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Latest Innovations in the field of AI & ML

Artificial Intelligence can replicate human intelligence to perform actions, logical reasoning, learning, perception, and creativity. An intelligent machine developed by humans to input requests and receive the desired output.

Machine Learning is an artificial intelligence subdiscipline and technique for developing self-learning computer systems. ML platforms are gaining popularity because of high definition algorithms that perform with the utmost accuracy.

Neural Networks a technique of Artificial Intelligence modeled similar to the human brain, can learn and keep improving with the experience and learns with each task.

Deep learning is unsupervised learning, the next generation of artificial intelligence computers that teach themselves to perform high-level thought and perception-based actions.

Market Size:

Global Machine Learning Market was valued at $1.58 billion in 2017 expected to reach $8.8 billion by 2022 and  $20.83 billion by 2024.

Artificial Intelligence predicted to create $3.9 trillion of value for business and cognitive and AI systems will see worldwide investments of $77.6 billion by 2022.

AI and ML have the capability of creating an additional value of $2.6 Trillion in Sales & Marketing and $2 Trillion in manufacturing and supply chain planning by the year 2020.

Unmanned ground vehicles have registered revenues of $1.01 billion globally, in 2018 and expected to reach $2.86 billion by 2024.

Autonomous Farm Equipment market worldwide is projected to reach over $86.4 Billion by the year 2025.

Key Players in Artificial Intelligence:

  • Apple
  • Nvidia Corporation
  • Baidu
  • Intel Corp.
  • Facebook
  • AlphaSense
  • Deepmind
  • iCarbonX
  • Iris AI
  • HiSilicon
  • SenseTime
  • ViSenze
  • Clarifai
  • CloudMinds

Industries Artificial Intelligence Serves:

  • Retail
  • HR & Recruitment
  • Education
  • Marketing
  • Public Relations
  • Healthcare and Medicine
  • Finance
  • Transportation
  • Insurance

Artificial Intelligence can be applied in:

  • Face Recognition
  • Speech Recognition
  • Image Processing
  • Data Mining
  • E-mail Spam Filtering
  • Trading
  • Personal Finance
  • Training
  • Job Search
  • Life and Vehicle Insurance
  • Recruiting Candidates
  • Portfolio Management
  • Consultation
  • Personalized marketing
  • Predictions

Key Players in Machine Learning

  • Google Inc.
  • SAS Institute Inc.
  • FICO
  • Hewlett Packard Enterprise
  • Yottamine Analytics
  • Amazon Web Services
  • BigML, Inc.
  • Microsoft Corporation
  • Predictron Labs Ltd.
  • IBM Corporation
  • Fractal Analytics
  • H2O.ai
  • Skytree
  • Ad text

Industries Machine Learning Serves:

  • Aerospace
  • BFSI
  • Healthcare
  • Retail
  • Information Technology
  • Telecommunication
  • Defense
  • Energy
  • Manufacturing
  • Professional Services

Machine Learning can be applied in:

  • Marketing
  • Advertising
  • Fraud Detection
  • Risk Management
  • Predictive analytics
  • Augmented & Virtual Reality
  • Natural Language Processing
  • Computer Vision
  • Security & Surveillance

Future of AI & ML:

Artificial Intelligence and Machine Learning can support in every task, predict the damages, ease the processes, bring better control and security to the applications and make businesses profitable. Overcome the challenges of every field with AI & ML technology.

In the future, the subsets of AI like Natural language generation, speech recognition, face recognition, text analytics, emotion recognition, and deep learning.

Natural Language Generation converts the data into text for computers to understand and communicate with the user. It can generate reports and summaries using applications created by Digital Reasoning, SAS, Automated Insights, etc.

Speech recognition understands the human language and these interactive systems respond using voice. The apps with voice assistants are preferred by many who don’t prefer text or have typing constraints and lets you pass on instructions while you are busy in other work, cooking, cleaning or driving, etc. E.g. Siri, Alexa, etc. Companies that offer speech recognition services are OpenText, Verint Systems, Nuance Communications, etc.

Virtual Agents interact with humans to provide better customer service and support. Commonly used as chatbots these are becoming easy to build and use. Companies providing virtual agents are Amazon, Apple, Microsoft, Google, IBM, and a few others.

Text Analytics helps machines to structure the sentences and find the precise meaning or intention of the user to improve the search results and develop machine learning.

NLP – Natural language processing helps applications to understand human language input, analyze large amounts of natural language data. It converts unstructured data to structured data for a speedy response to queries.

Emotion Recognition is AI technology that allows reading human emotions by focusing on the face, image, body language, voice, and feelings they express. It captures intention by observing hand gestures, vocabulary, voice tone, etc. E.g. Affectiva Emotion AI is used in industries such as gaming, education, automotive, robotics, healthcare industries, and other fields

Deep learning a machine learning technology that involves neural circuits to replicate the human brain for data processing and creating patterns for decision-making. Companies offering deep learning services are Deep Instinct, Fluid AI, MathWorks, etc.

Every sub-discipline of AI technology is worth exploring. Present-day applications are using these technologies to some extent and in the future, we will see outbursts and advance applications to benefit society and industries.

AI & ML innovations

1. Searches: AI technology has improved the way people search for information online, the text, image and speech search enabled with the recommendations from the search engines. Optimum search in minimum effort and time, faster response rate and relevant results along with the options to suit your requirements are what you can expect as a user. Better search optimizes web content, helps in lowering marketing and advertising expenses, increase in sales and productivity. Eg. Amazon Echo, Google Home, Apple’s Siri, and Microsoft’s Cortana deliver the best search experience. Google’s assistant receives voice instructions for about 70% of its searches.

2. Web Design: Companies know of the fact that how important it is to keep the websites working, creating a user-friendly website that is less expensive. Updating websites is another challenge. AI applications can empower you with pre-built designs of websites; assist you in creating one without any technical expertise, by uploading some basic content, images, etc. Select the buttons for call to action, themes, and formats to create a website that can interact with the user. Better user experience considers the location, demographics, interactions and the speed of analyzing the search and personalizing web experiences. Great web experience has a high probability of conversion. You may even add a chatbot to the website for faster query resolution and increased sales.

3. Banking and Payments: AI can automate transactions, help to schedule transactions and make general and utility payments. Personalized banking can let the banks focus on customer wise preferences and share product information of utmost relevance. Customers investing in the FDs, stocks, NFOs or even based on age to approach with specific marketing material. Loans and its procedures can be automated and the basic level information is shared using chatbots. Perform KYC checks necessary for continuing service from the banks. E.g. Simudyne is an AI-based platform for investment banking. Secure is AI and ML-based identity verification system for KYC.

4. E-Commerce: Retailers achieved a competitive edge using AI technology. It has recommendation systems based on location, age, gender, past purchases, stored preferences, (customer-centric search, etc. Tailor-made recommendations increase the chances of customers visiting the site and making a purchase or even return at a later stage to avail discounts. Chatbots are used for 24×7 customer support, image search lets users find the product faster without entering any text, better the decision making by comparisons and after-sales service. Companies benefit in inventory management, data security, customer relationship management and sales improvement using AI technology. IBM’s Watson assists customers with independent research about the factors relating to the product, its advantages, specifications, restrictions and multiple products that match the criterion.

5. Supply Chain and Logistics: This industry has benefited from the AI technology in improving operations, reducing shipping costs, easy tracking of vehicles, maintenance of vehicles, know about the condition in which the parcel was delivered, real-time reporting and feedback. It can help in quality checks for manufacturing, managing the supply chain vendors, keeping records of warehouse entries, forecasting the demand for products, reducing freights, planning and scheduling deliveries, etc. AI can automate many functions of supply chain and logistics for increased sales and better customer care.

6. Marketing and Sales: AI automation along with ML can give customers better options of products and prices, personalize the recommendations, eliminate geographical constrains, lower the cost of customer acquisition and maintaining touch with the existing customers. The intelligent algorithms predict what users want and what companies can provide to match the best possible. AI can even predict price trends, manage inventory, and help in decision making for stocking. Marketing activities can be channelized based on preferences and consumer behaviors. Services by companies like Phrasee and Persado can determine the perfect subject line for an e-mail, organize e-mail in a way that attracts the user to take desired actions. After-sales and customer care is an important aspect for companies expecting returning customers.

Overall it will increase the profitability of organizations and improve sales and marketing performance. AI can identify new opportunities for business and suggest an effective method too. Predictive analysis is of great help in customer service companies like Netflix and Spotify that run on subscriptions, would like to know if enough registrations are on the way for next month. Decide on additional schemes or marketing efforts are needed for increasing sales.

