A Simplified Approach to Finding the Right Machine Learning Algorithm

How do you select the right machine learning algorithm to get the most relevant results? With the multitude of algorithms out there, it can be a daunting process. There is a huge repertoire of data as well. Selecting the best algorithm will depend on the quality of the data, the size and nature of this data and the outcome you are expecting from the algorithm. It is a method of trial and error, and even the most experienced data scientists have to experiment before finding the solution.
However, we have identified some steps to simplify the approach to resolving most machine learning problems.

Finding the Right Fit

Your data can be categorized by input or by output. If your data is in a structured format with clearly labeled data, then it can be categorized as a supervised learning problem. Supervised learning algorithms look at past data and make future predictions based on past outcomes. For example, if we want a machine learning algorithm to predict the future price of petrol, then the petrol price of past years, along with supportive date is patterns are studied. This could include city-wise petrol price, political events, geographical events, economic growth, GDP and other related data. Once a pattern is identified, the supervised learning algorithm will use this pattern to predict unlabeled data, in this case, it would be the future price of petrol.

  • Supervised Learning
    Supervised learning can further fall into 3 categories. When existing data is being used to assign the data into groups, then that is called Classification. Two-choice classifications like assigning images as tea or coffee are called binomial classifications and multiple options are called multi-class classification. Regression is when future predictions are based on past information, like in the case of petrol prices. And Anomaly Detection is identifying data patterns that are suspect or unusual. This is done by using existing data as a reference point. Anything different from this pattern will trigger an alarm.
  • Unsupervised Learning
    Data that is unlabeled falls under the unsupervised learning problem category. Also, there’s the reinforcement learning problem which is when you want to optimize an objective function through interactions with the environment.
    In unsupervised learning, data points have no labels associated with them. There is no dependent variable. The data is organized using cluster analysis to find patterns or some relation between unlabeled information. This will help in bringing about some structure to the unorganized data and also to simplify it for better understanding.
  • Reinforcement Learning
    Reinforcement Learning is optimizing a situation by letting the algorithm learn the situation and enabling interaction with the environment. For example, a robot can learn how to play chess by finding patterns of existing data of a chess player’s moves to predict the next move it should take. The algorithm is rewarded when the correct moves are made, based on which the algorithm tries to further improve the results of the next move. Reinforcement learning is commonly used in Robotics and IoT applications.
  • Activate the Algorithms

    Once you’ve categorized the problem, you’ll be able to identify a set of algorithms that can be implemented, using the tools available. Implement all of the algorithms using a machine learning pipeline and carefully identify the evaluation criteria. It will compare the performance of each of the algorithms and the best one will automatically get selected.

A Quick Guide to Digital Transformation for Your Business

Digital Transformation is a complete shift in the way present day business functions are conducted in organizations. The traditional rules of trade, consumerization and market study will no longer hold good. The old processes will have to be rewritten based on new thinking and supported by new technology. Business and operational activities, processes and sales models will incorporate technologies to leverage the digital capabilities and accelerate business growth. This will welcome new age thinking that considers and addresses complex societal problems with the support of new technology.

While digital transformation is currently a hot topic among corporates and business, it also impacts public sector organizations and associations. Digital transformation will help tackle complex challenges like addressing water shortage within cities, pollution, future job prospects and other key factors that impact the economic growth of a country.

1.Chalk out a digital transformation strategy
Businesses must put together a digital transformation strategy to leverage the possibilities that new digital technologies provide them. This strategy will accelerate the business capabilities and create an impact faster for future success.

Digital transformation cannot happen over-night and requires a staged approach with a clear route map that involves various departments of the business. They key is to keep updated with digital innovations and imbibe them in business processes. This will help ensure continued optimization within individual departments and will, in turn, improve the performance of the organization.

2.Let technology fuse with business goals
Digital transformation is not just about the latest technology, but how this technology has a direct impact on business decisions. Digital, now has increased capabilities to identify societal requirements, consumer problems and also actively work towards tackling these issues for an enhanced experience.
Some of the newest technologies like Big Data, Artificial Intelligence, IoT, Augmented Reality (AR) act as enablers of digital change and is one of the main reasons for digital transformation. These applications provide an inside view into customer experience, employee satisfaction, business outcomes, customer feedback among others and have brought digital transformation to the forefront.

