AI and data-driven intelligence is king

What is meant by Artificial Intelligence?

From an industrial point of view, AI means algorithm-based and data-driven computer systems that enable machines and people with digital capabilities such as perception, reasoning, learning, and even autonomous decision making.

What is meant by data-driven?

When a company employs a “data-driven” approach, it means it makes strategic decisions based on data analysis and interpretation. A data-driven approach enables companies to examine and organize their data with the goal of better serving their customers and consumers.

What is data-driven in AI?

Data-driven characteristics include well-integrated data of good quality and algorithmic automation, including artificial intelligence (AI). Being or becoming data-driven is a response to a major cultural and economic transformation in progress

Artificial Intelligence, What It Is and Why It Matters

Artificial intelligence (AI) is a wide-ranging branch of computer science concerned with building smart machines capable of performing tasks that typically require human intelligence. It refers to any human-like behavior displayed by a machine or system. In AI’s most basic form, computers are programmed to “mimic” human behavior using extensive data from past examples of similar behavior. This can range from recognizing differences between a cat and a bird to performing complex activities in a factory environment

AI makes it possible for machines to learn from experience, adjust to new inputs and perform human-like tasks. Most AI examples that you hear about today – from chess-playing computers to self-driving cars – rely heavily on deep learning and natural language processing. Using these technologies, computers can be trained to accomplish specific tasks by processing large amounts of data and recognizing patterns in the data.

Every industry has a high demand for AI capabilities – including systems that can be used for automation, learning, legal assistance, risk notification, and research. Specific uses of AI in the industry include Health Care, Retail, Manufacturing, Banking, and many more.

A powerful tool for businesses and organizations

Artificial intelligence can be a very powerful tool for both large corporations generating significant data and small organizations that need to process their calls with customers more effectively. AI can streamline business processes, complete tasks faster, eliminate human error, and much more.

Importance of AI

AI automates repetitive learning and discovery through data. Instead of automating manual tasks, AI performs frequent, high-volume, computerized tasks. And it does so reliably and without fatigue. Of course, humans are still essential to set up the system and ask the right questions.

AI adds intelligence to existing products. Many products you already use will be improved with AI capabilities, much like Siri was added as a feature to a new generation of Apple products. Automation, conversational platforms, bots and smart machines can be combined with large amounts of data to improve many technologies. Upgrades at home and in the workplace, range from security intelligence and smart cams to investment analysis.

AI adapts through progressive learning algorithms to let the data do the programming. AI finds structure and regularities in data so that algorithms can acquire skills. Just as an algorithm can teach itself to play chess, it can teach itself what product to recommend next online. And the models adapt when given new data. 

AI analyses more and deeper data using neural networks that have many hidden layers. Building a fraud detection system with five hidden layers used to be impossible. All that has changed with incredible computer power and big data. You need lots of data to train deep learning models because they learn directly from the data. 

AI achieves incredible accuracy through deep neural networks. For example, your interactions with Alexa and Google are all based on deep learning. And these products keep getting more accurate the more you use them. 

AI gets the most out of data. When algorithms are self-learning, the data itself is an asset. The answers are in the data. You just have to apply AI to find them. Since the role of the data is now more important than ever, it can create a competitive advantage. If you have the best data in a competitive industry, even if everyone is applying similar techniques, the best data will win.

How AI Works

AI works by combining large amounts of data with fast, iterative processing and intelligent algorithms, allowing the software to learn automatically from patterns or features in the data. AI is a broad field of study that includes many theories, methods and technologies

AI Artificial intelligence includes the following elements:

  • Models of human behavior
  • Models of human thought
  • Systems that behave intelligently
  • Systems that behave rationally
  • A set of specific applications that use techniques in machine learning, deep learning and others

In the larger picture artificial intelligence (AI) can encompass (among others):

  • Semantic search engines used to identify query matches based on words and their context.
  • Data mining algorithms, that are adapted from machine learning, and are applied to explore and analyze data sets for new relationships.
  • Learning algorithms exposing internet-based services with natural language interfaces
  • Autonomous vehicles.
  • Automated speech recognition and generation.
  • “Deep” neural nets to recognize images.

Types of Artificial Intelligence?

Artificial intelligence is classified into two main categories: AI that’s based on functionality and AI that’s based on capabilities.

Types of Artificial Intelligence: Based on functionality

  1. Reactive Machines
  • Reactive Machine – This AI has no memory power and does not have the ability to learn from past actions. IBM’s Deep Blue is in this category.
  • Reactive Machines perform basic operations. This level of A.I. is the simplest. These types react to some input with some output. There is no learning that occurs. This is the first stage to any A.I. system. A machine learning that takes a human face as input and outputs a box around the face to identify it as a face is a simple, reactive machine. The model stores no inputs, it performs no learning.
  • Static machine learning models are reactive machines. Their architecture is the simplest and they can be found on GitHub repos across the web. These models can be downloaded, traded, passed around and loaded into a developer’s toolkit with ease.
  1. Limited Memory
  • Limited Theory – With the addition of memory, this AI uses past information to make better decisions. Common applications such as GPS location apps fall into this category.
  • Limited memory types refer to an A.I.’s ability to store previous data and/or predictions, using that data to make better predictions. With Limited Memory, machine learning architecture becomes a little more complex. Every machine learning model requires limited memory to be created, but the model can get deployed as a reactive machine type.

