Human-centered AI (HCAI), as a design philosophy, advocates prioritizing humans in designing, developing, and deploying intelligent systems, aiming to maximize the benefits of AI technology to humans and avoid its potential adverse effects. While HCAI has gained momentum, the lack of guidance on methodology in its implementation makes its adoption challenging. After assessing the needs for a methodological framework for HCAI, this paper first proposes a comprehensive and interdisciplinary HCAI methodological framework integrated with seven components, including design goals, design principles, implementation approaches, design paradigms, interdisciplinary teams, methods, and processes. THe implications of the framework are also discussed. This paper also presents a "three-layer" approach to facilitate the implementation of the framework. We believe the proposed framework is systematic and executable, which can overcome the weaknesses in current frameworks and the challenges currently faced in implementing HCAI. Thus, the framework can help put it into action to develop, transfer, and implement HCAI in practice, eventually enabling the design, development, and deployment of HCAI-based intelligent systems.

Neural network architectures are most often conceptually designed and described in visual terms, but are implemented by writing error-prone code. PrototypeML is a machine learning development environment that bridges the dichotomy between the design and development processes: it provides a highly intuitive visual neural network design interface that supports (yet abstracts) the full capabilities of the PyTorch deep learning framework, reduces model design and development time, makes debugging easier, and automates many framework and code writing idiosyncrasies. In this paper, we detail the deep learning development deficiencies that drove the implementation of PrototypeML, and propose a hybrid approach to resolve these issues without limiting network expressiveness or reducing code quality. We demonstrate the real-world benefits of a visual approach to neural network design for research, industry and teaching.

SAN FRANCISCO – Engineers need a new class of tools to keep up with the complexity of designing today's semiconductors, said a keynoter at the International Solid State Circuits Conference (ISSCC) here Monday (Feb. Separate tools need to target today's four major markets using new techniques and assumptions including machine learning, said Cliff Hou, vice president of R&D at TSMC. "We need a new design paradigm to overcome chip design challenges," said Hou. "It's time for us to evolve our design paradigm, we've only covered a small portion of" the design space, he said. Over the last 10 years the industry has been driven by mobile, building its design databases around smartphone SoCs. "Now we realize mobile is OK as a starting point but we also have to optimize circuits for automotive, high- performance systems and IoT where the considerations are very different," Hou said, showing four different SRAM designs TSMC uses just for a range of mobile and wearable designs. Hou's keynote gave a laundry list of knotty challenges where TSMC is seeing some progress.