Transforming your Business with       Artificial Intelligence

AI and ML is Redefining Businesses

Not so long ago, technology that could learn and solve problems on its own was found only in science fiction. Today, devices with these advanced capabilities are emerging all around us, representing the latest wave of rapid progress. Artificial Intelligence (AI) is redefining our way of life, enabling machines to do what people once thought only humans could do. It is also revolutionizing the way we do business.

Businesses need AI and ML so that they can:

Make their infrastructure intelligent to drive insights and better decisions.

Improve their security posture to secure every endpoint.

Enrich every customer experience to deliver more engaging, personalized products and interaction.

Transform processes and business models to drive efficiency and productivity.

Hire, retain, and empower talent to improve workforce efficiency and engagement.

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  What is Artificial Intelligence?

Imagine a world where every car is connected and communicating with the cars and traffic signals around it to automatically optimize traffic flows using analytics of vehicle volumes and speeds. Or a city where sensors and guides can provide freedom of movement to the visually impaired.

Consider the possibilities if every robot in a manufacturing facility were connected and providing data about product volumes for just-in-time ordering of raw materials, and metrics to enable proactive, preventive maintenance to eliminate downtime.

Artificial intelligence is still a new and amorphous field, but one widely agreed-on definition is “a system capable of rationally solving complex problems or taking appropriate actions to achieve its goals in whatever real-world circumstances it encounters.”² In short, it’s a computer that can solve problems without direct assistance from a person.

  Machine Learning

There are two essential kinds of problems AI can solve. The first involves scenarios where theoretical modeling can be applied o approximate and predict behavior. The second focuses on problems where diligent parametric observation provides enough data to fit a mathematical formula.

The first type of problem, known as knowledge engineering, involves coders and experts who team up to explicitly program human expertise into a computer so it can act independently.

The second scenario, machine learning, focuses on training algorithms—often called learners—to discover their own problem-solving methods with massive amounts of data.
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  Neural Networks

Artificial neural networks (ANN) or connectionist systems are computing systems vaguely inspired by the biological neural networks that constitute animal brains. Such systems "learn" to perform tasks by considering examples, generally without being programmed with task-specific rules.

An ANN is based on a collection of connected units or nodes called artificial neurons, which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit a signal to other neurons. An artificial neuron that receives a signal then processes it and can signal neurons connected to it.

In ANN implementations, the "signal" at a connection is a real number, and the output of each neuron is computed by some non-linear function of the sum of its inputs. The connections are called edges. Neurons and edges typically have a weight that adjusts as learning proceeds. The weight increases or decreases the strength of the signal at a connection. Neurons may have a threshold such that a signal is sent only if the aggregate signal crosses that threshold.

Appliying Artificial Intelligence

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Digital Production Assistants

Automobile manufacturer BMW is harnessing the potential of AI in innovative automation and flexible assistance systems in production.

At its factory in Leipzig, Germany, lightweight robots work directly on the production line together with the human workforce.

Their versatility, modest space requirements, and high level of safety grant people access to areas that used to be off limits to everyone except robots.

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Hedge Funds Redefine Trading

Financial services players have access to rich sources of data. To make the most of it, today’s data-centric hedge funds increasingly rely on AI to support innovative new trading engine models.

Each day, after analyzing everything from market prices and volumes to macroeconomic data and corporate accounting documents, these AI engines make their own market predictions and then “vote” on the best course of action.

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Intelligent Production Management

Procter & Gamble, which operates 130 plants worldwide, has had success integrating AI and other smart factory technologies into its manufacturing operations.

There’s nothing for us that could be more strategic to the company than to really drive better efficiencies and a better end-to-end operation in our supply chain,” said Jim Fortner, P&G’s vice president for information technology and services

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