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Innovative chatbots

Science fiction has always depicted AI as a kind of machine that can communicate and behave like humans in society. While research on artificial intelligence had already begun by the late 1950s, it wasn’t until the advent of Big Data that AI picked up and became the technological behemoth it is today. 

Artificial intelligence has expanded into countless use-cases and industries, enriching the lives of humans globally. AI started as small computer programs that could play checkers and solve word problems. It has since grown into being able to predict stock markets and election results. While we still don’t have talking robots, or at least not too many, we do have easy access to chatbots. And, some of them have developed into the most strange but interesting use-cases for artificial intelligence the world has ever seen. 

Innovative chatbots

While chatbots may seem human, it’s important to note that they are just lines of code, a string of 1s and 0s floating through cyberspace. However, what they lack in human behavior, they make up for with superior natural language processing, neural networks, and deep learning. This makes chatbots awesome devices that can simulate humans and keep them hooked. Here are some of the most popular ones:

ALICE

It’s hard to talk about chatbots without mentioning ALICE: Artificial Linguistic Internet Computer Entity. It is the first chatbot ever popularized on the web. Developed by Dr. Richard Wallace over 25 years ago, ALICE holds up reasonably well to this day. While her answers don’t always hit the mark, it’s easy to forget how old this code is after even a short conversation with ALICE. 

Sometimes ALICE gives answers that sounded more futuristic than expected at the time. People will remember ALICE for how it spurred the development of numerous, arguably more advanced chatbots over the next couple of decades. ALICE was also the inspiration for the AI computer in the 2013 Academy Award-winning science-fiction film, Her.

Endurance

Designed to help Alzheimer’s patients feel less lonely Endurance is one of the most helpful chatbots for older individuals who suffer from short-term memory loss and dementia. This open-source chatbot was created to identify deviations in conventional branches that could mean problems for immediate recollection, which can be quite the challenge for natural language processing systems. 

Anyone can contribute to the project’s code-base. Though it isn’t fully fleshed out yet, it could potentially provide researchers and scientists more insight into how memory loss works, and how it can be helped. Since the platform is based on the cloud, doctors and family members can review communication logs instantly. This helps identify potential degradation in short-term memory. Further, it takes the burden off family members to constantly be available as a companion.

U-Report

Global child advocacy non-profit organization, United Nations Children’s Fund (UNICEF) is now using chatbots to help citizens in developing countries to speak out about their communities’ urgent needs. The U-Report chatbot enables large-scale data gathering via polls. By regularly sending out polls prepared according to a range of urgent social issues, users can respond with what needs to be tackled first. UNICEF is then able to use this feedback to create or amend potential policy recommendations. 

In Liberia, this rather basic chatbot was able to create major impact waves by finding out whether teachers were convincing students to have sex in exchange for better grades. 86% of the 13,000 Liberian children involved responded about the issues, which led to a collaborative project between UNICEF and Liberia’s Minister of Education to eradicate the problem.

Woebot

Woebot is a chatbot with a cartoon mascot that helps reduce depression through active listening. Psychologist Alison Darcy developed it at Stanford University. It also praises the user with funny GIFs, memes, and encouragement. The New York Times, Wired, and Business Insider has featured this chatbot multiple times over the years.

They praised its, “gentle attentiveness and sensitivity to emotions.” The chatbot is available to those who need a confidence boost, guidance, or even just a friend. Woebot is available for download on  Google Play Store and Apple’s App Store, and users have applauded its funny and likable personality, despite some scripted lines popping up here and there. 

Insomnobot 3000

Created by Casper, which specializes in mattresses and pillows, this is the most well-named chatbot on the list. Casper developed Insomnobot 3000 to help insomniacs find someone to talk to as they lie awake in bed. The chatbot’s website reads, “A friendly, easily distracted bot designed to keep you company when you just can’t fall asleep. Extra chatty between 11 pm-5 am.”The interface is also much more intuitive, as users don’t need to visit a particular website or download an app to talk to the chatbot. Instead, they are required to text the number provided on the website, similar to how you would text a friend late at night. The bot does bring up some product suggestions here and there, but it never becomes overbearing. It was also included on Forbes as one of the most amazing examples of online chatbots in practice. 

DoNotPay

Everyone cannot afford legal counsel, especially when wrongly accused. DoNotPay is a chatbot developed to help users dispute parking tickets, by guiding you through the process without any lawyer interaction. 

If user reviews are to be believed, the service has been quite successful at achieving its goal and empowers users to settle legal matters from the palm of their hands. It’s also helping individuals actively learn about their rights as citizens. While this isn’t the most conversational chatbot, it serves its purpose well and is one of the most strange but interesting use-cases of artificial intelligence. 

Robot Vera

It is a networking Legal and HR chatbot that business enterprises use to solve many issues relating to the recruitment, legal paperwork, etc. smoothly. Robot Vera can improve the workflow and productivity of companies.

Robot Vera assists HR teams by automatically analyzing resume databases and calling candidates that make a good fit. The conversational AI filters out the applications received for a position to merely 10% of the best suitable ones. It then informs candidates about the job description, schedules and conducts telephonic interviews or video chats.

Robot Vera can evaluate the answers of applicants and perform recruitment tasks almost 10 times faster than humans perform. It can hire employees, handle complex office situations, and even fire them if needed.

