Artificial Intelligence Terms
AI has become a catchall term for applications that perform complex tasks that once required human input such as communicating with customers online or playing chess. The term is often used interchangeably with its subfields, which include machine learning (ML) and deep learning. There are differences, however. For example, machine learning is focused on building systems that learn or improve their performance based on the data they consume. It’s important to note that although all machine learning is AI, not all AI is machine learning.
To get the full value from AI, many companies are making significant investments in data science teams. Data science, an interdisciplinary field that uses scientific and other methods to extract value from data, combines skills from fields such as statistics and computer science with business knowledge to analyze data collected from multiple sources.
Discover the possibilities of AI
AI and Developers
Developers use artificial intelligence to more efficiently perform tasks that areotherwise done manually, connect with customers, identify patterns, and solveproblems. To get started with AI, developers should have a background in mathematicsand feel comfortable with algorithms.
When getting started with using artificial intelligence to build an application, it helps to start small. By building a relatively simple project, such as tic-tac-toe, for example, you’ll learn the basics of artificial intelligence. Learning by doing is a great way to level-up any skill, and artificial intelligence is no different. Once you’ve successfully completed one or more small-scale projects, there are no limits for where artificial intelligence can take you.
Get started with AI
How AI Technology Can Help Organizations
The central tenet of AI is to replicate—and then exceed—the way humans perceive and react to the world. It’s fast becoming the cornerstone of innovation. Powered by various forms of machine learning that recognize patterns in data to enable predictions, AI can add value to your business by
- Providing a more comprehensive understanding of the abundance of data available
- Relying on predictions to automate excessively complex or mundane tasks
Learn about use cases for AI and ML
AI in the Enterprise
AI technology is improving enterprise performance and productivity by automating processes or tasks that once required human power. AI can also make sense of data on a scale that no human ever could. That capability can return substantial business benefits. For example, Netflix uses machine learning to provide a level of personalization that helped the company grow its customer base by more than 25 percent.
Most companies have made data science a priority and are investing in it heavily. A 2021 McKinsey survey on AI discovered that companies reporting AI adoption in at least one function had increased to 56 percent, up from 50 percent a year earlier. In addition, 27% of respondents reported at least 5% of earnings could be attributable to AI, up from 22% a year earlier.
AI has value for most every function, business, and industry. It includes general and industry-specific applications such as
- Using transactional and demographic data to predict how much certain customers will spend over the course of their relationship with a business (or customer lifetime value)
- Optimizing pricing based on customer behavior and preferences
- Using image recognition to analyze X-ray images for signs of cancer
How Enterprises Use AI
According to the Harvard Business Review, enterprises are primarily using AI to
- Detect and deter security intrusions (44 percent)
- Resolve users’ technology issues (41 percent)
- Reduce production management work (34 percent)
- Gauge internal compliance in using approved vendors (34 percent)
What's Driving AI Adoption?
Three factors are driving the development of AI across industries.
- Affordable, high-performance computing capability is readily available. The abundance of commodity compute power in the cloud enables easy access to affordable, high-performance computing power. Before this development, the only computing environments available for AI were non-cloud-based and cost prohibitive.
- Large volumes of data are available for training. AI needs to be trained on lots of data to make the right predictions. The emergence of different tools for labeling data, plus the ease and affordability with which organizations can store and process both structured and unstructured data, is enabling more organizations to build and train AI algorithms.
- Applied AI delivers a competitive advantage. Enterprises are increasingly recognizing the competitive advantage of applying AI insights to business objectives and are making it a businesswide priority. For example, targeted recommendations provided by AI can help businesses make better decisions faster. Many of the features and capabilities of AI can lead to lower costs, reduced risks, faster time to market, and much more.
Find out how to achieve more than you thought possible
5 Common Myths About Enterprise AI
While many companies have successfully adopted AI technology, there’s also quite a lot of misinformation about AI and what it can and can’t do. Here, we explore five common myths about AI.
- Myth #1: Enterprise AI requires a build-it-yourself approach.
Reality: Most enterprises adopt AI by combining both in-house and out-of-the-box solutions. In-house AI development allows businesses to customize to unique business needs; prebuilt AI solutions enable you to streamline your implementation with a ready-to-go solution for more common business problems.
- Myth #2: AI will deliver magical results immediately.
Reality: The path to AI success takes time, thoughtful planning, and a clear idea of the deliverables you want to accomplish. You need a strategic framework and an iterative approach to avoid delivering a random set of disconnected AI solutions.
- Myth #3: Enterprise AI doesn’t require people to run it.
Reality: Enterprise AI isn’t about robots taking over. The value of AI is that it augments human capabilities and free your employees up for more strategic tasks. Moreover, AI relies on people to feed it the right data and work with it the right way.
- Myth #4: The more data, the better.
Reality: Enterprise AI needs smart data. To get the most effective business insights from AI, your data needs to be high quality, up to date, relevant, and enriched.
- Myth #5: Enterprise AI needs only data and models to succeed.