3.Develop core capabilities across business functions

For a business to incorporate digital transformation processes smoothly, they must focus on 3 core factors. Firstly, an active awareness of the latest trends and innovations in the market, secondly, taking informed and researched decisions while implementing new processes and thirdly, fast implementation and execution to ensure minimum disturbance to business activities.

Digital business transformation needs to be an integrated activity that brings about an improvement in each of the business functions. The Business Process needs to realign to incorporate newer, innovative processing techniques. Business Activity Centers like marketing, finance, customer care will have to move towards an agile, automated revamp. Workplace employees and channel partner relations can be improved by incorporating more friendly and focused initiatives to ensure satisfaction. Digital transformation will bring about more focus on people and customers by using advanced technology.

A Change in the system calls for digital disruption

Digital disruption takes place when implementing a new business process or system that is different from the rest of the players. This new approach to the way of doing business is usually taken up by tech companies or startups that have an in-depth understanding of business activities. They are able to come up with innovative solutions that cause a change in customer behavior and overall market value, forcing other players to change their business model as well.

A holistic picture

Digital transformation is bound to seep into every industry segment; be it technology, retail, manufacturing, hotel and service, transportation, etc. In the end, it is about the experience of the customer and focuses on future improvements. Digital transformation calls for acting on the insights that digital technologies provide us and making changes to improve the daily lives, business offerings and services of fellow humans.

The Right Approach to Digital Transformation Demands in Customer-Focused IT Field

Traditionally, IT departments have been the quiet back-end team that takes care of software processes. The IT engineers conduct quality-checks, maintain the IT systems, fix bugs and act as a support team for a smooth functioning of all business activities.

However, with the continuous advances in technology, we cannot ignore the value that this team brings in. We can see a quick and active transformation taking place in the way organizations function. The IT department now forms an integral part of important marketing and sales discussions. Inputs from the engineers and developers are required to tweak services and products to improve customer experience. It is time for the IT guys to move into the limelight and this will require adjusting to this culture shift.

We have seen new inventions in digital spaces with modern innovations and cool interfaces. However, many of them have disappeared from the market due to ignoring customer experience and ease of usability. It is time to focus on the customer experience first and then create technology to support it, rather than the other way around.

The New Role of IT in Digital Customer Experience

Business owners and senior management are beginning to understand this change. Hence with the highly competitive digital economy, management is now working closely with technology teams to solve individual customer problems. And they are putting in research and processes for the same. IT systems are being created to improve customer experience rather than that to just meet the needs of internal teams. To stand apart from the competition, customers need to have a compelling experience with the product.

Although customer experience has been the primary responsibility of the marketing team. Now, the IT team will also have an equally influential role to play. The digital customer experience is affected both by the media channels, offers and promotions as well as by the back-end system and user interface. The technical team’s role is going to grow much larger to capture as they can track customer browsing history, website journey, predict new interests and future requirements. These activities will require internal and external data sources and more intricate technical development.

A Makeover for the Office Tech Teams

There is a new-found expectation from the technical teams. The company looks towards them for suggestions and inputs to make changes that could improve the sales, customer experience and lead to much higher business returns. It is time for them to upgrade their skills, abilities and outlook towards their work contribution.

One new area for them to focus would be customer browsing patterns, research of competition in the market and understanding customer grievances. An in-depth study of these new areas will help in creating technology that increases customer engagement and also improves the design and back-end processes.

The easiest way for IT departments to increase understanding of customer interactions is by using their expertise to create technologies that collate and analyze customer data. The information collected will provide a rich source of insights into customer behavior.

The Best Tactics & Tools for Cross-Platform Mobile App Development

Native mobile app development is giving way to cross-platform mobile app development. The market needs are constantly changing and by the end of 2018 more than 50% of mobile apps are expected to be hybrid. Today, 60% of online traffic comes from mobile devices and more that 80% of mobile time is spent on apps. We can expect the number of mobile apps optimized for mobility to grow substantially in the next year.

These studies prove the future doesn’t belong to one mobile platform taking a lion’s share of the market. Rather, we can expect to see a multitude of new entrants in the smartphone OS space. To reach a wider audience base mobile app developers are creating apps that cater to various mobile platforms.

