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How chatbots are redefining customer experience

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

Why Customer Experience Is Important For Every Business?

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

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

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

How Has Customer Experience Changed Over The Years?

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

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

What Are Chatbots?

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

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

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

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

Why Do We Need Chatbots?

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

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

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

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

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

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

Why Chatbots Are The Future Of Customer Service?

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

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

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

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

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

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

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

How Are Chatbots Used In Business?

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

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

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

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

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

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

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

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

The Future Of Customer Experience And Chatbot

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

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

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

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

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

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

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

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

How artificial intelligence is transforming ecommerce

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

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

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

Evolution Of E-Commerce

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

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

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

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

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

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

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

Usage Of Data In Artifical Intelligence Systems

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

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

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

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

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

Artificial Intelligence Systems & E-Commerce

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

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

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

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

Advance Visual Search Engine

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

AI frameworks enable Marketers to Easily Target Specific Customers

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

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

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

AI recommendations can Help Improve Search Results 

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

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

Make Progressively Effective Deals

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

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

AI Chatbots

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

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

Personalization

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

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

Better Decision Making

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

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

Future Prospects

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

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

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

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

what is content moderation and why companies need it

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

Why do we need content moderation?

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

Content moderation comes with a lot of benefits such as:

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

How do content moderation systems work?

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

Pre-moderation

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

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

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

Post-moderation

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

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

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

Reactive moderation

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

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

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

AI Content Moderation

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

Current shortcomings of content moderation

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

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

Conclusion

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

8 industries artificial intelligence is transforming

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

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

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

Healthcare

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

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

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

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

Finance

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

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

Transport

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

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

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

Business Intelligence

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

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

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

Human Resources

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

Blind contracting

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

Retail/E-Commerce

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

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

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

Agriculture

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

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

Advertising

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

What to expect in the next decade?

Cyborgs

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

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

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

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

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

Top 7 ai trends in 2019

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

AI-Enabled Chips

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

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

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

Augmented Reality

Augmented reality AI trend in 2019

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

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

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

Neural Networks

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

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

The convergence of AI and IoT

IoT & AI trends in 2019

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

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

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

Computer Vision

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

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

Facial Recognition

Facial recognition AI trends in 2019

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

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

Open-Source AI

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

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

Conclusion

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

8 common myths about machine learning

Artificial Intelligence and the idea of it has always been around be it research or sci-fi movies. But the advances in AI wasn’t drastic until recently. Guess what changed? The focus moved from vast AI to components of AI such as machine learning, natural language processing, and other technologies that make it possible.

Learning models which form the core of AI started being used extensively. This shift of focus to Machine Learning gave rise to various libraries and tools which make ML models easily accessible. Here are some common myths surrounding Machine Learning:

Machine Learning, Deep Learning, Artificial Intelligence are all the same

In a recent survey by TechTalks, it was discovered that more than 30% of the companies wrongly claim to use Advance Machine Learning models to improve their operations and automate the process. Most people use AI and ML synonymously. How different are AI, ML and Deep Learning?

Machine Learning is a branch of Artificial Intelligence which has learning algorithms powered by annotated data which learn through experiences. There are primarily two types of learning algorithms.

Supervised Learning algorithms draw patterns based on the input and output values of the datasets. It starts predicting the outputs from the training data sets with possible input and output values.

Unsupervised learning models look at all the data fed into the model and find out patterns in the data. It uses unstructured and unlabeled data sets.

Artificial Intelligence, on the other hand, is a very broad area of Computer Science, where robust engineering and technological advances are used to build systems that need minimal or no human intelligence. Everything from the auto-player in video games to predictive analytics used to forecast sales fall under the same roof using some Machine Learning algorithms

Deep Learning uses a set of ML algorithms to model abstraction in data sets with system architecture. It is an approach used to build and train neural networks.

All data is useful to train a Machine Learning model

Another common myth around Machine learning models is that all the data is useful to improve the outputs of the model. The raw data is never clean and representative of the outputs.

To train the Machine Learning models to learn the accurate outputs expected, data sets need to be labeled with relevance. Irrelevant data needs to be removed.

The accuracy of the model is directly correlated to the quality of the data sets. The quality of the trained data sets results in better accuracy rather than a huge amount of raw/unlabelled data.

