AI and the Speed of Integrity

AI's Speed To The Finish

If there's one thing everybody agrees on, artificial intelligence cannot be stopped. It is inevitable and will continue to move forward. However, this is with a bit of caution.

Artificial intelligence is the new "gold rush. " Like today's news, AI development is a push to be first rather than accurate.

The chase for artificial intelligence is so overwhelming that the integrity of the data can easily be compromised in the pursuit of results and innovation. Suppose I had to say this in simple terms. In that case, I have witnessed clients and customers fully enamored with the pursuit of digital transformation and the results of automated data that neither would question the accuracy of the source or results. And when prompted with this statement, it's almost as if it didn't matter. Even though digital transformation is the grounded effort of integrating digital technology to transform a business on how it operates or delivers value, it has been incorporating artificial intelligence [ai].

What appears to be an inspirational perspective witnessing our evolution actually comes with the risk of artificial intelligence.

Dangers of Artificial Intelligence [to name a few]

• Copyright Infringement
• Privacy violations
• Algorithmic bias caused by bad data
• Bias and discrimination
• Autonomous weapons
• Job loss
• Lack of transparency
• Deepfakes
• Invasion of privacy
• Social manipulation
• Socioeconomic inequality
• Ai causes complacency
• Job automation
• Market volatility
• Risks of artificial intelligence
• Social manipulation through AI algorithms
• AI race
• Danger to humans
• Disinformation
• Existential risks

Generative AI Has an Intellectual Property Problem

AI seems so amazing.? The AI generation has outstanding images and provides profound compilations of articles and essays. But to believe that AI is generating this from scratch is not the truth. AI platforms are streaming through volumes of data and billions of parameters of archives and text. And they are establishing their own patterns and rules to create a response. This process is crossing the line of legal risk. Intellectual property and infringement: In many cases, ongoing claims are being litigated. ?In a case filed in late 2022, Andersen v. Stability AI et al., three artists formed a class to sue multiple generative AI platforms based on the AI using their original works without a license to train their AI in their method.? It's unclear who owns the content that's been compiled because it's been developed by a sea of copyrighted, patented, and trademarked information. Regardless, currently, it's completely legal to write a book using AI generation.?

This isn’t the first time data transformation and copyright laws have battled.? Google has had to defend itself against the use of scraping text from books to create its search engine.

Unintended consequences

? There are more traps headed your way. If you are a company or person using generator AI. In using AI-generated contracts by vendors or customers, there are unintended consequences, whether direct or unintentional. If a business is unaware that data used could have been derived from unlicensed work or AI-generated unauthorized work not covered by fair use, that business could be held liable for a willful infringement at a costly rate of damages for each use instance. Not to mention the accidental use or sharing of confidential information. While so many users are so eager to feed the AI so that it “LEARNS”, is a dangerous game.? Businesses should take steps to evaluate how their contracts are being written. They should at least have terms of service to ensure that AI platforms are not infringing on unlicensed or improper use of training data. Possibly require vendors or customers to provide disclosures if either party uses a generative AI to ensure that intellectual property rights are understood and protected on both sides.

Is AI really learning?

?AI is far from being autonomous. In other words, AI can not think like humans or make conscious decisions. Yeah, it has evolved from early computers, and even to this day, it uses a sea of data as part of its learning, but it's still based on rules-based systems developed by humans. In other words, there is still bias in some of that program's language. And maybe rightfully so, so if the computer ever becomes fully autonomous, we don't want them to take over the world.

Every time you get an answer from an AI generator, ask these two words. "Explain Yourself". Can it explain why it gave you that answer? Or did it just compile results scraped from the ether of the internet?

Types of AI

Machine Learning

AI uses current and historical data and algorithms to make software more accurate at predicting outcomes. Although its goal is to perform this without being programmed, it is still fundamentally built on a degree of rules [rule-based systems using if-then conditions].

Deep Learning

Deep learning is a type of machine learning that imitates the way humans gain knowledge. It makes the process of collecting, analyzing, and interpreting large amounts of data faster and easier. It is based on utilizing neural networks.

Neural Networks

A neural network is an artificial intelligence method that teaches computers to process data in a way that imitates how the human brain functions. This method uses interconnective nodes of neurons in a layered substructure that resembles the human brain.

What do we do if AI can't be stopped?

Assure the Source of Data

I work in project controls. There is high importance in the value of data integrity. Our projects are hinged on our professional input of the forecast of budget/cost and schedule completion. The value of reporting depends heavily on the source of our collected data but includes but is not limited to: the agreed-upon estimate, the reporting cycle and its cutoff dates, and compliance with industry best practices.

Develop Legal Protection

Establishing a series of company rules and legal language to incorporate into your current and future contracts can provide a layer of protection against the unintended consequences of potential infringement from vendors and clients.

Establish Standards or Guidelines

Most global professional organizations have already established departments to prepare for the future use of artificial intelligence and digital transformation. If you have a high moral compass and feel the need to influence these changes, it is highly suggested that you get involved with these groups in order to participate in the development of these future and existing guidelines.

Published by

Cesar Ramos

This is a summary of my thoughts. This topic is quite intriguing, and since I'm involved with multiple companies pursuing data transformation and/or machine learning, I look at it from a pragmatic perspective. Not so much with the excitement of the fancy charts and results but more of the integrity of the data it comprised. These types of things are what I call a high moral compass when it comes to artificial intelligence. Most industries have adapted quickly to AI. However, in the engineering and construction space, where I primarily consult, I find it lagging in pursuing artificial intelligence. It's not because we are failing, but there are many reasons why things cannot be automated just yet. Some of these construction jobs are uniquely built, and automation requires at least standards and things that would make automation or modularization possible. This is one of many topics that have been on my mind here lately. Please feel free to comment, question, agree, or disagree. I don't think I'm on this knowledge quest on my own.

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