Big Data

Big data refers to extremely large and diverse collections of structured, unstructured, and semi-structured data that continues to grow exponentially over time. These datasets are so huge and complex in volume, velocity, and variety, that traditional data management systems cannot store, process, and analyze them. 

The amount and availability of data is growing rapidly, spurred on by digital technology advancements, such as connectivity, mobility, the Internet of Things (IoT), and artificial intelligence (AI). As data continues to expand and proliferate, new big data tools are emerging to help companies collect, process, and analyze data at the speed needed to gain the most value from it. 

Big data describes large and diverse datasets that are huge in volume and also rapidly grow in size over time. Big data is used in machine learning, predictive modeling, and other advanced analytics to solve business problems and make informed decisions.

Big data examples

Data can be a company’s most valuable asset. Using big data to reveal insights can help you understand the areas that affect your business—from market conditions and customer purchasing behaviors to your business processes. 

Here are some big data examples that are helping transform organizations across every industry: 

• Tracking consumer behavior and shopping habits to deliver hyper-personalized retail product recommendations tailored to individual customers
• Monitoring payment patterns and analyzing them against historical customer activity to detect fraud in real time
• Combining data and information from every stage of an order’s shipment journey with hyperlocal traffic insights to help fleet operators optimize last-mile delivery
• Using AI-powered technologies like natural language processing to analyze unstructured medical data (such as research reports, clinical notes, and lab results) to gain new insights for improved treatment development and enhanced patient care
• Using image data from cameras and sensors, as well as GPS data, to detect potholes and improve road maintenance in cities
• Analyzing public datasets of satellite imagery and geospatial datasets to visualize, monitor, measure, and predict the social and environmental impacts of supply chain operations

The Vs of big data

Big data definitions may vary slightly, but it will always be described in terms of volume, velocity, and variety. These big data characteristics are often referred to as the “3 Vs of big data” and were first defined by Gartner in 2001. 

Volume

As its name suggests, the most common characteristic associated with big data is its high volume. This describes the enormous amount of data that is available for collection and produced from a variety of sources and devices on a continuous basis.

Velocity

Big data velocity refers to the speed at which data is generated. Today, data is often produced in real time or near real time, and therefore, it must also be processed, accessed, and analyzed at the same rate to have any meaningful impact. 

Variety

Data is heterogeneous, meaning it can come from many different sources and can be structured, unstructured, or semi-structured. More traditional structured data (such as data in spreadsheets or relational databases) is now supplemented by unstructured text, images, audio, video files, or semi-structured formats like sensor data that can’t be organized in a fixed data schema. 

In addition to these three original Vs, three others that are often mentioned in relation to harnessing the power of big data: veracity, variability, and value.  

• Veracity: Big data can be messy, noisy, and error-prone, which makes it difficult to control the quality and accuracy of the data. Large datasets can be unwieldy and confusing, while smaller datasets could present an incomplete picture. The higher the veracity of the data, the more trustworthy it is.
• Variability: The meaning of collected data is constantly changing, which can lead to inconsistency over time. These shifts include not only changes in context and interpretation but also data collection methods based on the information that companies want to capture and analyze.
• Value: It’s essential to determine the business value of the data you collect. Big data must contain the right data and then be effectively analyzed in order to yield insights that can help drive decision-making. 

Big data benefits

Organizations that use and manage large data volumes correctly can reap many benefits, such as the following:

• Enhanced decision-making. An organization can glean important insights, risks, patterns or trends from big data. Large data sets are meant to be comprehensive and encompass as much information as the organization needs to make better decisions. Big data insights let business leaders quickly make data-driven decisions that impact their organizations.
• Better customer and market insights. Big data that covers market trends and consumer habits gives an organization the important insights it needs to meet the demands of its intended audiences. Product development decisions, in particular, benefit from this type of insight.
• Cost savings. Big data can be used to pinpoint ways businesses can enhance operational efficiency. For example, analysis of big data on a company's energy use can help it be more efficient.
• Positive social impact. Big data can be used to identify solvable problems, such as improving healthcare or tackling poverty in a certain area.

