Text Summarization In NLP

Text Summarization In NLP

What Is Text Summarization

The advancements in NLP have led to the development of machine learning algorithms such as text summarization that can automatically shorten longer texts and extract summaries of sections of text without losing the message. In a society constrained by time and obsessed with efficiency, text summarization is a leading-edge way to process information. It reduces reading time and makes researching, processing, and digesting the right information easier.

In the modern business landscape, data is a commodity. A comparison can be made that data in the 21st century is akin in value to oil in the 20th century. The analysis and use of vast amounts of data is applied to decision-making for many businesses across industries. Research predicts that by 2025 the total amount of data globally will exceed 180 zettabytes. A meteoric rise from the projected 4.4 zettabytes of data circulating the world in 2013.

The vast amount of data available to the world represents tremendous opportunity especially with tapping into unstructured data. Natural Language Processing (NLP) is a subset of Artificial Intelligence (AI) in which computers can interpret and analyze human language in efficient and useful ways. It is a way to get a human-level understanding of the language for machines. While tools such as content analytics platforms can understand the context of language, a computer is yet to “read between the lines” or intuit language the way a human does.

Text Summarization Examples

NLP extracts the context from a human language using machine learning. In NLP, text summarization shortens a set of data computationally to create a subset of the most meaningful information. It works in two different ways:

Extraction-Based Summarization

 This summarization technique operates by extracting keywords from the document and combining

them into a summary. The text remains unchanged as the extraction of keywords is made according to predefined metrics.

Extraction-Based Examples

Source text

Jill and Jack went to a farm to pick roses in bloom in Vermont. Jill picked a yellow rose to give to Jane.

Extractive Summary

Jill and Jack pick roses in Vermont. Jill gives it to Jane.

The words in bold have been used to create a summary. Because keywords are extracted, the summary may not always be grammatically accurate.

Abstraction-Based Summarization

 This summarization technique works by paraphrasing and shortening parts of the source document. The benefit of abstraction being used in text summarization is that it does not struggle with the same grammatical inconsistencies. Also, abstractive text summarization is more natural at summarizing language like humans. It creates new phrases and sentences with only the most relevant information from the text.

Abstraction-Based Summarization Examples

Abstractive Summary

 Jack and Jill went to the farm to pick roses with Jane.

Executive Summary

 Jack, Jill, and Jane went to the farm.

Conclusion

Abstraction-based summarization is more accurate in capturing the grammatical syntax and context of the text. However, abstraction-based summarization algorithms are harder to develop therefore extraction-based summarization remains the most popular used method. As NLP technology progresses to understand the nuances of language so will the text summarization algorithms to make information more accessible and meaningful to businesses.

What Is Natural Language Processing

What Is Natural Language Processing

Natural Language Processing (NLP) is a subset of AI in which computers can analyze and interpret human language in an efficient and useful way. It is a way to get a human-level understanding of language for machines. Considering the nuances of language, NLP is an interesting development in the field of AI. While tools such as content analytics platforms can understand the context of language, a computer is yet to “read between the lines” or intuit language the way a human does.

Technological advancements have changed the paradigm for doing business. As part of a digital transformation, businesses are exploring means to innovate and scale past the competition. The field of Artificial Intelligence (AI) is one arena that has seen a rapid acceleration in practical application. Nowadays, AI is no longer an abstract theory or a figment of science fiction but a tangible tool for businesses to gain a competitive advantage.

On a high level, NLP leverages computer programs to interpret unstructured content using AI and machine learning. This mechanism allows computers to understand the context of language much as human brains do. Advancements in AI neural networks have taught computers how to mimic the neural networks of human beings. In much the same way that a human being learns by continually being exposed to stimuli, a computer gets more efficient in interpreting language through exposure.

By structuring and interpreting unstructured information, computers generate insights from untapped data for businesses. This enables businesses across industries to get a deeper understanding of the public perception around consumer behavior, products, and brand equity. Decisions that were previously made on guesswork are now guided by data-driven insights. This is because machines are better, more accurate, and more consistent in interpreting language than humans. NLP processes data in real-time, 24/7, for consistent and accurate results. As a competitive advantage, NLP can be used to analyze large data sets such as social media monitoring, customer support, and online reviews.

Natural Language Processing Examples 

AI tools are most effective when they find an application in daily life. Here are some examples of the way NLP has improved daily life across industries:

NLP and Healthcare

 In healthcare, NLP tools can predict diseases based on electronic health records and the patterns of a patient’s speech. This innovation has been used to diagnose health conditions in areas such as mental health and cardiovascular health. As healthcare moves to telehealth, NLP can be used to help in service delivery.

