Artificial Intelligence (AI) is doing a very good job and continues to provide many benefits to our modern world, but with good, inevitably negative consequences. The sooner we start to think about what they are, the better prepared we can be to reduce and manage the risks.
Legendary physicist Stephen Hawking shared this ominous warning: “Success in creating effective AI may be the biggest event or worst in our civilization’s history.
The first step in preparing for the negative effects of artificial intelligence is to examine what some of those negative effects are. Here are some of the highlights:
1. AI bias
Because AI algorithms are built by humans, those who deliberately or inadvertently put them into the algorithm have a built-in bias. If AI algorithms are built with a bias, or if the data in the training sets they are given to learn is biased, they may yield biased results. This reality may lead to unintended consequences that we have become racist with non-discriminatory recruiting algorithms and Microsoft’s Twitter chatbot. As companies build AI algorithms, they need to develop and train them responsibly.
2. Loss of some jobs
Many jobs are created by artificial intelligence, and although most jobs predict a net increase, or at least the same amount of money created to replace those lost thanks to AI technology, the jobs that machines do today are people who take over. This will require changes in training and education programs to prepare our future workforce and help current workers move to new positions that utilize their unique human capabilities.
3. Change in human experience
If AI takes on menial tasks and allows humans to significantly reduce the amount of time they have to spend on the job, the extra freedom may seem utopian at first glance. However, in order to feel that they have a purpose for life, human beings have to turn their newfound freedom into new activities that give them the social and psychological benefits used to provide their job. It may be easier for some individuals and communities than for others. There may also be financial considerations when machines receive the money that humans pay. The economic benefits of increased efficiency are very clear on the profit-loss statements of businesses, but the overall benefits to society and the human condition are somewhat opaque.
4. Global Regulations
If our world is much smaller than it is today due to technology, this means that AI solutions & technology needs to be decided between different governments, requiring new laws and regulations to allow safe and efficient global interactions. Since we are no longer isolated from one another, the actions and decisions of artificial intelligence in one country can easily affect others. We are already seeing Europe adopt a robust regulatory framework to ensure compliance and transparency, while the US and China, in particular, allow their companies to apply AI more liberally.
5. Fast hacking
Artificial intelligence increases the speed at which it can be achieved and in many cases, it exceeds our ability to follow humans. With automation, malicious activities such as phishing, software distribution of viruses, and AI systems benefiting from the way they view the world, are difficult for humans to uncover until there is a real danger.
6. AI Terrorism
Similarly, there may be new AI-enabled terrorism to deal with: the proliferation of autonomous drones and the delivery of robotic groups to remote attacks or disease transmission via Nanorobots. Our law enforcement and defense agencies need to adjust to this current threat.
It will take time and extensive human reasoning to determine the best way to prepare for the future with more artificial intelligence applications, which, while likely to have negative effects with its subsequent adoption, will be minimized as much as possible.
As with any disruptive event, these are not easy conditions to solve, but as long as we are still human in determining solutions, we will reduce and reduce the many benefits of artificial intelligence. No adverse effects.
In the end, the cameras themselves became cheaper, ubiquitous and even better; Cameras mounted on drones can effectively see the entire city.
The result was a level of intelligence that was impossible some years ago.The ACLU report published Thursday that “Dawn of Robot Surveillance” does not record ourselves as “AI-aided video surveillance,” but judges us based on their perception of our actions, emotions, skin color, clothing, voice, and more.Let’s take the technologies one at a time.
Facial recognition technology is constantly improving, facilitating the enormous storage of tagged photographs we provide to Facebook and other social media sites, as well as government-issued photos in the process of issuing ID cards and driver’s licenses.
Let’s discuss how AI will impact the video surveillance industry shortly.Real-time monitoringBasically, in the days of CCTV cameras, a video was used to live on TV screens, but very little was done to make any meaningful analysis of the security incident.
In those days, video surveillance solutions were always reactive and continued to be used in large parts of the world.
Most agencies for CCTV footage are only in the event of an incident or a massive threat awareness.
Machine Learning Solutions enables businesses to discover new trends easily and patterns from large and unique data sets.
Deep Learning Software is a subsection of Machine Learning.https://www.appcodemonster.com/machine-learning-solutions/
In other words, Machine Learning Application algorithms are continually working to achieve an optimal probability that predictions are correct as the data evolves.
As newer and more recent data is added to the model, the optimization is dynamic.Why AI / ML?Intent-based networks provide easier operations for today’s complex software-defined networks.
But, as these networks grow more and more, the great programming capacity of the devices and the flexibility in their configuration leads to unimaginable levels of complexity.
This short manual will explain why we need AI / ML, the basics of AI / ML related to network analysis, and the role they play in intent-based networks.First, let’s take a look at the challenges IT teams face today:There is a proliferation of client devices that connect to the network, such as laptops, smartphones, cameras, sensors, machines, robots, thermostats, lighting, etc.
Additionally, the wireless environment is highly dynamic, and performance can vary depending on the number of users, services, and applications, and levels of interference.Applications are moving to the cloud.
An ML system can automate the processes involved in managing orders, inventory, shipping, and warehousing by making data-driven decisions without human intervention.The Importance of Framing, Scoping, and Defining Problems in Machine Learning Models:Phew!
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