To understand how AI/ML and data systems are implemented in cloud architecture we first have to discuss what the “Cloud” is. Cloud computing defined by the National Institute of Standards and Technology is a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction. This cloud model promotes availability and is composed of five essential characteristics, three service models, and four deployment models. In brief the five essential characteristics are On-demand self-service, Broad network access, Resource pooling, Rapid elasticity, and Measured service. The three service models are Information as a Service, Platform as a Service, and Software as a Service. Finally, the four deployment models are public, private, community, hybrid. The readings provided more detail specifically Rountree’s Basics of Cloud Computing regarding the basic definitions of cloud followed by more in-depth detail of the abstractions (service models) and deployment models in Ruparelia’s Cloud Computing. For me though I understand cloud as the ability to manipulate data with on-demand availability of computer system resources from hosting companies (Amazon, Google, IBM, Microsoft). Cloud takes away the need for physical infrastructure by virtualizing everything from physical hardware to applications.  

Now how that applies to AI/ML is that the main questions so let’s now go to IBM’s Virtual Assistant Watson. Most of the case studies were based on business interactions and this virtual assistant was built to help companies “provide customers with fast consistent and accurate answers across any application, device or channel.” I took this to mean a business version of Apple’s Siri working for companies customer service operations. IBM states that Watson utilizes what IBM calls AutoML with “meta-learning” techniques, what I conjure as a form of blackboxing. I think Watson is another neural network that uses the similar techniques in the virtual assistant’s discussion in week 8, in which virtual assistants receive an input and run that input through various hidden layers in a neural network to produce an output. However, Watson does this in a form of conversational user interfaces i.e. advance chatboxes. IBM argues that they are the leader in this technology for companies due to advance in their natural language processing capabilities and new natural language understanding. Since all my information from them is from their own publishing I would argue that it may be bias.

Anyway, how this combines with cloud computing is that businesses can pay for hosting companies computer services like Watson to collect, organize, and analyze their data and provide appropriate responses without investing in their own physical and virtual resources to do the same. This thereby produces a new source of efficiency and innovation. Companies like Siemens have used this tool and created CARL an HR interactive chatbot for employees. CARL which stands for Cognitive Assistant for Interactive User Relationship and Continuous Learning runs on IBM Cloud and is powered by IBM Watson. This is a good way of viewing the interaction between Cloud and AI/ML. I like to look at it in the sense the Watson provided the adaptability behind the process i.e. algorithms, NLP, and AI/ML to answer users inquiries while the cloud provided the scalability to accommodate users, languages and topics through IBM’s vast databases, storage centers, and computation services.

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Now the ethics behind this are interesting. As opposed to other emerging technology that can be used for nefarious reasons like AI/ML, cloud computing to me is just an extension of computing services on a different platform. So, the risk I see is in the inherent design of the system due to its reliance on the internet namely the vulnerabilities to attacks and security and privacy. Regarding vulnerabilities to attacks, I mostly agree with Floridi’s argument in Ethics of Cloud Computing we have to look more at the users and impose proscriptive measures preventing uses of certain businesses with private and personal information to use these systems, for now. I believe that companies should continue to advance their software and find resolutions to the inherent vulnerabilities in cloud computing, an until then certain businesses should avoid using cloud computing. Regarding security and privacy, if only four companies own and operate cloud services which are slowly becoming the go to “unifying” architecture I instantly think of security and privacy violations of user’s data. In this sense I disagree with Floridi and argue that something beyond technological neutral regulations should be thought of that can both limit big tech’s monopoly of this service while also sustaining innovation. There is a middle ground we just need more dialogue and de-blackboxing these concepts for the general public.