Category: Machine Learning

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Latest Innovations in the field of AI & ML

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

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

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

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

Market Size:

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

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

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

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

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

Key Players in Artificial Intelligence:

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

Industries Artificial Intelligence Serves:

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

Artificial Intelligence can be applied in:

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

Key Players in Machine Learning

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

Industries Machine Learning Serves:

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

Machine Learning can be applied in:

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

Future of AI & ML:

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

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

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

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

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

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

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

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

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

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

AI & ML innovations

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

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

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

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

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

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

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

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

Digital Advertising

Outline:

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

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

Development tools for AI and ML

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

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

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

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

Tools for AI and ML

Tools for AI and ML:

Google ML Kit for Mobile:

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

Features:

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

Pros:

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

Cons:

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

Accord.NET:

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

Features:

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

Pros:

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

Cons:

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

Tensor Flow:

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

Features:

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

Pros:

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

Cons:

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

Infosys Nia:

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

Features:

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

Pros:

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

Cons:

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

Apache Mahout:

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

Features:

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

Pros:

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

Cons:

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

Shogun:

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

Features:

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

Pros:

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

Cons:

Some may find its API difficult to use.

Scikit:

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

Features:

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

Pros:

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

Cons:

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

Final Thoughts:

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

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

Machine Learning

What is Machine Learning?

Machine learning (ML) is fundamentally a subset of artificial intelligence (AI) that allows the machine to learn automatically. No explicit programs are needed instead of coding you gather data and feed it to the generic algorithm. It is a scientific study of algorithms and statistical models used by computers to perform specific tasks.

The machine builds a logic based on that data. It can access data and teach itself from various instructions, interactions, and queries resolved. ML forms data patterns that help in making better decisions. The machines learn without human interference even in fields where developing a conventional algorithm is not workable. ML includes data mining, data analysis to perform predictive analytics.

Machine learning facilitates the analysis of substantial quantities of data. It can identify profitable opportunities, risks, returns and much more at a very high speed and accuracy. Costs and resources are involved in training the agent to process large volumes of information gathered.

Working of Machine Learning:

Machine Learning algorithm obtains skill by using the training data and develops the ability to work on various tasks. It uses data for accurate predictions. If the results are not satisfactory, we can request it to produce other alternative suggestions. ML can have supervised, semi-supervised, unsupervised or reinforcement learning.

Supervised learning is the machine is trained by the dataset to predict and take decisions. The machine applies this logic to the new data automatically once learned. The system can even suggest new input after adequate training and can even compare the actual output with the intended output. This model learns through observations, corrects the errors by altering the algorithm. The model itself finds the patterns and relationships in the dataset to label the data. It finds structures in the data to form a cluster based on its patterns and uses to increase predictability.

Semi-supervised learning uses labeled and unlabelled data for the training purpose. This is partly supervised machine learning, and it considers labeled data in small quantities and unlabelled data in large quantities. The systems can improve the learning accuracy using this method. If the companies have acquired and labeled data; have skilled and relevant resources in order to train it or learn from it they choose semi-supervised learning.

Unsupervised machine learning algorithms are useful when the information used to train is not classified or labeled. Studies that include unsupervised learning prove how systems can conclude a function to depict a hidden structure from the unlabelled data. The system explores data supposition to describe the obscure structures from the unlabelled data.

Reinforcement machine learning, these algorithms can interact with its environment by generating actions. It can find the best outcome from some trial and errors and the agent earns reward or penalty points to maximize its performance. The model trains itself to predict the new data presented. The reinforcement signal is a must for the agent to find out the best action from the ones its suggestions.

Future of ML

Evolution of Machine Learning:

Machine learning has evolved over a period and experiences continuous growth. It developed the pattern recognition and non-programmed automated learning of computers to perform simple and complex tasks. Initially, the researchers were curious about whether computers can learn with the least human intervention just with the help of data. The machines learn from the previous methods of computations, statistical analysis and can repeat the process for other datasets. It can recommend the users for the product and services, respond to FAQs, notify for subjects of your choice, and even detect fraud.

