Developing ethical Artificial Intelligence is an increasingly important goal for companies and organizations. Aside from ensuring that AI solutions don’t cause harm to human beings, ethically sound AI implementations require organizations to hire ethicists to work with their developers and corporate decision makers. Organizations should also have an ethics code for AI, organize an external review board, implement an audit trail for AI solutions, and train employees. And when AI solutions do cause harm, the organization should provide a way to undo the harm caused by those solutions.
Philosophy of Artificial Intelligence
In his Philosophy of Artificial Intelligence, John Haugeland makes the argument that AI will be a revolution in how computers think, act, and feel. While the concept of AI is an exciting new effort to build computers with minds, some researchers see it differently. The Westworld TV series, for example, is a great way to explore this notion. Luger and Stubblefield describe AI as the automation of intelligent behavior.
AI also has strong ties to reasoning. It uses probabilistic, inductive, and logic-based methods to make decisions. In Glymour’s 1992 book, for example, he tells us about the first-order logic that Frege developed, which consists of a series of step-by-step inferences. Though the idea of AI and its use in daily life seems futuristic, its philosophical roots go back to Descartes.
In addition to exploring the philosophical questions surrounding the possibility of AI, this field examines the architecture of intelligent machines. This is a branch of philosophy that addresses the question of whether humans and animals are machines, or are simply inferring human-like behavior. The most important questions in AI lie at the intersection of semantic contents of thought and computation, and the question of what constitutes rationality. The philosophy of artificial intelligence revolves around Kurt Godel’s famous Incompleteness Theorem and questions about the nature of human-like reasoning powers.
Philosophers have long debated how AI can affect the world and society. The current state of AI is an integral part of the course. Students will discuss ethical issues, dangers, and current research in this field. Moreover, students will learn about the history and current developments in the field. They will also learn about the benefits and potential dangers of AI. So, the philosophy of artificial intelligence will be crucial in this world. For the sake of humanity, it is essential to know the history of artificial intelligence, including its philosophical and social implications.
Applications of Artificial Intelligence
Since the 1950s, games have been a key application of AI. In a variety of games, including chess, go, poker, and general game playing, AIs have achieved superhuman results. Most chess programs today use AI Algorithms to replace hand-coded algorithms. Likewise, AIs can read and upload files, classify them into the proper accounting codes, and find hidden trends. Unlike human players, AIs never tire or make human mistakes.
Using an artificial intelligence voicebot, for example, can help a business contact hundreds of thousands of people via the phone. These programs can respond to simple questions and pass interested leads to a live sales representative. Previously, this would have been impossible for most businesses to accomplish, but now it’s possible. Such AI applications are known as AIOps. This technology helps organizations achieve their goals of digital transformation by automating complex business processes and freeing staff time for more important tasks.
AI-powered algorithms can improve the efficiency of manufacturing processes by detecting patterns in demand across socioeconomic groups, time, and geographies. These algorithms also help determine inventory levels, raw material sourcing, financing decisions, and human staffing. Machine-to-machine communication with AI is also possible. Moreover, AI tools can predict the malfunction of factory tools. A leading example of how AI is impacting the automotive industry is self-driving cars. With the help of 5G technology, autonomous vehicles will be connected to each other and can share vital data.
Financial services have long utilized AI-based systems to identify fraudulent charges and claims. In 1987, the Security Pacific National Bank launched a fraud prevention taskforce to combat the spread of unauthorized debit card use. Other examples of AI-based systems in the financial industry include Moneystream and Kasisto. Medical applications of AI include concept-processing technology in EMR software. A machine-made AI that understands probabilities is an effective medical diagnostic tool.
Neural nets
Neural networks are a type of computer network that is loosely modeled on the human brain. They are composed of thousands or millions of simple processing nodes that are highly interconnected and are referred to as “neural networks.” Most neural nets are organized in layers with each layer being linked to the next using a connection called a “feedforward” connection. An individual node may be connected to multiple nodes in the layer below or above.