BabyCentre UK

BabyCentre UK belongs to Johnson & Johnson, a reputed name in childcare. This bot helps answer questions about pregnancy and childbirth. It can calculate the due date of would-be mothers and guide them for preparing for childbirth. It also provides many resources on self-care for moms going through all stages of motherhood.

The bot provides information on how to get pregnant, weekly articles during pregnancy, health care of the mother and the baby, when to call a doctor for your baby, why your baby does not sleep, among many others. It also provides mothers with a forum to share their doubts and experiences.

Watson Assistant

IBM, a leader in the computer manufacturing space, developed this advanced chatbot. It holds the content of varied industries and is pre-trained for industry-specific use cases, using data relevant to that industry. It can understand historical chat, call logs, search queries and also respond to issues using its knowledge base.

The Watson Assistant can be part of your company’s website, messaging channels, customer service tools adapted, and your mobile apps. The chatbot offers a visual dialog editor making your zero experience of coding your power in developing a new feature.

Replika

Replika is a virtual companion chatbot that intends to promote mental wellness. Users can complain or talk about anything, from their failed relationships to their deepest desires. Replika helps people meet their emotional needs. It soothes them when they feel anxious or heartbroken and need an inspiring or comforting chat. Users can select various options for self-motivation, depending on their choice.

Conclusion

As chatbots continue to become an integral part of our society, the trust placed on user privacy and data collection only increases. Today chatbots can send you money, order your pizza, and as shown above, even settle your legal disputes. These can be frightening from the perspective of AI becoming intelligent enough to make humans obsolete. And this is a question developers keep in mind and debate frequently within the AI community. 

AI bots

Whether you’re tracking short-term memory loss or just need a friend, there is a chatbot out there for you. And they continue to learn with every conversation they have.. They’re on Facebook Messenger, Reddit, and even Slack. The last time you opened a chat with a customer service representative, an AI chatbot most likely handled it. If Google Duplex has shown us anything, it’s that AI might become a much more integral part of our lives than previously envisioned. 

Humans have become more comfortable with chatbots and AI. Artificial intelligence systems are learning more about how humans communicate, behave and function. Perhaps we can live in a world where humans and machines live in harmony. After all, most of the time, we just need someone to talk to.

Virtual Assistants - Alexa, Siri, Google Assistant

Siri was introduced as a feature of the iPhone 4S in 2010. While it could only answer simple questions such as “What’s today’s weather like?” and “Who is Barack Obama?”, users praised the potential of the new voice assistant. Quite a feat for that time for a virtual assistant.

Expectations were high, and Siri fell short. Users complained about inaccurate responses to simple questions or commands. If Siri didn’t know the answer to a question, she’d crack a bad joke, which can seem like an unacceptable excuse for not having the ability to answer a question.

While Apple made improvements to its voice assistant, it wasn’t able to meet a lot of high expectations, and that frustrated users.

Alexa

Three years later, Amazon introduced its own voice assistant named Alexa, and it was instantly pitted against Apple’s alternative. Users observed that Alexa was quicker with responses, and was answering more questions right than wrong. Alexa fell short next to Siri when it comes to the fluidity and flow of requests and conversations. Siri could respond to commands better, and it had no problems understanding multiple sentence structures that conveyed the same message.

In 2016, Google came out with an answer to Siri and Alexa in the form of Google Assistant. It became the gold standard for how natural language processing (NLP) should be implemented with a voice assistant. The drawback of Google Home was that it didn’t have the broad integrations that Alexa had with Amazon’s devices.

These three voice assistants are the most popular in the market and each of them has their own strengths and weakness. But, how exactly do they stand against each other? 

The main tests we will conduct for these voice assistants are commands, conversation flow, music requests, home automation, and technology. MKBHD and Undecided with Matt Farell have given us interesting demonstrations and questions that can be used to test each of these three voice assistants. Let’s compare them using the following parameters:

Commands

Voice assistants started off as devices that could answer simple questions such as the time and the weather. Accuracy of response is key here and speed is an additional bonus.

What’s the weather?

Siri, Alexa, and Google Home have no problem answering this. Google tends to have a slight delay in its response generally, but nothing that could test a user’s patience.

How far away is London?

Siri and Google answered this right in miles as the crow flies, while Alexa provided an inaccurate response, or the answer to a different London (there are 29 places in the world called London). 

Conversation Flow 

When humans have conversations, the talking points build naturally and flow from one topic to another seamlessly. For a voice assistant, understanding context while having a conversation is key. 

Conversation

The following questions were asked one after the other to each voice assistant separately.

Who is the 45th President of the United States?

All three voice assistants provide the right answer. Siri cites the source and asks users if they’d like more information.

Where is he from?

When asked immediately after the previous question, Siri and Google fail. Alexa seems to handle context better than its two competitors.

Music

Since all voice assistants communicate with speakers, they need to understand song, artist and album requests. But before we get into their ability to play a track on-demand, its important to note that each voice assistant only plays music from a select set of streaming services. Alexa wins here as it plays from most major services. Google works only with Google Play Music, YouTube Music, Spotify, and Deezer. And Siri, not surprisingly, only plays from Apple Music.