7. Digital Advertising: AI is supporting in marketing and sales, certainly it can assist in better focus for advertisements shown to the users. Google Adwords lets you focus on demographics, interests and other aspects of the audience. Facebook and Google ads are the platforms that use ML & AI for intelligent and accurate displays of relevant ads. Next is an audience management service that uses machine learning to automate the handling of ads for maximum response and it tests it on a variety of an audience to find the most active participation and likely conversions. The highest conversion rates received because of the increased performance of ads using ad text makes a business profitable.

Digital Advertising

Outline:

The continuous progress of Artificial Intelligence and emerging sub-disciplines will lead to customization and improvement in products and services. Human to Chatbot conversations are new but bot to bot conversations, actions, negotiations and much more awaited and is in the developing stage.

The existence of technology will add value to human life, create reliance and businesses will have new openings and challenges to deal with. Intelligent tools will deliver smart solutions and give rise to innovation to cut the competition.

Financial Services

Artificial Intelligence and innovative services and products are spreading like fire. The companies and individuals who are a fan of technological developments follow the developments. AI provides multiple services and people unaware of new technology even use it extensively.

The modern approach towards the finance industry is the result of multiple technological interventions.

Current Market Size of the Finance Industry:

Presently the expansion phase of the finance sector in India is calling for innovation. The foreign portfolio investors have reached $899.12 million on November 22, 2018. Total Asset Under Management (AUM) in the Mutual Fund industry was on peak, at $340.48 in April 2018 till February 2019. IPOs (Initial Public Offers) raised in the period from April to June 2018 have increased to $1.2 billion.

Investments and Developments provide a new horizon to the upcoming future.

Financial Services and AI

Future of Financial Services:

Leading financial services firms are achieving a higher market share with the AI initiatives they enroll. The finance sector is enthusiastic,about 70% of firms are part of the ML and 60% use NLP.  The future of this sector varies in terms of revenues independent of the technology.

Artificial Intelligence brings dependability in the service sector and the finance industry is a prime service provider. The trust built over the last few years is changing the budgeting and strategy for involving AI. It provides an advantage to meet customer expectations and to gain a competitive advantage over others.

The scope of investments by 45% of frontrunner financial services firms are nearing to $5M. Risk takers are likely to win, as they are pro technological changes.

AI adoption increases the ability to solve the operational problems of a repetitive nature, or simple tasks like primary conversations with the basic level of Artificial Intelligence technology. Advanced level of AI brings in understanding power, perception and decision power.

Mobile wallet transactions in India expected to touch $492.6billion by 2022.

The Association of Mutual Funds in India (AMFI) is targeting nearly five-fold growth in assets under management (AUM) to Rs 95 lakh crore (US$ 1.47 trillion) and three times growth in investor accounts to 130 million by 2025.

Artificial Intelligence helps in credit decisions, risk management, fraud prevention, trading, personalized banking, process automation and enhancement of customer experience. AI is making the dream come true for the people who had weird ideas that machines can do wonders.

Humans are optimistic about AI technology, with expectations that it will bring transactional security, improved digital assistants, a high level of transparency in handling accounts, introducing process automation and foremost significant is the thorough checks of transactions and processes.

Types of Financial Services:

  1. Banking Services
  2. Investment Services
  3. Insurance
  4. Foreign Exchange
  5. Accounting
  6. Brokerage
  7. Mortgage
  8. Wealth Management

Software and mobile applications are improving accessibility to financial services and Artificial Intelligence is easing the process. Availing services was never so easy. Automation with AI, ML and NLP is a boon for service recipients.

Scope for AI-based automation:

1. Commercial banking Services: These financial services help businesses to raise money from market sources like bonds and equity. The primary activities of commercial banks if powered with AI can bring discipline to internal banking processes. Investment banking and retail banking are already exploring AI.

2. Venture Capital: It is a service that provides outside investors to companies with the potential of high growth. There is a surety of business when these investors bring in money for the business. AI can help in calculating risks and returns for the investors.

3. Angel Investment: An informal investor (angel investor) typically shares the resources and funds their investment capital. There are groups and networks of angel investors. AI can improve networking for connecting the right investment seekers and investors based on preferences.

4. Conglomerates: A financial services company is functioning in multiple sections that provide services such as life insurance, asset management, retail management, and investment banking can draw advantages with AI-based support apps.

5. Financial Market Utilities: Stock exchanges, clearinghouses and interbank networks and such organizations provide specialized services that require precision. AI-powered trading and banking are in high demand.

AI can assist in simplifying the service and improving its quality.

1. Smart Sales: The AI-based Chatbots are better in solving basic queries and responding using FAQs. With no or minimum human intervention, a virtual salesperson can take the customer through the stages of sales right from inquiry until closure.

2. Compliance: An enormous amount of financial data that is generated in banking and other financial services sector creates challenges for the service providers. Ai can identify the malpractices, manipulation and any loopholes found in personalized and classified services.

3. Evaluate Risks: Artificial Intelligence can consider the concerns and treat the user requests accordingly. Each financial transaction, loan or investment is accompanied by various risks that affect the business and thus the help of technology improves decision making.

4. Trading: Financial markets are prone to fluctuations yet many algorithms that try predicting the trends, using the old data. It can independently suggest, buy, sell or hold the stocks and notify us for the transactions or alert based on fed instructions.

5. Predictive Analytics: The spending habits, purchase frequency, other choices, investment portfolio, and transactional data lets AI guide for improving financial decisions and shares investment ideas.

6. Data Enrichment: Transaction data is simplified enough for the customers to understand and take control over their spending habits, budgeting, managing the credit score.

7. Smart Loans: The banks and financial institutes consider the credit score of the customer to approve the loans. Their banking history, income, tax payments, current financial situation, and past loan records are maintained by AI. It can easily bridge the gaps between creditworthy loan seekers and lenders.

8. Personalized Wealth Management: This service is for customers that have either huge bank balances or active investors in both the cases they are the favorite sales targets. The AI-based advisors provide the best advice to the customers based on the customer data available.

AI Performed Banking Activities:

1. Issue checkbooks

2. Credit cards

3. Interacting with customers for balances

4. Loan information and procedures

5. Online transactions

6. Electronic fund transfer  

7. Pending documents

8. Send dispatch information

9. Make bill payments

10. Schedule payments

11. Utility bills

12. Repayment of loans

13. Assist in tax planning

14. Aid in foreign exchange

15. Foreign exchange processing and remittance

16. Send info on upcoming investment options in debt and equity

17. Calculate and inform about brokerage for transactions

18. Guidance for wealth management

19. Help buy an insurance policy, send quotes and renewals

20. Book new FDRs and renewal of FDRs

21. Ease to operate the accounts

Innovations that have changed this industry with traditional mindset functions are:

  1. Cleo: An AI-powered data-driven messenger helps manage their finances. It allows the users to link bank accounts and send money to their contacts of FB messenger. You can set a limit for savings and Cleo can keep that spare amount aside. Checks if you should spend money and is it affordable. It can warn users when they do not follow the financial limits and overspend.
  2. ZestFinance: This ML automated platform is an underwriting solution that assesses borrowers with no credit information. AI-powered platform can be implemented by the companies to automate lending and reduce losses occurring due to inaccurate data. Zest Finance can predict the risk and improve the business.
  3. Scienaptic Systems: It provides an underwriting platform that gives banks and credit institutions better transparency about the customers. It successfully holds 10 crores of customers. Scienaptic Systems uses myriad unstructured and structured data, transforms the data and learns from interactions to offer contextual underwriting intelligence. It could save $151 million of loss for a major credit card company.
  4. Eva Money:  This AI-based mobile app is available on iOS and Android. It is voice and chat enabled and replies to all your queries relating to personal finances. Link the Eva Money app to your bank accounts and it provides a picture of your current financial holdings. It can even recommend increasing savings, improving credit score and other financial decisions.
  5. Trim: It analyzes your expenses and assists in saving money. It can even cancel the unused facilities or high-cost subscriptions, get you better options on investment and insurance requirements and even negotiate bills for you. VentureBeat reported Trim to save $6.3 million of 50,000 users.
  6. DataRobot:  It provides machine learning-based software for data scientists, business analysts, software engineers, and IT professionals. DataRobot helps to build accurate predictive models that can enhance decision making for financial services. It deals with issues like fake credit card transactions, digital wealth management, direct management, and lending.
  7. WinZip: AI-powered finance app delivers automated financial services like investments, savings, and payments. The conversational AI ‘Misa’ is the most powerful financial chatbot, MintZip takes the support of Misa to consider the behavioral sciences and financial sciences for continuous training on financial aspects. It assists users in financial planning.
  8. Kesho: This software provides machine intelligence and data analytics to leaders in the finance industry. Kesho also used cloud computing and NLP, this speeds up the response to the questions from users. Kesho could predict the pound rate drop as mentioned in Forbes article.
  9.  AlphaSense: This AI-powered search engine serves the banks, investment firms, and Fortune 500 companies. Natural language processing analyzes keyword search within research, news, and transcripts to discover the trends of financial markets. AlphaSense is providing great value to financial professionals, organizations, companies, traders, and brokers with the latest information on private and public companies. AI analyzes large and complex data and uses algorithms for quantitative trading that can automate trade and make them profitable.
  10. Kavout: It uses machine learning and quantitative analysis to process massive data that is unstructured. Identifies financial market patterns for price and SEC filings in real-time. Higher Kai Score shows outperformance of stock, it is an AI-powered stock ranker. Kavout selected stocks to have a higher annual growth rate.
  11. Kasisto: A conversational AI platform Kai improves customer experiences and reduces the volume of customers approaching call centers.  Kasisto is the creator of KAI, a conversational AI platform used to improve customer experiences in the finance industry. Chatbots recommend daily financial decisions based on calculations. Kaisisto can be integrated with mobile apps to provide real-time support to customers.
  12. Shape Security: Top banks in the US use Shape Security restrains frauds of credit application, credential stuffing, cracking gift cards and other frauds by investigating and identifying fake users. The ML models are trained to identify between real customers and bots. Its Blackfish network uses AI-enabled bots to detect logins from different IPs, machines, and phones and alerts the customers and companies for the breach.
  13. Able AI: A virtual financial assistant that integrates with Google Home, Amazon Alexa, SMS, Facebook, web, and mobile to make banking more convenient. Able AI provides services like customer support, personal financial management, and conversational banking. The app also helps in budgeting, tracking expenses and working on savings.
Innovations in financial Services Industry

