This is a summary of the book titled “Deeply responsible business: A global history of values driven leadership” written by Geoffrey Jones and published by Harvard UP, 2023. Global. The author is a Harvard professor who contends that business leaders and their companies can reimagine capitalism to be beyond profit seeking and prioritize social purpose and philanthropy. This is especially relevant now when there are many companies coming into new exploiting their wealth and position to manipulate politics and the law. This approach requires the company to demonstrate their stated values. History has shown that such reimagining has borne success. During the industrial revolution, some business leaders still valued human dignity more than anything else. Many business leaders outside the west became wealthy capitalists. A textile manufacturer in India helped build a better economy. After the second world war, US business leaders came to value social responsibility. Value-driven business demonstrate responsibility and better integrate into thriving communities. Social responsibility in business requires strong incentives. Strong values motivate responsible business leaders.

During the Industrial Revolution, some business leaders prioritized human dignity over profit. In Britain, workers faced stagnant wages, dangerous conditions, and child labor. Some employers believed that profits should not come at the expense of their impoverished workers. Textile manufacturer Robert Owen provided his employees with a community, schools, and cultural center, recognizing that improving their lives would benefit his company. Quakers in Britain were prominent entrepreneurs, running their own schools and apprenticeship programs. George Cadbury, heir to his father's chocolate manufacturing business, embraced religious and moral convictions, allowing workers to buy houses at cost and provide mortgages.

As industrialization spread across Western Europe and North America, a wealth gap had built up between the industrialized West and the rest of the world. Members of India's Parsi community excelled in responsible business and entrepreneurship, emphasizing the importance of improving people's lives. Indian industrial pioneer Jamsetji Nusserwanji developed modern cotton textile factories and became wealthy, but his company focused on serving its communities, including employee housing, healthcare, and education.

Before India's independence in 1947, the country faced social and economic challenges. Textile manufacturer Kasturbhai Lalbhai, from an affluent Jain merchant family, supported India's independence and played a pivotal role in developing the country. Lalbhai launched India's first dye manufacturing company with American technical and financial help, aiming to generate jobs in rural areas and create residential communities. He believed in the importance of social responsibility in business and the moral and spiritual responsibility of business leaders to improve their communities. After World War II, some US business leaders began to value social responsibility, such as George Romney, who promoted the Rambler as an alternative to larger cars. However, the compact market was taken away by cheaper cars like the Volkswagen Beetle in the early 1960s.

In the 1960s, a new generation of businesspeople emerged, bringing new social and cultural norms, and rejecting the gray establishment of the 1950s. This generation included hippies, rock music acolytes, Vietnam War protesters, environmentalists, and feminists. They were critical of capitalism and the existing relationship between business and society. Values-driven businesses emerged in fields that could facilitate social or environmental change, such as energy use and retailing. Entrepreneurs like John Mackey and Anita Roddick aimed to transform the beauty industry and society's perception of women. Responsible businesses like Sekem and Ambootia incorporated into thriving communities, adhering to Rudolf Steiner's global principles. Social responsibility in business requires strong incentives, and the availability of capital is the principal enabler. Responsible business leaders who turn to social and environmentally beneficial practices can shape the availability of capital and create incentives to invest. Environmental, Social, and Governance (ESG) investing is growing in global markets, but its practical relevance relies on voluntary information and can be easily distorted.

Responsible business leaders create products or services with social value, treating stakeholders with dignity and respect. They recognize business organizations as human social institutions and accept responsibility for employees, customers, and the natural world. These leaders value communities, provide dignified housing, educational opportunities, and cultural life, and improve people's well-being by improving life in their communities.

 Small and medium businesses aka SMBs are targets of cyberattacks and their strategies to cope with these threats are much different from those of enterprises. This article lists a few of them.

