langkilde, Co-founder and CEO of Kognic
Many experts agree that artificial intelligence (AI) is one of the most important technologies of our time, and predict that its impact could be comparable to that of previous industrial and scientific revolutions. If used properly, for example, AI could improve our lives in many ways-from safe, automated mobility to novel drug discovery. With the continuing exponential increase in computational power and data, AI systems should become far more capable, possibly equaling or exceeding human-level performance at many intellectual tasks.
Building upon the rapid innovation we experienced in 2023, here are just a few AI-related developments we can expect to see in the coming year:
1. Embodied AI is poised for real-world application. Up to this point, much of the media attention about artificial intelligence has been focused on Generative AI, a branch of AI that generates text, images and other types of media based on the patterns and structure of input training data. While chatbots, image generation, and other forms of virtual AI will undoubtedly prove to be useful to many businesses, the really big breakthrough will occur when AI enters our physical world. In Embodied AI, everything that moves will need autonomy as intelligent agents interact with our environment, such as robots and autonomous vehicles. Although robots have captured the imagination of writers and artists for decades, most people have still not interacted with one, because they have not yet become a common element of everyday life. In 2024, we will see an acceleration of Embodied AI into human space more frequently.
2. The future of automotive product development hinges on managing evolving datasets. As one of the first major applications of Embodied AI, autonomous driving has seen great investment from the automotive industry. Even with significant efforts, the rollout has been challenging, and AI has clearly not lived up to consumer expectations. However, the problem is not one of intent. Manufacturers of automated vehicles want to ensure that their products behave as passengers expect by not making dangerous turns, obeying traffic rules, driving on safe surfaces and keeping pedestrians safe. Solving these real-world use cases are inextricably linked to the data that is used to train the AI models. The size, frequency and complexity of data, coming from different types of sensors such as radar, cameras and LiDAR, contributes to a complicated landscape of hardware and software. All stakeholders in the autonomous driving value chain must now shift to programming with data versus programming with code as demanded by Machine Learning.
3. AI Alignment will become even more integral to ensure the safe and ethical development of AI systems. As Embodied AI moves into more complex scenarios and becomes more commonplace, we can expect it to encounter many of the challenges that the automotive sector encountered in 2023 with autonomous vehicles. Consequently, tighter alignment is necessary to ensure that AI systems achieve intended and desired outcomes. AI alignment is about defining clear and consistent expectations and desired outcomes. In order for Embodied AI systems to perform well, we need to teach them the language of human preferences. This will be especially critical over the next 12 months. Accelerating alignment is the key to solving many AI-related problems, across practically every industry.
4. Business leaders need to participate in teaching machines to understand what humans want. Business leaders often perceive AI and ML to be domains of technology experts, but that is no longer the case. The world of business leadership is increasingly data-driven. The shifts we have seen in every department across the enterprise-from marketing, finance and operations-has felt the impact of access to more complex data. AI is no different, and it needs data to grow and provide value. In order to function properly, AI products need to be fine-tuned with human feedback to be valuable as automation will only get you so far. This will require contributions from business leaders in all industries. In order to minimize the potential negative impacts of AI, we need to ensure that humans remain in charge so that we can provide feedback and consistently optimize the systems.
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ABOUT THE AUTHOR
Daniel Langkilde is a world-renowned expert in Machine Learning, and is passionate about the potential of ML and AI to address a wide range of important issues. As Co-founder and CEO of Kognic, he spearheads a team of data scientists, developers and perception experts in Artificial Intelligence (AI) alignment to provide the leading dataset management platform for performance-critical AI applications such as autonomous driving. Prior to founding Kognic, Daniel gained extensive experience in delivering machine learning at global scale at Recorded Future, the world’s largest intelligence company. Daniel earned his M.Sc. in Engineering Mathematics at Chalmers University of Technology, where he also served as President of the Student Union and a Member of the Board of Directors, and has been a Visiting Scholar at both MIT and UC Berkeley.