AI-based financial services mobile app development is in full swing. Customers are focusing on the things that make a difference in their lives instead of looking for processes and trying to understand the terminology.

Implications:

The evolution of financial services with the advancement of AI technology allows us to manage business risks, improves forecasts, assists trading, provides cybersecurity, detects fraud, betters personal banking and brightens user experience.

The technology of today is the future of industries tomorrow. Hammer the iron when it is hot applies to the adoption of advanced technology in every sector and finance is no exception. Financial services are awaiting a brighter future where humans are relieved of the pressure to perform better. Let AI guarantee the uninterrupted services for your valuable customers.

Top 10 most Innovative Chatbots developed today

October 16, 2019 | AI, Chatbots | No Comments

Innovative chatbots

Chatbots are becoming popular with the increasing capacity to perform thousands of tasks. There are 23,552 identified number of tasks related to lifestyle, games, music, smart home, travel & tourism, shopping, communication, shopping, and many more areas. AI can multiple employee productivity within the organizations and speed up innovation.

Big brands that extensively use Chatbots are Lyft, Spotify, MasterCard, Staples, Pizza Hut, Starbucks, Fandango, Tata Capital, TCS, Club Mahindra, Godrej Agrovet, etc. The versatile applications powered by AI keeps the chatbots running at high speed and have the capacity to speed up the information exchange and response rate.

Microsoft and IDC Asia Pacific states that around 77% of business leaders consider Artificial Intelligence is increasing business competitiveness by 2.3x by 2021.

Innovative chatbots

Chatbots are making a mark in sectors like healthcare and medicine, education, edutainment, real estate, travel, customer service, gaming, and E-commerce.

Top 10 Most Innovative Chatbots:

AI bots

Artificial Intelligence chatbots deliver quality output and the deep learning algorithms ensure the relevance of content shared. It can sense the user’s intentions and personalize the response.

1. Mitsuku: A popular AI-powered online chatbot developed using Artificial Linguistic Internet Computer Entity – A.L.I.C.E. database. The advanced machine learning techniques enhance its conversation skills allowing anyone to talk with it. However, it is not created for any specific purpose but aims to entertain users.

Mitsuku is the most human-like chatbot and can make human-like conversations. It uses NLP- natural language processing that allows the bot to understand everything we say. It is five times the award winner of the Loebner Prize Turing Test.

Mitsuku is the best conversation chatbot that performs well compared to other bots. You can ask what all can it do for you or have a general conversation, knowledgeable conversation, ask about history, or something that happened between selected dates, ask it to show a horoscope, top 40 songs across the globe and much more.

It can talk about anything and with anyone; it has no topic or age limitations. It can be funny some times and while discussing sensitive topics it takes a neutral stand.

2. Hipmunk:  It is one of the most innovative AI chatbots supported by a user interface. It has wonderful travel ideas. A bot is supported by a user interface (UI) that can be more efficient. Hipmunk It understands your need and helps you schedule the travel, search for best flight options, book hotels or rent a car.

Hipmunk can search and compare the price and options from other listings and get you the best deal. Easily integrate them with social media pages or Skype. The bot uses the location data to determine work accordingly for the search input and optimizes the search for the user.

It lets you do your work or relax while it smartly handles the information found from multiple sites, and places it right to suit your requirements. It allows you to share the maps.

Hipmunk is not chatty bot yet efficient to complete the transactions for the user. Bots need not to be chatty.

3.Duolingo: A language-learning app gained popularity because of the number of languages it lets you practice. Duolingo enables you to develop conversational skills in other languages and you can even practice aloud.

Duolingo saves you from the embarrassment of speaking a foreign language and lets you overcome the fear of conversing in front of others.

Learn almost 30 foreign languages through this chatbot. It provides plenty of self-paced exercises that can develop a better understanding of those languages.

It simplifies the recruitment process and is capable of interviewing thousands of candidates simultaneously and in a given period it can complete the interviews.

4.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 the company.

Human Resources team’s efforts to screen and select the CV’s form the job portals, inspect the CV’s, sort the documents, and e-mails received from not suitable candidates. Robot Vera automatically analyzes resume databases and calls candidates that are fit for a new opening in the organization.

AI-powered chatbot Robot Vera filters out the applications received for a position to merely 10% of the best suitable ones form all the sources of resumes. It then informs 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.

5. Replies: Virtual companion chatbots are the ones, which people can flirt with. They can even complain or talk loud about their failed relationships. Replika helps people to meet their emotional needs and 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.

It has over thirty thousand members on the Facebook group. It lets you feel good especially with the care and compassion received from the virtual companion. Replika mimics the speech and behavior of the user. You can download and teach this app everything about yourself. It can have an in-depth conversation about the things you want to engage in.

Replika can even follow you on social media and continue to ask you some questions.

6. TechCrunch: This smart conversational chatbot gives a personalized experience over the content you want, how frequently you want within the selective topics, authors and type of content available on TechCrunch.

If you wish, to track specific types of articles or the industry-specific development stories and news it serves you with the best and relevant content. There is a lot of content on the internet and you cannot read all and cannot afford to miss what is important for you and your business.

Conversational double intent lets you get info on two searches at a time e.g. news on Mahindra and Tata. Get personalized news recommendations

TechCrunch customizes to the user’s choice and helps companies create a brand image. Sending the content that the user enjoys lets them relate to the products and services they provide. Companies get traffic from people interested in their products and the target customers automatically reach out.

7.BabyCentre UK: It belongs to Johnson & Johnson a reputed name in childcare. This bot helps query resolution about pregnancy and childcare. It can calculate the due date of would-be mothers and guide them for preparing for childbirth. Many articles are available on self-care for moms on all stages of motherhood.

BabyCentre UK’s facebook messenger Bot responds to the questions for a concern area the parent faces for different age groups, it asks for the child’s age and problem. Personalized bits of advice suggested by the bot helps to a great extent. Targeted content adds to illustrate the answers given by the bot.

They have information on how to get pregnant, receive weekly articles during pregnancy, health care of mother and baby, when to call a doctor for your baby, why your baby does not sleep, and you can share your opinion in the community.

If the toddler is weaning, it can suggest if the child is ready for solid food and extends the conversation by asking other indications they should check out. E-mail content received by the parents is personalized as per the child’s age opted by them.

The BabyCentre’s bot could avoid the spam filters and achieve a read rate of 84% and a higher engagement rate than the e-mail channels.

8.Acebo:  It is a bot that tracks expenses, checks to-do lists, and intelligent task management to improve the productivity and efficiency of the team. The most convenient way to store the expense records, images and receipts to export at the selected date to the accounting system or expense format. Find the tasks, expenses, polls, and results in a central and easily accessible location.

You can personalize the survey, create engaging surveys such as emotion-enabled surveys, conventional surveys, chat-based surveys, and automatically track sections of feedback received from customers.