SMBs also hold valuable sensitive data such as employee and customer records, financial transaction information, intellectual property, and access to business finances and larger networks critical to their success. Cybercriminals recognize both the vulnerability and the value of SMBS. Among the different types of attacks on SMB, the common attacks include malware developed to manipulate or compromise target systems, malware free attacks that don’t leave artifacts and move laterally to compromise target systems, vulnerabilities in systems and applications that can be compromised to gain unauthorized access to computer systems, phishing and email based scams that impersonate credible people and organizations to steal credentials, compromised credentials in the form of stolen identity and account data, insider threats where employees become accomplices, and zero-days where new and unprecedented exploits are leveraged to mount planned and targeted attacks.

Traditional methods such as virus and malware detection based on signatures are no longer sufficient. In addition, penetration into the SMB assets can be leveraged for lateral movement and data exfiltration which significantly increases the loss. Data theft, ransomware, extortion and hacktivism are only some of the examples.

Strategies to counter these attacks include:

1. Understand the reality of cyberattacks: There are hundreds of adversary groups that launch cyberattacks. Sensitive data is always a prime target regardless of what business owns it. Antivirus and firewall are not sufficient. Sometimes breaches go undetected for hundreds of days. Costs for continuity and recovery can be so high that SMBs may not recover.

2. Implement basic cybersecurity hygiene practices. These include strong password policies, enforcing multi-factor authentications, performing regular backups of critical data, keeping current with security patches and updates, locking down cloud environments, implementing and testing threat detection and response, and securing your network.

3. Employee upskilling, education and training and regular assessments: Inform the employees of improvements to authentication channels and continuously test their responsiveness with asking them to identify fraudulent messages.

4. Overcoming limited resources and expertise: When expert resources seem out of reach, there can be automations and dedicated teams to set policies and monitor, respond to and stop attacks.

A managed detection and response service or solution may also be a best fit for your business.

 The previous article talked about bias in AI applications. This one covers some of the emerging trends in AI Applications.

First, the chatbots, generative AI and search applications will continue to leverage better, cheaper, and more purposeful Large Language Models aka LLMs. Those same LLMs also come in useful to create next generation eCommerce search and product discovery solutions to produce high-quality, hyper-personalized customer search results. According to Gartner, Generative AI and search bear a reciprocal relationship because the models used in the search are trained on domain datasets. Generative AI such as ChatGPT, on the other hand, is trained on large corpus. Therefore, they are used in combination. The highly-relevant and personalized search together with a conversational capabilities of a chatbot is immense power to both traditional use cases for shoppers as well as emerging uses cases for personalized campaigns for individuals but small and medium businesses struggle to realized that potential due to scale.

Second, social media is a valuable channel for establishing and building relationships with billions of consumers and getting them to consider products and brands by facilitating 1 to 1 personalization at scale is now possible via Generative AI. One specific demonstration of this is with A/B testing where entire campaigns and content can be generated and customers can leverage that to see more ads that better suit their taste.

Third, product catalogs can become more informative with GenAI with pages enticing customers to add the product to their cart. The multimodal nature of AI allows image, text, and other content types to be brought together assisting retailers with both informative and personalized product pages. When combined with databases like Mongo DB for its flexibility with hierarchy and labeling, these algorithms can leverage product attributes and make hyper personalized recommendations especially with new products that have little history.

Fourth, the site navigation application gets a whole new facelift which shores up losses from cart abandonment, lost sales, and customers. In addition, it facilitates dynamic navigation, filters and search experiences that are hyper-personalized to the individual customer. Site search is one of the primary use cases of search and as with the discussion of AI models, the results can be highly relevant.

Fifth, checkout and delivery that is frequently associated with past purchase behavior, current browsing session data, and real-time inventory levels can be improved by showcasing “others bought” or “style your purchase” upsells which is not only using collaborative filtering but embedded semantics to promote personalization.

Sixth, customer nurturing applications with review solicitations, loyalty and reward programs and generating word-of-mouth feedback provide a new level of personalization for the shoppers. This results in an improved site experience feeding back into the virtuous cycle of retail shopping.

One of the main challenges against all these applications is the overcrowding of relevant but useless suggestions and the way to fix it is to with intensive oversight, continuous monitoring and the best practices of AI safety and security.