Big data challenges

There are common challenges for data experts when dealing with big data. They include the following:

• Architecture design. Designing a big data architecture focused on an organization's processing capacity is a common challenge for users. Big data systems must be tailored to an organization's particular needs. These types of projects are often do-it-yourself undertakings that require IT and data management teams to piece together a customized set of technologies and tools.
• Skill requirements. Deploying and managing big data systems also requires new skills compared to the ones that database administrators and developers focused on relational software typically possess.
• Costs. Using a managed cloud service can help keep costs under control. However, IT managers still must keep a close eye on cloud computing use to make sure costs don't get out of hand.
• Migration. Migrating on-premises data sets and processing workloads to the cloud can be a complex process.
• Accessibility. Among the main challenges in managing big data systems is making the data accessible to data scientists and analysts, especially in distributed environments that include a mix of different platforms and data stores. To help analysts find relevant data, data management and analytics teams are increasingly building data catalogs that incorporate metadata management and data lineage functions.
• Integration. The process of integrating sets of big data is also complicated, particularly when data variety and velocity are factors.

Analytics

Analytics is the process of discovering, interpreting, and communicating significant patterns in data. . Quite simply, analytics helps us see insights and meaningful data that we might not otherwise detect. Business analytics focuses on using insights derived from data to make more informed decisions that will help organizations increase sales, reduce costs, and make other business improvements.

Business analytics

Business analytics is ubiquitous these days because every company wants to perform better and will analyze data to make better decisions. Organizations are looking to get more from analytics—using more data to drive deeper insights faster, for more people—and all for less. To meet those goals, you need a robust cloud analytics (PDF) platform that supports the entire analytics process with the security, flexibility, and reliability you expect. It needs to help you empower your users to do self-service analysis without sacrificing governance. And it must be easy to administer.

The business value of analytics

A new way to work

The nature of business is changing, and with that change comes a new way to compete. Keeping up with the demands of today’s tech-savvy workforce means having a method for creating value and running quickly. Deliver speed and simplicity to your users while maintaining the highest standards for data quality and security. A centralized analytics platform where IT plays a pivotal role should be a fundamental part of your business analytics strategy. The combination of both business-led and IT-led initiatives is the sweet spot for innovation.

Uncover new opportunities

Advancements in analytics technology are creating new opportunities for you to capitalize on your data. Modern analytics are predictive, self-learning, and adaptive to help you uncover hidden data patterns. They are intuitive as well, incorporating stunning visualizations that enable you to understand millions of rows and columns of data in an instant. Modern business analytics are mobile and easy to work with. And they connect you to the right data at the right time, with little or no training required.

Visualize your data

You want to see the data signals before your competitors do. Analytics provides the ability to see a high-definition image of your business landscape. By mashing up personal, corporate, and big data, you can quickly understand the value of the data, share your data story with colleagues, and do it all in a matter of minutes.

Regenerative Agritech

Regenerative AgriTech is a farming and land management approach that enhances ecosystems and food production sustainability through technology. It prioritises soil health, biodiversity, and carbon sequestration by minimising soil disturbance, using cover crops, crop rotation, and livestock integration. 

This practice emulates natural ecological processes to build resilient and productive agricultural systems, addressing climate change, biodiversity, and food security while preserving the environment. Technology plays a pivotal role in optimising resource management, improving efficiency, and supporting data-driven decision-making for eco-conscious farmers.

Benefits of  Regenerative Agriculture on Farmers

Regenerative agriculture comes as an innovation in farming practices, focusing on the rehabilitation and restoration of the soil used for agriculture. It presents a compelling case for farmers, with the prospects of improved long-term land productivity and resilience against climate change. However, the transition isn't as easy as it sounds. There are challenges that need to be braved, including acquiring new knowledge and skills, as well as potentially investing in new equipment. There is also risk of temporary yield loss and impact on farm-level revenue. However, these are mainly short-term pains that pave the way for long-term gains.

In a study conducted by CSU Chico, it has been found that integrating regenerative agriculture into mainstream farming has the potential to restore the water cycle, which significantly contributes to the overall health and longevity of the soil. This transition also helps in carbon sequestration, actively helping in the fight against the dire impacts of climate change.

Another study from DTNPF also showcases how regenerative agriculture can be beneficial. It found that these practices drastically reduced the input costs involved in farming, while concurrently enhancing the yields. Practices such as recycling, the regular addition of compost or biochar, and cultivating crops and vegetation that capture carbon from the atmosphere are essential components of regenerative agriculture, according to the study. Regenerative farming can reduce pesticide use by 50-100%.

