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Role of Artificial Intelligence in Financial Analysis

Artificial Intelligence replicates human intelligence in the automated processes that machines perform. Machines require human intelligence to execute actions. These computer processes are data learning-based and can respond, recommend, decide and autocorrect on the basis of interactions.

Financial Analysis is a process of evaluating business and project suitability, the company’s stability, profitability, and performance. It involves professional expertise. It needs a lot of financial data from the company to analyze and predict.

Types of Financial Analysis:

Types of Financial Analysis
  1. Cash Flow: It checks Operating Cash Flow, Free Cash Flow (FCF).
  2. Efficiency: Verify the asset management capabilities of the company via Asset turnover ratio, cash conversion ratio, and inventory turnover ratio.
  3. Growth: Year over year growth rate based on historical data
  4. Horizontal:  It is comparing several years of data to determine the growth rate.
  5. Leverage: Evaluating the company’s performance on the debt/equity ratio
  6. Liquidity: Using the balance sheet it finds net working capital, a current ratio
  7. Profitability: Income statement analysis to find gross and net margins
  8. Rates of Return: Risk to return ratios such as Return on Equity, Return on Assets, and Return on Invested Capital.
  9. Scenario & Sensitivity: Prediction through the worst-case and best-case scenarios
  10. Variance: It compares the actual result to the budget or the forecasts of the company
  11. Vertical Analysis: Income divided by revenues.
  12. Valuation: Cost Approach, Market Approach, or other methods of estimation.

Role of AI in Financial Analysis:

The finance industry is one of the major data collectors, users, and processors. Financial Services sector and its services are specialized and have to be precise.

Finance organizations include entities such as retail and commercial banks, accountancy firms, investment firms, loan associations, credit unions, credit-card companies, insurance companies, and mortgage companies.

Artificial intelligence can teach machines to perform these calculations and analysis just as humans do. We can train machines, the frequency of financial analysis can be set, and accessibly to reports has no time restrictions.

How AI is implemented in Financial Analysis?

AI implementation in Financial Analysis

Artificial intelligence adopted by Financial Services is changing the customer expectation and directly influences the productivity of this sector.

Implementation of Artificial intelligence in the Finance Sector:

  • Automation
  • To streamline processes
  • Big data processing
  • Matching data from records
  • Calculations and reports
  • Interpretations and expectations
  • Provide personalized information

Challenges these financial institutions face in implementing AI is the number of trained data scientists, data privacy, availability, and usability of data.

Quality data helps in planning and budgeting of automation, standardizing processes, establishing correlation. Natural language processing –NLP used in AI is quite a communicator still with over 100 languages spoken in India and 6500 languages across the globe, the development of interactive sets is challenging.

Add Virtual assistants/ Chatbots to the website, online portals, mobile applications and your page on the social media platform. Chatbots can indulge in basic level conversations, reply FAQs, and even connect the customer to a live agent. Machine Learning technology lowers costs of customer service, operations, and compliance costs of financial service providers. AI provides input to the financial analysts for in-depth analysis.

Advantages of AI in Financial Analysis

Advantages of Artificial Intelligence in Financial Analysis:

  1. Mining Big Data: AI uses Big data to improve operational activities, investigation, research, and decision-making. It can search for people interested in financial services and other latest finance products launched in the market.
  2. Risk Assessment: AI can assess investment risks, low-profit risks, and risks of low returns. It can study and predict the volatility of prices, trading patterns, and relative costs of services in the market.
  3. Improved Customer Service: Catering customers with their preset preferences is possible with virtual assistants. Artificial Intelligence understands requests raised by customers and is able to serve them better.
  4. Creditworthiness & Lending: AI helps to process the loan applications, highlights risks associated, crosscheck the authenticity of the applicant’s information, their outstanding debts, etc.
  5. Fraud Prevention: Systems using Artificial Intelligence systems can monitor, detect, trace, and interrupt the identified irregularities. It can identify any transaction involving funds, account access, and usage all that indicate fraud. This is possible with the data processing it does on the historic data, access from new IPs, repetitive errors or doubtful activities and activations.
  6. Cost Reduction: AI can reduce costs of financial services and reduce human efforts, lessens the requirement of resources, and adds to accuracy in mundane tasks. Sales conversion is faster due to the high response rate and saves new customer acquisition costs. Maximizing resources can save time and improve customer service, sales, and performance.
  7. Compliances: Financial data is personal hence, data security, and privacy-related compliances based on norms, rules, and regulations of that region being met. While companies use and publish data, General Data Protection Regulation (GDPR) laws protect individuals and abide by companies to seek permission before they store user data.
  8. Customer Engagement: Recommendations and personalized financial services by AI can meet unique demands and optimize offerings. It can suggest the investment plans considering existing savings, investment choices, habits, and other behavioral patterns, returns expected in percentage as well as in long term or short term, future goals.
  9. Creating Finance Products: AI can help finance industry to create intelligent products from learning’s from the financial datasets. Approaching existing clients for new products or acquiring new is faster with AI technology.
  10. Filtering information: AI helps faster search from a wide range of sources. Search finance services, products, credit-scores of individuals, ratings of companies and anything you need to improve service.
  11. Automation: Accuracy is crucial in the finance sector and while providing financial services. Human decisions are prone to influence of situations, emotions, and personal preferences but AI can follow the process without falling into any loopholes. It can understand faster and convey incisively. Automation of processes can improve with face recognition, image recognition, document scanning, and authentication of digital documents, confirmation of KYC documents, and other background checks; necessary for selective finance services.
  12. Assistance: Text, image and speech assistance helps customers to ask questions, get information, and download or upload documents, connect with company representatives, carry out financial transactions and set notifications.
  13. Actionable items: Based on the financial analysis the insights generated to provide a competitive advantage to the company. A large customer base and its complex data are simplified by AI and send information to the concerned department for scheduling actions. These insights are gathered from all modes of online presence i.e. Website, social media, etc.
  14. Enhanced Performance: Business acceleration, increase in productivity and performance is a result of addition to the AI knowledge base. The overall use of AI technology is adding to opportunities in the finance sector.

Companies utilizing Artificial Intelligence in Financial Analysis:

  1. Niki.ai: This company has worked on various chatbot projects e.g. HDFC bank FB chat provides banking services and attracts additional sales. It created a smartphone application for Federal Bank. Niki the chatbot can guide the customers looking for financial services, e-commerce and retail business with its recommendations. It can assist in end-to-end online transactions for online hotel and cab, flight or ticket booking.
  2. Rubique:  It is a lender and applicant matchmaking platform. The credit requirements of applicants are studied before recommendation from this AI-based platform. It has features like e-KYC, bank statement analysis, credit bureau check, generating credit memo & MCA integration. It can track applications in real-time and help to speed up the process.
  3. Fluid AI: It is committed to solving unique and big problems of finance, marketing, government and some other sectors using the power of artificial. It provides a highly accurate facial recognition service that enhances security.
  4. LendingKart: This platform serves by tackling the process of loans to small businesses and has reached over 1300 cities. LendingKart developed technology tools based on big data analysis to evaluate borrower’s creditworthiness irrespective of flaws in the cash flow or past records of the vendor.
  5. ZestFinance: It provides AI-powered underwriting solutions to help companies and financial institutions, find information of borrowers whose credit information is less and difficult to find.
  6. DataRobot: It has a machine learning software designed for data scientists, business analysts, software engineers, and other IT professionals. DataRobot helps financial institutions to build accurate predictive models to address decision-making issues for lending, direct marketing, and fraudulent credit card transactions.
  7. Abe AI: This virtual financial assistant integrates with Amazon Alexa, Google Home, Facebook, SMS, web, and mobile to provide customers convenience in banking. Abe released a smart financial chatbot that helps users with budgeting, defining savings goals and tracking expenses.
  8. Kensho: The company provides data analytics services to major financial institutions such as Bank of America, J.P. Morgan, Morgan Stanley, and S&P Global. It combines the power of cloud computing, and NLP to respond to the complex financial questions.
  9. Trim: It assists customers in rising saving by analyzing their spending habits. It can highlight and cancel money-wasting subscriptions, find better options for insurance and other utilities, the best part is it can negotiate bills.
  10. Darktrace: It creates cybersecurity solutions for various industries by analyzing network data. The probability-based calculations can detect suspicious activities in real-time, this can prevent damage and losses of financial firms. It can protect companies and customers from cyber-attacks.

Conclusion:

The future of Artificial Intelligence in Financial Analysis is dependent on continuous learning of patterns, data interpretation, and providing unique services. Financial Analysis and Artificial Intelligence have introduced new management styles, methods of approaching and connecting with customers for financial services. The considerations of choices increase the comfort level of customers and sales. Organizations become data-driven and it helps them to launch, improve, and transform applications.

The insights, accuracy, efficiency, predictions, and stability have created a positive impact on the finance sector.

Understanding What Is Conversational AI

For the last couple of hundred years, the total of what correspondence has been verbal, composed, or visual. We talked with our mouths, hands, and utilizing different mediums like braille or a PC. Discussions, specifically, required two distinct things.

Various people and an approach to impart. Things have since taken a noteworthy improvement. We have now opened better approaches to discuss legitimately with our innovation in a conversational setting utilizing a conversational chatbot.

Conversational AI alludes to the utilization of informing applications, discourse-based collaborators and chatbots to computerize correspondence and make customized client encounters at scale. Countless individuals use Facebook Messenger, Kik, WhatsApp and other informing stages to speak with their loved ones consistently. Millions more are exploring different avenues regarding discourse-based colleagues like Amazon Alexa and Google Home.