9.Instalocate: This chatbot saves you from reading complex customer rights documents of various airlines. Get a refund from airlines in case of delayed or canceled flights and even overbooking. Yes, this is legal airlines owes you the compensation and in the currency not some coupons.

It is simple to use just track your flight with few details like airlines, flight no. and the date of travel. The chatbot notifies you automatically to apply for the compensation once you are eligible.

It provides you a stress-free travel experience with the information Instalocate shares with the user. Flight-related information like delay alerts, security wait time, web check-in, baggage allowance, etc. You can inform your friends and family while you are onboard and helps to get you a cab as soon as your flight lands.

Instalocate is your travel assistant available 24×7 that plans travel, books flights suggests where you can eat or stay, updates you with flight details in real-time.

10.Watson Assistant: IBM a leader in AI space developed this advanced chatbot. It holds the content of varied industries and is pre-trained for industry-specific. It uses data content relevant to that industry. It can understand historical chat, call logs, search and respond from the knowledge base.

Furthermore, it inquires for more clarity from the customer to serve them better. It can decide on its own when to direct the user to human representatives. Level one work is repetitive is taken care of by the Watson Assistant. The bot is smart enough to recommend for the training it requires improving on its conversational abilities.

Watson Assistant can be part of your company 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.

How Chatbots are becoming our need and reliable partners?

Whether business or personal life we have too many things to handle and an intelligent friend like Chatbot is a relief in heck. How badly a human need someone to take care of them yet not interrupts in their personal space.

Chatbots are digital friends, assistants, planners, tutors, therapists, and partner in day-to-day life. Book flights, hotels, cabs, dinner, medical checkups, listen to music, do the shopping for clothes, cosmetics, groceries, buy insurance, get educated, or perform financial and banking transactions.

There are bots that you can use on your website, Facebook page, Skype, to speed business process. Lakhs of bots and over 23,000 skills makes it interesting and challenging for the programmers to create unique solutions.

To create innovative chatbots, identify a unique problem or need, chart out the probable solutions, break down in tasks, what and how can you automate, are your data ready, and can it serve multiple industries or the selected one with features that do not exist.

Closing Thoughts:

Intelligence is mandatory for innovation in Artificial Intelligence technology. Chatbots got created to reduce human interaction, conflicts, and arguments but on the contrary, the same attributes of human nature are the food for innovation. There are always good ideas that can be improved so are the systems. The future of chatbots is the conversations predicted to save $8 billion per annum by 2022.

Development tools for AI and ML

Artificial Intelligence a popular technology of computer science is also known as machine intelligence. Machine Learning is a systematic study of algorithms and statistical models.

AI creates intelligent machines that react like humans as it can interpret new data. ML enables computer systems to perform learning-based actions without explicit instructions.

AI global market is predicted to reach $169 billion by 2025. Artificial Intelligence will see increased investments for the implementation of advanced level software. Organizations will strategize technological advancements.

Various platforms and tools for AI and ML empower the developers to design powerful programs.

Tools for AI and ML

Tools for AI and ML:

Google ML Kit for Mobile:

Software development kit for Android and IOS phones enables developers to build robust applications with optimized and personalized features. This kit allows developers to ember the machine learning technologies with cloud-based APIs. This kit is integration with Google’s Firebase mobile development platform.

Features:

  1. On-device or Cloud APIs
  2. Face, text and landmark recognition
  3. Barcode scanning
  4. Image labeling
  5. Detect and track object
  6. Translation services
  7. Smart reply
  8. AutoML Vision Edge

Pros:

  1. AutoML Vision Edge allows developers to train the image labeling models for over 400 categories it capacities to identify.
  2. Smart Reply API suggests response text based on the whole conversation and facilitates quick reply.
  3. Translation API can convert text up to 59 languages and language identification API forms a string of text to identify and translate.
  4. Object detection and tracking API lets the users build a visual search.
  5. Barcode scanning API works without an internet connection. It can find the information hidden in the encoded data.
  6. Face detection API can identify the faces in images and match the facial expressions.
  7. Image labeling recognizes the objects, people, buildings, etc. in the images and with each matched data; ML shares the score as a label to show the confidence of the system.

Cons:

  1. Custom models can grow in huge sizes.
  2. Beta Release mode can hurt cloud-based APIs.
  3. Smart reply is useful for general discussions for short answers like “Yes”, “No”, “Maybe” etc.
  4. AutoML Vision Edge tool can function successfully if plenty of image data is available.

Accord.NET:

This Machine Learning framework is designed for building applications that require pattern recognition, computer vision, machine listening, and signal processing. It combines audio and image processing libraries written in C#. Statistical data processing is possible with Accord. Statistics. It can work efficiently for real-time face detection.

Features:

  1. Algorithms for Artificial Neural networks, Numerical linear algebra, Statistics, and numerical optimization
  2. Problem-solving procedures are available for image, audio and signal processing.
  3. Supports graph plotting & visualization libraries.
  4. Workflow Automation, data ingestion, speech recognition,

Pros:

  1. Accord.NET libraries are available from the source code and through the executable installer or NuGet package manager.
  2. With 35 hypothesis tests including two-way and one-way ANOVA tests, non-parametric tests useful for reasoning based on observations.
  3. It comprises 38 kernel functions e.g. Probabilistic Newton Method.
  4. It contains 40 non-parametric and parametric statistical distributions for the estimation of cost and workforce.
  5. Real-time face detection
  6. Swap learning algorithms and create or test new algorithms.

Cons:

  • Support is available for. Net and its supported languages.
  • Slows down because of heavy workload.

Tensor Flow:

It provides a library for dataflow programming. The JavaScript library helps in machine learning development and the APIs help in building new models and training the systems. Tensorflow developed by Google is an opensource Machine Learning library that aids in developing the ML models and numerical computation using dataflow graphs. Use it by installing, use script tags or through NPM.

Features:

  1. A flexible architecture allows users to deploy computation on one or multiple desktops, servers, or mobile devices using a single API.
  2. Runs on one or more GPUs and CPUs.
  3. It’s yielding scheme of tools, libraries, and resources allow researchers and developers to build and deploy machine-learning applications effortlessly.
  4. High-level APIs accedes to build and train for ML models efficiently.
  5. Runs existing models using TensorFlow.js, which acts as a model converter.
  6. Train and deploy the model on the cloud.
  7. Has a full-cycle deep learning system and helps in the neural network.

Pros:

  1. You can use it in two ways, i.e. by script tags or by installing through NPM.
  2. It can even help for human pose estimation.
  3. It includes the variety of pre-built models and model subblocks can be used together with simple python scripts.
  4. It is easy to structure and train your model depending on data and the models with you are training the system.
  5. Training other models for similar activities is simpler once you have trained a model.

Cons:

  1. The learning curve can be quite steep.
  2. It is often doubtful if your variables need to be tensors or can be just plain python types.
  3. It restricts you from altering algorithms.
  4. It cannot perform all computations on GPU intensive computations.
  5. The API is not that easy to use if you lack knowledge.

Infosys Nia:

This self-learning knowledge-based AI platform accumulates organizational data from people, business processes and legacy systems. It is designed to engage in complicated business tasks to forecast revenues and suggest profitable products the company can introduce.

Features:

  1. Data Analytics
  2. Business Knowledge Processing
  3. Transform Information
  4. Predictive Automation
  5. Robotic Process Automation
  6. Cognitive Automation

Pros:

  1. Organizational Transformation is possible with enhanced technologies to automate and increase operational efficiency.
  2. It enables organizations to continually use previously gained knowledge as they grow and even modify their systems.
  3. Faster data processing adds to the flexibility of data visualization, analytics, and intelligent decision-making.
  4. Reduces human efforts involved in solving high-value customer problems.
  5. It helps in discovering new business opportunities.

Cons:

  1. It is difficult to understand how it works.
  2. Extra efforts needed to make optimum use of this software.
  3. It has lesser features of Natural Language Processing.

Apache Mahout:

Mainly it aims towards implementing and executing algorithms of statistics and mathematics. It’s mainly based on Scala and supports Python. It is an open-source project of Apache.
Apache Mahout is a mathematically expressive Scala DSL (Domain Specific Language).

Features:

  1. It is a distributed linear algebra framework and includes matrix and vector libraries.
  2. Common maths operations are executed using Java libraries
  3. Build scalable algorithms with an extensible framework.
  4. Implementing machine-learning techniques using this tool includes algorithms for regression, clustering, classification, and recommendation.
  5. Run it on top of Apache Hadoop with the help of the MapReduce paradigm.