The Changing Scenario of Mobile Apps

With so many apps in the market, it can be cumbersome and expensive for 100% native i0S and android app development. It is becoming increasingly necessary to move to cross-platform development. However, before deciding whether you should make the shift to cross-platform development, do a thorough research to understand the implications, the work involved and effect on the end user.

With so many apps in the market, it can be cumbersome and expensive for 100% native i0S and android app development. It is becoming increasingly necessary to move to cross-platform development. However, before deciding whether you should make the shift to cross-platform development, do a thorough research to understand the implications, the work involved and effect on the end user.

The Benefits of Cross-Platform Mobile App Development

Cross-platform development tools can be credited to 3 markup languages – HTML5, CSS3 and JavaScript. We’ve been able to see tremendous technological advances in the mobile world with them. These languages are easy to master and due to their cross-platform abilities, we can overlook the minor disadvantages it has over native languages.

Native apps are much sturdier in comparison. There is a big developer base and toolkits, and faster creation process due to existing codes. However, native apps need dedicated developers for each platform and additional costs of testers and porting. Also, resources are very expensive and hard to retain due to the constant demand.

Cross-platform apps can easily cover these faults in native apps with enhancement of their capabilities. Experts speculate that if HTML5 capabilities are enhanced, we should be able to see the desired results.

Key Tools that are Enabling Cross-Platform Applications

Mobile developers have a list of favorite cross-platform application tools that are helping change the mobile app development scenario. Here’s a list of some of the best tools that developers swear by:

React Native – React Native is the next gen of React which is a JavaScript code library. React Native has a good network of experienced developers and has a quick development process.

Xamarin – Xamarin provides superior software quality with its ability to avoid bugs and reuse code. Platform-specific errors can be decreased to a great extent with the use of this tool.

Appcelerator Titanium – Appcelerator Titanium framework allows the absence of a browser engine which gives enhanced responsiveness and fluidity.

PhoneGap – PhoneGap is a very flexible tool that’s compatible with all the main mobile platforms. Programmers and developers will find this app to be highly user-friendly.

Mobile technology is highly dynamic and evolving at a fast pace to allow newer and sophisticated possibilities. The future lies in using the capabilities of both native and cross-platform app development to ensure maximum benefit for the end user.

Explaining EAFs and Simplifying the Development of M2M Devices

Some high-end Embedded Application Frameworks like Linux, Android and others are highly optimized for M2M application development. Embedded Application Frameworks enable Machine to Machine communication or (M2M) communication. M2M Technology allows users to connect and manage remote devices over the air. A number of industries can benefit from this new technology advancement. Now, users will be able to centrally control remote industrial equipment, track vehicles or cabs, manage cloud services and give consumer devices more competencies. M2M can change the face of the functioning of most industries.

Earlier integrating M2M technologies was a costly and time-consuming affair. The process required system designers to create the entire M2M architecture from start. However, with Embedded Application Frameworks (EAFs), this process has been simplified to a large extent. Developers can now deploy connected services through pre-packaged Real-time Operating Systems (RTOSs) and libraries that are directly embedded into the communications module. This new advancement has halved the time and effort required to develop new M2M hardware. Developers can now focus on creating more innovated and connected applications.

M2M Devices and Its Challenges

To explain simply, M2M technology is used to boost a device or piece of equipment with intelligent services and this device is connected to a back-end software that can monitor and control it. There are two core components required to make this possible – a wireless modem that can communicate with the back-end systems, and a software that runs the services.

Wireless devices have been in the market for many decades now. And these devices were made using easily available hardware components and traditional multichip architecture. The engineers just had to assemble the hardware and software that would then run on a microprocessor with external memory. Early developers had a very limited choice and option to experiment. Prepackaged software market that supported M2M connectivity was yet to be established.

However, M2M technology has a few drawbacks. Developing a device with M2M technology and integrating the infrastructure from scratch is time-consuming, and can take up to a year to go to market. Nowadays, we do not have the liberty of this much time due to competition. Moreover, the operational costs tend to be very high. Especially if there is an in-house team working on the project. Also, in-house development would require heavy investment in an RTOS with full-blown processing power and it’s not used for every M2M application. Lastly, M2M technology required a devoted device developer and their core expertise lies in is not in multichip computing architectures.