Applications of Conversational AI

Accordingly, informing and discourse-based stages are quickly uprooting conventional web and portable applications to turn into the new vehicle for intuitive discussions. At the point when joined with robotization and man-made reasoning (AI), these associations can interface people and machines through menial helpers and chatbots.

However, the genuine intensity of conversational AI lies in its capacity to all the while complete exceptionally customized connections with huge quantities of individual clients. Conversational AI can on a very basic level change an association, furnishing more methods for speaking with clients while encouraging more grounded communications and more noteworthy commitment.

Human-made consciousness is a term we’ve started to turn out to be exceptionally acquainted with. When covered inside your most-loved science fiction motion picture, AI is currently a genuine, living, powerhouse of its own.

Conversational AI is in charge of the rationale behind the bots you manufacture. It’s the cerebrum and soul of the chatbot. It’s what enables the bot to carry your clients to a particular objective. Without conversational AI, your bot is only a lot of inquiries and answers.

Conversational AI

Few Examples Of Conversational AI

Facebook Messenger

Facebook has bounced completely on the conversational trade temporary fad and is wagering enormous that it can transform its mainstream Messenger application into a business informing powerhouse.

The organization originally incorporated shared installments into Messenger in 2015, and after that propelled a full chatbot API so organizations can make cooperations for clients to happen inside the Facebook Messenger application. You can request blooms from 1–800-Flowers, peruse the most stylish trend and make buys from Spring, and request an Uber, all from inside a Messenger talk.

Operator

Administrator considers itself a “demand organize” expecting to “open the 90% of business that is not on the web.” The Operator application, created by Uber fellow benefactor Garrett Camp, interfaces you with a system of “administrators” who act like attendants who can execute any shopping-related solicitation.

You can request show passes, get blessing thoughts, or even get inside plan proposals for new furnishings. Administrator is by all accounts situating itself towards “high thought” buys, greater ticket buys requiring more research and skill, where its administrators can increase the value of an exchange.

Administrator’s specialists are a blend of Operator workers, in-store reps, and brand reps. The organization is additionally creating man-made consciousness to help the course ask for. Almost certainly the administration will wind up more astute after some time, joining AI for productivity and human mastery for quality suggestions.

Amazon Echo

Amazon’s Echo gadget has been an unexpected hit, coming to over 3M units sold in under a year and a half. Albeit some portion of this achievement can be ascribed to the gigantic mindfulness building intensity of the Amazon.com landing page, the gadget gets positive surveys from clients and specialists the same and has even incited Google to build up its own adaptation of a similar gadget, Google Home.

What does the Echo have to do with conversational business? While the most widely recognized utilization of the gadget incorporates playing music, making educational inquiries, and controlling home gadgets, Alexa (the gadget’s default addressable name) can likewise take advantage of Amazon’s full item inventory just as your request history and brilliantly complete directions to purchase stuff. You can re-request normally requested things, or even have Alexa walk you through certain alternatives in buying something you’ve never requested.

Snapchat Discover + Snapcash

Brands are falling over themselves to connect to Snapchat, and the ultra-well known informing application among youngsters and Millennials has as of late been offering some enticing sign that it will end up being a considerably all the more convincing internet business stage sooner rather than later.

In 2015, Snapchat propelled Snapcash, a virtual wallet which enables clients to store their charge card on Snapchat and send cash between companions with a basic message.

While this was a restricted test, it demonstrates that Snapchat sees potential in empowering direct trade (likely satisfied through Snapcash installments) inside the Snapchat application, opening the entryway to many fascinating better approaches to brands to interface and offer items to Snapchatters.

AppleTV and Siri

With a year ago’s invigorate of AppleTV, Apple brought its Siri voice partner to the focal point of the UI. You would now be able to ask Siri to play your preferred TV appears, check the climate, look for and purchase explicit kinds of motion pictures, and an assortment of other explicit errands.

Albeit a long ways behind Amazon’s Echo as far as expansiveness of usefulness, Apple will no uncertainty grow Siri’s joining into AppleTV, and its reasonable that the organization will present another adaptation of AppleTV that all the more legitimately contends with the Echo, maybe with a voice remote control that is continually tuning in for directions.

Businesses and conversational AI

Organizations can utilize Conversational AI to robotize clients confronting touchpoints all over – via web-based networking media stages like Facebook and Twitter, on their site, their application or even on voice aides like Google Home. Conversational AI frameworks offer an increasingly clear and direct pipeline for clients sort issues out, address concerns and arrive at objectives.

Both the terms ‘Chatbot‘ and ‘Conversational AI’ have a similar significance.

How It Works To Engage Customers

1) It’s convenient, all day, every day

The greatest advantage of having a conversational AI arrangement is the moment reaction rate. Noting inquiries inside an hour means 7X greater probability of changing over a lead. Clients are bound to discuss a negative encounter than a positive one. So stopping a negative survey directly from developing in any way is going to help improve your item’s image standing.

2) Customers incline toward informing

The market shapes client conduct. Gartner anticipated that ‘40% of versatile collaborations will be overseen by shrewd specialists by 2020. ’ Every single business out there today either has a chatbot as of now or is thinking about one. 30% of clients hope to see a live visit alternative on your site. 3 out of 10 shoppers would surrender telephone calls to utilize informing. As an ever-increasing number of clients start anticipating that your organization should have an immediate method to get in touch with you, it bodes well to have a touchpoint on a detachment.

3) It’s connecting with and conversational

We’ve just lauded the advantages of having a direct hotline for clients to contact you. Be that as it may, the conversational angle is the thing that separates this strategy from some other.

Chatbots make for incredible commitment devices. Commitment drives tenacity, which drives retention — and that, thus, drives development.

4) Scalability: Infinite

Chatbots can quickly and effectively handle an enormous volume of client questions without requiring any expansion in group size. This is particularly helpful on the off chance that you expect or abruptly observe a huge spike in client questions. A spike like this is a catastrophe waiting to happen in case you’re totally subject to a little group of human operators.

How Businesses Can Use Conversational AI

Your business is speaking with a client for the duration of the time they’re utilizing your item. As far as we can tell conveying conversational AI answers for undertakings, we’ve seen that some utilization cases can use such innovation superior to other people.

Our rundown of the best performing use cases is underneath:

  • Ushering a client in (Lead Generation): Haptik’s Lead Bots have seen 10Xbetter change rates contrasted with standard web structures.
  • Answer questions and handle grumblings when they come in (Customer Support): Gartner predicts that by 2021, 25% of endeavors over the globe will have a remote helper to deal with help issues.
  • Keeping current clients glad (Customer Engagement): Our customers have seen a 65% expansion in degrees of consistency essentially by stopping an intuitive utility chatbot inside their application.
  • Learning from clients to improve your item after some time (Feedback and Insights): Customers are 3X bound to impart their input to a Bot than fill study structures.

Organizations are no special case to this standard, as an ever-increasing number of clients presently expect and incline toward talk as the essential method of correspondence, it bodes well to use the numerous advantages Conversational AI offers. It’s not only for the client, but your business can also decrease operational expenses and scale tasks hugely as well.

By guaranteeing that you’re accessible to tune in and converse with your client whenever of the day, Conversational AI guarantees that your business consistently wins good grades for commitment and availability. So, Conversational AI works all over the place.

Any business in any space that has a client touchpoint can utilize a Conversational virtual specialist. It’s better for clients and for the business. Nothing else matters.

10 common challenges in building high-quality ai training data

Artificial Intelligence is a wonderful computer science that creates intelligent machines to interact with humans. These machines play an analytical role in learning, planning as well as problem-solving. The technical and specialized aspects that AI data covers, can give an advantage over the conceptual designs.

AI was founded in the year 1956, motivated the transfer of human intelligence to machines that can work on specified goals. This led to the development of 3 types of artificial intelligence.

Types of AI

  1. Artificial Narrow Intelligence – ANI 
  2. Artificial General Intelligence – AGI 
  3. Artificial Super Intelligence – ASI 

Speech recognition and voice assistants are ANI, general-purpose tasks handled the way a human would is AGI while ASI is powerful than human intelligence. 

Why AI is Important?

AI performs the frequent and high-volume tasks with precision and the same level of efficiency every time. It adds capabilities to the existing products. This technology revolves around large data sets to perform faster and better.

The science and engineering of making intelligent machines is flourishing on technology. 

The ultimate aim is to make computer programs that can conveniently solve problems with the same ease as humans do. 

According to Market and Markets, the global autonomous data platform is predicted to become a USD 2,210 billion industry and AI market size to reach USD 2,800 million by the year 2024. The data analysis, storage, and management market in life sciences are projected to reach USD 41.1 billion by the year 2024.

The growth of artificial intelligence is due to ongoing research activities in the field. 

AI Models: The top 10 AI models based on their algorithms understand and solve the problems. 

  1. Linear regression
  2. Logistic regression
  3. Linear Discriminant Analysis – LDA
  4. Decision Trees
  5. Naive Bayes
  6. K-Nearest Neighbors
  7. Learning Vector Quantization – LVQ
  8. Support Vector Machines
  9. Bagging & Random Forest
  10. Deep Neural Networks

AI can accustom to gradually developing learning algorithms that let the data do the programming. The right model can classify and predict data. AI can find and define structures and identify regularities in data to help the algorithm acquire new skills. The models can adapt to the new data fed during training. It can use new techniques when the suggested solutions are not satisfactory and the user demands more solutions.

AI-powered models help in development and advancements that cater to the business requirements. The selection of a model depends on parameters that affect the solutions you are about to design. These models can enhance business operations and improve existing business processes.

