The Research

Building a Strong Base in State-of-the-Art Research

Our methods are rooted in SoTA progress: shallow and deep learning, computer vision, AI agency, and multimodal models. We consistently enhance our strategies to guarantee our analyses are founded on the most current global research findings*.

*Visual analysis can offer valuable insights into plant diagnoses, particularly in early detection. However, it should not be viewed as a comprehensive replacement for soil and leaf tissue analysis.

'Using Deep Learning for Image-Based Plant Disease Detection' - 2016

"Crop diseases are a major threat to food security, but their rapid identification remains difficult in many parts of the world due to the lack of the necessary infrastructure. The combination of increasing global smartphone penetration and recent advances in computer vision made possible by deep learning has paved the way for smartphone-assisted disease diagnosis. Using a public dataset of 54,306 images of diseased and healthy plant leaves collected under controlled conditions, we train a deep convolutional neural network to identify 14 crop species and 26 diseases (or absence thereof). The trained model achieves an accuracy of 99.35% on a held-out test set, demonstrating the feasibility of this approach. Overall, the approach of training deep learning models on increasingly large and publicly available image datasets presents a clear path toward smartphone-assisted crop disease diagnosis on a massive global scale."

Conclusions

"Finally, it's worth noting that the approach presented here is not intended to replace existing solutions for disease diagnosis, but rather to supplement them. Laboratory tests are ultimately always more reliable than diagnoses based on visual symptoms alone, and oftentimes early-stage diagnosis via visual inspection alone is challenging. Nevertheless, given the expectation of more than 5 Billion smartphones in the world by 2020—of which almost a Billion in Africa (GSMA Intelligence, 2016)—we do believe that the approach represents a viable additional method to help prevent yield loss. What's more, in the future, image data from a smartphone may be supplemented with location and time information for additional improvements in accuracy. Last but not least, it would be prudent to keep in mind the stunning pace at which mobile technology has developed in the past few years, and will continue to do so. With ever improving number and quality of sensors on mobiles devices, we consider it likely that highly accurate diagnoses via the smartphone are only a question of time."

Sharada P. Mohanty1,2,3  David P. Hughes4,5,6  Marcel Salathé1,2,3*
  • 1Digital Epidemiology Lab, EPFL, Geneva, Switzerland
  • 2School of Life Sciences, EPFL, Lausanne, Switzerland
  • 3School of Computer and Communication Sciences, EPFL, Lausanne, Switzerland
  • 4Department of Entomology, College of Agricultural Sciences, Penn State University, State College, PA, USA
  • 5Department of Biology, Eberly College of Sciences, Penn State University, State College, PA, USA
  • 6Center for Infectious Disease Dynamics, Huck Institutes of Life Sciences, Penn State University, State College, PA, USA

- END OF THE STUDY

'Plant Disease Identification using Artificial Intelligence: Machine Learning Approach' - 2018

"Computerization in the field of agriculture sees an extraordinary achievement in numerous farming perspectives, including detection of different plant diseases. The focal point of pretty much every nation has moved towards the mechanization of agriculture to achieve exactness and precision and to serve the consistently expanding request of food. Among the significant difficulties in agriculture, plant disease detection is a critical factor influencing the result of cultivating. Quality of vegetables, organic products, vegetables and grains is influenced by plant disease, and hefty misfortune underway and therefore monetary loses are watched, so there is a prerequisite of quick and viable plant disease detection and evaluation strategies. This paper investigates the manners by which machine learning models can be applied to improve the cycle of plant disease detection in beginning phases to improve grain security and manageability of the agro-biological system."

Conclusions

"Applications of machine learning and deep learning in the field of agriculture are picking up energy. Strategies of image preparing are utilized for precise discovery and grouping of harvest disease and the exact location and order of the plan disease’s significant for the productive development of the crop. Several industrially available items are turning out to be well-known step by step to distinguish plant diseases and recognize recuperation arrangements and help farmers in improving their yield profitability and like these benefits."

