Explainable AI – The new wave of revolution in the field of AI

What is Explainable Artificial Intelligence (XAI)?

Explainable Artificial Intelligence (XAI) refers to methods and processes that enable humans to understand and trust the outcomes of machine learning algorithms. As AI technologies become increasingly sophisticated, the ability to interpret how these systems make decisions is critical. Explainable AI aims to bridge this gap by making AI models more transparent and interpretable.

Explainable AI provides insights into model accuracy, fairness, transparency, and outcomes, which are crucial for building trust in AI systems. This trust is essential for the responsible deployment of AI in various sectors, ensuring that the technology is used ethically and effectively.

The Importance of Explainable AI

In the current landscape, AI often operates as a “black box,” where even developers may struggle to understand how specific decisions are made. This lack of transparency can be problematic, especially in high-stakes environments like healthcare, finance, and legal systems. Explainable AI addresses these challenges by providing clarity on the decision-making processes of AI systems.

Key benefits of Explainable AI include:

  • Enhanced Trust: By understanding how AI decisions are made, users are more likely to trust and rely on these systems.
  • Regulatory Compliance: Explainability helps meet legal and ethical standards, especially in regulated industries.
  • Bias Detection: By revealing decision-making processes, biases can be identified and mitigated, leading to fairer outcomes.

Evolution and Techniques of Explainable AI

Explainable AI is not a new concept. Early AI systems, such as expert systems, included explanations based on predefined rules. These systems were transparent but less capable compared to modern machine learning models. Today’s techniques aim to balance prediction accuracy with interpretability.

Historical Context

In the early days of AI, expert systems were developed to emulate the decision-making ability of human experts. These systems were based on rules and logic that were easy to understand and explain. However, as AI evolved, machine learning models, particularly deep learning networks, began to outperform traditional rule-based systems. These advanced models, while powerful, often lacked transparency, making it difficult to understand how they reached specific decisions.

Modern Techniques

Some popular methods in explainable AI include:

– Local Interpretable Model-Agnostic Explanations (LIME): This technique explains individual predictions by approximating the black-box model locally with an interpretable model.

– Shapley Additive exPlanations (SHAP): SHAP values help explain the output of any machine learning model by computing the contribution of each feature to the prediction.

– Layer-wise Relevance Propagation (LRP): Primarily used in neural networks, LRP assigns relevance scores to each input feature, indicating its contribution to the final decision.

Market Size and Projections

The explainable AI market is growing rapidly. According to a report by Statista, the global AI market is projected to reach $62 billion by 2029, with a significant portion attributed to explainable AI technologies. This growth is driven by the increasing need for transparency and accountability in AI systems, especially in industries such as healthcare, finance, and autonomous systems.

Case Studies and Applications

Healthcare

Explainable AI is revolutionizing healthcare by providing transparency in diagnostic processes and treatment recommendations. For example, AI systems that analyze medical images can now explain their findings, helping doctors make better-informed decisions. In one instance, a study showed that explainable AI improved the accuracy of breast cancer detection by providing clear visual explanations of its analysis, which doctors could then verify.

Finance

In the financial sector, explainable AI enhances trust in automated decision-making processes, such as loan approvals and fraud detection. By understanding the factors that influence these decisions, financial institutions can ensure compliance and fairness. For instance, a leading bank implemented an explainable AI system that significantly reduced the incidence of biased loan approvals, ensuring that decisions were based on fair and transparent criteria.

Autonomous Systems

For autonomous vehicles and other AI-driven systems, explainability is crucial for safety and regulatory compliance. AI systems must be able to justify their actions to gain user trust and meet legal standards. Companies like Tesla and Waymo are incorporating explainable AI to provide insights into how their self-driving cars make decisions, which is essential for both user acceptance and regulatory approval.

Evaluating Explainable AI Systems

Explanation Goodness and Satisfaction

Explanation goodness refers to the quality of explanations provided by AI systems, while explanation satisfaction measures how well users feel they understand the AI system after receiving explanations. Ensuring high levels of both is essential for building user trust and facilitating effective interaction with AI.

Measuring Mental Models

Mental models are users’ internal representations of how they understand AI systems. Methods to elicit mental models include think-aloud tasks, retrospection tasks, structured interviews, and diagramming tasks. These methods help gauge how well users comprehend AI decision-making processes.

Measuring Trust in XAI

Trust in AI systems is crucial for their adoption and effective use. Various scales and methods exist to measure trust, including surveys and behavioral analysis. Trust should be measured as a dynamic process that evolves with user interaction and system performance.

Measuring Performance

The performance of XAI systems can be evaluated based on user performance, system performance, and overall work system performance. Metrics include task success rates, response speed, and correctness of user predictions. Continuous model evaluation helps businesses troubleshoot and improve model performance while understanding AI behavior.

Future Directions & Challenges

While significant progress has been made, challenges remain in the field of explainable AI. Balancing model complexity with interpretability is a key issue. More complex models often provide better performance but are harder to explain.

