Artificial Intelligence
Artificial intelligence (AI) refers to the modelling of human intelligence in machines programmed to think like people and mimic their actions. Artificial intelligence algorithms are designed to make decisions, often using real-time data.
Artificial intelligence systems typically exhibit some of the following behaviours related to human intelligence: planning, learning, reasoning, problem solving, knowledge representation, perception, movement, and manipulation, and to a lesser extent, social intelligence, and creativity.
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Types of AI
AI technologies are classified based on their ability to mimic human characteristics, the technology they use to do so, their real-world applications, and the theory of mind.
Narrow AI
Narrow AI is focused, designed to perform specific tasks – such as facial recognition, speech / voice assistants, driving a car or searching the Internet – and is very agile to perform the specific task for which it is programmed. Narrow artificial intelligence does not mimic or copy human intelligence, it simply mimics human behaviour based on a narrow range of parameters and contexts.
Examples of Narrow AI:
- Rank brain by Google / Google Search
- Siri by Apple, Alexa by Amazon, Cortana by Microsoft, and other virtual assistants
- IBM’s Watson
- Image / facial recognition software
- Email spam filters / social media monitoring tools for dangerous content
- Self-driving cars
General AI
General artificial intelligence or “strong” AI refers to machines that exhibit human intelligence. In other words, general AI can successfully perform any intellectual task that a human can perform. This is the kind of AI we see in movies like “Elle” or other science fiction movies in which humans interact with conscious, sensitive, and emotionally motivated machines and operating systems. and self-awareness.
Artificial Superintelligence
Artificial superintelligence (ASI) is a fictional AI that not only mimics or understands human intelligence and behaviour, ASI machines become reliable and exceed the capacity of human intelligence and skills. Machines will be capable of exhibiting intelligence that we have not seen in the brightest amongst us.
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Benefits with AI
Eradicating human error
With the rise of AI models that can achieve human parity, algorithms are now widely used in various fields. The reason is that they can perform low-level tasks such as data entry or customer service with high efficiency at low cost. Apart from offering better efficiency, well-trained AI algorithms do not make mistakes. Human error has been almost completely removed from the equation, introducing much better efficiency at a lower cost.
Automatic customer interactions
Most customer interactions require human interaction including email, social media conversations, telephone calls and online chat. However, with AI, companies can automate many of these communications. By analysing previous communication data, computers can be programmed to give customers accurate answers and deal with their inquiries. In addition, when AI is combined with machine learning, the platform interacts more, making it better to communicate with the customer.
Incomparable efficiency
Machines integrated with AI are very efficient. In the case of AI systems, the assurance of error-free productivity is 99.9% because unlike humans, they do not forget. Additionally, AI is more efficient at aiming at its target and staying with it without distracting. The system reacts to change with incredible speed and skill. These characteristics are very beneficial for more advanced systems for the development of the human world.
Predicting outcomes
AI can predict results based on data analysis. For example, it detects patterns in customer data that indicate whether a product is likely to sell at the current sale and the quantity they are. It can also predict when demand for these products will decline. This is basic information to help the business buy the right stock – and the right amount.
Delegating routine tasks
A problem that many companies face but they are not sure how to deal with effectively is the cumbersome task of maintaining an active cybersecurity network. Now, it is no secret that AI is used to automate various tasks in many industries, so why should it be different in cybersecurity? Instead of wasting your resources, you can automate repetitive and routine tasks for your AI counterparts, and let your experienced IT staff focus on complex issues and the integration of modern security measures.
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Challenges in AI
AI human interface
As a new technology, there is a severe shortage of manpower with data science and data analytics skills. In turn, they can be represented to obtain the maximum performance of artificial intelligence. As AI advances, companies lack the trained professionals to meet their requirements and use this technology. Business owners need to train their professionals to take advantage of this technology.
Data security
Machine learning and decision making in AI and AI applications are based on vast amounts of sensitive data, which is often confidential and personal. This makes it prone to serious issues like data breach and identity theft. For the most part, businesses and governments seeking profit or power use AI-powered tools, usually linked together in a global network, making them difficult to regulate or contain.
Fewer human jobs
With more automation, a decline in the workforce is inevitable. Unfortunately, the victims of efficiency gains are those who currently hold these positions. If jobs are cut due to the integration of artificial intelligence solutions, the company must either find new jobs for its employees or release them all together. While this is the reality of doing business, logistics companies need to address this issue before investing in replacement AI systems.
Cost
Small and medium-sized organizations have a hard time when it comes to adopting artificial intelligence techniques because it is an expensive business. Large companies such as Facebook, Apple, Microsoft, Google, Amazon also allocate a separate budget for the implementation and enforcement of artificial intelligence technologies.
Ethical challenges
Ethics and morality are some of the major AI challenges that have yet to be addressed. The way developers can perfectly technically refine AI bots to perfectly mimic human conversations make it increasingly difficult to distinguish a machine from a real customer service representative. Artificial intelligence algorithms make predictions based on the training provided. The algorithm labels things according to the assumptions of the trained data. Therefore, the accuracy of the data is ignored.
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Future of AI
The line between computer programs and AI is not clear. It is relatively easy to emulate narrow elements of human intelligence and behaviour but creating a machine version of human consciousness is a different story altogether.
While AI is still in its infancy and the search for powerful AI has long been considered science fiction, breakthroughs in machine learning and deep learning indicate that we may need to -be more realistic about the possibility of achieving general AI in our lives.
It is scary to imagine a future in which machines are better than humans at what makes us human. We cannot accurately predict all the implications of artificial intelligence for our world, but it is not incomprehensible to eradicate things like disease and poverty. Right now, the biggest challenge civilization faces with narrow artificial intelligence technologies is the prospect of efficient and targeted automation, which makes many human jobs outdated.