Jubin Dipakkumar Kothari1
  • 1Department of Information Technology, Campbellsville University, Campbellsville, Kentucky

- END OF THE STUDY

'How Convolutional Neural Networks Diagnose Plant Disease' - 2019

"Deep learning with convolutional neural networks (CNNs) has achieved great success in the classification of various plant diseases. However, a limited number of studies have elucidated the process of inference, leaving it as an untouchable black box. Revealing the CNN to extract the learned feature as an interpretable form not only ensures its reliability but also enables the validation of the model authenticity and the training dataset by human intervention. In this study, a variety of neuron-wise and layer-wise visualization methods were applied using a CNN, trained with a publicly available plant disease image dataset. We showed that neural networks can capture the colors and textures of lesions specific to respective diseases upon diagnosis, which resembles human decision-making. While several visualization methods were used as they are, others had to be optimized to target a specific layer that fully captures the features to generate consequential outputs. Moreover, by interpreting the generated attention maps, we identified several layers that were not contributing to inference and removed such layers inside the network, decreasing the number of parameters by 75% without affecting the classification accuracy. The results provide an impetus for the CNN black box users in the field of plant science to better understand the diagnosis process and lead to further efficient use of deep learning for plant disease diagnosis."

 Conclusion

"In this study, we evaluated an array of visualization methods to interpret the representation of plant diseases that the CNN has diagnosed. The experimental results show that some simple approaches, such as naive visualization of the hidden layer output, are insufficient for plant disease visualization, whereas several state-of-the-art approaches have potential practical applications. Feature visualization and semantic dictionary can be used to extract the visual features that are heavily used to classify a particular disease. To understand what part of the input image is important, the interpretation of attention maps is a favorable choice. However, the behavior of some approaches for generating attention maps was different from what the original study suggested because previous experiments utilized the general object recognition dataset (i.e., ImageNet), which requires the extraction of fine-grained differences, unlike the plant disease diagnosis. Our task is similar to domain-specific fine-grained visual categorization (FGVC) [], which occasionally makes the problem more challenging. This is somewhat related to the datasets of natural images (e.g., iNaturalist dataset []) that contain categories with a similar appearance. It is important to further understand what the deep networks learn for such fine-grained categorization tasks."

Yosuke Toda1,2  Fumio Okura 1,3
  • 1Japan Science and Technology Agency, 4-1-8 Honcho, Kawaguchi, Saitama 332-0012, Japan
  • 2Institute of Transformative Bio-Molecules (WPI-ITbM), Nagoya University, Chikusa, Nagoya 464-8602, Japan
  • 3Department of Intelligent Media, Institute of Scientific and Industrial Research, Osaka University, 8-1 Mihogaoka, Ibaraki, Osaka 567-0047, Japan

- END OF THE STUDY

'Convolutional Neural Networks for the Automatic Identification of Plant Diseases' - 2019

"Deep learning techniques, and in particular Convolutional Neural Networks (CNNs), have led to significant progress in image processing. Since 2016, many applications for the automatic identification of crop diseases have been developed. These applications could serve as a basis for the development of expertise assistance or automatic screening tools. Such tools could contribute to more sustainable agricultural practices and greater food production security. To assess the potential of these networks for such applications, we survey 19 studies that relied on CNNs to automatically identify crop diseases. We describe their profiles, their main implementation aspects and their performance. Our survey allows us to identify the major issues and shortcomings of works in this research area. We also provide guidelines to improve the use of CNNs in operational contexts as well as some directions for future research."

 Conclusion

"In this paper, we identified some of the major issues and shortcomings of works that used CNNs to automatically identify crop diseases. We also provided guidelines and procedures to follow in order to maximize the potential of CNNs deployed in real-world applications. Many already-published solutions based on CNNs are not currently operational for field use mostly due to a lack of conformity to several important concepts of machine learning. This lack of conformity may lead to poor generalization capabilities for unfamiliar data samples and/or imaging conditions, which lowers the practical use of the trained models. Nevertheless, the studied works show the potential of deep learning techniques for crop diseases identification. Their findings are definitely promising for the development of new agricultural tools that could contribute to a more sustainable and secure food production."

Justine Boulent1,2,3*  Samuel Foucher2  Jérôme Théau1,3  Pierre-Luc St-Charles2
  • 1Department of Applied Geomatics, Université de Sherbrooke, Sherbrooke, QC, Canada
  • 2Vision and Imagery Team, Computer Research Institute of Montréal, Montréal, QC, Canada
  • 3Quebec Centre for Biodiversity Science (QCBS), Montreal, QC, Canada

- END OF THE STUDY

'Review of the State of the Art of Deep Learning for Plant Diseases: A Broad Analysis and Discussion' - 2020

"Deep learning (DL) represents the golden era in the machine learning (ML) domain, and it has gradually become the leading approach in many fields. It is currently playing a vital role in the early detection and classification of plant diseases. The use of ML techniques in this field is viewed as having brought considerable improvement in cultivation productivity sectors, particularly with the recent emergence of DL, which seems to have increased accuracy levels. Recently, many DL architectures have been implemented accompanying visualisation techniques that are essential for determining symptoms and classifying plant diseases. This review investigates and analyses the most recent methods, developed over three years leading up to 2020, for training, augmentation, feature fusion and extraction, recognising and counting crops, and detecting plant diseases, including how these methods can be harnessed to feed deep classifiers and their effects on classifier accuracy."

