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How Machine Learning and AI is Influencing Logistics, Supply Chain & Transportation Management

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How Machine Learning and AI is Influencing Logistics, Supply Chain & Transportation Management

More than 65% of top transportation professionals agree that logistics and supply chain management is in the midst of a revolution – a period of incremental transformation. And, the most potent drivers of change are none other than machine learning and artificial intelligence.

Top notch companies are already found leveraging the tools of artificial intelligence and machine learning for fine-tuning its superior strategies, including warehouse location scouting and enhancing real-time decision-making. Though these advanced technologies nurture large chunks of data, the logistic industry has for long been hoarding piles of data. Today, the difference lies in the gargantuan volume of data, as well as the existence of powerful algorithms to inspect, evaluate and trigger the process of understanding and its respective action.

Below, we will understand how AI streamlines logistics and transportation functionalities, influencing profitability and client satisfaction. Day by day, more companies are fusing Artificial Intelligence with Internet of Things to administer logistics, inventory and suppliers, backed by a certain amount of precision and acumen. Let’s delve deeper!

Predictive Maintenance

AI-powered Sensors monitor operational conditions of machines; thus can detect discrepancies even before the scheduled machine servicing based on manufacturer’s recommendation. Then they alert the technicians prior to any potential equipment failure or service disorientation. Thanks to real-time wear and tear!

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Shipping Efficiency

Powerful algorithms are constantly used to tackle last minute developments, including picking the best alternate port in case the main port is non-operational or something like that, planning beforehand if the main carrier cancels a booking and even gauging times-of arrival.

Machine Learning capabilities are also put to use for estimating the influence of extreme weather conditions on shipping schedules. Location specific weather forecasts are integral to calculate potential delays in shipments.

Warehouse Management

Machine learning has the ability to determine inventory and dictate patterns. It ascertains the items which are selling and are to be restocked on a priority basis, and items which need sound remarketing strategy.

Voice recognition is a key tool that uses AI to ensure efficiency and accuracy through successful Warehouse Management System – a robotic voice coming out of a headset says which item to pick and from where, enabling a fast process of warehousing and dispatching of goods.

Once, the worker founds the item, he/she reads out the number labeled on them, which the system then tallies with its own processed data list through speech recognition and then confirms the picked item for the next step.  The more the system is put to use, the more trained it gets. Over time, the system learns the workers’ tone and speech patterns, resulting into better efficiency and faster work process.

Delivery

 A majority of shipping companies are competing with each other to have the most robust and efficient delivery service, because delivery is the final leg of a logistic journey. And it’s vitally important – predictive analytics is used to constantly maneuver driver routes, and plan and re-plan delivery schedules.

DHL invests on semi-autonomous vehicles that drive independently without human intervention carrying deliverables to people across urban communities. Another company, Starship Technologies, founded by the co-founders of Skype employs six-wheeled robots across London packed with hi-tech cameras and GPS. The robots are stuffed with cutting edge technology, but are controlled by humans so that they can take charge as and when required minimizing any negative outcomes.

Overall, artificial intelligence and machine learning has started augmenting human role for efficient logistics and transportation management. With all the recent developments in the technology sphere, it’s only a matter of time until AI becomes a necessary management part of supply chain.

Data Science Machine Learning Certification

And of course all this excites us to the core! If you are excited too, then please check out our brand new Machine Learning Using Python training courses. We combine theoretical knowledge merged with practical expertise to ensure students get nothing but the best!

The blog has been sourced from:

https://www.forbes.com/sites/insights-penske/2018/09/04/how-artificial-intelligence-and-machine-learning-are-revolutionizing-logistics-supply-chain-and-transportation/#eb663dd58f5d
https://aibusiness.com/streamline-supply-chain-ai
 


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How Machine Learning is Driving Out DDoS, The Latest Hazard in Cyber Security

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How Machine Learning is Driving Out DDoS, The Latest Hazard in Cyber Security

It is common knowledge that the computer world is under constant threat of security breaches. Furthermore, cyber attacks are becoming more dangerous by the day. Over three trillion dollars are wasted every year owing to cyber crimes. And this huge wastage of money is likely to double by 2021. In a time where the number of internet users is increasing exponentially, it seems surreal to expect that threats can be completely eradicated.

