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Tuesday, October 26, 2021

Netflix Browser Guide

I was a bit bored over the weekend.. did some digging and found access to Netflix genre codes.  Wrote a quick script to generate a webpage ... Now browse the inner storage of Netflix and find hidden gems organized by genre :-). Enjoy!


Wednesday, January 20, 2021

Blockchain, Security and Interoperability


The technology’s unique aspects and vulnerabilities must be considered during the design process

One of the major selling points of blockchain is the ability to converge it with other core technologies of the Fourth Industrial Revolution in creative ways. Starbucks, for example, announced plans to develop an artificial intelligence-based demand forecast and logistics management system called Deep Brew. The artificial intelligence aspect of the system relies on data aggregated through the Internet of Things from the coffee company’s equipment and products, which can then be traced transparently through blockchain. Many governments have begun lending support to businesses to pursue such efforts by funding and developing the infrastructure necessary for smart cities and smart factories. As with most other technologies, the ultimate level of security that is afforded by blockchain technology boils down to decisions made during the design process. While many aspects of blockchain design, such as evaluating trade-offs among the confidentiality, integrity, and availability of data, are similar to or the same as those that apply to other cybersecurity evaluations, blockchain also involves often unique, added elements such as decentralization, consensus mechanisms, cryptography, and smart contracts.

Given the relatively early stage of the technology’s development, our understanding of blockchain’s vulnerabilities and related best practices grows every day. Its nascent state of development is an important consideration when it comes to scaling applications built on top of blockchain - especially those containing sensitive data. Another important technical consideration where blockchain is involved is interoperability. 

Currently, the options for communicating between blockchain systems are limited - which means often replicating or creating new siloes for information-sharing. There are several efforts underway to explore potential new commercial interoperability solutions, such as such as the open-source project Polkadot, the decentralized Cosmos network, and the Interledger network of exchanges, though they are all still at relatively early stages. Meanwhile standardization efforts, which are key to achieving cross-protocol information sharing, are also relatively immature when it comes to blockchain - and so far suffer from both limited coordination and uptake. In addition, blockchain interoperability needs to be envisioned beyond the infrastructure layer, and should involve platform considerations, like consensus mechanisms and authentication, in addition to business aspects like commercial models and legal frameworks.


AI and COVID-19

The pandemic may lead to a better understanding and appreciation of the value of human collaboration and interaction

COVID-19 has had inconsistent consequences on artificial intelligence. The pandemic has stressed the extraordinary nature of AI as both a front-line technology, and one that counts on the status quo, for example. Pioneering AI systems have played a role in tackling the health crisis by following its range, detecting possible drug treatments, and scrutinizing through thousands of published papers on the topic for insights. At the same time, the pandemic poses primary challenges to AI techniques. 

The version of AI now in everyday use, machine learning, relies on historic training data and adopts that the patterns recognized in that data are still pertinent. However, during unparalleled circumstances, this type of supposition can be knotty. Approaches to addressing this problem include using human know-how to recognize the places where the causal rules of the process still apply, and collecting new training data that more accurately reflect the changed conditions. 

As the pandemic lingers, we should be able to accumulate enough real-world examples of its impact to underpin AI systems that can do things like detect COVID-19 in lung scans, or automatically filter out harmful misinformation about the pandemic.

However, we must not push aside the principles that govern AI use in our rush to address the crisis. Contact tracing apps, for example, have raised concerns about the collection of sensitive personal health and location data, and while it may be tempting to make exceptions during a crisis it may prove challenging to close these doors once they are opened. There has also been growing concern that the pandemic will accelerate the replacement of human workers with AI. While we might expect greater automation in situations where safety and distancing measures for a workplace are costly or infeasible, high levels of pandemic-related unemployment may actually reduce the cost of human labour and therefore bolster hiring in other areas. 

AI is still a relatively new technology, and its adoption requires investment and risk that companies in a crisis mode may not be ready for. And, many of the jobs most affected by the pandemic require face-to-face human interaction - the skill AI is least able to learn. It is possible that the pandemic will therefore lead to a better appreciation of the value of human contact, and new ideas about how to preserve it in the future.