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Understanding Artificial Intelligence and How to Use it

Artificial Intelligence is one of the most exciting and rapidly growing fields in technology. AI is changing the way we live and work. This blog post will define artificial intelligence and illustrate how it is being utilized in today’s business environment. We will identify areas of your organization where AI-driven tools could be used to help increase productivity and promote efficiency. Finally, we will demonstrate how automation tools can use AI to turbocharge your workflows.

What is Artificial Intelligence?

Computer science defines AI research as the study of “intelligent agents,” any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals.

In practical terms, AI applications can be deployed in several ways, including these real-world examples.

  • Alphabet (Google) uses AI to power their self-driving car, Waymo, search results, and their personal assistant, Duplex.
  • Amazon uses AI to power its Alexa digital assistant, product recommendations, etc.
  • Microsoft uses AI throughout its company and products, from Power Automate to Cortana.
  • Apple uses AI to improve security with tools such as Face ID for biometric authentication.
  • Meta (Facebook) uses AI to power the information and advertisements you consume on its platform.
  • Healthcare organizations are using IBM Watson to diagnose and treat cancer and financial institutions to prevent fraud.

Now, these companies all have massive R&D budgets that have helped them create and refine their AI tools, so you may be thinking that none of these will apply to your business or personal effectiveness, and that is where you would be wrong.

You can use AI to increase your productivity in several ways, and we will explore some of them later but first, it is crucial to understand the different types of AI and how they are utilized for business.

Different types of AI and how they are used.

Automaion and AI

Narrow AI

Narrow AI is an artificial intelligence designed to execute just one task, such as checking the weather or playing chess, to generate journalistic stories.

Narrow AI is not as intelligent, sentient, or motivated by emotion as humans are.

Even if Narrow AI appears to be more advanced than that, it operates within a predetermined scope.

Narrow AI is a term used to describe today’s machine intellect. Narrow AI includes technologies like Google Assistant, Amazon Alexa, Siri, and other natural language processing systems.

Narrow AI has relieved us of many tedious, routine, mundane tasks that we don’t want to do.

All AI in use today is Narrow AI. The rest remains theoretical.

General or Strong AI

In contrast, general AI (sometimes called “strong AI”) is artificial intelligence that has been programmed to perform any intellectual task that a human being can. General AI does not exist yet, but it is the long-term goal of many artificial intelligence researchers.

Computers can now process data at a speed that surpasses that of humans. can make informed judgments or develop innovative ideas. This form of intellect distinguishes us from machines but is challenging to define since our capacity for consciousness primarily governs it.

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Artificial Super Intelligence (ASI)

Artificial Super Intelligence (ASI) is still a long way off, though it is the ultimate aim of many AI researchers. AGI is anticipated to be able to reason, solve problems, make judgments under uncertainty, plan, learn, integrate prior knowledge into decision-making, and be innovative, inventive, and creative.

Artificial Super Intelligence will outpace human intellect in nearly every field, from creativity to broad knowledge to problem-solving. Machines will be able to exhibit intelligence that no one on earth has previously been able to achieve.

Don’t worry! ASI does not yet exist, and it is doubtful that it will for at least 30 years, or maybe ever. Several significant technological challenges must be overcome for this to become a reality. Most of these hurdles are still unknown at this time.

Machine Learning

Machine learning is a type of data analysis that allows you to automate the development of analytical models. It’s part of artificial intelligence, which holds that systems may learn from data, find patterns, and make judgments without requiring human assistance. Almost all AI in use today is using Machine Learning.

Machine learning is a process of teaching computers to learn from data without being explicitly programmed. The term machine learning was coined in 1959 by Arthur Samuel, an American pioneer in the field of computer gaming and Artificial intelligence.

Machine learning algorithms build models based on sample data, known as training data. These models can then make predictions about new data. The iterative nature of machine learning is critical because models can evolve as they are exposed to new data. They learn from past computations in order to provide dependable, reproducible judgments and results.

Machine learning is closely related to statistics and optimization modeling. It has strong ties to mathematical foundations such as linear algebra and calculus, making it an accessible tool for many data scientists.

