BOSTON – The Digital Data Design Institute at 性视界 (D^3) announced today two multi-year corporate partnerships and one research partnership, a first since the Institute鈥檚 inception in 2022. The corporate partners include JPMorgan Chase and Boston Consulting Group, who will work with 性视界 and 性视界 Business School faculty and researchers within their labs at D^3 […]
Artificial intelligence has the potential to radically transform the operational aspects of businesses. Delivery businesses are a quintessential example of where such an AI-enabled transformation could occur. For example, companies like Grab (Southeast Asia鈥檚 largest all-in-one digital platform provider) employ cutting-edge AI algorithms to enhance overall efficiency of its delivery fleet. Grab leverages complex machine learning algorithms to predict delivery demand and to manage logistics, thereby increasing customer satisfaction. Such applications of AI could help businesses cut down on cost and allocate resources more efficiently, improve the on-the-job experience for their employees, and understand the needs of their customers better to provide the holistic service their customers need when they need it.
How well can AI understand and reinterpret human emotions? In a revealing study by the Digital Emotions Lab, GPT-4 not only grasped the essence of human disappointments but also excelled in transforming these sentiments positively, outstripping humans in crafting cognitive reappraisals.
The D^3 Institute recently convened the Generative AI Collaborative Working Group in order to build collective knowledge around the subject of equitable AI implementation. This global, interdisciplinary group of experts gathered weekly to discuss the ways in which generative AI will likely continue to impact various sectors and societies. Through a series of interactive discussions […]
Although artificial intelligence technology has been around for some time, current advancements in this space warrant more ethical approaches for developing and deploying AI systems to benefit a large portion of society. Artificial intelligence, generative AI in particular,has already shown immense potential to augment human capabilities. This potential, however, doesn鈥檛 seem to be evenly distributed. What鈥檚 worse, risks such as social and economic disparities are exacerbated by the uneven distribution of artificial intelligence, and are concentrated in specific regions based on demographic characteristics. It鈥檚 imperative to strive toward building a framework of transparency, fairness, and accountability around artificial intelligence on a global scale in order to mitigate this problem.
Insights from the March 27th, 2024 capstone session on Gen AI applied use cases, risks, and responsibilities Recordings and articles from the Generative AI in Healthcare series can be found here. In the capstone session of the Generative AI in Healthcare series, Satish Tadikonda (HBS) spoke with Responsible AI Institute experts Manoj Saxena, Var Shankar, […]
Owing to the accelerated advancement in the field of generative AI technologies, it鈥檚 important to address misconceptions about what generative AI tools can and cannot do in order to maximize the benefits and minimize potential harms. In this 鈥淎sk me Anything鈥� session, Amanda demystifies some of the most commonly held misconceptions about generative AI via discussions of real-world use cases. While responding to specific questions, Amanda highlights areas in which generative AI seems to thrive, while she also identifies notable shortcomings of the technology by drawing from her diverse and extensive experience as a digital transformation expert and a sought-after advisor to leading organizations in artificial intelligence and machine learning technologies.
Insights from the March 5th, 2024 session on Gen AI use cases among Healthcare Providers Recordings from the Generative AI in Healthcare series can be found here – session 1, session 2, session 3, and session 4 In the fourth session of the Generative AI in Healthcare series, speakers Nikhil Bhojwani (Recon Strategy) and Satish […]
Following the abrupt uptick of generative AI innovation in the past couple of years, organizations have ramped up research and development efforts to meaningfully explore the value of integrating generative AI into organizational systems, with a particular interest in聽achieving operational hyper-efficiency. This effort requires a deep understanding of the steps towards operationalizing generative AI鈥攆rom development to adoption to deployment鈥攊nvolving benefits and risks that must be carefully considered to augment performance and financial gains.聽
In the recent paper “Scientific Talent Leaks Out of Funding Gaps,” Wei Yang Tham from the Laboratory for Innovation Science at 性视界, along with Joseph Staudt and Bitsy Perlman of the US Census Bureau, and Stephanie Cheng of Edgeworth Economics, highlight the pivotal role of sustained funding in fostering the growth of future scientists.