TaskMatrix.AI: Completing Tasks by Connecting Foundation Models with Millions of APIs

Artificial Intelligence (AI) has made incredible progress recently. On the one hand, advanced foundation models like ChatGPT can offer powerful conversation, in-context learning and code generation abilities on a broad range of open-domain tasks. They can also generate high-level solution outlines for domain-specific tasks based on the common sense knowledge they have acquired. However, they still face difficulties with some specialized tasks because they lack enough domain-specific data during pre-training or they often have errors in their neural network computations on those tasks that need accurate executions. On the other hand, there are also many existing models and systems (symbolic-based or neural-based) that can do some domain-specific tasks very well. However, due to the different implementation or working mechanisms, they are not easily accessible or compatible with foundation models. Therefore, there is a clear and pressing need for a mechanism that can leverage foundation models to propose task solution outlines and then automatically match some of the sub-tasks in the outlines to the off-the-shelf models and systems with special functionalities to complete them. Inspired by this, we introduce TaskMatrix.AI as a new AI ecosystem that connects foundation models with millions of APIs for task completion. Unlike most previous work that aimed to improve a single AI model, TaskMatrix.AI focuses more on using existing foundation models (as a brain-like central system) and APIs of other AI models and systems (as sub-task solvers) to achieve diversified tasks in both digital and physical domains. As a position paper, we will present our vision of how to build such an ecosystem, explain each key component, and use study cases to illustrate both the feasibility of this vision and the main challenges we need to address next

Numerical Simulation of LVAD Inflow Cannulas with Different Tip

The tip structure of LVAD inflow cannula is one of major factors to lead adverse events such as thrombosis and suction leading to obstruction. In this research, four kinds of tips that had been used in inflow cannulas were selected and designed. The flow field of the four inflow cannulas inserted into the apex of left ventricle (LV) was numerically computed by computational fluid dynamics. The flow behavior was analyzed in order to compare the blood compatibility and suction in left ventricle and cannulas after the inflow cannulas with different tips were inserted to the apex of LV. The results showed that the cannula tip structure affected the LVAD performance. Among these four cannulas, the trumpet-tipped inflow cannula owned the best performance in smooth flow velocity distribution without backflow or low-velocity flow so that it was the best in blood compatibility. Nevertheless, the caged tipped cannula was the worst in blood compatibility. And the blunt-tipped and beveled tipped inflow cannulas may obstruct more easily than trumpet and caged tipped inflow cannulas because of their shape. The study indicated that the trumpet tip was the most preferable for the inflow cannula of long-term LVAD

Central aortic valve coaptation area during diastole as seen by 64-multidetector computed tomography (MDCT)

As multiple new procedures now require better visualization of the aortic valve, we sought to better define the central aortic valve coaptation area seen during diastole on multi-detector row cardiac computed tomography (MDCT). 64-MDCT images of 384 symptomatic consecutive patients referred for coronary artery disease evaluation were included in the study. Planimetric measurements of this area were performed on cross-sectional views of the aortic valve at 75% phase of the cardiac cycle. Planimetric measurement of central regurgitation orifice area (ROA) seen in patients with aortic regurgitation and Hounsfield units of the central aortic valve coaptation area were performed. Mean area of the central aortic valve coaptation area was 5.34 ± 5.19 mm2 and Hounsfield units in this area were 123.69 ± 31.31 HU. The aortic valve coaptation area (mm2) measurement in patients without AR was: 4.90 ± 0.17 and in patients with AR: 10.53 ± 0.26 (P = <0.05). On Bland–Altman analysis a very good correlation between central aortic valve coaptation area and central ROA was found (r = 0.80, P = <0.001). Central aortic valve coaptation area is a central area present at the coaptation of nodules of arantius of aortic cusps during diastole; it is incompetent and increased in size in patients with aortic regurgitation