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Day: 20 December 2018

What Is Organisational Change Management?

Posted on by Imarticus Learning

Last Updated on 11 months ago by Imarticus Learning

Organisational change management is a systematic approach to managing the effects of new business models, changes in organisational structure or the change in the culture of the organisation. A framework for organisational change becomes inevitable as it guides the people in the organisation to learn and adapt to the new behavior and skills.

Being transparent about the change about to take place, setting good communication tools and being proactive about change keeps the interests of the stakeholders intact, and they provide co-operation through the process of implementation of organisational change.

Successful organisational change management should incorporate the following:

• Understanding the vision of change and not being competitive to initiate the change.
• Efficient leadership who can communicate the vision for the proposed change and its association with the organisational objectives
• Changes expected in the working of employees due to the implementation of change.
• Having a concrete plan to depict the success rate of the change. Frequent monitoring and measuring of the implemented change to make sure if the change has fulfilled its intended objectives.
• Having organisational change management approaches enables in a smooth transition of individuals and the organisation when bringing about a change.

The process of organisational change management: First and the foremost step in the change management courses is to clearly define the change and its connection with the goals of the organisation.

Once the change is defined you need access to the impact of such a change at various levels of the organisation. Developing communication tools will ease out the process of change management in Organization through the levels of organisational structure and towards the employees.

Effective communication strategies must include timely and incremental communication of important messages regarding the change. Employees need to be properly trained and made competent by equipping them with the necessary skills and knowledge for successful change management.

Keep evaluating your change management for determining its efficiency in attaining the organisational goals.

Future of change management:
It is expected to have a meteoric rise in the Future of change management as increasing understanding and application of change is getting technical and complicated. In future, the organisation that fails to adapt to change is less likely to survive.

Hence, change management in an organisation is an inevitable part of a successful organisation. Innovation is the key to stay in the market, and hence constant improvements in the products and services will help in thriving through change. In future passionate change, leadership teams within an enterprise will be the ones responsible for a successful change.

Conclusion
Being focused and building the momentum for change through all levels of the organisation is crucial. Being creative and expecting a change can make a huge difference with respect to avoiding the change.

Shared vision and shared approach of the management and staff will be a vital point in handling change. So, fasten your seat belts and drive your organisation to the path of success by adopting the right change in the organisation.

What Is The Change Management Process In Healthcare?

Posted on by Imarticus Learning

Last Updated on 4 years ago by Imarticus Learning

Change is the only constant thing in the world. People need to equip and adapt themselves to the changing environment and technology to cope up with the pace at which the world is travelling ahead. The medical field is a very competitive field with a lot of responsibility. Health care management works hard to manage change and get equipped and maintain the expertise necessary to carry out their professional tasks.

As the speed at which change is travelling has doubled it vital to managing change which is more about dealing with the complexity of the process of change management in healthcare.

For a successful adaptation of a change management process in healthcare, the following points have to be implemented:

Change in consumer behavior patterns: The ultimate aim of marketers is to drive the interests of the consumer by making their experience more personalized. Such is the expectation of the consumers with the health care industry. The consumer expects their involvement in their health care decisions.

So, health care management is working towards developing more consumer-centred approaches and decisions to give them a personalized and satisfactory experience.

Be ready to accept and expect the change anytime: iNstead of avoiding the change being proactive about it can make a humongous difference. Voluminous and disruptive change can be tackled with ease if you expect and accept change.

Be competent and ready for a change in organisational culture, change in technology or a different business model. Understanding the change and implementing it for the benefit of the organisational goals is the best process of change management in health care.

Excellent communication within healthcare: Change in healthcare may affect multiple levels in the organisation. So, the internal team needs to make timely and transparent communication with other members for smooth adoption of change. Communication is the key to an efficient transition of change within healthcare.

Predictable change may give you more time to react whereas unpredictable change is difficult to handle as it comes without notice. Changes in healthcare are more likely to be unpredictable. Senior management and managers need a broader perspective of change to have a better framework for change management.

Bringing about the change in healthcare requires sheer perseverance and patience. The commitment of the managers and focus is also a quintessential part of the process to learn change management courses.