Addressing Technical Challenges

Current technical limitations include hardware constraints and the need for high-performance computing to handle the computational load of explainable AI techniques. Researchers are working on developing more efficient algorithms and hardware solutions to make explainable AI more accessible and scalable.

Ethical and Social Implications

Explainable AI also plays a crucial role in addressing the ethical and social implications of AI deployment. By making AI decisions transparent, organizations can better ensure that these systems do not perpetuate or exacerbate existing biases. For example, research has shown that AI systems used in hiring processes can unintentionally favor certain demographics. Explainable AI can help identify and correct these biases, promoting fairness and equity.

Integrating Human Expertise

Combining AI systems with human insights can improve decision-making and trust. This hybrid approach leverages the strengths of both AI and human judgment, leading to more robust and reliable outcomes. For instance, in medical diagnostics, AI can provide a preliminary analysis, which is then reviewed and confirmed by a human expert, ensuring accuracy and building trust in the system.

Explainable AI – The Inevitable Future

As AI continues to integrate into various sectors, the need for explainable AI becomes more critical. By making AI systems more transparent and understandable, organizations can build trust, meet regulatory requirements, and ensure the ethical use of technology. The ongoing research and development in this field promise to make AI systems not only more powerful but also more aligned with human values and societal needs.

Explainable AI is pivotal in making AI systems transparent, accountable, and trustworthy. By enhancing decision-making processes, improving compliance, and promoting ethical AI practices, XAI is set to revolutionize various industries. 

Want to set up productive & profitable AI Systems?

At Crafsol, we specialize in integrating cutting-edge explainable AI technologies into your operations. Our expertise ensures a seamless transition, empowering your organization to harness the full power of AI.

Contact us today to learn more about our explainable AI solutions and how we can help your company stay ahead in the digital era. Book a free consultation and start your transformation journey with Crafsol.

A girl using AR VR device.

The future of workforce training – AR and VR in action

As we advance into an era defined by rapid technological evolution, traditional training methodologies are increasingly falling short of industry demands. Industries such as automotive and life sciences are pioneering the use of augmented reality (AR) and virtual reality (VR) to revolutionize their training paradigms. These immersive technologies are not just enhancing the training experience; they are driving superior skill acquisition, bolstering safety protocols, and delivering significant cost efficiencies.

Imagine a scenario where a life sciences professional can practice intricate surgical procedures in a highly realistic virtual environment, or an automotive engineer can assemble an engine in a virtual workshop with unparalleled precision and feedback. These technologies provide a level of engagement and interaction that traditional methods simply cannot match.

This blog delves into how AR and VR are redefining workforce training. By examining real-world applications and projecting future innovations, we will illustrate how these advanced technologies are setting new benchmarks in training effectiveness.

Crafsol’s Innovative Approach with AR and VR

At Crafsol, we specialize in integrating AR and VR technologies into training programs to deliver exceptional learning experiences. Here’s how we make a difference:

  • Immersive Learning Environments: By creating realistic and interactive virtual scenarios, we ensure that learners can practice and hone their skills in a safe and controlled setting. This immersive approach enhances engagement and retention.
  • Remote Accessibility: With our AR and VR technologies, training can be accessed remotely, breaking down geographical barriers and allowing learners from different locations to participate in the same high-quality training programs.
  • Customized Training Programs: We tailor our AR and VR solutions to meet the specific needs of each client, ensuring that the training content is relevant and directly applicable to their industry and workforce requirements.
  • Data-Driven Insights: Our solutions include analytics and reporting features that track learner progress and performance. This data helps organizations identify areas for improvement and measure the effectiveness of their training programs.

By leveraging the latest advancements in AR and VR, Crafsol is committed to delivering cutting-edge training solutions that drive employee development and organizational success.

The Impact of VR in the Automotive Industry

Many car manufacturers, including Volkswagen, Audi, and BMW, are embracing virtual reality (VR) to revolutionize their training programs and overall operations. Here’s how VR is making a difference:

Key Applications:

  • Training Programs: VR immerses trainees in realistic, three-dimensional environments, simulating real-world tasks and scenarios. This leads to better skill acquisition and a more engaging learning experience.
  • Design and Prototyping: Companies like Volkswagen use VR to create detailed 3D models of vehicle designs. This allows engineers to visualize and test new concepts early in the development process, saving time and resources.
  • Customer Experience:
    • Virtual Showrooms: Brands like Kia and Vroom offer virtual showrooms, allowing customers to explore new and used cars from the comfort of their homes.
    • VR Test Drives: Companies like Abarth provide potential buyers with VR test drives, enhancing the car shopping experience.
    • Brand Story Tours: VR tours of brand histories and behind-the-scenes looks at production processes help build deeper connections with customers.