NLP and Sentiment Analysis

 Businesses can listen on social media to what customers are saying by using NLP. Using this technique provides businesses with a lot of insights into customer’s choices and decision-making processes. Access to previously unmanageable large sets of unstructured data gives businesses access to data they did not even know they had on social media.

NLP and Email

Tech giants such as Google and Yahoo use NLP to filter and classify emails by instantly analyzing incoming text. This enables tech companies to filter and stop spam before it enters a user’s inbox.

NLP and Home Assistants

Apple’s Siri and Amazon’s Alexa are great examples of voice driven interfaces that use NLP to make everyday life better for humans. From turning off the lights at ome to playing favorite songs, these home assistants use voice recognition to help humans.

A document-heavy industry, legal is getting help from NLP to automate routine litigation tasks. It can help legal teams be more efficient and save money by having a better strategic focus.

What Industries Use NLP

Most industries will be disrupted by the availability of NLP technology including legal. While lawyers are not going to be replaced by technology, the way lawyers do their job will be changed. NLP automates the time-consuming, redundant tasks that slow down lawyers. This technology will save time and reduce errors by extracting meaning from content and

collecting data and documents instantaneously. Lawyers work on billable hours and NLP technology can save hours of labor allowing legal professionals to focus on cases and clients that matter the most. In addition, NLP may make legal support more affordable to a larger population of people.

Financial Services

More than other industries, the financial services industry is time sensitive. News around the community spreads quickly and can influence an investor’s decision-making. For an investor, filtering relevant information from the 24/7 news cycle is laborious and time-consuming. NLP technology can help make smart financial decisions by pre-filtering steps automatically. Also, financial services can use NLP technology to increase compliance in large amounts of paperwork eliminating mundane and repetitive steps in the process.

Healthcare

Since the COVID-19 pandemic, the healthcare industry has had a significant paradigm shift. With the prevalence of telehealth, efficient coordination of medical data is a top priority. NLP technology can be used to analyze doctor’s records and find hidden correlations between diagnoses, symptoms, medications, and treatment. These new insights give healthcare professionals the ability to manage quality care initiatives and better predict treatment outcomes for patients. /

The Two Subfields Of Natural Language Processing (Understanding & Generation)

Given the high-level definition of NLP and the extent of its capability, it is no wonder that NLP has many subfields. More specifically, there is Natural Language Understanding (NLU) and Natural Language Generation (NLG).

So, what is the difference? NLU is more focused on the process of reading and interpreting language such as giving a command to Apple’s Siri. NLG is geared towards the process of generating a natural language. For example, after asking Siri a question about the weather in Portland, OR the computer would use NLG to respond with “rainy with a chance of thunderstorms”.

To further understand the subsets of NLP, let’s put them in the context of the eCommerce industry. The ecommerce industry is focused on the virtual customer experience and interactions. NLU would drive site search in eCommerce. It is activated when users have a search query, they expect to be understood and to get an appropriate search result based on the query. In eCommerce, NLG would be used to power chatbots that generate language. Chatbots speak back to the users and facilitate an interactive and informative user experience for the customer. Together, NLG and NLU make eCommerce more powerful and responsive to user’s needs and preferences. This in turn drives customer loyalty and increases revenue.

NLP, what is the future?

Given the capabilities of NLP in select industries and the increased reliance on technological advances across the business sector, the future for NLP looks bright. Perhaps, what is most surprising to the average person is how NLP technology has seeped into and improved everyday aspects of life. By touching all areas of life and improving daily interactions, NLP can have a positive effect on the individual’s quality of life as well as society.

Foremost, NLP technology makes communication easier and more efficient. This ensures that every interaction we have is in alignment with our goals and has a positive outcome. NLP also influences mental health and helps individuals overcome personal obstacles and psychological barriers. Because NLP mimics the human brain, it augments tools and techniques inherent to us without us being aware of its psychological impact.

The human brain is prone to error and inconsistency. By relying on machine cognition, everything becomes a lot faster and more automated. The adoption of NLP technology has allowed major organizations to become more agile and flexible with decision-making and strategic initiatives. For businesses, access to unstructured data, data they did not even know they had provided new insights. The intelligence gathered from the data by NLP-powered machines changed the way businesses make decisions.

The prediction is that in the future NLP will become more mainstream. Such NLP practices as text summarization, NLP consulting, and text mining will become more sophisticated with new developments.NLP technology will not only influence select industries but empower decision-making across global markets.