Play Get Lucky by Daft Punk

Simple task. No losers here.

Play the song that goes “like the legend of the phoenix”

Alexa fails here while Siri and Google Assistant get it right.

Home Automation

Home automation refers to command-based control over home appliances such as fans, den lights, television, heaters, etc. Here’s how the voice assistants fared with the following two questions.

Turn off the den lights

All assistants successfully turned the lights off. 

Set the room temperature to 70 F

Google Assistant and Siri got this right, while Alexa adjusted the room temperature to a value between 65 and 70. 

Technology

Siri primarily works on Natural Language Processing (NLP) integrated with Machine Learning (ML), and voice recognition. Alexa operates on similar tech such as Automated Speech Recognition (ASR), and Natural Language Understanding (NLU). The technology isn’t too different from google either, its voice assistant employs NLP and ML.

Yes, the three voice assistants use ML and NLP to understand what the user is saying and to make suggestions or respond to the user’s language input. While the primary technology is the same or at least similar, the end result is what separates the three. As observed in the tasks assigned to them earlier, certain aspects of each voice assistant’s tech, such as the ability to understand speech patterns and words,  give them an advantage and a disadvantage.

Conclusion

The aim isn’t to be diplomatic, but there isn’t exactly a winner among the three. All the voice assistants can, for the most part, do the same things. Alexa has the largest home-integration options among the three, while Google Assistant and Siri are a lot more natural to talk to. 

Virtual Assistants

If you’re big on home automation and having wide music streaming options, Alexa is the voice assistant for you.

If you find yourself comfortable with Google’s streaming services such as Google Music and Youtube, Google Assistant is a smart pick. It also comes with a formidable range of home automation.

And finally, if your household is equipped with Apple’s products, it’s a no brainer to pick Siri, who’s device also has the best speakers among the three. Siri also has an advantage concerning privacy, as it encrypts all data, unlike its competitors that use it for targetted ad campaigns.

As a consumer, your goal is to see which one of these fits your requirement and aligns with what you’re looking for from a voice assistant.

Machine Learning and AI to cut down financial risks

Under 70 years from the day when the very term Artificial Intelligence appeared, it’s turned into a necessary piece of the most requesting and quick-paced enterprises. Groundbreaking official directors and entrepreneurs effectively investigate new AI use in money and different regions to get an aggressive edge available. As a general rule, we don’t understand the amount of Machine Learning and AI is associated with our everyday life.

Artificial Intelligence

Software engineering, computerized reasoning (AI), once in a while called machine knowledge. Conversationally, the expression “man-made consciousness” is regularly used to depict machines that emulate “subjective” capacities that people partner with the human personality.

These procedures incorporate learning (the obtaining of data and principles for utilizing the data), thinking (utilizing standards to arrive at surmised or positive resolutions) and self-redress.

Machine Learning

Machine learning is the coherent examination of counts and verifiable models that PC systems use to play out a specific task without using unequivocal rules, contingent upon models and induction. It is seen as a subset of man-made thinking. Man-made intelligence estimations manufacture a numerical model reliant on test information, known as “getting ready information”, in order to choose figures or decisions without being explicitly adjusted to playing out the task.

Financial Risks

Money related hazard is a term that can apply to organizations, government elements, the monetary market overall, and the person. This hazard is the risk or probability that investors, speculators, or other monetary partners will lose cash.

There are a few explicit hazard factors that can be sorted as a money related hazard. Any hazard is a risk that produces harming or undesirable outcomes. Some increasingly normal and particular money related dangers incorporate credit hazard, liquidity hazard, and operational hazard.

Financial Risks, Machine Learning, and AI

There are numerous approaches to sort an organization’s monetary dangers. One methodology for this is given by isolating budgetary hazards into four general classes: advertise chance, credit chance, liquidity hazard, and operational hazard.

AI and computerized reasoning are set to change the financial business, utilizing tremendous measures of information to assemble models that improve basic leadership, tailor administrations, and improve hazard the board.

1. Market Risk

Market hazard includes the danger of changing conditions in the particular commercial center where an organization goes after business. One case of market hazard is the expanding inclination of shoppers to shop on the web. This part of the market hazard has exhibited noteworthy difficulties in conventional retail organizations.

Utilizations of AI to Market Risk

Exchanging budgetary markets naturally includes the hazard that the model being utilized for exchanging is false, fragmented, or is never again legitimate. This region is commonly known as model hazard the executives. AI is especially fit to pressure testing business sector models to decide coincidental or rising danger in exchanging conduct. An assortment of current use instances of AI for model approval.

It is likewise noticed how AI can be utilized to screen exchanging inside the firm to check that unsatisfactory resources are not being utilized in exchanging models. An intriguing current utilization of model hazard the board is the firm yields. which gives ongoing model checking, model testing for deviations, and model approval, all determined by AI and AI systems.

One future bearing is to move more towards support realizing, where market exchanging calculations are inserted with a capacity to gain from market responses to exchanges and in this way adjust future exchanging to assess how their exchanging will affect market costs.

2. Credit Risk

Credit hazard is the hazard organizations bring about by stretching out credit to clients. It can likewise allude to the organization’s own acknowledge hazard for providers. A business goes out on a limb when it gives financing of buys to its clients, because of the likelihood that a client may default on installment.

