Artificial intelligence (AI) is the science of training machines — that is, computers — to execute human tasks. AI is rooted in the understanding that a computer can absorb information from its surroundings and learn accordingly, just like a human.
AI helps make sense of the increasingly vast amount of data available to businesses today, including the quickly changing preferences and make-up of customers. AI and data provide actionable insights that can boost revenue, improve customer experience, increase productivity and efficiency, and drive business growth and transformation.
Types of Artificial Intelligence
AI is a general field that encompasses several disciplines and technologies. To name a few:
Rules-based systems use IF-THEN-ELSE and TRUE-FALSE decisions to establish a system based on rules.
Machine learning is an application of AI that enables systems to learn automatically and improve through experience without the assistance of explicit programming.
Neural networks mimic the human brain’s workings, using interconnected nodes or units the way the brain uses neurons: each unit applies a relatively simple process or calculation to incoming data, then passes the output to another layer, which applies another simple process or calculation, etc. Each layer of the network learns from the previous one; multiple data passes determine connections and derives meaning.
Deep learning leverages massive neural networks to learn more complex patterns from vast pools of data. Applications of deep learning include image and speech recognition.
Predictive analytics applies statistical techniques to predict cause-effect relationships.
Pattern recognition allows an AI system to classify and interpret input from its surroundings. Applications of pattern recognition include natural language processing and computer vision.
Cognitive computing is a field of AI that strives for natural, human-like interactions with machines.
Technologies that Support AI
The advancement of AI is predicated on the wide-scale availability of massive amounts of data and improvements in related technologies, including:
Graphical processing units, providing the heavy compute power required for iterative processing to train neural networks with Big Data
The Internet of Things (IoT), which generates massive amounts of data from connected devices
Advanced algorithms to analyze more data faster and at multiple levels. This processing allows for identifying and predicting events, understanding complex systems, and optimizing unique scenarios
Application programming interfaces (APIs) to add AI functionality to existing products and services
Data warehouses, particularly cloud-based data warehousing, enable instant access to shared data in real-time, across and between organizations. For AI, frictionless, real-time sharing of data allows businesses to collaborate and work together to gain deeper insights
While artificial intelligence is a wide-ranging concept that describes machines carrying out tasks in a “smart” way, machine learning is an applied solution of AI in which machines are given access to data and enabled to self-train and learn from this data to produce insightful results. Embracing machine learning as well as AI and data science leads to improved business intelligence.
Artificial Intelligence versus Applied Intelligence
Within AI are applied intelligence and general intelligence. Applied intelligence is the branch of AI that focuses on a system’s ability to match or surpass human intelligence for a specified task. Applied intelligence incorporates analytics and automation into the AI process.
Benefits of Artificial Intelligence for Business
With access to massive amounts of data and artificial intelligence technologies, businesses can improve engagement with their customers, automate business processes, and improve productivity and revenue.
The benefits of AI to businesses include:
Process automation, especially of repetitive activities
Better understanding of customers
More effective, targeted marketing activities
Improved products and services
More reliable customer service
Artificial Intelligence is driving discovery and innovations in education, finance, government, retail, and many other industries.
There are many ways to use artificial intelligence in business. The most common AI uses are:
Example: Enhancing routing and predicting delays for logistics and time-sensitive processes
Example: Planning store and site locations to maximize sales and support based on local demand and competition
Example: Identifying customer churn risks following behavior changes or interactions for proactive retentions
Example: anticipating failures of critical machinery and equipment, enabling proactive repairs to be scheduled
Interpreting unstructured data
Example: Reading customer data, emails, tickets, etc., and routing the query based on type, priority, and urgency
Example: Estimating product, service, and staffing demand at a hyper-local level base on trends and events
Trend and anomaly detection
Example: Identifying and preventing new cases of fraud based on suspicious activities and behavior changes
Example: Customizing offers for every individual customer based on their history and personal profile
Examples: intelligent customer service chatbots and personalized recommendation engines
AI Growth and Opportunity
Artificial intelligence technology today is virtually limitless. As such, artificial intelligence products can help you grab a larger share of the marketplace.
Artificial intelligence vendors vary. Some only provide infrastructure, while others can support AI projects with industry-specific apps. Whether through business analytics, robotic process automation, DevOps or data warehousing, hundreds of startups are impacting the AI industry.
Companies with the right machine learning platform architecture can push forward with AI initiatives and introduce artificial intelligence products to all aspects of their business and everyday life.
Snowflake’s platform was built from the ground up to support machine learning and AI-driven data science applications.
Snowflake enables your team to prepare data and build ML models with ease. Customers don’t need to worry about complex integrations or related expenses. Instead, they can focus on AI initiatives.