 Mitigating Bias in AI applications:

Applications such as generative Artificial Intelligence aka GenAI, computer vision and Natural Language Processing aka NLP can find insights, make predictions, and streamline operations. But as these applications become more sophisticated in learning and reasoning, they are highly dependent on data used to train them. These data inevitably contain biases. Biased algorithms limit the potential of AI and lead to flawed or distorted model outputs that can negatively impact marginalized groups.

For example, the bias demonstrated by Chatbots has surfaced as follows:

Racial bias in salary recommendations: A Stanford Law School study found that chatbots like ChatGPT 4 and Google's PaLM-2 suggested lower salaries for job candidates with Black-sounding names compared to white-sounding names. For instance, a candidate named Tamika was recommended a $79,375 salary as a lawyer, while Todd was suggested $82,485 for the same position.

Hidden racial bias in language perception: While AI models tend to use positive words when directly asked about Black people, they display negative biases towards African American English. Users of this dialect were often associated with "low status" jobs like cook or soldier, while Standard American English users were linked to "higher status" jobs like professor or economist.

Gender bias in translations: ChatGPT was found to perpetuate gender stereotypes when translating between English and languages with gender-neutral pronouns. For example, it tended to assign "doctor" to men and "nurse" to women.

Cultural biases: A comparison between ChatGPT (US-based) and Ernie (China-based) showed that ChatGPT displayed implicit gender biases, while Ernie showed more explicit biases, such as emphasizing women's pursuit of marriage over career.

Socioeconomic biases: Researches use medical images as training data to recognize diseases and even while it is infused with human expertise to annotate the images, the amount of data available might not only be skewed by those who could afford to have their images taken but also by those who could afford but had no reachability to the diagnostic imaging or representation in its collection.

From these examples, bias can be tracked to “blind spots” in training data. And as it takes many forms such as selection, exclusion, prejudice, cognitive, confirmation, historical and availability biases, outcomes often disenfranchise groups of people. That is not the only risk though. Algorithms trained on biased data can falsely predict risk, negatively impact hiring, lending and more, expose existing societal prejudices, create mistrust across boundaries and even lead to fines in regulated environments. How data is collected, who collects it, whether it is wholesome in representation, and such others determine how biased the data is. More often than not, collection of data is from existing literature and these sources already demonstrate influences.

Diverse and Inclusive data sets are the biggest antidote to biases. AI models trained on a broad swath of sources do better to respond to queries from a vast group of people. As part of this strategy, methods to detect and mitigate biases in data and continuous refinement and updates to datasets are mandatory. Some of this can be realized by having a large network of data contributors, facilitation of peer reviews, multimodal data collection, medallion architecture curation, inclusive data sources and robust coverage, timestamped or versioned data, securing data at rest and in transit, least-privilege access control, ubiquitous reach to and from data, and facilitation of asset intake and metadata collection

 These are the steps for an Azure subscriber to expose an endpoint on Azure Front Door:

1. **Create an Azure Front Door Profile**: If you don't already have one, create an Azure Front Door Standard/Premium profile.

2. **Obtain a Custom Domain**: Purchase a custom domain from a domain provider or use an existing one. If your DNS domains are hosted on Azure, delegate the domain provider's DNS to Azure DNS.

3. **Create a CNAME DNS Record**: Create a CNAME record with your domain provider to point to the Front Door default frontend host. This maps your custom domain name to the Front Door default hostname.

4. **Map the Temporary afdverify Subdomain**: Map the temporary `afdverify` subdomain to verify domain ownership.

5. **Add the Custom Domain to Azure Front Door**: Go to the Domains pane of your Azure Front Door profile. Select "+ Add" to add a new domain.

6. **Validate the Custom Domain**: Choose the domain type (Non-Azure validated domain or Azure pre-validated domain). If it's a Non-Azure validated domain, manually enter the custom domain name.

7. **Associate the Custom Domain with Your Endpoint**: Associate the validated custom domain with your Azure Front Door endpoint.

8. **Verify the Custom Domain**: Ensure the custom domain is correctly associated and verify it through the Azure portal.

9. **Configure HTTPS (Optional)**: If you want to secure your custom domain with HTTPS, configure it by following the steps to enable HTTPS for your custom domain.

