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AI Analytics: Deep Learning vs Machine Learning

AI Analytics: Deep Learning vs Machine Learning

How Is Artificial Intelligence Used in Analytics?

Machine learning is used to analyze enormous amounts of data. It does this by sifting through information and looking for patterns. This allows AI to find trends in data that would otherwise take years to identify. For example, if a company wants to see what products customers like most, they could give an AI program access to all their customer records. Once the AI has learned which products people prefer, it can then recommend products like those that were popular last year.

Artificial intelligence is also used for online shopping recommendations. There are websites that have a recommendation engine where users can type in items they want, and the site will find other things in the same category that they might be interested in. These machines are trained using human-generated content about how similar or different two web pages are. An example of this would be Amazon’s Alexa voice assistant. If you asked Alexa “What do you think I should buy today?” She will tell you what other topics her AI system thinks you may like based on your previous purchases.

AI is also employed in the healthcare industry. As we live longer, it is important that doctors make sure patients are healthy before prescribing certain drugs. By analyzing medical records, AI can predict diseases that patients may face in the future. Doctors can then prescribe the right medication before something goes wrong. It is an AI called DeepMind that helped create this technology.

There’s no denying the value of AI—it can revolutionize every sector of business and society — but there are still challenges overcoming for adoption beyond research labs.

The Internet of Things is rapidly becoming one of the biggest technological advances since the introduction of the smartphone. Today, IoT is already disrupting industries across the world and creating new opportunities for businesses of any size. And yet, despite the massive potential benefits, only 6% of companies as part of their digital strategy.

We worked with leading experts and real-world case studies to distill the key insights and lessons learned from deployments in manufacturing, retail, utilities, transportation and infrastructure, consumer goods, automotive, aerospace & defense, healthcare, home appliances, government, and public services sectors.

With over forty billion connected devices now in operation — up from 3.5 billion in 2017 — connected car solutions are starting to mature. In 2018, cars will become smarter, safer, and better integrated with our daily lives. But when it comes to connectivity, auto OEMs must prepare themselves for the coming wave of disruptive innovation.

We’re living in exciting times. Technological innovation moves quickly, and with it comes the ability to create real changes in businesses across the world.

Innovation itself isn’t enough though. In fact, innovation is often only one step in the complex process needed to drive positive change. Change requires a clear view of why you need to innovate; and how. And while an innovative idea alone won’t always result in transformation, it’s a useful starting point for the development of a strategy to succeed.

We define a successful innovation strategy as “a coherent plan to achieve sustainable competitive advantage, through an iterative approach involving four distinctive phases: pre-innovate, innovate, sustain, and reinforce.” Each phase builds upon the next, resulting in a cyclical model whereby you create opportunities, explore them, and turn them into reality. Let’s explore each element of the model in detail.

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AI Predictions

A lot of AI predictions are based on past events. If we look at the prediction that “the average person will spend $1,000 less per month online than they did five years ago,”, it’s not a bad guess. The internet was new back then, and now it’s becoming more mainstream. People are spending more time on social media, shopping online, and using apps, so the average person spends way more money online today than they did back then.

Business intelligence has remained stagnant for decades — but all that is about to change. BI technology has evolved exponentially over recent years, providing organizations with unprecedented insight into business operations.

Today, many people have access to powerful BI tools via desktop or mobile platforms, enabling them to analyze data and run reports in seconds. As a result, we see increased adoption of BI software by small and medium-sized enterprises, who can now access information easily and make important decisions faster.

But there remains a significant gap between SMEs and larger enterprise users. Although many large corporations use BI systems, most don’t consider deploying them internally according to the Gartner Magic Quadrant report. This could be because existing products tend to lack scalability and performance, which limit their usefulness and value.

However, the market is moving toward cloud-based analytics platforms that offer both flexibility and cost savings. These innovative approaches enable SMEs to adopt advanced analytical capabilities without having to invest in expensive hardware. To help address these challenges, we’ve identified three areas where AI innovations will transform BI:

Cognitive computing: AI enables machines to think like humans. It provides decision-making capabilities, allowing analysts to perform tasks such as sentiment analysis, forecasting and pattern recognition in real time. Such features allow BI solutions to assist employees in making day-to-day tasks easier and more productive. Cognitive technologies also provide the foundation for predictive maintenance and diagnostic services, which help companies identify problems before a breakdown occurs.

