- Generative AI, powered by Large Language Models (LLMs) like GPT-3 and GPT-4, has gained significant prominence in the AI and ML industry, with widespread adoption driven by technologies like ChatGPT.
- Major tech players such as Google and Meta have announced their own generative AI models, indicating the industry’s commitment to advancing these technologies.
- Vector databases and embedding stores are gaining attention due to their role in enhancing observability in generative AI applications.
- Responsible and ethical AI considerations are on the rise, with calls for stricter safety measures around large language models and an emphasis on improving the lives of all people through AI.
- Modern data engineering is shifting towards decentralized and flexible approaches, with the emergence of concepts like Data Mesh, which advocates for federated data platforms partitioned across domains.
The InfoQ Trends Reports provide InfoQ readers with an opinionated high-level overview of the topics we believe architects and technical leaders should pay attention to. In addition, they also help the InfoQ editorial team focus on writing news and recruiting article authors to cover innovative technologies.
In this annual report, the InfoQ editors discuss the current state of AI, ML, and data engineering and what emerging trends you as a software engineer, architect, or data scientist should watch. We curate our discussions into a technology adoption curve with supporting commentary to help you understand how things are evolving.
In this year’s podcast, InfoQ editorial team was joined by external panelist Sherin Thomas, software engineer at Chime. The following sections in the article summarize some of these trends and where different technologies fall in the technology adoption curve.
Generative AI, including Large Language Models (LLMs) like GPT-3, GPT-4, and Chat GPT, has become a major force in the AI and ML industry. These technologies have garnered significant attention, especially given the progress they made over the last year. We have seen wide adoption of these technologies by users, in particular driven by ChatGPT. Multiple players such as Google and Meta have announced their own generative AI models.
The next step we expect is a larger focus on LLMOps to operate these large language models in an enterprise setting. We are divided in whether prompt engineering will be a large topic in the future or whether the adoption will be so widespread that everyone will be able to contribute to the prompts used.
Vector Databases and Embedding Stores
With the rise in LLM technology there’s a growing focus on vector databases and embedding stores. One intriguing application gaining traction is the use of sentence embeddings to enhance observability in generative AI applications.
The need for vector search databases arises from the limitations of large language models, which have a finite token history. Vector databases can store document summaries as feature vectors generated by these language models, potentially resulting in millions or more feature vectors. With traditional databases, finding relevant documents becomes challenging as the dataset grows. Vector search databases enable efficient similarity searches, allowing users to locate the nearest neighbors to a query vector, enhancing the search process.
A notable trend is the surge in funding for these technologies, signaling investor recognition of their significance. However, adoption among developers has been slower, but it’s expected to pick up in the coming years. Vector search databases like Pinecone, Milvus, and open-source solutions like Chroma are gaining attention. The choice of database depends on the specific application and the nature of the data being searched.
In various fields, including Earth observation, vector databases have demonstrated their potential. NASA, for instance, leveraged self-supervised learning and vector search technology to analyze satellite images of Earth, aiding scientists in tracking weather phenomena such as hurricanes over time.
Robotics and Drone Technologies
Cost of robots is going down. In the past legged balancing robots were hard to acquire, but there are already some models available for around 1,500 dollars. This allows more users to use robot technologies in their applications. The Robot Operating System (ROS) is still the leading software framework in this field, but companies like VIAM are also developing middleware solutions that make it easier to integrate and configure plugins for robotics development.
We expect that advances in unsupervised learning and foundational models will translate into improved capabilities. For example, by integrating a large language model into the path planning part of the robot to enable planning using natural language.
Responsible and Ethical AI
As AI starts to affect all of humanity there is a growing interest in responsible and ethical AI. People are simultaneously calling for stricter safety around large language models, as well as being frustrated by the output of such models reminding users of the safeguards in place.
It remains important for engineers to keep in mind to improve the lives of all people, not just a select few. We expect a similar impact from AI regulation as GDPR had a few years ao.
We have seen some AI fail because of bad data. Data discovery, operations, data lineage, labeling and good model development practices are going to take center stage. Data is crucial to explainability.
The state of modern data engineering is marked by a dynamic shift towards more decentralized and flexible approaches to manage the ever-growing volumes of data. Data Mesh, a novel concept, has emerged to address the challenges posed by centralized data management teams becoming bottlenecks in data operations. It advocates for a federated data platform partitioned across domains, where data is treated as a product. This allows domain owners to have ownership and control over their data products, reducing the reliance on central teams. While promising, Data Mesh adoption may face hurdles related to expertise, necessitating advanced tooling and infrastructure for self-service capabilities.
Data observability has become paramount in data engineering, analogous to system observability in application architectures. Observability is essential at all layers, including data observability, especially in the context of machine learning. Trust in data is pivotal for AI success, and data observability solutions are crucial for monitoring data quality, model drift, and exploratory data analysis to ensure reliable machine learning outcomes. This paradigm shift in data management and the integration of observability across the data and ML pipelines reflect the evolving landscape of data engineering in the modern era.
Explaining the updates to the curve
With this trends report also comes an updated graph showing what we believe the state of certain technologies is. The categories are based on the book “Crossing the Chasm“, by Geoffrey Moore. At InfoQ we mostly focus on categories which have not yet crossed the chasm.
One notable upgrade from innovators to early adopters are the “AI Coding Assistants”. Although they were very new last year and hardly used, we see more and more companies offering this as a service to their employees to make them more efficient. It’s not a default part of every stack, and we are still discovering how to use them most effectively, but we believe that adoption will continue to grow.
Something which we believe is crossing the chasm right now is natural language processing. This will not come as a surprise to anyone as many companies are currently trying to figure out how to adopt generative AI capabilities in their product offering following the massive success of ChatGPT. We thus decided to make it cross the chasm already into the early majority category. There is still a lot of potential for growth here, and time will teach us more what the best practices and capabilities are for this technology.
There are some notable categories who did not move at all. These are technologies such as synthetic data generation, brain-computer interfaces and robotics. All of these seem to be consistently stuck in the innovators category. The most promising in this regard is the synthetic data generation topic, which is lately getting more attention with the GenAI hype. We do see more and more companies talking about generating more of their training data, but have not seen enough applications actually using it in their stack to warrant it moving to the early adopters category. Robotics has been getting a lot of attention for multiple years now, but its adoption rate is still too low for us to warrant a movement.
We also introduced several new categories to the graph. A notable one is vector search databases, something which comes as a byproduct of the GenAI hype. As we are gaining more understanding of how we can represent concepts as a vector there is also more need for efficient storing and retrieving said vectors. We also added explainable AI to the innovators category. We believe that computers explaining why they made a certain decision will be vital for widespread adoption to combat hallucinations and other dangers. However, we currently don’t see enough work in the industry to warrant a higher category.
The field of AI, ML, and Data Engineering keeps on growing year over year. There is still a lot of growth in both the technological capabilities as well as the possible applications. It’s exciting for us editors at InfoQ to be so close to the progress, and we are looking forward to making the same report next year. In the podcast we make several predictions for the coming year, which range from “there will be no AGI” to “Autonomous Agents will be a thing”. We hope you enjoyed listening to the podcast and reading this article, and would love to see your predictions and comments below this article.