Use of AI to Credit Risk

There is currently an expanded enthusiasm by establishments in utilizing AI and AI procedures to improve credit hazard the board rehearses, somewhat because of proof of inadequacy in conventional systems. The proof is that credit hazard the executives’ capacities can be essentially improved through utilizing Machine Learning and AI procedures because of its capacity of semantic comprehension of unstructured information.

The utilization of AI and AI systems to demonstrate credit hazard is certainly not another wonder however it is a growing one. In 1994, Altman and partners played out a first similar investigation between conventional measurable techniques for trouble and chapter 11 forecast and an option neural system calculation and presumed that a consolidated methodology of the two improved precision altogether

It is especially the expanded unpredictability of evaluating credit chance that has opened the entryway to AI. This is apparent in the developing credit default swap (CDS) showcase where there are many questionable components including deciding both the probability of an occasion of default (credit occasion) and assessing the expense of default on the off chance that default happens.

3. Liquidity Risk

Liquidity hazard incorporates resource liquidity and operational subsidizing liquidity chance. Resource liquidity alludes to the relative straightforwardness with which an organization can change over its benefits into money ought to there be an unexpected, generous requirement for extra income. Operational subsidizing liquidity is a reference to everyday income.

Application to liquidity chance

Consistency with hazard the executives’ guidelines is an indispensable capacity for money related firms, particularly post the budgetary emergency. While hazard the board experts regularly try to draw a line between what they do and the frequently bureaucratic need of administrative consistence, the two are inseparably connected as the two of them identify with the general firm frameworks for overseeing hazard. To that degree, consistency is maybe best connected to big business chance administration, in spite of the fact that it contacts explicitly on every one of the hazard elements of credit, market, and operational hazard.

Different favorable circumstances noted are the capacity to free up administrative capital because of the better checking, just as computerization diminishing a portion of the evaluated $70 billion that major money related organizations go through on consistency every year.

4. Operational Risk

Operational dangers allude to the different dangers that can emerge from an organization’s normal business exercises. The operational hazard class incorporates claims, misrepresentation chance, workforce issues, and plan of action chance, which is the hazard that an organization’s models of promoting and development plans may demonstrate to be off base or insufficient.

Application to Operational Risk

Simulated intelligence can help establishments at different stages in the hazard the boarding procedure going from distinguishing hazard introduction, estimating, evaluating, and surveying its belongings. It can likewise help in deciding on a fitting danger relief system and discovering instruments that can encourage moving or exchanging hazards.

Along these lines, utilization of Machine Learning and AI methods for operational hazard the board, which began with attempting to avoid outside misfortunes, for example, charge card cheats, is currently extending to new regions including the examination of broad archive accumulations and the presentation of tedious procedures, just as the discovery of illegal tax avoidance that requires investigation of huge datasets.

Financial Risks

Conclusion

We along these lines finish up on a positive note, about how AI and ML are changing the manner in which we do chance administration. The issue for the set up hazard the board capacities in associations to now consider is on the off chance that they wish to profit of these changes, or if rather it will tumble to present and new FinTech firms to hold onto this space.

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.

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

Artificial Intelligence and Machine Learning is transforming business operations across industries. From autonomous vehicles to financial services, AI has successfully found multiple use cases across virtually every space. For this piece, let’s focus on AI’s influence on healthcare. 

The healthcare industry has a variety of use cases for AI. With its ability to assist medical professionals with diagnoses and drug research, the healthcare community has welcomed AI and ML with open arms. Doctors can now track symptoms faster and effectively, while researchers can locate vaccine raw materials with minimum manual procedures. Hospitals and medical research centers have adopted AI into the heart of their operations. Here’s how AI’s contributions are transforming healthcare:

Improved decision making

Medical professionals have the responsibility to suggest treatment alternatives to patients. With the assistance of AI, doctors can make such decisions a lot faster and more accurately. For example, doctors treating cancer patients can make use of Machine Learning algorithms that can detect cancer cells and their potential spread and impact. Using such algorithms, doctors can choose between various treatment methods available, from basic medication to extensive surgical procedures.

Healthy lifestyle management

Everyone wants to be healthy, and AI is making it easier than ever to stay so. By providing information on daily eating, sleeping, and fitness habits, AI-inspired interfaces can predict the health impact of a user’s lifestyle and suggest quick and long-term fixes.

Health assistant chatbots

Chatbots are the rage today in the customer service space. People love interacting with chatbots to solve queries and receive answers. The healthcare space is taking advantage of chatbots too. Health assistant chatbots can perform simple diagnoses for patients, and accordingly recommend whether the patient needs to visit a hospital or not. Advanced chatbots could also suggest off the shelf medication and dietary suggestions. During difficult times such as the coronavirus outbreak, people are making use of such chatbots to reduce the load on hospitals.

Health monitoring

Patients admitted to hospitals need their parameters monitored constantly. AI/ML models can study a patient’s health parameters and alarm surgeons and physicians regarding high-risk situations. For example, during child-birth, delivering mothers lose a lot of blood, and doctors can effectively measure the amount lost, and accordingly provide the required medical assistance.