Deep learning and neural networks: AI are already being applied across various industries including retail, healthcare, finance, manufacturing, and marketing. One example is Amazon Alexa, which uses deep learning techniques to recognize speech patterns and respond appropriately. Deep learning allows computers to learn from big data sets and automatically train themselves to perform specific tasks. With this capability, companies can deploy models and algorithms to automate processes and gain insights from vast amounts of unstructured data.

Natural language processing: NLP is another key area that will significantly enhance the productivity of BI teams. When combined with cognitive computing, NLP can be used to generate meaningful insights from text-based data sources. For instance, chatbots can answer customer questions and support calls seamlessly, saving valuable resources in IT departments. Furthermore, NLP can also be employed to extract data from customer service emails and ticketing systems.

As a result, businesses can improve operational efficiency, reduce costs, and simplify workflows. AI applications can also be deployed in multiple ways, from desktop to mobile to IoT devices, providing convenient access to BI solutions regardless of location.

In addition to improving BI and machine learning functions, AI can be used to power other business initiatives. In fact, some experts predict that by 2020, 80 percent of all jobs will require human judgment — but only 20 percent of workers will need advanced skills in math, science, technology, engineering, and mathematics. That means AI will play an increasingly key role in helping organizations recruit, develop, and retain top talent.

The potential benefits of AI in BI applications include these aspects:

• Accuracy and speed: Advanced technology has enabled biometric identification systems to quickly scan customers’ irises and faces, while facial recognition can track movements and detect changes over time. These capabilities help eliminate errors and ensure quick responses when things go wrong.

• Ease of use: AI-powered BI can deliver accurate analysis directly into the hands of end users, making tasks easier, quicker, and more efficient. Analysts can get immediate insights about issues and trends affecting the business when they arrive in email or Slack messages rather than waiting days or weeks for reports to be generated.

• Cost savings: Using AI, companies can save millions of dollars annually by automating manual processes and eliminating redundancies. This reduces waste, improves quality, and speeds up production.

• Productivity gains: AI eliminates repetitive tasks and offers faster results, reducing bottlenecks and freeing up time for higher — valuable activities. By taking away mundane tasks, AI frees up staff to focus on more challenging projects.

So, how does it work?

There are two main approaches to AI: machine learning and neural networks. Machine learning uses algorithms to train computers to recognize patterns in data. Neural networks take this concept further by training artificial neurons to mimic the function of actual neurons in our brains. Neural networks are often used for image recognition, speech processing, and natural language processing.

Machine learning requires extensive datasets as well as extensive computational power. With AI, you can automate many routine decisions that were previously manually performed using rules, formulas, and other techniques.

Neural network models offer three major advantages over machine learning: accuracy, speed, and flexibility. Unlike machine learning, which requires massive amounts of training data, neural networks don’t have any such limitations. They learn automatically, like brain cells do. However, their performance is not affected by the quantity of information available.

Furthermore, unlike humans, a neural network doesn’t make mistakes. It’s an ideal tool for analyzing complex problems because its model can look at thousands of factors simultaneously and process them instantly.

A neural network can analyze large volumes of data quickly and accurately. Its unique ability to self-learn makes it useful for predictive analytics, where predictions are based on past events. As the system learns, it incorporates new knowledge. Thus, it’s able to continuously adapt to changing conditions.

AI isn’t about replacing employees. It can also make businesses smarter, more productive, and more profitable. There are three primary areas of application:

1. Automation

How Biometrics Identify Customers

Biometrics is the science of measuring physical traits — such as fingerprints, palm prints, DNA, voiceprints, retinas, and even body heat — to identify individuals. The most common examples today involve authentication, but there are numerous possibilities. For instance, if your bank wants to verify your identity based on your thumbprint, the device scans your finger and compares it against a database of known prints.

In fact, fingerprint capture technology has become so advanced that some banks now allow customers to store their prints in a secure online vault. When it comes to customer authentication, it’s critical to minimize risks, increase security, and improve efficiency.

For example, if someone steals your credit card, he could access all your purchases without having to hack into your account. Instead, you’d need to enter your PIN whenever you place an order. But with biometrics, the verification would occur instantly. That way, if someone tries to charge something on your account, it will only succeed if your thumbprint matches what’s stored in the database.

Another benefit of biometrics is improving fraud prevention. If someone swipes a stolen credit or debit card in a retail environment, the thief can be identified through facial recognition software. This provides protection from fraudulent activity, while at the same time strengthening consumer confidence in financial institutions.