AI models help in resourcefully delivering innovative solutions.  

AI Training Data

Human intelligence is achievable by assembling vast knowledge with facts and establishing data relations.

According to the survey of dataconomy, nearly 81% of 225 data scientists found the process of AI training difficult than expected even with the data they had. Around 76% were struggling to label and interpret the training data.

We require a lot of data to train deep learning models as they learn directly from the data. Accuracy of output and analysis depends on the input of adequate data.

AI training data

AI can achieve an unbelievable level of accuracy through training data. It is an integral part based on which the accurate results or predictions are projected.

Data can improve the interactions of machines with humans. Healthcare-related activities are dependent on data accuracy. The AI techniques can improve the routine medical checks, image classification or object recognition that otherwise would have required humans to accompany the machines.

AI data is the intellectual property that has high value and weight for the algorithms to begin self-learning. Ultimately, the solutions to queries are lying somewhere in the data, AI finds them for you, and helps in interpreting the application data. Data can give a competitive advantage over other industry players even when similar AI models and techniques are used the winner will be best and accurate data. 

Industries that need AI training data

  • Automotive: AI can improve productivity and help in decision making for vehicle manufacturing.
  • Agriculture: AI can track every stage of agriculture from seeding to final production.
  • Banking & Financial Services: AI facilitates financial transactions, investments, and taxation services.
  • FMCG: AI can keep the customers informed of the latest FMCG products and their offers.
  • Energy: AI can forecast in renewable energy generation, making it more affordable and reliable.
  • Education: Using AI technology and the student data helps the universities to communicate for the exams, syllabus, results and suggesting other courses. 
  • Healthcare: AI eases patient care, laboratory, and testing activities, as well as report generation after analyzing the complex data.

(Read here: 9 Ways AI is Transforming Healthcare Industry)

  • Industrial Manufacturing: The procedural precautions in manufacturing and the standardization is what AI can deliver.
  • Information Technology: AI can detect the security threat and the data they have can prepare companies in advance for the threat.
  • Insurance: AI bridges the gaps in insurance renewals and benefits the customers and companies both.
  • Media & Entertainment: AI can initiate notifications relating to the news and entertainment as per the data preferences stored.
  • Sales & Marketing: AI can smoothen and automate the process of ordering or promoting the products.
  • Telecom: AI can personalize recommendations about telecom services.
  • Travel: AI can facilitate travel decisions, booking tickets and check-in at airports.
  • Transport & Warehousing: AI can track, notify, and crosscheck the in transit and warehousing details.
  • Retail: AI can remind the frequent buyers of the list of products to the customers who prefer to buy from retail outlets.
  • Pharmaceuticals: The medicine formulation and new inventions are where AI can be helpful.

All functions in the industry’s improvement are possible only based on historic and ground-level data. The data dependency can add to challenges as the relational database and its implementation only make AI effective. AI training data is useful to companies; for automation of customer care, production, and operational activities. AI technology helps in cost reduction once implemented.

Read here: 8 Industries AI is transforming

Common AI Training Data Challenges

AI is programmed to perform selective tasks, assigning new tasks can be challenging. The limited experience and data can create obstacles in training the machines for new and creative methods of using the accumulated data. The costs of implementing AI technology are higher restricting many from using it. Machines are likely to replace human jobs but on the other hand, we can expect quality work assigned to humans. Ultimately the induced thought process cannot replace what humans can do hence the machine cannot innovatively perform tasks.

AI can take immediate actions but the accuracy is related directly to the quality of data stored. If the algorithms suit the type of task you want the machines to perform, the results will be satisfactory else, dissatisfaction will mount.

Ten most common challenges companies face in AI training data:

  1. Volumes of Data: Repetitive learning is possible with the use of existing data, which means that a lot of data, is required for training. 
  2. Data Presentation: The computational intelligence, statistical insights, processing, and presentation of data are of utmost importance for establishing a relationship with data. Limited data and faulty presentation can interrupt the predictive analysis for which AI data is built.
  3. Proper use of Data: Automation based on the data, the base that improves many technologies. This data is useful in creating conversational platforms, bots, and smart machines.
  4. Variety of Data: AI needs data that is comprehensive to perform automated tasks. Data from computer science, engineering, healthcare, psychology, philosophy, mathematics, finance, food industry, manufacturing, linguistics, and many more areas are useful.
  5. AI Mechanics: We need to understand the mechanisms of artificial intelligence to generate, collect, and process data; for the computational procedures, we want to handle smartly. 
  6. Data Accuracy: Data itself is a challenge especially if erroneous, biased, or insufficient. Even unusable formats of data, improper labeling of data or the tools used in data labeling can affect the accuracy. Data collected vary in formats and quality as collected from diverse sources such as e-mails, data-entry forms, surveys, or company website. Consider the pre-processing requisites for bringing all the attributes to proper structures for making data usable. 
  7. Additional Efforts on Data: Nearly 63% of enterprises have to build automation technology for labeling and annotation. Data integration requires extra attention even before we start labeling.
  8. Data Costs: Data generation for AI is costly but implementing it in projects can result in cost reduction. Missing links of data can add to the costs of data correction. The initial investment is huge hence; the process and strategies require proper planning and implementation.
  9. Procuring Data: Obtaining large data sets requires a lot of effort for companies. Other than that de-duplication, removing inconsistencies are some of the major and time-consuming activities. Transferring the learning from one set of data to another is not simple. The practical use of AI data in training is complex than it looks due to a variety of data sets on industries.
  10. Data Permissions: Personal data, if collected without permission, can create legal issues. Data theft and identity theft are some allegations, which no company would like to face. Choose the right data for representing that criteria or population. 

With a lack of training data or quality issues, can stall AI projects or be the principal reason for project failure. AI technology is reliable but the human capabilities are restricted with the dependencies they create. 

Read here: 7 Best Practices for creating High-quality Training Data

Another viewpoint is something humans already know cannot be erased. With the help of AI technology, enhance the speed, and accuracy of tasks. Human has superiority in terms of thinking, getting the tasks done and even automating them with AI. Human life is precious and in risky situations, while experimenting, the AI machines are worth considering.

Like all the technologies, AI comes with its own set of pros and cons and we need to adapt it wisely.

9 ways artificial intelligence is transforming healthcare

Man-made brainpower (artificial intelligence) is the recreation of human knowledge forms by machines, particularly PC frameworks. These procedures incorporate learning (the procurement of data and guidelines for utilizing the data), thinking (utilizing principles to arrive at inexact or unmistakable resolutions) and self-remedy. 

AI systems in medicinal services are the utilization of complex calculations and programming to evaluate human perception in the examination of muddled restorative information. In particular, AI is the capacity for PC calculations to rough ends without direct human info. What recognizes AI innovation from conventional advancements in medicinal services is the capacity to pick up data, process it and give a well-characterized yield to the end-client. Computer-based intelligence does this through AI calculations. 

The essential point of wellbeing related AI applications is to investigate connections between counteractive action or treatment strategies and patient results. Artificial intelligence projects have been created and connected to practices, for example, analysis forms, treatment convention advancement, tranquilize improvement, customized prescription, and patient checking and care.

HISTORY OF HEALTHCARE

The historical backdrop of drugs demonstrates how social orders have changed in their way to deal with ailment and sickness from antiquated occasions to the present. The Indians are said to have presented the ideas of therapeutic finding, forecast, and propelled restorative morals. In the Middle Ages, careful practices acquired from the antiquated bosses were improved and after that systematized in Rogerius’ The Practice of Surgery. Colleges started orderly preparing doctors around 1220 CE in Italy. 

The innovation of the magnifying instrument was an outcome of improved comprehension. Preceding the nineteenth century, humorist was thought to clarify the reason for illness yet it was bit-by-bit supplanted by the germ hypothesis of ailment, prompting successful medicines and even solutions for some irresistible infections. General wellbeing measures were grown particularly in the nineteenth century as the quick development of urban areas required orderly sterile measures. Propelled research focuses opened in the mid-twentieth century, regularly associated with real emergency clinics. The mid-twentieth century was described by new organic medicines, for example, anti-infection agents. These headways, alongside improvements in science, hereditary qualities, and radiography prompted present-day prescription. The drug was intensely professionalized in the twentieth century.

 AI AND HEALTHCARE

The intensity of Artificial Intelligence is reverberating crosswise over numerous enterprises. Be that as it may, its effect on social insurance is genuinely extraordinary. With its capacity to mirror human psychological capacities, AI systems are bringing a change in outlook in the social insurance industry. 

This transformative innovation is reforming the wellbeing parts from numerous points of view. From medication advancement to clinical research, AI has improved patient results at decreased expenses, by the use of AI data training. Furthermore, the presentation of this innovation in social insurance guarantees simple access, reasonableness, and adequacy.

Research

Medication research and disclosure is one of the later applications for AI in social insurance. By guiding the most recent advances in AI to streamline the medication disclosure and medication repurposing forms there is the possibility to fundamentally slice both an opportunity to advertise for new medications and their expenses. Research has always been an integral part of AI and healthcare.

Training

Man-made intelligence permits those in preparing to experience naturalistic reproductions such that basic PC driven calculations can’t. The coming of common discourse and the capacity of an AI PC to draw immediately on an enormous database of situations, implies the reaction to questions, choices or guidance from a learner can challenge such that a human can’t. What’s more, the preparation program can gain from past reactions from the learner, implying that the difficulties can be ceaselessly changed to meet their adapting needs. 

Furthermore, preparing should be possible anyplace, with the intensity of AI inserted on a cell phone, fast get up to speed sessions, after a precarious case in a center or while voyaging, will be conceivable.