Pros:

  1. It is a simple and extensible programming environment and framework to build scalable algorithms.
  2. Best suited for large datasets processing.
  3. It eases the implementation of machine learning techniques.
  4. Run-on the top of Apache Hadoop using the MapReduce paradigm.
  5. It supports multiple backend systems.
  6. It includes matrix and vector libraries.
  7. Deploy large-scale learning algorithms using shortcodes.
  8. Provide fault tolerance if programming fails.

Cons:

  1. Needs better documentation to benefit users.
  2. Several algorithms are missing this limits the developers.
  3. No enterprise support makes it less attractive for users.
  4. At times it shows sporadic performance.

Shogun:

It provides various algorithms and data structures for unified machine learning methods. Shogun is a tool written in C++, for large-scale learning, machine learning libraries are useful in education and research.

Features:

  1. Huge capacity to process samples is the main feature for programs with heavy processing of data.
  2. It provides support to vector machines for regression, dimensionality reduction, clustering, and classification.
  3. It helps in implementing Hidden Markov models.
  4. Provides Linear Discriminant Analysis.
  5. Supports programming languages such as Python, Java, R, Ruby, Octave, Scala, and Lua.

Pros:

  1. It processes enormous data-sets extremely efficiently.
  2. Link to other tools for AI and ML and several libraries like LibSVM, LibLinear, etc.
  3. It provides interfaces for Python, Lua, Octave, Java, C#, C++, Ruby, MatLab, and R.
  4. Cost-effective implementation of all standard ML algorithms.
  5. Easily combine data presentations, algorithm classes, and general-purpose tools.

Cons:

Some may find its API difficult to use.

Scikit:

It is an open-source tool for data mining and data analysis, developed in Python programming language. Scikit-Learn’s important features include clustering, classification, regression, dimensionality reduction, model selection, and pre-processing.

Features:

  1. Consistent and easy to use API is also easily accessible.
  2. Switching models of different contexts are easy if you learn the primary use and syntax of Scikit-Learn for one kind of model.
  3. It helps in data mining and data analysis.
  4. It provides models and algorithms for support vector machines, random forests, gradient boosting, and k-means.
  5. It is built on NumPy, SciPy, and matplotlib.
  6. BSD license lets you use commercially.

Pros:

  1. Easily documentation is available.
  2. Call objects to change the parameters for any specific algorithm and no need to build the ML algorithms from scratch.
  3. Good speed while performing different benchmarks on model datasets.
  4. It easily integrates with other deep learning frameworks.

Cons:

  1. Documentation for some functions is slightly limited hence challenging for beginners.
  2. Not every implemented algorithm is present.
  3. It needs high computation power.
  4. Recent algorithms such as XGBoost, Catboost, and LightGBM are missing.
  5. Scikit learns models take a long time to train, and they require data in specific formats to process accurately.
  6. Customization for the machine learning models is complicated.
AI and ML development

Final Thoughts:

Twitter, Facebook, Amazon, Google, Microsoft, and many other medium and large enterprises continuously use improved development tactics. They extensively use tools for AI and ML technology in their applications.

Various tools for AI and ML can ease software development and make the solutions effective to meet customer requirements. Make user-friendly mobile applications or other software that are potentially unique. Using Artificial Intelligence and Machine Learning create intelligent solutions for improved human life. New algorithm creation, using computer vision and other technology and AI training requires skills and development of innovative solutions that need powerful tools.

Computer Vision Advances and Challenges

Computer Vision is a field of computer science using the technology of artificial intelligence. A part of robotics as artificial visual systems automatically processes images and videos. AI training lets the computers understand, identify, classify and interpret the digital images. Response from the machines to the images relies on the understanding of computer vision. The purpose of this technology is to automate the tasks consisting of human visual aspects.

Machines obtain information from images with computer vision technology. The input data processed by the vision sensor enables it to perform actions using high-level information. Machines can gain an understanding of the situations. AI uses pattern recognition and machine learning techniques that ease decision-making.
Computer Vision technology is now accessible and affordable for industries to adopt changes and extract benefits.

History:

Experimentation on computer vision began in the1950s and by 1970s; it could distinguish handwritten and typed text with optical character recognition. In 1966, a summer vision project to build a system that can analyze the scene and identify objects commenced at MIT. Initially, the project looked simple but to be decoded. The computer vision market is all set to reach a valuation of $48.32 billion by 2023. The estimation of the computer vision AI market, in 2019 for the healthcare industry is about $1.6billion.

Reason for popularity:

  1. Creation of a huge amount of visual data
  2. Improvement in mobile technology and computing power add to image data
  3. Its ability to process massive datasets
  4. Recognizing visual inputs faster than humans
  5. Accurate interpretation of images and videos
  6. Quick processing and high demand in robots across industries
  7. Defect detection assists corrective actions
  8. Analyze images on different parameters
  9. Maintain quality and safety
  10. Increases reliability and accuracy
  11. AI Training for computer vision
  12. New hardware and algorithms brought precision
  13. Cost-effective technology compared to other systems prevailing
  14. Automation, quality control, scrutiny is introduced
  15. Eases complicated industrial tasks
  16. Rise in online analysis of images
  17. Industries that widely use computer vision are automotive, aerospace, defense, education, healthcare, pharmaceuticals, food and packaging, beverages, manufacturing, government applications, etc.
Computer Vision

How does it work?

Machines understand process and analyze images with the information it can access on the topic. With the neural networks, the iterative learning process can be set. If you are looking forward to identifying the forest area all over the globe, the datasets used by neural networks require images and videos of green patches and dry patches. Tagged images and metadata helps the machine to reply correctly. Different pieces of image are recognized using pattern recognition by the neural networks.

Mainly the system uses various components of the machine vision system such as lens, image sensors, lighting, vision processing, and communication devices. Computers assemble visual images in bits like a puzzle put together. The pieces assembled into an image makes filtering and processing speedy. In the above example of identifying forests, the machines are not trained to see different tree types and leaves instead they are trained to recognize the green patches on earth. The training lets it create an image of the forest and match it with the data.

Deep Learning learns from large amounts of data and its algorithms are inspired by a human brain to result most accurately. This subset of machine learning can identify objects, people, tag friends, translate photos, translate voice, and translates text in multiple languages. Deep learning has transformed computer vision with its high level of accuracy that is beyond human capacity.

Difference between Computer Vision and Machine Learning:

Machine learning helps the computer to understand what they see and computer vision determines how they see. Machine learning is where the systems teach themselves based on the continuously populating data. CV requires artificial intelligence to train the system in performing varied tasks. CV does not learn from the training data available but makes data patterns to find relations between data and understand it for a visual representation of a preset result.
Computer vision is progressing towards replacing human vision that assists in complicated tasks. This requires intelligent algorithms and robust systems.

Examples of Computer Vision Applications:

Applications of Computer Vision

Augmented Reality:

  1. Geo Travel: Augmented Reality Geo Travel can be your travel guide, GPS enabled application gives you information on your exact location. Plan a trip for you using your searched data on the city with the result of Wikipedia pages that you can save for easy travel. Find a car with a car finder that saves your parking position for you to get back to your car easily.
  2. Web: The Augmented Web combines HTML5, Web Audio, WebGL, and WebRTC to improve the user experience when they visit existing pages. Image search, Google photos use face recognition, object recognition, scene recognition, geolocalization, Facebook takes care of image captioning, Google maps use aerial imaging and YouTube does content categorization with help of computer vision.

Automotive: In this field can save millions of people from tragic traffic accidents. Human error is possible due to multitasking, overthinking, tension and negligence. Self-driving cars are loaded with multiple cameras, radar, ultrasonic sensors and technology that detect 360-degree movement, developed by Google Labs. Tesla car warns drivers to take control of the steering wheel. The error proofing, presence, and absence of objects, responsible control on the machine all is possible with computer vision. Technology takes control by detecting objects, marks lanes, catches signs and understands traffic signals for us to drive safely.

Agriculture: Computer vision can check the quality of grain, identify weeds, and take actions to save crops by sprinkling herbicides on weeds using AI technology. It helps in the packaging of agricultural produce and products.

Healthcare and Medical Imaging: This technology helps healthcare professionals inaccurate presentation of data, reports, and illness-related information. It can save patients from getting improper treatments, study their medical data, which is image-based such as X-Rays, CT scans, sonography, mammography, and other monitoring activities of patients. Augmented Reality assisted surgery ensures better results than surgeries with human surveillance.

Get assistance in surgery from the analysis of various images with computer vision technology. Gauss Surgical is a blood monitoring solution that closely watches blood loss in real-time. It can save patients’ life during critical operations, facilitate blood transfusions, and make out hemorrhage. The images captured with help of iPad or Triton, processed by cloud-based computer vision and it estimates blood loss through intelligent machine learning algorithms. Computer vision can improve diagnosis ad automate pathology.

