To become a true AI-fuelled organization, you need to fundamentally rethink the way humans and machines interact within working environments. Executives must consider how to deploy Machine Learning or deep learning to support data-driven decision-making. Artificial Intelligence will offer a multitude of benefits to your organization, however, only when viewed through a strategic business lens rather than as an IT project. One must identify the enterprise’s main objectives, then align the AI strategy to achieve those outcomes. You may choose solutions that reduce costs, improve productivity, reduce risk, or extract greater meaning from data.
How can I use AI to achieve a competitive advantage?
AI is a broad category that includes natural language processing, computer vision, Machine Learning, and more. Consider your organization’s vertical industry. For example, in the financial services, a firm may want to initiate its AI pilots by creating a robo-adviser or chatbot that can offer customers one-on-one investment advice.
How can Deep Learning help my business?
The buzz phrase “big data” has been invoked repeatedly for several years. Most companies have jumped on that bandwagon. CEOs and CTOs recognized there was value in collecting all this data around their business processes. That was phase one. Phase two was about finding more sophisticated ways to query that data – the basic business analytics trend. Phase three, brings Machine Learning techniques on the data. Machine Learning will help businesses develop models that are less backwards looking and more predictive in terms of outcome. Now, Deep Learning is the latest evolution of this incredible technology as it mimics the way our brain works. It will be more instructive when it comes to what to do or build or offer in your business.
So, what do you know about AI, ML and DL?
ARTIFICIAL INTELLIGENCE (AI):
Artificial Intelligence is a technique which enables machines to mimic human behavior. Ultimate aim of AI is to make intelligent machines that can perform human-like behavior and take its own smart decision. A machine completes tasks based on a set of rules that solve problems (algorithms). For example, such machines can move and manipulate objects, recognize whether someone has raised their hands, or solve other problems.
MACHINE LEARNING (ML):
ML is a subset of artificial intelligence. In fact, it’s simply a technique for realizing AI. Training in Machine Learning entails giving a lot of data to the algorithm and allowing it to learn more about the processed information. The intention of ML is to enable machines to learn by themselves using the provided data and make accurate predictions.
DEEP LEARNING (DL):
Deep Learning is a subset of ML; It is a technique for realizing Machine Learning. In other words, DL is the next evolution of Machine Learning.
DL algorithms are roughly mimicking the information processing patterns found in the human brain. The brain usually tries to decipher the information it receives. It achieves this through labelling and assigning the items into various categories.
Whenever we receive new information, the brain tries to compare it to a known item before making sense of it — which is the same concept deep learning algorithms employ. Artificial neural networks (ANNs) are a type of algorithm that aim to imitate the way our brains make decisions.
For example, while DL can automatically discover the features to be used for classification, ML requires these features to be provided manually.
To learn more about the evolution of AI into deep learning, talk to Ingram Micro’s AI team.