Medical imaging

The healthcare community has adopted computer vision to study medical images and provide insights for physicians. In radiology, AI models can locate tumors and predict their development. Dermatology also makes use of computer vision by studying various skin disease cases and identifying the ones at hand. With such technology, dermatologists can assist patients (such as the ones suffering from eczema) with more accurate treatment options. 

Early symptom identification

With AI-inspired health monitoring equipment, doctors can identify potential threats to a patient’s health. Health conditions such as diabetes and heart disease can be addressed in advance and treated, thus eliminating the chances of a condition getting more complicated.

Epidemic spread

If an epidemic’s spread can be analyzed with high precision, populations can mitigate a virus outbreak by adopting healthy practices. For example, during this coronavirus outbreak, understanding the virus’s spread has helped people practice social distancing and regular hand-washing. Two effective ways to tackle COVID-19.

Vaccine research

The coronavirus outbreak has forced pathologists to search for suitable vaccine raw materials. Machine learning can help researchers locate protein structures and eliminate futile alternatives. Businesses across the globe are looking for ways to use AI for vaccine research and identification.

End of life care

With every decade, people’s lifespans have increased, and AI is poised to increase that even further. Conditions such as dementia and osteoporosis are common health issues faced by the elderly. AI models, coupled with a humanoid design, can interact with people suffering from such issues, to keep themselves distracted, and their minds active.

Conclusion

The healthcare field, as displayed, is filled with AI/ML use cases. New generation AI tools, and models, are helping doctors understand their patients’ conditions better, and provide advanced treatment solutions. While implementation still has a long way to go, AI in healthcare has started on the right footing; with technology that promotes quality diagnoses and maintains the importance of medical professionals.

The future of medicine is here, with AI paving its path.

How chatbots are redefining customer experience

Chatbots’ reliability and consistency in serving customers have changed the way the world created the customer experience. A company that regularly communicates with customers can experiment and improve using AI-based chatbots. Digital transformation can favor the customer service and experience. The world is moving fast and so are the technological advancements. If you intend to draw benefits from implementing the latest technology, there is no reason for further delay.

Why Customer Experience Is Important For Every Business?

Customer experience is a trophy that companies receive for something they do with pride. Companies focusing on improved customer experience know the worth of single positive feedback, share, comment and rebound effect it creates. New customer acquisition and maintenance of existing customers are crucial for market sustainability. Returning customers are solid proof of the experience you created for them. 

Customer loyalty is not achievable with marketing tactics it is a long-term investment in the customer relationship. The customers, who have a guarantee towards service or product, trust the companies. The companies in return continue to provide flawless service. Customer experience is a key feature in brand building. Attracting new customers is challenging and bringing back a lost customer is even tougher. 

Customer satisfaction has a direct impact on revenues and the company’s reputation. Thus, customer experience is of ultimate importance to every business.

How Has Customer Experience Changed Over The Years?

The customer experience has changed with the availability of the internet and loads of information that influences the decisions. The power of researching about the product, services, and the competitor’s brands raises the overall expectations. The features, the price, functionality, use of advanced technology, and response from the company all such expectations have changed with the market. The launch of the latest technology based affordable solutions is changing their demand.

Customer support is no more just issue resolution team; the general queries related to product, price, and availability are part of customer service. The location constraint; faced by customer care is removed by chatbots and it eases the process. It has changed the way the pre and post-sales interactions take place. Customer experience should be enjoyable, useful, and reliable. B2C businesses have a great opportunity to create a better customer experience.

What Are Chatbots?

Chatbots are AI-based conversational robots designed for the specific needs of the company and its services. The software executes automated tasks like communicating with users without any human control over the bot. These chat platforms either independent or via websites are effective through the internet. The chatbots developed with specific purposes as discussion and basic plus extended conversation with humans are just like instant messages.

The response to the queries is spontaneous and machine learning helps them process the requests. Chatbots can respond to the text and voice inquiries and perform the required actions. The knowledgebase helps chatbots to search for accurate response by combining information to communicate. The best examples of chatbots are Alexa from Amazon, Siri by Apple, Microsoft’s Cortana, and Google Home.

Companies like Pizza Hut, Uber, eBay, Lyft, Emirates, Bank of America, MongoDB, LeadPages, TechCrunch, and many more are already using chatbots to deliver a better experience to the customers.

Grand View Research Report says that the chatbot market globally is predicted to reach USD 1.2 billion in just ten years. The report says that the demand for intelligent virtual assistants is rising with automatic speech recognition and text to speech conversion. 

Why Do We Need Chatbots?

These instant messengers create a personal and real life-like experience. The speed and precision it brings to the customer service are securing chatbots position in businesses. The growth of the business is a factor that invites companies to get their own chatbots. 

Customization of messages is the next step for the improvement in chatbots. Repeating the same messages does not make sense hence learning from the customer behavior helps. Companies use chatbots by keeping their goals in mind; bringing relevance to the user journey, create intimate experiences, and engage with users.

Chatbots used uniquely for sending product updates, promotional messages, and product comparisons can deliver a better experience. We can collect user data, offer services, and replicate human interactions. The search for information is simplified, communicating can be easier, and personalization of information is possible too.

Chatbots take care of the basic level of communication. In case of inability to solve or in case of customer dissatisfaction; it passes to human handled customer service process.