Machine Learning as of today:

Machine Learning has gained popularity for its data processing and self-learning capacity. It is involved in technological advancements and its contribution to human life is noteworthy. E.g. Self-driving vehicles, robots, chatbots in the service industry and innovative solutions in many fields.

Currently, ML is widely used in :

1. Image Recognition: ML algorithms detect and recognize objects, human faces, locations and help in image search. Facial recognition is widely used in mobile applications such as time punching apps, photo editing apps, chats, and other apps where user authentication is mandatory.

2. Image Processing: Machine learning conducts an autonomous vision useful to improve imaging and computer vision systems. It can compress images and these formats can save storage space, transmit faster. It maintains the quality of images and videos.

3. Data Insights: The automation, digitization, and various AI tools used by the systems provide insights based on an organization’s data. These insights can be standard or customized as per the business need.

4. Market Price: ML helps retailers to collect information about the product, its features, its price, promotions applied, and other important comparatives from various sources, in real-time. Machines convert the information to a usable format, tested with internal and external data sources, and the summary is displayed on the user dashboard. The comparisons and recommendations help in making accurate and beneficial decisions for the business.

5. User Personalisation: It is one of the customer retention tactic used in all the sectors. Customer expectations and company offerings have a commercial aspect attached; hence, personalization is introduced on a wide variety of forms. ML processes massive data of customers such as their internet search, personal information, social media interactions, and preferences stored by the users. It helps companies increase the probability of conversion and profitability with reduced efforts with ML technology. It can help branding, marketing, business growth and improve performance.

6. Healthcare Industry: Machine learning assists to improve healthcare service quality; reduce costs, and increase satisfaction. ML can assist medical professionals by searching the relevant data facts and suggest the latest treatments available for such illnesses. It can suggest the precautionary measures to the patient for better healthcare. AI can maintain patient data and use it as a reference for critical cases in hospitals across the globe. The machines can analyze images of MRI or CT Scan, process clinical procedures videos, check laboratory results, sort patient information and use efficiently. ML algorithms can even identify skin cancer and cancerous tumors by studying mammograms.

7. Wearables: These wearables are changing patient care, with strong monitoring of health as a precaution or prevention of illness. They track the heart rate, pulse rate, oxygen consumption by the muscles and blood sugar level in real-time. It can reduce the chances of heart attack or injury, and can recommend the user for medicine dose, health check-up, type of treatment, and help the faster recovery of the patient. With an enormous amount of data that gets generated in healthcare, the reliance on machine learning is unavoidable.

8. Advanced cybersecurity: Security of data, logins, and personal information, bank and payment details is necessary. The estimated losses that organizations face because of cybercrime are likely to reach $6 trillion yearly. Threat is raising the cybersecurity costs and increasing the burden on the operational expenses of organizations. The ML implementation protects user data, their credentials, saves from phishing attacks and maintains privacy.

9. Content Management: The users can see sensible content on their social media platforms. The companies can draw the attention of the target audience and it reduces their marketing and advertising costs. Based on human interactions these machines can show relevant content.

10. Smart Homes: ML does all mundane tasks for you, maintaining the monthly grocery, cleaning material, and regular purchase lists. It can update the list when there are input and order material on the scheduled date. It increases the security at home by keeping the track of known visitors and barring the other from entering the premise or specifies suspicious activities.

11. Logistics: Machine learning can keep track of the user’s choices for delivery and can suggest based on the instructions and addresses they use often. The confirmations, notifications, and feedback about the delivery is processed by the machines more efficiently and in real-time.

Future of ML:

Do not be surprised if we are found learning dance, music, martial arts, and academic subjects from the Bots. We will shortly experience improved services in travel, healthcare, cybersecurity, and many other industries as the algorithms can run throughout with no break, unlike humans. They not only deal but respond and collect feedback in real-time.

Researchers are developing innovative ways of implementing machine-learning models to detect fraud, defend cyberattacks. The future of transportation is great with the wide-scale adoption of autonomous vehicles.

The voice, sound, image, and face recognition, NLP is creating a better understanding of customer requirements and can serve better through machine learning.

