H2O – An Opensource Artificial Intelligence

Posted on April 7, 2022 - 4:45 pm by Bhaskar Sharma

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H2O – An Opensource Platform for AI Model development

H2O – An open-source artificial intelligence (AI) platform available through H2O.ai. It is trusted by many data scientists and has become the go-to platform for AI-based model development. Many widely used statistical models and algorithms can be developed using H2O, adding to its popularity amongst data analysts and practitioners. Deep learning, generalized linear models, and boosted machines are some of the models supported by H2O.

One of the main features of H2O is that it automatically runs through all the algorithms (using its AutoML functionality) to produce a leaderboard of all the best applications. H2O provides its API support for Python, R, Scala, and as a GUI framework Flow making it quite accessible to programmers with different skillsets.[/text]

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[text]There are some alternatives to H2O such as Splunk, KNIME, Amazon SageMaker, Microsoft Azure Machine Learning Studio, RapidMiner, and IBM Watson Studio that are also available.[/text][clear by=”40px” id=”” class=””]

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However, H2O remains the framework of choice due to the following reasons:

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It has AutoML functionality

Also has, Bigdata support with H2O’s Sparkling Water

Readily available algorithms, easy to use in the analytical projects

Faster than Python scikit learn (in machine learning supervised learning area)

It is available as an opensource

Its development is supported on local machines, AWS, Azure, IBM, GCP

Its deployment is supported across a broad range of production environments (as a REST service (Local and Cloud) as well as an AWS lambda function)

Access to the core development team, speed of problem resolution, and feature additions are excellent

In addition to H2O, H2O.ai offers the following Enterprise platforms:

H2O wave – it is used to build visually attractive images and interactive Artificial Intelligence applications.

H2O Driverless AI – it is like a web application with its own UI and UX; it is used to tune parameters while configuring models saving some time for the data scientists.

There are no major challenges with H2O. Of course, more detailed documentation could be made available for H2O. Overall, H2O – An open-source artificial intelligence (AI) is one of the most popular frameworks available for AI and ML-based model building. At NextGen Invent, our data scientists are experienced in building many such models using various frameworks including H2O.

H2O – An open-source artificial intelligence (AI)

We would love to hear from you about your framework of choice!

To find out more about H2O and how we can help you, please visit our website at www.nextgeninvent.com[/text][clear by=”40px” id=”” class=””]

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Automatic Labelling to the rescue

Posted on April 7, 2022 - 3:22 pm by Bhaskar Sharma

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Artificial Intelligence, Data + AI+ Analytics

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Automatic Labelling: A big leap in data prepration for AI/ML Models

Tagging or labeling of data is an essential step in training computer vision models. With more and more data being needed for training, it is imperative to label the data in a hassle-free and less time-consuming fashion. This is where automatic labeling comes into the picture.

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Challenge

[/custom_heading][text]When we started the computer vision model that can identify objects in an image and video, we never realized that the objects, we need to identify may take us over a year to label. We needed to label thousands of objects in millions of images to train our model. Innovation is the mother of necessity and we were forced to come up with options to automate our instrument labeling task.

We could not use solutions from companies such as https://thehive.ai/ as those label objects in rectangle boxes and we needed to label exact boundaries of instruments.[/text][custom_heading id=”” class=”” style=”margin-bottom: 0px;”]

Solution

[/custom_heading][text]We analyzed multiple tools which could help us in reducing the time and cost of labeling. The following are the tools for evaluation:

Tool	Pros	Cons
Amazon Sagemaker Ground Truth	Accurately labeled data can manage big data, competitive pricing ($8/100 objects)	Need machine learning experience to carry out labeling jobs
Lionbridge AI	Highly accurate labeled data, better project management features	Higher pricing
V7 Darwin	Speeds up labeling time dramatically	Bugs are not managed
Label	Opensource tool, user friendly	No project management features

Based on our selection criteria,3-4 seconds for labeling, we selected Label.

We needed to select a model which can help in automating the labeling of objects. Among the various options below, we selected Detectron2.

This approach allowed us to finish the labeling task in a week.[/text][image float=”center” lightbox=”” width=”” is_gallert_item=”” src=”14840″ alt=”” href=”” title=”” popup_content=”” id=”” class=”” style=””][custom_heading id=”” class=”” style=”margin-bottom: 0px;”]

Results and Learning:

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With the advent of Big Data, the future of  data labeling is active learning.
Data labeling requires quality control, manual intervention , and collaboration to produce high-quality training data.
The cost of data annotation was scaled down by 5 times.
Too many data points were created by automation, hence, another algorithm was created to reduce the number of data points.

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[text]Our Automatic Labelling to the rescue solution is available now to all our customers at no charge for the models which we are developing. In case you have any queries on how to auto-label the images, please contact us for more information at [email protected].

Visit our website at www.nextgeninvent.com[/text]

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Demystifying AI for Future Leaders in Healthcare

Posted on April 7, 2022 - 2:55 pm by Bhaskar Sharma

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Artificial Intelligence, Digital Health

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Demystifying Artificial Intelligence for Healthcare leaders

Artificial Intelligence (AI), as defined by the English Oxford Living Dictionary “The theory and development of computer systems capable of doing activities that require human intellect, such as visual perception, speech recognition, decision-making, and language translation.”

AI works on a set of rules underlined by algorithms without any supervision. When used for the right purpose and goals with ethics AI can deliver tremendous value. Many people fear that higher dependency on AI could result in unethical practices in the future.[/text]

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[text]Many questions and misconceptions concerning Artificial Intelligence (AI) in healthcare are still prevalent. These challenges and misconceptions are especially relevant because healthcare is the next frontier for using AI technology.