All this data points to a future where agriculture works hand-in-hand with nature, which could lead to increased profitability and sustainability for farmers. The potential rewards, such as healthier soils, increased biodiversity, and reduced emissions from food systems clearly outweigh the challenges in view of many farmers who are making this transition.

We must invest more in regenerative agriculture sooner rather than later. It is essential to look at regenerative agriculture not just as a farming trend, but as an investment in our long-term future. The process might entail initial prospective challenges, but the long-term benefits for our planet are innumerable and the expected returns for the farmers, lucrative.

Main Criticisms of Regenerative Agriculture

Regenerative agriculture, despite potential benefits including improved yields, resilience against climate impacts, and reduced emissions from food systems, comes under scrutiny from critics, primarily bounty its scalability, the inherent risks associated with transition, and debates surrounding its scientific basis.

The scalability critique leans heavily on the contrast between small-scale farms and vast commercial agricultural enterprises. While naysayers acknowledge the benefits regen ag practices bring to small-scale farms, they raise eyebrows when discussions arise of applying regen ag on a broader spectrum or larger scale. The core argument centers around the feasibility of making such a mammoth transition from conventional farming, given the enormous initial investments required and the imminent risk of decrease in predictability and yields, at least initially.

Another point of criticism lies in the risks related to transitioning from conventional methods to regen ag. A transition to regen ag is seen as challenging due to numerous risks including temporary yield losses, lower farm-level revenue, and potential long-term damage to productivity and income. Many critics posit that not all farms can afford the proverbial leap into the dark associated with transitioning to regen ag.

Additionally, critics often point out that smallholder farmers might find the transition especially daunting owing to their limited capacity and resources. These farmers often lack the financial robustness to weather short ebb in the productivity or revenue, heightening the financial risks associated with the transition for them. However, it's important to note that organizations are cropping up that work to help smallholder farmers make the transition more smoothly and affordably.

Furthermore, a lack of consensus on the science behind regen ag also serves as a common point of critique. Detractors suggest there is little empirical, large-scale data available to unequivocally support the touted benefits of regen ag. Despite various studies showing the positive impacts of regen ag on soil health, carbon sequestration, and biodiversity, critics argue these findings are often specific to certain climates or soil types, and not universally applicable.

Investment potential of regen ag tech and public-private partnerships in sustainable agriculture too, come under some scrutiny. While government investment in the U.S. may be smaller than the private market investment in food and ag tech, the relationship and favorability between the two is viewed as crucial for advancing regenerative agricultural practices. Critics argue that the significant amount of private capital required for these ventures may narrow the scope of engagement, exclude marginalized communities, and inadvertently reinforce existing inequalities in access to regen ag tech and resources.

Remote Care

Remote Care is a medical service that allows providers to have access to real-time health evaluations of patients outside the care of a hospital by using remotely connected monitoring and medical devices. It is used in combination with telemedicine or for routine and preventative care by measuring vital signs. The medical staff read the results and decides the appropriate point of care. If health vitals indicate worsening symptoms or irregularities, remote care alerts caretakers to contact the patient and assess for emergency care.

Remote care uses remote patient monitoring devices (RPM) that transmit patients’ vitals to their providers. Devices – like glucose monitors, pressure sensors, internet-connected blood tests, and EKG – are set up to be used in a patient’s home to provide essential diagnostics that make it easier to promote patient medication adherence and routine self-care. Providers can use health data from RPM devices to make critical changes to medication, diet, and exercise routines to improve patients’ overall health conditions. 

Many RPM devices are powered through the Internet of Things, allowing various applications and equipment to exchange data through a connected network. Patient data from digitally connected technologies enables providers to understand symptoms and chronic conditions better, driving better patient experiences and health outcomes.

How does Remote Care work?

Remote care typically has three aspects: a wearable device, a connectivity platform, and a console. Patients wearing wearable devices transmit medical information into a connected platform that interprets this information into diagnostics and insights visualized on a console used by a provider.  

Devices such as smartphones, intelligent pacemakers, and wearables capture vital health information that allows caretakers to have round-the-clock details to help make better treatment plans after significant surgeries or chronic symptom management. Patients can also manually enter health data - like weight, blood pressure, pulse oximetry, and temperature - into a Remote Care app or software on a computer that gets transmitted to a nurse for real-time clinical observations. Patients can make a video call with a nurse after they observe real-time vitals, along with the ability to view historical health data.