Making M2M Communication Intelligent with EAF

Some of the challenges of M2M technology can be corrected with Embedded Application Frameworks (EAF). EAFs can simplify and also reduce the costs of deploying an M2M system. The delivery timeline from planning to final launch, which usually takes one year can be completed in six months. Moreover, deploying software in an EAF eliminates the requirement of a separate RTOS, which saves on a big chunk of heavy investment.

Core Components to Consider in the EAF Model

While integrating the EAF model in M2M devices we must consider a few key core components. Firstly, the app should have a lightweight OS optimized for M2M. The OS should be optimized to allow voice control, data call, messaging and also this will provide direct access to the communications stack.

Another key feature is access to a variety of software libraries and development tools. Libraries will allow functions like GSP connectivity, internet connectivity protocols among others. Development tools will make it easy to code, debug, monitor M2M applications and maintain the backend. Finally, the EAF should have tools for cloud-based management of connected devices to handle device monitoring and software or firmware upgrades.

Twitter’s New AI Tool Can Automatically Highlight the Best Part of Your Photo

Artificial Intelligence (AI) is slowly seeping into every aspect of our lives. And while the changes may not be prominent enough for all to see, you will notice small improvements that affect the overall experience. With the help of Machine Learning, researchers at Twitter have also made one some small, yet important tweaks to its photo-cropping AI Tool. Twitter will now be able to focus on the most important part of the photos in their newsfeed, and not just a random part of the image.

Like on all other social media channels, photographs and images have been an integral part of tweets as well. However, regular twitteratis can vouch for the fact that twitter hasn’t been the most friendly when it comes to highlighting images. We’re forced to overlook the slightly off-focus, oddly cropped thumbnails that appear on our newsfeed, and instead click to view the full image only if it’s an interesting read.

New Image-Cropping Method with Improved AI Features

Twitter has had the photo sharing feature since 2011, but the previous methodology used for image-cropping wasn’t viewer friendly. The reason for this was that Twitter’s previous AI was trained only for face detection, and if there weren’t any faces, it focused on the center of the image. This meant that any image with buildings or animals resulted in an off-focus thumbnail.

Recently, Twitter undertook some in depth research to overcome the limitations in its photo-cropping tool that led to awkwardly cropped preview images. Twitter researchers found two important AI tricks to detect the important parts of an image:

Identifying the Saliency of an Image

‘Saliency of an image’ is measured based on the part of an image that the human eye is most attracted to or that a person is most likely to focus on. The part of an image on which a person focuses is called a ‘highly salient region’. Academics have measured ‘saliency’ by using eye trackers that record those pixels that people fixated on while focusing on or looking at an image. People generally tend to focus on faces, text, animals and other objects and regions of high contrast. Using this data, neural networks and other algorithms can be trained to predict what people might like looking at.

Twitter will be using this feature to crop images so that it focuses on a region that the viewer will find most interesting. And twitter will do it at such speed that users will not experience any lag time while uploading images, everything will happen in real time.

Training Algorithms through Knowledge Distillation

The neural networks that deal with identifying the saliency of an image are usually too slow. As a result, these cannot be used for cropping the millions of images that are uploaded on twitter by the millisecond. So twitter found an alternative and made some optimizations to help its system perform 10 times faster that its standard method. This resulted in a highly advanced AI that can do intelligent cropping of photographs as and when they are being uploaded.

The development team at twitter used a technique called ‘knowledge distillation’. This enables to train their existing algorithms or data to quickly identify the most ‘salient’ parts of the photos or the area that catches the viewer’s eye the most.

Pruning of Images for Identification

The engineers at twitter also used a technique called ‘pruning’ to let the algorithm skip over features of the image that are not relevant for the identification. So, now twitter displays images that their previous algorithm could not detect properly, and these too in real time with better crops. This new AI tool also shows images of objects that were cropped previously as they could not ‘sit’ in the middle of the image but now appropriately cropped using the new tool. It also shows that the new AI feature recognizes text and can also adjust the crop to include a sign.

A Dramatic Feel to twitter Feeds

It’s a small yet important update that will dramatically improve the look and feel, and overall user experience of the twitter feed. This new update is being rolled out to all twitter users, and hopefully, it will put an end to all those awkwardly cropped thumbnail images.

This latest offering from twitter is now being available for twitteratis to use on desktop, iOS, and Android apps as reported by the company. So, next time when you use twitter, do not forget to thank the neutral networks for the enhanced images.