Among a plethora of threats, the most infamous one is DDoS, which stands for distributed denial of service attack. In this malicious form of attack, normal traffic for the targeted server, network or service is disrupted by flooding it and its neighboring infrastructure with tremendous internet traffic. This new evil in cyber security has wreaked havoc with business processes.

The tech ecosystem is becoming increasingly dominated by machine learning. ML techniques provide a new approach to eradicate DDoS attacks. In this blog, we discuss a newly researched ML technique that helps restrain DDoS attacks.

SIP and VoIP

A team of researchers from University of Aegean, Greece, headed by Z Tsiatsikas, has published a study about tackling DDoS with machine learning in SIP-based VoIP systems. The popularity of VoIP systems in hardware ecosystems is the primary reason for choosing it for this study. In this age of internet, VoIP is the common choice for voice as well as multimedia communications.

Session Initiation Protocol (SIP) is the preference for initiating VoIP sessions. The basic structure of SIP/VoIP architecture has been described below:

User Agent (UA): This represents the endpoints of SIP, which are active units of the session. For example, in the case of voice communication, the caller and receiver represent endpoints for the session.

SIP Proxy Server: This entity acts both as client and server during the session. The tasks of the server are:

  • Maintaining send and receive requests
  • Transferring information between users

Registrar: Authentication processes and requests to register for UA are managed by this entity.

The VoIP provider keeps a record of the SIP communication. This is an important step as it gives out information to service providers regarding billing and accounting based activities of users. In addition to this essential data, it may also give out data about intrusion or dubious activities happening in a network. Hence, it is very important to monitor this area. If neglected, it may turn into a hotbed for DDoS attacks.

Combining ML Methods in VoIP

The researchers have employed these five standard ML algorithms in experiments:

  • Sequential minimal optimization
  • Neural networks
  • Naïve Bayes
  • Random Forest
  • Decision trees

In the experiment, communications are taken care of through these algorithms. The network is made anonymous using HMAC (keyed-hash method authentication code) and classification features are created. These algorithms are tested using 15 different DDoS attack situations. This is done using a ‘test bed’ of DDoS simulations. The design, as done by researchers, is shown below:

Image source: Analytics India

Following are some of the parameters of the experiment:

  • 3 to 4 types of Virtual Machines (VMs) have been used for SIP proxy, legitimate users, and for generating attack traffic based on the scenario.
  • Particularly for SIP proxy, popular VoIP server Kamailo (kam, 2014) has been employed.
  • sipp v.3.21 and sipsak2 tools have been employed to simulate patterns for legitimate and DoS attack traffic.
  • For simulation of DDoS attack, SIPpDD tool has also been used
  • Weka tool has been used for machine learning analysis.

Performance

Compared to non-ML detection, these algorithms perform well. Speaking from an intrusion detection viewpoint, Random Forest and decision trees work best. With the rise in attack traffic, there’s drop in the rate of intrusion detection, which signifies the presence of DDoS.

To conclude, it can be said that machine learning surpass traditional methods of detecting attacks. This latest development in cyber security is another example of the rapid progress that machine learning is bringing into every field.

Interested in joining machine learning courses in Delhi? Wait not. Contact DexLab Analytics Right Now and get yourself enrolled for the best machine learning training in Delhi.

 

This article has been sourced from: www.analyticsindiamag.com/machine-learning-chasing-out-ddos-cyber-security

 

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LinkedIn Suggests How to Find Machine Learning Experts across Diverse Career Pathways

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LinkedIn Suggests How to Find Machine Learning Experts across Diverse Career Pathways

Machine learning skill is fast picking up pace amongst more and more businesses. Each day, a large number of employees are being sucked into the booming field of big data analytics. But, recruiting them can be a tad bit challenging, on the part of employers. In this regard, LinkedIn recently shared some valuable data that defines the standard career path of a machine learning professional, offering insights as to how enterprises can themselves build and nurture such talent.

In the process of conducting such an intensive analysis, LinkedIn scrutinized various profiles across the globe having at least one machine learning skill listed in their profiles. The analysis of profiles spanned from April 2017 to March 2018.

The result of the analysis is interesting; it highlighted the skills the professionals share with each other and at what point of their career they need to adapt to these skills. It also sheds light on what kind of skills are developed just before machine learning – and they are data mining, R and Python, respectively.

