14,687 research outputs found
Empowering the Knowledge Worker with Artificial Intelligence (AI) Deploying IBM Watson at MITRE
No full textIn the past few years, major advancements in the artificial intelligence (AI) sector have peaked the interest of many companies. In 2019, experts see growing confidence that this smart, predictive technology, bolstered by learnings has picked up in its initial deployments and can be rolled out at a wholesale level across all business operations (Marr, 2019). Many businesses are asking if this is the right time to invest in the technology, and one of them is the MITRE corporation.
Our proposal will explore the feasibility of introducing IBM Watson, an AI tool, into the MITRE organization. We will conduct a full analysis of the following areas: Technical, Cyber Risk Analysis, Ethical, Finance and Business Case. This paper will highlight pros and cons of investing in IBM Watson, so that MITRE can make a decision accordingly
Human-AI Collaboration in Academic Writing: towards a Synergy Model and A Case to Include AI as a Co-Author
No full textAs generative AI systems such as ChatGPT and Gemini 2.5 become increasingly integrated into academic workflows, the question of their legitimacy, limitations, and potential in scholarly writing has become urgent. This paper presents a reflexive case study of a sustained collaboration between a domain expert in consciousness studies and Gemini 2.5, culminating in the co-authorship of a peer-reviewed research article. By analyzing exactly 37,440 words of recorded interactions, we identify patterns of synergy, including recursive refinement, conceptual amplification, and accelerated manuscript development. We argue that when guided by a knowledgeable human author, AI can act as a cognitive partner rather than a passive tool—amplifying scholarly creativity and improving efficiency without compromising academic rigor. The case supports a '1+1=3' synergy model for co-authorship, in which human steering and AI fluency converge to produce novel insights and polished output faster and more effectively than either could achieve alone. The findings advocate for a paradigm shift from prohibitive policies to the responsible, expert-guided integration of AI in academic research and writing, grounded in transparency and accountability, and present arguments for why the AI tool should be listed as a co-author despite current injunctions against such practice
Learning Occupational Task-Shares Dynamics for the Future of Work
No full text© 2020 Copyright held by the owner/author(s). The recent wave of AI and automation has been argued to differ from previous General Purpose Technologies (GPTs), in that it may lead to rapid change in occupations' underlying task requirements and persistent technological unemployment. In this paper, we apply a novel methodology of dynamic task shares to a large dataset of online job postings to explore how exactly occupational task demands have changed over the past decade of AI innovation, especially across high, mid and low wage occupations. Notably, big data and AI have risen significantly among high wage occupations since 2012 and 2016, respectively. We built an ARIMA model to predict future occupational task demands and showcase several relevant examples in Healthcare, Administration, and IT. Such task demands predictions across occupations will play a pivotal role in retraining the workforce of the future
A Review of Real-Time Strategy Game AI
No full textThis literature review covers AI techniques used for real-time strategy video games, focusing specifically on StarCraft. It finds that the main areas of current academic research are in tactical and strategic decision-making, plan recognition, and learning, and it outlines the research contributions in each of these areas. The paper then contrasts the use of game AI in academia and industry, finding the academic research heavily focused on creating game-winning agents, while the indus- try aims to maximise player enjoyment. It finds the industry adoption of academic research is low because it is either in- applicable or too time-consuming and risky to implement in a new game, which highlights an area for potential investi- gation: bridging the gap between academia and industry. Fi- nally, the areas of spatial reasoning, multi-scale AI, and co- operation are found to require future work, and standardised evaluation methods are proposed to produce comparable re- sults between studies.</jats:p
Report to the President for year ended June 30, 2021, MIT IBM Watson AI Lab
No full textThis report contains the following sections: Goals and Priorities; Industry Research Collaborations; Selected Research Overview; Student Engagement; Undergraduate Research Opportunities; Master and Graduate Student Opportunities; Community Outreach and Events; Communications; Administration and Governanc
Report to the President for year ended June 30, 2023, MIT-IBM Watson AI Lab
No full textThis report contains the following sections: Overview; Goals and Priorities; Industry Research Collaborations; Selected Research Overview; Student and Young Researcher Engagement; Postdoctoral, master’s, graduate, and undergraduate student opportunities in the Lab; Community Outreach and Events; Communications; Administration and Governanc
Report to the President for year ended June 30, 2025, MIT-IBM Watson AI Lab
No full textThis report contains the following sections: Goals and Priorities, Industry Research Collaborations, Selected Research Overview, Student and Young Researcher Engagement, Community Outreach and Events, Communications, and Administration and Governance
Report to the President for year ended June 30, 2024, MIT-IBM Watson AI Lab
No full textThis report contains the following sections: Goals and Priorities, Industry Research Collaborations, Selected Research Overview, Student and Young Researcher Engagement, Community Outreach Events, Communications, and Administration and Governance
Artificial Intelligence reassessed through IBM’s Watson: The Dawning of AI Expertise
No full textI denne afhandling præsenterer jeg min forskning om kunstig intelligens (KI) og de forudsigelser der er forbundet med det. Jeg undersøger også en af de nuværende former - Watson, teknologi som er udviklet af IBM, som har fået verdensomspændende opmærksomhed efter at have vundet 'Jeopardy! "Over menneskelige deltagere. Endvidere vil jeg udnytte min analyse af aktuelle udvikling i KI - Watson, med henblik på at afgøre, om KI er i stand til at udvikle ekspertise. Til det formål jeg analyserer, hvad der udgør ekspertise i mennesker, og undersøge, om de nødvendige bestanddele er til stede i Watson, ved at antage at den nuværende KI generelt er bygget modellering menneskelig kognition, i betragtning af at det er en kognitiv teknologi.In this thesis I present my research on artificial intelligence (AI) and predictions associated with it. I also examine one of its current forms – Watson, cognitive technology developed by IBM, which has gained worldwide attention after winning ‘Joepardy!’ over human contestants. Further, I utilize my analysis of current development in AI – Watson, in order to determine if AI is capable of developing expertise. For that purpose I analyze what constitutes expertise in humans and examine if the necessary constituents are present in Watson, presuming that current AI is generally build modeling human cognition, considering that it is a cognitive technology. <br/
Meaningful human control: actionable properties for AI system development
No full textHow can humans remain in control of artificial intelligence (AI)-based systems designed to perform tasks autonomously? Such systems are increasingly ubiquitous, creating benefits - but also undesirable situations where moral responsibility for their actions cannot be properly attributed to any particular person or group. The concept of meaningful human control has been proposed to address responsibility gaps and mitigate them by establishing conditions that enable a proper attribution of responsibility for humans; however, clear requirements for researchers, designers, and engineers are yet inexistent, making the development of AI-based systems that remain under meaningful human control challenging. In this paper, we address the gap between philosophical theory and engineering practice by identifying, through an iterative process of abductive thinking, four actionable properties for AI-based systems under meaningful human control, which we discuss making use of two applications scenarios: automated vehicles and AI-based hiring. First, a system in which humans and AI algorithms interact should have an explicitly defined domain of morally loaded situations within which the system ought to operate. Second, humans and AI agents within the system should have appropriate and mutually compatible representations. Third, responsibility attributed to a human should be commensurate with that human’s ability and authority to control the system. Fourth, there should be explicit links between the actions of the AI agents and actions of humans who are aware of their moral responsibility. We argue that these four properties will support practically minded professionals to take concrete steps toward designing and engineering for AI systems that facilitate meaningful human control.Interactive IntelligenceDesign AestheticsCyber SecurityHuman-Robot InteractionEthics & Philosophy of TechnologyHuman Information Communication DesignWeb Information System
- …