Healthcare teams can also optimize remote care with health questionaries taken from an iPhone or tablet, capturing disease-specific qualitative data.

Healthcare providers observe patient data from an internal system, software, or online platform that organizes health insights into informational charts, dashboards, and databases. 

When conducting video consultations, providers receive document notes during the live interaction, instantly updating the information to the patient’s Electronic Health Record (EHR). 

Remote care can benefit healthcare organizations in the following ways: 

• Reduced Costs: Patients manage health vitals under medical supervision from the comfort of their home, opening vital hospital spaces and lowering readmission rates. Information on vitals is automated, reducing the costs of manual efforts. 
• Patient engagement: Patients provide real-time health updates through RPM-connected devices, giving providers accurate health insights for better diagnostics. Patients become active in managing their health, often leading to improved outcomes. 
• Improved access to care: Providers have more time to see patients traditionally underserved or who do not have access to transportation. Scheduling is made easier with routine check-ups enabled by video calls, keeping patients up to date on essential health needs and requirements. Insights from RPM devices can prioritize patients who need medical evaluation, care, and treatment in time-sensitive situations. 
• Better clinical outcomes: When patients use RPM devices to monitor their health at home, it allows for more outstanding education on modifying daily routines that can contribute to better health. Those with chronic or multiple diseases can easily monitor symptoms with real-time updates, allowing them to take charge of a medical issue before it becomes severe. 

Natural Language Processing (NLP)

Natural language processing (NLP) is a machine learning technology that gives computers the ability to interpret, manipulate, and comprehend human language. Organizations today have large volumes of voice and text data from various communication channels like emails, text messages, social media newsfeeds, video, audio, and more. They use NLP software to automatically process this data, analyze the intent or sentiment in the message, and respond in real time to human communication.

Why is NLP important?

Natural language processing (NLP) is critical to fully and efficiently analyze text and speech data. It can work through the differences in dialects, slang, and grammatical irregularities typical in day-to-day conversations.

Companies use it for several automated tasks, such as to:

• Process, analyze, and archive large documents
• Analyze customer feedback or call center recordings
• Run chatbots for automated customer service
• Answer who-what-when-where questions
• Classify and extract text

You can also integrate NLP in customer-facing applications to communicate more effectively with customers. For example, a chatbot analyzes and sorts customer queries, responding automatically to common questions and redirecting complex queries to customer support. This automation helps reduce costs, saves agents from spending time on redundant queries, and improves customer satisfaction.

What is natural language processing used for?

Some of the main functions and NLP tasks that natural language processing algorithms perform include the following:

• Text classification. This function assigns tags to texts to put them in categories. This can be useful for sentiment analysis, which helps the natural language processing algorithm determine the sentiment, or emotion, behind a text. For example, when brand A is mentioned in X number of texts, the algorithm can determine how many of those mentions were positive and how many were negative. It can also be useful for intent detection, which helps predict what the speaker or writer might do based on the text they're producing.
• Text extraction. This function automatically summarizes text and finds important pieces of data. One example of this is keyword extraction, which pulls the most important words from the text, which can be useful for search engine optimization. Doing this with natural language processing requires some programming -- it isn't completely automated. However, there are plenty of simple keyword extraction tools that automate most of the process -- the user just sets parameters within the program. For example, a tool might pull out the most frequently used words in the text. Another example is entity recognition, which extracts the names of people, places and other entities from text.
• Machine translation. In this process, a computer translates text from one language, such as English, to another language, such as French, without human intervention.
• Natural language generation. This process uses natural language processing algorithms to analyze unstructured data and automatically produce content based on that data. One example of this is in language models like the third-generation Generative Pre-trained Transformer (GPT-3), which can analyze unstructured text and then generate believable articles based on that text.

