Question

Artificial Intelligence and Cognitive Psychology

How can you engineer human emotion in artificial intelligence? Do not copy and paste from other websites without giving credit or putting things in quotes. For example, don't say it is possible to engineer human emotion by using engineering principles. What kind of engineering principles? How does engineering even come into the picture?

Answer 1

DEFINITION:

Artificial intelligence (AI), sometimes called machine intelligence, is intelligence demonstrated by machines. The term was first used at a conference at Dartmouth College in 1956. In contrast to the natural intelligence displayed by humans and other animals. In computer science, AI research is defined as the study of "intelligent agents": any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals. Colloquially, the term "artificial intelligence" is applied when a machine mimics "cognitive" functions that humans associate with other human minds, such as "learning" and "problem-solving" In answering the question of what artificial intelligence is, and what the term means today, we need to consider what constitutes intelligence. This is not as simple as many people assume it should be. For example, would you consider all animals to be intelligent? Or rather, to have intelligence?

"Some animals, such as cats, octopuses, and even dolphins, among others, demonstrate high levels of intelligence. When comparing two different animals, such as a mouse and a gorilla, there are a number of ways that scientists can measure their relative intelligence. But objectively defining and measuring intelligence is difficult.

Principles of Artificial Intelligence :

Applied Research Engineers at Magic Leap lead the efforts in developing novel algorithmic architecture towards the end goal of solving and building Artificial General Intelligence for Mixed Reality. In a collaborative environment, across multiple disciplines, we develop solutions to fundamental questions in mixed reality experiences using machine learning and AI.Applied Research Engineers leverage their domain knowledge and explore the use of machine learning approaches to solve broad cognition and perception problems. Now at the Wharton School at the University of Pennsylvania, I am looking for opportunities to meet people with interesting AI-related ideas and learn about the newest innovations within the AI ecosystem. Within the first two months of business school, I connected with Nicholas Lind, a second-year Wharton MBA student who interned at IBM Watson as a data scientist. Immediately recognizing our common passion for AI, we produced a lunch-and-learn about AI and machine learning (ML) for our fellow classmates.

Using the following deck, we sought to:

• define artificial intelligence and describe its applications in business
• decode buzzwords such as “deep learning” and “cognitive computing”
• highlight analytical techniques and best practices used in AI / ML

The lunch-and-learn was well received. When it became apparent that it was the topic at hand and not so much the free pizzas that attracted the overflowing audience, I was amazed at the level of interest. It was reassuring to hear that classmates were interested in learning more about the technology and its practical applications in solving everyday business challenges. Nick and I are now laying a foundation to make these workshops an ongoing effort so that more people across the various schools of engineering, design, and Penn at large can benefit.

How does engineering even come into the picture?

A prominent feature in science fiction over the years has been artificial intelligence. Since the earliest days of computing, scientists and other thinkers have been fascinated by the notion of creating a machine capable of replicating the human brain. It used to be thought that the analogy of the human brain is like a computer ran deep. However, we now know that the picture is much more complicated, the way that the brain works goes beyond a simple computer.

We still do not fully understand how consciousness arises in the human brain, and there is still much debate surrounding whether consciousness can be separated from advanced intelligence. But artificial intelligence need not be this complex; we see far simpler examples of what we might describe as artificial intelligence on a regular basis. Artificial intelligence is increasingly finding its way into industrial and manufacturing contexts. There are even AIs being used to conduct high-frequency trading on the stock market.Some of the most exciting current and prospective uses of artificial intelligence are within the field of engineering. The AIs that are used in the engineering sector combine both software and hardware components. Think of the robots on a car assembly line and the software that controls them. Some major fields are below:

Machine Learning:

One of the most significant technological concepts for the future of artificial intelligence-led engineering is machine learning. Machine learning is the study of exactly how machines learn. The ultimate goal of artificial intelligence isn't just to have machines that can learn but to have machines that are capable of self-analysis.

Natural Language Processing:

Natural language processing is a field of study dedicated to improving the ability of humans and machines to communicate. In particular, natural language processing aims to improve the sophistication with which machines can respond to the human voice. Like with machine learning, natural language processing makes heavy use of large data sets and algorithm-based learning. Think of the voice assistant in your smartphone. If you have owned a number of smartphones over the last decade or so, then you may well have noticed how much the accuracy with which they hear and transcribe our voices has improved. While your phone might be able to identify the words that you've said, this isn't the same as understanding.

Image Processing:

When humans see an object, it is because a light is entering the eye and being converted into an electric signal. This signal is then carried to the brain via the optic nerve. The brain turns this electronic signal into an image, it is this image that we 'see'. Machines work in a very similar way. We can set up a camera in order to record an image, and we can display this image to a user. However, this is not the same as the machine understanding the image. With image processing algorithms, we can have machines analyze what they see and react accordingly. From an engineering perspective, this means we could have machines which are able to identify structural abnormalities and other issues that have identifiable visible signs.

Internet of Things:

Today, we are used to having vast amounts of data flying through the airwaves all around us. As smart devices become more common in our homes, we are also beginning to see the practical potential of being able to link devices together. The Internet of Things refers to a hypothetical network, which would connect everyday devices and things together, in the same way, that the internet connects computers from around the world. Allowing the various devices in our lives to collect and share data would open up some exciting new possibilities.

Al Affects Blockchain and Cryptocurrency Tech:

A fantastic illustration of how innovative use of AI can affect cryptocurrencies and blockchain technology is the Magnus Collective. They comprise a decentralized network of AIs, including sensors, hardware, computers, robots, and human. It is a hybrid token, might be an evolution of the ICO concept. Artificial intelligence has had an impact on just about every conceivable industry and sector, engineering is no exception. There are a number of different applications of artificial intelligence that are of considerable use to engineers. From allowing more intuitive and innovative interactions with software and machinery, to keep a watchful eye over the work of engineers as well as other machines, artificial intelligence has many roles to play. We have always understood that both of these concepts can yield impressive results, but the transformative nature that they both have on engineering demonstrate that they are even more powerful than we once thought.