Most people are not familiar with the concept of Artificial Intelligence (AI). As an example, when asked about 1,500 senior business leaders in the United States about AI in 2017, only 17 percent said they were familiar with it. Most of them do not know what it is or how it affects their particular organizations. They understand that there is considerable potential to change business processes, but it is not clear how AI can be implemented in their own organizations.
In this paper, we discuss novel applications in finance, national security, health care, criminal justice, transportation, and smart cities, and address issues such as data access issues, algorithmic bias, AI ethics and transparency, and legal responsibility for AI decisions. We are contrary to the regulatory policies of the U.S. and the European Union and close by making many recommendations to benefit more from AI while protecting important human values.
To maximize the benefits of AI, we recommend nine steps forward:
· Inspiring greater information access for scientists without negotiating users personal privacy,
· Invest more government funding in unclassified AI research,
· Encourage new models of digital education and AI workforce development as employees have the skills they need in the 21st-century economy
· Generate a federal AI suggested committee to make policy recommendations,
· Engage with state and local authorities so that they implement effective policies,
· Control AI principles that are broader than specific algorithms.
· AI does not reflect historical injustice, injustice, or discrimination in data or algorithms, because bias complaints are taken seriously
· Conduct mechanisms for human monitoring and control and Penalize malicious AI behavior and promote cybersecurity.
QUALITIES OF ARTIFICIAL INTELLIGENCE
Although not uniformly agreed upon by definition, AI generally refers to “machines that respond to stimuli in accordance with traditional responses from humans, giving the human capacity for meditation, judgment, and purpose.” According to researchers Zuband and Vijay, these software systems “usually make decisions that require a human level of expertise” and help people deal with problems or problems. As such, they act in a deliberate, intelligent and positive manner.
Intentionality: Artificial intelligence algorithms are designed to make decisions, often using real-time data. They are different from passive machines capable of only mechanical or predetermined responses. Using sensors, digital data, or remote inputs, they combine information from a variety of sources, instantly analyze the material, and act on the insights gained from that data. With tremendous improvements in storage systems, processing speed and analytical methods, they have excellent sophistication in analytics and decision making. AI is usually undertaken in conjunction with machine learning and data analysis. Machine learning takes data and looks for underlying trends. If it identifies a practical problem, software designers can take that knowledge and use it to diagnose specific problems. Data are robust enough that algorithms can detect usage patterns. Data can come in the form of digital information, satellite images, visual information, text, or structured data.
Adaptability: AI systems have the ability to learn and adapt when making decisions. In the transportation area, for example, semi-autonomous vehicles have the means of notifying drivers and vehicles about impending congestion, potholes, highway construction or other traffic interruptions. Vehicles can take advantage of the experience of other vehicles on the road, without human involvement, and the entire corpus of their “experience” is immediately and completely transferred to other similarly configured vehicles. Their advanced algorithms, sensors, and cameras have experience in current operations and use dashboards and visual displays to display information in real-time, allowing human drivers to understand the ongoing traffic and vehicle conditions. And in the case of fully autonomous vehicles, sophisticated systems can fully control a car or truck and make all navigational decisions.
APPLICATIONS IN DIVERSE SECTOR
AI is not the vision of the future, but it is here today and connected to different fields. This includes finance, national security, health care, criminal justice, transportation, and smart cities. There are many examples where AI is already impacting the world and enhancing human capabilities in significant ways.
Finance: Investments in financial AI in the United States tripled between 2013 and 2014, reaching a total of $ 12.2 billion. According to observers in the field, “decisions about lending can now take into account a variety of nuanced data about the borrower, rather than a credit score and a background check.” Robo-Advisors Hours. These advances are designed to eliminate emotion from investment and make decisions based on analytical considerations and make these choices in a matter of minutes.
A prominent example of this is happening in stock exchanges, where high-frequency trading by machines has replaced human decision-making. People submit buy and sell orders and computers compare them in the blink of an eye without human intervention. Machines can detect trading inefficiencies or market distortions at very low levels and execute money-making trades, according to investors. These tools, which are powered by sophisticated computing in some places, have great potential for storing information because they are based on “quantum bits” that can store multiple values in each space, not zero or one. This dramatically increases storage capacity and reduces processing time.
Fraud detection represents another way that AI can help in economies of scale. Detecting fraudulent activity in large organizations is sometimes difficult, but AI can detect abnormalities, outliers, or twisted cases that require further investigation. Managers can help them find problems early in the cycle before they reach dangerous levels.
Health care: AI tools are helping designers to improve computational sophistication in health care. For example, Merandix is a German company that applies deep learning to medical issues. It is used in medical imaging to “detect lymph nodes in the human body in computer tomography (CT) images.” Humans can do this, but radiologists charge $ 100 per hour and can only read four images per hour. If there are 10,000 images, the cost of this process is $ 250,000, which is very expensive if done by humans.
The deeper practice in this situation is that train computers in the data sets look normal compared to the disordered lymph node. After imaging exercises and respecting the accuracy of labeling, radiological imaging professionals can apply this knowledge to real patients and determine the extent to which someone is at risk for cancer lymph nodes. It is a matter of identifying an unhealthy and healthy node, as only a few can test positive.
AI has also been applied to congestive heart failure, affecting 10 percent of senior citizens and costing $ 35 billion annually in the United States. AI tools are helpful because they “predict potential challenges in advance and allocate resources to patient education, sensing and active interventions that keep patients out of the hospital.”