Conclusion

How will humans respond to a major integration of NLP?

Much like with the adoption of AI, the proliferation of NLP in society has been met with enthusiasm rather than disdain. Humans see the potential of technological advancements to make work and life easier. Humans becoming more comfortable with advanced technology can be attributed to the widespread adoption of smartphones and the increased demand for elevated customer service. Nowadays, humans see the power of automating the mundane and repetitive tasks in life so they can focus on higher value-added activities requiring human intelligence, ingenuity, and creativity.

In the future, NLP technology will become an integral part of life. As AI continues to become more advanced, the next level of progress for these technologies to be able to “read between the lines” for human language. To become as sensitive and nuanced to the context of human language as the human brain. This will cause the AI and NLP robots to become more sophisticated and useful in the workplace. As these technological capabilities are becoming more mainstream, they are ushering in a new era of work.

How Artificial Intelligence Transformed Influencer Marketing

How Artificial Intelligence Transformed Influencer Marketing

How Marketing Adopted AI

The meteoric rise of Artificial Intelligence (AI) has significantly changed marketing practices. The core principle of marketing remains to “get attention”. But the way businesses go about getting attention from their customers has changed from alluring advertising to data-driven decisions. As marketing businesses continue to invest in AI to build their brand, they have leveraged the technology to transform influencer marketing as part of a profitable marketing strategy.

Influencer marketing has evolved from the early days of social media to sophisticated lifestyle marketing. Early adopters of social media recognized influencer marketing as way to leverage a popular personality’s audience on a social media channel. Early metrics were based on the number of followers and influencer engagement to promote products. Sometimes even influencers with the most curated lifestyle feeds found themselves involved in scandals that poorly reflected on brands and damaged sales.

Marketing businesses had to get smarter about viable influencer-marketing relationships but how?

The Use Of AI In Influencer Marketing

During the COVID 19 pandemic, marketers recognized how powerful influencer marketing can be. As people spent more time online and online sales skyrocketed, influencers in the niches of beauty, fashion, and food gained visibility across social media platforms. However, for a marketer finding an influencer to partner with is more complex than just a follower count. While many personalities online sell the idea of a great life finding the right influencer for a business is time-consuming. It could take hours on any social media platform for a business to go through an influencer’s posts and gauge their follower’s engagement.

To solve such problems, marketers turned to AI algorithms to predict the success of influencer marketing more accurately. AI is a technology that mimics human cognition automating redundant and mundane tasks which allows humans to focus on higher value-added activities that require

intelligence and ingenuity. When applied to influencer marketing, AI is an iterative process. It learns from what works and what does not to become more efficient. To gauge a follower count, it would take human hours and multiple spreadsheets to complete. An AI algorithm can go through large sets of data in minutes and cross-reference the posts to give an accurate representation of an influencer’s following.

Finding The Right Influencer With AI

As marketers leverage AI, they refine the AI algorithm used for influencer marketing. The business can identify whether partnering with an influencer is the right strategy for the brand. It can answer integral questions such as: what makes an influencer right for the brand? What traits does an influencer need to display? Furthermore, granular influencer analysis with AI can uncover new opportunities. Traditionally, businesses were looking to partner with influencers with large followings. However, data analytics has shown the power of micro-influencers. These are influencers that may not have a large following but have more engagement with followers in a targeted niche. Brands can leverage micro-influencers for a stickier message that would drive more sales than a generic message.

Conclusion

As a practice, marketing needs to remain flexible and responsive to technological innovation. Marketers have become more sophisticated with tools such as AI in developing and executing strategy and branding. AI has transformed influencer marketing to become more effective. A business can leverage AI to determine which influencer partnership would be most beneficial for the brand. AI remains a human-guided technology. Without humans setting guidelines and limits on AI, progress in marketing would not be possible.

What Is An Artificial Neural Network

What Is An Artificial Neural Network

The exploration of Artificial Intelligence (AI) has posed an important question: “Can machines think like humans?”. As technology evolves and scientists push the limits of possible with AI, new advancements make the answer to that question more of a reality. Artificial Neural Networks simulate the structure of the brain. They are intended to make computers more humans and help machines master human-level intelligence.

It is undoubtedly machines have rivaled humans when it comes to reasoning and problem-solving. However, there are still capabilities of human intelligence such as imagination and inspiration that remain to be replicated by machines. That is why researchers are focusing on artificial neural networks to bridge the gap between artificial intelligence and human minds.