Smartphones: These handy tools for perfect pictures and AI are transforming the arena of development in computer vision. It scans QR codes, has portrait and panorama modes of photography. The face and smile detection, anti-blur technology is computer vision.

Insurance: It will compare the images of patients, reports and insurance forms to settle claims of hospitalization. In case of car or property insurance, this technology can analyze the damage, inspect the property and process claims. Automation in the insurance sector can result in speedy resolution of queries and settlements.

Manufacturing: Computer vision can predict the equipment maintenance, quality issues of product, monitor the production line and product quality to reduce the defects in manufacturing.

Google Translate App: Need to learn a foreign language just to travel for pleasure and leisure is eliminated with the introduction of computer vision. Pointing to a text or sign translates the foreign language in the selected output language. The accurate recognition of any sign is possible due to optical character recognition and augmented reality for exact translation.

Challenges of Computer Vision:

Challenges of Computer Vision
  1. The human visual system is too good to be simulated. The capacity of the human eye and brain in coordination with each other can recognize things, people and places are better. Computer systems can fail to recognize the faces with a variety of expressions or variant lighting.
  2. Initial research for industry-specific tasks can be expensive. The technology is changing rapidly but the complexities of integrating computer vision systems are a higher-level challenge.
  3. Face recognition is an annoyance and breach of privacy and business ethics in the hospitality, finance and banking industry. Multiple and adverse uses of technology are a threat and San Francisco has banned facial recognition.
    The algorithms for each talk about a particular industry may not be accurate or updated and the results may not match the preordained results.
  4. The misuse of computer vision is the result of faulty inputs or intentionally tampered images to form flawed patterns that harm the learning models.
  5. Object classification is challenging as the label is assigned to the entire image for classification. Handwritten documents are difficult for computer vision, due to a variety of handwriting styles, curves and shapes formed while writing for each alphabet.
  6. Object Detection is more complicated than image classification as there can be multiple objects in an image and the request can be for single objects or combinations.

Insufficient visual data sets or image reconstruction used to fill in for the missing parts of the image damages or corrupts the versions of photos.

Supposition:

Computer vision technology of Artificial Intelligence (AI) is witnessing a global rise in market revenues from $1700 million in 2015 to $5500 in 2019.

Image processing a subset of computer vision that performs to imitate the human vision and goes beyond human accuracy. It can enhance images by processing and making them identifiable for future use. Defect-free manufacturing, automotive, pharmaceuticals, overall many industries, products, and services is achievable. Increased adoption of computer vision AI-based technology is facilitating market growth.

The future of computer vision is accelerating and the image, photo and video data are growing enormously. The data upload, download and access are opening new opportunities for computer vision-based solutions.

Scope to improve performance and create a better user experience is a source of innovation towards the problem-solving capabilities of systems. The food industry will demonstrate the highest growth rate by applying computer vision technology in manufacturing and packaging operations.

The relationship of images and users is changing and the equation of visual data and its processing is harmonizing.

Jobs Artificial Intelligence

In the previous couple of years, computerized reasoning has progressed so rapidly that it presently appears to be not a month passes by without a newsworthy Artificial Intelligence (AI) achievement. In territories as wide-running as discourse interpretation, medicinal analysis, and interactivity, we have seen PCs beat people in frightening manners.

This has started an exchange about how AI will affect work. Some dread that as Artificial intelligence improves, it will replace laborers, making a consistently developing pool of unemployable people who can’t contend monetarily with machines.
This worry, while reasonable, is unwarranted. Truth be told, AI will be the best employment motor the world has ever observed.

2020 will be a significant year in AI-related work elements, as indicated by Gartner, as AI will turn into a positive employment helper. The number of occupations influenced by Artificial Intelligence will shift by industry; through 2019, social insurance, the open division, and instruction will see constantly developing employment requests while assembling will be hit the hardest. Beginning in 2020, AI-related occupation creation will a cross into positive area, arriving at 2,000,000 net-new openings in 2025, Gartner said in a discharge.

Numerous huge advancements in the past have been related to change the time of impermanent occupation misfortune, trailed by recuperation, at that point business change and AI will probably pursue this course.

Jobs by Artificial Intelligence (AI) and ML

JOBS CREATED BY AI AND MACHINE LEARNING

A similar idea applies to AI. It is an instrument that individuals need to figure out how to utilize and how to apply to what’s going on with as of now. New openings are now being made that are centered around applying AI to security, improving basic AI methods, and on keeping up these new apparatuses.

Plenty of new openings will develop for those with mastery in applying center Artificial Intelligence innovation to new fields and applications. Specialists will be expected to decide the best sort of AI (for example master frameworks or AI), to use for a specific application, create and train the models, and keep up and re-train the frameworks as required. In fields, for example, security, where sellers have enabled security programming with AI, it’s up to clients – the security investigators – to comprehend the new capacities and put them to be the most ideal use.

Training is another field where AI and machine learning is making new openings. As of now, over the US, the main two situations in the rundown of scholastic openings are for Security and Machine Learning specialists. Colleges need more individuals and can’t discover educators to show these fundamentally significant subjects.

FUTURE JOBS PROSPECTS BECAUSE OF AI AND MACHINE LEARNING

In a few businesses, AI will reshape the sorts of employments that are accessible. What’s more, much of the time, these new openings will be more captivating than the monotonous errands of the past. In assembling, laborers who had recently been attached to the generation line, looking for blemished items throughout the day, can be redeployed in increasingly profitable interests — like improving procedures by following up on bits of knowledge gathered from AI-based sensor and vision stages.

These are increasingly specific errands and retraining or uptraining might be important for laborers to successfully satisfy these new jobs — something the two organizations and people should address sooner than later.

Man-made intelligence-based arrangements in any industry produce monstrous measures of information, frequently from heterogeneous sources. Successfully saddling the intensity of this information requires human abilities. Profound learning researchers have come to comprehend that setting is basic for preparing powerful AI models — and people are important to clarify this information to give set in uncertain circumstances and help spread all this present reality varieties an AI framework will experience.

Keeping that in mind, Appen utilizes more than 40,000 remote contractual workers a month to perform information explanation for our customers, drawing from a pool of more than 1 million talented annotators around the world.

These occupations wouldn’t exist without the profound learning innovation that makes AI conceivable. As researchers and designers make immense advances in innovation, organizations and laborers may need to adopt new mechanical aptitudes to remain aggressive.

Simulated intelligence is helping drive work creation in cybersecurity

As the worldwide economy is progressively digitized and mechanized, effectively unavoidable criminal ventures – programmers, malware, and different dangers – will develop exponentially, requiring engineers, analyzers, and security specialists to alleviate dangers to crucial open framework and meet expanding singular personality concerns.

In the previous couple of years there has been an enormous increment in cybersecurity work postings, a large number of which stay unfilled. With this deficiency of cybersecurity experts, most security groups have less time to proactively protect against progressively complex dangers. This interest has made a significant specialty for laborers to fill.

The stream down impact of industry-wide digitalization

In a roundabout way, the efficiencies and openings that profound learning and computerization empower for organizations can make a great many employments. While mechanized conveyance strategies, for example, self-driving conveyance trucks will take a great many drivers off the street, an ongoing Strategy + Business article proposes that, “In reality as we know it where organizations are progressively made a decision on the nature of the client experience they give, you will require representatives who can consolidate the aptitudes of a client care specialist, advertiser, and sales rep to sit in those trucks and connect with clients as they make conveyances.”

Additionally, the higher profitability and positive development empowered by AI will positively affect employing as organizations will just need to procure more laborers to take on existing assignments that require human abilities. Consider client support, publicists, program administrators, and different jobs that require abilities, for example, compassion, moral judgment, and inventiveness.

Growing new aptitudes to endure and flourish

It’s anything but difficult to perceive any reason why laborers and administrators the same may be hesitant to execute AI-controlled mechanization. Be that as it may, as their rivals receive this innovation and start to outpace them in deals, creation, and development, it will expect them to adjust. The two organizations and laborers should put resources into developing new innovative aptitudes to enable them to remain significant in this information-driven scene. If they can do this, the open doors for business and expert development are perpetual.

Development in AI and ML jobs

DEVELOPMENT IN THE FIELD OF AI and ML

Man-made reasoning is a method for making a PC, a PC controlled robot, or a product think keenly, in the comparative way the insightful people think.
Man-made brainpower is a science and innovation dependent on orders, for example, Computer Science, Biology, Psychology, Linguistics, Mathematics, and Engineering. A significant push of Artificial Intelligence (AI) is in the advancement of PC capacities related to human knowledge, for example, thinking, learning, and critical thinking.