Understanding the vision and mission of the organisation and implementing only the required changes is the key to success. Measuring and monitoring the implemented change is also essential for knowing if the change has fulfilled its purpose or not.

Conclusion
 If you master the process of change management, then change will be a beneficial factor rather than being scary or frightening agenda. It is all about being resilient and accepting change as and when it comes to you.

The ultimate goal should be to thrive through the change and not survive the change. Change management in healthcare entails continuous improvements in the existing capabilities of the organisation and empowering more support for the leaders to implement the change.

 

What Do You Need To Know For AI

Posted on by Imarticus Learning

Last Updated on 5 years ago by Imarticus Learning

In a world where technology is developing at a rapid rate, fields that focus on automation and artificial intelligence are becoming the most lucrative. With many artificial intelligence courses available, it is important to remember that a strong base is fundamental.

So, the question is – What do you need to know before you can venture into a field like artificial intelligence?

Before heading to the pre-requisites, it is important to understand that AI is a field that is multi-dimensional. It can be used for anything from medicine to education. This also means programming AI is diverse, akin to law, where you constantly need to educate yourself on the updates of the technologies available in your field of AI.

Finally, the different fields of AI can have specific requirements, but on a broader scale, most AI in any field requires strong foundations that are basically the same. Here are a few things you need to know before studying about artificial intelligence.

Numbers Are Key
A strong understanding of mathematics is a must when venturing into artificial intelligence. The key here isn’t just knowing basic math. If you hope to venture into artificial intelligence, a deep understanding of discrete mathematics is part of the core foundation of the field.

Most artificial intelligence is based on various algorithms, and an understanding of these algorithms, as well as the ability to mathematically analyse them for errors and solutions, are considered the most basic requirement for AI.

Programming
 Much like math, programming is an essential part of artificial intelligence, implementing the mathematical data into code in a manner where you can not only develop but maintain and enhance machine learning is also part of the core foundation of AI. This means you must be able to code at a high level and find a way to be creative with code to improve the functions of a developing AI system.

In-depth knowledge of Python is often considered a mandatory pre-requisite to learning artificial intelligence as this open sourced programming language is currently the most popular and widely used.

Analyzing Data
 While programming and math are the foundations, the ability to analyse and interpret data is considered a cornerstone for anyone involved with developing AI. This skill is important as this is where the error guidance and solution base of AI stems from. Imagine a world where you create an algorithm and program that algorithm into a robot to vacuum.

This works successfully as a single task. Imagine now that you integrate another code into the same robot to do the dishes. The robot accidentally breaks the dishes or uses bleach to wash the dishes. This error is because the codes can overlap and create a fault or a bug. Data interpretation is essential to identify faults and bugs in order to rectify them.

Conclusion
While the three pre-requisites mentioned above are core tools for those studying AI, they aren’t the only ones you need. The field of AI you venture into may require knowledge of the field itself. An example of this is medical AI, where you will need an in-depth knowledge of medicine and how medicine functions. AI is ever growing, and its complexities are deep.

No matter the type of AI you choose to learn, a strong understanding of math, programming and the ability to analyse data accurately are a must.

Getting on the Right Artificial Intelligence Path

Posted on by Imarticus Learning

Last Updated on 4 years ago by Imarticus Learning

 

Are you looking to expand your current skill-sets? Does Artificial Intelligence pique your interest? Artificial Intelligence uses software or machines to use intelligence similar to that of humans. Even the humble calculator is an example of artificial intelligence. The field of AI is currently focusing on creating systems that can reason, learn, present knowledge, plan, and understand natural language amongst many others.

If you want to jump into this new and exciting field of innovation, you might want to make sure that you have your basics covered. There are several artificial intelligence courses in India that you can enrol in. However, if you are looking to explore on your own, you can follow the path given below to give you an understanding of how AI functions.

Brush Up On Your Math
A strong understanding of mathematics is key to your ability to move forward in the field of AI. Knowing as much math as you can will definitely help you later, but at the start, you can focus on statistics, calculus, and optimization. There are several resources available online for these topics, and you can also brush the dust off your old math textbooks.