But even beyond fraud detection, biometric technologies can provide tremendous value. For example, if a business collects data on who its customers are, how they interact with its products/services, and where they spend their money, it can use biometrics to gather this information in real time. In addition, it can monitor each customer’s behavior and predict future spending trends, thereby increasing customer satisfaction and retention.

2. Optimization

Optimizing Your Website

If you think the Internet was created solely for fun, marketing, and commerce, then you’re wrong. The World Wide Web was built for one reason: communication. From birth, we’ve been conditioned to communicate efficiently via email, text messaging, social media, and search engines.

The web has changed a lot since its inception. Today, approximately 2.5 billion users share 3.8 trillion messages every day. Although these statistics tell us that people still prefer to communicate face-to-face, there’s no denying the popularity of mobile devices. According to Cisco Systems, global mobile traffic increased 39% year over year during 2016. As a result, marketers must rework their strategies to ensure that their websites and digital campaigns have a positive impact.

The key to optimizing your website lies in understanding which metrics are important to your company’s success. For example, you want to attract new customers. To do this, you’ll need to know which keywords drive visitors to your site. You may also want to know which pages on your site convert visitors into leads or sales. Likewise, you might want to optimize your site to reduce bounce rates, keep customers engaged, and increase overall brand loyalty.

Once you understand what metrics matter, you can invest in the right tools to help you achieve them. For example, let’s say you want to enhance user experience because you want customers to feel happy when visiting your website. One tool you can use is Google Analytics. With Google Analytics, you can see which parts of your site have the highest volume of page views. By knowing this information, you can prioritize UX improvements.

3. Personalization

Personalized Customer Service

As consumers continue using smart devices and grow accustomed to personalized experiences, companies must adapt to meet their needs. They need to anticipate what their customers want and create targeted experiences that resonate with them.

One way to make such an improvement is through personalization. In other words, businesses should tailor content, services, and offers to individual customers. This can include product recommendations, customized promotions, and timely notifications.

In fact, according to Salesforce, almost half of B2B buyers expect to receive personalized emails and texts. That means brands must proactively deliver tailored content and offer relevant information. And according to Gartner, 72 percent of organizations will deploy AI solutions in 2018. So as technology advances, so does the sophistication and effectiveness of AI.

4. Security

Security Through Biometrics

Today, most security systems rely on passwords. But password management is difficult for users, especially when employees and customers move around constantly. A study by RSA found that 77 percent of respondents were worried about forgetting their logins.

Biometrics, however, eliminates this problem. Instead of requiring people to remember complex codes, biometric authentication relies on unique physical characteristics like fingerprints, eye scans, hand geometry, and voiceprints. These methods allow individuals to access secure areas without having to enter a code.

According to IDC, more than 50 percent of enterprises plan to introduce biometric technologies by 2019. Because humans cannot change their fingerprints, they’re safe from hackers who gain unauthorized access to online accounts. In addition, biometrics doesn’t require users to be physically present. This makes it easy for businesses to implement across branches, partners, and vendors.

5. Chatbots

Chatbots Are Here to Stay

For many years, chatbot applications had limited functionality. However, today’s platforms are much more robust and conversational thanks to advances in AI.

These bots can conduct simple tasks like finding a restaurant near you or sending a message on behalf of another person. They even respond if a customer asks questions about products and services.

Gartner forecasts that 75 million business processes will be automated by 2023. And while email automation is widely adopted, only 10% of email messages are answered. That leaves plenty of room for growth in the bot area.

6. Virtual Assistants

Virtual Assistants are becoming increasingly popular among businesses.

The reason? They save money, time, and resources.

But before making any investments, it’s important to identify how virtual assistant’s work. Some functions involve interacting with chatbots. Other programs automate routine tasks using AI technology. Either way, these tools help you delegate manual labor and focus your efforts elsewhere.

7. IoT

The Internet of Things Is on Its Way Up

If you own a home, chances are you’re already familiar with the term “smart home.” You may use smart thermostats and lighting fixtures, and some devices might even warn of potential dangers.

But what happens when we start adding new things into the mix? Imagine if every appliance could communicate directly with one another. For instance, your refrigerator will know when milk has spoiled. Or you’d get alerts whenever you leave the house.

That’s where the IoT comes in. According to McKinsey & Company, there will be 5 billion connected devices by 2020. And Gartner predicts that 55.8 trillion objects will connect to the internet by 2025.

