If someone asked you what your definition of AI or artificial intelligence was, what would you say?
What Exactly Is Artificial Intelligence?
Artificial intelligence is a complex topic. For that reason, there are several definitions that you might encounter. Here is one of the most accurate ones by Google:
“The theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision making, and translation between languages.”
In my own seminars, I try to keep things simple, defining AI as using computers to do things that normally require human intelligence. For a more detailed and complete definition, however, I personally like this one from the online publication Quartz:
“Artificial intelligence is software or a computer program with a mechanism to learn. It then uses that knowledge to make a decision in a new situation, as humans do. The researchers building this software try to write code that can read images, text, video, or audio, and learn something from it. Once a machine has learned, that knowledge can be put to use elsewhere.”
In other words, we might say that AI is the ability of machines to use algorithms to learn from data, and use what has been learned to make decisions like a human would. Unlike humans, though, AI-powered machines don’t need to take breaks or rest and they can analyze massive volumes of information all at once. The ratio of errors is also significantly lower for machines that perform the same tasks as their human counterparts.
This article aims to provide examples of the ways that the development and adaptation of artificial intelligence will open up new opportunities and challenges to both the business world and society as a whole. For this reason, you won’t find a lot of detailed explanations of the technical aspects of AI here. However, at the end of this section, you can find a list of resources that you can consult if you’d like to dive deeper into the technical world of artificial intelligence.
The idea that computers or software programs can both learn and make decisions is particularly significant and something that we should be aware of, as their processes are growing exponentially over time. Because of these two skills, AI systems can now accomplish many of the tasks that were once reserved for humans.
AI-based technologies are already being used to help humans benefit from significant improvements and increased efficiency in nearly every area of life. As the development of AI continues to grow, it will change the ways we live and work more and more.
Another benefit of AI is that it allows machines and robots to perform tasks that humans consider to be difficult, boring, or dangerous. In turn, this will enable humankind to do things that were once thought impossible.
One drawback to AI technologies is that machines will also be able to perform many tasks that currently require a human touch, which will significantly disrupt the labor market. AI also has the potential to cause political power struggles. We’ll cover both of these topics in other sections of this book.
AI can be applied to just about every situation and offers the possibility of transforming our experiences, making things better and more effective.
Here are just a few of the fast-growing technical applications for AI that are currently in place:
- Static Image Recognition, Classification and Tagging: These tools are helpful in a wide array of industries.
- Algorithmic Trading Strategy Performance Improvements: This has already been implemented in various ways in the financial sector.
- Efficient, Scalable Processing of Patient Data: This helps to make patient care more effective and efficient.
- Predictive Maintenance: This is yet another tool that is widely applicable to different industries.
- Object Detection and Classification: This can be seen in the self-driving car industry, but has potential for use in many other sectors as well.
- Content Distribution on Social Media: This is primarily a marketing tool used with social media, but can also be used to raise awareness for non-profit organizations or to quickly spread information as a public service.
- Protection from Cybersecurity Threats: This is an important tool for banks and systems that send and receive payments online.
While some of the examples above are a little more technical, it is clear to see that AI will give us the potential to better see, hear, and understand the world around us. Because this was once a uniquely human characteristic, AI will open up a whole new world of possibilities.
AI will be able to make our lives easier by offering suggestions and predictions relating to important questions in our lives, impacting areas like our health, wellbeing, education, work, and how we interact with others.
It will also transform the way we do business by providing competitive advantages to the companies that seek to understand and apply these tools quickly and effectively.
Sometimes the term “artificial intelligence” can tend to scare people off. One top AI expert, Sebastian Thrun, thinks it would be better to call it “data science,” a less intimidating term, which would probably lead to increased public acceptance.
Machine learning is one of the primary approaches to artificial intelligence. In short, machine learning is an aspect of computer science in which computers or machines have the ability to learn without being explicitly programmed. A typical result would be suggestions or predictions in a particular situation.
Consider the first personal computers that became available to consumers in the 1980s. These machines were explicitly programmed to be able to do certain things. In contrast, thanks to machine learning, many technical devices that you’ll use in the future will gain experience and insight from the way they are used to be able to offer a personalized user experience for each individual.