Learn A Language
No, we don’t mean French. You need to learn the right programming languages in order to be able to delve into Artificial Intelligence. Focus your time on learning Python, C, and C++. These languages come with well-stocked toolkits and libraries which will help you navigate your future projects. Each of these languages has their own benefits and limitations, but starting with Python is a good bet. Look up artificial intelligence online courses offered by Imarticus Learning.

Solve a Problem you Know
One of the best ways to get started on AI is to practice with a problem you know and are interested in. It will keep you motivated as you continue to delve deeper into the intricacies of AI. The problem should interest you and must come with ready to access data that can be worked with a single machine. You could also start with the Titanic Competition that is tailormade for beginners like you.

Make Your Own Bot
A BOT is a type of weaker AI that can complete automated tasks. Try your hand at building your very own Chatbot. An example of an advanced chatbot is the Google Search Engine. It has three basic components – input text, send button, and output text. You can explore open source platforms like XPath and Regex in order to build your very own chatbot. This chatbot can be complex or funny and helpful. You can choose what your bot does for you.

Participate in an Actual Kaggle Competition
Kaggle has many real-time competitions that see hundreds of enthusiasts try to solve a problem. You can test out your knowledge and also learn where you need to explore more. This opportunity also allows you to connect to other AI enthusiasts. The forums are a rich resource on problem-solving and debugging.

Free Resources
There are many places on the internet and artificial intelligence courses which will help you expand your knowledge of AI one skill at a time. A great free resource is the Intel AI Academy which provides much-needed support, tech, and other tools for beginners like yourself.

Popular Tools to Analyze Data

Posted on by Imarticus Learning

Last Updated on 5 years ago by Imarticus Learning

Big Data is now an inevitable part of how many companies operate. While we all leave our footprint on the internet, companies ranging from IT to manufacturing firms are reaping the benefits of data analytics.

Knowing how to extract the information and trends, you require from the vast pool of data is imperative. Data analytics courses lets companies leverage this information for creating new plans, products, trends, offers, and more.

There are many tools that can be used effectively for analyzing data. Each of these tools has their own benefits and strengths. Once you are familiar with the capabilities of these tools, you will be able to employ the right tool for the right analysis. Tools for data analysis can be categorized into three main types.

  • Open Source Tools

KNIME

KNIME Analytics Platform is one of the most popular choices available to data scientists. It lets you model and manipulates data with more than 1000 modules, ready-to-run examples, a comprehensive set of integrated tools, and a large collection of advanced algorithms.

RapidMine

This tool is similar to KNIME in that it is a visual program. This tool has a unified environment making it easy to run through the entire gamut of the analytical workflow. You can use this tool for everything from data prep to machine learning to model validation to deployment.

  • Tools for Data Visualizations

Datawrapper

This is an effective tool used by news rooms around the world to create easy understand graphics and interactive charts. During elections, for example, newsrooms will plug in data collected by various resources and journalists on the ground to create charts that the layman can use.

The data can be populated according to race, ethnicity, age, gender, qualification, and more in order to understand the trend of the elections. Politicians in turn can use the same data to understand where they have popularity and with whom their ideologies resonate.

Google Fusion Tables

This is an amped up version of Spreadsheets backed by the powerful mapping tools of Google. You can use preexisting tables and combine two or more tables to create a visualization for both sets of data. You can choose to map, graph, chart the data which can then be shared or embedded into any page. This tool is great for collaboration as all the data organisation is saved on Google Drive.

  • Sentiment Tools

SAS Sentiment Analysis

Going back to the elections example, sentiment techniques can be used to assess sentiments in real time. The SAS tool extracts and interprets sentiments in real time or over a time period that you can specify. The tool features natural language processing and statistical modelling. The language processing is rule-based, and so you can choose the specific trend or emerging topic. This tool can be used to find the current feeling a population has towards a particular electoral candidate. This can be further developed to reflect the sentiments based on age, employment, gender, and sexual orientation.

Opinion Crawl

This is a great data analytics tool for all data scientists. It allows you to get sentiment analysis based on topic. This could be a person, a real-time event, a company, a product, or more. This tool provides the data in the form of a pie chart representing the real-time sentiment of the topic along with related images, a few headlines, and, most importantly, key semantic concepts related to the topic according to the public.