Autonomous Vehicles like self-driving cars can reduce traffic-related problems like accidents and keep the driver safe in case of a mishap. ML is developing powerful technologies to let us operate these autonomous vehicles with ease and confidence. The sensors use the data points to form algorithms that can lead to safe driving.

Deeper personalization is possible with ML as it highlights the possibilities of improvement. The advertisements will be of user choice as more data is available from the collective response of each user for the text or video they see.

The future will simplify the machine learning by extracting data from the devices directly instead of asking the user to fill the choices. The vision processing lets the machine view and understands the images in order to take action.

You can now expect cost-effective and ingenious solutions that will alter your choices and change your set of expectations from the companies and products.

According to the survey by Univa 96% of companies think there will be outbursts in Machine Learning projects by 2020. Two out of ten companies have ML projects running in production. 93% of companies, which participated in the survey, have commenced ML projects. (344 Technology and IT professionals were part of the survey)

Approximately 64% of technology companies, 52% of the finance sector, 43% of healthcare, 31% of retail, telecommunications, and manufacturing companies are using ML and overall 16 industries are already using machine-learning processes.

Final Thoughts:

Machine Learning is building a new future that brings stability to the business and eases human life. Sales data analysis, streamlining data, mobile marketing, dynamic pricing, and personalization, fraud detection, and much more than the technology has already introduced, we will see new heights of technology.

Machine Learning and AI to cut down financial risks

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

Artificial Intelligence

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

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

Machine Learning

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

Financial Risks

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

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

Financial Risks, Machine Learning, and AI

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

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

1. Market Risk

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

Utilizations of AI to Market Risk

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

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

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

2. Credit Risk

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

Use of AI to Credit Risk

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

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

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

3. Liquidity Risk

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

Application to liquidity chance

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

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

4. Operational Risk

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

Application to Operational Risk

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

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

Financial Risks

Conclusion

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

Relationship between Big Data, Data Science and ML

Data is all over the place. Truth be told, the measure of advanced data that exists is developing at a fast rate, multiplying like clockwork, and changing the manner in which we live. Supposedly 2.5 billion GB of data was produced each day in 2012.

An article by Forbes states that Data is becoming quicker than any time in recent memory and constantly 2020, about 1.7MB of new data will be made each second for each person on the planet, which makes it critical to know the nuts and bolts of the field in any event. All things considered, here is the place of our future untruths.

Machine Learning, Data Science and Big Data are developing at a cosmic rate and organizations are presently searching for experts who can filter through the goldmine of data and help them drive quick business choices proficiently. IBM predicts that by 2020, the number of employments for all data experts will increment by 364,000 openings to 2,720,000

Big Data Analytics

Big Data

Enormous data is data yet with tremendous size. Huge Data is a term used to portray an accumulation of data that is enormous in size but then developing exponentially with time. In short such data is so huge and complex that none of the customary data the board devices can store it or procedure it productively.

Kinds Of Big Data

1. Structured

Any data that can be put away, got to and handled as a fixed organization is named as structured data. Over the timeframe, ability in software engineering has made more noteworthy progress in creating strategies for working with such sort of data (where the configuration is notable ahead of time) and furthermore determining an incentive out of it. Be that as it may, these days, we are predicting issues when the size of such data develops to an immense degree, regular sizes are being in the anger of different zettabytes.

2. Unstructured

Any data with obscure structure or the structure is delegated unstructured data. Notwithstanding the size being colossal, un-organized data represents various difficulties as far as its handling for inferring an incentive out of it. A regular case of unstructured data is a heterogeneous data source containing a blend of basic content records, pictures, recordings and so forth. Presently day associations have an abundance of data accessible with them yet lamentably, they don’t have a clue how to infer an incentive out of it since this data is in its crude structure or unstructured arrangement.

3. Semi-Structured

Semi-structured data can contain both types of data. We can see semi-organized data as organized in structure however it is really not characterized by for example a table definition in social DBMS. The case of semi-organized data is a data spoken to in an XML document.