Today AI is being used to:

Minimize human errors
Automate large and complex computations to improve diagnostics and treatments
Automate time-consuming contract negotiations with insurance companies
Provide better overall outcomes and more cost-effective patient experiences.
Automate time-consuming processes, and elimination of menial tasks

Thus, Healthcare startups or established enterprises in healthcare are looking to get ahead of the curve and starting to incorporate AI in their business model. Artificial Intelligence (AI) will be at the forefront of healthcare and future leaders in healthcare will bring new business models. The adoption of AI in healthcare solutions will eliminate human error, increase patient safety, and mitigate risks while reducing administrative costs. It is therefore essential to demystify the application of AI for future leaders in the healthcare domain.[/text][clear by=”40px” id=”” class=””]

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Artificial Intelligence has become a growth engine for Healthcare Startups

[/custom_heading][text]In the quest to provide solutions to healthcare problems (prevent, cure, and treat any disease), entrepreneurs are now taking the help of Artificial Intelligence. Healthcare startups that bring Data, Analytics, and AI-powered solutions to the market are insight-driven organizations. Being an insight-driven organization helps healthcare startups develop, strengthen, increase productivity, and scale their organization by reaching the target audience.

According to the report published by Research Expert Shanhong Liu, Statista,2020, marketing and sales get more benefit from adopting artificial intelligence (AI) technologies. The McKinsey Global Survey on Artificial Intelligence also showed that organizations are using AI to generate value in terms of revenue.

Adopting Artificial Intelligence by healthcare startups in their business model leads to:

Increase in annual revenue
Reduction in operational cost
Gaining an advantage over competitive organizations
Increases the capability to bring new products through innovation
Monetization of Data Assets

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How can AI-enabled healthcare enterprises be the future?

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1. Understanding the development and deployment of AI-enabled solutions

[/custom_heading][text]AI can execute healthcare duties similarly or better than humans in many cases. However, implementation issues may delay the large-scale automation of AI-based services in healthcare. To become future leaders in AI-based solutions for healthcare, organizations should know about identifying the right AI tool for different challenges and opportunities. Apart from this, organizations need exemplary implementation and project management skills to manage AI projects in healthcare. In addition, the decision-making body of such organizations should be aware of the significant trends in cybersecurity, ethics, and bias in algorithms, telemedicine, and clinical decision support.

AI applications are moving into domains that were previously regarded as only the domain of human ability. This is only possible because of recent advances in digitized data collecting, machine learning, and computing infrastructure. Therefore, the application of AI is common for diagnoses and treatment recommendations, patient engagement and adherence, and repeated administrative duties.

Thus, healthcare organizations should implement AI-based solutions and involve personnel with a technical background for a smooth deployment. This will help meet the patients’ high expectations from smart hospitals regarding services and outcomes.[/text][custom_heading id=”” class=”” style=”margin-bottom: 0px;”]

2. Understanding the current scenario and predicting the future trends in the following-

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• Artificial Intelligence in Healthcare Financial Management

[/custom_heading][text]In the e-commerce and financial sector, AI has improved customer experience, supply chain management, operational efficiency, and mate size. Their main goal is to create an inexpensive model high in quality, reliability, and which has a more extensive reach. Healthcare organizations, biopharmaceutical firms, etc., also use these models to predict and learn from the data in a similar approach.

Most existing and useful AI applications in healthcare finance focus on robotic process automation (RPA). The goal is to automate time-consuming and labor-intensive tasks like combining billing data from many sources or performing monthly account reconciliations. Machine learning is another AI technology relevant to claims and payment administration. Healthcare Insurance enterprises can use AI for probabilistic data matching across different databases. AI-based solutions can reliably find, analyze, and correct coding issues and incorrect claims. This will save all the stakeholders involved, a lot of time and money.

Hospitals and other Healthcare enterprises can use Artificial intelligence in many ways. Some of them are mentioned below:

Data and medical records management- Data management is the most visible application of artificial intelligence in healthcare. Gathering, storing, standardizing, and tracking patient information records is the blood for any solution. It is the first stage for developing any healthcare solution. Digital healthcare can be brought to market only when data is integrated, automated, powered by AI.
Tasks involving repetitive work- AI can analyze tests, X-rays, CT (Computed Tomography) scans, data input, and other laboratory and diagnostic tasks faster and more accurately than humans. Especially in cardiology and radiology, the amount of data to analyze can be overwhelming. Thus, incorporating AI-based solutions will save time and improve diagnosis and treatment.
Customized designing of treatments- Data analysis from medical images obtained through MRIs, CT scans, ultrasounds, and x-rays, can be carried out quickly with the help of AI. This helps in rapid diagnosis and choosing suitable treatment options for every patient specifically.
Consultation with the help of AI– In healthcare, the primary purpose of AI is to improve patient participation. Telemedicine through a smartphone can provide real-time assistance to patients, handle prescriptions, provide information on a wide range of medications, and suggest the dose range.
Drug designing– With several breakthroughs, machine learning algorithms are now being used to decrease drug discovery times. Artificial intelligence (AI) can make parts of the drug development process faster, cheaper, and safer.
Diagnosing diseases- Disease diagnosis and treatment have been a focus of AI since its introduction in healthcare applications. Artificial intelligence has been proven effective in the healthcare sector in several studies, and it can effectively detect and cure disease. Cancer diagnosis, genetic disease monitoring, mental illness treatments, diabetic management, and other applications are only a few examples, from the studies.
Robotically assisted surgery (RAS)– RAS using AI addresses the limits of prior minimally invasive surgical treatments and boosts ‘open surgery’ surgeons’ capability.
Patient engagement- To overcome the difficulties of patient engagement, big data and artificial intelligence are increasingly being deployed. Across the healthcare sector, machine learning and business rules engines are increasingly being employed to develop complicated interventions.