How is Machine learning already being used?

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Machine learning is already being used in a variety of ways. Some of the most common applications include:

  • The heavily hyped, self-driving Google car? The essence of machine learning.
  • Online recommendation offers, such as those from Amazon and Netflix? Machine learning applications for everyday life.
  • Knowing what customers are saying about you on Twitter? Machine learning combined with linguistic rule creation.
  • Fraud detection? Machine learning determines transactions that don’t fit a consumer’s typical spending habits.

Deep Learning

Deep learning is a type of machine learning that trains a computer to perform human-like tasks, such as recognizing speech, identifying images, or making predictions.

Instead of organizing data to run through predefined equations, deep learning sets up basic parameters about the data and trains the computer to learn on its own by recognizing patterns using many layers of processing.

What is the difference between Deep Learning and Machine Learning?

Machine Learning requires human involvement, while Deep Learning is autonomous. Machine Learning needs data that has been pre-labeled so that artificial intelligence can find the patterns within it. On the other hand, Deep Learning can take unlabeled data and autonomously label it itself according to the patterns it discerns.

Deep Learning can also learn at a much faster pace than Machine Learning because it does not rely upon human interaction.

Artificial Neural Networks

An Artificial Neural Network (ANN) is a type of Artificial Intelligence designed to mimic how the human brain processes information. It does this by creating a network of interconnected processing nodes, or artificial neurons, that can learn to recognize patterns of input data.

A neural network is a framework for many different machine learning algorithms to collaborate and handle complex data inputs. It’s not an algorithm itself but rather a platform for other Machine Learning techniques to interact and analyze big data.

Such systems “learn” to perform tasks by considering examples, generally without being programmed with any task-specific rules.

ANNs are often used for image recognition, facial recognition, pattern recognition, and classification tasks. For example, in image recognition, they might learn to identify images that contain cats by analyzing example images that have been manually labeled as “cat” or “no cat” and using the results to identify cats in other photos.

They do this without any prior knowledge about cats, for example, that they have fur, tails, whiskers, and cat-like faces. Instead, they automatically generate identifying characteristics from the learning material they process.

One of the best current applications of ANNs is predicting the selling price of a home in applications such as Zillow. The system’s neural network interprets numerous input variables, including square footage, location, and past selling prices of comparable properties in the region. It also analyzes Macroeconomic factors (e.g., unemployment rate, interest rates). It then performs a variety of internal calculations using temporary values and then gives the user a single output. With every home sold, the model gets better as it has additional and newer data to learn from.

How can Artificial Neural Networks be used in business?

here are many ways that Artificial Neural Networks can be used in business. Some of the most common applications include:

  • Automated customer service: ANNs can be used to create chatbots that can handle customer service inquiries without the need for human intervention.
  • Sales prediction: As mentioned above, Artificial Neural Networks can be used to predict future sales patterns based on past data. This technology can be used to make more informed decisions about inventory levels, marketing campaigns, and other factors that affect sales.
  •  Marketing optimization: ANNs can be used to analyze customer data and create targeted marketing campaigns.
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Robotic Process Automation (RPA)

Robotic process automation (RPA) is a type of business process automation technology that uses metaphorical software robots (bots) or artificial intelligence (AI)/digital employees. It’s also known as software robotics, and it’s not the same thing as robot software.

In traditional workflow automation tools, a software developer produces a list of actions to automate a task and interface to the back-end system using internal application programming interfaces (APIs) or dedicated scripting language.

In contrast, RPA systems develop the action list by watching the user perform that task in the application’s graphical user interface (GUI) and then complete the automation.

RPA bots interpret trigger responses and communicate with other systems to perform various repetitive tasks. They follow the rules and workflows just like their human counterparts.