LinkedIn has a valuable suggestion for the recruiters – it says companies can seek job candidates that have these abovementioned skills, only to develop machine learning skill later.

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Some of the other skills worthy of professionals’ interest are Java and C++ – these programming languages are gaining importance day by day.

The data given below even illustrates which industry absorbs the majority of machine learning talent. Unsurprisingly, one third of professionals powered by machine learning skill falls under higher education and research category, more than a quarter of ML professionals are from software and internet industry and the rest are scattered amongst other industry types.

Following the insights, LinkedIn suggests that enterprises should look beyond their respective industries to seek right ML candidates. According to last year’s data, 22% of people possessing ML skill changed their jobs and amongst them, 72% changed industries.

Moreover, the data helps recruiter identify the right candidate by checking out the combination of his skills as a whole and the skills a ML professional should possess. For example, ML professionals belonging from the finance and banking sector are more likely to be specialized in business analytics, Tableau and SAS, while ML professionals hailing from software industry should have a vast knowledge on a broad spectrum of programming language skills.

Future of Machine Learning

Machine learning is another flourishing branch of AI. While the early AI programs were mostly rule-based and human-dependent, the latest ones possess the striking ability to teach and formulate their own operational rules.

2017 was smashing for witnessing growth of scope and capabilities of machine learning, while 2018 harbors potential for widespread business adoption, says a research from Deloitte.

As parting thoughts, AI is nothing but tools adopted to tackle high-end business problems. Designing a proper application of machine learning includes asking the right questions to the right people to get hold of right solutions.

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References:

zdnet.com/article/looking-for-machine-learning-experts-linkedin-data-shows-how-to-find-them

techrepublic.com/article/machine-learning-the-smart-persons-guide
 

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How Python Introduces New Audiences to the Exciting World of Computer Programming

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How Python Introduces New Audiences to the Exciting World of Computer Programming

What was the motivation behind the birth of Python? The language has been searched by American Google users more often than Kim Kardashian in the last one year! And the rate of queries related to Python has trebled since 2010.

Dutch computer scientist, Guido van Rossum, fed up with the shortcomings in commonly used programming languages, developed Python as his Christmas project in 1989. He wanted a language that was simple to read, allowed users to create their own modules for special-purpose coding and then made this package available to others. And lastly he wanted a ‘’short, unique and slightly mysterious’’ name. He named the package after the British comedy group, Monty Python. And Cheese Shop was the chosen name for the package repository.

Nearly three decades after this ground-breaking Christmas invention, the popularity of Python is still growing. According to stats from Stack Overflow, a programming forum, approximately 40% of developers use it and 25% intend to do so. But the programming language isn’t admired by the community of developers alone; it is well-liked the public in general. According to Codecademy, a website that has taught different programming languages to over 45 million novices, Python has the highest demand. Python aficionados, known as Pythonistas, have contributed over 145,000 packages to the Cheese Shop and these cover diverse realms, such as astronomy and game development.

Image source: Economist

Decoding Python’s Fame

Python isn’t perfect. There are other languages that have higher processing efficiency and give users better control over the computer’s processor. However, Python possesses some killer features, which make it a great general purpose language. It has easy-to-learn syntax that simplifies coding. Python is a versatile platform that has a variety of applications.

 

  • The Central Intelligence Agency uses it for hacking
  • Pixar employs it for work related to films
  • Google uses it for crawling web pages
  • Spotify recommends songs with the help of Python

 

Python is also widely used for tasks that are grouped under ‘’non-technical’’. Following are some examples:

 

  • Marketers build statistical models with the help of Python to judge the effectiveness of campaigns.
  • Lecturers use it to find out if the grading system is accurate or not
  • Journalists use codes written in Python for grazing the web for data

 

Professionals who need to trawl through spreadsheets find Python highly valuable for their work. EFinancialCareers, a website dealing with jobs, has reported a fourfold increase between 2015 and 2018 in job listings that mention Python. Citigroup, the reputed American bank, organizes crash courses in Python to train newly hired analysts.

