AI

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 combines statistics, computer science, and business knowledge to extract value from various data sources.

 

AI and Developers

Developers use artificial intelligence to more efficiently perform tasks that are otherwise done manually, connect with customers, identify patterns, and solve problems. To get started with AI, developers should have a background in mathematics and 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.

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

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 percent of respondents reported at least 5% of earnings could be attributable to AI, up from 22 percent 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. Ease of data labeling and affordable storage and processing of structured and unstructured data is enabling more algorithm building and training.
• 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.

AI Model Training and Development

There are multiple stages in developing and deploying machine learning models, including training and inferencing. AI training and inferencing refers to the process of experimenting with machine learning models to solve a problem.

For example, a machine learning engineer may experiment with different candidate models for a computer vision problem, such as detecting bone fractures on X-ray images.

To improve the accuracy of these models, the engineer would feed data to the models and tune the parameters until they meet a predefined threshold. These training needs, measured by model complexity, are growing exponentially every year.

Infrastructure technologies key to AI training at scale include cluster networking, such as RDMA and InfiniBand, bare metal GPU compute, and high performance storage.

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, machine learning projects are often computationally expensive. They’re also complex to build and require expertise that’s in high demand but short supply. Knowing when and where to incorporate these projects, as well as when to turn to a third party, will help minimize these difficulties.

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 includes self-repairing autonomous databases and premade models for image recognition and text analysis on various datasets.

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 can streamline monitoring with a cloud platform that integrates all data and automatically tracks thresholds and 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.

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

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.

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.

For a successful AI transformation journey that includes strategy development and tool access, find a partner with industry expertise and a comprehensive AI portfolio.

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.