The functions listed above are used in a variety of real-world applications, including the following:

• Customer feedback analysis. Tools using AI can analyze social media reviews and filter out comments and queries for a company.
• Customer service automation. Voice assistants on a customer service phone line can use speech recognition to understand what the customer is saying, so that it can direct their call correctly.
• Automatic translation. Tools such as Google Translate, Bing Translator and Translate Me can translate text, audio and documents into another language.
• Academic research and analysis. Tools using AI can analyze huge amounts of academic material and research papers based on the metadata of the text as well as the text itself.
• Analysis and categorization of healthcare records. AI-based tools can use insights to predict and, ideally, prevent disease.
• Plagiarism detection. Tools such as Copyleaks and Grammarly use AI technology to scan documents and detect text matches and plagiarism.
• Stock forecasting and insights into financial trading. NLP tools can analyze market history and annual reports that contain comprehensive summaries of a company's financial performance.
• Talent recruitment in human resources. Organizations can use AI-based tools to reduce hiring time by automating the candidate sourcing and screening process.
• Automation of routine litigation. AI-powered tools can do research, identify possible issues and summarize cases faster than human attorneys.
• Spam detection. NLP-enabled tools can be used to classify text for language that's often used in spam or phishing attempts. For example, AI-enabled tools can detect bad grammar, misspelled names, urgent calls to action and threatening terms.

Benefits of natural language processing

The main benefit of NLP is that it improves the way humans and computers communicate with each other. The most direct way to manipulate a computer is through code -- the computer's language. Enabling computers to understand human language makes interacting with computers much more intuitive for humans.

Other benefits include the following:

• Offers improved accuracy and efficiency of documentation.
• Enables an organization to use chatbots for customer support.
• Provides an organization with the ability to automatically make a readable summary of a larger, more complex original text.
• Lets organizations analyze structured and unstructured data.
• Enables personal assistants such as Alexa to understand the spoken word.
• Makes it easier for organizations to perform sentiment analysis.
• Organizations can use NLP to better understand lead generation, social media posts, surveys and reviews.
• Provides advanced insights from analytics that were previously unreachable due to data volume.

Bioinformatics

Bioinformatics is an emerging branch of biological science that emerged from the combination of both biology and information technology. It is an interdisciplinary field of study that uses Biology, Chemistry, Mathematics, Statistics, and Computer Science that have merged to form a single discipline. This sector is mainly involved in analyzing biological data, and developing new software using biological tools.

According to the NCBI- National Center for Biotechnology Information, the branch of NLM- National Library of Medicine and NIH- National Institutes of Health, Bioinformatics is defined as the analysis, collection, classification, manipulation, recovery, storage and visualization of all biological information using computation technology.

The term Bioinformatics was first coined in the year 1960 by the two Dutch biologists named Paulien Hogeweg and Ben Hesper. According to their research and discoveries, Bioinformatics was defined as the study of information processes in biotic systems.

Application of Bioinformatics

Bioinformatics is mainly used to extract knowledge from biological data through the development of algorithms and software.

Bioinformatics is widely applied in the examination of Genomics, Proteomics, 3D structure modelling of Proteins, Image analysis, Drug designing and a lot more. A significant application of bioinformatics can be found in the fields of precision and preventive medicines, which are mainly focused on developing measures to prevent, control and cure dreadful infectious diseases.

The main aim of Bioinformatics is to increase the understanding of biological processes.

Listed below are a few applications of Bioinformatics.

• In Gene therapy.
• In Evolutionary studies.
• In Microbial applications.
• In Prediction of Protein Structure.
• For the Storage and Retrieval of Data.
• In the field of medicine, used in the discovery of new drugs.
• In Biometrical Analysis for identification and access control for improvising crop management, crop production and pest control.

Principles of Biotechnology

Modern biotechnology is highly dependent on genetic engineering and bioprocess engineering.

Genetic Engineering

The principle of genetic engineering is to manipulate and modify the genetic material of an organism to incorporate desirable traits. Recombinant DNA technology is the main pillar of genetic engineering.

Recombinant DNA Technology is a technique to alter the genes of an organism. The desired gene is inserted into host using recombinant DNA technology. The host shows the desired trait phenotypically, which is governed by the inserted gene.

The recombinant DNA technology involves the following main steps:

• Selection of the desired gene
• Selection of vector for the transfer of the gene known as a cloning vector, e.g. plasmid
• Insertion of recombinant DNA into the host
• Maintaining the introduced DNA in the host so that it is passed on to the next generation
• Recombinant DNA Technology requires various tools like vector, host and enzymes such as restriction enzymes, ligases, polymerases, etc.