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• Artificial Intelligence and Machine Learning in Health Insurance

[/custom_heading][text]Artificial Intelligence in healthcare is not limited to hospitals and pharmaceuticals. Healthcare Insurance providing enterprises also need AI. The use of Artificial Intelligence models in Health Insurance can be of many ways, including:

Use of Chatbots
Faster Claim Settlements
Personalized Health Insurance Policies
Cost Efficiency
Fraud Detection
Choosing the right health insurance plan for customers

AI-based enterprises can develop sophisticated models for Healthcare Insurance companies to offer services for patients suffering from any chronic condition. Models like home delivery pharmacies through healthcare insurance plans are also possible. Case managers can use cognitive systems to effectively screen situations, assess the data produced by AI more precisely, and make informed judgments. For example, usage-based insurance (UBI) plans that are highly dynamic and customized to the behavior of individual clients are becoming increasingly popular.[/text][clear by=”40px” id=”” class=””]

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Conclusion

[/custom_heading][text]Studies have shown that AI can perform better and faster than humans in many crucial healthcare activities. A few ongoing studies on AI applications in healthcare paint a picture of a future where healthcare systems use is more cohesive and human-like. Algorithms are already surpassing radiologists in detecting dangerous tumors and advising researchers on building cohorts for expensive clinical trials.

Today, in this fast-changing world, AI is being used to address many of our problems. However, it is essential to know that as we study more and demystify AI, we will be able to explore several different uses of it. With such advancement in science, humans can explore uncharted territories in engineering and medical sciences, which once seemed impossible. AI for future leaders will involve discovering the phenomenal secrets of successful technology implementation in global health care. Thus, AI for future healthcare leaders will bring a new source of revenue, enhance innovations, generate a better lifestyle, and limitless possibilities for the greater benefit of society. Thus, enterprises involved in healthcare and pharmaceuticals should include AI-based business and service models and be the next leaders in the world of MedTech.[/text][clear by=”40px” id=”” class=””]

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What AI and its adoption means to business?

Posted on April 5, 2022 - 8:40 pm by Bhaskar Sharma

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Digital Transformation, Data + AI + Analytics

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What Artificial Intelligence and its adoption means to business?

AI is no longer a dream for the future: it has become a reality across all industries and spheres of life, especially the business world. As with anything new, it can be a challenge to adopt AI within existing systems. So, how can you harness the power of AI?

It all starts at the top, business leaders are the change agents, and game-changers, it falls upon them to enlighten their colleagues, employees, and others. They articulate the pros and cons of AI as well as how it can help accelerate business growth.[/text]

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The Benefits of AI for Business

[/custom_heading][text]Regardless of your niche, there’s a way that AI can help you. A few broad advantages you can expect with AI integration include:

Improved employee efficiency – Automate processes can save time and effort that employees can direct towards other goals instead of being bogged down with repetitive manual tasks.
Improve Quality – Using Machine learning for analysis and processes can bring accuracy. That delivers consistently better results and avoid room for error with manual work
Proactive than Reactive – AI and its adoption can provide you with predictive analytics allowing you to tackle unforeseen problems, meet customer demands with ease, and find better ways to grow your business.

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How Do You Increase AI Adoption?

[/custom_heading][text]Most businesses are well aware that AI could help them innovate but not many understand how exactly that happens. Change agents struggle on how to reap the benefits of AI via driving its adoption. To maximize benefits and increase adoption, we recommend a 2-pronged approach:[/text][custom_heading id=”” class=”” style=”margin-bottom: 0px;”]

1. Enabling Workforce with data and Insights

[/custom_heading][text]Our first recommendation is to initially focus on building the main KPI-based dashboards so that power is in the hands of employees and managers. The process of defining these KPIs and dashboards will help you refine your business goals and truly figure out what you need your AI system to accomplish.[/text][custom_heading id=”” class=”” style=”margin-bottom: 0px;”]

2. Build on the success of the prior step using a four-step approach: discover, plan, act, and optimize.

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Discovery involves accessing the state of your data, the capabilities of your people, the feasibility of your system, etc. Assessing your data value is the single most important thing you can do to ensure the success of your AI system. Data is the lifeblood of any AI system and having structured, clean data can make or break AI initiatives.
Plan out your mission and vision for your AI solution by defining the obstacles in your business and converting insights into potential solutions.
Then we move on to the act – Implementation of the planned activities. We suggest choosing the right problem to solve based on its impact and adoption rate. AI use cases impact is calculated based on which business workflow it can be part of.
Lastly, you need to optimize systems, and processes. AI systems thrive on learning and constantly optimizing will ensure that insight is real and valid with changing business, and your system stay trustworthy.

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Decision Making Framework for AI based Product businesses

Posted on April 5, 2022 - 6:50 pm by Bhaskar Sharma

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Artificial Intelligence, Digital Transformation

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Decision Making Framework for AI based Product businesses

NextGen Invent Corporation has built a decision-making framework for entrepreneurs and business innovators. To address the following questions that arise when you have a business idea using AI and workflow enhancement.

I have an AI business idea, but what’s next?
What will increase the adoption of my AI solutions?
How can I make sure that my data science team is going to build an accurate model?
What are the success factors and forces that go into making my AI model effective?
What are the risks associated with my AI product/service adoption and sales cycle?

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[text]A business framework (decision-making framework) for AI-based products.

services include evaluating AI models and streamlining workflow automation that can contribute to business growth.

An agile business framework is key to avoiding expensive mistakes and ensuring a positive business outcome. Whether the idea is to start a new AI-based product/service business or to transform an existing product, a business framework plays a crucial role.