The result? More opportunities for businesses to increase productivity and streamline operations. By leveraging IoT technology, companies can create better experiences for consumers and improve efficiency throughout the supply chain.

8. Augmented Reality

Augmented reality brings together digital and real-world elements.

Today, augmented reality apps can provide directions to the nearest store or give tips on how to solve problems at home.

In the future, AR will grow beyond navigation and shopping malls. It’ll be available on almost any device — including smartphones. Thanks to advancements in 3D printing, engineers will have access to custom prototypes. As a result, businesses will be able to make improvements faster and test out ideas in advance.

9. Robotics

Robots Will Automate Human Jobs

Today, robots perform repetitive tasks. But as computing power improves, robotic systems will become smarter, allowing them to do more complicated jobs.

From manufacturing to healthcare, robotics will be utilized to reduce the burden on human workers. A study conducted by Oxford University found that over the next 15 years, approximately 47 percent of U.S. occupations could be replaced by machines.

10. Cybersecurity

Cybercrime Costs $600B Annually

According to McAfee, cybercrime costs the U.S businesses $600 billion annually. Hackers target websites, steal sensitive information, and destroy valuable assets.

To minimize risk, IT departments should consider investing in cybersecurity solutions. Many solutions include software, hardware, and consulting services.

Customer Stories in Business Using AI

As well as being an effective way to reduce seizures, the ketogenic diet has helped many people lose weight, improve memory, control mood swings, and manage chronic pain. The diet has also shown promise in treating some types of cancer and neurological diseases like epilepsy.

What is AI? AI stands for artificial intelligence. It’s a branch of computer science dealing with creating intelligent machines. In business, AI is used to analyze enormous amounts of data to find patterns and predict trends. This allows companies to identify which products sell best, who their customers are, and where they’re going wrong. AI helps businesses understand customer behavior and make educated decisions about strategy and marketing.

How does it work? AI uses machine learning techniques such as neural networks, deep learning, evolutionary computation, genetic algorithms, and reinforcement learning. These methods allow computers to learn from data without explicitly programming each step of the process. Instead, they rely on the system itself to figure out how to proceed based on its experience.

Why is it important? Businesses around the world are adopting AI. If you want to stay competitive in today’s market, you need to adopt AI too. Today, 80% of global economic growth is expected to come from emerging markets like India and China. To thrive in this market, you must adapt to changing consumer demands and deliver personalized experiences across all channels.

AI gives your business:

– New insights into your customers

– Better operational efficiency

– Faster time-to-market

– Greater profitability

If you want to get started using AI, I recommend looking at these resources:

– The Future of Work Report

– McKinsey Global Institute

– IBM Watson Research Center

– TechRepublic

The future of work report predicts that automation will eliminate 7.5 million U.S. jobs over the next decade. This means more than fifty million Americans will need new skills or education.

By 2030, AI and robotics technologies will lead to a third wave of automation, according to a 2018 Accenture report. Industries most likely to see job displacement include transportation, organization, health care, financial services, government, and hospitality.

When will we see widespread adoption? According to Gartner, there will be a tipping point between 2020 and 2025 when more than 75% of enterprises will start deploying AI. By 2022, half of all digital interactions will be automated. When this happens, humans will no longer be needed to take care of routine processes.

That said, not everyone is embracing AI. Some industries, such as finance, law, and medicine, require highly regulated environments. Other countries still have regulatory barriers. There are also concerns about privacy and security. As a result, only 20% of organizations use AI today. That number might increase by 2021, but many organizations won’t adopt until after 2025.

How can I prepare?

Here are my top five recommendations:

1) Understand what AI is and why it’s so important.

2) Hire people who are willing to innovate.

3) Develop new processes to ensure consistency.

4) Adopt cloud computing.

5) Create a flexible workforce.

Machine learning vs Deep Learning?

Deep learning is a subset of machine learning. It uses neural networks to analyze enormous amounts of data and learn from experience. The most well-known example of this type of artificial intelligence is Google’s AlphaGo program which beat world champion Lee Sedol at Go.

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What makes deep learning different?

• Neural networks contain multiple layers of neurons. Each layer has smaller units called nodes.

• Nodes in one layer make predictions about nodes in the next layer. They are trained with input features.

• Training involves adjusting weights.

• A node learns by accumulating evidence through repeated training cycles.

How do you apply deep learning?