It starts with the way humans interpret real-world situations in multi-sensory ways that computers have yet to learn. In the 1950s, as science was pioneering AI, neural networks were explored to see if a computer can learn and make decisions like a human being.

What makes artificial neural networks interesting is that it mimics the structure of the human brain. Neuroscience has discovered that different parts of the brain are responsible for processing different parts of information and the whole brain is arranged as interconnected brain cells. The artificial neural networks (ANN) are designed to replicate the structure of the human brain as well as the mechanism.

How Do Neural Networks Work

Much like the human brain, Artificial Neural Networks works in complex ways. Neural networks are organized into layers. These layers contain several “interconnected nodes” with “activation functions”. The mechanism of Artificial Neural Networks contains an intricate interaction between “input layer”, “connections”, and “output layer”, all of which communicate. The interesting fact about Artificial Neural Networks is that they are trained much like the human brain. The more the human brain is exposed

to stimuli, the more connections it makes between neurotransmitters and builds neural networks. These neural networks are “neuroplastic”, they get stronger or weaker when the pathway is repeatedly exposed to the same stimuli. In this way, human beings learn from experience. In the same way, Artificial Neural Networks learns by example and gets better by repetition.

Neural Network Examples

Artificial Neural Networks is a fascinating technological advancement but what real-world applications do they have? Artificial Neural networks has been used in forecasting and identifying patterns of data in many industries such as:

– Customer service

– Sales forecasting

– Marketing

– Data sciences

– Risk management

– Security

CONCLUSION

The human brain remains a mystery to neuroscience and technology to this day. While it functions as a network of interconnected neurons, the way it processes inputs and information to output creativity, inspiration, and talent is still to be understood. The goal of Artificial Neural Network is to replicate human intelligence so AI systems can more accurately replicate human cognition. The Artificial Neural Network mechanism closely mimics its biological counterpart in the way it processes and builds on information and stimuli. Artificial Neural Network is used to solve problems and provide solutions for an array of industries from marketing to risk management. These solutions are meant to further progress for the industry and society.

How To Leverage Artificial Intelligence

How To Leverage Artificial Intelligence

Artificial Intelligence (AI) has gone from a futuristic theory to a practical tool for businesses that want to design the future. The impact and benefits of AI are industry agnostic, span a diverse range from healthcare to financial services and beyond. Before a business adopts AI as part of its technological arsenal, it needs to ask itself certain questions that can illuminate if AI can help the bottom line.

Google searches for the term “artificial intelligence” and “machine learning” have become prevalent in the last few years. A recent poll determined 80% of C-suite executives see the need for AI in their organization but most of them do not know how to go about successfully implementing AI.

By adopting AI, a business increases productivity and efficiency as well as the opportunity for innovation.

3 Ways Businesses Can Benefit From AI

While there is a lot of hype surrounding the practical application of AI in the business sector, here are some ways a business can leverage AI:

1. Enhanced Productivity

One of the most well-known benefits of AI is improved productivity. The introduction of AI into the workflow minimizes redundant tasks and reduces human error which frees up human intelligence for more value-added tasks and ingenuity. More productivity is critical for the success of the business which relies on human capital to bring value to the business.

2. Access To Innovation 

AI can be used not only to boost productivity but also to accelerate the ability of a business to tap into innovation. The pursuit of innovation is something that eludes most business which focuses mostly on production and operations. The power of AI is that it can compile and execute decisions based on a large data set. This data analysis enables the business to recognize trends, opportunities, and themes it typically would not consider. This new information gives the business insights necessary for higher performance and data-driven decision-making.

3. Improved Customer Service

In an ideal world, businesses would have streamlined customer service and service delivery with minimal service recovery. Just a few years ago, the idea that a business can staff a 24- hour customer service team seemed costly and labor-intensive. The advent of AI has redefined the relationship between businesses and customers. For example, banking uses AI chatbots to improve the online banking experience. AI chatbots can answer questions, request necessary documentation, build relationships, and service the customer 24/7. Moreover, AI has become beneficial in a world with an evolved definition of customer service. These days customers want to access support anywhere at any time from multiple different channels. AI helps businesses be instantly available to customers without human error or inconsistencies.

Conclusion

In the new digital era of work, technological advancements are changing the way business is being done. The evolution of AI from theoretical beginnings to practical applications has tremendously benefited businesses. Across industries, it enabled businesses to be more efficient and productive in operations. Businesses leveraged data analysis to access more innovation driven by data insights and trends to highlight opportunities. Finally, AI was used to redefine customer service in an age where instant access and consistency are required. AI has shifted the perceptions of what was possible when it comes to doing business.