Most entrepreneurs would agree that they had a million-dollar AI-based product idea but were unable to bring it to reality because they were unaware of the direction they were supposed to take.

Therefore, having a solid business framework to guide decision-making processes is an important tool.

NextGen Invent’s decision-making framework is based on the experience gathered from over three hundred AI model developments and from interviews with 50+ executives and 20+ successful entrepreneurs. The proposed business framework highlights five key principles that play a vital role in determining the course of your AI business:[/text][image lightbox=”” width=”” is_gallert_item=”” src=”14786″ alt=”” href=”” title=”” popup_content=”” id=”” class=”” style=””][text]An entrepreneur should consider these five key principles to be successful in defining an AI/ML/Deep Learning model for bringing innovative products and services to the market. Let’s understand each key principle in detail.[/text][clear by=”40px” id=”” class=””]

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Be the Model User

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If you are an entrepreneur with a goal to launch an AI model-based product or service, you should imagine yourself to be the model user. Define your expectations from the model. This will help you determine the input format, the manner in which it is processed.
The output is given to your users.
After receiving the input, you must understand, as a model, the success rate of the output provided. In other words, ask yourself how knowledgeable you should be as a model used to provide the input to utilize model output.
You should make sure that your model output is relevant to users’ needs based on vast data knowledge.
If you are an entrepreneur and not a data scientist then this step brings you closer to understanding your AI product and services at a deeper level.
This step will help you in asking the right questions as well.

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Entry barrier

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In Michael Porter’s five forces, the most critical force for the success of a product or service is Entry Barrier. In most cases, the entry barrier for an AI model comes from its input data.
Most companies are moving towards gathering Real World Data, gaining exclusive data rights, and improving data quality.
360-degree data approach along with strong data quality standards are crucial ingredients in making a unique model.
value proposition and an entry barrier to your business.
Once your model has an entry barrier and value to the customer, it is important yet difficult to maintain that position.
Continuous learning, additional data to increase model intelligence, and expanding strategic partnerships/distribution channels may help you in staying ahead of curve.

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Ethics

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“Ethics is knowing the difference between what is right and what is wrong”.
Unfortunately, there will always be a gray area as law and social awareness will change over time.
As an entrepreneur, the boundaries you set and the decisions you make in regard to ethics.
Having an AI model with high standards for data privacy will tend to provide sustainable products and services.

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Model Risk

[/custom_heading][text]Like all other businesses, AI-based products and services have their own set of unique risks. We can categorize these risks as follows:[/text][custom_heading id=”” class=”” style=”margin-bottom: 0px;”]

1. Trust

[/custom_heading][text]An AI model is seen as a black box, i.e. input is provided to receive an output, but is it very hard to gain insight into what happened inside the box.

Even though a model is using several black-box components one should spend extensive time in quality test data and scenarios.

Trust in a model normally comes from well-documented benefits achieved from the AI model in real scenarios.[/text][custom_heading id=”” class=”” style=”margin-bottom: 0px;”]

2. Future Redundancy

[/custom_heading][text]Due to ever-changing and fast-paced technology, the model that is considered worthy today might end up being redundant in the future. Therefore, plan to periodically audit and analyze your model’s performance in different scenarios.[/text][custom_heading id=”” class=”” style=”margin-bottom: 0px;”]

3. Data Compliance

[/custom_heading][text]In most cases, the data used for your AI model has to follow a set of rules and regulations like HIPAA, GDPR, etc. As these laws are changing, a model that is compliant today may not be in the future.

Staying ahead of the curve and using technologies such as federated learning are commonly implemented approaches in these cases.[/text][clear by=”40px” id=”” class=””]

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Model Adoption

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This is where an entrepreneur or other business executives spend most of their time.
Executives should keep working on how to increase model value and make it easy to use.
The adoption of AI is one of the hardest dimensions in any organization.
It must adopt at all levels of the organization for it to be successful.
Entrepreneurs and executives should be aware of the above business framework and related key principles.
We would love to know how this framework has helped your business.
To get more information or discuss your AI initiative, please click here.

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Rise of Digital Healthcare/Telemedicine during Covid

Posted on April 5, 2022 - 4:09 pm by Bhaskar Sharma

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Digital Health, Digital Transformation, Telemedicine

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Rise of Digital Healthcare/Telemedicine during Covid

Prior to the Coronavirus pandemic, digital healthcare or telemedicine was a relatively new form of healthcare delivery that was still far from mass adoption in the industry. However, Covid-19 completely changed existing healthcare delivery models in the blink of an eye. Now, telemedicine has become a mainstay of healthcare delivery and is definitely here for the foreseeable future.

As the pandemic kept people restricted to their homes, no one could go to a hospital or clinic unless it was an emergency. It became necessary to deliver healthcare remotely as much as possible to prevent the further spread of the pandemic. This was a huge boost in the adoption of digital health measures like telemedicine, remote monitoring, and digital health tracking.[/text]

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The Use Of Technology And Analytics In Healthcare:

[/custom_heading][text]The healthcare sector is slow in adoption of technology. There is hesitancy in healthcare providers to depend too much on technology. After all, medicine does require a human touch. However, technology can provide the necessary data to drive these human decisions leading to better patient outcomes and this can be done without sacrificing empathy and compassion.