Conclusions

"This review has discussed and analysed contemporary shallow and deep architectures and their highest achieved accuracy levels for plant disease detection and crop management. The use of realistic datasets, augmentation methods and different pre-training backbone models has also been analysed. Despite the successes that have been achieved in this field, there are still some challenges facing researchers and future orientations to be suggested:

- Mild symptoms of some plant diseases in their early life cycles.
- Some lesion spots have no determined shapes.
- Plant health considerations via monitoring growth and ripeness life cycle of fruits and leaves.
- Automated labelling and auto segmentation of image samples based GANs.
- The usage of hyperspectral data to feed deep classifiers is a recently developed technique that is recommended for the early detection of plant disease life cycles and the healthy leaf life cycle to differentiate it from a diseased leaf.
- Lastly, future work will include several deep learning models for early classification and detection of plant disease due to huge improvements in deep learning models and the availability in plant datasets. Therefore, that will reflect positively on the quality of plants for future generations."

Reem Ibrahim Hasan1,2 Suhaila Mohd Yusuf1  Laith Alzubaidi2,3  

  • 1School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia, Skudai, Johor 81310, Malaysia
  • 2Al-Nidhal Campus, University of Information Technology & Communications, Baghdad 00964, Iraq
  • 3Faculty of Science & Engineering, Queensland University of Technology, Brisbane, QLD 4000, Australia

- END OF THE STUDY

'Identification of Plant-Leaf Disease Using CNN and Transfer-Learning Approach' - 2021

"The timely identification and early prevention of crop diseases are essential for improving production. In this paper, deep convolutional-neural-network (CNN) models are implemented to identify and diagnose diseases in plants from their leaves, since CNNs have achieved impressive results in the field of machine vision. Standard CNN models require a large number of parameters and higher computation cost. In this paper, we replaced standard convolution with depth=separable convolution, which reduces the parameter number and computation cost. The implemented models were trained with an open dataset consisting of 14 different plant species, and 38 different categorical disease classes and healthy plant leaves. To evaluate the performance of the models, different parameters such as batch size, dropout, and different numbers of epochs were incorporated. The implemented models achieved a disease-classification accuracy rates of 98.42%, 99.11%, 97.02%, and 99.56% using InceptionV3, InceptionResNetV2, MobileNetV2, and EfficientNetB0, respectively, which were greater than that of traditional handcrafted-feature-based approaches. In comparison with other deep-learning models, the implemented model achieved better performance in terms of accuracy and it required less training time. Moreover, the MobileNetV2 architecture is compatible with mobile devices using the optimized parameter. The accuracy results in the identification of diseases showed that the deep CNN model is promising and can greatly impact the efficient identification of the diseases, and may have potential in the detection of diseases in real-time agricultural systems."

Conclusions

"There are many developed methods in the detection and classification of plant diseases using diseased leaves of plants. However, there is still no efficient and effective commercial solution that can be used to identify the diseases. In our work, we used four different DL models (InceptionV3, InceptionResnetV2, MobileNetV2, EfficientNetB0) for the detection of plant diseases using healthy- and diseased-leaf images of plants. To train and test the model, we used the standard PlantVillage dataset with 53,407 images, which were all captured in laboratory conditions. This dataset consists of 38 different classes of different healthy- and diseased-leaf images of 14 different species. After splitting the dataset into 80–20 (80% of whole data for training, 20% whole images for testing), we achieved the best accuracy rate of 99.56% in EfficientNetB0 model. On average, less time was required to train the images in the MobileNetV2 and EfficientNetB0 architectures, and it took 565 and 545 s/epoch, respectively, on colored images. In comparison with other deep-learning approaches, the implemented deep-learning model has better predictive ability in terms of both accuracy and loss. The required time to train the model was much less than that of other machine-learning approaches. Moreover, the MobileNetV2 architecture is an optimized deep convolutional neural network that limits the parameter number and operations as much as possible, and can easily run on mobile devices."