Some common uses for RPA include:

  • Banking and finance process automation
  • Mortgage and lending processes
  • Customer care automation
  • eCommerce merchandising operations
  • Optical character recognition applications
  • Data extraction process
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Big Data and Analytics

The amount of data we generate is massive, but what do we do with the information we create? That’s where analytics comes in. Analytics is the process of turning data into insights that can help us make decisions. Artificial intelligence (AI) is a subset of analytics that involves using computers to do things that would typically require human intelligence, such as understanding natural language and recognizing trends.

There are many different types of AI analytics, but some common examples used in business are as follows.

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Descriptive Analytics – What Happened?

At the bottom of the big data value chain, descriptive analytics or data mining might help reveal patterns that provide insight. Assessing credit risk is a simple example of descriptive analytics, using past financial performance to forecast a customer’s future financial success.

Descriptive analytics can be useful in the sales cycle, for example, to categorize customers by their likely product preferences and sales cycle.

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Diagnostic analytics – Why did it Happen?

Diagnostic analytics aims to figure out what caused an issue and when it occurred. For example, you could use descriptive analytics to assess the number of posts, mentions, followers, fans, page views, reviews, pins, and so on in a social media marketing campaign.

In some cases, it might be beneficial to look at a few dozen or even a couple of hundred website mentions in isolation. This analysis can help you determine what worked and what didn’t in your past campaigns.

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Predictive Analytics – What will happen?

Predictive analytics is at the top of the big data value chain and is about making predictions about future events. It’s similar to diagnostic analytics, but predictive analytics uses historical data and new data to make predictions.

For example, a retail store could use predictive analytics to forecast inventory needs based on past sales data, weather patterns, and special events.

Some businesses have gone one step further, employing predictive analytics throughout the sales process to examine the lead source, number of conversations, communication methods, social media interaction types, and document usage.

Predictive analytics crossovers with Artificial Intelligence (AI), as AI can be used to create predictive models.

Where is Prescriptive Analytics being used?

Prescriptive analytics is the next step after predictive analytics. While predictive analytics tells you what will happen, prescriptive analytics tells you how to make it happen.

For example, a company might use historical sales data to predict that a specific product will be in high demand during the holiday season. Prescriptive analytics would then tell the company how many units to produce, what price to charge, and what promotions would be most effective to maximize profits.

The goal of prescriptive analytics is to find the best possible course of action, given a set of constraints and objectives. It gives you a laser-like focus to answer specific questions.

In the healthcare industry, for example, you may better manage a patient population by using prescriptive analytics to count the number of clinically overweight people and then apply conditions like diabetes and low-density lipoprotein cholesterol levels to determine where treatment should be focused.

Prescriptive Analytics – How can we make it happen?

Prescriptive analytics is the next step after predictive analytics. While predictive analytics tells you what will happen, prescriptive analytics tells you how to make it happen.

For example, a company might use historical sales data to predict that a specific product will be in high demand during the holiday season. Prescriptive analytics would then tell the company how many units to produce, what price to charge, and what promotions would be most effective to maximize profits.

The goal of prescriptive analytics is to find the best possible course of action, given a set of constraints and objectives. It gives you a laser-like focus to answer specific questions.

In the healthcare industry, for example, you may better manage a patient population by using prescriptive analytics to count the number of clinically overweight people and then apply conditions like diabetes and low-density lipoprotein cholesterol levels to determine where treatment should be focused.

How can you use AI to turbocharge your productivity?

One way you can use AI is by utilizing Automation tools. Automation is the process of using technology to perform a task or series of tasks without human intervention.

Many people think of automation as replacing human workers with machines. However, automation can also refer to the use of software to perform tasks that would otherwise be done manually.

For example, you can use an AI-powered tool like Microsoft Excel to generate graphs and charts from data you input automatically—saving you a significant amount of time when creating reports or presentations.

Alternatively, you can utilize a program like Jasper AI to assist with content creation. Coming up with excellent copywriting might be challenging at times. If you have to create a lot of it (for example, for website content), the job may feel quite daunting. That’s where Jasper.ai comes in – this AI-driven tool produces high-quality, optimized copy at superhuman speeds.

If you want to learn more great AI-driven tools to increase your personal effectiveness, be sure to sign up for some of our courses!

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