Chatbots are available full time; they eliminate the waiting period for attendance by a customer care representative. They save money on companies spent on calls and customer care activities. You save on hiring and training costs of customer care executives.

Chatbots have no dependency on moods, feelings, interpretations and have no perception of who should behave how nor do they respond considering this. Chatbots can be effective at any given time and can do mundane tasks with the same precision every time without being bored.

Why Chatbots Are The Future Of Customer Service?

A survey by Business Insider suggests that 80% of the enterprises will use chatbots by the year 2020.

Businesses like banks, telecom, retail chains, e-commerce, and many industries use chatbots as virtual assistants for customer support. Initial training costs are higher but the inquiry management and response save costs and time in the long run. It works on FAQs, the questions that are similar but framed differently by the users. The software allows the bot to explore the existing data about the user and the information stored on the topic. 

The ability to understand the queries, recognition of terminology, dialogues, and presentation of the query is machine learning. A chatbot can identify if it is a statement or problem, select a proper template for the response, cross-check with the user if the understanding of the question is correct. 

The data is collected from various sources by the bot; it is cleaned, segregated, marked, and classified for reference. The data built from the customer service center e-mails, manual chats, training material, and call recordings are useful in improving customer experience. The dialogues that happen in this process are repetitive and this helps template creation and standardization of responses. The personal information from this data removed intelligently works in favor of companies. The intention is to extract the question-answer sets for further use.

The sequencing of data helps in organic search for the chatbot reducing the mistakes in understanding the questions. Chatbots can rectify typo errors and reframe the question-received input. Speak the language your audience uses not in terms of spoken language but the latest terms. Solve actual problems by asking relevant questions. Avoid missing opportunities by being available 24X7. A single chatbot can enter into multiple conversations that earlier needed a lot of employees.

Independently owned company or a large organization both can benefit from AI Chatbots. The companies with fewer resources or high frequency of customer conversations, in both the cases the chatbots, can serve more practically. Salesforce survey indicates that 64% of the agents can solve complex problems as AI Chatbots deal with the basic ones. 

The customer experience is changing and the expectations are rising with the immediate response in 42% cases and response in less than 5 mins in 36% cases. The speed with which chatbots communicate, businesses will certainly churn information fast to serve faster. (Salesforce.com)

How Are Chatbots Used In Business?

Businesses and customers can get a reliable solution from assistance AI-based chatbots provide.

  • Answering questions 
  • Redirecting to FAQs
  • Providing detailed explanations 
  • Resolving complaints 
  • Bill payments 
  • Flight or restaurant booking 
  • Schedule meetings
  • Purchase items 
  • Managing subscriptions
  • Creating a brand image

How Are AI Chatbots Bettering Customer Experience And How Data Is Enabling This?

Artificial intelligence involves machine learning. AI creates intelligent machines, and ML creates systems that can learn from experience. The eBay chatbot enables a user to chat using a smartphone or Google Home and it can purchase a product at the lowest price with your instructions.

The data collected by asking questions on chat, collected from surveys or any brochures/e-books the user downloads are stored for future use. This data helps to communicate with the user in the future. The preferences of users are stored; this creates a strong rapport and good impression. The feeling that the company knows the customer is special. The customer can relate to how well a company deals with data. The latest offers during the chat process ease registration, with existing information. There is no need for the user to create logins.

The data AI chatbots uses increases customer engagement rate, build brand awareness, and creates a personalized experience. The amounts of e-mails read less or not opened, due to flooded inboxes. The chatbots allow us to share the same amount of information at a faster pace. Chatbots can send text, image, pdf, or message in any form. This restriction less communication introduces increased activities of marketing and promotion.

Chatbots are effective and soon may replace the search window on the websites. Creating a chatbot requires an understanding of the business as well as a target customer. If your customer base for the product is the 16-30 age group of chatbot can be a perfect solution. For the age group of 55-65 maybe the design with voice command or connect calls would work better instead. The internet connectivity is the dependency for chatbot hence the drops in the internet or limited availability can be an obstacle in serving efficiently.

The AI data is useful for training purposes, analysis, and serving the customers better. The situations that arise occasionally and some that arise regularly are included in training the customer representatives with the accumulated data.

The Future Of Customer Experience And Chatbot

AI chatbots are preferred by most of the companies as it saves time, money, and efforts. About 46% of internet users in the US would choose live support instead of a chatbot as per a survey by usabilla.com.

Machine learning increases the accuracy level of chatbots. ML allows the system to learn from the data but AI helps in decision-making. ML finds the solution for a user but AI will find an optimal solution. The advanced systems can go beyond the general chat. They let the user know that they are speaking to a Chatbot. This can change the way they ask questions and the response received from the bot can become more acceptable.

According to the report by Global Market Insights, the market worth of chatbot will be $1.34 billion by the year 2024 and nearly 42% will be dedicated to customer service.

Connect the AI Chatbots created by you with facebook messenger, Alexa, Siri or any of the reliable bots to increase efficiency. Chatbots can help take actions that are interaction or information-based. The user can actually complete the task of purchase, shopping, booking from the same chat window. There remains no need for a user to search for other ways of completing the task. It saves time and effort of the users and the companies get faster conversions.

AI can hold conversations as humans do, these dialogues create comfort and trust for users to participate in product/service-related feedback or surveys. The simple and complex form of communication with the prospects and existing customers is levered by the chatbots.