The basic concept of neural networks is that neurons in a network perform mathematical computations when posed with a problem. These neurons read the inputs and determine whether enough information exists to pass it on to the next one. In the simplest example, the neurons add up the data inputs and fire whenever the sum is above a threshold. These neurons are very powerful in detecting anomalies, which makes them an excellent choice for use in machine learning.
The neural network approach was originally designed to mimic the human brain and has evolved to fit various tasks. They have supported tasks as diverse as computer vision, speech recognition, speech and text mining, social network filtering, board games, and even medical diagnosis. Today, neural networks are used in financial operations, enterprise planning, trading, and business analytics. While they were once considered difficult to understand, they have gained wide adoption in many industries.
Before the advent of neural networks in artificial intelligence, they were an important area of research in computer science and neuroscience. However, their widespread use was suppressed in the early 1970s by the controversial work of Marvin Minsky and Seymour Papert. However, this newer approach has returned to the forefront of computer science due to the increased processing power of graphics chips. So what are neural nets and how do they work?
Reactive machines
Reactive machines in Artificial Intelligence are essentially software programs that are programmed to react to external stimuli. These computers can be easily identified by their spam filters and Netflix recommendations. Other examples of reactive machines are the chess-playing supercomputers Deep Blue and AlphaGo. While these programs are extremely impressive, they lack creativity and generalization. Hence, they’re apt for tasks such as self-driving cars.
Reactive AI systems can mimic the human mind, but without the ability to learn from past experiences. They respond to current situations only, and do not make any predictions or use their memory to make better decisions. IBM’s Deep Blue is an example of a reactive AI system. But it’s not enough to replicate human thought patterns – we need to make AI systems that can think and make decisions. Reactive machines have the capacity to mimic basic human behavior, and are reliable for repetitive tasks.
The other main class of AI systems is the reactive type. The name “reactive” refers to a system that uses data it has already been fed. This type of AI system makes use of large data sets to build a reference model for solving future problems. For example, an image recognition AI system is trained using thousands of pictures and their labels. Similarly, a theory of mind AI model could make decisions based on observations, emulating human emotions and making empathy.
Reactive AI systems cannot learn from experience, and they fall under narrow AI. By contrast, the theory of mind envisions machines with feelings, thoughts, and decisions. Several real-world attempts are already underway to develop AI systems with these characteristics. Robots like Kismet, for example, are already attempting to mimic human emotions. In addition to their advanced cognitive abilities, reactive machines can also help prevent accidents and improve cancer diagnosis.
Impact on the economy
While the immediate impact of AI on the economy is uncertain, the long-term effects could be substantial. By 2030, AI investment could augment employment by 5 percent, and its productivity effect could increase employment by 10 percent. In other words, AI could create new job opportunities at a lower cost than human labor. However, the longer-term effects of AI on the economy will likely be more complex. There is a need to plan ahead for this disruptive technology.
According to a recent Accenture report, AI will add an additional $13 trillion to global GDP by 2030, which is around $22 trillion. It is projected to increase labor productivity by up to 40 percent in these countries. While it will increase GDP in all sectors, the US will benefit the most. Its GDP will increase by 4.6 percent by 2035, making it the largest growth rate among developed nations. Conversely, Italy will see a smaller GDP increase, up to 1.8 percent a year.
Although AI is not widely adopted in developing countries, the US and China are well-positioned to reap the rewards of AI adoption. These advanced economies are motivated to capitalize on the benefits of AI, especially given their slow productivity growth. Other developed economies may lag behind the leaders, but they have strengths in specific areas. Meanwhile, developing countries lack the talent and investment capacity needed for AI adoption. It is essential to plan ahead for these effects, and implement plans to prepare for them.
The AI revolution will impact the economy overall, but its effects on employment may not be as immediate as many economists believe. While AI may reduce employment in some areas, it may be positive for skilled labor. In addition, the employment benefits of automation appear to outweigh the costs of automation. In the long-term, automation is likely to increase employment in plants that automate. However, the negative correlation between automation and aggregate employment may be due to labor market frictions. It is important to consider education and labor market policies as a means of determining the impact of AI on employment.