Some of the most appealing packages within the Cheese shop harness the power of AI. Mr. Van Rossum declares that Python is the preferred language for AI researchers. They use it for creating neural networks and identifying patterns from huge data sets. However, the high demand for learning Python comes with certain risks. Novices who know how to use different tools but don’t know their intricacies well are prone to make faulty conclusions without proper supervision.

One solution for this problem is to educate students from an early age. Generally, teaching programming languages is limited to STEM students in American universities. A radical proposal is to offer computer science classes to primary school children. Anticipating a future filled with automated jobs, 90% American parents have expressed desire that their children receive computer programming classes in school.

Presently, 67% of 10-12 year olds have accounts in Code.org. In university level, Python has been ranked the most popular programming language for 2014. While nobody can predict how much longer Python will keep reigning, one thing is for sure, Mr. Rossum’s Christmas invention is truly smart and purposeful.

To the dismay of Pythonistas, on 12th July 2018, he stepped down from the position of supervising the community. The reason being his discomfort with the rising fame!

Well, we hope Python’s glory continues for years to come! To read more blogs on the latest developments in the world of technology, follow DexLab Analytics. If you’re interested in mastering machine learning using Python, then you must check our machine learning courses in Delhi.

 

Reference: economist.com/science-and-technology/2018/07/19/python-has-brought-computer-programming-to-a-vast-new-audience

 

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A Comprehensive Guide on the Functioning of Chatbots

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A Comprehensive Guide on the Functioning of Chatbots

Chatbot is a technology that is rapidly growing and is likely to power 85% of customer service by 2020. And this is already mid 2018. Though this technology is booming, many are new to the concept of chatbots. To help such newbies, on this blog we will discuss what a chatbot really is and also talk about the different parameters related to it.

So, what is a chatbot?

A chatbot is a computer program that interacts with human users through simulated conversations using the Internet. The chatbot cannot set commands by itself. It simply provides solutions to human queries through the most natural medium of communication, which is chatting and messaging in the language of customers.

The next question that comes to our mind is-

What are the tasks that a chatbot can perform?

In this regard, it must be kept in mind that chatbots are basically programs that automate tasks. The tasks span over a variety of fields, including customer support, appointment scheduling, performing surveys and lead generation. Here are some areas of the business areas where chatbots have been very beneficial:

  1. A chatbot answers FAQs and gives the information customers want about different products and services. In short, businesses keep chatbots to handle all the customer queries. In fact, bots are able to respond to multiple queries at a time!
  2. It helps customers schedule appointments, plan trips and informs them if a product is available or not.

It has been found that companies that use the services of chatbots can save up to 60% of their time!

Why have Chatbots become the talk of town?

Most important reason for their growing popularity is that they allow the company to be present on a platform that is extensively used by customers– online. With the advent of chatbots, brands can be in the same space as their customers, without being physically present. Customers are able to interact with businesses 24/7. Thus, bots act like sales representatives online that are ready to assist customers. This directly leads to higher sales for many businesses. Moreover, chatbots respond depending on the industry it’s employed in and the customer it’s interacting with. Hence, it helps deliver personalized responses to every single user.

Working of a chatbot:

Chatbots are basically a form of AI that is developed by means of complicated programming. There are two main types of chatbots. Some chatbots function through a set of structured questions and answers and some function mainly through machine learning algorithms. The later is more complicated. However, both may look the same to users.

Scripted and structured bots: The chatbots working with structured question and answers have a limited knowledge base. Their skills are limited to correctly answering only specific questions which the bots are programmed to answer. There might be questions that aren’t included in the programming, to which the bot is likely to respond with ‘’I’m sorry, I didn’t understand the question.’’ These bots are as smart as the programming behind them permits. These types of bots are generally used for marketing in Messenger platforms. They perform tasks like sending daily mails and content pieces, generating leads, performing surveys, etc.

Source: DZone

NLP based chatbots: These bots understand language very well and deviations from the standard set of questions won’t baffle them easily. NPL (natural language processing) is a part of machine learning and the incorporation of NPL is what enables these bots to understand the nuances of language so well. Obviously, it takes a lot more work to develop these intelligent chatbots. There are three main concepts in NPL- intent, entity and utterance. Intent and entities are responsible for structuring the chatbot, whereas utterance is responsible for improving the bots with use. The best part about machine learning chatbots is that the more they are interacted with, the cleverer they become.