Data Science

Data science is an idea used to handle huge data and incorporates data purifying readiness, and investigation. A data researcher accumulates data from numerous sources and applies AI, prescient investigation, and opinion examination to separate basic data from the gathered data collections. They comprehend data from a business perspective and can give precise expectations and experiences that can be utilized to control basic business choices.

Utilizations of Data Science:

  • Internet search: Search motors utilize data science calculations to convey the best outcomes for inquiry questions in a small number of seconds.
  • Digital Advertisements: The whole computerized showcasing range utilizes the data science calculations – from presentation pennants to advanced announcements. This is the mean explanation behind computerized promotions getting higher CTR than conventional ads.
  • Recommender frameworks: The recommender frameworks not just make it simple to discover pertinent items from billions of items accessible yet additionally adds a great deal to the client experience. Many organizations utilize this framework to advance their items and recommendations as per the client’s requests and the significance of data. The proposals depend on the client’s past list items

Machine Learning

It is the use of AI that gives frameworks the capacity to consequently take in and improve for a fact without being unequivocally customized. AI centers around the improvement of PC programs that can get to data and use it learn for themselves.

The way toward learning starts with perceptions or data, for example, models, direct involvement, or guidance, so as to search for examples in data and settle on better choices later on dependent on the models that we give. The essential point is to permit the PCs to adapt naturally without human mediation or help and alter activities as needs are.

ML is the logical investigation of calculations and factual models that PC frameworks use to play out a particular assignment without utilizing unequivocal guidelines, depending on examples and derivation. It is viewed as a subset of man-made reasoning. AI calculations fabricate a numerical model dependent on test data, known as “preparing data”, so as to settle on forecasts or choices without being expressly modified to play out the assignment.

The relationship between Big Data, Machine Learning and Data Science

Since data science is a wide term for various orders, AI fits inside data science. AI utilizes different methods, for example, relapse and directed bunching. Then again, the data’ in data science might possibly develop from a machine or a mechanical procedure. The principle distinction between the two is that data science as a more extensive term centers around calculations and measurements as well as deals with the whole data preparing procedure

Data science can be viewed as the consolidation of different parental orders, including data examination, programming building, data designing, AI, prescient investigation, data examination, and the sky is the limit from there. It incorporates recovery, accumulation, ingestion, and change of a lot of data, on the whole, known as large data.

Data science is in charge of carrying structure to huge data, scanning for convincing examples, and encouraging chiefs to get the progressions adequately to suit the business needs. Data examination and AI are two of the numerous devices and procedures that data science employments.

Data science, Big data, and AI are probably the most sought after areas in the business at the present time. A mix of the correct ranges of abilities and genuine experience can enable you to verify a solid profession in these slanting areas.

In this day and age of huge data, data is being refreshed considerably more every now and again, frequently progressively. Moreover, much progressively unstructured data, for example, discourse, messages, tweets, websites, etc. Another factor is that a lot of this data is regularly created autonomously of the association that needs to utilize it.

This is hazardous, in such a case that data is caught or created by an association itself, at that point they can control how that data is arranged and set up checks and controls to guarantee that the data is exact and complete. Nonetheless, in the event that data is being created from outside sources, at that point there are no ensures that the data is right.

Remotely sourced data is regularly “Untidy.” It requires a lot of work to clean it up and to get it into a useable organization. Moreover, there might be worries over the solidness and on-going accessibility of that data, which shows a business chance on the off chance that it turns out to be a piece of an association’s center basic leadership ability.

This means customary PC structures (Hardware and programming) that associations use for things like preparing deals exchanges, keeping up client record records, charging and obligation gathering, are not appropriate to putting away and dissecting the majority of the new and various kinds of data that are presently accessible.

Therefore, in the course of the most recent couple of years, an entire host of new and intriguing equipment and programming arrangements have been created to manage these new kinds of data.

Specifically, colossal data PC frameworks are great at:

  • Putting away gigantic measures of data:  Customary databases are constrained in the measure of data that they can hold at a sensible expense. Better approaches for putting away data as permitted a practically boundless extension in modest capacity limit.
  • Data cleaning and arranging:  Assorted and untidy data should be changed into a standard organization before it tends to be utilized for AI, the board detailing, or other data related errands.
  • Preparing data rapidly: Huge data isn’t just about there being more data. It should be prepared and broke down rapidly to be of most noteworthy use.