AI and machine learning can be vital tools for doctors, helping them make smarter and quicker decisions. The Coronavirus pandemic has hastened the use of telemedicine by both healthcare providers and patients, that has helped save many lives in these times.[/text][clear by=”40px” id=”” class=””]

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Digital Healthcare During The Pandemic

[/custom_heading][text]The Coronavirus pandemic has familiarized the industry with several digital transformations including:[/text][custom_heading id=”” class=”” style=”margin-bottom: 0px;”]

Telemedicine

[/custom_heading][text]Telemedicine involves doctor-patient consultations happening online, usually through video conferencing. As video conferencing became a part of daily life, patients and doctors alike are now more comfortable with online consultations. Telemedicine is perfect for remote care of chronic conditions, minor emergencies, counseling, follow-up care, and more.[/text][custom_heading id=”” class=”” style=”margin-bottom: 0px;”]

Remote Monitoring

[/custom_heading][text]Remote monitoring and digital tracking of health parameters have made it possible for doctors to safely monitor their patients at home. This allows the patients to remain comfortable at home while also allowing the doctor better monitoring through tracking devices, sensors, etc.[/text][custom_heading id=”” class=”” style=”margin-bottom: 0px;”]

Mobile applications

[/custom_heading][text]The pandemic has driven the use of mobile apps by patients and caregivers to track symptoms, treatment, and outcomes. This is also an easy way for doctors to track multiple patients and use the data to drive better treatment over time. Mobile apps also played an important role in contact tracing.[/text][clear by=”40px” id=”” class=””]

[text]Other digital transformations during the pandemic include the use of AI bots for tele-consulting, predictive analytics, AR training, and bringing sustainability to the healthcare supply chain.

The future of digital health is bright with the entire industry rapidly adopting cloud computing, big data analytics, and digital communication. As this field expands, the question of data security around patient records is at the forefront of every discussion. Only time will tell how far digital health can take us.[/text][clear by=”40px” id=”” class=””]

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Will Artificial Intelligence replace physicians?

Posted on April 5, 2022 - 4:00 pm by Bhaskar Sharma

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Artificial Intelligence, Digital Health

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Will Artifical Intelligence replace physicians and face of healtcare?

[/custom_heading][share facebook=”true” twitter=”true” linkedin=”true” email=”true” size=”small” id=”” class=”” style=”margin-top: 10px;”][clear by=”15px” id=”” class=””][text]Imagine yourself walking into the hospital with a humanoid robot greeting you with a calming voice, asks you about your symptoms, and reassures you while giving you a prescription with a smile. While this may sound like complete science fiction, the question remains, “Will Artificial Intelligence replace your doctor in the future?” While we realize the advancement in technology especially in the field of neural networks has been remarkable, AI is enabling physicians with tools and decision-making power but not replacing them, at least not anytime soon.[/text]

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[text]Here is our CEO, Deepak Mittal’s opinion about AI replacing physicians: Click Here[/text]

[text]The same opinion other leaders have expressed in a poll conducted by NGI. Results were quite clear, 63% of the respondents believe that AI won’t replace physicians:[/text][text]Artificial Intelligence (AI) acts as an enabler to medical care. AI/ML shines the most when it is assisting physicians in making better medical decisions. More than accuracy, we as humans need human empathy from a physician along with effective treatment.[/text][text]Al/ML is bringing the power of object identification, classification along with question/answering, but a physician’s power lies in linking various pieces of information to make decision. Diagnosing a condition is an np-complete problem (specifically set cover: http://en.wikipedia.org/wiki/Set_cover_problem) and even with quantum computers, np-complete problems cannot be solved in polynomial time. The point can be well proven by the experiment of Waldo.[/text]

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Waldo experiment:

[/custom_heading][text]Waldo wears a stocking cap, even in the summer, is skinny, usually wears a striped shirt, needs a haircut, and always hangs out with lots of other characters. Can you find him in the image? And if you did, how long did it take?

The viral video where a robot built with Google AI finds Waldo from a cluster of images within seconds which a human eye would normally take minutes. But if we change the question to who all needs a haircut in the picture then will the computer do that. That’s where in our opinion, a physician’s power lies. Or better question will be which haircut will look best on which person based on his liking/ethnicity etc. Please note that we are only highlighting two of the thousand parameters that might be going in a physician’s mind. Unfortunately, not all decisions are black and white in our human world.

Even if we consider fully automated surgeries, we have history books to offer wisdom. The advent and progress in AI has been remarkable, and we have had our fair share of lessons from shortcomings and mistakes like Therac-25 in the past, where admittedly so we’ve realized there is a long, long way to go before AI can even dream of replacing surgeon.

I would also like to point to the problem with malpractice insurance and FDA approval. Think of a hospital having 1000 physicians and malpractice points to one physician vs malpractice. Pointing to AI that does the work of 1000 physicians. Imagine a situation where If we may run out of physicians overnight because of one blunder or mistake.

In nutshell, we believe that it is unlikely we’ll get humanoid “robot physicians” for a long time to come. Though technology will replace some of the more routine aspects of medical care, or improve it but for now. The physicians are here to stay.[/text][clear by=”40px” id=”” class=””]

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AI Adoption: Why Business leaders should excel it?

Posted on April 1, 2022 - 9:41 pm by Bhaskar Sharma

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Artificial Intelligence, Data + AI + Analytics

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AI Adoption: Why Business leaders should accelerate it?

[/custom_heading][share facebook=”true” twitter=”true” linkedin=”true” email=”true” size=”small” id=”” class=”” style=”margin-top: 10px;”][clear by=”15px” id=”” class=””][text]Business Leader’ decisions and actions have widespread consequences on company success and as such, great leaders excel at preparing their company to beat the competition. They lead the company and employees, spearhead strategy, and inspire confidence in the set targets and goals. Equipping business leaders with more sophisticated and powerful tools enables better decision-making. In this aspect, the importance of Artificial Intelligence (AI) cannot be overlooked.

