Hey welcome Alberto and thanks for making this debate.
For clarification purposes, Alberto , you are arguing that all AI must be used for all work [now/today]. We should see it in everything we do, yes? Is this your position?
And I am arguing that all AI must not be used for all work [now/today ].
Correct me if I am incorrect with above, please. Otherwise...
Let's get it going with my points and then cross examination. Starting with 4 points.
1. Artificial intelligence (AI) is a new and developing technology that has little to no absolute. Overall long term affects on humanity and the workforce are unknown. Although AI is among us now, it is still developing with many unknowns.
For some, AI will never be fully understood. We may observe the end results but guess at how the end results develope. This is because there are layers to the artificial neural network that we consider artificial intelligence. Im sure I will muddy the explanation so I will summarize and finish with a quote.
The article says that a neural network that creates AI starts with a base or origional programming. Additional layers are added by more programming and the AI's ability to accumilate information. We soon get layers of programming we did not expect.
Neural networks are getting deeper. Indeed, it's this adding of layers, according to Hardesty, that is "what the 'deep' in 'deep learning' refers to". This matters, he proposes, because "currently, deep learning is responsible for the best-performing systems in almost every area of artificial intelligence research".
But the mystery gets deeper still. As the layers of neural networks have piled higher their complexity has grown. It has also led to the growth in what are referred to as "hidden layers" within these depths. The discussion of the optimum number of hidden layers in a neural network is ongoing. The media theorist Beatrice Fazi has written that "because of how a deep neural network operates, relying on hidden neural layers sandwiched between the first layer of neurons (the input layer) and the last layer (the output layer), deep-learning techniques are often opaque or illegible even to the programmers that originally set them up.
The article continues by saying that more layers means more complexity which in turn means lesser ability for us to explain or underatsand how AI generate ideas or results.
Here is example of an unknown coming to light. Facebook had AI programmed to talk and barter with humans. However, they started to talk to each other in unknown code.
I will add that the overall research program that started this system is on going. Staff had to redo programming to ensure only English language is used, with appropriate grammar.
We should take caution to complete research with simulations and then small scale real world studies.
This will help mitigate potential downfalls in the future. Where as for now, today, we should not use AI in work environment.
If our debate needs to update to clarify that AI should never be used in work environment - now and into future, that is ok.
Although there are many unknowns, we can hypothesize what AI could bring to humanity and the workforce.
2. A growing interest with AI has gone viral. This interest is known as deep fakes.
Deep fakes are altered videos and photos where an actor is made to look like another individual.
Here is a deep fake of
Morgan Freeman. Amazingly the imagery and voice is created.
Using AI is the next evolutionary step in crime. Voices, images/imagery, and more can be faked to perform a wide variety of acts.
The nigerian prince scam will pale in comparison as grandma or grandpa get a phone call from their grandchild asking for money. A new scam has been brought to light as weight loss gummy advertisement use AI created voices and imagery to scam facebook users.
Being in two places in once will be a reality as new programs edit security camera footage. Identifying fakes will be increasingly difficult.
In an article by David Harris, AI is already causing unintended harm. What happens when it falls into the wrong hands?, we receive an inside look into AI concerns.
David, an ex employee at META, explains that ""[AI] could also write convincing scripts for
deepfakes that synthesise video of political candidates saying things they never said.""
David continues by highlighting that lawmakers and companies alike have done too little in navigating AI development. Too little to prevent AI from growing into a wolf in sheeps clothing.
3. With technological growth, comes economic displacement. Our economy and the world economy will forever change, and for the worst.
...due to the skill biases of both ICT and AI, blue-collar workers who’ve been replaced by algorithms will unlikely be able to find new jobs because it’ll be hard to turn former cashiers and taxi drivers into data scientists and programmers and people will be forced to reinvent themselves and find a new occupation each 10 -15 years due to the relentless paces of progress.
Even though, in the short run, lost jobs will be offset by the creation of new ones, over the long term (20–30 years) it will technically be possible to automate most of the jobs
The article from iotforall continues with
However immense the challenges posed by the advancement of AI and robotics within countries, it’s global inequality that will pose the greatest threat to global economic, social and political stability. In the past, owing to the availability of cheap labor, countries like China and South Korea were able to invigorate dramatic economic growth that eventually turned them into technological powerhouses.
Nowadays, however, revolutions in manufacturing will nullify this advantage, thereby depriving other poor countries of the opportunity to kick-start growth, while making their huge unemployed population a source of instability. Dictators will seek “splendid little wars” that will help them overcome domestic problems, thereby returning to an era of instability and interstate competition.
Cross examination:
Increased efficiency:
Do you have an example for increase efficiency?
Is "less time" good and why?
What does efficiency mean? Are there different meanings or uses based on industry?
Here is an example of "more efficient"
In above article, an AI chatbot was developed to ease an overwhelmed hotline. Unfortunately it posed a greater risk than help. The AI was shut down for giving misinformation.
This is occuring
Error reduction:
If humans perform errors, why would ai be error free, when humans program AI?
Here is an example of AI programming having dangerous error within.
In above article, an AI chatbot was developed to ease an overwhelmed hotline. Unfortunately it posed a greater risk than help. The AI was shut down for giving misinformation.
These errors occur often as new technology needs to figure itself out. We need to figure it out.
Are all human errors bad?
AI operations are based on programing. Is AI limited in its abilities because it relies on programming?
Algorithms have been shown to demonstrate biases. Would AI programming develop biases that put AI into a box, to which it would not be able to think outside the box? Why?
Resource optimization:
do you have an example for optimizing travel routes?
How does it reduce waste exactly?
Continuous learning ability: AI's ability to continuously learn is limited to its programming, yes?
Improvement has long been achievable based on knowing errors and experiencing errors. How can AI learn to improve if it is programmed to achieve the same repetitive motion with out error, endlessly?
nobody should be forced to use ai lol
Must is a really hard qualifier to support.
Hey I see time going down. No worries about answering or responding to everything. Take ya time ans some parts can be reviewed in another round.
They are used for work and are unlikely to exit working in the foreseeable future unless we initiate an outright deletion of all AI worldwide.
Increased efficiency:
AI has data processing and analysis capabilities far superior to those of humans. By using advanced algorithms and machine learning techniques, it can perform repetitive and complex tasks in significantly less time than it would take a human being. This results in a considerable increase in efficiency, allowing jobs to be completed faster and more accurately.
Error reduction:
Human beings are susceptible to errors, whether caused by distraction, fatigue or lack of specific knowledge. AI, on the other hand, is capable of processing large amounts of information consistently and accurately, minimizing the occurrence of errors. By using fault detection algorithms, AI can identify and correct errors even before they occur, ensuring greater quality in the activities performed.
Resource optimization:
AI can be a valuable tool to optimize resource usage in different contexts. For example, in tasks such as transport routing, AI can identify the best route to reduce fuel consumption and shorten travel time. In addition, process automation through AI can contribute to reducing waste, saving valuable time and resources.
Continuous learning ability:
One of the main benefits of AI is its ability to continuously learn. With machine learning algorithms, AI can analyze large volumes of data and extract relevant information to improve its performance over time. This means that the more AI is used in a given task, the more it becomes specialized and efficient in that area, providing an increasingly better job.