Want a Successful AI Strategy?
Start With Leadership and Vision

Information on artificial intelligence (AI) is flooding the market, media and social channels. Without doubt, it’s certainly a topic worth the attention. But, it can be difficult to sift through market hype and grandiose promises to understand exactly how AI can be applied in practical and reliable solutions. Like most technological advances, incorporating new technology into business processes requires significant leadership and effective direction that all stakeholders can easily understand.

Great leaders become great by balancing strategy with tactics, future vision with current reality and strengths with weaknesses – all with the goal of accomplishing a clearly defined objective. Great leaders also understand that people are the most valuable resources within their organization. To drive and inspire their success, you must optimize strengths while recognizing inherent weaknesses.

Many of our daily human experiences and interactions involve machines or devices of some sort. Technology is an integral part of our lives. Because of that, it’s time to evaluate how we can better use the strengths of machines (while acknowledging their weaknesses) to augment our ability to understand and improve the world around us.

Advances in machine learning have allowed us to create systems that can automate complex tasks through constant learning. We might be inclined to say that these computers are intelligent based on the tasks they accomplish and the way they interact with us while performing those tasks. Indeed, computers can learn, understand and make assessments about the world based on information we provide to them.
We have evolved beyond telling these machines what to do with our data. Now, machines can learn from patterns and anomalies they find in data on their own. These are patterns and anomalies that our human minds can’t feasibly find, due to the sheer size and complex intricacies that exist within the data. A computer’s strength comes from its ability to reliably, efficiently and accurately analyze large volumes of data
without fatigue.

But, the computer doesn’t understand strategy. It is limited to a specific task, which it executes in a very intelligent manner. Its ability to learn and provide insights is limited in scope. It still requires humans to take those insights and determine what role they will play in a larger strategy that accomplishes our identified objectives.

If we can harness the strengths of machines and artificial intelligence, while acknowledging the weaknesses, we can use current technologies to achieve future success.

Understanding the Basics of Artificial Intelligence

To understand artificial intelligence as it stands today, it’s important to define the term and understand its foundation.

Artificial intelligence is the science of training systems to emulate human tasks through learning and automation. At its core is the ability for the machine to learn how to apply logic and reason to gain an understanding from very complex data. Simply put: The machine learns from data it receives by identifying patterns and relationships within the data itself.

The machine can ingest massive amounts of information, extract key features, determine a method of analysis, write the code to execute the analysis and produce intelligent output – all through an automated process. Once operational, this automated process occurs with minimal intervention (though substantial influence) from its human counterparts.

Foundational Building Blocks and Key Elements

Artificial intelligence has the following core technologies: machine learning and deep learning, computer vision, natural language processing, and forecasting and optimization. The foundational skills a machine needs to learn from data and produce a result are not new. SAS has been a pioneer in machine learning for more than 40 years and we have nearly 30 years of expertise in natural language processing. Our foundation is strong and stable; our analytical technologies are innovative.

Building AI Capabilities

These elements (machine learning and deep learning, computer vision, natural language processing, and forecasting and optimization) can be used independently or together to build an AI capability A capability is the operational task you want a machine to perform, and it requires you to consider the objective you want to achieve. AI capabilities can continually learn and adjust to changing conditions in data.

Here are some ideas for how you could use AI capabilities in a business context:

  • Pattern recognition. Understand typical trends or behaviors for customer financial transactions and spot anomalies in an account’s spending data to identify potentially fraudulent behavior.
  • Prediction. Capture short- and long-term variability in data to improve forecasting of energy consumption.
  • Classification. Examine animal track images and group them by species type to support wildlife conservation efforts.
  • Image recognition. Determine if nodes on a raw CT scan are malignant or benign.
  • Speech to text. Transcribe customer call center voice messages to text for detection of sentiment and further analysis.
  • Cognitive search. Offer personalized recommendations to online shoppers by matching their interests with other customers who purchased similar items.
  • Natural language interaction (NLI). Tell a software application to generate a report on sales revenue predictions without having to run the reports yourself.
  • Natural language generation (NLG). Get summaries of everything that has been analyzed from a large document collection.

Source
Pegasus Knowledge Solutions



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