AI Without Math: Making AI and ML Comprehensible

If we want nontechnical stakeholders to respond to artificial intelligence developments in an informed way, we must help them acquire a more-than-superficial understanding of artificial intelligence (AI) and machine learning (ML). Explanations involving formal mathematical notation will not reach most people who need to make informed decisions about AI. We believe it is possible to teach many AI and ML concepts without slipping into mathematical notation

The Governance of AI-based Information Technologies within Corporate Environments

Artificial Intelligence (AI) is making significant progress in recent times and is gaining a strong foothold in business. Currently, there is no generally accepted scholarly framework for the governance of AI-based information technologies within corporate environments. Boards of directors who have the responsibility of overseeing corporate operations need to know how best to govern AI technologies within their companies. In response, this dissertation aims to understand the key elements that can assist boards in the governance of AI-based information technologies. Further, it attempts to understand how AI governance elements dynamically interact within a holistic system. As AI governance is a novel phenomenon, an exploratory investigation was conducted via a qualitative approach. Specifically, the study adopted a grounded theory methodology, within the constructivist paradigm, with the intent of generating theory instead of validating existing theory. Data collection included in-depth interviews with key experts in AI research, development, management, and governance processes in corporate and academic settings. Data were further supplemented with data received from conference presentations given by AI experts. Findings from this dissertation elicited a theoretical model of AI governance that shows various AI governance areas and constituting elements, their dynamic interaction, as well as the impact of these elements in enhancing the organizational performance of AI-based projects and reducing the risks associated with those projects. This dissertation provides a scholarly contribution by comparing governance elements within the IT governance domain and the new AI governance domain. In addition to theoretical contributions, this study provides practical contributions for the benefit of the boards of directors. These include a holistic AI governance framework that pictorially represents twenty-two AI governance elements that boards can use to build their own custom AI governance frameworks. In addition, recommendations are provided to assist boards in starting or enhancing their AI governance journeys.ThesisDoctor of Philosophy (PhD)Artificial Intelligence (AI) refers to a set of technologies that seek to perform cognitive functions associated with human minds, such as learning, planning, and problem-solving. AI brings abundant opportunities as well as substantial risks. Major companies are trying to figure out how best to benefit from AI technologies. Boards of directors, with the responsibility of overseeing company operations, need to know how best to govern such technologies. In response, this study was conducted to uncover key AI governance elements that can assist boards in the governance of AI. Data were collected through in-depth interviews with AI experts and by attending AI conference presentations. Findings yield a theoretical model of AI governance that can assist scholars in enhancing their understanding of this emerging governance area. Findings also provide a holistic framework of AI governance that boards can use as a practical tool to enhance their effectiveness of the AI governance process

Incorporation of in situ generated 3,3′-(sulfanediyl)bis(1-methyl-1,3-imidazolidine-2-thione) into a one-dimensional CuIcoordination polymer with sulfur-bridged {CuI4S10} n central cores

Funding information JPJ acknowledges the NSF–MRI program (grant No. CHE1039027) for funds to purchase the X-ray diffractometer.Peer reviewedPublisher PD

Separation of copper(II) from manganese-(II), cobalt(II), nickel(II) and zinc(II) using Acetylacetone

823-824The separation of copper(II) from binary mixtures of divalent metal ions, Cu-Mn, Cu-Co, Cu-Ni and Cu-Zn has been studied using acetylacetone(Hacac) in chloro-form as the extractant. The pH of the aqueous phase has been kept at 4.34 using Britton-Robinson buffer (CH3COOH-NaOH). From the plot of log Kd versus log[Hacac], the nature of the extracted species is found to be Cu(CH3COO) (acac)

4-Ethyl-1-(4-meth­oxy­benzyl­idene)thio­semicarbazide

In the title compound, C11H15N3OS, the dihedral angle between the aromatic ring and the thio­urea unit is 4.28 (7)° and an intra­molecular N—H⋯N hydrogen bond generates an S(5) ring. In the crystal, mol­ecules are linked into (001) sheets by N—H⋯S hydrogen bonds

Monoclinic modification of 1,2-bis­(diphenyl­seleno­phosphino­yl)ethane

The complete mol­ecule of the title compound, C26H24P2Se2, is generated by crystallographic 2-fold symmetry, with the rotation axis bisecting the central C—C bond. The dihedral angle between the terminal aromatic rings is 74.1 (1)°

Diaqua­bis­(pyridine-2-sulfonato-κ2 N,O)cobalt(II)

The title complex, [Co(C5H4NO3S)2(H2O)2], lies on a twofold rotation axis that relates the two water mol­ecules and the two pyridine-2-sulfonate ions. The CoII atom exists in an slightly distorted octa­hedral environment. The N-donor atoms are cis to each other. In the crystal, adjacent mol­ecules are linked by O—H⋯O hydrogen bonds into a layer motif extending along (001)