Kellen Thomas O'Connor
In recent months it has been impossible to avoid the term “Fake News”. Regardless of political affiliation it’s impossible to deny that our current digital media platforms like Facebook, Twitter, Instagram, and YouTube can very easily be made into effective and manipulative digital media tools. A recent study conducted by MIT found that there are vast swathes of content on our social media sites that are generate by AI bots, and shared by them too. The study also found that most of this content is written as what is called click-bait, or eye catching / provocative content meant to capture the readers attention, regardless of accuracy. Fake media content, on average seemed to reach consumers six times quicker than legitimate, non-clickbait content.
As we continue using these digital eco-systems, and as their use continues to expand into the developing world, what are some safeguards that we can put in place to ensure content is legitimate? Do media platforms have the responsibility of verifying their content?
Original Article: https://tinyurl.com/yarr4w5e
Artificial Intelligence has made massive strives in the areas of business intelligence, research, robotics, analytics, and even in effectively modeling how the human brain works with artificial neural networks. Different from more basic forms of artificial intelligence, neural networks involve the creation of a series of logical nodes with mathematical weights and connections to other logical nodes. A system of these nodes will receive input, and this input will eventually run through the system of nodes, each node with it’s own mathematical weight or criteria. Based on how the system is set up, a neural network will take that output and come to some eventual result that is hopefully actionable to either an analyst or some program that may be utilizing the neural network.
This artificial system is a direct mirror to how our brains supposedly operate, with these logical nodes representing individual neurons in a human brain. Artificial systems like these can be refined to extreme precision for classification, decision making, or mathematical calculation and many prominent firms already use complex neural networks. Organizations like YouTube, Google, Microsoft and Amazon already use neural networks to direct content, and advertisements on their platforms.
Feel free to answer one of these questions below:
Are the complexity of AI systems outrunning how fast human’s are able to understand them? What good is it having a complex system if you can’t discern how eventual decisions are being made?
The IT world is filled with a variety of work structure methodologies. Terms like Agile and Waterfall make up just two of a number of ways to approaching the software development and IT life cycle. A new methodology named ‘DevOps’ has gained a lot of steam in the last five years and has really turned the industry upside down. The key differences between other IT methodologies and DevOps is the removal of barriers between development teams and operations teams, involving everyone to some degree in all parts of the IT life cycle. Some of the most important take-aways from DevOps are
- A more decentralized approach to project planning to incorporate more ideas and oversight in the development process.
- The idea of continuous deployment. Being able to update applications and launch new changes with 0 downtime in regards to the end-user experience. IE: No maintenance time..
- DevOps has evolved with cloud infrastructure explicitly in mind and is focused on helping organizations scale painlessly.
DevOps is growing as an enterprise IT methodology and will probably become more and more present in our lives as we get more involved in our workplaces, do you think decentralization is a good trend? How important do you think it is to remove silo’s between development and operations teams?
Feel free to comment.