Individual Health Virtual Assistant 

In the present time, a great many people approach a cell phone. They are probably going to have their menial helper on their cell phones. Propelled AI calculations control associates like Cortana, Google Assistant, Siri. At the point when joined with human services applications, they will give a huge incentive to the clients. 

Human services applications will go about as an individual wellbeing partner. They will likewise be utilized to give drug alarms, and human-like associations will likewise be conceivable. Man-made intelligence as an individual aide will likewise help in helping the patients when the clinical staff isn’t accessible. 

Diagnosis 

With the presentation of AI systems in the restorative field, diagnosing sicknesses has turned into significantly simpler. Gone are those occasions when specialists needed to arrange a few sweeps to discover where a knot was or if that is even a lump. AI applications with imaging and diagnosing methods help in keeping away from mistakes that people are inclined to submitting. Man-made intelligence frameworks can discover issues by simply taking a gander at the outputs. 

Likewise, AI programs for use in cardiology and radiology have been created. These frameworks can recognize malignant growth cells in beginning periods and can keep the sickness from spreading. The same goes for heart assaults – the AI framework grew so far can investigate the examined pictures and discover issues with the report. However, the presentation of AI will tackle these sorts of issues and will keep blunders from occurring in any case.

Treatment

Past checking wellbeing records to enable suppliers to recognize incessantly sick people who might be in danger of an unfavorable scene, artificial intelligence can enable clinicians to adopt an increasingly extensive strategy for infection the board, better arrange care plans and help patients to more readily oversee and agree to their long haul treatment programs. 

Robots have been utilized in medicine for over 30 years. They go from straightforward research center robots to profoundly complex careful robots that can either help a human specialist or execute tasks without anyone else. Notwithstanding medical procedure, they’re utilized in emergency clinics and labs for dreary assignments, in recovery, active recuperation and on the side of those with long haul conditions. 

Virtual Nursing Assistants

Consider virtual nursing assistants like an Alexa for your medical clinic bedside. These menial helpers duplicate the run of the mill conduct of an attendant by helping patients with their everyday schedules, reminding them to take meds or go to arrangements, helping answer restorative inquiries and then some. The virtual systems alone are responsible for cutting as much as $20 billion in expenses. 

End life care

We are living longer than past ages, and as we approach the part of the arrangement, we are biting the dust more alternately and slowly, from conditions like dementia, heart disappointment, and osteoporosis. It is additionally a period of life that is regularly tormented by dejection. 

Robots can possibly reform part of the bargain, helping individuals to stay autonomous for more, diminishing the requirement for hospitalization and care homes. Artificial intelligence joined with the headways in a humanoid configuration is empowering robots to go much further and have ‘discussions’ and other social connections with individuals to continue maturing minds sharp.

Radiology

The forte that has picked up the best consideration in the field of Radiology. A capacity to decipher imaging results may help clinicians in recognizing a moment change in a picture that a clinician may inadvertently miss. An examination at Stanford made a calculation that could distinguish pneumonia at that particular site, in those patients required, with a superior normal F1 metric (a measurable measurement dependent on exactness and review), then the radiologists associated with that preliminary. The radiology gathering Radiological Society of North America has executed introductions on AI in imaging during its yearly gathering. The rise of AI training data in radiology is seen as a risk by certain masters, as the innovation can accomplish upgrades in certain factual measurements in confined cases, instead of pros. 

Growing Care to Developing Nations 

With an expansion in the utilization of AI systems, more care may wind up accessible to those in creating countries. Man-made intelligence keeps on growing in its capacities and as it can decipher radiology, it might most likely determine more individuals to have the requirement for fewer specialists as there is a lack in a large number of these nations. The objective of AI is to show others on the planet, which will at that point lead to improved treatment, and in the long run more prominent worldwide wellbeing. Utilizing artificial intelligence in creating countries that don’t have the assets will decrease the requirement for re-appropriating and can utilize AI training data to improve patient consideration. For instance, Natural language preparing, and AI are being utilized for directing malignancy medicines in spots, for example, Thailand, China, and India. Scientists prepared an AI application to utilize NLP to mine through patient records, and give treatment. A definitive choice made by the AI application concurred with master choices 90% of the time

These are a portion of the extraordinary things that artificial intelligence can do. Be that as it may, it isn’t constrained to that. The medicinal services industry could be made a beeline for one more cutting edge makeover (even as it keeps on adjusting to the appearance of electronic wellbeing records frameworks and other social insurance IT items) as man-made brainpower (AI) improves. Could AI applications become the new ordinary crosswise over basically every part of the human services industry? Numerous specialists trust it is inescapable and coming sooner than you may expect. As advancement pushes the limits of social insurance, better answers for spare time, cash, and proficiency will be conceivable.

what is content moderation and why companies need it

Content Moderation refers to the practice of flagging user-generated submissions based on a set of guidelines in order to determine whether the submission can be used or not in the related media.  These rules decide what’s acceptable and what isn’t to promote the generation of content that falls within its conditions. This process represents the importance of curbing the output of inappropriate content which could harm the involved viewers. Unacceptable content is always removed based on their offensiveness, inappropriateness, or their lack of usability.

Why do we need content moderation?

In an era in which information online has the potential to cause havoc and influence young minds, there is a need to moderate the content which can be accessed by people belonging to a range of age-groups. For example, online communities which are commonly used by children need to be constantly monitored for suspicious and dangerous activities such as bullying, sexual grooming behavior, abusive language, etc. When content isn’t moderated carefully and effectively, the risk of the platform turning into a breeding ground for the content which falls outside the community’s guidelines increases.

Content moderation comes with a lot of benefits such as:

  • Protection of the brand and its users
    Having a team of content moderators allows the brand’s reputation to remain intact even if users upload undesirable content. It also protects the users from being the victims of content which could be termed abusive or inappropriate.
  • Understanding of viewers/users
    Pattern recognition is a common advantage of content moderation. This can be used by the content moderators to understand the type of users which access the platform they are governing. Promotions can be planned accordingly and marketing campaigns can be created based on such recognizable patterns and statistics.
  • Increase of traffic and search engine rankings
    Content generated by the community can help to fuel traffic because users would use other internet media to direct their potential audience to their online content. When such content is moderated, it attracts more traffic because it allows users to understand the type of content which they can expect on the platform/website. This can provide a big boost to the platform’s influence over internet users. Also, search engines thrive on this because of increased user interaction.

How do content moderation systems work?

Content moderation can work in a variety of methods and each of them holds their pros and cons. Based on the characteristics of the community, the content can be moderated in the following ways:

Pre-moderation

In this type of moderation, the users first upload their content after which a screening process takes place. Only once the content passes the platform’s guidelines is it allowed to be made public. This method allows the final public upload to be free from anything that’s undesirable or which could be deemed offensive by a majority of viewers.

The problem with pre-moderation is the fact that users could be left unsatisfied because it delays their content from going public. Another disadvantage is the high cost of operation involved in maintaining a team of moderators dedicated to ensuring top quality public content. If the number of user submissions increases, the workload of the moderators also increases and that could stall a significant portion of the content from going public.

If the quality of the content cannot be compromised under any circumstances, this method of moderation is extremely effective.

Post-moderation

This moderation technique is extremely useful when instant uploading and a quicker pace of public content generation is important. Content by the user will be displayed on the platform immediately after it is created, but it would still be screened by a content moderator after which it would either be allowed to remain or removed.

This method has the advantage of promoting real-time content and active conversations. Most people prefer their content online as soon as possible and post moderation allows this. In addition to this, any content which is inconsistent with the guidelines can be removed in a timely manner.

The flaws and disadvantages of this method include legal obligations of the website operator and difficulties for moderators to keep up with all the user content which has been uploaded. The number of views a piece of content receives can have an impact on the platform and if the content strays away from the platform’s guidelines, it can prove to be costly. Considering the fact that such hurdles exist, the content moderation and review process should be completed within a quick time slot.

Reactive moderation

In this case, users get to flag and react to the content which is displayed to them. If the members deem the content to be offensive or undesirable, they can react accordingly to it. This makes the members of the community responsible for reporting the content which they come across. A report button is usually present next to any public piece of content and users can use this option to flag anything which falls outside the community’s guidelines.

This system is extremely effective when it aids a pre-moderation or a post-moderation setup. It allows the platform to identify inappropriate content which the community moderators might’ve missed out on. It also reduces the burden on community moderators and theoretically, it allows the platform to dodge any claims of their responsibility for the user-uploaded content.

On the other hand, this style of moderation may not make sense if the quality of the content is extremely crucial to the reputation of the company. Interestingly, certain countries have laws which legally protect platforms that encourage/adopt reactive moderation.

AI Content Moderation

Community moderators can take the help of artificial intelligence inspired content moderation as a tool to implement the guidelines of the platform. Automated moderation is commonly used to block the occurrences of banned words and phrases. IP bans can also be established using such a tool.

Current shortcomings of content moderation

Content moderators are bestowed with the important responsibility of cleaning up all content which represents the worst which humanity has to offer. A lot of user-generated content is extremely harmful to the general public (especially children) and due to this, content moderation becomes the process which protects every platform’s community. Here are some of the shortcomings experienced by modern content moderation:

  • Content moderation comes with certain dangers such as continuously exposing content moderators to undesirable and inappropriate content. This can have a negative psychological impact but thankfully, companies have found a way to replace them with AI moderators. While this solves the earlier issue, it makes the moderation process more secretive.
  • Content moderation presently has its fair share of inconsistencies. For example, an AI content moderation setup can detect nudity better than hate speech, while the public could argue that the latter has more significant consequences. Also, in most platforms, profiles of public figures tend to be given more leniency compared to everyday users.
  • Content Moderation has been observed to have a disproportionately negative influence on members of marginalized communities. The rules surrounding what is offensive and what isn’t aren’t generally very clear on these platforms, and users can have their accounts banned temporarily or permanently if they are found to have indulged in such activity.
  • Continuing from the last statement, the appeals process in most platforms is broken. Users might end up getting banned for actions they could rightfully justify and it could take a long period of time before the ban is revoked. This is a special area in which content moderation has failed or needs to improve.