The 2019 study from Microsoft and IDC Asia/Pacific, “Future Ready Business: Assessing Asia Pacific’s Growth Potential Through AI”.[/text]

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[text]Which surveyed 200 business leaders and 202 workers, concluded that the rate of innovation improvements and employee productivity gains were estimated to rise by 2.2 times and 2.3 times, respectively. For the organizations that have implemented AI initiatives.[/text][clear by=”35px” id=”” class=””]

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The top five business drivers to adopt the technology were:

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Higher competitiveness (24% of respondents chose it as the number one driver)
Accelerated innovation (21%)
Better customer engagement (15%)
Higher margins (14%)
Productive employees (9%)

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[text]Therefore,the biggest advantages of Artificial Intelligence (AI) come in the form of reducing the load of repetitive tasks for employees and providing deeper insights over extended periods of time. AI is a great tool to bring in agility, filtering signals from noise. Also, it has the ability to handle big data easily, extract meaningful patterns and models.

Finally, make responsive dashboards that cannot be overstated.[/text][clear by=”35px” id=”” class=””]

[text]AI adoption can also serve to make the organization agile, initiating the development of new products through careful listening and appropriate action.

Trailblazing leaders can build a knowledge base and disseminate that information consistently, motivating continuous learning in an environment that seeks to progress rapidly and radically.[/text][clear by=”40px” id=”” class=””]

[text]In addition, the benefits of AI come with some considerations. To implement AI effectively in your business, you need big data in diverse forms. To utilize this data, you will need more computing power and storage than you’ve used before, and additional skills to integrate this software and hardware into your current technology and business teams.

As you start to use AI technology, the more data you get, the more ways you’re likely to discover how to use it. Successful use of AI technology to improve business is an ongoing and continuous effort.[/text][clear by=”40px” id=”” class=””]

[text]In conclusion, as a business leader, you are at the helm of this effort, helping others to adjust and thrive.

You can also, steer how an organization prepares for and responds to the opportunities and risks around AI in business.[/text][clear by=”40px” id=”” class=””]

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Machine Learning – Supervised, Unsupervised, & Reinforced Learning

Posted on April 1, 2022 - 9:32 pm by Bhaskar Sharma

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Artificial Intelligence, Machine Learning

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Machine Learning – Supervised, Unsupervised, & Reinforced Learning

[/custom_heading][share facebook=”true” twitter=”true” linkedin=”true” email=”true” size=”small” id=”” class=”” style=”margin-top: 10px;”][clear by=”15px” id=”” class=””][text]Machine learning is a vast topic with so many intricacies that it can be confusing where to start. Machine learning is the force behind many of the algorithms that govern our lives like Amazon’s recommendation engine, fraud detection software, financial market tracking, and supply chain logistics management across the industry.

The fundamental function of all these algorithms is their ability to learn. Artificial intelligence and machine learning models can learn in many ways. Each of these learning methods can sound complicated if you don’t have in-depth technical knowledge, so let’s dive in for a simple explainer on different learning models in machine learning.[/text]

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Supervised Learning

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Supervised learning is the simplest to understand. You need to provide an input to which you already know what the output should be. What you don’t know is how you can reach this output, and this is what the model will study and base a prediction on.

Supervised learning involves the use of existing data to train models. The input data is training data. The algorithm works on this to produce a prediction. It can compare the output produced to the intended output and find errors that is modified as needed.

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Unsupervised learning

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Unsupervised learning involves the use of unlabelled data to train the machine to identify patterns and cluster the data. It is best for situations where you have complex data and are not sure what your desired outcome is. This method of learning gives you a better understanding of the inner relationships within your data that you may process further.

The two most important types of unsupervised learning are clustering and association. Clustering involves the grouping of items based on similarities. Association learning is used when you want to find the link between data columns.

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Reinforced learning

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In reinforced learning, models are created to incorporate rewards and punishments. This encourages the model to chase rewards and minimize punishments, teaching it how to make decisions. The model learns to recognize relevant signals and decide the best action to maximize the reward. When a loop is completed, a reinforcement signal is needed to give feedback to the model on how to proceed further.

Reinforced learning is a sort of middle ground between supervised and unsupervised learning. The model is provided labeled data, like in supervised learning but the model is able to make judgments by itself, like unsupervised learning. Recommendation algorithms often function on reinforced learning.

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Machine learning as a field is expanding by the day, with more accurate and complex algorithms. A solid understanding of the basic way these algorithms learn will help you get a better understanding of what you can achieve with AI and machine learning systems. Much like how we learn, an abundance of patience and effort we need to ensure your AI system learns well. Remember, it only gets better with every round of learning!

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AI Driving 2022 Future Business Trends

Posted on April 1, 2022 - 9:25 pm by Bhaskar Sharma

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Data + AI + Analytics, Machine Learning

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Artificial Intelligence, driving 2022 future business trends

[/custom_heading][share facebook=”true” twitter=”true” linkedin=”true” email=”true” size=”small” id=”” class=”” style=”margin-top: 10px;”][clear by=”15px” id=”” class=””][text]The role of AI in work life and business is undisputed. As we move forward, this role will only expand to include more functionalities and use cases like artificial intelligence, machine learning, and data processing becomes more advanced and efficient. In the past few years alone, we’ve seen AI grow by leaps and bounds and this growth shows no signs of slowing down just yet.