Building an ML system is easy with unsupervised learning and ‘Black Box Models’

The most business decision will require very specific evaluation, to make strategic data-driven decisions. Unsupervised and ‘Black Box’ models use algorithms randomly and highlight data patterns making it biased towards patterns which aren’t relevant.

The usability and relevance of these patterns to the objective the business the focus is on are a lot less when these models are used. Black box systems do not reveal what patterns they have used to arrive at certain conclusions. Supervised or Reinforcement learning trained with curated, labeled data sets can surgically investigate the data and give us the desired outputs.

ML will replace people and kill jobs

The usual notion around any advanced technology is that it will replace people and make people jobless. According to Erik Brynjolfsson and Daniel Rock, with MIT, and TomMitchell of Carnegie Mellon University, ML will kill the automated or painfully redundant tasks, not jobs.

Humans will spend more time on decision making jobs rather than repetitive tasks which ML can take care of. The job market will see a significant reduction in repetitive job roles but the wave of ML, AI will create a new sector of jobs to handle the data, train it and derive outcomes based on the ML systems.

Machine Learning can only discover correlations between objects and not causal relationships

A common perception of Machine Learning is that it discovers easy correlations and not insightful outputs. Machine Learning used in conjunction with thematic roles and relationship models of NLP will provide rich insights. Contrary to common belief, ML can identify causal relationships. This is commonly used to try out different use cases and observing the consequences of the cases.

Machine learning can work without human intervention

Most decisions from the ML models will need human intelligence and intervention. For examples, an airlines company may adopt ML algorithms to get better insights and influence best ticket prices. Data sets are constantly updated and complex algorithms may be run on it.

But, to decide the price of a flight by the system itself has a lot of loopholes, the company will hire an analyst who will analyze the data and sets prices with the help of models and their analytical skills, not just relying on the model alone.

The reasoning behind the decision making is still a human intelligence one. Complete control should not be rested on models for optimal results.

Machine Learning is the same as Data mining

Data mining is a technique to examine databases and discover the properties of data sets. The reasons its often confused is because Data Analytics uses these data sets using data visualization techniques. Whereas, Machine Learning is a subfield which uses curated data sets to teach systems the desired outputs and make predictions.

There is similarity when unsupervised learning Ml models use datasets to draw insights from them, which is precisely what data mining does. Machine Learning can be used for data mining.

The common confusion between the two arises due to a new term being used extensively, Data Science. Most Data mining-focused professionals and companies are leaning towards using Data science and analytics now causing more confusion.

ML takes a few months to master and is simple

To be an efficient ML Engineer, a lot of experience and research is needed. Contrary to the hype, ML is more than importing existing libraries in languages and using Tensor Flow or Keras. These can be used with minimal training but takes an experienced hand to provide accuracy.

A lot of intense Machine Learning focussed products require intense research on topics and even coming up with approaches using methods that are in discussion at a university or research level. Already existing libraries solve very generic problems people are trying to solve and not really insightful data. A deeper understanding of algorithms is needed to create an accurate model with an improved f1(accuracy) score.

To sum up, there is an overlap of concepts and models in Machine Learning, Artificial Intelligence, Data Science and Deep Learning. However, the goal and science of the subfields vastly vary. To build completely automated AI systems, all the fields become crucial and play a distinct role.

5 common misconceptions about AI

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

“Can Artificial Intelligence solve any/all problems?“

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

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

“Is AI infallible?“

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

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

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

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

“AI must be expensive.”

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

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

“Will Artificial Intelligence be the end of humanity?”

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Understanding the difference between AI, ML & NLP models

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

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

What is Artificial Intelligence?

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

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

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

History of AI

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

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

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

The areas of application of AI include

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

What is Machine Learning?

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

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

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

History of Machine Learning

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

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

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

Different types of machine learning approaches are:

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

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

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

ML has several applications in various fields such as

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

What is Natural Language Processing?

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

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

History of NLP

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

Few Applications of NLP include

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

What is Deep Learning?

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

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

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

History of Deep Learning

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

Applications of Deep Learning are

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

Key Differences between AI, ML, and NLP

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

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

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

Drone Revolution | Blog | Bridged.co

It’s a bird, it’s a plane… Oh, wait it’s a Drone!