The issue with conventional PC frameworks wasn’t that there was any hypothetical obstruction to them undertaking the preparing required to use enormous data, yet by and by they were excessively moderate, excessively awkward and too costly to even consider doing so.

New data stockpiling and preparing ideal models, for example, have empowered assignments which would have taken weeks or months to procedure to be embraced in only a couple of hours, and at a small amount of the expense of progressively customary data handling draws near.

The manner in which these ideal models does this is to permit data and data handling to be spread crosswise over systems of modest work area PCs. In principle, a huge number of PCs can be associated together to convey enormous computational capacities that are similar to the biggest supercomputers in presence.

ML is the critical device that applies calculations to every one of that data and delivering prescient models that can disclose to you something about individuals’ conduct, in view of what has occurred before previously.

A decent method to consider the connection between huge data and AI is that the data is the crude material that feeds the AI procedure. The substantial advantage to a business is gotten from the prescient model(s) that turns out toward the part of the bargain, not the data used to develop it.

Conclusion

AI and enormous data are along these lines regularly discussed at the same moment, yet it is anything but a balanced relationship. You need AI to get the best out of huge data, yet you don’t require huge data to be capable use AI adequately. In the event that you have only a couple of things of data around a couple of hundred individuals at that point that is sufficient to start building prescient models and making valuable forecasts.

How chatbots are redefining customer experience

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

Why Customer Experience Is Important For Every Business?

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

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

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

How Has Customer Experience Changed Over The Years?

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

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

What Are Chatbots?

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

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

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

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

Why Do We Need Chatbots?

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

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

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

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

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

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

Why Chatbots Are The Future Of Customer Service?

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

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

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

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

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

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

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

How Are Chatbots Used In Business?

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

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

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

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

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

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

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

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

The Future Of Customer Experience And Chatbot

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

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

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

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

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

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

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

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

8 resources to get free training data for ml systems

The current technological landscape has exhibited the need for feeding Machine Learning systems with useful training data sets. Training data helps a program understand how to apply technology such as neural networks. This is to help it to learn and produce sophisticated results.

The accuracy and relevance of these sets pertaining to the ML system they are being fed into are of paramount importance, for that dictates the success of the final model. For example, if a customer service chatbot is to be created which responds courteously to user complaints and queries, its competency will be highly determined by the relevancy of the training data sets given to it.

To facilitate the quest for reliable training data sets, here is a list of resources which are available free of cost.

Kaggle

Owned by Google LLC, Kaggle is a community of data science enthusiasts who can access and contribute to its repository of code and data sets. Its members are allowed to vote and run kernel/scripts on the available datasets. The interface allows users to raise doubts and answer queries from fellow community members. Also, collaborators can be invited for direct feedback.

The training data sets uploaded on Kaggle can be sorted using filters such as usability, new and most voted among others. Users can access more than 20,000 unique data sets on the platform.

Kaggle is also popularly known among the AI and ML communities for its machine learning competitions, Kaggle kernels, public datasets platform, Kaggle learn and jobs board.

Examples of training datasets found here include Satellite Photograph Order and Manufacturing Process Failures.

Registry of Open Data on AWS

As its website displays, Amazon Web Services allows its users to share any volume of data with as many people they’d like to. A subsidiary of Amazon, it allows users to analyze and build services on top of data which has been shared on it.  The training data can be accessed by visiting the Registry for Open Data on AWS.

Each training dataset search result is accompanied by a list of examples wherein the data could be used, thus deepening the user’s understanding of the set’s capabilities.

The platform emphasizes the fact that sharing data in the cloud platform allows the community to spend more time analyzing data rather than searching for it.

Examples of training datasets found here include Landsat Images and Common Crawl Corpus.

