Author: SciEncephalon

Navigating the Ethics of AI: Strategies for Governance in the Era of AI Content Writers like ChatGPT

Posted on by SciEncephalon

As we move further into the era of Artificial Intelligence (AI), it is crucial that we also consider the ethical implications of this technology. One of the most pressing concerns is how to govern AI in a way that is responsible and fair. This is particularly important for AI content writers like GPT-3, which can generate large amounts of text relatively quickly.

One of the main challenges in governing AI is that it is a rapidly evolving field. As AI technology improves, new ethical considerations arise. This means we must be willing to constantly re-evaluate our approach to governing AI and make adjustments as needed.

One way to govern AI is through the use of regulation. This can include laws, regulations, and guidelines that govern the development and use of AI technology. These regulations can help ensure that AI is developed and used in a way consistent with ethical principles, such as privacy, accountability, and transparency.

Another way to govern AI is through the use of industry standards. These standards can help ensure that AI is developed and used consistently with best practices in the field. For example, industry standards for explainable AI (XAI) can ensure that AI systems are transparent, interpretable, and auditable, which can help prevent unethical AI models’ usage.

There is also an additional way to govern AI through the use of Self-regulation. This includes creating internal ethical and compliance teams with the company, which are responsible for monitoring the development and usage of AI to ensure they are aligned with the company’s ethical principles and standards.

It is also important to note that when it comes to AI like GPT-3, it may have the potential to produce harmful or biased content. Therefore, the developers must ensure that these models are fine-tuned and trained on diverse and unbiased data.

In conclusion, governing AI in the era of AI content writers like GPT-3 is a complex task that requires a multi-faceted approach. It is essential to consider both regulation and industry standards, as well as self-regulation, to ensure that AI is developed and used in a way that is responsible and fair. We must be willing to continuously re-evaluate and adjust our approach to AI governance as the technology evolves and its impact on society.

Applications of AI/ML in the Internet of Things (IoT)

Posted on by SciEncephalon

The IoT (Internet of Things ) has been rising in recent times. According to the IDC (International Data Corporation), more than 50 billion things will be connected to the Internet by 2025; consider that over the last ten years:

  • The unit price of sensors has gone from $1.30 to $0.60.
  • There is a decline in bandwidth cost by 40 times.
  • There is a reduction in processing costs by 60 times.

IInterest and revenues have grown in everything from smartwatches
and other wearables to smart cities, homes, and smart cars. Let’s take a closer look:

IoT Wearables

According to IDC, vendors shipped 56.8 million units of wearables in 2019, up more than 133% from 2018. By 2025, IDC forecasts
annual shipment volumes of 126.1 million units, resulting in a five-year CAGR(compound annual growth rate) of 45.1%; this fuels big data streams for healthcare research and development in commercial markets and academia.

Smart Cities

With the tremendous increase in the world’s population expected to live in urban cities, we will see the rapid expansion of city borders, driven by population increases and infrastructure development. By 2023, there will be 30 megacities globally; this, in turn, will require attention on smart cities to make them more sustainable and low-carbon cities by putting initiatives in place that make these places more livable and attractive to investors. The market will continue to grow by 2022, almost touching $1.5 trillion through diverse areas such as transportation, buildings, infrastructure, energy, and security.

Smart Homes

According to BI Intelligence, smart home devices are forecasted to ship at a CAGR of more than 68% over the next five to six years and will reach 1.8 billion units by 2023. Smart devices include security systems, energy equipment like smart meters, refrigerators, washers and dryers. By 2023, its forecasted that it will represent approximately 27% of total IoT product shipments.

Autonomous Cars

Autonomous vehicles (AVs), also known as self-driving cars, are already disrupting many industries. Although the exact timing of maturity, technology, and sales are unclear, these smart vehicles could eventually play a significant role in the world economy. According to McKinsey & Co., in addition to other advantages, smart vehicles could reduce the incidence of car accidents by up to 90%, saving billions of dollars annually.

There are many use cases related to Data Analytics and Machine Learning, including the computational gap between CPU storage, modeling machine failure in the IoT, networks on the IoT, modeling the data for the smart city of the future. Here are some of the Case studies, including but not limited to:

  • Spark Streaming- to predict any public transportation equipment failure
  • Usage of a new IoT app for traffic monitoring in Singapore
  • Smart City Traffic pilot in the Oulu city of Finland
  • An ongoing longitudinal study by using personal health data to reduce cardiovascular disease
  • Data analytics is in use to minimize the risk in human space missions under NASA’s Orion program

We should also consider the ethics perspective of Data Analytics related to the algorithms that control the issues in the Internet of Things. There is a critical need for further discussions related to IoT data and a window of opportunities to influence the moral essence involved in using the IoT.

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