Looking to the future, how will AI impact work and business? It can be hard to predict but as an overview, we can expect the following changes globally:[/text]

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1. Productivity will increase

[/custom_heading][text]One of the biggest advantages of integrating AI into your business is the major boost in productivity that you receive. An efficient and effective AI system can reduce human error, find new solutions to old problems, and help you avoid pitfalls in the future. As the technology behind artificial improves, we can expect this productivity boost to keep getting better. The goal is to reach a stage where less training is needed for your AI model in order to get more things done. This will directly result in enhanced productivity across industries.[/text][clear by=”35px” id=”” class=””]

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2. Consolidation

[/custom_heading][text]Services via companies using the power of Artificial intelligence will have lower operations costs and will be able to scale operations faster and cheaper. That will accelerate consolidation in different industries. Think of the Chrome browser, it’s so ubiquitous in its market that many users forget that other browsers exist.[/text][clear by=”35px” id=”” class=””]

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3. Personalization

[/custom_heading][text]Consolidation can spell doom for smaller companies unless they pivot and adapt. Personalization of products and services should be the focus of small companies to stay competitive and profitable. This means personalization not only at a customer segment level but delving deeper so that each customer becomes a segment unto themselves. For example, personalized medicine is one such opportunity for hyper-personalization.

Integrating AI systems into their businesses models is the only way for small companies to make this shift towards personalization. The strength of a company’s AI capabilities will likely be one of the biggest factors for business success in the future.[/text][clear by=”35px” id=”” class=””]

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4. Less time from ideation to launch

[/custom_heading][text]The current turnaround time for various product development clocks in at about 6 months, from ideation to launch. This production window varies based on the industry or product. Overall, we can expect a shortening of the time taken to ideate, develop, test, and launch new products and services. Artificial intelligence and machine learning can play vital roles in reducing the development process. Zara is known today for taking less time from idea to launch. But in the future, this duration will be in hours and days and not in weeks or months. Further along, AI can speed up the testing process and help products launch quicker and with fewer setbacks.[/text][clear by=”40px” id=”” class=””]

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5. Convergence of physical and digital world

[/custom_heading][text]By 2030, about $5.5 trillion to $12.6 trillion of value will be unlocked globally using IoT products and services. Virtual Reality, Mixed Reality, and extended reality are continually challenging customer experience and re-writing the rules. The convergence of the digital and physical worlds is going to be a reality in the future.

AI and machine learning are now a mainstay of the business. As the field grows, it’s still not too late to get in on the action. Adopting AI in your business can help you grow and explore exciting new opportunities. AI can give your company a competitive edge while ensuring that your technological strategy stays updated. Now is a great time to invest in AI and see the wonders it can do for your business.[/text]

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Changes needed to promote AI Adoption

Posted on April 1, 2022 - 8:02 pm by Bhaskar Sharma

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Artificial Intelligence, Data + AI + Analytics

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Deeper look at change needed to promote AI adoption

[/custom_heading][share facebook=”true” twitter=”true” linkedin=”true” email=”true” size=”small” id=”” class=”” style=”margin-top: 10px;”][clear by=”15px” id=”” class=””][text]The nature of work and the status quo of business are rapidly changing. To beat the competition, leaders are challenged today to embrace the change required within the company to transform it into an AI-enabled company first with AI Adoption. The rise of Artificial Intelligence, Machine Learning, and automation has changed the way we train labor, how we leverage machines, and how we improve productivity. In these exciting times, no one can afford to be left behind.

AI is changing the face of business every day. From fully automated customer service to guiding banking decisions, the scope of work undertaken by AI is expanding. Firstly, not every business has been able to harness the power of AI in its processes and goals.[/text]

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Tapping Into the Power Of AI

[/custom_heading][text]Truly mobilizing the power of AI requires a huge shift in mindset, company culture, and company processes. Acceptance and understanding of AI among managers and executives is the biggest change and is absolutely necessary for a shift towards an AI-powered future.

At the leadership level, one of the major roadblocks is taking a plug-and-play approach to AI Adoption. This happens when company leaders rush into their AI implementation and build their systems with a narrower view of their goals. But this isolated, small-scale approach can backfire leading to a failure of company-wide adoption.

The second roadblock leaders might face is taking a narrow perspective on their AI goals and a lack of understanding of the capabilities that AI has. Leaders first need to analyze their goals, align their company culture and mission to their goals, and work with experts to build AI systems that will stand the test of time.[/text][clear by=”35px” id=”” class=””]

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What Changes Are Needed For AI Adoption?

[/custom_heading][text]The first change is always a change in your strategy. Think about what your goals are, what processes you need to change or initiate, and how you can maintain this long-term. Once company leaders lock in this strategy, they can then move towards educating their peers and employees on the pros and cons of AI and machine learning.

Broadly, these are three changes you will need to consider:[/text][custom_heading id=”” class=”” style=”margin-bottom: 0px;”]

1. Creating a collaborative work culture

[/custom_heading][text]AI thrives on data and the best way to create valuable data is to encourage cross-departmental collaboration. This will help you take a broad view of your challenges and goals and avoid the pitfalls of a limited or one-sided perspective. By involving people across different disciplines, you can enrich your insights and help create a more robust AI system. Additionally, it is important that leaders address the fear of failure for a company to embrace innovation.[/text][custom_heading id=”” class=”” style=”margin-bottom: 0px;”]

2. Shifting to data-driven decision-making

[/custom_heading][text]This onus falls squarely on company leaders. Gone are the days when we relied on purely leader-driven decision-making. Company leaders must now lead the charge towards data-driven decision-making. Trusting your AI system and augmenting company decisions with data and analytics is an important step in seamless AI adoption.[/text][custom_heading id=”” class=”” style=”margin-bottom: 0px;”]

3. Accepting a more dynamic, agile workflow

[/custom_heading][text]AI works best with practice. This means it may not be perfect at first. It takes time to learn and gets better with every iteration. This means companies will have to adjust to a trial-and-error mentality with the resilience to learn from failures.