Market Growth:

  • Current Value: $759.3 million in 2019
  • Projected Growth: Expected to reach $14,727.9 million by 2027

Benefits of VR in Automotive:

  • Efficiency: VR reduces the need for physical prototypes, which saves time and reduces costs.
  • Enhanced Collaboration: Virtual environments facilitate better teamwork and communication, even across different locations.
  • Cost Savings: By minimizing the need for physical materials and travel, VR significantly cuts down expenses.
  • Innovation: VR allows for early testing and iterative design processes, leading to more innovative and customer-focused products.

Pandemic Influence:

The COVID-19 pandemic accelerated the adoption of VR, helping automotive companies maintain operations and customer engagement despite lockdowns and social distancing measures.

Future Outlook:

As VR technology continues to evolve, its applications in the automotive industry are set to expand further. From improving training programs to enhancing customer interactions, VR is poised to play a crucial role in shaping the future of car manufacturing and marketing.

AR & VR Global landscape

The global AR & VR market is on a trajectory of remarkable growth. In 2024, it is projected to generate US$40.4 billion in revenue. This market is expected to grow at a compound annual growth rate (CAGR) of 8.97% from 2024 to 2029, reaching a market volume of US$62.0 billion by 2029. The largest segment within this market is AR Software, anticipated to achieve a market volume of US$13.0 billion in 2024.

The United States is at the forefront, projected to lead with a market volume of US$10.9 billion in 2024. On a global scale, the number of AR & VR users is expected to reach 3.728 billion by 2029, with a user penetration rate projected to increase from 52.8% in 2024 to 56.5% by 2029. The average revenue per user (ARPU) is estimated to be US$11.9. Countries like China and the United States are leading in innovation and adoption, driving significant growth in the AR & VR landscape [source: Statista].

Future Prospects and Innovations in AR and VR Training

Emerging Trends in AR and VR

Haptic Feedback: The integration of haptic feedback in VR training modules is revolutionizing the training landscape by adding tactile sensations that enhance realism. This innovation allows users to feel textures, resistance, and vibrations, making the virtual experience more immersive and effective. For instance, in medical training, surgeons can practice procedures with realistic feedback, improving precision and confidence.

AI Integration: Artificial Intelligence (AI) is being incorporated into AR and VR platforms to create adaptive learning environments. AI algorithms analyze trainee performance and customize training modules to individual needs, providing personalized feedback and accelerating the learning process. This tailored approach is particularly beneficial in complex fields like medicine and engineering, where personalized training can significantly enhance skill acquisition.

Improved Accessibility: Advancements in AR and VR hardware and software are making these technologies more affordable and accessible. Cost-effective VR headsets and applications are now available on a variety of devices, broadening the reach of these technologies. This democratization of AR and VR is expected to drive widespread adoption across various industries, making advanced training tools accessible to more organizations.

Expanding Applications

Remote Collaboration: AR and VR are transforming remote collaboration by creating virtual spaces where teams can interact in real-time. These environments facilitate seamless communication and teamwork, regardless of geographical boundaries. VR platforms enable virtual meetings, collaborative design sessions, and remote technical support, enhancing productivity and fostering innovation.

Simulation and Modeling: Industries such as aerospace and architecture are leveraging AR and VR for complex simulations and 3D modeling. These technologies allow for detailed visualization and testing of designs, reducing the need for physical prototypes and accelerating the development process. For example, architects can conduct virtual walkthroughs of buildings, enabling clients to experience designs before construction begins.

Healthcare Advances: The future of VR in healthcare is promising, with applications ranging from advanced surgical simulations to virtual therapy for mental health. VR is being used for exposure therapy, pain management, and rehabilitation, providing immersive experiences that enhance patient outcomes. These applications not only improve the quality of care but also offer new avenues for medical training and patient engagement.

Potential Challenges and Solutions

Technical Challenges: Current technical limitations of AR and VR include hardware constraints such as limited battery life and the need for high-performance computing. These challenges can hinder the seamless integration of AR and VR into training programs. However, advancements in hardware technology, such as more efficient processors and longer-lasting batteries, are addressing these issues and paving the way for more robust VR systems.

Adoption Barriers: The widespread adoption of AR and VR technologies faces barriers such as high initial costs, the need for specialized training, and resistance to change. To overcome these challenges, companies are developing scalable training programs and cloud-based VR services that lower the entry barrier and make it easier for organizations to adopt these technologies.

Innovative Solutions: Innovative solutions are being developed to overcome the challenges of AR and VR adoption. Cloud-based VR services allow for scalable and cost-effective deployment of VR training programs. Additionally, continuous advancements in software development are making AR and VR applications more user-friendly and accessible, facilitating wider adoption.

Conclusion

AR and VR technologies are set to revolutionize training across various industries. By enhancing skill development, improving safety, reducing costs, and providing a competitive edge, these technologies offer immense potential. As AR and VR continue to evolve, their applications will expand, bringing about innovative solutions that will shape the future of training and beyond. Embracing these advancements will not only enhance organizational capabilities but also position companies as leaders in their respective fields.