UCI Machine Learning Repository

Run by the School of Information & Computer Science, UC Irvine, this repository contains a vast collection of ML system needs such as databases, domain theories, and data generators. Based on the type of machine learning problem, the datasets have been classified. The repository has also been observed to have some ready to use data sets which have already been cleaned.

While searching for suitable training data sets, the user can browse through titles such as default task, attribute type, and area among others. These titles allow the user to explore a variety of options regarding the type of training data sets which would suit their ML models best.

The UCI Machine Learning Repository allows users to go through the catalog in the repository along with datasets outside it.

Examples of training data sets found here include Email Spam and Wine Classification.

Microsoft Research Open Data

The purpose of this platform is to promote the collaboration of data scientists all over the world. A collaboration between multiple teams at Microsoft, it provides an opportunity for exchanging training data sets and a culture of collaboration and research.

The interface allows users to select datasets under categories such as Computer Science, Biology, Social Science, Information Science, etc. The available file types are also mentioned along with details of their licensing.

Datasets spanning from Microsoft Research to advance state of the art research under domain-specific sciences can be accessed in this platform.

GitHub.com/awesomedata/awesomepublicdatasets

GitHub is a community of software developers who apart from many things can access free datasets. Companies like Buzzfeed are also known to have uploaded data sets on federal surveillance planes, zika virus, etc. Being an open-source platform, it allows users to contribute and learn about training data sets and the ones most suitable for their AI/ML models.

Socrata Open Data

This portal contains a vast variety of data sets which can be viewed on its platform and downloaded. Users will have to sort through data which is currently valid and clean to find the most useful ones. The platform allows the data to be viewed in a tabular form. This added with its built-in visualization tools makes the training data in the platform easy to retrieve and study.

Examples of sets found in this platform include White House Staff Salaries and Workplace Fatalities by US State.

R/datasets

This subreddit is dedicated to sharing training datasets which could be of interest to multiple community members. Since these are uploaded by everyday users, the quality and consistency of the training sets could vary, but the useful ones can be easily filtered out.

Examples of training datasets found in this subreddit include New York City Property Tax Data and Jeopardy Questions.

Academic Torrents

This is basically a data aggregator in which training data from scientific papers can be accessed. The training data sets found here are in many cases massive and they can be accessed directly on the site. If the user has a BitTorrent client, they can download any available training data set immediately.

Examples of available training data sets include Enron Emails and Student Learning Factors.

Conclusion

In an age where data is arguably the world’s most valuable resource, the number of platforms which provide this is also vast. Each platform caters to its own niche within the field while also displaying commonly sought after datasets.  While the quality of training data sets could vary across the board, with the appropriate filters, users can access and download the data sets which suit their machine learning models best. If you need a custom dataset, do check us out here, share your requirements with us, and we’ll more than happy to help you out!

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.

10 free image training data resources online

Not too long ago, we would have chuckled at the idea of a vehicle driving itself while the driver catches those extra few minutes of precious sleep. But this is 2019, where self-driving cars aren’t just in the prototyping stage but being actively rolled out to the public. And, remember those days when we were marveled by a device recognizing it’s users face? Well, that’s a norm in today’s world. With rapid developments, AI & ML technologies are increasingly penetrating our lives. However, developments of such systems are no easy task. It requires hours of coding and thousands, if not millions, of data to train & test these systems. While there are a plethora of training data service providers that can help you with your requirements, it’s not always feasible. So, how can you get free image datasets?

There are various areas online where you can discover Image Datasets. A lot of research bunches likewise share the labeled image datasets they have gathered with the remainder of the network to further machine learning examine in a specific course.

In this post, you’ll find top 9 free image training data repositories and links to portals you’re ready to visit and locate the ideal image dataset that is pertinent to your projects. Enjoy!

Labelme

Free image training dataset at labelme | Bridged.co

This site contains a huge dataset of annotated images.

Downloading them isn’t simple, however. There are two different ways you can download the dataset:

1. Downloading all the images via the LabelMe Matlab toolbox. The toolbox will enable you to tweak the part of the database that you need to download.