Process

• Restriction enzymes are known as molecular scissors that cut the desired sequence of DNA.
• This DNA is then ligated into the vector with the help of ligases before inserting it into the host organism.
• The DNA-vector combination is known as the Recombinant DNA which is then transformed into the host.
• This recombinant DNA along with the foreign DNA gets multiplied within the host.
• It is then provided with optimum conditions to induce the expression of the target protein. This protein is known as the recombinant protein.
• Many genetically modified crops are produced using this technology, e.g. Bt cotton, a pest-resistant variety of cotton.

Bioprocess Engineering

Modern biotechnology is responsible for the advancement of the pharmaceutical industry. It helped in the production and storage of products like antibiotics, enzymes, vaccines, etc. on a large scale.

A large amount of culture can be obtained by carrying out the multiplication of organisms in the bioreactors under sterile and optimum conditions. We get a higher yield of the required product using bioprocess engineering.

Process

• The host organism containing the rDNA is cultured in a sterile bioreactor by providing suitable growth conditions. The products formed are either released in the growth medium or accumulated inside the cells
• The obtained products are subjected to a series of processes before being marketed.
• The products are purified by a process called downstream processing and formulated by various processes.
• The product undergoes a strict quality check before it is subjected to further trials.
• The modern processes in biotechnology are used for human welfare and have a significant impact on our life. The products have greatly enhanced various medicines and food production. Extensive research is going on in this field to combat various diseases and improve quality of life.

Artificial Intelligence

Artificial intelligence is the simulation of human intelligence processes by machines, especially computer systems. Specific applications of AI include expert systems, natural language processing, speech recognition and machine vision.

How does AI work?

As the hype around AI has accelerated, vendors have been scrambling to promote how their products and services use it. Often, what they refer to as AI is simply a component of the technology, such as machine learning. AI requires a foundation of specialized hardware and software for writing and training machine learning algorithms. No single programming language is synonymous with AI, but Python, R, Java, C++ and Julia have features popular with AI developers.

In general, AI systems work by ingesting large amounts of labeled training data, analyzing the data for correlations and patterns, and using these patterns to make predictions about future states. In this way, a chatbot that is fed examples of text can learn to generate lifelike exchanges with people, or an image recognition tool can learn to identify and describe objects in images by reviewing millions of examples. New, rapidly improving generative AI techniques can create realistic text, images, music and other media.

AI programming focuses on cognitive skills that include the following:

• Learning. This aspect of AI programming focuses on acquiring data and creating rules for how to turn it into actionable information. The rules, which are called algorithms, provide computing devices with step-by-step instructions for how to complete a specific task.
• Reasoning. This aspect of AI programming focuses on choosing the right algorithm to reach a desired outcome.
• Self-correction. This aspect of AI programming is designed to continually fine-tune algorithms and ensure they provide the most accurate results possible.
• Creativity. This aspect of AI uses neural networks, rules-based systems, statistical methods and other AI techniques to generate new images, new text, new music and new ideas.

Types of Artificial Intelligence

• Narrow AI: Also known as Weak AI, this system is designed to carry out one particular job. Weak AI systems include video games like personal assistants like Amazon's Alexa and Apple's Siri. Users ask the assistant a question, and it answers it for you.
• General AI: This type includes strong artificial intelligence systems that carry on the tasks considered to be human-like. They tend to be more complex and complicated and can be found in applications like self-driving cars or hospital operating rooms.

Why is artificial intelligence important?

AI is important for its potential to change how we live, work and play. It has been effectively used in business to automate tasks done by humans, including customer service work, lead generation, fraud detection and quality control. In a number of areas, AI can perform tasks much better than humans. Particularly when it comes to repetitive, detail-oriented tasks, such as analyzing large numbers of legal documents to ensure relevant fields are filled in properly, AI tools often complete jobs quickly and with relatively few errors. Because of the massive data sets it can process, AI can also give enterprises insights into their operations they might not have been aware of. The rapidly expanding population of generative AI tools will be important in fields ranging from education and marketing to product design.

Indeed, advances in AI techniques have not only helped fuel an explosion in efficiency, but opened the door to entirely new business opportunities for some larger enterprises. Prior to the current wave of AI, it would have been hard to imagine using computer software to connect riders to taxis, but Uber has become a Fortune 500 company by doing just that.