Technologically sound, data-driven processes involving skilled people are the future of business. however, don’t rush to adopt AI systems without first analyzing your company, your goals, and your processes. Evaluate each step and analyze generated data, and always consult with experts for guidance.[/text]

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Future of Digital Healthcare: Where are we heading?

Posted on April 1, 2022 - 4:54 pm by Bhaskar Sharma

[blog_5_secrets_of_successful_data heading=”Future of Digital Healthcare: Where are we heading?” description=”The most outstanding entrepreneur Steve Jobs once said, “We cannot connect the dots by looking forward but can connect them by looking backward.” Thus, to understand where we are heading with digital healthcare, it is essential to know what developmental changes digital health has been through to date.” image=”17634″][/blog_5_secrets_of_successful_data]

Below is the timeline of a few important milestones in Digital Healthcare to date:

1897-1980

Telemedicine, which is now mainstream treatment in healthcare, dates to 1897. Adam Darkins and Margaret Cary’s book “Telemedicine and Telehealth: Principles, Policies, Performances and Pitfalls” mentions the first reported use of telemedicine on a young child with croup illness. However, the subsequent use of telemedicine for diagnosis was reported after nine decades post-1987, during the Antarctica expeditions and space missions.

Similarly, during the mid-1960s, Lockheed developed an electronic clinical information system that laid the foundation for Electronic Health Records (EHR). By the 1980s, hospital administrative efforts were made to use EHR among medical practices.

1990-1999

As the world entered from the 80s to 90s, digital health met its golden period. Polygraph lie-detector test was invented in 1921. It was the first machine to include sensors that measured Galvanic Skin Response (GSR), pulse rate and blood pressure. The technology used back then is now commonly found in fitness trackers. In 1938, the first wearable hearing aid was developed. Healthcare delivery through digital communication showed potential to upgrade the relationship between patients and healthcare providers. Many professional associations appeared in the USA and across the globe. A few examples include the International Medical Informatics Association, the American Telemedicine Association, and the European Health Telematics Association.

Significant technological advances during 1950-1999 lead to the invention of ultrasound imaging techniques, artificial organs, and DNA sequencing. These techniques laid the following founding base on using technology in medicine for patient benefit.

2000-2015

2003 witnessed the world’s first fully digital pacemaker where a physician can download patient information in just 18 seconds. To enhance the experience during physical exercise, Nike and iPod launched a fitness tracking wireless system in 2006. Physician Tom Ferguson invented the word “e-patient” and wrote the first white paper on the concept of e-patient in 2007. The physician created a website epatient.net and wrote blogs. The primary intention behind e-patient is to make patients aware of using the internet to socialize, stay well-informed, and take healthcare into their own hands.

Similarly, in 2010, Health keynote speaker Engelen started the #PatientsIncludedmovement. The movement’s goal is to empower the patient to be the caretaker of their health and increase patient literacy. Delocalization of healthcare (Telemedicine) using technological developments is also the goal of #PatientsIncludedmovement.

In 2014, the British Medical Journal created the Patient Panel to take patient and public partnership to the next level of scientific research publication. The journal realized that affordable, safe, quality, and effective healthcare could be possible if patient perspectives were also given importance. Thus, BMJ brought the following changes:

Calling on authors to involve them in the production of their papers
Requesting authors of research papers to highlight how they involved patients in designing the research question.
Also included papers reviewed by patients in their standard peer-review process.

2017-2019

In 2017, USFDA launched the Digital Health Unit to expand the opportunities for digital health tools to become part of general healthcare. The American Medical Association in 2018 published its Artificial Intelligence Policy. The goal was to get doctors involved in the development of healthcare A.I. It also stressed patient and physician education on the potentials and limitations of A.I.

2020

“Innovations in Digital Healthcare during COVID-19 Pandemic”

During the COVID-19 pandemic, Telehealth saw massive growth. Online Services like COVID test from the comfort of your home, booking an appointment with a consultant physician, with pathology labs to collect blood, urine, or other samples as directed by the physician, and ordering medicine from a pharmacy shop et al. have increased dramatically. Virtual healthcare became the new norm. Artificial Intelligence-based diagnostic testing and over-the-counter tests for accurate and fast diagnosis of COVID-19 also came into the picture.

Apart from this, here are a few innovations that we witnessed during the COVID-19 pandemic in terms of digital health:

Artificial Intelligence (AI) designed 3D-printed swabs
Ultra-wideband (UWB) technology to monitor social distancing
Light signal processing technology to detect COVID-19 via smartphone

Forbes News

Posted on August 13, 2020 - 2:52 pm by Bhaskar Sharma

"Fit the position to the person."

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The best hires come from referrals, but you don’t always have to try to find the “right” person to fill a specific position. In some cases, it is wise to create a job for the right person to ensure your business doesn’t miss out on talented individuals.

[/text][text]Deepak Mittal
Founder and CEO[/text][text]

Image Source- Forbes

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Ensure You Make The Right Hire For Your Business With These 15 Tips

[/custom_heading][divider direction=”start” icon_fontawesome=”” style=”” id=”” class=””][text]While the hiring process is important, it can also be challenging and time-consuming. You and your hiring team must sort through résumés, schedule interviews, meet with candidates and choose the right person for the job.

The subsequent onboarding process also requires further time and resources—and if you make the wrong hiring choice, you need to start the whole process all over again. To help you choose the right hire the first time, follow these tips from 15 members of Forbes Business Council.[/text][link target=”blank” lightbox=”” href=”https://www.forbes.com/sites/forbesbusinesscouncil/2020/07/16/ensure-you-make-the-right-hire-for-your-business-with-these-15-tips/#3af7c77b5ad4″ title=”” popup_content=”” id=”” class=”” style=””]Click Here to read the full article[/link]