Conclusion

While the topic of content moderation comes with its achievements and failures, it completely makes sense for companies and platforms to invest in this. If the content moderation process is implemented in a manner which is scalable, it can allow the platform to become the source of a large volume of information, generated by its users. Not only can the platform enjoy the opportunity to publish a lot of content, but it can also be moderated to ensure the protection of its users from malicious and undesirable content.

8 industries artificial intelligence is transforming

Man-made reasoning popularly known as Artificial Intelligence depicts the propelled procedure for a machine to settle on choices dependent on the rationale. Computer-based intelligence has effectively had a worldwide effect on the making of conversational chatbots, self-driving vehicles, and proposal frameworks. Artificial intelligence is developing in its notoriety among business pioneers as a rising advantage for the workforce and is by and by finding in different ventures as of now, changing how organizations and social orders work.

The use of Artificial Intelligence is on the rise and every industry seems to want a piece of it. Over the past couple of years, Artificial Intelligence and Machine Learning are being rigorously used to improve business processes and everyday new technology is being researched or developed to handle more and more complex processes.

A good number of industries have already started using Artificial Intelligence and Machine Learning in their businesses and have been able to take advantage of them to massively improve processes within the organization. Let’s have a quick look at some of the industries Artificial Intelligence is taking over and in what ways below.

Healthcare

With the whole world becoming health-conscious, this is an industry that has humongous potential.

Artificial intelligence is on the ascent inside the medicinal services industry, taking care of an assortment of issues, setting aside cash and clearing new streets to a more extensive comprehension of wellbeing sciences. AI innovations in the health insurance industry are for the most part used to productively gather singular patient information. AI has helped anesthesia conveyance and expert AI support during medicinal techniques. As per Health IT Analytics, progressive changes have been taking place in the wellness and health insurance sector with the utilization of AI-based wellbeing and medical services or devices.

Computer Vision backed by Artificial Intelligence has been very successful in analyzing data to determine diseases. With NLP and ML leading the space to study the demographics and identify health issues in that population.

Surgeries can now be made using AI-assisted bots that are more accurate and help by lowering the risk of infections, help with reducing the blood loss during surgeries and also shorten the healing time.

Finance

Artificial Intelligence and Machine learning are taking over the Finance industry by storm. It’s now been noticed that AI and ML have been able to surpass humans in a lot of important processes, from gathering financial data, analysis of this data and managing investments. Finance has been using Artificial Intelligence coupled with predictive analytics to track the changes in the stock market and identify potential investment opportunities.

Most of the leading financial institutions have also started incorporating chatbots that are very well developed specifically for the finance industry using very refined training data. JPMorgan Chase is now using AI in the form of an image recognition software with character recognition to scan and extract specific information from a huge set legal documents in just a few seconds, which would practically take months for humans to do it.

Transport

Transport is another industry where Artificial Intelligence is taking over drastically. Self-driven cars and self-driven trucks are the more popular developments in this industry but there are a lot of significant developments that have been happening in the industry in terms of incorporating Artificial Intelligence and Machine Learning.

Figuring out the best routes in terms of distance and fuel efficiency has been one of the most trusted processes for Artificial Intelligence. The Transport industry is benefitted the most by using Artificial Intelligence to gather information from an assortment of sources to streamline and alter the delivery courses and improve distribution systems.

Extensive research and development have been going on to develop self-driven cargo ships which can determine the safest and shortest route based on weather and obstructions on the way. New AI technology is being developed that can detect any type of malfunctions and hence reduce marine accidents.

Business Intelligence

Business Intelligence is an industry that is on the boom currently. The volume of data that is generated from clients is extremely valuable and Artificial Intelligence applications have been able to better analyze this data and give better insights. It has been very precise in exploring the data and giving out more refined recommendations. It is also automated which reduces the human effort significantly.

Humans no longer need to go through various charts and dashboards to speculate the important parameters, the AI integrated tools do it much more effectively and deliver more accurate results.

Artificial Intelligence has revolutionized the way we work with data. With the main goal of Business Intelligence is getting the right data to the point where a decision can be made in the shortest time possible. The demand for such AI or ML applications is increasing exponentially with new emerging requirements and data being generated.

Human Resources

Utilization of Artificial Intelligence and Machine learning in recruitment and human resources has increased substantially over the past couple of years because it decreases human effort while making the whole process more streamlined.

Blind contracting

Blind contracting is a procedure for choosing applicants without seeing them. ML calculations can analyze candidate information under determined pursuit parameters that are exclusively dependent on experience and accreditations as opposed to statistical data. This can help groups more diverse regarding abilities, instruction foundation, sexual orientation, ethnicity, and unique attributes that potential applicants bring to the table.

Retail/E-Commerce

E-Commerce is one of the biggest industries that has taken advantage of Artificial Intelligence and Machine Learning to streamline complicated processes. From analyzing online traffic, predicting accurate suggestions and optimizing the delivery process to analyzing competitor data and producing critical decision-making outputs, AI has been a harpoon to this industry.

Artificial intelligence can customize buying suggestions for clients while helping retailers to enhance valuing and rebate techniques by interest gauging.

With most of the big players in the industry even focusing on developing a user-friendly chatbot to assist consumers with picking the right product, the experience has been revolutionized. The chatbots are now capable of analyzing what product would interest the consumer and accurately suggest them which has skyrocketed sales. With the scope of further implementation of AI and ML across various processes, E-Commerce can be considered one of the biggest industries that Artificial Intelligence has taken over.

Agriculture

Agriculture is another industry where Computer Vision backed by Artificial Intelligence has changed the game. Large agricultural lands are now captured by drones and using computer vision the exact areas where weeds grow can be predicted. This has been a revolutionary step in the field of agriculture as the efficiency can be increased monstrously. This also eliminates the human effort of manually detecting key areas of the agricultural land. The data is reliable, efficient and economical.

This helps in identifying the problematic areas and also help in getting rid of the weeds and hence maximize the output.

Advertising

Businesses would normally spend thousands of dollars to run test ads to figure out the target audience. But AI-powered campaigns can provide better results with the existing data itself thereby reducing costs by more than half. This would be a game-changer in the marketing realm as brands and businesses would have a sure shot avenue to place their money in. Connecting with potential clients, creating leads and changing over them to deals, distinguishing the piece of the overall industry of another item before dispatch and rivalry research could all end up simpler with brilliant nostalgic investigation instruments.

What to expect in the next decade?

Cyborgs

In the future, we will probably expand ourselves with PCs and upgrade our very own large number of normal capacities. Although a considerable lot of these conceivable cyborg upgrades would be included for comfort, others may fill a progressively useful need. Computer-based intelligence will wind up valuable for individuals with severed appendages, as the mind will almost certainly speak with a mechanical appendage to give the patient more control. This sort of cyborg innovation would fundamentally decrease the impediments that amputees manage.

Industries being transformed with the rise of AI systems, Artificial Intelligence can take up dangerous jobs, they are in fact rambles, being utilized as the physical partner for defusing bombs, however requiring a human to control them, as opposed to utilizing AI. Whatever their order, they have spared a great many lives by assuming control more than one of the most hazardous employments on the planet. Welding is another good example of producing toxic substances, intense heat, and earsplitting noise, which could be outsourced to robots in most cases. Robot Worx explains that robotic welding cells are already in use and have safety features in place to help prevent human workers from fumes and other bodily harm.

Artificial Intelligence has not yet been developed perfectly to make robots that are capable of understanding emotions. But it is an area where a lot of pioneers are focusing on developing currently.

Most robots are as yet aloof and it’s difficult to picture a robot you could identify with. In any case, an organization in Japan has made the primary huge strides toward a robot friend—one who can comprehend and feel feelings. Soon, we will have robot friends who can understand our emotions and can relate to it. They can act as therapists providing mental therapy.

Further advancements will take place in all currently existing AI technologies the future will have more robust AI and ML applications that can be deeply personalized to suit every individual’s choices. The future of AI is exciting and promising. We can safely conclude saying AI and ML will change the world in ways unimaginable.

Top 7 ai trends in 2019

Artificial Intelligence is a method for making a system, a computer-controlled robot. AI uses information science and algorithms to mechanize, advance and discover worth escaped from the human eye. Most of us are pondering about “what’s next for AI in 2019 paving the way to 2020?” How about we explore the latest trends in AI in 2019.

AI-Enabled Chips

Companies over the globe are accommodating Artificial Intelligence in their frameworks however the procedure of cognification is a noteworthy concern they are confronting. Hypothetically, everything is getting more astute and cannier, yet the current PC chips are not good enough and are hindering the procedure.

In contrast to other programming technologies, AI vigorously depends on specific processors that supplement the CPU. Indeed, even the quickest and most progressive CPU may not be capable to improve the speed of training an AI model. The model would require additional equipment to perform scientific estimations for complex undertakings like identifying objects or items and facial recognition.

In 2019, Leading chip makers like Intel, NVidia, AMD, ARM, Qualcomm will make chips that will improve the execution speed of AI-based applications. Cutting edge applications from the social insurance and vehicle ventures will depend on these chips for conveying knowledge to end-users.