Explore the Future of Training with Crafsol

At Crafsol, we specialize in integrating cutting-edge AR and VR technologies into training programs tailored to your specific needs. Our expertise ensures a seamless transition and empowers your organization to leverage the full potential of digital transformation.

Ready to Transform Your Training Programs?

Contact us today to learn more about our AR and VR solutions and how we can help you stay ahead in the digital era. Book a free consultation and start your transformation journey with Crafsol.

Mastering Security in Software Development with DevSecOps

Introduction


In today’s digital era, where security breaches are costly and disruptive, integrating security at every stage of the software development process is crucial. DevOps emphasizes collaboration and automation between software development and IT operations, streamlining the delivery of high-quality software products. It focuses on breaking down silos between development, operations, and quality assurance teams, promoting faster and more reliable software releases. Building upon the foundation of DevOps, DevSecOps transforms traditional development practices by embedding security from the beginning.

The Essence of DevSecOps


DevSecOps extends the DevOps principles by integrating security practices across all phases of the software development lifecycle (SDLC). This approach not only facilitates better security but also enhances operational efficiencies, making security a shared responsibility among all stakeholders involved in the development process.

DevSecOps in the Software Development Lifecycle

From requirement analysis to deployment, DevSecOps infuses security:

  • Planning and Coding: Security begins in the earliest stages with developers ensuring code is free of vulnerabilities.
  • Building and Testing: Tools like AWS CodePipeline and Jenkins automate security testing, ensuring vulnerabilities are caught before deployment.
  • Deployment and Operations: Continuous monitoring and automated security patches are applied, maintaining integrity post-deployment.

Why DevSecOps Matters

The benefits of implementing DevSecOps are significant:

  • Early Detection of Vulnerabilities: By shifting security left, teams identify and mitigate risks early, reducing the cost and effort needed for remediation.
  • Streamlined Compliance: Automated tools help adhere to regulations, enhancing compliance with standards like HIPAA or GDPR.
  • Enhanced Collaboration: DevSecOps fosters a culture where development, operations, and security teams work together, leading to more secure and robust software.

Industry Impact and Examples

DevSecOps is being rapidly adopted across various sectors:

  • Financial Services: Banks and financial institutions use DevSecOps to protect sensitive financial data against breaches and ensure compliance with financial regulations.
  • Healthcare: Healthcare organizations implement DevSecOps to secure patient data and medical records, complying with health regulations while innovating patient care technologies.
  • Retail: Retail companies leverage DevSecOps to secure e-commerce platforms, ensuring customer data protection and smooth online transactions.

Practical Implementation of DevSecOps

Successful DevSecOps implementation involves several key components:

  • Continuous Integration and Delivery: AWS CodePipeline and similar tools integrate security at every step of CI/CD, automating the build-and-test phases for rapid deployment.
  • Security as Code: Teams implement security policies as code, ensuring consistent enforcement across all environments.
  • Automated Security Tools: Tools like static and dynamic security testing (SAST/DAST), and interactive application security testing (IAST) are essential for detecting potential vulnerabilities in real-time.

DevSecOps and SAP

In the context of SAP, which is a widely used enterprise resource planning (ERP) software, DevSecOps means ensuring that every change or update made to SAP systems considers security implications from the outset. For example, when new features are being added to SAP applications or when existing processes are being modified, security concerns are addressed right from the planning phase. This ensures that any potential vulnerabilities are identified and fixed early on, making the overall system more secure and resilient against cyber threats.

SAP environments, being complex and often highly customized, may indeed face unique challenges when it comes to integrating DevSecOps practices. For example, SAP systems typically involve a combination of custom code, third-party integrations, and core SAP modules, each requiring careful consideration of security implications.

In some cases, SAP may offer built-in tools or features that support security practices, such as role-based access controls and security notes for patching vulnerabilities. However, these may not cover all aspects of DevSecOps, particularly in terms of code analysis and continuous integration/continuous deployment (CI/CD) processes.

Therefore, while the principles of DevSecOps apply to SAP environments, organizations may need to adapt and customize DevSecOps practices to suit their specific SAP implementation, considering factors such as tooling, automation capabilities, and collaboration between development, security, and operations teams.

Challenges and Solutions

Despite its benefits, integrating DevSecOps presents challenges, such as the need for cultural shifts within organizations and the complexity of tool integration. Overcoming these challenges requires:

  • Comprehensive Training: Equipping teams with the necessary skills in both security and DevOps practices.
  • Tool Integration: Seamless integration of security tools into the development pipeline to support continuous delivery without sacrificing security.

Conclusion

DevSecOps is transforming software development by ensuring that security is not an afterthought but a fundamental aspect of the development process. By adopting DevSecOps, organizations not only safeguard their applications but also embrace a proactive approach to security, aligning it with today’s fast-paced software development cycles.

Cybersecurity concerns from dark web

More than 94% of the world’s information resides in the deep and dark webs. Only 6% is available on the surface web and accessible using the usual browsers.