Chatbots were in making since the 1950s but today they have shape conversations using the triggers as keywords. Chatbots are better listeners and thus provide better solutions to the problems. The designing of chatbot involves humans hence the customization is programmable. 

The chatbot applications are useful in customer service, social media marketing, and order processing. Sectors like BFSI, Media& Entertainment, Healthcare, Retail, and Travel & Tourism are widely using these solutions. The deployment of Chatbots can be on-premise or cloud, both opens easy ways of dealing with customers. 

With gradual development, the concerns of delay in response, irrelevant suggestions, sharing of inaccurate information, misunderstood requests, or unhelpful responses have become a checkpoint. This is not the failure of chatbot but the development stage, which can assure improvement by the involvement of AI companies. The continuous growth in AI technology is the commitment of experts for the betterment of human life including the business aspects.

How artificial intelligence is transforming E-commerce

Web-based business or e-Commerce means purchasing and selling of merchandise, items, or administrations over the web. Exchange of cash, assets, and information is additionally considered as e-Commerce. These business exchanges should be possible in four different ways: Business to Business (B2B), Business to Customer (B2C), Customer to Customer (C2C), Customer to Business (C2B). The standard meaning of E-business is a business exchange which is occurred over the web. 

The historical backdrop of e-commerce starts with the first-ever online deal. On 11 August 1994, a man sold a CD by the band Sting to his companion through his site NetMarket, an American retail stage. This is the primary cause of a buyer buying an item from a business through the internet. From that point forward, e-commerce has advanced to make items simpler to find and buy through online retailers and commercial centers. Autonomous consultants, private ventures, and huge organizations have all profited by internet business, which empowers them to sell their merchandise and services at a scale that was impractical with customary disconnected retail. Worldwide e-commerce business deals are anticipated to reach $27 trillion by 2020. 

History of online business is inconceivable without Amazon and eBay which were among the first Internet organizations to permit electronic exchanges. Because of these companies we currently have an attractive web-based business division and appreciate the purchasing and selling points of interest of the Internet. Presently there are 5 biggest and most acclaimed overall Internet retailers: Amazon, Dell, Staples, Office Depot and Hewlett Packard. 

Evolution Of E-commerce

CompuServe, a key critical internet business organization was built up by Dr. John R. Goltz and Jeffrey Wilkins by using a dial-up association in 1969. This was the first run through the web-based business was presented. Michael Aldrich developed electronic shopping in the year 1979, he is additionally considered as originator or designer of web-based business. This was finished by associating an exchange handling PC with an altered TV through a phone association. This was accomplished for the transmission of secure information. 

This proceeded with the development of innovative AI systems, prompted the dispatch of the principal web-based business stages by Boston Computer Exchange in 1982. 

The 90s took the online business to the following level by presenting Book Stacks Unlimited as an online book shop by Charles M. Stack. It was one of the principal web-based shopping website made around then. Internet browser apparatus presented by Netscape Navigator in 1994. It was utilized on the Windows stage. The year 1995 denoted the notable improvement throughout the entire existence of web-based business as Amazon and eBay were propelled. Amazon was founded by Jeff Bezos, while Pierre Omidyar started eBay. 

PayPal was the first online business installment framework in 1998 that began as an instrument to make payments online. Alibaba began its web-based shopping stage in 1999 with more than $25 million as capital. Step-by-step it ended up becoming an e-commerce mammoth. 

Google kickstarted the advertisements promoting apparatus named Google AdWords as an approach to assist retailers with utilizing the compensation per-click (PPC) setting in 2000. Amazon Prime’s enrollment was propelled by Amazon in 2005 to enable clients to get free two-day shipping at a yearly charge. 

Significant changes that have occurred in the web-based business industry from 2017 to show. Huge retailers are pushed to sell on the web. Private companies have seen an ascent, with nearby merchants currently working together via web-based networking media stages. 

Operational expenses have been let down in the B2B area. Package conveyance expenses have seen a noteworthy ascent. A few internet business commercial centers have risen to empower more vendors to sell on the web. Coordinations has developed with the presentation of robotization instruments and AI. Online life has turned into an apparatus to build deals and market brand. The purchasing propensities for clients have essentially changed. 

Usage Of Data In Artifical Intelligence Systems

With regards to AI, there is nothing of the sort as information over-burden. Truth be told, it’s a remarkable inverse—the more information, the better. Since AI frameworks can process colossal measures of information, and their precision increments alongside information volume, the interest for information keeps on developing. 

Artificial intelligence makes it feasible for machines to gain insights, as a matter of fact, learn under new inputs and perform human-like errands. Most AI models that you find today, from chess-playing PCs to self-driving vehicles, depend intensely on profound learning and common language handling. Utilizing these innovations, PCs can be prepared to achieve explicit errands by handling a lot of information and perceiving designs in the information. 

Online businesses have two things in plenitude. One is an interminable rundown of items and the other is information. Web-based businesses need to manage a ton of information consistently. This information can be similar to everyday deals, the all-out number of things sold, the number of requests got in a territory, and so forth. It needs to deal with client information too. 

Dealing with that measure of information isn’t workable for a human. Artificial intelligence systems can not just gather this information in a progressively organized structure but, also, create appropriate bits of knowledge out of this information. 

