Most people are not very familiar with the concept of artificial intelligence (AI). As an example, when 1,500 senior corporate leaders in the United States were questioned about artificial intelligence in 2017, only 17% of them said they were familiar with the term. Many of them had no idea what it was or how it would impact their specific businesses. They understood there was considerable potential for altering business processes, but they were unsure of how AI could be used within their own organizations. Despite a widespread lack of familiarity, artificial intelligence is a technology that is revolutionizing all aspects of life. Experts say it is a versatile tool that helps individuals to combine information, evaluate data, and use the insights obtained to enhance decision-making.
Qualities of artificial intelligence
Despite the fact that there is no universally accepted definition, AI generally refers to a “machine that responds to stimulus consistent with traditional responses from humans, given the human ability for deliberation, judgement, and intention”.
Researchers claim that these software programmes “make decisions which ordinarily demand a human degree of skill” and assist users in foreseeing challenges or resolving them when they arise. As a result, the user acts in an intentional, intelligent, and adaptive manner.
Intentionality
Algorithms for artificial intelligence are created to make judgements, often using real-time data. AI differs from passive machines, which can only make mechanical or preset decisions. They differ from passive machines, which can only make mechanical or predetermined decisions.
Artificial intelligence combines data from numerous sources, instantaneously assess the information using sensors, digital data, or remote inputs, and then takes action based on the findings they draw from the data. Artificial intelligence is capable of making decisions with a high level of sophistication thanks to significant advancements in storage systems, computing speeds, and analytical approaches.
Intelligence
Machine learning and data analytics are typically used in AI projects. Data is analysed by machine learning to find underlying trends. Software developers can utilize this information to investigate certain problems if it identifies anything that is pertinent to a real-world situation.
All that artificial intelligence need is data that has strong algorithms to recognize valuable patterns. Some examples of data are digital information, satellite images, visual information, text, and unstructured data.
Adaptability
Artificial intelligence decision-making systems have the capacity to learn and adapt. For instance, Semi-autonomous vehicles have features that alert drivers and other vehicles about impeding traffic jams, potholes, highway construction, or other potential roadblocks. AI advanced algorithms, sensors, and cameras combine dashboards and visual displays that show information in real-time so that human drivers can comprehend changing traffic and vehicle circumstances.
Applications in diverse sectors
Experts say, AI is not a futuristic concept, but it is a reality that is being implemented in a number of industries today. Finance, national security, healthcare, criminal justice, transportation, and smart cities are a few examples of these. There are several instances when AI is already changing the world and significantly enhancing human capabilities. The enormous prospects for economic growth that AI offers are one of the factors contributing to its expanding role in society.
Artificial intelligence technology “may enhance global GDP by $15.7 by 2030,” according to a PriceWaterhouseCoopers report. For example, advancements of $7 trillion have been made in China, $3.7 trillion in North America, $1.8 trillion in Northern Europe, $1.2 trillion in Africa and Oceania, $0.9 trillion in the rest of Asia excluding China, $0.7 trillion in Southern Europe, and $0.5 trillion in Latin America. China is advancing quickly because it has declared a national objective to invest $150 billion in AI and take the lead globally by 2030.
A study conducted by the McKinsey Global Institute on China revealed that, “AI-led automation can offer the Chinese economy a productivity injection that would add 0.8 to 1.4 percentage points to GDP growth yearly.” The sheer size of China’s AI market offers those country significant prospects for pilot testing and future advancement; even though their experts claim that the country is lagging behind the United States and the United Kingdom in AI adoption.
Finance
American investments in financial AI increased three times 2013-2014, reaching $12.2 billion. According to analysts in that industry, “Decisions about loans are now being made by software that can take into account a variety of finely parsed data about a borrower, rather than just a credit score and a background check.”
The use of stockbrokers and financial advisers is eliminated by so-called robo-advisers, who “build tailored investment portfolios.” The goal of these developments is to remove emotion from investing so that judgements may be made quickly and solely on analytical factors.
A prominent example of this is taking place in stock exchanges, where high-frequency trading by machines has largely supplanted human decision-making. People submit buy and sell orders, and computers instantly match them without any human involvement. On a very small scale, machines can identify trading inefficiencies or market disparities and carry out trades in accordance with investor instructions. Powered in some places by advanced computing, these tools have much greater capacities for storing information because of their emphasis not on a zero or a one, but on “quantum bits” that can store multiple values in each location. That dramatically increases storage capacity and decreases processing times. This significantly shortens processing times and boosts storage capacity.
Another way artificial intelligence benefits financial systems is in fraud detection. In huge businesses, it can be challenging to spot fraudulent activity, but AI can spot anomalies, outliers, or incidents that call for further inquiry. This aids managers in identifying issues early on in the cycle, before they spiral out of control.
National security
Artificial intelligence has a significant impact on national security. The American military is using AI as part of Project Maven “to sift through the massive troves of data and video captured by surveillance and then alert human analysts of patterns or when there is abnormal or suspicious activity.” According to Deputy Secretary of Defense Patrick Shanahan, the objective of developing technologies in this field is “to meet our warfighters’ needs and to boost the]speed and agility of technology development and procurement.”
As huge amounts of data are sorted in almost real-time, the big data analytics associated with AI will significantly change intelligence analysis, giving commanders and their staffs a degree of intelligence analysis and productivity previously unseen. Command and control will also be impacted as human commanders outsource mundane and, in some cases, crucial choices to artificial intelligence platforms, drastically cutting the time between the decision and the action that follows.
Experts say, in the end, combat is a race against time, with the winner typically being the side that can make a decision and carry it out the quickest. In fact, artificial intelligence-enhanced command and control systems can move decision support and decision-making at a speed that is noticeably faster than that of conventional methods of fighting wars.
A new term, hyper war, has been established explicitly to embrace the pace at which war will be fought to describe how quickly this process will proceed, especially if it is combined with automatic decisions to deploy artificially intelligent autonomous weapons systems capable of devastating effects.
Healthcare
Artificial intelligence tools are helping designers improve computational sophistication in health care. For instance, the German business Merantix uses deep learning to solve medical problems. It can be used to “identify lymph nodes in the human body in Computer Tomography (CT) pictures” in the field of medical imaging.
The trick, according to the system’s creators, is marking the nodes and spotting any potential troublesome lesions or growths. Although radiologists may only be able to carefully read four photos in an hour and charge $100 per hour. This method would cost $250,000 if there were 10,000 photos, which is too expensive to be completed by humans.
Deep learning can be used in this case to teach computers how to distinguish between lymph nodes that appear normal and those that do not. Radiological imaging specialists can apply this knowledge to actual patients and ascertain the degree to which someone is at risk of malignant lymph nodes after practising labelling accuracy through imaging exercises. It is an issue of determining the unhealthy versus healthy node because only a small percentage of samples are likely to test positive. Congestive heart failure, which affects 10% of senior individuals and costs the US $35 billion annually, has also been the subject of AI research.
Artificial intelligence tools are helpful because they predict in advance potential issues and allocate resources to patient education, sensing, and proactive interventions that keep patients out of the hospital.