What Is White Label Artificial Intelligence

What Is White Label Artificial Intelligence

As businesses become more comfortable with the adoption of AI, the demand for white label AI increased. White label Artificial Intelligence is the practice of customizing AI systems to be a value add for a business. This ensures that AI is configured to support business systems and works as a solution to improve the margins of the business.

Technological advances have redefined the way business is done across industries. To keep up, many businesses have undergone digital transformations and adopted technologies such as Artificial Intelligence (AI) and Machine Learning (ML) to augment operations. In the new era of work, technology dictates business performance which has been greatly impacted by the recent COVID-19 pandemic. As the business world went remote, a poll indicated that 80% of C suite executives want AI to be implemented in their organizations and most of these executives did not know how to go about the process.

Benefits Of White Label Artificial Intelligence

There are numerous benefits to adopting AI technologies into business operations. Not only does AI improve profit margins but it increases the amount of work a business does with customers. For example, banks have leveraged AI chatbots to manage service delivery. AI chatbots are available 24/7 to answer customer questions and perform basic account management without human error or inconsistency. The reliance on AI chatbots has improved the quality of customer service in banks where customer retention is a priority.

Other benefits of AI include the automation of repetitive and mundane tasks to enable employees to concentrate more on higher value add activities requiring human intelligence and ingenuity. Going back to the banking example, AI technologies automate the verification of account information and credit reports so by the time an employee touches a customer it is regarding a more complex transaction. When business systems adopt AI, they augment services and strengthen the quality of customer relationships.

Benefits Of A Customizable Microservice

One of the more significant benefits of businesses using AI is to gain a competitive advantage. At Scion Analytics, we realized the tremendous potential of AI in the business sector with the introduction of the Content Analytics Platform (CAP). The CAP leveraged AI and ML technologies to create a customized microservice for a business that would automate processes. Scion Analytics use cases have found that each time a client uses 1 microservice for every use it saves the client 8 hours. When 8 hours is saved in tangent with 30 employees at the same time, 240 hours is saved per use.

The creation and customization of microservices contribute to the presence of white label AI in a business. Automation helps businesses improve existing products and processes as well as drive innovation. As a result of the exploration of data analytics and the application of white label AI, businesses make progress towards innovation. As businesses leverage new insights and make actionable data-driven decisions, they free up the operational bandwidth to successfully innovate.

What Is Artificial Intelligence?

What Is Artificial Intelligence?

The concept of Artificial Intelligence (AI) has been hard to understand since its emergence. When asked about AI, the average person can confuse it with high-powered robots from TV shows and ideas from Elon Musk. Depictions in popular culture such as Terminator or “Star Trek” do little to demystify the myth. In the boardroom, executives when asked about AI know for certain that they want the technology a part of their organization but lack more than cursory knowledge as to how it operates. The mystery surrounding the term enables those cautious of advancements in technology to issue warnings that AI will dominate humans, endanger individual freedom, and destroy the remaining shreds of any personal privacy we have through a digital coup.

But does the emergence of AI signify the decline of human intellectual freedom? To make that determination, a definition of AI needs to be established. At a basic level, AI is a way to replicate human intelligence in machines. Since the 1950s, every attempt by a scientist at AI was to answer the fundamental question, “Can machines think?”.

The pioneer of AI was Alan Turing. In the 1950s, he was credited with engineering “thinking machines” that were capable of reasoning at a human being level. The “Turing Test” was used to see if computers could do reasoning puzzles as well as humans to be assessed as “having a human capacity for contemplation, judgment, and intention”. Further scientific research would separate the actions and the behavior of computers to see if they could simulate thinking humanly and rationally as well as acting humanly and rationally. Such developments as white label AI and artificial neural network would come to define the leading edge of the field.

How Is Artificial Intelligence Measured?

There are different metrics by which AI is measured, one of which is intentionality. AI algorithms are designed to make decisions with real-time data. Insights derived from unstructured, semi-structured, or structured data can be used to do instant analysis to reach decisions. The dynamic nature of AI inputs differentiates them from passive machines that are only capable of predetermined responses. The intentionality of AI machines

makes them pliable to human values. Unlike popular culture and imagination, there is little risk of AI posing a risk to human beings. The presence of AI helps and augments human capabilities rather than endangers them.

Another feature of AI is intelligence. Oftentimes, AI is used with machine learning and data analytics to look for underlying trends. Identifying such patterns leads researchers to intelligent decision-making on specific issues.