This aide in understanding the client’s behavior just as of an individual purchaser. Understanding the client’s purchasing behavior can make e-commerce make changes any place required and predict what purchases the client might make in the future.

Artificial Intelligence Systems & E-Commerce

With regards to shopping, numerous clients have chosen to take their business on the internet. Insights have assessed that the number is relied upon to ascend to more than 2 billion by 2021. 

This interest in online shopping has made organizations progressively inventive in the way they interact with consumers on the net. 

Gone are the days when clients had to search for an online business store. Presently, it’s the ideal opportunity for e-commerce businesses empowered with an Artificial Intelligence system that is changing the plan of action of numerous brands. The headway of new advancements has totally changed the present situation of the business. 

Henceforth, incorporating artificial intelligence systems in internet business has raised the advertising standards as well. These artificial intelligence systems can break down informational indexes, recognize designs and mak a customized understanding. This makes a one of a kind methodology that is more effective than any person. 

Advance Visual Search Engine

Recently AI presented the visual search motor in the e-commerce segment. It is one of the most invigorating innovations that allow a client to find what they need with only a solitary snap. We can say that AI is a determined innovation that empowers visual hunt. With a straightforward snap, the client can get fitting outcomes. 

AI frameworks enable Marketers to Easily Target Specific Customers

Artificial intelligence removes the mystery with regard to engaging perfect purchasers. Rather than making a one-size-fits-all advertisement, organizations would now be able to make promotions that are focused on explicit purchasers relying on their online conduct. 

Advertising and AI recommendation tools make it simpler to gather purchaser information, make dynamic advertisements that consider this data and disseminate significant promotions and substance on stages where perfect purchasers are probably going to see it.

AI training data have even prompted increasingly successful retargeting techniques. Presently, companies like Facebook make it simpler for organizations to retarget advertisements in spots where clients go on the web. 

Artificial Intelligence recommendations can Help Improve Search Results 

An advertiser can make the most captivating and viable web duplicate on the planet. Be that as it may, it won’t enable them to arrive at their business objectives if clients can’t discover it. An ever-increasing number of clients are discovering items utilizing search engines. 

An easy to use website with important keywords, meta depictions, and labels can go far in reaching the perfect customer. Therefore, AI systems can enable advertisers to drive more traffic to their site and arrange content in a manner that urges purchasers to consistent course through your internet business store. The present advertisers are vigorously worried about the client experience and creating sites that rank high on web crawlers. 

Make Progressively Effective Deals

If you need to make a solid deals message that reached the customer at the perfect time on the correct stage, at that point incorporating AI into your CRM is the best approach. 

Numerous AI chatbots empower common language learning and voice info, for example, Siri or Alexa. This enables a CRM framework to answer client inquiries, tackle their issues and even recognize new open doors for the business. Some AI-driven CRM frameworks can even perform various tasks to deal with every one of these capacities and the sky is the limit from there. 

Artificial Intelligence Chatbots

The web-based business destinations currently offer every minute of everyday help and this is a result of chatbots. Before this, AI chatbots just offered standard answers, presently they have transformed into wise machines which see all issues that need to be managed. 

A few web-based shopping locales presently have AI chatbots to help individuals settle on purchasing choices. Indeed, even applications like Facebook Messenger have AI chatbots through which potential clients can speak with the merchant site and offer help with the purchasing procedure. These bots convey by utilizing either discourse or message or both. 

Personalization

With advances in computerized reasoning and AI training data, new profound personalization procedures have entered internet business. Personalization is the capacity to utilize mass-shopper and individual information to tweak content and web interfaces to the client. 

Personalization stands apart from customary promoting enabling balanced discussions with purchasers. Great personalization can expand commitment, transformations, and diminishing time to exchange. For instance, online retailers can track web conduct over various touch focuses (portable, web, and email). 

Better Decision Making

Ecommerce can settle on better choices with the use of artificial insight. Information experts need to deal with a great deal of information consistently. This information is unreasonably tremendous for them to deal with. Also, breaking down the information likewise turns into a troublesome undertaking. 

Man-made reasoning has secured the basic leadership procedure of e-commerce. Man-made intelligence calculations can without much of a stretch distinguish the mind-boggling designs in the information by anticipating client conduct and their obtaining design.

Future Prospects

New examinations anticipated that the overall e-commerce deals will arrive at another high by 2021. Online business organizations ought to envision a 265% growth from $1.3 trillion in 2014 to $4.9 trillion in 2021, according to statista. This demonstrates the fate of a relentless upward pattern without any indications of decay. 

As the lines obscure between the physical and advanced condition, numerous channels will turn out to be increasingly pervasive in clients’ way to buy. This is proved by 73% of clients utilizing different channels during their shopping venture. 

Online business is a consistently extending world. With the escalating obtaining intensity of worldwide shoppers, the expansion of online life clients, and the ceaselessly advancing foundation and innovation, the eventual fate of eCommerce in 2019 and past is still progressively energetic as ever. 

AI training data and AI recommendations have made life simpler for the retailers just as purchasers. Web-based business sites are seeing an exponential climb in their deals. Man-made consciousness has helped E-Commerce sites in giving better client experience.

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.