1 1  2 2  3 

  • 1Department of Information Technology, North Eastern Hill University, Shillong, Meghalaya 793022, India
  • 2Department of Electrical Engineering Fundamentals, Wroclaw University of Science and Technology, 50-370 Wroclaw, Poland
  • 3Faculty of Law, Administration and Economics, University of Wroclaw, 50-145 Wroclaw, Poland

- END OF THE STUDY

'Plant diseases and pests detection based on deep learning: a review' - 2021

"Plant diseases and pests are important factors determining the yield and quality of plants. Plant diseases and pests identification can be carried out by means of digital image processing. In recent years, deep learning has made breakthroughs in the field of digital image processing, far superior to traditional methods. How to use deep learning technology to study plant diseases and pests identification has become a research issue of great concern to researchers. This review provides a definition of plant diseases and pests detection problem, puts forward a comparison with traditional plant diseases and pests detection methods. According to the difference of network structure, this study outlines the research on plant diseases and pests detection based on deep learning in recent years from three aspects of classification network, detection network and segmentation network, and the advantages and disadvantages of each method are summarized. Common datasets are introduced, and the performance of existing studies is compared. On this basis, this study discusses possible challenges in practical applications of plant diseases and pests detection based on deep learning. In addition, possible solutions and research ideas are proposed for the challenges, and several suggestions are given. Finally, this study gives the analysis and prospect of the future trend of plant diseases and pests detection based on deep learning."

Conclusions

"In summary, with the development of artificial intelligence technology, the research focus of plant diseases and pests detection based on machine vision has shifted from classical image processing and machine learning methods to deep learning methods, which solved the difficult problems that could not be solved by traditional methods. There is still a long distance from the popularization of practical production and application, but this technology has great development potential and application value. To fully explore the potential of this technology, the joint efforts of experts from relevant disciplines are needed to effectively integrate the experience knowledge of agriculture and plant protection with deep learning algorithms and models, so as to make plant diseases and pests detection based on deep learning mature. Also, the research results should be integrated into agricultural machinery equipment to truly land the corresponding theoretical results."

Jun Liu1, Xuewei Wang1

  • 1Shandong Provincial University Laboratory for Protected Horticulture, Blockchain Laboratory of Agricultural Vegetables, Weifang University of Science and Technology, Weifang, 262700, Shandong, China

- END OF THE STUDY

'Convolutional Neural Networks in Detection of Plant Leaf Diseases: A Review' - 2022

"Rapid improvements in deep learning (DL) techniques have made it possible to detect and recognize objects from images. DL approaches have recently entered various agricultural and farming applications after being successfully employed in various fields. Automatic identification of plant diseases can help farmers manage their crops more effectively, resulting in higher yields. Detecting plant disease in crops using images is an intrinsically difficult task. In addition to their detection, individual species identification is necessary for applying tailored control methods. A survey of research initiatives that use convolutional neural networks (CNN), a type of DL, to address various plant disease detection concerns was undertaken in the current publication. In this work, we have reviewed 100 of the most relevant CNN articles on detecting various plant leaf diseases over the last five years. In addition, we identified and summarized several problems and solutions corresponding to the CNN used in plant leaf disease detection. Moreover, Deep convolutional neural networks (DCNN) trained on image data were the most effective method for detecting early disease detection. We expressed the benefits and drawbacks of utilizing CNN in agriculture, and we discussed the direction of future developments in plant disease detection."

Conclusions

"CNN methods are widely used in the detection of plant diseases. It has solved the problems of traditional object detection and classification methods. In this study, we presented a detailed review of CNN-based research on plant leaf disease detection in crops over the last five years. A total of 100 publications were reviewed based on detection methods and model performance evaluation, comparison of popular CNN frameworks, detailed description of CNN applications in agricultural fields, dataset preparation, the problem and solution related to plant leaf disease detection, and publicly released datasets in the relevant field. We addressed highly related research articles to present a comparative analysis of various CNN models.

Most studies used CNN approaches, and they note that pre-training models compared with training from scratch models on plant leaf datasets can quickly improve performance accuracy, especially if there is a sufficient dataset for each class to train the models. Moreover, we found that most CNN approaches have many problems and challenges, one of which is the lack of dataset, a severe challenge that researchers face while doing their work. However, an essential future impact would be to develop highly efficient detection approaches employing large datasets with different plant leaf diseases. This would also address the class imbalance by requiring large generalized datasets."