Also known as Unmanned Aerial Vehicles (UAVs), drones have no human pilot onboard and are controlled by either a person with a remote control/smartphone on the ground or autonomously via a computer program.

These devices are already popular in various industries like Defense, Film making and Photography and are gaining popularity in fields like Farming, Atmospheric research, and Disaster relief. But even after so much innovation and experimentation, we have not explored the full capacity of data gained from drones.

We at Bridged AI are aware of this fact and are contributing to this revolution by helping the drone companies in perfecting their models by providing them with curated training data.

Impact of Drones

Drones inspecting power lines

Drones are being used by companies like GE to inspect their infrastructure, including power lines and pipelines. They can be used by companies and service organizations to provide instant surveillance in multiple locations instantly.

Surveillance by drones

They can be used for tasks like patrolling borders, tracking storms, and monitoring security. Drones are already being used by some defense services.

Border patrolling
Drones surveying farms

In agriculture, drones are used by farmers to analyze their farms for keeping a check on yield, unwanted plants or any other significant changes the crops go through.

Drones at their best

Drones can only unlock their full potential when they are at a high degree of automation. Some sectors in which drones are being used in combination with artificial intelligence are:

Image Recognition

Drones can only unlock their full potential when they are at a high degree of automation. Some sectors in which drones are being used in combination with artificial intelligence are:

Image Recognition

Drones use sensors such as electro-optical, stereo-optical, and LiDAR to perceive and absorb the environment or objects in some way.

Computer Vision

Computer Vision is concerned with the automatic extraction, analysis, and understanding of useful information from one or more drone images.

Deep Learning

Deep learning is a specialized method of information processing and a subset of machine learning that uses neural networks and huge amounts of data for decision-making.

DJI’s Drone

Drones with Artificial Intelligence

The term Artificial intelligence is now routinely used in the Drone industry.

The goal of drones and artificial intelligence is to make efficient use of large data sets as automated and seamless as possible.

A large amount of data nowadays is collected by drones in different forms.

This amount of data is very difficult to handle, and proper tools and techniques are required to turn the data to a usable form.

Combination of drones with AI has turned out to be very astounding and indispensable.

AI describes the capability of machines that can perform sophisticated tasks which have characteristics of human intelligence and includes things like reasoning, problem-solving, planning and learning.

Future with Drones and AI

In just a few years, drones have influenced and redefined a variety of industries.

When on the one hand the business tycoons believe that automated drones are the future, on the other hand, many people are threatened by the possibility of this technology becoming wayward. This belief is inspired by many sci-fi movies like The Terminator, Blade Runner and recently Avengers: The Age of Ultron.

What happens when a robot develops a brain of its own? What happens if they realize their ascendancy? What happens if they start thinking of humans as an inferior race? What if they take up arms?!

“We do not have long to act,” Elon Musk, Stephen Hawking, and 114 other specialists wrote. “Once this Pandora’s box is opened, it will be hard to close.”

Having said that, it is the inherent nature of humans to explore and invent. The possibilities that AI-powered drones bring along are too charming and exciting to let go.

At Bridged AI we are not only working on the goal of utilising AI-powered drone data but also helping other AI companies by creating curated data sets to train machine learning algorithms for various purposes — Self-driving Cars, Facial Recognition, Agri-tech, Chatbots, Customer Service bots, Virtual Assistants, NLP and more.

Development of artificial intelligence - a brief history | Blog | Bridged.co

The Three Laws of Robotics — Handbook of Robotics, 56th Edition, 2058 A.D.
1. First Law — A robot may not injure a human being or, through inaction, allow a human being to come to harm.
2. Second Law — A robot must obey the orders given it by human beings except where such orders would conflict with the First Law.
3. Third Law — A robot must protect its own existence as long as such protection does not conflict with the First or Second Laws.

Ever since Isaac Asimov penned down these fictional rules governing the behavior of intelligent robots — in 1942 — humanity has become fixated with the idea of making intelligent machines. After British mathematician Alan Turing devised the Turing Test as a benchmark for machines to be considered sufficiently smart, the term artificial intelligence was coined in 1956 at a summer conference in Dartmouth University, USA for the first time. Prominent scientists and researchers debated the best approaches to creating AI, favoring one that begins by teaching a computer the rules governing human behavior — using reason and logic to process available information.