It is important to understand the difference between Deep Web from Dark Web. The Deep Web is not accessible to search engines for various functional and operations reasons. This may include over 90% of the entire web. The dark web includes all sorts of information, with restricted access, but usually of great value if exploited. The data could include research, medical/financial/identity records, important defence documents or protocols to holding all kinds of illegal marketplaces for drugs, weapons, cryptocurrencies and more. The Dark Web is that part of the Deep Web that could use encryption software to make the user’s identities and IP addresses undetectable.

Thus, the most difficult-to-access part of the Deep Web is the Dark Web or the Darknet in another synonym. The anonymization in dark web leads to the predominance of malicious and criminal activities in that hidden and encrypted environment.

Various crimes and heinous actions are prevalent in the dark web to make gains through extortion, sabotaging networks, or stealing organizations’ data. On a social scale many crimes such as children pornography and pedophile networks, drugs and arms trade, human trafficking, terrorism and recruitment of extremists, planning terrorist attacks, murderers for hire, hacked digital media trade, counterfeit documents, fraud, are also reported on the dark web.

It is well known that cyber criminals are interested in databases, financial transactions, emails, identities and login credentials. Typically, hackers steal this data through phishing attacks and through use of malware. And all of this is strategized, planned and executed in the Dark Web.

Traditionally, cybersecurity investments have been largely limited to protection and prevention of IT infrastructure and perimeter defence. Monitoring the dark web does not seem urgent and immediate concern for most organizations. But in the current scenario, dark web remains to be the place from where most cyberattacks are initiated and managed. The covert operations from dark web can expose sensitive data and trade secrets to  damage business beyond repair.

Knowing where to look is the key to protecting your assets before a cyberattack occurs. And the dark web makes it highly complicated for any individual or organisation. But, organisations cannot just be content with cybersecurity protection measures for their core IT assets. As dark web becomes the root cause of cyber attacks, detection, discovery and decoding the signals will actually determine the status of every orngaisation’s security posture.

Today, a large portion of hacking includes acts of theft and threats to organized gangs, supported financially to earn profits on a large scale. Unlike the past, the hacking acts are not limited to individuals. The shift to an organized crime to achieve financial gains or meet covert political goals makes it extremely difficult to trace such acts of cyberthreats.

In terms of cybersecurity threats, hacking communities are active on Dark Web platforms, where hackers exchange experiences and share information, in addition to circulating hacking tools, malware, ransomware, breached data, and planning large-scale cyberattacks resembling a pattern of an organized crime

Organizations must now reconsider conventional methods and shift to contemporary techniques to outpace with the evolution of cyberattacks.

Cyber Threat Intelligence (CTI)

Cyber Threat Intelligence is gathering demand and increasing interest from researchers and security practitioners, and users. CTI provides evidence-based know-how about cyber threats. Considering the gained knowledge, organizations can make cybersecurity decisions, including detecting, preventing, and recovering from cyberattacks

CTI provides information related to: Who, What, Where, How, and When of cyber attacks from the dark web. CTI is expected to utilise data from multiple sources. Sources can be internal (such as network events log files, firewall logs, alerts, responses to previous incidents, the malware used for attacks, and network flows), or external (such as reports from other institutions or governments, and experts’ blogs). The CTI framework is also organised to address cybersecurity risks at various levels – strategic, tactical and operational.

CTI is a data-driven process involves several phases of collecting, processing, and analyzing the data as per the security threats perceived and experienced by the organization.

The key phases in CTI are as outlined below:

  1. Intelligence planning/strategy
  2. Data collection and aggregation
  3. Threat analytics
  4. Intelligence usage and dissemination

To understand the intelligence an organization requires, it should acquire several components, including inspecting the existing security domain, determining the current cyber threats, monitoring its cyber assets, and modeling potential directions of future threats.

The threat from dark web is persistent, advancing and ever-changing to become complex and intertwined. For cybersecurity threats from the dark web, not feeling secure is the best strategy to feel safe. Staying invested before the attacks occur can go a long way in enhancing the security of your data, assets and competitive edge.

Crafsol has the expertise to help you build and implement cyber threat intelligence that goes beyond the traditional methods of monitoring and securing your organisation to address the threats posed by dark web. Get in touch with us at connect@crafsol.com

Opportunities in the Metaverse – separating the reality from the hype

Is Metaverse the next big thing? What is it, really? Who owns it? What will it mean to our businesses? Or is it all personal? 

For now, Metaverse is more about questions surrounding it rather than firm and clear answers.

Imagine someone asking you about the internet in 1970s. It was all possibilities. The same is the case with Metaverse today with its many possibilities and scenarios. But if there is one thing that we can be sure about is that the Metaverse is going to change our lives, sooner than later. Therefore, we need to learn, explore and adapt. 

What is Metaverse, really?