Augmented Reality

Augmented reality AI trend in 2019

Augmented reality (AR) is one of the greatest innovation patterns at this moment, and it’s just going to become greater as AR cell phones and different gadgets become increasingly available around the globe. The best examples could be Pokémon Go and Snapchat.

Objects generated from computers coexist together and communicate with this present reality in a solitary, vivid scene. This is made conceivable by melding information from numerous sensors such as cameras, gyroscopes, accelerometers, GPS, and so forth to shape a computerized portrayal of the world that can be overlaid over the physical one.

AR and AI are distinct advancements in the field of technology; however, they can be utilized together to make one of a kind encounters in 2019. Augmented reality (AR) and Artificial Intelligence (AI) advances are progressively relevant to organizations that desire to pick up a focused edge later on the work environment. In AR, a 3D portrayal of the world must be developed to enable computerized objects to exist close by physical ones. With companies such as Apple, Google, Facebook and so on offering devices and tools to make the advancement of AR-based applications simpler, 2019 will see an upsurge in the quantity of AR applications being discharged.

Neural Networks

A neural network is an arrangement of equipment as well as programming designed after the activity of neurons in the human cerebrum. Neural networks – most commonly called artificial neural networks are an assortment of profound learning innovation, which likewise falls under the umbrella of AI.

Neural networks can adjust to evolving input; so, the system produces the most ideal outcome without expecting to overhaul the yield criteria. The idea of neural networks, which has its foundations in AI, is quickly picking up prominence in the improvement of exchanging frameworks. ANN emulate the human brain. The current neural network advances will be enhanced in 2019. This would empower AI to turn out to be progressively modern as better preparing strategies and system models are created. Areas of artificial intelligence where the neural network was successfully applied include Image Recognition, Natural Language Processing, Chatbots, Sentiment Analysis, and Real-time Transcription.

The convergence of AI and IoT

IoT & AI trends in 2019

The most significant job AI will play in the business world is expanding client commitment, as indicated by an ongoing report issued by Microsoft. The Internet of Things is reshaping life as we probably are aware of it from the home to the workplace and past. IoT items award us expanded control over machines, lights, and door locks.

Organizational IoT applications would get higher exactness and expanded functionalities by the use of AI. In actuality, self-driving cars is certifiably not a commonsense plausibility without IoT working intimately with AI. The sensors utilized by a car to gather continuous information is empowered by the IoT.

Artificial intelligence and IoT will progressively combine at edge computing. Most Cloud-based models will be put at the edge layer. 2019 would see more instances of the intermingling of AI with IoT and AI with Blockchain. IoT is good to go to turn into the greatest driver of AI in the undertaking. Edge devices will be furnished with the unique AI chips dependent on FPGAs and ASICs.

Computer Vision

Computer Vision is the procedure of systems and robots reacting to visual data sources — most normally pictures and recordings. To place it in a basic way, computer vision progresses the info (yield) steps by reading (revealing) data at a similar visual level as an individual and along these lines evacuating the requirement for interpretation into machine language (the other way around). Normally, computer vision methods have the potential for a more elevated amount of comprehension and application in the human world.

While computer vision systems have been around since the 1960s, it wasn’t until recently that they grabbed the pace to turn out to be useful assets. Advancements in Machine Learning, just as the progressively skilled capacity and computational devices have empowered the ascent in the stock of Computer Vision techniques. What follows is also an explanation of how Artificial Intelligence is born. Computer vision, as a region of AI examines, has entered a far cry in a previous couple of years.

Facial Recognition

Facial recognition AI trends in 2019

Facial recognition is a type of AI application that aides in recognizing an individual utilizing their digital picture or patterns of their facial highlights. A framework utilized to perform facial recognition utilizes biometrics to outline highlights from the photograph or video. It contrasts this data and a huge database of recorded countenances to find the right match. 2019 would see an expansion in the use of this innovation with higher exactness and dependability.

In spite of having a lot of negative press lately, facial recognition is viewed as the Artificial Intelligence applications future because of its gigantic prominence. It guarantees a gigantic development in 2019. The year 2019 will observe development in the utilization of facial recognition with greater unwavering quality and upgraded precision.

Open-Source AI

Open Source AI would be the following stage in the growth of AI. Most of the Cloud-based advancements that we use today have their beginning in open source ventures. Artificial intelligence is relied upon to pursue a similar direction as an ever-increasing number of organizations are taking a gander at a joint effort and information sharing.

Open Source AI would be the following stage in the advancement of AI. Numerous organizations would begin publicly releasing their AI stacks for structuring a more extensive encouraging group of people of AI communities. This would prompt the improvement of a definitive AI open source stack.

Conclusion

Numerous innovation specialists propose that the eventual fate of AI and ML is sure. It is the place where the world is headed. In 2019 and beyond these advancements are going to support as more organizations come to understand the advantages. However, the worries encompassing the dependability and cybersecurity will keep on being fervently discussed. The ML and AI trends for 2019 and beyond hold guarantees to enhance business development while definitely contracting the dangers.

The need for training data in ai and ml models

Not very long ago, sometime towards the end of the first decade of the 21st century, internet users everywhere around the world began seeing fidelity tests while logging onto websites. You were shown an image of a text, with one word or usually two, and you had to type the words correctly to be able to proceed further. This was their way of identifying that you were, in fact, human, and not a line of code trying to worm its way through to extract sensitive information from said website. While it was true, this wasn’t the whole story.

Turns out, only one of the two Captcha words shown to you were part of the test, and the other was an image of a word taken from an as yet non-transcribed book. And you, along with millions of unsuspecting users worldwide, contributed to the digitization of the entire Google Books archive by 2011. Another use case of such an endeavor was to train AI in Optical Character Recognition (OCR), the result of which is today’s Google Lens, besides other products.

Do you really need millions of users to build an AI? How exactly was all this transcribed data used to make a machine understand paragraphs, lines, and individual words? And what about companies that are not as big as Google – can they dream of building their own smart bot? This article will answer all these questions by explaining the role of datasets in artificial intelligence and machine learning.

ML and AI – smart tools to build smarter computers

In our efforts to make computers intelligent – teach them to find answers to problems without being explicitly programmed for every single need – we had to learn new computational techniques. They were already well endowed with multiple superhuman abilities: computers were superior calculators, so we taught them how to do math; we taught them language, and they were able to spell and even say “dog”; they were huge reservoirs of memory, hence we used them to store gigabytes of documents, pictures, and video; we created GPUs and they let us manipulate visual graphics in games and movies. What we wanted now was for the computer to help us spot a dog in a picture full of animals, go through its memory to identify and label the particular breed among thousands of possibilities, and finally morph the dog to give it the head of a lion that I captured on my last safari. This isn’t an exaggerated reality – FaceApp today shows you an older version of yourself by going through more or less the same steps.

For this, we needed to develop better programs that would let them learn how to find answers and not just be glorified calculators – the beginning of artificial intelligence. This need gave rise to several models in Machine Learning, which can be understood as tools that enhanced computers into thinking systems (loosely).

Machine Learning Models

Machine Learning is a field which explores the development of algorithms that can learn from data and then use that learning to predict outcomes. There are primarily three categories that ML models are divided into:

Supervised Learning

These algorithms are provided data as example inputs and desired outputs. The goal is to generate a function that maps the inputs to outputs with the most optimal settings that result in the highest accuracy.

Unsupervised Learning

There are no desired outputs. The model is programmed to identify its own structure in the given input data.

Reinforcement Learning

The algorithm is given a goal or target condition to meet and it is left to its devices to learn by trial and error. It uses past results to inform itself about both optimal and detrimental paths and charts the best path to the desired endgame result.

In each of these philosophies, the algorithm is designed for a generic learning process and exposed to data or a problem. In essence, the written program only teaches a wholesome approach to the problem and the algorithm learns the best way to solve it.

Based on the kind of problem-solving approach, we have the following major machine learning models being used today:

  • Regression
    These are statistical models applicable to numeric data to find out a relationship between the given input and desired output. They fall under supervised machine learning. The model tries to find coefficients that best fit the relationship between the two varying conditions. Success is defined by having as little noise and redundancy in the output as possible.

    Examples: Linear regression, polynomial regression, etc.
  • Classification
    These models predict or explain one outcome among a few possible class values. They are another type of supervised ML model. Essentially, they classify the given data as belonging to one type or ending up as one output.

    Examples: Logistic regression, decision trees, random forests, etc.
  • Decision Trees and Random Forests
    A decision tree is based on numerous binary nodes with a Yes/No decision marker at each. Random forests are made of decision trees, where accurate outputs are obtained by processing multiple decision trees and results combined.
  • Naïve Bayes Classifiers
    These are a family of probabilistic classifiers that use Bayes’ theorem in the decision rule. The input features are assumed to be independent, hence the name naïve. The model is highly scalable and competitive when compared to advanced models.
  • Clustering
    Clustering models are a part of unsupervised machine learning. They are not given any desired output but identify clusters or groups based on shared characteristics. Usually, the output is verified using visualizations.

    Examples: K-means, DBSCAN, mean shift clustering, etc.
  • Dimensionality Reduction
    In these models, the algorithm identifies the least important data from the given set. Based on the required output criteria, some information is labeled redundant or unimportant for the desired analysis. For huge datasets, this is an invaluable ability to have a manageable analysis size.