Artificial Intelligence Apps can Challenge Humans

Posted on by Imarticus Learning

Last Updated on 5 years ago by Imarticus Learning

Try memorizing all the phone numbers from your contact list. Now recall the numbers of all people whose name begin with the letter ‘S’.

Possible? Maybe.

Easy? No.

Humans have finite limits, and that’s why man has trained artificial intelligence to mimic his learning.  Now, is there a future possibility of AI taking over humans?

Here are the latest artificial intelligence updates on software applications that challenge the human brain and its finite limits.

Latest AI Software Capabilities:

Deep Mind’s AlphaGo

The game is based on an abstract strategy of a board game with two players each trying to surround more areas than the rival. AlphaGo uses deep learning neural networks with advanced searches to win against humans. The software is an example of advanced AI learning on its own.

DeepStack

The game of Poker also fell to the might of DeepStack. Based on intuitive decisions and deep learning from self-play, the ML computes the possibilities instantly to base its decisions. It can be used in the fields of cybersecurity, finance, and health care.

Philip

MIT’s CS and AI Laboratory use a gamer “Philip” to kill multiple players. Using neural networks and deep self-learning on Nintendo games, the ML using Q learning and actor-critic techniques is successful most of the time.

COIN

This JPMorgan software COIN used in investment banking has made commercial contracts an instant process saving 360,000 human work-hours. The word “COIN” is coined from contract intelligence, and that’s what it exactly is! COIN uses ML which ingests data and picks up on error-free relationships and patterns.

AI Duet

The software is an artificial “pianist” and is created by Google’s Creative Lab using neural networks, Tensorflow and Tone.js.

LipNet

University of Oxford’s CS Department has eased disabilities by making lip reading easy. The software uses neural networks, video frames-to-text and spatiotemporal convolutions on variable length sentences to lip read. It definitely has a massive impact on the movie industry, disability-prone deaf, biometric identification, covert conversations, dictating silently in public spaces and so much more.

GoogLeNet

This Google system can detect cancer better than the most experienced pathologists. ML and smart algorithms can learn to scan and interpret images and predict a diagnosis with greater accuracy.

DeepCoder

Microsoft and Cambridge University have developed this software that writes its own code. They have trained the ML to forecast properties and outputs from inputs. The insights are used to augment searches for 3/6 line code. The DeepCoder uses the synthesis of programs and an SMT-solver to put the pieces together mimicking programmers. This eventually helps people who cannot code, but know where the problem lies.

In conclusion, it is true that humans have surmounted the challenges of artificial intelligence. Machine learning has taught and brought machines very close to mimicking human behavior and thinking. There could be a possibility of a clash between the abilities and capabilities in the human vs. AI war. Will machines and AI overtake us?

Not if we intelligently harness ML capabilities. We have to use our finite abilities to limit rogue applications. And that is a huge positive!

Technical Approaches for building conversational APIs

Posted on by Imarticus Learning

Last Updated on 4 years ago by Imarticus Learning

 

Today’s GUIs can understand human speech and writing commands like the Amazon Echo and Google Home. Speech detection and analysis of human sentiments are now being used in your daily life and on your smart devices like the phones, security systems and much more. This means learning the AI approach.

The six smart system methods:
The existing artificial intelligence process and systems are not learning-based on interactive conversations, grounded in reality or generative methodology. The system of AI training needs to be one of the following.

Rule-based systems can be trained to recognize keywords and preset rules which govern their responses. One does not need to learn an array of new commands. It does need trained workforce with domain expertise to get the ball rolling.

Systems that are based on data retrieval are being used in most applications today. However, with speech recognition and conversational Artificial Intelligence courses being buzzwords, the need to scale and update quickly across various languages, sentiments, domains, and abilities needs urgent skilled manpower to update and use knowledge databases which are growing in size and volume.

The Generative methodology can overcome the drawbacks of the previous methods. In simple language, this means that the language system could be trained to generate its own dialogues rather than rely on pre-set dialogues.
The popular generative and interactive systems today incorporate one or all of the following methods to train software.

• Supervised learning is used to develop a sequence-to-sequence conversation mapping customer input to responses that are computer-generated.