With the availability of free DIY chatbot platforms, chatbots can now be created without prior knowledge on coding. But, if you wish to be a pro in this field then acquire the necessary skills through the machine learning training in Gurgaon. For all the trending news on big data and related tech, follow DexLab Analytics. We are an institute that provides high-quality machine learning courses in India.

 

Reference: dzone.com/articles/here-is-a-complete-guide-of-chatbots

onlim.com/en/how-do-chatbots-work

 

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3 Recent Applications of AI will leave you Spellbound

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3 Recent Applications of AI will leave you Spellbound

AI technology has the potential to enhance societies in a number of ways. Here, we discuss some of latest developments in AI-based research.

AI can smell illness in your breath

According to a recent declaration made by Nvidia, AI can detect illness, including cancer, by analyzing the human breath. Researchers from Edinburgh Cancer Center in UK, Loughborough University, the University of Edinburgh and Western General Hospital have developed an AI program using deep learning methods that is  able to analyze compounds in human breath and predict illness. The motivation? Humans have a less developed sense of smell compared to other animals. Hence, a lot of information hidden in the air around us go unnoticed and can be perceived with a highly receptive olfactory system.

Source: news.developer.nvidia.com

The team of researchers said that this is the first machine learning model that can successfully detect compounds and ion patterns from raw GC-MS (Gas Chromatography and Mass Spectrometry) data. TensorFlow deep learning frameworks, cuDNN-accelerated Keras and Nvidia Tesla GPUs were used to develop neural networks for the program. The data utilized for expanding the neural networks was contributed by volunteers who had different forms of cancer and were undergoing radiotherapy. Artificial intelligence makes the process less expensive, and definitely more reliable and faster than humans analyzing a breath sample.

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AI is marking exam papers

A new concept is coming together in China’s education system. Experiments suggest that machine intelligence can contest a teacher’s marking capability and at times even surpass it! AI has long assisted humans in marking multiple choice exams and performed wonderfully in that. Chinese researchers have taken the examining powers of AI-driven machines a step forward and developed AI that can mark essays.

First, the system perceives general logic from the context and then links it to the meaning of words. It works like a human mind that first understands the theme of the story from the headline and then reads through the rest of the writing. The machine learning algorithm assesses the quality of the essay with human-like judgment. It grades the paper and provides remarks on areas where there’s scope of improvement. These remarks include the need to improve sentence structure and writing approach among others.

Source: Cambridge assessment

A case study conducted with 120 million students from 60,000 schools shows that both the algorithm and human teachers have the same average performance rating, which is 92%. However, the model is designed to automatically improve as it handles more tasks and is likely to outperform the teachers in future.

Secret Archives of Vatican being decoded with AI

Within the walls of the Vatican lies the most impressive collection of historical facts in the world. The Vatican Secret Archives contains records that date back to more than 12 centuries. Despite gazillions of pages stored in Vatican, only a selected few are available to researchers and scholars online.

Source: Serial Box

A new project named In Condice Ratio is combining optical-character-recognition (OCR) with artificial intelligence to help scan through all the information and upload it to online database. Traditional OCR method isn’t effective on handwritten documents. But the new OCR enhanced with AI, known as jigsaw segmentation, can recognize different pen strokes and turn the raw information into searchable data.

Source: In Condice Ratio

What the future holds

It seems like in the near future humans beings will need to use and depend on the judgment of AI applications on the daily. So, why not master the necessary skills needed to understand the workings of AI applications? Enroll for machine learning courses in Gurgaon and follow DexLab Analytics for the latest AI-tech blogs. We provide top-notch machine learning training in Delhi.

 

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New Artificial Intelligence Model Provides Smart Solution to Water Logging in Indian Cities

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New Artificial Intelligence Model Provides Smart Solution to Water Logging in Indian Cities

The primary reason why metro cities in India are hit by water logging during the monsoons is poor city planning. To tackle this problem, a team from Netaji Subhas Institute of Technology, headed by researchers Apoorva Gupta and Aman Bansal, have designed an AI model. This one-of–a-kind model predicts the severity of water logging in target locations by combining data on rainfall and traffic for a particular region.

