Starting from the current state of generative artificial intelligence (AIGC) and large language models (LLM), I will first discuss the basic principles and shortcomings of the latest AIGC products, such as ChatGPT and Sora, along with their future improvements and development trends. I will mainly elaborate on the important roles and value of AIGC in the biopharmaceutical field. Recently, ChatGPT outperformed 17 doctors by accurately diagnosing arare disease in a 4-year-old boy. This demonstrates that, when applied appropriately, AI canindeed become an assistant in diagnosing and treating diseases. However, a study published in JAMA by Brigham and Women's Hospital, affiliated with Harvard University, showed that ChatGPT's cancer treatment recommendations were only completely accurate in 62‥ of cases, indicating that its results should be applied cautiously. One solution to this issue is the use ofcontent detection tools, such as AIGC-X and ZeroGPT. The vast information behind ChatGPT is an advantage, but in specialized fields, it also brings the downside of excessive interference information. To address this, our team has developed a large language model knowledge vector library system for autism that reduces training time and achieves similar objectives using only a small amount of training data. This lecture will also introduce the use of AIGC indesigning new drug molecules. By inputting numerous small drug molecules related to the treatment of a particular disease into the AIGC system, new drug molecules can be generated. Coupled with our powerful AI drug screening capabilities, we have the potential to design new drugs suitable for specific targets.
Edge-cloud networks connect a wide range of systems and devices, ranging from large data centers to small IoT devices. The potential advantages of adopting edge and cloud networking include the quick adaptation of new technologies, including a faster rollout and adoption of software and feature updates, as well as better management of various resources, including network and edge devices. This talk provides an overview of some challenges in efficient support for running AI/ML algorithms in edge-cloud networks. Our focus is on low latency, connectivity, and local data processing while still achieving efficiency. We will look attwo specific examples of AI/ML implementations: one is the optimal offloading of AI/ML code from IoT/edge devices to the cloud, and the other is exploring network topology and connectivity for efficient decentralized federated learning.
Starting from the current state of generative artificial intelligence (AIGC) and large language models (LLM), I will first discuss the basic principles and shortcomings of the latest AIGC products, such as ChatGPT and Sora, along with their future improvements and development trends. I will mainly elaborate on the important roles and value of AIGC in the biopharmaceutical field. Recently, ChatGPT outperformed 17 doctors by accurately diagnosing a rare disease in a 4-year-old boy. This demonstrates that, when applied appropriately, AI can indeed become an assistant in diagnosing and treating diseases. However, a study published in JAMA by Brigham and Women’s Hospital, affiliated with Harvard University, showed that ChatGPT's cancer treatment recommendations were only completely accurate in 62% of cases, indicating that its results should be applied cautiously. One solution to this issue is the use of content detection tools, such as AIGC-X and ZeroGPT. The vast information behind ChatGPT is an advantage, but in specialized fields, it also brings the downside of excessive interference information. To address this, our team has developed a large language model knowledge vector library system for autism that reduces training time and achieves similar objectives using only a small amount of training data. This lecture will also introduce the use of AIGC in designing new drug molecules. By inputting numerous small drug molecules related to the treatment of a particular disease into the AIGC system, new drug molecules can be generated. Coupled with our powerful AI drug screening capabilities, we have the potential to design new drugs suitable for specific targets.
Sarcasm is used on social media to humorously criticize or hurt specific individuals or things by expressing emotions opposite to the literal meaning of the text. Therefore, sarcasm detection is necessary to analyze people's true feelings. Recent research has proposed sarcasm detection methods using commonsense. Commonsense refers to the general information that humans possess and is knowledge helpful in understanding everyday situations. The existing sarcasm detection model, as focused on in this study, utilizes only one specific type of commonsense. However, verification using other types of commonsense has not been conducted. Additionally, although the existing model also proposes three explicit knowledge selection strategies based on sentiment polarity to narrow down the generated commonsense, the optimal strategies for different datasets are not identical. Therefore, it is necessary to obtain useful and appropriate commonsense information for sarcasm detection at the stage of commonsense generation. In this study, we compare the performance of sarcasm detection models using various types of commonsense and aim to select appropriate types of commonsense. We also make improvements in the input text to the commonsense generation model and post-processing after commonsense generation. Furthermore, we attempt to concatenate multiple types of commonsense for further performance improvement. The experimental results show that the model performance varies depending on the type of commonsense across three datasets. It is verified that acquiring useful commonsense for sarcasm detection through appropriate type selection can improve performance compared to the existing model.
WiFi backscatter can transmit its data by reflecting or not reflecting the WiFi signal. It can be well integrated into existing Internet of Things (IoT) devices to realize the transmission of tag data and the connection between WiFi devices. However, achieving high-rate backscatter communication on commercial WiFi devices is a challenge, especially without frequency shift. This paper proposes SymbolBack, a new backscatter communication system. By manually controlling the media access control (MAC) payload, we make data subcarriers of the transmitted orthogonal frequency division multiplexing (OFDM) symbols exactly the same. We set unique reference data for WiFi packets of different protocols, which only use a small number of symbols as reference symbols. We design a decoding algorithm based on the prediction of subsequent bits and the calculation of the Hamming distance. Tag bits are decoded by comparing the current symbol with all possible reference symbols. Decoding is done within the packet, thus the system only needs one receiver and no frequency shift. We validate our system on simulated environments and system prototypes. We show that our system achieves less than 0.1% bit error rate (BER) and a transfer rate of 60kbps at most.
