The rise of Artificial Intelligence(AI) and machine learning over the previous couple of years has not helped, and even people that should know better are giving into fear. The very fact is, much of the fear around AI comes from overactive imaginations that machines will mimic our own bad behavior. As a result, today we are familiar with Artificial intelligence myth.
A lot of factual inaccuracies come along with all the excitement surrounding AI and machine learning. Let us distinguish fact from fiction.
So let’s break down the myths and realities around AI and every one of its offshoots, like machine learning and deep learning.
Myth: AI goes to require away jobs.
Reality: Mostly AI will change existing jobs and make new ones.
This Artificial intelligence myth is the only greatest fear, and it’s legitimate. AI is getting used to automate many boring, repetitive functions in areas as diverse as customer service, data center management, and radiology. Does that mean server admins are out of a job? No, it means they’re liberal to work on tougher tasks.
Some industries could be impacted and a few workers could also be displaced, but that went on constantly and frequently . the economic revolution of the late 1800s caused massive displacement. The car put the horse and buggy industry out of business. Early telephone calls couldn’t be avoided an operator, and AT&T had armies of them.
What will happen to people whose jobs are replaced by AI? They advance to other jobs. We’ve done that through all of human history.
Based on all the work doing with large companies, a displacement of lower level knowledge workers are seen.
AI is automating variety of tasks done by knowledge workers, operates 24/7 at a better level of accuracy, and also provides insights and forward looking views where the purchasers or market are going. But, in some situations, the degree of AI prediction exceeds what people can do.
According to 33% rule, 33% of data workers make the change easily, 33% want to form the change but require significant training to be a better level knowledge worker, and 33% are unable to regulate or be retrained. For that last 33%, the change is so great they need to advance to something else.
Myth: AI is smarter than people.
Reality: AI is as intelligent as you can program it.
This is the most popular Artificial intelligence myth. The smartest engine on earth is that the human brain and we’re not getting to build a better AI than the human brain. AI isn’t sentient, it isn’t conscious, and it’ll not get smarter than us.We project onto AI what we might do.
“There is no AI without people, the people that create the algorithms and knowledge that structure AI. We build it, teach it, and provides it the tools to form certain decisions on our behalf.”
Narrowly, AI are often utilized in some circles to form decisions faster than humans. That doesn’t mean the choices are always right or thoughtful or always the proper outcome.
AI isn’t socially aware. So, how does one teach AI to read the room? Some decisions can only be made by humans.
Myth: AIOps is predominantly supported event management and correlation.
Reality: Maybe initially but it’s evolving.
The initial wave of AIOps did revolve event management systems to perform noise reduction supported correlating alerts, like grouping of comparable alerts.This was a big breakthrough , as long as noise has long hindered the usability of event management systems.
But even greater benefits are on the horizon. The next wave has broadened to other areas of IT Operations like automation and monitoring/observability. Use cases would come with intelligent routing of tickets or automation supported learned patterns.
Myth: AI will make medical decisions and diagnoses.
Reality: Yeah, but it’s not the last word AI would have.
Today, radiologists are experts within the evaluation of X-rays, MRIs, CAT scans, and other medical imagery. One among the main efforts of AI is teaching image classifiers to acknowledge abnormalities like tumors. AI has the power to scan many images to find out to interpret scans faster and more thoroughly than any human could ever achieve.
However, a doctor or radiologist will still have the ultimate call in determining a diagnosis. It’s just that a diagnosis may are available minutes rather than days or weeks.
Myth: I even have no idea what the AI is doing and if I can trust it.
Reality: AI is far more transparent now.
This Artificial intelligence myth is responsible for much of fear. Early on, AIOps was perceived as being a “black box,” i.e. a mysterious framework that produced output without offering insights into what the underlying algorithm performed and why.
However, over time we are seeing these solutions mature, and more “white box” approaches that are gaining trust and adoption.
Although some frameworks do not have clarity, more and more software providers and AI systems have more insight as to why they did what they did.
The tricky thing is to supply appropriate transparency, to not overwhelm the user, to realize their trust and understanding.
Myth: I want a knowledge lake to coach my AI.
Reality: Drain the swamp.
Unstructured data is worse than structured data because it takes up space. to urge obviate it you’ve got to spend resources to sift through it all. For that reason, unstructured data for the sake of unstructured data are often worse than useless.
What the planet is functioning on now’s how can we structure and organize data to mine it and the way can we build historical algorithms and paradigms.
A touch unstructured data is okay but once we open floodgates on data points, you absolutely need to have a knowledge lake with the power to arrange and structure it later.
Myth: Modeling determines outcome.
Reality: You can’t be sure of that.
All AI initiatives begin as test projects. you’ll get excellent results during the testing phase, but find that your model is way less accurate once you deploy it into production. That’s because AI and machine learning models must be trained on data, which training data must be representative of the important data, or results will suffer.
Note too that the training of your AI model isn’t complete. As soon as you set your model to use within the world , its accuracy will begin to degrade. The speed of decline will depend upon how briskly the important world data changes (and customer preferences can change quickly), but sooner or later your model will need to be retrained with new data that represents the new state of the planet.
It’s a fragile task of defining your training data set. Your training data has got to be an equivalent as your production data. That’s the key to creating your programs successful. It’s a key you’ll got to address again and again throughout the lifetime of your AI model.