Already, basic examples of this include the personalization you see in social media sites like Facebook, or in Google search engine results.
Machine learning uses algorithms to learn from data patterns. For example, email spam filters use machine learning to detect which emails are spam and then separate those from legitimate emails. This is a simple example of how algorithms can be used to learn from data patterns, and the knowledge acquired can be used to make decisions.
Showcases three subsets of machine learning that can be used: supervised learning, unsupervised learning, and reinforcement learning.
In supervised learning, algorithms use data that has already been labeled or organized. With this method, human input is required to be able to provide feedback.
Unsupervised learning implements algorithms in which data is not labeled or organized ahead of time. Instead, relationships must be discovered without human intervention.
Lastly, with reinforcement learning, algorithms are able to learn from experience. They are not given explicit goals, except to maximize some reward.
One of the most powerful and fastest growing applications of artificial intelligence is deep learning, which is a sub-field of machine learning. Deep learning is being used to solve problems which were previously considered too complex, and normally involve huge amounts of data.
Deep learning occurs through the use of neural networks, which are layered to recognize complex relationships and patterns in data. The application of deep learning requires a huge dataset and powerful computational power in order to work. Deep learning is currently being used in speech recognition, natural language processing, computer vision, and vehicle identification for driver assistance.
One example of this can be seen in the translations being done at Facebook. Recently, Facebook revealed that thanks to deep learning they are able to make about 4.5 billion translations every day. These tend to be short translations for things like status updates posted by people to their Facebook profiles. Facebook AI tools are able to translate these messages automatically into different languages. It would be incredibly expensive and require a huge team of people to offer the same translations without deep learning.
For the sake of simplicity, in this book I mainly use the term artificial intelligence, although many times I might technically mean deep learning or machine learning. Keep in mind that artificial intelligence is often used in a more general sense throughout the book.
Artificial intelligence technologies and applications have started to become a leading topic in the news. Unfortunately, there are a lot of misleading stories and articles that have generated widespread confusion among the general public. One of the best and most trustworthy sources of up-to-date AI-related news is the AI Index.
Will Artificial Intelligence Be Able to See, Hear and Understand?
To better understand the huge impact that AI will have on our lives, it is helpful to know that AI technologies now have the ability to see (computer vision), hear (speech recognition), and understand (natural language processing) more than ever before. Figure 1.5. showcases this concept well.
AI scientists are achieving amazing new advances in each of these three fields. For example, Google has announced that it has developed computer vision technology that can add the appropriate colors to photographs and videos that were originally in black and white.
Google has also developed speech recognition technology that can hear and understand speech almost as well as humans can, with a 95 percent accuracy rate for English.
Another incredible accomplishment in the field of computer vision is that scientists at the Massachusetts Institute of Technology (MIT) have managed to develop AI that can see through walls by using radio frequency waves.
In the near future we will read about similar achievements in these three technologies. We can be sure that the help AI will offer to humans will be immeasurable when it is able to see, hear and understand perfectly.
While all three of these sensing capabilities will be important, computer vision may be the most significant, as it offers the most beneficial uses for things like self-driving cars, facial recognition, drones and robotics.
My own prediction is that in the future, computer vision will be used just about everywhere, including in almost every device in your home. For example, your refrigerator will use computer vision to see which items are missing so it can place the orders to replace them on its own. Also, most buildings will use computer vision for safety reasons, thereby eliminating the need for security guards. Computer vision will also be used in supermarkets and other retail stores, utilizing facial recognition technology to analyze your emotions based on your facial expressions, and use that information to suggest products for you to purchase.
Consider your own work for a moment. How could the application of one or more of these three AI technologies (computer vision, speech recognition, and natural language processing) help you to perform your work more efficiently?
As with many other AI technologies, computer vision is now accessible to any company, organization, government, or an individual via cloud-based computer vision services. Therefore, anyone can rent computer vision services rather than build a computer vision platform from nothing. This type of service is often referred to as “CV as a Service” or “CVaaS.”
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