    Examples: Principal component analysis, t-stochastic neighbor embedding, etc.
  • Neural Networks and Deep Learning
    One of the most widely used models in AI and ML today, neural networks are designed to capture numerous patterns in the input dataset. This is achieved by imitating the neural structure of the human brain, with each node representing a neuron. Every node is given activation functions with weights that determine its interaction with its neighbors and adjusted with each calculation. The model has an input layer, hidden layers with neurons, and an output layer. It is called deep learning when many hidden layers are encapsulating a wide variety of architectures that can be implemented. ML using deep neural networks requires a lot of data and high computational power. The results are without a doubt the most accurate, and they have been very successful in processing images, language, audio, and videos.

There is no single ML model that offers solutions to all AI requirements. Each problem has its own distinct challenges, and knowledge of the workings behind each model is mandatory to be able to use them efficiently. For example, regression models are best suited for forecasting data and for risk assessment. Clustering modes in handwriting recognition and image recognition, decision trees to understand patterns and identify disease trends, naïve Bayes classifier for sentiment analysis, ranking websites and documents, deep neural networks models in computer vision, natural language processing, and financial markets, etc. are more such use cases.

The need for training data in ML models

Any machine learning model that we choose needs data to train its algorithm on. Without training data, all the algorithm understands is how to approach the given problem, and without proper calibration, so to speak, the results won’t be accurate enough. Before training, the model is just a theorist, without the fine-tuning to its settings necessary to start working as a usable tool.

While using datasets to teach the model, training data needs to be of a large size and high quality. All of AI’s learning happens only through this data. So it makes sense to have as big a dataset as is required to include variety, subtlety, and nuance that makes the model viable for practical use. Simple models designed to solve straight-forward problems might not require a humongous dataset, but most deep learning algorithms have their architecture coded to facilitate a deep simulation of real-world features.

The other major factor to consider while building or using training data is the quality of labeling or annotation. If you’re trying to teach a bot to speak the human language or write in it, it’s not just enough to have millions of lines of dialogue or script. What really makes the difference is readability, accurate meaning, effective use of language, recall, etc. Similarly, if you are building a system to identify emotion from facial images, the training data needs to have high accuracy in labeling corners of eyes and eyebrows, edges of the mouth, the tip of the nose and textures for facial muscles. High-quality training data also makes it faster to train your model accurately. Required volumes can be significantly reduced, saving time, effort (more on this shortly) and money.

Datasets are also used to test the results of training. Model predictions are compared to testing data values to determine the accuracy achieved until then. Datasets are quite central to building AI – your model is only as good as the quality of your training data.

How to build datasets?

With heavy requirements in quantity and quality, it is clear that getting your hands on reliable datasets is not an easy task. You need bespoke datasets that match your exact requirements. The best training data is tailored for the complexity of the ask as opposed to being the best-fit choice from a list of options. Being able to build a completely adaptive and curated dataset is invaluable for businesses developing artificial intelligence.

On the contrary, having a repository of several generic datasets is more beneficial for a business selling training data. There are also plenty of open-source datasets available online for different categories of training data. MNIST, ImageNet, CIFAR provide images. For text datasets, one can use WordNet, WikiText, Yelp Open Dataset, etc. Datasets for facial images, videos, sentiment analysis, graphs and networks, speech, music, and even government stats are all easily found on the web.

Another option to build datasets is to scrape websites. For example, one can take customer reviews off e-commerce websites to train classification models for sentiment analysis use cases. Images can be downloaded en masse as well. Such data needs further processing before it can be used to train ML models. You will have to clean this data to remove duplicates, or to identify unrelated or poor-quality data.

Irrespective of the method of procurement, a vigilant developer is always likely to place their bets on something personalized for their product that can address specific needs. The most ideal solutions are those that are painstakingly built from scratch with high levels of precision and accuracy with the ability to scale. The last bit cannot be underestimated – AI and ML have an equally important volume side to their success conditions.

Coming back to Google, what are they doing lately with their ingenious crowd-sourcing model? We don’t see a lot of captcha text anymore. As fidelity tests, web users are now annotating images to identify patterns and symbols. All the traffic lights, trucks, buses and road crossings that you mark today are innocuously building training data to develop their latest tech for self-driving cars. The question is, what’s next for AI and how can we leverage human effort that is central to realizing machine intelligence through training datasets?

5 common misconceptions about AI

Ever wondered what your life would be without those perky machines lying around which sometimes/most times replaced a significant part of your daily routine? In Terminology fancied by Scientists, we call them AI (Artificial Intelligence,) and in plain layman or lazy man terms that is us, we fancy calling them machines and bots.

Let’s define the exact meaning of AI in terms of science because I hate disappointing aspiring scientists out there who don’t take puns lightly. For those that do, welcome to the fraternity of loose and lost minds. Let’s get down to business, shall we?

Definition: Artificial Intelligence or machine intelligence, is intelligence demonstrated by machines in contrast to the natural intelligence displayed by humans. Colloquially, the term "artificial intelligence" is often used to describe machines (or computers) that mimic "cognitive" functions that humans associate with the human mind such as "learning" and "problem-solving.”

Isn’t it evident I copied the above definition from Wikipedia? And did your natural intelligence decipher the meaning of the definition stated above?

Let me introduce you to the lazy man definition of Artificial Intelligence. Like all engineering scholars, I will take the absolute pleasure of dismantling the words and assembling it together again.

Artificial – Non-Human, something that can’t breathe air or respond to a feeling. 

Intelligence – the ability to display intellect, sound reasoning, judgment, and a ready wit.

Put the two words together and voila! Artificially intelligent machines are capable of displaying or mimicking human intellect, sound reasoning, and judgment towards it's surrounding.

Now that we got the definition of AI out of the way, look around you, what do you see? What’s in your hands? Do you not spot a single electronic device or bots?

Things or machines work a lot differently in this era. You must be awestruck of the skyrocketing shiny monuments. The big bird moving 33,000ft above your head carrying humans from one country to another, hospitals treating the diseased and the ill with technology your mind can’t fathom.

Fast cars, microwave and yes, we no longer communicate using crows or pigeons we have cell phones!

Don’t be surprised if I reveal that these are the necessity and an extension to our lives. And no, we cannot live without them anymore.

Our purpose of life has changed drastically, growing crops and putting food on the table isn’t what give us lines on the forehead. We built replacement models that take care of that too. We are living in a fast lane where technology, eventually, will slingshot us to the moon or another planet.

With such a drastic rise in AI and the current trend where all companies want a piece of it, there are some misconceptions about AI as well. With this blog, I try to debunk the misconceptions highlighting both the positive and negative aspects of artificial intelligence.

“If these machines are handling even the simplest of tasks, what are people going to do? Is it the destruction of jobs?”

Fret not. If there is technological advancement, there are always career opportunities as it is the human mind that does the ‘thinking.’ You are the master of your creation.

In fact, in 2020 there will be 2.3 million new jobs available thanks to AI, which results in less muscle power and more brainpower.

“Can Artificial Intelligence solve any/all problems?“

This question is debatable, while AI is designed to assist and make our jobs easier, it cannot save a human being from rubbing off cancers and illness.

Human intelligence hasn’t discovered a way to program the bots to predict or diagnose illness proactively. One must remember, bots act on what is fed/programmed by humans.

“Is AI infallible?“

If you thought it was, then I have slightly bad news. Humans are in a common misconception assuming the machines are no less than perfection and display little to no mistake. The non-sentient systems are trained by us, data selected and curated by us, and human tendency is to make mistakes and learn from them.

Artificial Intelligence is just as good as the training data used, which is created by humans. Any mistake with the training data will reflect on the performance of the system and the technology will be compromised. Ensuring you use a high-quality training dataset is critical to the success of the AI system.

Speaking of data being compromised, during the 2016 presidential election campaign, we witnessed the information of US citizens being evaluated by gaining access to their social media accounts. To proactively block their social media feeds with ads that will prove to be of interest. Therefore, stealing away the votes from the opposition.

We call this “data/information manipulation.” Sadly, the downside of Artificial Intelligence.

“AI must be expensive.”

Well, implementing a fully automated system doesn’t come easy and doesn’t come cheap. But depending on the needs and goals of the organization, it may be entirely possible to adopt AI and get the desired results without breaking your treasure chest.

The key is for each business to figure out what they want and apply AI as needed, for their unique goals and company scale. If businesses can workout their scalability and incorporate the right Artificial intelligence, it can be economical in the long run.

“Will Artificial Intelligence be the end of humanity?”

We are a work in progress, standing at the foyer of technological advancements with a long way to go. But, much like the misconception about robots replacing humans in the workforce, the question is more of smoke in the mirror.

The AI in its current level is not fully capable of self-conscious and decision making. Don’t let Star Trek, Iron Man and Terminator movies fool you into believing bots will lose their nuts (literally and hypothetically) and foreshadow the destruction of humanity. On the flip side, it is the natural disasters the bots are being designed to protect us from.

Oh, look what’s in every body’s hand, it’s what we call a cell phone. A device primarily designed to communicate with people that are at a greater distance.

Communication takes place using microwaves, very different from sand waves. Look closely and you’ll see people doing weird things using their fingers on the cell phone and a weird thing hanging from their ears going through to the same device. Yes, these devices are their partners for life.

Here we are, say Konnichiwa to the lady, don’t touch her! She’s just a hologram.

Welcome to the National Museum of Emerging Science and Innovation simply known as the Miraikan (future museum) where obsessiveness over technology has led us to build a museum for itself.

There’s Asimo, the Honda robot and, what you’re looking at isn’t another piece of asteroid that struck earth years ago, it is Geo-Cosmos. A high-resolution globe displaying near real-time events of global weather patterns, ocean temperatures, and vegetation covering across geographic locations.