2. Utilizing the images online using the LabelMe Matlab toolbox. This choice is less favored as it will be slower, yet it will enable you to investigate the dataset before downloading it. When you have introduced the database, you can utilize the LabelMe Matlab toolbox to peruse the annotation records and query the images to extricate explicit items.

ImageNet

Free image training dataset at ImageNet | Bridged.co

The image dataset for new algorithms is composed by the WordNet hierarchy, in which every hub of the hierarchy is portrayed by hundreds and thousands of images.

Downloading datasets isn’t simple, however. You’ll need to enroll on the website, hover over the ‘Download’ menu dropdown, and select ‘Original Images.’ Given you’re utilizing the datasets for educational/personal use, you can submit a request for access to download the original/raw images.

MS COCO

Free image training dataset at mscoco | Bridged.co

Common objects in context (COCO) is a huge scale object detection, division, and subtitling dataset.

The dataset — as the name recommends — contains a wide assortment of regular articles we come across in our everyday lives, making it perfect for preparing different Machine Learning models.

COIL100

Free image training dataset at coil100 | Bridged.co

The Columbia University Image Library dataset highlights 100 distinct objects — going from toys, individual consideration things, tablets — imaged at each point in a 360° turn.

The site doesn’t expect you to enroll or leave any subtleties to download the dataset, making it a simple procedure.

Google’s Open Images

Free image training data at Google | Bridged.co

This dataset contains an accumulation of ~9 million images that have been annotated with image-level labels and object bounding boxes.

The training set of V4 contains 14.6M bounding boxes for 600 object classes on 1.74M images, making it the biggest dataset to exist with object location annotations.

Fortunately, you won’t have to enroll on the website or leave any personal subtleties to get the dataset allowing you to download the dataset from the site without any obstructions.

On the off chance that you haven’t heard till now, Google recently released a new dataset search tool that could prove to be useful if you have explicit prerequisites.

Labelled Faces in the Wild

Free image training dataset at Labeled Faces in The Wild | BridgedCo

This portal contains 13,000 labeled images of human faces that you can readily use in any of your Machine Learning projects, including facial recognition.

You won’t have to stress over enrolling or leaving your subtleties to get to the dataset either, making it too simple to download the records you need, and begin training your ML models!

Stanford Dogs Dataset

Image training data at Stanford Dogs Dataset | Bridged.co

It contains 20,580 images and 120 distinctive dog breed categories.

Made utilizing images from ImageNet, this dataset from Stanford contains images of 120 breeds of dogs from around the globe. This dataset has been fabricated utilizing images and annotation from ImageNet for the undertaking of fine-grained picture order.

To download the dataset, you can visit their website. You won’t have to enroll or leave any subtleties to download anything, basically click and go!

Indoor Scene Recognition

Free image training data at indoor scene recognition | Bridged.co

As the name recommends, this dataset containing 15620 images involving different indoor scenes which fall under 67 indoor classes to help train your models.

The particular classifications these images fall under incorporated stores, homes, open spaces, spots of relaxation, and working spots — which means you’ll have a differing blend of images used in your projects!

Visit the page to download this dataset from the site.

LSUN

This dataset is useful for scene understanding with auxiliary assignment ventures (room design estimation, saliency forecast, and so forth.).

The immense dataset, containing pictures from different rooms (as portrayed above), can be downloaded by visiting the site and running the content gave, found here.

You can discover more data about the dataset by looking down to the ‘scene characterization’ header and clicking ‘README’ to get to the documentation and demo code.

Well, here are the top 10 repositories to help you get image training data to help in the development of your AI & ML models. However, given the public nature of these datasets, they may not always help your systems generate the correct output.

Since every system requires it’s own set of data that are close to ground realities to formulate the most optimal results, it is always better to build training datasets that cater to your exact requirements and can help your AI/ML systems to function as expected.

The need for training data in ai and ml models

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

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

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

ML and AI – smart tools to build smarter computers

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

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

Machine Learning Models

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

Supervised Learning

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

Unsupervised Learning

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

Reinforcement Learning

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

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

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

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

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

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

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

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

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

The need for training data in ML models

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

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

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

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

How to build datasets?

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

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

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

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

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