There was plenty of hype and excitement about AI and several countries started funding research as well. Two decades in, the progress made did not deliver on the initial enthusiasm or have a major real-world implementation. Millions had been spent with nothing to show for it, and the promise of AI failed to become anything more substantial than programs learning to play chess and checkers. Funding for AI research was cut down heavily, and we had what was called an AI Winter which stalled further breakthroughs for several years.

Gary Kasparov vs IBM Deep blue | Blog | Bridged.co

Programmers then focused on smaller specialized tasks for AI to learn to solve. The reduced scale of ambition brought success back to the field. Researchers stopped trying to build artificial general intelligence that would implement human learning techniques and focused on solving particular problems. In 1997, for example, IBM supercomputer Deep Blue played and won against the then world chess champion Gary Kasparov. The achievement was still met with caution, as it showcased success only in a highly specialized problem with clear rules using more or less just a smart search algorithm.

The turn of the century changed the AI status quo for the better. A fundamental shift in approach was brought in that moved away from pre-programming a computer with rules of intelligent behavior, to training a computer to recognize patterns and relationships in data — machine learning. Taking inspiration from the latest research in human cognition and functioning of the brain, neural network algorithms were developed which used several ‘nodes’ that process information similar to how neurons do. These networks have multiple layers of nodes (deep nodes and surface nodes) for different complexities, hence the term deep learning.

Representation of neural networks | Blog | Bridged.co

Different types of machine learning approaches were developed at this time:

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

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

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

Along with better-written algorithms, several other factors helped accelerate progress:

Exponential improvements in computing capability with the development of Graphical Processing Units (GPUs) and Tensor Processing Units have reduced training times and enabled implementation of more complex algorithms.

Data repositories for AI systems | Blog | Bridged.co

The availability of massive amounts of data today has also contributed to sharpening machine learning algorithms. The first significant phase of data creation happened with the spread of the internet, with large scale creation of documents and transactions. The next big leap was with the universal adoption of smartphones generating tons of disorganized data — images, music, videos, and docs. We have another phase of data explosion today with cloud networks and smart devices constantly collecting and storing digital information. With so much data available to train neural networks on potential scores of use-cases, significant milestones can be surpassed, and we are now witnessing the result of decades of optimistic strides.

  • Google has built autonomous cars.
  • Microsoft used machine learning to capture human movement in the development of Kinect for Xbox 360.
  • IBM’s Watson defeated previous winners on the television show Jeopardy! where contestants need to come up with general knowledge questions based on given clues.
  • Apple’s Siri, Amazon’s Alexa, Google Voice Assistant, Microsoft’s Cortana, etc. are well-equipped conversational AI assistants that process language and perform tasks based on voice commands.
Developments in AI | Blog | Bridged.co
  • AI is becoming capable of learning from scratch the best strategies and gameplay to defeat human players in multiple games — Chinese board game Go by Google DeepMind’s AlphaGo, computer game DotA 2 by OpenAI are two prolific instances.
  • Alibaba language processing AI outscored top contestants in a reading and comprehension test conducted by Stanford University.
  • And most recently, Google Duplex has learned to use human-sounding speech almost flawlessly to make appointments over the phone for the user.
  • We have even created a Chatbot (called Eugene Goostman) that passed the Turing Test, 64 years after it was first proposed.

All the above examples are path-breaking in each field, but they also show the kind of specialized results that we have managed to attain. In addition, such achievements were realized only by organizations which have access to the best resources — finance, talent, hardware, and data. Building a humanoid bot which can be taught any task using a general artificial intelligence algorithm is still some distance away, but we are taking the right steps in that direction.

Bridged's service offerings | Blog | Bridged.co

Bridged is helping companies realize their dream of developing AI bots and apps by taking care of their training data requirements. We create curated data sets to train machine learning algorithms for various purposes — Self-driving Cars, Facial Recognition, Agri-tech, Chatbots, Customer Service bots, Virtual Assistants, NLP and more.