AI has become central to many of today's largest and most successful companies, including Alphabet, Apple, Microsoft and Meta, where AI technologies are used to improve operations and outpace competitors. At Alphabet subsidiary Google, for example, AI is central to its search engine, Waymo's self-driving cars and Google Brain, which invented the transformer neural network architecture that underpins the recent breakthroughs in natural language processing.

Advantages of AI

The following are some advantages of AI.

• Good at detail-oriented jobs. AI has proven to be just as good, if not better than doctors at diagnosing certain cancers, including breast cancer and melanoma.
• Reduced time for data-heavy tasks. AI is widely used in data-heavy industries, including banking and securities, pharma and insurance, to reduce the time it takes to analyze big data sets. Financial services, for example, routinely use AI to process loan applications and detect fraud.
• Saves labor and increases productivity. An example here is the use of warehouse automation, which grew during the pandemic and is expected to increase with the integration of AI and machine learning.
• Delivers consistent results. The best AI translation tools deliver high levels of consistency, offering even small businesses the ability to reach customers in their native language.
• Can improve customer satisfaction through personalization. AI can personalize content, messaging, ads, recommendations and websites to individual customers.
• AI-powered virtual agents are always available. AI programs do not need to sleep or take breaks, providing 24/7 service.

Disadvantages of AI

The following are some disadvantages of AI.

• Expensive.
• Requires deep technical expertise.
• Limited supply of qualified workers to build AI tools.
• Reflects the biases of its training data, at scale.
• Lack of ability to generalize from one task to another.
• Eliminates human jobs, increasing unemployment rates.

Machine Learning

Machine learning (ML) is a discipline of artificial intelligence (AI) that provides machines with the ability to automatically learn from data and past experiences while identifying patterns to make predictions with minimal human intervention.

Machine learning methods enable computers to operate autonomously without explicit programming. ML applications are fed with new data, and they can independently learn, grow, develop, and adapt.

Machine learning derives insightful information from large volumes of data by leveraging algorithms to identify patterns and learn in an iterative process. ML algorithms use computation methods to learn directly from data instead of relying on any predetermined equation that may serve as a model.

The performance of ML algorithms adaptively improves with an increase in the number of available samples during the ‘learning’ processes. For example, deep learning is a sub-domain of machine learning that trains computers to imitate natural human traits like learning from examples. It offers better performance parameters than conventional ML algorithms.

While machine learning is not a new concept – dating back to World War II when the Enigma Machine was used – the ability to apply complex mathematical calculations automatically to growing volumes and varieties of available data is a relatively recent development.

Today, with the rise of big data, IoT, and ubiquitous computing, machine learning has become essential for solving problems across numerous areas, such as

• Computational finance (credit scoring, algorithmic trading)
• Computer vision (facial recognition, motion tracking, object detection)
• Computational biology (DNA sequencing, brain tumor detection, drug discovery)
• Automotive, aerospace, and manufacturing (predictive maintenance)
• Natural language processing (voice recognition)

Why is machine learning important?

Machine learning has played a progressively central role in human society since its beginnings in the mid-20th century, when AI pioneers like Walter Pitts, Warren McCulloch, Alan Turing and John von Neumann laid the groundwork for computation. The training of machines to learn from data and improve over time has enabled organizations to automate routine tasks that were previously done by humans -- in principle, freeing us up for more creative and strategic work.

Machine learning also performs manual tasks that are beyond our ability to execute at scale -- for example, processing the huge quantities of data generated today by digital devices. Machine learning's ability to extract patterns and insights from vast data sets has become a competitive differentiator in fields ranging from finance and retail to healthcare and scientific discovery. Many of today's leading companies, including Facebook, Google and Uber, make machine learning a central part of their operations.

As the volume of data generated by modern societies continues to proliferate, machine learning will likely become even more vital to humans and essential to machine intelligence itself. The technology not only helps us make sense of the data we create, but synergistically the abundance of data we create further strengthens ML's data-driven learning capabilities.

What will come of this continuous learning loop? Machine learning is a pathway to artificial intelligence, which in turn fuels advancements in ML that likewise improve AI and progressively blur the boundaries between machine intelligence and human intellect.