AI is a man-made consciousness-based method for creating PC frameworks that learn and advance dependent on experience. Some basic AI applications incorporate working self-driving autos, overseeing speculation reserves, performing legitimate disclosure, making therapeutic analyses, and assessing inventive work. A few machines are in any event, being educated to mess around.

Man-made intelligence and MACHINE LEARNING isn’t the eventual fate of innovation — it’s nowhere. Simply see how voice aides like Google’s Home and Amazon’s Alexa have turned out to be increasingly more unmistakable in our lives. This will just proceed as they adapt more aptitudes and organizations work out their associated gadget biological systems. The accompanying can be viewed as a portion of the significant advancements in the field of AI.

Artificial intelligence in Banking and Payments

This report features which applications in banking and installments are most developed for AI. It offers models where monetary organizations (FIs) and installments firms are as of now utilizing the innovation, talks about how they should approach actualizing it, and gives depictions of merchants of various AI-based arrangements that they might need to think about utilizing.

Computer-based intelligence in E-Commerce

This report diagrams the various uses of AI in retail and gives contextual analyses of how retailers are increasing a focused edge utilizing this innovation. Applications incorporate customizing on the web interfaces, fitting item suggestions, expanding the hunt significance, and giving better client support.

Computer-based intelligence in Supply Chain and Logistics

This report subtleties the variables driving AI appropriation in-store network and coordinations, and looks at how this innovation can decrease expenses and sending times for activities. It likewise clarifies the numerous difficulties organizations face actualizing these sorts of arrangements in their store network and coordinations tasks to receive the rewards of this transformational innovation.

Artificial intelligence in Marketing

This report talks about the top use cases for AI in advertising and looks at those with the best potential in the following couple of years. It stalls how promoting will develop as AI robotizes medicinal undertakings, and investigates how client experience is winding up increasingly customized, pertinent, and auspicious with AI.

CONCLUSION

To close, AI introduces a colossal open door for venturesome individuals. Representatives have the chance to jump into another field and conceptual their business to another, more significant level of investigation and vital worth. Businesses need to help these moves and for the most part remain open to representatives rethinking themselves as they hold onto innovations, for example, AI.

Artificial Intelligence Applications

Man-made brainpower has significantly changed the business scene. What began when in doubt based mechanization is currently fit for copying human communication. It isn’t only the human-like abilities that make man-made consciousness extraordinary.

A propelled AI calculation offers far superior speed and unwavering quality at a much lower cost when contrasted with its human partner’s Artificial insight today isn’t only a hypothesis. It, indeed, has numerous viable applications. A 2016 Gartner research demonstrates that by 2020, at any rate, 30% of organizations universally will utilize AI, in any event, one piece of their business forms.

Today businesses over the globe are utilizing computerized reasoning to advance their procedure and procure higher incomes and benefits. We contacted some industry specialists to share their point of view toward the uses of man-made reasoning. Here are the experiences we have gotten: 

What is AI?

Computerized reasoning, characterized as knowledge shown by machines, has numerous applications in the present society. Simulated intelligence has been utilized to create and propel various fields and enterprises, including money, medicinal services, instruction, transportation, and the sky is the limit from there. 

Man-made knowledge systems will typically indicate most likely a part of the going with practices related to human understanding: orchestrating, getting the hang of, thinking, basic reasoning, learning depiction, perception, development, and control and, to a lesser degree, social information and creative mind. 

Applications of Artificial Intelligence for business

Human-made intelligence is omnipresent today, used to suggest what you should purchase next on the web, to comprehend what you state to menial helpers, for example, Amazon’s Alexa and Apple’s Siri, to perceive who and what is in a photograph, to spot spam, or recognize Mastercard extortion. 

Utilization of Artificial Intelligence in Business 

• Improved client administrations. 

In the event that you run an online store, you’ve absolutely seen a few changes in client conduct. 30% of every single online exchange presently originate from portable. Despite the fact that cell phone proprietors invest 85% of their versatile energy in different applications, just five applications (counting delivery people and web-based life) hold their consideration.

So as to empower versatile application selection, the world’s driving retailers like Macy’s and Target introduce signals and go to gamification. Facebook and Kik went significantly further and propelled chatbot stages. A chatbot (otherwise known as “bot” or “chatterbot”) is a lightweight AI program that speaks with clients the manner in which a human partner would.

Despite the fact that H&M, Sephora and Tesco were among the principal organizations to get on board with the chatbot fleeting trend, bots’ potential stretches a long way past the web-based business area. The Royal Dutch Airlines have constructed a Facebook bot to assist voyagers with registration docs and send notices on flight status.

Taco Bell built up a menial helper program that oversees arranges through the Slack informing application. HP’s Print Bot empowers clients to send records to the printer directly from Facebook Messenger.

As per David Marcus, VP of informing items at Facebook, 33 thousand organizations have just constructed Facebook bots — and now they’re “beginning to see great encounters on Messenger”; 

• Workload computerization and prescient support. 

By 2025, work mechanization will prompt an overall deficit of 9.1 million US employments. In any case, AI applications won’t cause the following work emergency; rather, savvy projects will empower organizations to utilize their assets all the more viably. Engine, an electric firm from France, utilizes rambles and an AI-controlled picture preparing application to screen its foundation.

The London-based National Free Hospital joined forces with DeepMind (an AI startup claimed by Google) to create calculations distinguishing intense kidney wounds and sight conditions with next to zero human impedance. General Electric battles machine personal time by gathering and breaking down information from savvy sensors introduced on its hardware. On account of the Internet of Things and technology, organizations can lessen working costs, increment profitability and inevitably make a learning-based economy; 

• Effective information the executives and examination.

 Before the current year’s over, there will be 6.4 billion associated contraptions around the world. As more organizations start utilizing IoT answers for business purposes, the measure of information produced by savvy sensors increments (and will arrive at 400 zettabytes by 2018). On account of Artificial Intelligence, we can come this information down to something significant and increase superior knowledge into resources and workforce the board.

The LA-based startup built up an AI application that sweeps a client’s internet-based life presents on recognize unsuitable substances (bigotry, savagery, and so forth.). About 43% of organizations get to potential workers’ online life profiles. Presently you can confide in the undertaking to a savvy calculation and spare your HR’s time (especially as a human wouldn’t locate a bigot tweet posted two years prior); 

• Evolution of showcasing and publicizing.

New innovations have changed the manner in which advertisers have been working for a considerable length of time. Utilizing the AI Wordsmith stage, you can have a news story composed (or created!) in negligible seconds. The cunning Miss Piggy bot talks away with fans to advance the Muppet Show arrangement. Facebook uses AI calculations to follow client conduct and improve advertisement focusing on.

Airbnb has built up a shrewd application to upgrade settlement costs considering the hotel’s area, regular interest, and well-known occasions held close by. With Artificial Intelligence, advertisers can computerize an incredible portion of routine errands, obtain significant information and commit more opportunity to their center duties — that is, expanding incomes and consumer loyalty.

Applications of Artificial Intelligence for Business

1. Media and web-based business 

Some AI applications are equipped towards the investigation of varying media substances, for example, motion pictures, TV programs, ad recordings or client produced content. The arrangements regularly include PC vision, which is a noteworthy application region of AI. 

Ordinary use case situations incorporate the examination of pictures utilizing object acknowledgment or face acknowledgment procedures, or the investigation of video for perceiving important scenes, articles or faces. The inspiration for utilizing AI-based media and technology can be in addition to other things the assistance of media search, the making of a lot of enlightening watchwords for a media thing, media content approach observing, (for example, confirming the appropriateness of substance for a specific TV review time), discourse to content for chronicled or different purposes, and the discovery of logos, items or big-name faces for the situation of significant notices.

AI applications are additionally generally utilized in E-trade applications like visual hunt, chatbots, and technological tagging. Another conventional application is to build search discoverability and making web-based social networking content shoppable. 

2. Market Prediction 

We are utilizing AI in various conventional spots like personalization, natural work processes, upgraded looking and item suggestions. All the more as of late, we began preparing AI into our go-to-showcase activities to be first to advertise by anticipating what’s to come. Or on the other hand, would it be advisable for me to state, by “attempting” to anticipate what’s to come? Google search is presently upgraded with AI calculations giving clients significant substance — and that is one reason why customary SEO is gradually biting the dust.