As of now, the Metaverse is best defined broadly, rather than exactly. Broadly, Metaverse is a shift of technologies that will possibly combine the physical world with the virtual cyberspace for an integrated and immersive experience. From 3D world, Augmented and Virtual Reality to digital economy which includes crypto currencies, NFTs assets, there are several technologies that are driving this shift. 

In more simple words, Metaverse is both physical and digital. It’s a unified living experience where we can work, play, relax, socialise and transact in a completely virtual yet immersive environment. 

But has it not existed already? Yes, there are quite a few examples such as Fornite, Second Life, several gaming apps which have already shown what a Metaverse could feel like. In the future, the integrations between these platforms will be wide and far and shape the Metaverse as a seamless experience touching many aspects of our lives.

It’s important to recognise the key differences between the what drives the internet today and connected world that will drive the Metaverse. While most of the interactions and transactions on the internet today are centrally controlled and monitored, metavers is based on a decentralised, peer-to-peer architecture. The internet today still offers digital payments that can be claimed in the physical world. On the contrary, metavers is going to be entirely driven by digital currencies and tokens. In the today’s scenario digital assets are not always transferable. In the Metaverse, you can buy or sell digital assets, just as you would do in the physical world. Metaverse is not a mere extension of the today’s internet but an explosion of possibilities surpassing its current limitations.

Why Metaverse now?

Not a day would pass without someone announcing an entry into Metaverse. Take a look at some recent developments for instance:

  • Facebook has renamed it Meta
  • Microsoft’s $69 billion acquisition of Activision to get into Metaverse
  • HSBC buying a virtual plot of land in Sandbox
  • Infosys launched a metaverse foundry to build use cases and educate customers
  • China has set up a regulatory body for Metaverse companies

There are several factors driving the growth of metaverse. The pandemic has created a compulsive need for environment with connected, shared digital experiences. Users are looking to extend their experiences further through the meta verse. Several technologies such as AR, VR are now converging to enable immersive experiences. The internet penetration is peaking fast. The need for communities based on shared values and interests, and cutting across the physical boundaries of the world, is being increasingly felt. The ecosystem for participation is ripe, as the convergence of enabling technologies leads to rapid growth in metaverse use cases.

Metaverse Applications – what’s going to take-off in near future?

Beyond social

From facebook announcing a clear intent to create a metaverse of social interactions to increasing digital communities wanting to develop common virtual experiences in gaming, business, governance, learning and many other areas, social interactions are going to shift to the metaverse environment in near future.

Continue reading →

Unsupervised Machine Learning and Its Application

What is Unsupervised Learning?

Unsupervised Machine Learning a machine learning technique that uses Machine learning algorithms to analyze data. It doesn’t need anyone to supervise the model. On the contrary, the model works on its own to determine patterns and information hidden in the data. No labels are given to the learning algorithm. No targets are given to the model while training. Unsupervised learning does not require any human intervention. At Crafsol, we understand the different algorithms and suggest the model for Machine Learning accordingly.

The training data that we feed comprises of two important components:-

  • Unstructured data: It may contain data that is meaningless, incomplete, or unknown data.
  • Unlabelled data: The data contains a value for input parameters but not for the output.

Why Unsupervised Learning?

There are multiple reasons for which Unsupervised Learning is important.

  1. With human intervention, there are chances we might miss out on a certain pattern. Unsupervised Machine Learning finds all kinds of unknown patterns.
  2. Large datasets are very expensive, especially if everything needs to be labeled. Computers can mostly give unlabelled data so only a few of them can be labelled manually.
  3. With the help of clustering, it can find features that can help in the categorization of data.
  4. It can help in scenarios where we don’t know how many or what classes is the data divided.

Types of Unsupervised Learning

  • Clustering: The most common unsupervised learning method involves the Clustering method that involves exploring data, the grouping of data, and finding hidden structures. This technique is used to find natural clusters if they exist in the data. Further, you can also modify the number of clusters that the algorithm can identify.
  • Association: This is a rule-basedtechnique that finds out useful relation between two parameters of a large data set. This technique is used in shopping stores which helps in finding the relationship between two sales. This helps in understanding user behavior.

Supervised vs. Unsupervised Machine Learning

Supervised LearningUnsupervised Learning
In supervised learning the data is trained using labelled dataIn Unsupervised Learning the data is trained using unlabelled data
Both Input and Output variables are givenOnly input variable is given. Output can’t be predicted
The algorithms are trained using labelled dataAlgorithms are used against unlabelled data
Supervised Learning needs supervision to train the algorithm modelUnsupervised learning doesn’t require any human intervention.
Supervised Learning can be categorized in Classification and Regression problemsUnsupervised Learning can be classified in Clustering and Association problems
Supervised learning model produces accurate resultUnsupervised learning produces less accurate result
Continue reading →

How Deep Learning works and it’s advantages

Do you know how Google Translator is able to translate paragraphs from one language to another in few milliseconds? Or how self-driving cars work? How do you get your favorite music streamed or Netflix and YouTube know your choices and favorites or how Facebook tags your friends whenever you upload new photos? How Deep Learning works?