• Augmented learning addresses the above issues and allows optimization for resolution, rewards, and engaging human interest.

• Adversarial learning improves the output of neural dialog which use testing and discriminatory networks to judge the output. The ideal training should involve productive conversations and overcome choice of words, indiscriminate usage and limitations on prejudging human behavior.

Methods relying on the ensemble that use the method most convenient to the context are being used in chatbots like Alexa. Low dialogue levels and task interpretation are primarily addressed. This method though cannot provide for intelligent conversations like human beings produce.

Learning that is grounded uses external knowledge and context in recognizing speech patterns and suggesting options. However, since human knowledge is basically in sets of data that is unstructured, the chatbots find it difficult to make responses of such unstructured data that are not linked to text, images or forms recognized by the computer.

The use of networking neural architecture into smaller concept based parts and separating a single task into many such components instantly while learning and training can help situational customization, external memory manipulation and integration with knowledge graphs can produce scalable, data-driven models in neural networks.

Learning interactively is based on language. Language is always developing and interactive when being used to enable collaborative conversations. The operator has a set goal based on the computer’s control and decisions. However, the computer with control over decision making cannot understand the language. Humans can now use SHRDLURN to train and teach the computer with consistent and clear command instructions. Based on experience it was found that creative environments were required for evolving models.

Which method to use is and how is where the creativity of human operators counts! To learn machine learning or an artificial intelligence and the systems of deploying it is the need of the hour no matter which technical method you use.

Are Fintechs Really An Enabler For The Traditional Banks

Posted on by Imarticus Learning

Last Updated on 5 years ago by Imarticus Learning

Fintech or Financial Technologies is the new branch of technology that aims to improve and automate the delivery of financial services. In the beginning, Fintech was employed for the back-end applications of the financial institutes.

But, since then, the technology has taken a diversion towards the consumer-oriented services. Fintech is expected to change the face of the banking sector in the next two decades. Whenever a new technology is introduced, a battle for the market domain is typical between the old guards and new entrants. The story is not different for Fintech.

So many organisations are out there with an opinion that Fintech is going to make a negative impact on the traditional banking services. This article discusses how the Fintech is going to act as an enabler to the traditional banks rather than being a challenge.

The Technological Challenges: Past and Present

In the past, the financial institution has proven to be slow towards the innovation. Yet, showing great resilience towards the challenges in the past. More than 450 attackers such as digital currencies, networks, wallets etc. attempted to challenge the traditional institutes in earlier days. Fewer than 5 of them have survived to the date. Other than PayPal no one has really disrupted the banks.

But the time has changed. The modern markets and new generation banking customers are promoting the new age financial services. Various reports are suggesting that millennials are more faithful towards the Fintech companies than the traditional banks. The expectations of customers are on the rise, and it is favouring the Fintech companies. With the evolution of the digital economy, the rise is expected to continue.

In short, the transformative forces are seemingly unstoppable with the current social environments. The traditional banks need to elevate their digital experience to survive. However, a complete one-on-one competition between traditional banks and Fintech companies is not going to take place.

The Collaboration

The progress brought by the Fintech companies presents large opportunities for the traditional banking institutes. Rather than disrupting each other, a collaborative movement can benefit both banks and Fintech companies. Following are the few important ways how Fintech can be used to improve the banking services.

Reduced operational costs – The efficiency of staff and other elements can be increased through Fintech thereby reducing the operating cost.

Expansion – The limitations of the legacy systems can be overcome through the Fintech. This competitive advantage can be used to expand the organisation into foreign markets and new customer segments.

Revenue Growth – The Fintech can be used to scale the less capital-intensive business such as insurance and wealth management.

The Fintech also needs these collaborations to succeed. The regulatory challenges and difficulties with scaling the customer base are a great barrier for Fintech companies to overcome. Many researchers believe a “better together” policy is going to profit both players rather than a stealing business strategy.

Clearly, the young generation of banking customers is in need of a better digital experience. It has led the traditional banks to collaborate with these Fintech companies. With banking giants recognizing the potential of this new branch of technology, the demand for well-equipped experts is expected to reach new heights. You can prepare for this lucrative opportunity ahead through various Fintech courses available.

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