There are three major kinds of machine learning models that achieve this Limited Memory type:

  1. Reinforcement learning

These models learn to make better predictions through many cycles of trial and error. This kind of model is used to teach computers how to play games like Chess, Go, and DOTA2.

  1. Long Short Term Memory (LSTMs)

Researchers intuited that past data would help predict the next items in sequences, particularly in language, so they developed a model that used what was called the Long Short Term Memory. For predicting the next elements in a sequence, the LSTM tags more recent information as more important and items further in the past as less important.

  1. Evolutionary Generative Adversarial Networks (E-GAN)

The E-GAN has memory such that it evolves at every evolution. The model produces a kind of growing thing. Growing things don’t take the same path every time, the paths get to be slightly modified because statistics is a math of chance, not a math of exactness. In the modifications, the model may find a better path, a path of least resistance. The next generation of the model mutates and evolves towards the path its ancestor found in error.

In a way, the E-GAN creates a simulation similar to how humans have evolved on this planet. Each child, in perfect, successful reproduction, is better equipped to live an extraordinary life than its parent.

  1. Limited Memory Types in practice

While every machine learning model is created using limited memory, they don’t always become that way when deployed.

Limited Memory A.I. works in two ways:

  1. A team continuously trains a model on new data.
  2. The A.I. environment is built in a way where models are automatically trained and renewed upon model usage and behavior.

For a machine learning infrastructure to sustain a limited memory type, the infrastructure requires machine learning to be built-in to its structure.

  1. Theory of Mind
  • Theory of Mind – This AI is still being developed, with the goal of its having a very deep understanding of human minds.
  • We have yet to reach Theory of Mind artificial intelligence types. These are only in their beginning phases and can be seen in things like self-driving cars. In this type of A.I., A.I. begins to interact with the thoughts and emotions of humans.
  • Presently, machine learning models do a lot for a person directed at achieving a task. Current models have a one-way relationship with A.I. Alexa and Siri bow to every command. If you angrily yell at Google Maps to take you another direction, it does not offer emotional support and say, “This is the fastest direction. Who may I call and inform you will be late?” Google Maps, instead, continues to return the same traffic reports and ETAs that it had already shown and has no concern for your distress.
  1. Self-Aware
  • Self-Aware AI – This AI, which could understand and evoke human emotions as well as have its own, is still only hypothetical.
  • This kind of A.I. exists only in story, and, as stories often do, instils both immense amounts of hope and fear into audiences. A self-aware intelligence beyond the human has an independent intelligence, and likely, people will have to negotiate terms with the entity it created. What happens, good or bad, is anyone’s guess.

Types of Artificial Intelligence: Based on capabilities

  • Artificial Narrow Intelligence (ANI) – A system that performs narrowly defined programmed tasks. This AI has a combination of reactive and limited memory. Most of today’s AI applications are in this category.
  • Artificial General Intelligence (AGI) – This AI is capable of training, learning, understanding and performing like a human.
  • Artificial Super Intelligence (ASI) – This AI performs tasks better than humans due to its superior data processing, memory and decision-making abilities. No real-world examples exist today.

In short, we can say that, whichever way you break down A.I., know that it A.I. is a strong software tool for the future that’s here to stay. A.I. is eliminating repetitive tasks in the workforce and elevating humans to reach higher selves, embracing constant states of change and creativity.

AI-Driven Analytics Is Essential for Data-Driven system

Businesses today are relying on analytics powered by artificial intelligence (AI) as a “must have” when it comes to digital transformation. Any data-driven company that needs to manage its operations with data as the salient light can attest to this.

However, many enterprises find it quite challenging to not only collect huge amounts of data but to make sense of the data and apply it in the right context. As a result, they are failing to get the most out of their growing information resources.

Getting Started with AI Analytics 

Although analytics is not a new field, we’ve seen the analytics tool stack undergoing transformation due to advances in areas such as AI and machine learning:

AI-driven analytics can help all sorts of companies — from e-commerce outfits to fintech start-ups and even telcos — make better decisions for their new business models and contribute to three pillars of business success: increasing revenue, controlling costs and ensuring high-quality user experiences.

Organizations that do not use AI-based analytics can expect challenges. They might end up spending lots of money on big data that isn’t being analyzed holistically or fast enough to make the greatest impact. Any business today should assume its competitors are using AI/ML or will be soon.

In short we can say that by ensuring data reliability and building an AI-driven culture, organizations can better equip themselves to compete in the age of digital business.

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