Types of Machine Learning

1. Supervised machine learning

This type of ML involves supervision, where machines are trained on labeled datasets and enabled to predict outputs based on the provided training. The labeled dataset specifies that some input and output parameters are already mapped. Hence, the machine is trained with the input and corresponding output. A device is made to predict the outcome using the test dataset in subsequent phases.

For example, consider an input dataset of parrot and crow images. Initially, the machine is trained to understand the pictures, including the parrot and crow’s color, eyes, shape, and size. Post-training, an input picture of a parrot is provided, and the machine is expected to identify the object and predict the output. The trained machine checks for the various features of the object, such as color, eyes, shape, etc., in the input picture, to make a final prediction. This is the process of object identification in supervised machine learning.

The primary objective of the supervised learning technique is to map the input variable (a) with the output variable (b). Supervised machine learning is further classified into two broad categories:

• Classification: These refer to algorithms that address classification problems where the output variable is categorical; for example, yes or no, true or false, male or female, etc. Real-world applications of this category are evident in spam detection and email filtering.

Some known classification algorithms include the Random Forest Algorithm, Decision Tree Algorithm, Logistic Regression Algorithm, and Support Vector Machine Algorithm.

• Regression: Regression algorithms handle regression problems where input and output variables have a linear relationship. These are known to predict continuous output variables. Examples include weather prediction, market trend analysis, etc.

Popular regression algorithms include the Simple Linear Regression Algorithm, Multivariate Regression Algorithm, Decision Tree Algorithm, and Lasso Regression.

2. Unsupervised machine learning

Unsupervised learning refers to a learning technique that’s devoid of supervision. Here, the machine is trained using an unlabeled dataset and is enabled to predict the output without any supervision. An unsupervised learning algorithm aims to group the unsorted dataset based on the input’s similarities, differences, and patterns.

For example, consider an input dataset of images of a fruit-filled container. Here, the images are not known to the machine learning model. When we input the dataset into the ML model, the task of the model is to identify the pattern of objects, such as color, shape, or differences seen in the input images and categorize them. Upon categorization, the machine then predicts the output as it gets tested with a test dataset.

Unsupervised machine learning is further classified into two types:

• Clustering: The clustering technique refers to grouping objects into clusters based on parameters such as similarities or differences between objects. For example, grouping customers by the products they purchase.

Some known clustering algorithms include the K-Means Clustering Algorithm, Mean-Shift Algorithm, DBSCAN Algorithm, Principal Component Analysis, and Independent Component Analysis.

• Association: Association learning refers to identifying typical relations between the variables of a large dataset. It determines the dependency of various data items and maps associated variables. Typical applications include web usage mining and market data analysis.

Popular algorithms obeying association rules include the Apriori Algorithm, Eclat Algorithm, and FP-Growth Algorithm.

3. Semi-supervised learning

Semi-supervised learning comprises characteristics of both supervised and unsupervised machine learning. It uses the combination of labeled and unlabeled datasets to train its algorithms. Using both types of datasets, semi-supervised learning overcomes the drawbacks of the options mentioned above.

Consider an example of a college student. A student learning a concept under a teacher’s supervision in college is termed supervised learning. In unsupervised learning, a student self-learns the same concept at home without a teacher’s guidance. Meanwhile, a student revising the concept after learning under the direction of a teacher in college is a semi-supervised form of learning. 

4. Reinforcement learning

Reinforcement learning is a feedback-based process. Here, the AI component automatically takes stock of its surroundings by the hit & trial method, takes action, learns from experiences, and improves performance. The component is rewarded for each good action and penalized for every wrong move. Thus, the reinforcement learning component aims to maximize the rewards by performing good actions.

Unlike supervised learning, reinforcement learning lacks labeled data, and the agents learn via experiences only. Consider video games. Here, the game specifies the environment, and each move of the reinforcement agent defines its state. The agent is entitled to receive feedback via punishment and rewards, thereby affecting the overall game score. The ultimate goal of the agent is to achieve a high score.

Reinforcement learning is applied across different fields such as game theory, information theory, and multi-agent systems. Reinforcement learning is further divided into two types of methods or algorithms:

• Positive reinforcement learning: This refers to adding a reinforcing stimulus after a specific behavior of the agent, which makes it more likely that the behavior may occur again in the future, e.g., adding a reward after a behavior.
• Negative reinforcement learning: Negative reinforcement learning refers to strengthening a specific behavior that avoids a negative outcome.