Key Insights into the Application of Robotics Process Automation ( RPA) in Supply Chain Management

Supply Chain Management is an integral and vital function that dictates the overall success of an organization. Global associations depend on seamless operations that allow the smooth flow of goods and services. A well-planned supply chain takes care of many activities that involve procuring raw materials from providers, screening existing inventory, moving finished goods to consumers and tracking shipments to ensure on-time delivery. These various small yet inter-dependent activities can be made more efficient by incorporating Robotic Automation Processes or RPA.

Robotic Automation Processes can significantly improve the production network and bring in higher output standards in the coming years. Both product and service industries are beginning to understand the importance of utilizing RPA innovation. Incorporating RPA will create simple production processes and remove weighty, time-consuming and unproductive activities that eat up important man hours.

We provide you with a detailed brief on RPA utilization to update supply and coordination techniques and create much better work flow processes. Which parts of the inventory network are suitable for automation and how can RPA be used to streamline the whole supply chain, increase efficiencies and reduce cost.

How Does Robotics Process Automation Work?

Robotic Process Automation is an automation process where software and intelligent data algorithms are used to imitate the activities that humans perform. Latest technological advances allow employees to configure their computer software or ‘bots’ to collate, capture and decipher existing applications and respond by processing a transaction, triggering an action and communicating with other digital devices. And all these activities are done with minimal or zero assistance from humans. RPA helps in reducing operations costs, eliminating human error and increasing efficiency, which in turn will lead to better output volumes and business performance.

Currently, a majority of supply chain experts are implementing Robotic Process Automation for individual tasks. However, industry-level processes are multi-faceted and involve various inter-related activities. It becomes a laborious and time-consuming process for supervisors to review the performance of each of these individual activities.

Robotic Automation for End-To-End Tasks

Enterprise Process Robots can automate a whole business process in relation to the regular computerized devices that have a singular approach. These Enterprise Robots remove silo activities and permit the processing of an entire module of procedures that involve various mini-activities. For example, quality-check, packaging, stock management and transportation can be clubbed together as one automated process.

The engineer who oversees these activities can automate the process by instructing the product robot about how an occupation is finished, which is called inserted process know-how. The tasks are organized as various individual processes which are then brought together, enabling the interdependent areas to work in tandem. For instance, if the robotic autonomy arrangement could discover or know that a stock room is full, it immediately ends stock obtainment or moves stock to another room that is accessible.

RPA is gaining inroads into various large supply chain management companies to expedite tasks that were once done manually. High-end bots can automate any business process and can be configured to set up tasks assigned by the user, supporting the existing core system. They are code free, scalable and can even be taught to take up additional jobs. Transactions happen within seconds and fasten the entire operational process.

Here is a quick example of a logistics company that was struggling to deal with customer requests to reduce product price, and at the same time tackling carrier demands for more money. The company was also faced with various other operational issues like multiple EDI systems, scalability, seasonal requirements, customer feedback, over-worked employees, among others. The company created a cost-effective digital workforce of 400 RPAs to automate many of these operational jobs, including order placements, billing, scheduling, delivery and customer service. They were able to triple their output and cut down costs significantly.

The Beginning of Intelligent Automation In Supply Chain Management

There are many advantages of incorporating RPA in Supply Chain Management. Although we are not yet in a position to automate all tasks, especially the exercises that take place in the front office, RPA can take over a majority of the inventory networking activities. RPA can work using existing processes with minimal re-engineering. With RPA, management can identify inconsistencies, structure date, interact with multiple systems and deliver vital insights to the user. Product items can be moved from the producer to provider to the client in a proficient and systematized way. The entire production network will have its own inbuilt intelligent system to make learned decisions and consistently improve performance.

The Future of Robotics and Automation

Looking forward, there is great potential and a world of possibilities that Robotics and Automation have brought to the production and services industry. We can expect to see vast improvements in products, delivery, outputs and even pricing if Robotics Process Automation tools are used efficiently. Production network engineers can further update these intelligent procedures and frameworks to grasp new information and utilize the information at a much higher capacity.

What remains to be seen is how rapidly and to what degree robots and computers can collaborate to create significant process changes in other industries and functions. The Future of robotics companies will be dependent on how well they can adjust to the quick changes taking place in sourcing, generation and conveyance as it is happening today, and how adaptable are they to exploit these new innovations.

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