Reality: Data, algorithms, and models are a start. But an AI solution must be scalable to meet changing business needs. To date, most enterprise AI solutions have been handcrafted by data scientists. These solutions require extensive, manual setup and maintenance, and they don’t scale. To successfully implement AI projects, you need AI solutions that will scale to meet new requirements as you move forward with AI.
Learn more about AI myths
The Benefits and Challenges of Operationalizing AI
There are numerous success stories that prove AI’s value. Organizations that add machine learning and cognitive interactions to traditional business processes and applications can greatly improve user experience and boost productivity.
However, there are some stumbling blocks. Few companies have deployed AI at scale, for several reasons. For example, if they don’t use cloud computing, AI projects are often computationally expensive. They are also complex to build and require expertise that’s in high demand but short supply. Knowing when and where to incorporate AI, as well as when to turn to a third party, will help minimize these difficulties.
Learn how ML operations can help your ML efforts
AI Success Stories
AI is the driving factor behind some significant success stories.
- According to the Harvard Business Review, the Associated Press produced 12 times more stories by training AI software to automatically write short earnings news stories. This effort freed its journalists to write more in-depth pieces.
- Deep Patient, an AI-powered tool built by the Icahn School of Medicine at Mount Sinai, allows doctors to identify high-risk patients before diseases are even diagnosed. The tool analyzes a patient’s medical history to predict almost 80 diseases up to one year prior to onset, according to insideBIGDATA.
Ready-to-Use AI Is Making Operationalizing AI Easier
The emergence of AI-powered solutions and tools means that more companies can take advantage of AI at a lower cost and in less time. Ready-to-use AI refers to the solutions, tools, and software that either have built-in AI capabilities or automate the process of algorithmic decision-making.
Ready-to-use AI can be anything from autonomous databases, which self-heal using machine learning, to prebuilt models that can be applied to a variety of datasets to solve challenges such as image recognition and text analysis. It can help companies achieve a faster time to value, increase productivity, reduce costs, and improve relationships with customers.
How to Get Started with AI
Communicate with customers through chatbots. Chatbots use natural language processing to understand customers and allow them to ask questions and get information. These chatbots learn over time so they can add greater value to customer interactions.
Monitor your data center. IT operations teams can save huge amounts of time and energy on system monitoring by putting all web, application, database performance, user experience, and log data into one cloud-based data platform that automatically monitors thresholds and detects anomalies.
Perform business analysis without an expert. Analytic tools with a visual user interface allow nontechnical people to easily query a system and get an understandable answer.
Roadblocks to Realizing AI's Full Potential
Despite AI’s promise, many companies are not realizing the full potential of machine learning and other AI functions. Why? Ironically, it turns out that the issue is, in large part...people. Inefficient workflows can hold companies back from getting the full value of their AI implementations.
For example, data scientists can face challenges getting the resources and data they need to build machine learning models. They may have trouble collaborating with their teammates. And they have many different open source tools to manage, while application developers sometimes need to entirely recode models that data scientists develop before they can embed them into their applications.
With a growing list of open source AI tools, IT ends up spending more time supporting the data science teams by continuously updating their work environments. This issue is compounded by limited standardization across how data science teams like to work.
Finally, senior executives might not be able to visualize the full potential of their company’s AI investments. Consequently, they don’t lend enough sponsorship and resources to creating the collaborative and integrated ecosystem required for AI to be successful.
Creating the Right Culture
Making the most of AI—and avoiding the issues that are holding successful implementations back—means implementing a team culture that fully supports the AI ecosystem. In this type of environment
- Business analysts work with data scientists to define the problems and objectives
- Data engineers manage the data and the underlying data platform so it’s fully operational for analysis
- Data scientists prepare, explore, visualize, and model data on a data science platform
- IT architects manage the underlying infrastructure required for supporting data science at scale, whether on premises or in the cloud
- Application developers deploy models into applications to build data-driven products
Find out how your data science team can work together more efficiently
From Artificial Intelligence to Adaptive Intelligence
As AI capabilities have made their way into mainstream enterprise operations, a new term is evolving: adaptive intelligence. Adaptive intelligence applications help enterprises make better business decisions by combining the power of real-time internal and external data with decision science and highly scalable computing infrastructure.
These applications essentially make your business smarter. This empowers you to provide your customers with better products, recommendations, and services—all of which bring better business outcomes.
Learn more about the transformational power of Oracle’s SaaS applications with embedded AI
AI as a Strategic Imperative and Competitive Advantage
AI is a strategic imperative for any business that wants to gain greater efficiency, new revenue opportunities, and boost customer loyalty. It’s fast becoming a competitive advantage for many organizations. With AI, enterprises can accomplish more in less time, create personalized and compelling customer experiences, and predict business outcomes to drive greater profitability.
But AI is still a new and complex technology. To get the most out of it, you need expertise in how to build and manage your AI solutions at scale. A successful AI project requires more than simply hiring a data scientist. Enterprises must implement the right tools, processes, and management strategies to ensure success with AI.
Best Practices for Getting the Most from AI
The Harvard Business Review makes the following recommendations for getting started with AI:
- Apply AI capabilities to those activities that have the greatest and most immediate impact on revenue and cost.