1 1  1  

  • 1Department of Computer Engineering, Ankara University, Ankara 06830, Türkiye

- END OF THE STUDY

'Leaf disease identification and classification using optimized deep learning' - 2023

"Diseases that affect plant leaves stop the growth of their individual species. Early and accurate diagnosis of plant diseases may reduce the likelihood that the plant will suffer further harm. The intriguing approach needed more time, exclusivity, and skill. Images of leaves are used to identify plant leaf diseases. Research on deep learning (DL) appears to have a lot of potential for improved accuracy. The substantial advancements and expansions in deep learning have created the opportunity to improve the coordination and accuracy of the system for identifying and appreciating plant leaf diseases. This study presents an innovative deep learning technique for disease detection and classification named Ant Colony Optimization with Convolution Neural Network (ACO-CNN).The effectiveness of disease diagnosis in plant leaves was investigated using ant colony optimization (ACO). Geometries of colour, texture, and plant leaf arrangement are subtracted from the provided images using the CNN classifier. A few of the effectiveness metrics used for analysis and proposing a suggested method prove that the proposed approach performs better than existing techniques with an accuracy rate concert measures are utilized for the execution of these approaches. These steps are used in the phases of disease detection: picture acquisition, image separation, nose removal, and classification."

Conclusions

"Crop protection in organic farming is not an easy chore.This needs thorough knowledge about weeds, pathogens, possible pests, and the crop being grown. Early identification of leaf diseases is essential to the agricultural industry. Here is the basic concept of plant leafinfection identification and plant leaf infection symptoms.For test real-time images for leaf disease identification the traditional method has been used. The proposed method can provideprovision for farmerstodetect and recognize plant leaf diseases.Here theACO-CNN optimization approach is proposed for leaf disease detection.ACOwas used for the feature extraction and CNN classifier hasused for the organization.The proposed method is used to detect the infected leaf from the healthy leaf."

Yousef Methkal Abd Algani1  Orlando Juan Marquez Caro2  Liz Maribel Robladillo Bravo2  Chamandeep Kaur3  Mohammed Saleh Al Ansari4  B. Kiran Bala5
  • 1Department of Mathematics, The Arab Academic College for Education in Israel-Haifa, Israel
  • 2Universidad César Vallejo, Peru
  • 3Dept of IT, Jazan University, Saudi Arabia
  • 4College of Engineering, Department of Chemical Engineering, University of Bahrain, Bahrain
  • 5Department of Artificial Intelligence and Data Science, K.Ramakrishnan College of Engineering, Trichy, Tamil Nadu, India

- END OF THE STUDY

'An advanced deep learning models-based plant disease detection: A review of recent research' - 2023

"Plants play a crucial role in supplying food globally. Various environmental factors lead to plant diseases which results in significant production losses. However, manual detection of plant diseases is a time-consuming and error-prone process. It can be an unreliable method of identifying and preventing the spread of plant diseases. Adopting advanced technologies such as Machine Learning (ML) and Deep Learning (DL) can help to overcome these challenges by enabling early identification of plant diseases. In this paper, the recent advancements in the use of ML and DL techniques for the identification of plant diseases are explored. The research focuses on publications between 2015 and 2022, and the experiments discussed in this study demonstrate the effectiveness of using these techniques in improving the accuracy and efficiency of plant disease detection. This study also addresses the challenges and limitations associated with using ML and DL for plant disease identification, such as issues with data availability, imaging quality, and the differentiation between healthy and diseased plants. The research provides valuable insights for plant disease detection researchers, practitioners, and industry professionals by offering solutions to these challenges and limitations, providing a comprehensive understanding of the current state of research in this field, highlighting the benefits and limitations of these methods, and proposing potential solutions to overcome the challenges of their implementation."

Conclusions

"The DL and ML technologies have greatly improved the detection and management of crop and plant infestations. Advances in image recognition have made it possible to identify complicated diseases and pests. However, most research in this area is limited to lab-based studies and heavily relies on collected plant disease and pest photos. To enhance the robustness and generalization of the model, it’s important to gather images from various plant growth stages, seasons, and regions. Early identification of plant diseases and pests is crucial in preventing and controlling their spread and growth, thus incorporating meteorological and plant health data, such as temperature and humidity, is necessary for efficient identification and prediction. Unsupervised learning and integrating past knowledge of brain-like computers with human visual cognition can aid in DL model training and network learning. Achieving the full potential of this technology requires collaboration between specialists from agriculture and plant protection, combining their knowledge and experience with DL algorithms and models, and integrating the results into farming equipment. The paper explores the recent progress in using ML and DL techniques for plant disease identification, based on publications from 2015 to 2022. It demonstrates the benefits of these techniques in increasing the accuracy and efficiency of disease detection, but also acknowledges the challenges, such as data availability, imaging quality, and distinguishing healthy from diseased plants. The study finds that the use of DL and ML has significantly improved the ability to identify and detect plant diseases. The novelty of this research lies in its comprehensive analysis of the recent developments in using ML and DL techniques for plant disease identification, along with proposed solutions to address the challenges and limitations associated with their implementation. By exploring the benefits and drawbacks of various methods, and offering valuable insights for researchers and industry professionals, this study contributes to the advancement of plant disease detection and prevention."

