The question whether algorithmic trading systems (ATS) can improve human trading in terms of effectiveness is eliciting an increasingly relevant debate among traders and investors, as well as quantitative studies that address this issue through numerical testing [[9]]. In recent years, the discussion regarding whether algorithmic trading systems (ATS) can surpass human traders in terms of efficiency, consistency, and adaptability has gained significant traction in both academic and professional circles. Empirical evidence indicates that algorithmic strategies tend to exhibit superior performance in volatile or declining markets, whereas human-managed funds may retain a relative advantage during upward market trends due to behavioral and intuitive factors [[2]]. Moreover, large-scale behavioral studies reveal that algorithms largely eliminate well-known cognitive biases such as the disposition effect that continue to affect human traders [[23]]. Complementary research has also emphasized the growing integration of artificial intelligence and machine learning methods in modern ATS, which enhances predictive accuracy and execution speed [[7]]. Nonetheless, experimental findings suggest that algorithmic trading may still be constrained by design limitations, challenging the notion of its absolute superiority over human decision-making [[16]]. These findings collectively indicate that algorithmic and human trading approaches might be best viewed as complementary, each offering unique strengths under different market conditions.

Trading range breakout (TRB) is a key method in the technical analysis of financial trading, widely employed by traders in financial markets such as stocks, futures, and foreign exchange. However, distinguishing between true and false breakout and providing the correct rationale cause significant challenges to investors. Recently, large language models have achieved success in various downstream applications, but their effectiveness in the domain of financial breakout detection has been subpar. The reason is that the unique data and specific knowledge are required in breakout detection. To address these issues, we introduce BreakGPT, the first large language model for financial breakout detection. Furthermore, we have developed a novel framework for large language models, namely multi-stage structure, effectively reducing mistakes in downstream applications. Experimental results indicate that compared to GPT-3.5, BreakGPT improves the accuracy of answers and rational by 44%, with the multi-stage structure contributing 17.6% to the improvement. Additionally, it outperforms ChatGPT-4 by 42.07%. Our Code is publicly available: https://github.com/Neviim96/BreakGPT

Doctors believe Artificial Intelligence is now saving lives, after a major advancement in breast cancer screenings. A.I. is detecting early signs of the disease, in some cases years before doctors would find the cancer on a traditional scan. Artificial intelligence could hold the key to "the fountain of youth" for America's aging population, as AI-powered fitness equipment stakes a bigger claim in the world of health, according to the CEO of a high-tech gym franchise. When COVID-19 pandemic restrictions kept Americans from public spaces, including gyms, people flocked to innovative ways to stay in shape, including downloading apps such as FitnessAI, which generates personalized workouts using AI, or buying personal expensive AI-powered equipment. The Exercise Coach, which has fitness studios across the country and overseas in Japan, has risen in popularity since 2020 by the tune of 125%, according to the company's CEO Brian Cygan.

When San Francisco went into COVID-19 lockdown on March 17, the last thing 32-year-old tech entrepreneur Niket Desai had to worry about was staying fit. His regular spot, Barry's, would be closed indefinitely, but Desai had installed the Tempo Studio, an all-in-one home fitness device designed to turn 30 square feet of your living room into an artificial- intelligence-powered micro gym. Tempo is a six-foot-tall weight cabinet (weights included!) While similar devices, like Tonal, offer digital resistance training at home, Tempo is the first one to deploy 3D movement analysis, combined with machine learning and AI to improve your form and curate your workouts. Its screen streams more than 200 live and on-demand classes, from a ten-minute high-intensity workout to an hour of mobility training, while its motion sensors and AI isolate up to 25 different joints at 30 frames per second.

Streptococcus pneumoniae is the most common human respiratory pathogen, and β-lactam antibiotics have been employed to treat infections caused by S. pneumoniae for decades. However, the high variability of PBPs in clinical isolates and their mosaic gene structure hamper the predication of resistance level according to the PBP gene sequences. A research group led by Prof. FENG Jie at Institute of Microbiology of the Chinese Academy of Sciences developed a systematic strategy for applying supervised machine learning (SL) to predict antimicrobial susceptibility testing (AST) of β-lactam antibiotic resistance. The study was published in Briefings in Bioinformatics. The published PBP sequences with minimum inhibitory concentration (MIC) values and the sequences from NCBI database without MIC values were served as labelled data and unlabeled data, respectively. The performances of SL models were evaluated by cross-validation: the labelled data set was randomly split into 80% training set and 20% test set 100 times.