The answer to all this is Deep Machine Learning.

With the Technology evolving so quickly, terms like Machine Learning, Artificial Intelligence, and Deep Machine Learning have been of great advantage. Every day, we create roughly 2.5 quintillion bytes of staggering data. With such a huge amount of unstructured data, Deep Learning has gain wide popularity.

What is Deep Learning?

It is popularly known as deep structure learning.  It is a Machine Learning technique that teaches computers to do tasks that require human intelligence. It represents a set of algorithms trained on data inspired by the structure of a human brain. It’s very similar to the way, we as humans learn from our experiences. Deep Learning performs the same task over several iterations by changing it a bit every time.

How does Deep Learning work?

Deep Learning algorithms use a multi-layered structure of algorithms called Neural networks. Artificial Neural networks use a similar concept as our brain. When we receive new information, our brain tries to compare it with known objects. We can group or sort unstructured to similar data among the large volume of data. They have the unique capability to perform deep learning models to solve difficult tasks that Machine learning models can never solve.

Difference between Deep Learning and Machine Learning

In the Traditional Machine Learning model, new knowledge is extracted from a large array of data with the help of human interaction. New features need to be identified by users accurately and then applied

In Deep Learning models, they can solve the problem from start to finish. New features are created and added automatically. In short, deep learning develops its functionality by itself at any given time by using a hierarchical approach. It determines the most important characteristics to compare followed by the less important ones

Benefits or Advantages of Deep Learning

  1. Deep Learning can solve problems without any human control or intervention. They are self-controlled and managed machines.
  2. The important features are automatically identified to compare and optimally tuned for the desired outcome.
  3. The same approach can be applied to a variety of applications and data types.
  4. It solves complex problems with the large, diverse, unstructured amount of data

Implementation of Deep Learning in a variety of Industries

  1. Healthcare and Manufacturing: For diagnosis of diseases, providing personalized medicines based on genome, research, and manufacturing of medicines by pharma and medicine companies
  2. E-commerce and Entertainment: for personalized shopping on eCommerce sites like Amazon. Online platforms like Netflix, Disney Hotstar, and Amazon understand individual choices for entertainment,
  3. Information Technology: Chabot and Service bots, Virtual Assistants like Alexa or Siri,
  4. Travel and Transport: Facial recognition at Security, Translations for travelers or even businesses. Vision for driverless delivery trucks or drones and autonomous cars

Businesses can gain substantial benefits from Deep Learning. It’s imperative for companies can implement it in their projects to improve the way they process data.

Our Company, Crafsol Technology Solutions is a Deep Machine Learning Consulting Service provider based out in Pune, India.  With our expertise, we have already implemented Deep Machine Learning and provided Consulting Services on a variety of projects in Healthcare, Pharmaceuticals, Research firms, and other businesses. For more information on our projects, please

Digital dashboard for better coordination and insights

A smart watch is not enough to track your health.

Having a smart watch will seem great to anyone who is serious about improving their health. It can track how much you exercised, how many calories you burned, and even your pulse.
But does it track a person’s mental health? No.

An vital factor concerning overall health is not being measured. If there were a single platform collect and meaningfully display all data concerning mental and physical health, it would be easier to measure and improve a person’s health.

Likewise just the ERP or CRM alone may not be enough to track the health of your business, especially in uncertain times such as COVID. For times like this, there has to be a unified dashboard to track four key areas:

  • Financial health
  • Production health
  • Order booking health
  • Resources health

All these aspects are important and interlinked. For example, a quality manager’s main concern is the number of rejections and how to reduce them, but data about budget, resources, and access to order book will help improve his performance. Similarly, the marketing team could leverage information about production to build a better marketing strategy.

However, with your team scattered and business being affected by uncertainty, keeping track of vast data and coordinating with you team becomes challenging.

A common platform where data is collected and presented meaningfully for entire team to access would make considerable difference to your business. That’s exactly what a digital dashboard does.

What a digital dashboard can do for you

A digital dashboard is an online tool that uses advanced business intelligence technology to collect information from various sources, and display it in simple yet meaningful manner, and make it easy for you to share it with your team. While digital dashboards have already appeared on the business horizon, advanced versions of these tools use business intelligence programming to bring better visualization and insights to data. This technology ensures seamless coordination and insightful decision-making. While your production team can upload excel sheets relating to inventory, the dashboard can also be linked to your social media accounts to fetch data about how your online marketing campaign is doing.

Why implement digital dashboard

  • Faster to deploy and easier to use than conventional softwares.
  • Can collect data from digital accounts as well as uploaded digital documents.
  • Puts all your data on a secure server making it easy for your team to access it remotely.
  • While whole data is available in one place, access can be restricted by setting up permissions.
  • Offers control over which data you want to monitor, and helps make insightful decisions.

Conclusion

Digital dashboards are emerging as a preferred way to seamlessly collect data from teams and achieve better coordination and decision-making for businesses. At Crafsol Technologies, we work to make these tools available to you at the right price points. Crafsol’s digital dashboard makes use of advanced business intelligence programming to collect and present data insights.

