Artificial intelligence (AI) is a complex and rapidly-growing field, and it can be difficult to keep up with all of the terminology. In this blog post, we will discuss 8 key terms that you need to know when talking about AI. By understanding these concepts, you will be able to have more informed discussions about the future of AI!
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Siri, self-driving cars, Netflix recommendations – most of us are familiar with AI whether we know it or not. At its most basic, Artificial Intelligence (AI) is a field of study that combines computer science (programming), mathematics, and data science to build predictive models.
AI is also commonly thought of as the ability of a computer to mimic human intelligence. Human intelligence is generally characterized by the combination of many diverse abilities, hence why research in AI has focused on a variety of components of intelligence including learning, reasoning, problem solving, perception, and the use of language.
Here at Resua we are passionate about using AI in a new and intelligent way to solve problems and increase people’s capabilities.
TLDR: Artificial Intelligence (AI) is intelligence generated by computers.
Blockchain – it’s the latest buzzword in the technology world, and it’s got people talking about its potential to revolutionize everything from banking to supply chains to voting.
But what is blockchain, exactly? In its simplest form, blockchain is a digital ledger of transactions; however, that analogy doesn’t quite capture all that it is. When a transaction is made, it is recorded on a “block”, and each block is chained together in a chronological chain. This chain is stored across a network of computers, and each computer in the network verifies the transaction before it is added to the blockchain.
This verification process makes blockchain incredibly secure, and because blockchain is decentralized, it doesn’t require a central authority to manage or approve transactions. That’s why many people believe blockchain has the potential to upend traditional business models and create new opportunities for individuals and organizations alike. The decentralized nature of blockchain also makes it highly resistant to hacking. Even if someone were able to gain control of a majority of computers on the network, they would still be unable to make changes to the blockchain without being detected. This makes blockchain an appealing option for applications where security and transparency are paramount.
TLDR: Blockchain is an emerging database technology that uses cryptography and consensus to create immutable, or unchangeable, records.
AI integration is the process of incorporating AI into existing systems and applications. This can be done in a number of ways, depending on the needs of the organization, but the goal is always to improve efficiency and productivity by automating tasks that are currently performed manually.
AI integration can be used for a wide variety of tasks, from customer service to data entry. Various AI systems can be integrated into existing applications to add functionality and improve performance, including natural language processing and machine learning algorithms. In many cases, AI-powered systems can perform these tasks more accurately and rapidly than humans, leading to improved efficiency and cost savings.
In addition, AI-powered systems can often provide insights that humans would not be able to identify on their own, leading to better decision making. Facial recognition image processing, speech processing and personalized content are only a few examples of AI features that can be integrated into existing systems and applications. As AI technology continues to evolve, the potential applications for AI integration will only continue to expand.
TLDR: AI Integration is the linking of different AI technologies to work together in the context of existing systems and applications.
Non-fungible Token (NFT)
Non-fungible tokens (NFTs) are digital assets that are stored on a blockchain. NFTs can represent anything from a piece of art to a video game item to a piece of music. One of the key benefits of NFTs is that they are unique. Unlike traditional assets, NFTs cannot be duplicated or destroyed, making them ideal for storing digital art, music, and other forms of creative expression.
NFTs also offer greater transparency than traditional assets, as the ownership history of an NFT is publicly available on the blockchain. NFTs can also be used to represent ownership of physical assets, such as real estate or collectibles. The unique nature of NFTs has led to a boom in their popularity, with prices for some NFTs reaching millions of dollars. While NFTs are still a relatively new technology, they have the potential to revolutionize the way we interact with digital media.
TLDR: NFTs are non-fungible tokens that act as digital certificates of ownership and can represent anything.
Data Science is a growing, multidisciplinary field that combines the use of statistics, mathematics, computer science and business to collect and analyze data in order to solve problems or to optimize existing systems. This process involves the collection of all different types of data, which then gets analyzed for patterns or useful insights with tools such as forecasting, machine learning, and predictive analytics, amongst many others.
Why is this so useful? In a world where massive amounts of data are now able to be collected and analyzed, having the ability to find these trends and create better solutions for people is a win-win for companies and for consumers. In this way, data science is transforming the business landscape in some incredibly helpful ways! Not only does it mean that your streaming services are better able to cater to your preferences (one example of data science at work), it also means that businesses can find ways to streamline time consuming processes and optimize them for the better. Whether that means finding the best routes for deliveries, tracking delayed flights and being able to notify passengers in real time, or companies having better internal processes to serve their own employees — data science is a tool for the future, that is here now.
TLDR: Data Science is an interdisciplinary field that uses scientific and other methods to analyze and extract value from data.
Data Analytics is a branch within the tree of Data Science. It is a smaller subset of this huge field (defined above). Data Analytics is the process of examining raw data in order to identify patterns, and then making conclusions from that information. There are a number of different types of Data Analytics that can be used in order to optimize processes and enhance efficiency for organizations. Some of these types of data analytics include:
- Descriptive Analytics
- Diagnostic Analytics
- Predictive Analytics
- Prescriptive Analytics
All of these methods can be employed to gain insights and answer a variety of questions for a business. Descriptive Analytics might simply show what happened over a specific period of time; whereas Diagnostic Analytics could be employed to then answer the question of why something happened. Beyond this, Data Analytics can help to predict what will happen in the future, or help us understand the most efficient way to employ a variety of processes.
TLDR: Data Analytics is the science of analyzing raw data to make conclusions about that information.
Optimization is another word that’s heard often in conversations about AI. It’s a pretty great word, and it sounds fancy, but what does it actually mean? Optimization is the process or methodology of making something as effective, as efficient, or as functional as possible.
The term can be used in a variety of contexts. In business, optimization often refers to the process of making a product or service more efficient or profitable. This can involve improving existing features of a product, eliminating unnecessary components, finding new ways to reach customers, or decreasing expected costs for any given process.
Optimization is an ongoing process that requires continual experimentation and testing. Ultimately, optimization is about finding the best way to do something, whether it be in terms of time, money, or other resources.
TLDR: Optimization is the process or methodology of making something as effective, as efficient, or as functional as possible.
Machine learning (ML) is a branch of artificial intelligence that deals with the construction and study of algorithms that can learn from and make predictions on data. These algorithms can be used to find patterns in data, make predictions about future events, and help make decisions.
The main goal of machine learning is to enable computers to learn from data and to improve their performance at tasks through experience. Machine learning is a relatively new field, but it has already had a major impact on many different areas, including medicine, finance, and manufacturing. As machine learning algorithms continue to improve, they are likely to have an even bigger impact on our world.
TLDR: Machine learning (ML) is the application of artificial intelligence where computer programs use algorithms to find patterns in data.
We hope this article has helped you to understand some of the key AI concepts and definitions. As more and more businesses start to adopt AI-powered solutions, it will become increasingly important for everyone to have a basic understanding of what these technologies are and what they can do. If there are any other terms you’d like us to define, let us know in the comments below.
Should you find yourself looking for more information or wanting to learn about specific applications of AI, don’t hesitate to reach out to us! Our team is passionate about democratizing AI and helping individuals, businesses and organizations of all kinds harness the power of artificial intelligence.