You must be contemplating why has mankind reached such level of advancement? Let’s go back to the last question “Will AI be the end of humanity?”

The seismometer, a device that responds and records the ground motions, earthquake, and volcanic eruptions. There are a lot of countries that have lost far too many lives to even comprehend the tragic events of active earthquakes.

This device is a way to predict and bring citizens of Japan to safe grounds. Artificial Intelligence will not be the end of humanity, it can, in fact, be the opposite and could be an answer to humanity’s biggest natural calamities and disasters.

The human mind is something to behold, from its complex neural nerves in the brain to the nerves connecting to every part of the body to achieve motor functions. To replicate or clone it using artificial chips and wires is nearly impossible in the current era but the determination we hold and our adamant nature drives us to dream, the dream of one day successfully cloning the human consciousness into nuts and bolts of a bot.

One day to look at the stars and send bots for space exploration. To look for a suitable second home in an event of space disasters that humans have no control over. And, why send bots into deep space and not humans to add a feather to the hat of achievement?

Simply because we breathe, we starve, and our very own nervous system advertently detects the brutal nature of space above the earth. In this case, Artificial Intelligence and robots are in fact helping humans explore the possibilities of life in outer space. Which is against the misconception that AI will be the end of humanity.

So, there we have it, all the major misconceptions about artificial intelligence and what the reality is. End of the day, it all comes down to how we incorporate artificial intelligence and what we use it for.

If used in the right way, there will be a revolution in the way humans work. Which makes it important for all of us to work on educating people about artificial intelligence and using it to make the world a better place.

Understanding the difference between AI, ML & NLP models

Technology has revolutionized our lives and is constantly changing and progressing. The most flourishing technologies include Artificial Intelligence, Machine Learning, Natural Language Processing, and Deep Learning. These are the most trending technologies growing at a fast pace and are today’s leading-edge technologies.

These terms are generally used together in some contexts but do not mean the same and are related to each other in some or the other way. ML is one of the leading areas of AI which allows computers to learn by themselves and NLP is a branch of AI.

What is Artificial Intelligence?

Artificial refers to something not real and Intelligence stands for the ability of understanding, thinking, creating and logically figuring out things. These two terms together can be used to define something which is not real yet intelligent.

AI is a field of computer science that emphasizes on making intelligent machines to perform tasks commonly associated with intelligent beings. It basically deals with intelligence exhibited by software and machines.

While we have only recently begun making meaningful strides in AI, its application has encompassed a wide spread of areas and impressive use-cases. AI finds application in very many fields, from assisting cameras, recognizing landscapes, and enhancing picture quality to use-cases as diverse and distinct as self-driving cars, autonomous robotics, virtual reality, surveillance, finance, and health industries.

History of AI

The first work towards AI was carried out in 1943 with the evolution of Artificial Neurons. In 1950, Turing test was conducted by Alan Turing that can check the machine’s ability to exhibit intelligence.

The first chatbot was developed in 1966 and was named ELIZA followed by the development of the first smart robot, WABOT-1. The first AI vacuum cleaner, ROOMBA was introduced in the year 2002. Finally, AI entered the world of business with companies like Facebook and Twitter using it.

Google’s Android app “Google Now”, launched in the year 2012 was again an AI application. The most recent wonder of AI is “the Project Debater” from IBM. AI has currently reached a remarkable position

The areas of application of AI include

  • Chat-bots – An ever-present agent ready to listen to your needs complaints and thoughts and respond appropriately and automatically in a timely fashion is an asset that finds application in many places — virtual agents, friendly therapists, automated agents for companies, and more.
  • Self-Driving Cars: Computer Vision is the fundamental technology behind developing autonomous vehicles. Most leading car manufacturers in the world are reaping the benefits of investing in artificial intelligence for developing on-road versions of hands-free technology.
  • Computer Vision: Computer Vision is the process of computer systems and robots responding to visual inputs — most commonly images and videos.
  • Facial Recognition: AI helps you detect faces, identify faces by name, understand emotion, recognize complexion and that’s not the end of it.

What is Machine Learning?

One of the major applications of Artificial Intelligence is machine learning. ML is not a sub-domain of AI but can be generally termed as a sub-field of AI. The field of machine learning is concerned with the question of how to construct computer programs that automatically improve with experience.

Implementing an ML model requires a lot of data known as training data which is fed into the model and based on this data, the machine learns to perform several tasks. This data could be anything such as text, images, audio, etc…

 Machine learning draws on concepts and results from many fields, including statistics, artificial intelligence, philosophy, information theory, biology, cognitive science, computational complexity and control theory. ML itself is a self-learning algorithm. The different algorithms of ML include Decision Trees, Neural Networks, SEO, Candidate Elimination, Find-S, etc.

History of Machine Learning

The roots of ML lie way back in the 17th century with the introduction of Mechanical Adder and Mechanical System for Statistical Calculations. Turing Test conducted in 1950 was again a turning point in the field of ML.

The most important feature of ML is “Self-Learning”. The first computer learning program was written by Arthur Samuel for the game of checkers followed by the designing of perceptron (neural network). “The Nearest Neighbor” algorithm was written for pattern recognition.

Finally, the introduction of adaptive learning was introduced in the early 2000s which is currently progressing rapidly with Deep Learning is one of its best examples.

Different types of machine learning approaches are:

Supervised Learning uses training data which is correctly labeled to teach relationships between given input variables and the preferred output.

Unsupervised Learning doesn’t have a training data set but can be used to detect repetitive patterns and styles.

Reinforcement Learning encourages trial-and-error learning by rewarding and punishing respectively for preferred and undesired results.

ML has several applications in various fields such as

  • Customer Service: ML is revolutionizing customer service, catering to customers by providing tailored individual resolutions as well as enhancing the human service agent capability through profiling and suggesting proven solutions. 
  • HealthCare: The use of different sensors and devices use data to access a patient’s health status in real-time.
  • Financial Services: To get the key insights into financial data and to prevent financial frauds.
  • Sales and Marketing: This majorly includes digital marketing, which is currently an emerging field, uses several machine learning algorithms to enhance the purchases and to enhance the ideal buyer journey.

What is Natural Language Processing?

Natural Language Processing is an AI method of communicating with an intelligent system using a natural language.

Natural Language Processing (NLP) and its variants Natural Language Understanding (NLU) and Natural Language Generation (NLG) are processes which teach human language to computers. They can then use their understanding of our language to interact with us without the need for a machine language intermediary.

History of NLP

NLP was introduced mainly for machine translation. In the early 1950s attempts were made to automate language translation. The growth of NLP started during the early ’90s which involved the direct application of statistical methods to NLP itself. In 2006, more advancement took place with the launch of IBM’s Watson, an AI system which is capable of answering questions posed in natural language. The invention of Siri’s speech recognition in the field of NLP’s research and development is booming.

Few Applications of NLP include

  • Sentiment Analysis – Majorly helps in monitoring Social Media
  • Speech Recognition – The ability of a computer to listen to a human voice, analyze and respond.
  • Text Classification – Text classification is used to assign tags to text according to the content.
  • Grammar Correction – Used by software like MS-Word for spell-checking.

What is Deep Learning?

The term “Deep Learning” was first coined in 2006. Deep Learning is a field of machine learning where algorithms are motivated by artificial neural networks (ANN). It is an AI function that acts lie a human brain for processing large data-sets. A different set of patterns are created which are used for decision making.

The motive of introducing Deep Learning is to move Machine Learning closer to its main aim. Cat Experiment conducted in 2012 figured out the difficulties of Unsupervised Learning. Deep learning uses “Supervised Learning” where a neural network is trained using “Unsupervised Learning”.

Taking inspiration from the latest research in human cognition and functioning of the brain, neural network algorithms were developed which used several ‘nodes’ that process information like how neurons do. These networks have multiple layers of nodes (deep nodes and surface nodes) for different complexities, hence the term deep learning. The different activation functions used in Deep Learning include linear, sigmoid, tanh, etc.…

History of Deep Learning

The history of Deep Learning includes the introduction of “The Back-Propagation” algorithm, which was introduced in 1974, used for enhancing prediction accuracy in ML.  Recurrent Neural Network was introduced in 1986 which takes a series of inputs with no predefined limit, followed by the introduction of Bidirectional Recurrent Neural Network in 1997.  In 2009 Salakhutdinov & Hinton introduced Deep Boltzmann Machines. In the year 2012, Geoffrey Hinton introduced Dropout, an efficient way of training neural networks

Applications of Deep Learning are

  • Text and Character generation – Natural Language Generation.
  • Automatic Machine Translation – Automatic translation of text and images.
  • Facial Recognition: Computer Vision helps you detect faces, identify faces by name, understand emotion, recognize complexion and that’s not the end of it.
  • Robotics: Deep learning has also been found to be effective at handling multi-modal data generated in robotic sensing applications.

Key Differences between AI, ML, and NLP

Artificial intelligence (AI) is closely related to making machines intelligent and make them perform human tasks. Any object turning smart for example, washing machine, cars, refrigerator, television becomes an artificially intelligent object. Machine Learning and Artificial Intelligence are the terms often used together but aren’t the same.

ML is an application of AI. Machine Learning is basically the ability of a system to learn by itself without being explicitly programmed. Deep Learning is a part of Machine Learning which is applied to larger data-sets and based on ANN (Artificial Neural Networks).

The main technology used in NLP (Natural Language Processing) which mainly focuses on teaching natural/human language to computers. NLP is again a part of AI and sometimes overlaps with ML to perform tasks. DL is the same as ML or an extended version of ML and both are fields of AI. NLP is a part of AI which overlaps with ML & DL.