Having intelligent design is important for another AI criteria which is adaptability. AI systems are dynamic, they must learn and adapt to incoming information and factor it into decisions. As circumstances and conditions shift, AI algorithms must adjust to new changes and update to reflect them.

Artificial Intelligence Examples

While the perception of AI varies from the average person to the executive, there are everyday uses and examples of AI that often go unnoticed by the untrained eye. Here are some common uses of the technology:

1. Siri 

 Smartphones have become ubiquitous since their inception. Everyone that used an iPhone is familiar with Siri, Apple’s assistant. Siri is a pseudo-intelligent digital personal assistant that is a great example of machine learning (ML) technology. With every use, Siri gets smarter when it comes to the user’s requests to send messages, add events to the calendar, and find information.

2. Alexa

 Alexa from Amazon is the smart home hub that has been made welcome in many homes. It is a good example of AI because it can recognize and decipher speech from anywhere in the room. It helps us do every day, useful tasks such as schedule appointments and set alarms so we could focus on more value-added tasks around the home.

3. Tesla

 These self-driving vehicles are not just a glimpse of the future but also a great example of AI in action. Due to their over-the-air updates, Tesla cars get smarter as you get a better driving experience.

4. Amazon.com

 Amazon started as a bookseller and has turned into the number one shopping destination on the internet powered by AI.

While it cannot predict and ship products to us before we even know we need them it is not too far off yet. Amazon’s AI algorithms can predict a lot of purchasing behavior based on a consumer’s online behavior. The massive amount of data that Amazon collects refines the algorithms with predictive analytics.

5. Netflix

 Everyone’s favorite home movie app relies on AI to make decisions on what movies to suggest. Collecting billions of records, Netflix suggests movies based on consumer’s previous reactions and viewing history. As the data set grows so does the accuracy of personal predictions for Netflix users.

6. Pandora

This is a beloved music app that is used by many people to curate musical playlists. Pandora analyzes songs and “musical DNA” to personalize recommendations for people using AI. Such a level of personalization is what attracts users to adopt the app and customize the experience.

Artificial Intelligence & NLP

AI is a broad term that encompasses how machines simulate human intelligence. While there are different levels of AI, such as weak AI and strong AI, there are also subsets of the technology such as Natural Language Processing (NLP) and Machine Learning (ML).

AI powers systems that mimic cognitive abilities, like learning and solving problems. To master such tasks, NLP capabilities help computers understand and translate the human language. While AI and NLP cannot yet interpret the nuance and context of the text, the machine can translate, extract keywords, classify topics, and map content.

NLP is also used as a capability in data analytics tools that perform content analysis. It can be used to analyze large batches of content to map relationships, assign responsibility, and extract insights. This enables businesses to gain valuable insights, move the business forward and scale.

Artificial Intelligence Benefits

AI Benefits At Work

The evolution of technology has benefited society on a large scale. Specifically, AI has moved the economy forward by ushering in a new era of work. Robots and AI make work more efficient by augmenting capabilities and automating redundant tasks. By utilizing machine learning, AI improves over time making a business more efficient at using resources and scaling. It reduces the margin of human error and uses data patterns to cut costs. These advancements can benefit an array of industries from scientific research where researchers use AI to map cancer cells to space exploration companies where futurists design rockets that land on Mars. There are plenty of enhanced capabilities and opportunities powered by AI in the future.

AI Benefits At Home

The reach of AI extends beyond the economy into the home of the everyday individual. AI makes everyday life more efficient and information more accessible, it helps people create new opportunities in their environment. As a result, it improves job satisfaction, savings, and revenue generation.

As mentioned in the examples of AI like Pandora and Netflix, AI uses search algorithms to provide targeted information for a better user experience. Whether listening to music or watching a show, the presence of AI guarantees that these lifestyle choices will be curated. Also, AI in the home handles mundane tasks such as data entry and answering emails freeing up the individual to pursue their interests in their free time.

The proliferation of AI is innovation. Just like electricity marked an era of human progress, so does the evolution of AI expand human creativity and ingenuity by automating the redundant and mundane tasks necessary for accomplishing a greater vision. When the power of the human mind and the efficiency of AI collaborate the quality of human life improves.

Industries That Use Artificial Intelligence

Given the plethora of AI benefits on the economy, numerous industries have adopted the technology into their business practices.

Here are some examples:

1. Healthcare

 Healthcare was one of the early adopters of AI given its far-reaching benefits in the medical field. AI has been used for: digital consultations, robotic surgery, data, and health record-keeping, and changing the service delivery in the healthcare setting.