You can find real-world applications in image recognition, speech recognition, self-driving cars, computer vision, natural language processing, recommendation systems, fraud detection, and medical diagnosis. You can also use it to improve customer service, product development, sales forecasting, supply chain management, and organization.

Deep learning was invented by Yann LeCun who created Facebook’s face recognition technology. He later joined Facebook Artificial Intelligence Research Group where he developed AlexNet, an early convolutional network for object recognition. In 2012, Geoff Hinton introduced deep belief networks that became a popular technique for unsupervised feature extraction.

What types of datasets support deep learning?

Data sets used to train and test deep learning models usually fall into three categories:

• Image classification.

• Text classification.

• Speech/text transcription

Image Classification

The simplest classifiers are based on convolutional neural networks . CNNs have two main parts:

1) Convolutional layers

2) Pooling layers. CNNs are effective because they capture local correlations within images. They work best with 8×8 pixel images or larger. This means they’re most often applied to photographs, videos, or similar formats. However, some researchers say they could eventually be useful for text analysis.

Text Classification

Most applications of deep learning focus on understanding words rather than images. So, researchers first need to convert text into numerical vectors representing word embeddings. Word embedding algorithms look for patterns in how frequently words appear together. If a phrase occurs much more frequently than expected, then it must reflect something meaningful. For instance, phrases like “the sky is blue” and “I am excited!” convey the same meaning — both describe positive emotions. On the other hand, phrases like “it is raining” and “I hate it.” convey negative feelings.

Speech Recognition

Deep neural networks help machines understand human speech even if the audio signal varies over time. Researchers use acoustic models to recognize sounds and phonemes. Then, they feed these results to a hidden Markov model. This combination helps computers identify individual spoken words.

Data Analysis — What Types of Models?

Types of Models

There are four basic types of models used in statistics and predictive analytics. They include regression models, clustering models, classification models, and association rules. Regression models attempt to explain the relationships between independent variables and dependent variables. Clustering models group items according to similarity. Classifications assign each item to one of several categories based on common characteristics. Association rules are also called “if-then rules” because they state what happens when certain conditions hold true.

Regression Model

A linear regression model is the standard approach to predicting continuous values such as sales. Suppose we believe that customer satisfaction scores can vary depending on whether the product being sold has been preordered before, the product’s price, and the gender of the buyer. We can build a simple linear regression model using this information. As shown here, our relationship would look like this:

The Benefits of Machine Learning in Business

Are you looking for ways to improve your business but don’t know where to start?

Machine learning, also known as artificial intelligence, has been around since the 1960s. However, it wasn’t until recently that technology became accessible to everyone. Nowadays, you can buy pre-packaged solutions that allow you to get started with machine learning for free.

When most people think about machine learning, they’re thinking about an algorithm that learns from previous examples. There are hundreds of these algorithms available today, each one trying to figure out some problem or answer some question. For example, if I ask you to name all the countries in Europe, you’d have no trouble producing answers like France, Germany, Italy, etc. You’ve learned all those names by memorizing them and repeating them repeatedly. This is called recall — it’s when you remember something after seeing it just once. You may not have heard about these tools before, so we want to help you gain a better understanding of why they’re important and how they work. We’ll also explain how they could benefit your organization.

Why Use Machine Learning?

  1. To create products and services that customers want.

  2. To create better business processes.

  3. To detect anomalies.

  4. To enable real-time decision-making.

  5. To automate repetitive tasks.

  6. To solve problems that humans cannot.

  7. To increase revenue.

  8. To save money.

  9. To reduce risk.

  10. To gain a competitive advantage.

  11. To meet regulatory requirements.

  12. To achieve organizational objectives.

  13. To enhance security.

  14. To drive innovation & creativity.

  15. To optimize resources.

  16. To manage complexity.

  17. To predict future outcomes.

  18. To make decisions faster.

  19. To prevent errors and omissions.

  20. To control costs.

In today’s digital age, every company needs to have its own data strategy. If your organization does not have a data strategy, then it is time to start planning one. Data is the new oil, and it has become an absolute necessity for any company to survive and thrive. When we talk about data, we mean all kinds of data — internal data as well as external data.

Some businesses focus more on the internal side of things whereas others prefer to look outward to see what the customer is saying about them. However, whatever the case may be, when it comes to data collection, it is important that you understand the importance of data analytics. It is also important that you understand how you can make use of the collected data to improve your business strategies.

Our team is always innovating and working on new projects that incorporate innovative technology. If you are interested in learning more, reach out to our team. marketing@pcsocial.app

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