3. Foreseeing Vulnerability Exploitation 

We’ve as of late begun utilizing AI to anticipate if a weakness in a bit of programming will wind up being utilized by aggressors. This enables us to remain days or weeks in front of new assaults. It’s an enormous extension issue, yet by concentrating on the straightforward arrangement of “will be assaulted” or “won’t be assaulted,” we’re ready to prepare exact models with high review. 

4. Controlling Infrastructure, Solutions, and Services 

We’re utilizing AI/ML in our cooperation arrangements, security, administrations, and system foundation. For instance, we as of late obtained an AI stage to manufacturing conversational interfaces to control the up and coming age of talk and voice aides. We’re additionally including AI/ML to new IT administrations and security, just as a hyper-joined framework to adjust the outstanding burdens of processing frameworks. 

5. Cybersecurity Defense 

Notwithstanding conventional safety efforts, we have received AI to help with the cybersecurity barrier. The AI framework continually breaks down our system parcels and maps out what is typical traffic. It knows about more than 102,000 examples on our system. The AI prevails upon customary firewall standards or AV information in that it works consequently without earlier mark learning to discover irregularities. 

6. Human services Benefits 

We are investigating AI/ML innovation for human services. It can help specialists with findings and tell when patients are breaking down so restorative intercession can happen sooner before the patient needs hospitalization. It’s a successful win for the social insurance industry, sparing expenses for both the emergency clinics and patients. The exactness of AI can likewise identify infections, for example, malignant growth sooner, hence sparing lives. 

7. Shrewd Conversational Interfaces 

We are utilizing AI and AI to manufacture smart conversational chatbots and voice abilities. These AI-driven conversational interfaces are responding to inquiries from habitually posed inquiries and answers, helping clients with attendant services in inns, and to give data about items to shopping. Headways in profound neural systems or profound learning are making a considerable lot of these AI and ML applications conceivable. 

8. Showcasing and man-made brainpower 

The fields of advertising and man-made consciousness unite in frameworks that aid territories, for example, showcase gauging, and mechanization of procedures and basic leadership, alongside expanded effectiveness of undertakings which would, as a rule, be performed by people. The science behind these frameworks can be clarified through neural systems and master frameworks, PC programs that procedure input and give profitable yield to advertisers. 

Man-made consciousness frameworks originating from social figuring innovation can be applied to comprehend interpersonal organizations on the Web. Information mining procedures can be utilized to dissect various kinds of interpersonal organizations. This examination encourages an advertiser to distinguish persuasive entertainers or hubs inside systems, data which would then be able to be applied to adopt a cultural promoting strategy. 

Conclusion

AI applications, systems, and technology can’t copy innovativeness or keenness. Nonetheless, it can remove the overwhelming work trouble with the goal that advertisers can focus on key arranging and innovativeness. Almost certainly, in not so distant future we will run over such huge numbers of versatile applications that will be fabricated utilizing most recent AI innovations and they will have an incredible capacity to make this world considerably more intelligent.

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.

The need for high-quality chatbot training data

Technology enhancements in Human-Computer interaction has allowed users to interact more seamlessly with computers over a period of time. From setting up medical appointments to online check-in for flights, AI chatbots that imitate human conversations have gained prominence recently. We’ve all used a chatbot like Alexa or Siri to simplify our lives and workload, but do we know how they actually work?

What is a Chatbot?

A chatbot is a type of software that can simulate a conversation with a real human user. The medium used for this exchange is through applications, websites, or telephone conversations. Technically, a chatbot is a piece of software that conducts a conversation via auditory or textual methods simulating how a human would behave as a conversational partner

Chatbots learn from human interactions and grow over time. Broadly speaking, there are two types of bots; one that works on pre-defined rules and the other that can learn from data, identify patterns and make decisions with minimal human intervention. Rule-based chatbots use predefined responses from a database. The bot pulls information from a database using keywords and executes specific commands associated with those keywords. Smart machine-based chatbots, on the other hand, use artificial intelligence and cognitive computing to synthesize data from various information sources while weighing context and conflicting evidence to suggest the best possible response.

what is a chatbot

So, why are chatbots so popular?

Artificial intelligence has a wide range of applications for several fields. Chatbots are one of the most popular examples of artificial intelligence. They are an important asset for many businesses, as they assist in customer support among other things. According to a 2011 study by Gartner, by 2020 around 85% of our interactions will be handled by chatbots rather than humans. Chatbots aren’t just used for answering questions, but they also play a vital role in collecting information, creating databases, etc.

Chatbots help with:

  • Customer service’s first priority is its customers. Their experience determines the success or failure of a company. When online shopping is considered, it has been observed that most shoppers need some kind of support system in place. They need help at each step of the purchasing process, which is where chatbots come in to make this process smooth and quick.
  • Customer information informs their customer service strategies based on the data that they collect about their consumers. Chatbots take information from the reviews and feedback and use that information to help determine how the company can make its product better.
  • Lesser workforce – The work done by one chatbot is better than getting it done by a large number of employees. Companies can cut down on costs by using chatbots that can handle a variety of customer interactions, thus making the work simpler and more efficient as the amount of human error is reduced.
  • Avoids redundancy – Tasks can be avoided within company call centers and the employees, that is, they will help in ensuring that the employees spend their time on important tasks rather than repetitive ones.

Chatbots today can answer simple questions using prebuilt responses. If a user says A, the chatbot will respond with B and so on. After this development, however, expectations have increased. We are now looking for more advanced chatbots that can perform several tasks.

Conversational AI chatbots can be divided into a number of categories based on their level of maturity:

Level 1: This is the basic level where the chatbot can answer questions with pre-built responses. It is capable of sending notifications and reminders.

Level 2: At this level, the chatbot can answer questions and also lightly improvise during a follow-up.

Level 3: The assistant is now capable of engaging in a conversation with the user where it can offer more than just the prebuilt answers. It gets an idea of the context and can help you make decisions with ease.

Level 4: Now, the conversational chatbot knows you better. It knows your preferences and can make recommendations based on them.

Level 5 and beyond: Now the assistant is capable of monitoring several assistants to perform certain tasks. They can do efficient promotions, help in specific targeting of certain groups based on trends and feedback.

So, what goes on behind building a chatbot?

Developing a conversational chatbot is a long process, one that requires innovation at every step. The first and the most important decision to be made is how the bot will process the inputs and produce the reply. Most systems today used rule-based or retrieval-based methods. Other areas of research are grounded learning and interactive learning.

  1. Rule-based
    The chatbots are trained using a set of rules that automatically convert the input into a predefined output or action. It is a simple system, but highly dependent on keywords.
  2. Retrieval-based
    With this system, the bot receiving the input locates the best response from a database and displays it. It requires a high level of data pre-processing and is difficult to personalize and scale.
  3. Generative
    As the demand for chatbots increases, more innovation is being demanded. The limitations of the above-mentioned systems are overcome by this one. Here, the bot is trained using a large amount of chatbot training data. Generative systems are trained end-to-end instead of step-by-step and the system remains scalable in the long run.
  4. Ensemble
    All advanced chatbots, like Alexa, have been built with ensemble methods that are a mixture of all the three approaches. They use different approaches for different activities, but these methods still need a lot of work.
  5. Grounded learning
    Most human knowledge isn’t in the form of structured datasets and is instead presented in the form of text and images. Grounded learning involves knowledge that is based on real-world conversations.

For a chatbot to function as per the requirements, it is important to provide it with high-quality chatbot training data. What exactly is AI training data?

A chatbot converts raw data into a conversation. This raw data is unstructured. For example, consider a customer service chatbot. The chatbot needs to have a rough basis of what questions people might ask as well as the answers to those questions. For this, it retrieves data from emails, databases, or transcripts. This is the training data.

The process of formulating a response by a chatbot

The Importance of High-Quality Chatbot Training Data

Most of the chatbots today don’t work properly because they either have no training or use very little data. The implementation of machine-learning technology to train the bot is what differentiates a good chatbot from the rest.

Training is an on-going process that consists of five stages:

  1. Warm-up training
    The client data is used to start the chatbot. This is the first and most important step.
  2. Real-time training
    The incoming conversations are tracked and tell the bot what people are asking or saying, instead of working purely based on assumptions.
  3. Sentiment training
    The way people are talking to the bot is used to train language and functions. For example, an angry user is dealt with differently as compared to a happy user.
  4. Effectiveness training
    In this method, the result of the conversations is analyzed and the bot is trained accordingly to reach more people faster.

These are just a few ways on how high-quality chatbot training data can enable a conversational bot to produce optimal results. After this, the chatbot is checked for improvement at every stage. 

Chatbots make interactions between people and organizations simpler, enhancing customer service, and they also allow companies to improve their customer experience and overall efficiency. Human intervention is vital to building, training, and optimizing the chatbot system.