2. Marketing

In some ways, all the departments in the business support marketing in the brand-building activities. Some of the marketing initiatives such as A/B testing can be automated with AI to figure out the best possible ad copy combination that would work.

3. Education 

Robots are used to fill in the gap between reading and social skills for students and students with disabilities. Smart scheduling assistants are used to schedule parent-teacher conferences, so a student’s needs are addressed consistently.

4. Retail And Ecommerce

AI-powered tools are used by e-commerce companies to manage sales and inventory. AI chatbots can interact with customers managing the customer service experience and boosting sales.

Conclusion

These days, AI is more than just a page out of a science fiction book. It has become ingrained in our society to empower the individual and the business to make progress and to move forward. From healthcare to tech, from smart driving cars to smart phones, AI touches every corner of our lives with computer intelligence that simulates our own intelligence so it could make our experience better in the moment.

Artificial Intelligence: A Look into the Future of Humans

Artificial Intelligence: A Look into the Future of Humans

Welcome To The Future With AI

Since the 1950s, Artificial Intelligence (AI) has foreshadowed a giant leap in progress for humans. As AI becomes ubiquitous, everyday individuals and businesses are relying on the power of computer intelligence to transform lives. This development has redefined society and encouraged innovation only to find that a glimpse into the future is not without consequence. Science fiction and popular culture have created a myth surrounding the proliferation of AI with films such as Ex Machina, Transcendence, and Her. These films portray the dark side of AI overpowering human consciousness and serve as a cautionary tale for scientific progress.

The evolution of AI also raises concerns about the ethics of the technology especially when it comes to industries such as medicine and politics. Should there be strict governance on the use of AI? If so, as humans can we accurately assess the risks versus the benefits of incorporating AI into our lives? How do advances in AI affect what it means to be human in the 21st century? These are questions that need to be answered as we witness the improvements AI has afforded us in quality of life.

What The Future Looks Like With AI

Medicine

AI has given rise to precision medicine that is customized based on a person’s genetics, environment, and lifestyle to prevent and treat disease. AI has made treatments more affordable, accessible, and accurate through digital therapeutics and custom-designed drugs. For humans, this is helping improve quality of life and access to healthcare.

Transportation 

Tesla has pioneered the market of driverless cars. It is estimated by 2035 that 70% of cars on the road will be autonomous. This projection also includes a dramatic decline in fatal car accidents that are caused by human error. Advancements in AI are making roads safer and cars more efficient across the globe.

Virtual Assistants

AI virtual assistants such as Siri and Alexa use natural language processing (NLP) to help make human lives easier. The trend of voice technology is going to take off, even more, when virtual assistants are connected to robots in the future.

Implantables

This is truly a wave from the future where brain-machine interfaces can augment human intelligence. Such a capability will allow humans to solve many medical problems that remain without a solution to date. Diagnoses such as blindness, anxiety, and addiction could become history as the science of implantable reprograms human biology. One of the pioneers of researching and developing implantables is Elon Musk’s company, Neuralink, which has enabled a monkey to play a game using his thoughts. If these are the future capabilities of monkeys, just imagine what humans are going to be able to achieve with enhanced cognition capabilities.

Conclusion

The adoption of AI into the everyday lives of individuals and businesses is making humans hopeful about the future. AI can be considered one of the greatest leaps of human progress in recent generations. Once driverless cars and virtual assistants become a reality of everyday life rather than an exception, humans will need to establish ethics and governance for AI. It is a bright promise of the future, the fact that AI can augment human intelligence to achieve things that were once science fiction.

What Is Semi-Structured Data?

What Is Semi-Structured Data?

Semi-structured is made up of partially unstructured data and partially data structure created by metadata. It is an interesting intersection between the two data types and it can yield transformational insights when analyzed. A good example of semi-structured data is an X-ray. An X-ray consists of a great many pixels. The sheer volume of pixels cannot be searched, queried, and analyzed like structured data. However, X-rays, like most files, contain metadata. This “data about data” is what enables semi-structured and unstructured data to be harnessed.

Every day data is used to shape the direction of businesses, develop new business offerings, and gain a competitive advantage. In a market where businesses pursue innovation and thrive off disruption, harnessing data is integral to success.

Data is everywhere. It comes in different shapes and sizes. There are three different types of data: structured data, unstructured data, and semi-structured data.

Structured data is what has traditionally been thought of as data. It exists in predefined formats and is easily accessible by the average user. Unstructured data is the new frontier of data. It lacks a predefined format akin to endless chaos of data full of powerful insights. The middle between structured data and unstructured data is semi-structured data.