Muhammad Shoaib1,2†  Babar Shah3  Shaker EI-Sappagh4,5  Akhtar Ali6  Asad Ullah2  Fayadh Alenezi7  Tsanko Gechev6,8  Tariq Hussain9*  Farman Ali10*†
  • 1Department of Computer Science, CECOS University of IT and Emerging Sciences, Peshawar, Pakistan
  • 2Department of Computer Science and Information Technology, Sarhad University of Science and Information Technology, Peshawar, Pakistan
  • 3College of Technological Innovation, Zayed University, Dubai, United Arab Emirates
  • 4Faculty of Computer Science and Engineering, Galala University, Suez, Egypt
  • 5Information Systems Department, Faculty of Computers and Artificial Intelligence, Benha University, Banha, Egypt
  • 6Department of Molecular Stress Physiology, Center of Plant Systems Biology and Biotechnology, Plovdiv, Bulgaria
  • 7Department of Electrical Engineering, College of Engineering, Jouf University, Jouf, Saudi Arabia
  • 8Department of Plant Physiology and Molecular Biology, University of Plovdiv, Plovdiv, Bulgaria
  • 9School of Computer Science and Information Engineering, Zhejiang Gongshang University, Hangzhou, China
  • 10Department of Computer Science and Engineering, School of Convergence, College of Computing and Informatics, Sungkyunkwan University, Seoul, Republic of Korea

- END OF THE STUDY

'The Shift from Models to Compound AI Systems' - 2024

"AI caught everyone’s attention in 2023 with Large Language Models (LLMs) that can be instructed to perform general tasks, such as translation or coding, just by prompting. This naturally led to an intense focus on models as the primary ingredient in AI application development, with everyone wondering what capabilities new LLMs will bring. As more developers begin to build using LLMs, however, we believe that this focus is rapidly changing: state-of-the-art AI results are increasingly obtained by compound systems with multiple components, not just monolithic models.

For example, Google’s AlphaCode 2 set state-of-the-art results in programming through a carefully engineered system that uses LLMs to generate up to 1 million possible solutions for a task and then filter down the set. AlphaGeometry, likewise, combines an LLM with a traditional symbolic solver to tackle olympiad problems. In enterprises, our colleagues at Databricks found that 60% of LLM applications use some form of retrieval-augmented generation (RAG), and 30% use multi-step chains. Even researchers working on traditional language model tasks, who used to report results from a single LLM call, are now reporting results from increasingly complex inference strategies: Microsoft wrote about a chaining strategy that exceeded GPT-4’s accuracy on medical exams by 9%, and Google’s Gemini launch post measured its MMLU benchmark results using a new CoT@32 inference strategy that calls the model 32 times, which raised questions about its comparison to just a single call to GPT-4. This shift to compound systems opens many interesting design questions, but it is also exciting, because it means leading AI results can be achieved through clever engineering, not just scaling up training."

Conclusions

"Generative AI has excited every developer by unlocking a wide range of capabilities through natural language prompting. As developers aim to move beyond demos and maximize the quality of their AI applications, however, they are increasingly turning to compound AI systems as a natural way to control and enhance the capabilities of LLMs. Figuring out the best practices for developing compound AI systems is still an open question, but there are already exciting approaches to aid with design, end-to-end optimization, and operation. We believe that compound AI systems will remain the best way to maximize the quality and reliability of AI applications going forward, and may be one of the most important trends in AI in 2024."

Matei Zaharia1, Omar Khattab1, Lingjiao Chen1, Jared Quincy Davis1, Heather Miller1, Chris Potts1, James Zou1, Michael Carbin1, Jonathan Frankle1, Naveen Rao1, Ali Ghodsi1   
  • BAIR researchers, Berkley Artificial Intelligence Research, UC Berkley, California

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