The issue of water logging directly translates to economic losses for the country. If people are stranded in a place, if they can’t go about their daily business, it means a significant drop in productivity and business revenue. Mumbia, India’s financial capital faces this problem every year. There’s urgency to find a solution. On top of economic loses, heavy rainfall equals to heavy traffic, which boils down to fuel and time wastage as vehicles are stuck in jams.

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Working of the technology

Firstly, the team collected relevant data and then fed this data into the machine learning platform they created. This data was then run through the platform. The AI-powered model analyzes data related to topography and other natural factors for a particular region. It can point out regions that are prone to water logging and this helps engineers make better decisions and avoid mistakes while planning the infrastructure of a city, like constructing the network of roads.

Neural networks, which are the brains of this system, have been able to identify areas that deserved extra attention while planning.  It can also spot new areas that are naturally prone to water logging. The study and research that went into building this model was conducted in Manilla, Philippines’ capital city, since the region has similar topography and environmental characteristics to India.

Aggregating the data

Previously, researchers gave Internet of Things (IoTs) a shot. It relied on setting up electronic devices in various locations to fetch data on traffic, accident prevalence and moisture. However, the technicalities of these projects made them economically impractical.

The latest advancement in the field of technology made it possible for researchers to gather the required information. For example, Uber provides easy access to travel time data. It has come to be a reliable source for real-time data on traffic.

Future scope:

The real-time access to data boosts the practicality of this tech. The fact that algorithms self-evolve and improve as new data gets added to the system broadens the horizons of this AI-powered model. In future, this system can analyze data and spot accident-prone zones and also sent alerts to travelers as they approach those areas.

This revolutionary tech can be also be used for many other purposes. One of them is pointing out the most suitable locations to place emergency services like ambulances and fire-fighting engines. It can be utilized for predicting traffic on roads during special occasions like festivals and celebrations. In short, this new tech will serve as a great tool for engineers working on the architectural planning in developing countries.

Did you know that AI works like the human mind? But with the advantage of identifying patterns within huge data sets.

The smartest human beings are joining the AI workforce. Don’t be left behind. Enroll for machine learning course in Gurgaon. A machine learning certification from a reputed institute like DexLab Analytics is sure to give your career a huge boost!

 

Reference: sanvada.com/2018/07/06/new-artificial-intelligence-model-may-help-prevent-water-logging-cities

 

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Adopt Machine Learning and Personalize Marketing Game Big Time

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Adopt Machine Learning and Personalize Marketing Game Big Time

In the last couple of years, Netflix and Spotify have altered our digital expectations. The technology that these fast-growing streaming media companies use to generate fulfilling customized experiences is a particular kind of Artificial Intelligence, known as Machine Learning.

Highly technical though it sounds, Machine Learning is the most valuable, new-age tool that all the marketers need to employ right now. To better explain the nuanced concept, we’ll start with an approach that preceded it.

Human-based Marketing: Limited Scope

Previously, rules and segmentation used to dominate marketing domains; most of the customized experiences in the past were delivered through a set of norms, created manually by a marketer based on some predetermined criteria. Though the approach worked, but its scope was very limited.

The hitch is that the humans wrote the rules, based on what they believed true and right. But, remember, each human being is unique, and so is their perception. Also, their intent varies from time to time. In short, there exists too much data for a normal human being to assess or sort without taking the help of machines, or in this case Machine Learning.

The Rise of Machine Learning

Instead of relying on human intuitions, machine learning algorithms offer an innovative way for marketers to curate incredible experiences for individuals. No longer does the computer follow any rules and commands, rather we’ve programmed it to learn everything about a particular person, so that it can conjure up the experience that appeals to him the most.

For improved machine-learning personalization, marketers should build and feed in own ‘recipes’ to the computers that tell the kind of information to consider, when formulating someone’s digital campaign.

 Sometimes, the algorithms can be pretty simple, such as showing trending topics or they can be very complex, like decision trees or collaborative filtering. It all depends on the marketers to devise a strategy that would ensure the best customized experience for the visitors, of course with Machine Learning using Python.

Decision-making Induced by Machine Learning

When you speak with a person, you know what to say next and when to stop, based on the idea of previous encounters with him/her. Now, if it’s for the first time you’re speaking with him, you behave in a way you are expected to, based on social interactions with others.

Machine learning functions in the same way. Based on recognition and remembering past situations, this type of learning creates a fluid pattern that controls next behaviors.