- Use AI to boost productivity with the same number of people, rather than eliminating or adding headcount.
- Begin your AI implementation in the back office, not the front office (IT and accounting will benefit the most).
Getting Help with Your AI Journey
There is no opting out of AI transformation. To stay competitive, every enterprise must eventually embrace AI and build out an AI ecosystem. Companies that fail to adopt AI in some capacity over the next 10 years will be left behind.
Though your company could be the exception, most companies don’t have the in-house talent and expertise to develop the type of ecosystem and solutions that can maximize AI capabilities.
If you need help developing the right strategy and accessing the right tools to succeed in your AI transformation journey, you should look for an innovative partner with deep industry expertise and a comprehensive AI portfolio.
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Artificial Intelligence Learning Library
- What is data science?
Businesses are actively combining statistics with computer science concepts like machine learning and artificial intelligence to extract insights from big data to fuel innovation and transform decision-making.
- What is machine learning?
Machine learning, a subset of artificial intelligence (AI), focuses on building systems that learn through data with a goal to automate and speed time to decision and accelerate time to value.
- AI news and opinions
Artificial Intelligence, Machine Learning, and Data Science are changing the way businesses approach complex problems to alter the trajectory of their respective industries. Read the latest articles to understand how the industry and your peers are approaching these technologies.
What is artificial intelligence (AI)? Artificial intelligence is the simulation of human intelligence processes by machines, especially computer systems. Specific applications of AI include expert systems, natural language processing, speech recognition and machine vision.What is the artificial intelligence? ›
Artificial intelligence (AI) is a set of technologies that enable computers to perform a variety of advanced functions, including the ability to see, understand and translate spoken and written language, analyze data, make recommendations, and more.What is artificial intelligence short definition? ›
Artificial intelligence leverages computers and machines to mimic the problem-solving and decision-making capabilities of the human mind.What is artificial intelligence quizlet? ›
Artificial Intelligence (AI) The study of computer systems that model and apply the intelligence of the human mind. Turing Test. A behavioural approach to determining whether a computer system is intelligent.What is artificial intelligence AI and how does it work? ›
Artificial intelligence (AI) is an area of computer science that involves building smart machines that are able to perform tasks which usually require human intelligence. Advances in deep learning and machine learning have allowed AI systems to enter almost every sector in the tech industries.What is artificial intelligence give one example? ›
Apple's Siri, Google Now, Amazon's Alexa, and Microsoft's Cortana are one of the main examples of AI in everyday life. These digital assistants help users perform various tasks, from checking their schedules and searching for something on the web, to sending commands to another app.
Artificial intelligence (AI) is the basis for mimicking human intelligence processes through the creation and application of algorithms built into a dynamic computing environment. Stated simply, AI is trying to make computers think and act like humans.What is artificial intelligence in sentence? ›
Artificial intelligence is about making computers act more like humans. All artificial intelligence designs are inspired by the human brain. Artificial intelligence is a type of computer technology which is concerned with making machines carry out work in an intelligent way, similar to the way a human would.Where is AI mostly used? ›
Manufacturing and Production. Live Stock and Inventory Management. Self-driving Cars or Autonomous Vehicles. Healthcare and Medical Imaging Analysis.What is the main benefit of AI? ›
AI drives down the time taken to perform a task. It enables multi-tasking and eases the workload for existing resources. AI enables the execution of hitherto complex tasks without significant cost outlays. AI operates 24x7 without interruption or breaks and has no downtime.
Voice assistants, image recognition for face unlock in cellphones, and ML-based financial fraud detection are examples of AI software currently being used in everyday life. Typically, just downloading AI software from an online store and having no other devices is required.How can AI help the world? ›
AI is important because it forms the very foundation of computer learning. Through AI, computers have the ability to harness massive amounts of data and use their learned intelligence to make optimal decisions and discoveries in fractions of the time that it would take humans.What are the 4 types of AI? ›
There are a lot of ongoing discoveries and developments, most of which are divided into four categories: reactive machines, limited memory, theory of mind, and self-aware AI.How is AI used today? ›
AI and Smart Assistants
AI is the backbone of smart assistants, which can be accessed through most phones on the market these days and are also being integrated into cars and smart home devices. As of 2022, more than 120 million U.S. adults use a smart assistant at least once a month.
Alexa and other virtual assistants, such as Google Assistant and Apple's Siri, rely heavily on artificial intelligence, automation, and machine learning technologies to respond to user input and perform tasks.What are the main types of AI? ›
- Artificial Narrow Intelligence.
- Artificial General Intelligence.
- Artificial Super Intelligence.
- What is artificial intelligence?
- Reactive machines.
- Limited memory.
- Theory of mind.
- Artificial Narrow Intelligence (ANI)
- Artificial General Intelligence (AGI)
- Artificial Super Intelligence (ASI)
These four types aren't all created equal: Some are far more sophisticated than others. Some of these types of AI aren't even scientifically possible right now. According to the current system of classification, there are four primary AI types: reactive, limited memory, theory of mind, and self-aware.