Artificial intelligence (AI) is a truly revolutionary feat of computer science that is poised to become a key component of all modern software in the coming years. This is both a threat and an opportunity.
Artificial intelligence (AI) will be used to supplement both defensive and offensive cyber operations. However, with increasing advancements in AI and self-evolving machines, the problems, too, will start assuming increasing complexities, exposing flaws of and prompting AI systems to evolve further.
AI’s appetite for enormous training data volume has already started amplifying the importance of data, making many of us redefine our data protection frameworks. This will demand prudent global governance to ensure that this game-changing technology brings about broadly shared safety and prosperity.
What is Human-AI?
Human AI is an approach that combines human talent and skills with AI capabilities. Human-AI is still a viable option, because we are still far from having reliable AI mechanisms.
Human-AI will almost probably not be able to take over human occupations. The truth is that, while today’s technologies are extremely capable, a human element is always essential. Because of its ability to automate, it has the potential to severely disrupt work. However, viewing this as a simple transfer of labour from humans to machines is greatly exaggerated. It might be considered as a revolution in the same vein as previous industrial revolutions.
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During the nineteenth century, for example, there was a major shift from agricultural employment to factory work. People panicked at the time, but it was more of an opportunity to provide work to the growing number of unemployed. Human-AI will help to improve the employment landscape in the same way that past industrial revolutions did. This improvement, on the other hand, will come in the form of enhanced efficiency and production. In the short term, employers are frequently looking to AI technology to supplement human workforces and enable workers to operate in new and better ways.Despite the fact that AI has started automating the process of data annotation, no 100% accurate training datasets are obtainable, without the final review from human annotation experts
Know the popular AI myths and the truth
Myth-1 AI and NLP are one and the same
Natural language processing (NLP) is a subset of artificial intelligence (AI), which is a broad concept.Conceptually, AI encompasses mathematical and algorithmic approaches and mechanisms to build self-learning systems from loads of available data. Technically, AI enables a machine to replicate human intelligence in the solution of a wide range of real-world problems.
NLP refers to machine reading comprehension, and natural language generation, in which a machine can express numerical data into human words. NLP allows computers to extract keywords and phrases, comprehend language intent, translate to another language, and provide responses to input. NLP only deals with text and language issues; it doesn’t address activities like decision-making, visual perception, and so forth.
Myth-2
AI isn’t applicable in my business
AI solutionscan already improve consumer interactions, analyse data more quickly, assist in decision-making, and offer early warnings of impending disruptions, among other things. Why should you deny yourself this pleasure? It also has a variety of industrial uses, including as computer vision/recognition, which allows it to detect a damaged item far more efficiently and swiftly than a human operator.
So, ignoring AI is akin to ignoring the benefits of automation at the expense of the company’s competitiveness.
Myth-3
AI is only for big corporations
Most business irrespective of their size, are becoming data-dependent and data-oriented. Social media-led strategies are exposing huge volumes of data to companies. AI is primarily beneficial to businesses that have access to data. When the number of consumers, items, and sales transactions exceeds a couple of hundreds, AI applications should be considered. From your sales transactions alone, AI can identify cross-selling opportunities, price potential, and churn threats. As a result, calculating a business case to identify the potential benefits is unquestionably worthwhile for small and medium-sized businesses.
Myth-4
AI can act against its creators
“Strong AI” and “Narrow AI” are two broad categories of AI. Human capabilities are anticipated to be matched, if not surpassed, by strong AI. After all, is it a Terminator or an I-Robot? There is no powerful AI today, despite what some AI pundits claim. So, definitely, at least at present, AI systems do not have the potential to act against their creators.
The concept of Narrow AI encompasses all extant AI systems. For example, a chess computer, a navigation system, or churn prediction software are examples of such computers that find answers to specific application issues. That implies you’ve almost certainly been utilising “poor” AI-based software products in your daily life for quite some time. Human control is required for such programs to develop.
Myth-5
AI will render humans less intellectual
Some AI pessimists believe AI will turn people into mindless automatons who can only respond to more advanced machines. “Automation can take a toll on our job, our abilities, and our lives,” writes author Nicholas Carr. According to the author, artificial intelligence (AI) will simplify our activities to the point where human involvement will be minimal. For example, the advent of GPS rendered people’s ability to read paper maps obsolete. With such technologies, it’s safe to assume that human skills are evolving and that some habits are fading.
It is important to analyse how they have enhanced our lives and how we have gained new abilities, such as the ability to use computers and explore the Internet. While technology has become an integral part of our daily lives, it is still true that learning new technology and developing new skills takes time and requires the application of our natural abilities. As a result, it’s acceptable to argue that human AI isn’t making people stupid. In fact, it improves our life by coping with our difficulties.
Myth-6
AI will kill jobs
It would be more realistic to argue that AI technologies will displace certain jobs while transforming others. To put it another way, AI will fundamentally alter the nature of employment, much as past industrial revolutions did, but it will not likely result in a reduction in the aggregate number of jobs.
Similarly to how robotization enabled the elimination of repetitive manual chores, AI enables the elimination of monotonous intellectual duties, freeing up capacity to operate in a new and more intelligent manner. And, like robotization, AI has the potential to be more efficient than humans in some tasks. Consider an AI-based program for examining lung X-rays that can diagnose disease much more quickly and accurately than radiologists. For instance, AI-based predictive systems forecast much accurate demand and sales. These systems haven’t taken away any job but has only started assist management in precise decision-making.
Myth-7
AI can destroy privacy
Artificial Intelligence (AI)require a lot of data, leading to the misconception that any instance AI can potentially violate data privacy. While AI systems have the capability and even the need to collect and analyse more data, the harm to privacy posed by non-AI systems today is comparable.
Personal data is already collected by a vast number of organisations. With AI, it isn’t going to change. It may result in the collection of additional data, but it will have no effect on privacy. Furthermore, data reviewed by AI will be subject to the same criteria that currently govern data use and privacy protection.
Way forward
Despite the various anxieties that humans have about Artificial Intelligence, it’s reasonable to conclude that AI will complement rather than replace humans in the evolution of society.
Herbert Simon, dubbed “The Father of Artificial Intelligence,” prophesied that a computer, rather than a human, would be a chess champion within ten years. However, this prophecy came true 40 years later. Simon’s views stemmed from the promising performance of early AI systems on simple instances. When tested on a larger number of problems and more challenging problems, however, these early systems almost always failed spectacularly.
Author Bio:
Vaibhavi is a Digital Marketing Executive at Indium Software, India with an MBA in Marketing and Human Resources. She is passionate about writing blogs on the latest trends in software technology. Her passion further encompasses writing blogs on fashion, religious views, and food. Singing, dancing & mandala artwork are her stress busters. Sticking to the point and being realistic is her mantra!