It uses real data to derive at decisions, just similar to a normal human being who would come to a conclusion after a conversation.

As parting thoughts, humans shouldn’t hand over everything to the machines; machine learning can be all so rosy and perfect, but it’s us who needs to define, examine and refine the algorithms to make them work and fulfill the overall objectives of one-to-one customization and superior brand experience for the clients.

Of course, machine learning has over-the-top advantages against traditional human-based approaches, but it’s us who have developed them. And that matters!

For business analyst training courses in Noida, drop by DexLab Analytics. They are specialists in a number of in-demand skills, including big data hadoop, SAS and R programming, amongst others.

 

The blog has been sourced from – https://www.entrepreneur.com/article/311931

 

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Want to Develop an AI Chatbot? Know How:

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Want to Develop an AI Chatbot? Know How:

As businesses are focusing on improving customer engagement and building personalized experiences for them, AI-powered chatbots are rapidly becoming the norm to meet user-centric tasks. Gartner proclaims that by 2020, 85% of interactions between customers and a brand will occur through chatbots. Microsoft’s CEO, Satya Nadella rightfully says, ‘’ Bots are the new apps.”

It is important for a chatbot to have a ‘’human touch’’. The key to that is its intelligent quotient.

So, you want to build a smart AI chatbot? In this blog, we shall discuss some important pointers to get you started.

  • Understand Customers:

The most important thing to keep in mind while building a chatbot is the goal of building it. So, a chatbot needs to understand what users demand from it very well. Hence, the better the designer understands the goals; the superior will be the quality of the bot. A chatbot needs to be familiar with the most commonly asked questions and also needs to provide relevant answers to those. The two common goals of building a chatbot are helping users or collecting information from them. Helper chatbots employ natural language processing (NLP) and have strong understanding capabilities. These bots can be used to carry out a variety of tasks, like buying products or booking hotel rooms. On the other hand, collector bots adhere to a pre-defined set of questions and don’t have the ability to respond when presented with new queries. However, by utilizing intelligent platforms, the performance of collector bots can be enhanced; they learn to respond to unknown queries by intelligently presenting the information they collect.

  • Designing Conversational Flow:

Creating a conversation flow chart is a crucial phase of building a smart chatbot. Here are the steps that you need to follow:

  1. Write down a standard conversation
  2. Jot down the possible ways in which a user can go off track
  3. Learn to deal with such off track queries. Here, interacting with existing online bots proves extremely useful. Ask questions in order to break their flow and note down the responses you get. Apply these to your flow. David Low, chief technology evangelist for Amazon Alexa, has stressed on the importance of creating a conversation script and testing it back-and-forth.
  4. It is advisable to present your bot as a non-human character. For example, to make it clear that your platform is a bot, greet users with a welcome message and state all the tasks your text platform can perform.

  • NLP and Machine Learning:

Natural language processing (NLP) platforms, like WIT, API and LUIS are the driving force behind intelligent chatbots. They analyze and resolve sentences into intent, agents, actions and contexts. NPL platforms help identifying links between words and determining parts of speech like nouns, verbs and adjectives. When it comes to leveraging machine learning or NPL for your bot, consider open and closed sources, generative and retrieval-based models before settling for the ideal model.

Want to Develop an AI Chatbot? Know How:

Conversations happening in social media platforms include a variety of topics and fall under open domain category. However, if you wish to regulate input and output for a bot then you must opt for a closed domain. Retrieval-based models work with predefined responses whereas; generative models have the ability to come up with new responses. A complex feature like sentiment analysis can also be incorporated in chatbots through NPL. This is useful in situations where a chatbot is unable to satisfy a customer. In such cases it transfers the problem to a human customer representative.

In future, companies will be increasing dependent on chatbots to boost their sales. Hence, professionals with expertise in this upcoming tech are likely to be highly valued. So, if you want to be part of that elite group then you must enroll for machine learning training in Delhi at Dexlab Analytics– our seasoned consultants offer the best machine learning courses in Delhi.

 

References:

https://moz.com/blog/chat-bot

https://intellipaat.com/blog/how-to-build-an-artificial-intelligence-chatbot/

https://www.marutitech.com/make-intelligent-chatbot/

 

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