Semi-Structured Data Examples

An X-ray is just one example of unstructured data. Upon further examination, numerous examples of unstructured data can be found in everyday business operations.

Email

Email is a great example of semi-structured data. The popularity of the Internet and the proliferation of social media has created a deluge of new data. This data comes in flexible formats collected from a vast population sample. Digital communications such as emails are a source of semi-structured data. This is because every email has:

– Subject

– “To” line

– “From” line

– Date stamp

– Time stamp

The above fields are sources of structured information. However, the text body of an email is a form of unstructured data. There is no defined format or character limit for the body of an email. Emails collected, searched, and analyzed across an enterprise can represent a powerful source of information and record-keeping. Furthermore, emails can provide data mining opportunities to:

– Analyze customer feedback

– Streamline customer support

– Target marketing initiatives

– Develop social media initiatives

– Shape strategic initiatives

Web Pages

The overwhelming popularity of the Internet has produced a lot of content. Another example of semi-structured data is web pages. Most web pages have an organization with tabs for:

– Home

– About Us

– Blog

– Services

– Contact

These tabs are easy to navigate and search thus representing structured data. However, the web pages are written in HTML containing text and data within each of these pages that have no structure. A wealth of information lies hidden in web pages across the internet. Once a business knows how to leverage digital resources, both internal and external, it will become more successful.

Unstructured Data Vs. Semi-Structured Data

The availability of semi-structured data poses the question: what is the difference between semi-structured and unstructured data? It is a grey area that leaves a lot open to interpretation. All documents, images, and other files have some form of data structure. Therefore, it is hard to distinguish where semi-structured data ends and unstructured data begins. Both semi-structured and unstructured data lack organization and rules that are present in a relational database of structured data.

Conclusion

As more technological advances of data analytics tools evolve, the understanding of semi-structured data and how it relates to unstructured and structured data will deepen. For now, semi-structured data remains a prolific presence on the internet capable of taking businesses forward into the future.

Pros And Cons Of Structured Data Pt:2

Pros And Cons Of Structured Data Pt:2

In today’s competitive market, technological advances are evolving at the speed of business. For a business to be competitive, it needs to innovate and change with the times. One of the most profound, recent developments has been the use of unstructured data.

Data is integral to any business. Historically, data can be of any size and shape classified into structured data and unstructured data. Most businesses have tapped into structured data and its benefits. It has a predefined format and structure that makes it easy to access for business intelligence. Examples of structured data include credit card information and Excel files.

Conversely, unstructured data is more difficult to access and analyze. It can be thought of as being in a chaotic state, an endless alphabet soup of data with hidden insights. Examples of unstructured data include social media posts and emails.

Businesses have been using structured data techniques for some time while unstructured data applications remain to be explored. As more applications are found for unstructured data, data analytics tool will evolve as well. Because unstructured data is still in nascent stages, there are distinct pros and cons to using one type of data over the other.

Pros of Structured Data

1. Ease Of Use

One of the benefits of structured data is how easily it can be used by the average user with a working understanding of the data. It enables self-service of structured data without an in-depth understanding of the complexities of the data. It must be noted that structured data is also easy to use by machine learning. The organized nature of unstructured data lends itself to analysis and manipulation.

2. Convenient Storage

Due to its predefined structure, structured data is conveniently stored in data warehouses. Data warehouses are optimized to save storage space for enterprises and to encourage easy data access. Conversely, unstructured data which is much less defined is stored in data lakes with much greater storage capacity.

3. Access To More Tools

Historically, structured data was the only option for businesses looking to quantify data. Data analytics tools and practices have been developed around structured data. While unstructured data is still in its infancy, data managers have efficient tools at their disposal to process structured data.

Cons Of Structured Data

1. Limitations On Use

Structured data is predefined in its format lending itself to be used for intended purposes. This organizational structure places limitations on its flexibility and use cases. The opposite is the case for unstructured data which has a free form format and can be repurposed for multiple use cases.

2. Limited Storage Options

Structured data is stored in data warehouses. Data warehouses have rigid rules about data storage. Any change to structured data is labor intensive requiring a lot of resources and time to update. Some businesses use cloud-based data warehouse to eliminate the need for on-prem equipment and increase scalability.

Conclusion

Traditionally, businesses have used structured data to gain greater insight into operations. Due to its longevity, structured data has better tools, convenient storage and is easy to use. As the predecessor to unstructured data, structured data remains the reliable and preferred source for data analytics.