If you want to improve data collection, coordination and decision-making of your team, get in touch with us. Write to us at connect@crafsol.com.

Cognitive RPA: The next step in automation

Are you caught between the digital and manual processes?

Your factory’s electrical systems are monitored via an advanced digital interface. Your facility has significant occurrences of over-voltage, but not all of them are serious. However, every other little fault in the electrical system is now logged and brought to you via a digital report.

The electrician must now sift manually through all these reports to determine which of the threats is actually serious and needs attention. Here, he is handling digital documents manually. True digital transformation implies eliminating the need for human interaction in digital systems.

Imagine having a software which identifies serious issues on its own, and only sends you a single e-mail when over-voltage instances cross an acceptable threshold.

What is Cognitive RPA?

Cognitive Robotic Process Automation is different than ordinary Robotic Process Automation in that it can handle more complicated tasks that need some human intervention. Cognitive RPA uses machine learning and natural language processing to understand patterns of human behaviour and improve data and services.

Plain RPA deployed in a bank will only process thousands of structured digital forms and update customer database. Cognitive RPA on the other hand can identify discrepancies in the name on the form and the name on ID of a customer.

Why implement cognitive RPA?

  • Automate information collection and validation tasks such as sending routine emails and forms.
  • Automate maintenance of inventory database, medical records and financial records in healthcare.
  • Create smart chat-bots that can interact with customers in a more humane manner.

Cognitive RPA is the next step in the evolution of Robotic Process Automation. Crafsol Technologies is making efforts to make this advanced technology available for Indian businesses at affordable costs. If your business needs to process thousands of documents and bridge gaps in data, Crafsol’s cognitive RPA solutions might be just what you need.

To speed up the digital transformation of your business with efficient technological tools, Write to use at connect@crafsol.com.

Artificial Intelligence (AI) in Healthcare

Dr. AI – How it is impacting healthcare? and Covid-19 in China?

Technological change is rapidly sweeping the world today. On one hand there is the digitalisation of many services, and on the other hand, there is Artificial Intelligence which is redefining various aspects of life. From education to production and logistics, AI is transforming the way we work, play and live. The field of medicine has not remained untouched by technology. Digitalization is already reshaping the age-old doctor-patient relationship, and now, Artificial Intelligence is poised to bring tectonic shifts in medicine and healthcare.

More than 32,000 potential cases of Covid-19 have been detected by using AI software to expedite diagnosis.

Chinese hospitals are deploying artificial intelligence to detect visual signs of the pneumonia associated with Covid-19 on images from lung CT scans. As of February, around 34 hospitals in China were using software to diagnose Covid-19 cases. The software used data fro over 5000 previous diagnosis to successfully detect Coroavirus differences in CT scans with an accuracy of 96% within 20 seconds.

Here’s a look at how AI is changing medicine

Digital Diagnosis

With systematic integration of AI, Deep Learning, and chat-bots, consultation has really advanced to the next level. AI enabled chat-bots are able to ask relevant questions and help doctors with correct and detailed information to provide accurate diagnosis in the shortest possible time. With Deep Learning processes even complicated and unstructured inputs can be transformed into accurate information and utilised to achieve a quick resolution. On a lighter note, AI can save doctors from tasks such as writing notes and reading scans, allowing them more time to connect with the patient.

Medicine Selection

After consultation, AI further helps doctors to quickly and efficiently select medicine to prescribe. It could offer pointers about whether the medicine is available near the patient’s location. AI could help quickly diagnose and treat common childhood conditions. It’s expertise in recognising patterns could also help predict whether an individual could develop Alzheimer’s. Similarly, AI could play a vital role in the treatment of potentially terminal illnesses such as cancer.

Robotic Operators

The use of tools such as robotic arms for surgeries goes to show how robotics has impacted medicine. The entry of AI into the operation theatre could further improve the efficiency and accuracy of surgeries. It could help doctors choose the right tools, and provide real-time data and risk-assessment while the surgery is in progress

Follow-up

Many a time, the follow-up visit to the doctor becomes more of a hassle for the patient than the actual treatment itself. Doctors find it difficult to devote enough time to follow-up and patients struggle to get an appointment. AI-enable chat-bots could help resolve many such issues with follow-up. As data about treatment is stored digitally and could be accessed easily, AI-enabled bots could instantly solve the patient’s problem. This could help the follow-up checkups hassle-free for both patients as well as doctors.

These are just a few areas of opportunity where AI could revolutionize medicine. There is a lot of ground AI expected to cover over a period of time. More accuracy is to be achieved while ensuring operational efficiency. Being a veteran player in the healthcare sector, Crafsol is committed to working towards the betterment of the healthcare industry in every way. The company’s leadership has deep expertise in healthcare and pharmaceutical domains and aims to play a pivotal role in driving innovation in this field.