Researchers should look at different approaches to AI in order for it to be more easily understood. Some explainability techniques focus on explaining AI's reasoning, while others offer an explanation that is independent of context. These explanations may be extremely unlikely. Others try to connect knowledge-based system and make explanations more pertinent to context. Whatever approach you take, ensure that you consider the context.
Interactive explanations are recommended
It is important to design an explicable artificial intelligence system in a way that is both interactive and beneficial for the system owner as well as the users. This is because people are influenced by their past experiences and preferences. System owners should be aware that they may interpret similar explanations in different ways. Interactivity is important as it shows that the system can be customized and adapted to individual needs.
The second step in creating an explainable artificial intelligence application is to consider the levels of detail that users need. A counterfactual explanation can be enough to explain the smallest change in the model's features, while an interactive explanation will require more work. Contrary to the counterfactual explanation, it will only describe the output of a system and not reveal its inner workings. This explanation is useful in protecting intellectual property.
Interactive AI systems should be capable of incorporating diverse data to produce relevant results. It is inappropriate for clinical use if the machine cannot give such details in its explanation. The machine's decision-making process must be understood and interpreted by human experts. This requires a high level of confidence and trust in the machine's decisions. Personalized medicine is going to require a high level of explainability.
To provide semantics that are meaningful, background knowledge should be used
This article will discuss how background knowledge can be used to provide meaningful semantics for explainable artificial intelligence systems. Domain knowledge is a good source of background knowledge. Experiments can also provide background knowledge. Background knowledge should be used as explanations because it facilitates the human-machine relationship. We will also see how background knowledge can be injected back into a sub-symbolic model to improve performance.
The importance of background knowledge for explainability is well-known, and it has been widely recognized in psychology. Researchers have demonstrated that explanations are socially-oriented. They also include semantic information. This is essential to effective knowledge transmission. Hilton (1990) states that explanations are a result of social interactions and semantic information. Kulesza et al. (2013) found a positive relationship between explanation property and mental models. Researchers also discovered a relationship between completeness and soundness as well as trust.
With AI becoming more common, there is an increasing demand for explanation. It is essential to have explanations that are transparent and trustworthy for AI systems. It is essential to understand the user level in order to develop explainable artificial intelligence systems that build trust. Ultimately, this will help AI systems build trust in humans. To understand how AI systems work, you should consider the following background information.
FAQ
What is the most recent AI invention?
Deep Learning is the most recent AI invention. Deep learning is an artificial intelligence technique that uses neural networks (a type of machine learning) to perform tasks such as image recognition, speech recognition, language translation, and natural language processing. Google was the first to develop it.
The most recent example of deep learning was when Google used it to create a computer program capable of writing its own code. This was achieved using "Google Brain," a neural network that was trained from a large amount of data gleaned from YouTube videos.
This enabled the system to create programs for itself.
IBM announced in 2015 that they had developed a computer program capable creating music. Also, neural networks can be used to create music. These are called "neural network for music" (NN-FM).
How does AI work
An algorithm refers to a set of instructions that tells computers how to solve problems. An algorithm can be described in a series of steps. Each step must be executed according to a specific condition. The computer executes each instruction in sequence until all conditions are satisfied. This is repeated until the final result can be achieved.
Let's take, for example, the square root of 5. You could write down every single number between 1 and 10, calculate the square root for each one, and then take the average. That's not really practical, though, so instead, you could write down the following formula:
sqrt(x) x^0.5
This will tell you to square the input then divide it twice and multiply it by 2.
A computer follows this same principle. It takes the input and divides it. Then, it multiplies that number by 0.5. Finally, it outputs its answer.
Is there another technology which can compete with AI
Yes, but not yet. There have been many technologies developed to solve specific problems. But none of them are as fast or accurate as AI.
Who invented AI?
Alan Turing
Turing was created in 1912. His father, a clergyman, was his mother, a nurse. He excelled in mathematics at school but was depressed when he was rejected by Cambridge University. He discovered chess and won several tournaments. After World War II, he was employed at Bletchley Park in Britain, where he cracked German codes.
1954 was his death.
John McCarthy
McCarthy was born in 1928. McCarthy studied math at Princeton University before joining MIT. He developed the LISP programming language. By 1957 he had created the foundations of modern AI.
He died on November 11, 2011.
Are there any risks associated with AI?
Of course. They always will. AI is a significant threat to society, according to some experts. Others believe that AI is beneficial and necessary for improving the quality of life.
The biggest concern about AI is the potential for misuse. It could have dangerous consequences if AI becomes too powerful. This includes robot dictators and autonomous weapons.
AI could also take over jobs. Many people fear that robots will take over the workforce. Others think artificial intelligence could let workers concentrate on other aspects.
For instance, economists have predicted that automation could increase productivity as well as reduce unemployment.
Is AI good or bad?
Both positive and negative aspects of AI can be seen. It allows us to accomplish things more quickly than ever before, which is a positive aspect. Programming programs that can perform word processing and spreadsheets is now much easier than ever. Instead, we can ask our computers to perform these functions.
People fear that AI may replace humans. Many believe that robots will eventually become smarter than their creators. This means they could take over jobs.
Is Alexa an AI?
Yes. But not quite yet.
Alexa is a cloud-based voice service developed by Amazon. It allows users to communicate with their devices via voice.
The Echo smart speaker first introduced Alexa's technology. However, since then, other companies have used similar technologies to create their own versions of Alexa.
These include Google Home as well as Apple's Siri and Microsoft Cortana.
Statistics
- In the first half of 2017, the company discovered and banned 300,000 terrorist-linked accounts, 95 percent of which were found by non-human, artificially intelligent machines. (builtin.com)
- By using BrainBox AI, commercial buildings can reduce total energy costs by 25% and improves occupant comfort by 60%. (analyticsinsight.net)
- A 2021 Pew Research survey revealed that 37 percent of respondents who are more concerned than excited about AI had concerns including job loss, privacy, and AI's potential to “surpass human skills.” (builtin.com)
- According to the company's website, more than 800 financial firms use AlphaSense, including some Fortune 500 corporations. (builtin.com)
- In 2019, AI adoption among large companies increased by 47% compared to 2018, according to the latest Artificial IntelligenceIndex report. (marsner.com)
External Links
How To
How to build a simple AI program
You will need to be able to program to build an AI program. There are many programming languages out there, but Python is the most popular. You can also find free online resources such as YouTube videos or courses.
Here's an overview of how to set up the basic project 'Hello World'.
First, open a new document. This can be done using Ctrl+N (Windows) or Command+N (Macs).
In the box, enter hello world. Enter to save this file.
Now, press F5 to run the program.
The program should show Hello World!
This is only the beginning. These tutorials will help you create a more complex program.