10. **Test the Configuration**: Test the setup by accessing your custom domain to ensure everything is working correctly.

 Problem statement: Given the root of a binary tree, check whether it is a mirror of itself (i.e., symmetric around its center).

Example 1:

                 1

           2 2

    3 4 4 3

Input: root = [1,2,2,3,4,4,3]

Output: true

Example 2:

                 1

           2 2

    null 3 null 3

Input: root = [1,2,2,null,3,null,3]

Output: false

Constraints:

The number of nodes in the tree is in the range [1, 1000].

-100 <= Node.val <= 100

/**

 * Definition for a binary tree node.

 * public class TreeNode {

 * int val;

 * TreeNode left;

 * TreeNode right;

 * TreeNode() {}

 * TreeNode(int val) { this.val = val; }

 * TreeNode(int val, TreeNode left, TreeNode right) {

 * this.val = val;

 * this.left = left;

 * this.right = right;

 * }

 * }

 */

class Solution {

    public boolean isSymmetric(TreeNode root) {

        List<Integer> serialized = new ArrayList<Integer>();

        InorderTraversal(root, serialized);

        return IsPalindrome(serialized);

    }

    public boolean isPalindrome(List<Integer> serialized) {

        int i = 0;

        int j = serialized.count() - 1;

        while (i < j) {

            if (serialized.getAt(i) != serialized.getAt(j)) {

                return false;

            }

            i++;

            j--;

        }

        return true;

    }

    public void InorderTraversal(TreeNode root, List<Imteger> serialized) {

        if (root == null) {

            serialized.Add(Integer.MinValue);

            return;

        }

        InOrderTraversal(root.left);

        serialized.Add(root.val);

        InOrderTraversal(root.right);

    }

}

#blogpost: https://1drv.ms/w/c/d609fb70e39b65c8/EcVxcTYDkE9Hro_eEthnuH8Bx-PIXIxbdNq2kXKuyr8TdA?e=a1ofbg

 This is a summary of the book titled “Smart Teams: How to move from friction to flow and work better together” written by Dermot Crowley and Wiley in 2023. The author recognizes the premise as one of reduced productivity in modern workplaces with growing numbers of emails, meetings, attention-demanding messages, and ad hoc work for which he offers help to eliminate the “noise” and friction by getting more done on time. His help includes valuable tips on how great leaders promote a culture of “superproductivity” which requires collaboration and coordination, removal of organizational friction, fostering culture to boost productive flow, and essentially creating “smart teams” who know who to find and focus on the specific actions that matter towards this goal.

To achieve maximum productivity, a team needs proper systems and processes, enabling members to communicate, congregate, and collaborate effectively. A productivity culture is essential, and leaders should promote it within their organization. This is particularly challenging in hybrid or remote work environments, where employees often rely on email for communication. To improve productivity, leaders should address practices that impede it, such as the "death-by-meeting" orientation and eliminate unnecessary meetings.

To increase productivity, managers should address information overload, unfocused meetings, distractions, and unnecessary urgency. By ensuring smooth internal communications, reducing unfocused meetings, addressing distractions and interruptions, and fostering a "slow work" culture, employees can focus on performing better instead of merely focusing on speed.

The corporate world needs a "slow work" movement that shifts away from non-productive, speedy reactivity and towards increased focused, thoughtful responsiveness. This would slightly slow the pace of work, allowing team members to focus on performing better instead of merely faster.

Team productivity is maximized when members maximize both individual and group productivity. A selfless mindset, similar to a service mindset, aims to eliminate unproductive friction and promote cooperation. Leaders can boost productivity by avoiding disruptive behavior and becoming "superproductive." Building smart teams involves working well together and agreeing on productive behaviors. To increase productivity, team members should practice four individual behaviors: being purposeful, mindful, punctual, and reliable.

Purposeful teams focus on understanding the team's goals and priorities, while mindful teams focus on minimizing friction and promoting workflow. Punctuality is crucial in the US Navy, as it ensures timely completion of tasks. Reliability is essential for team members to trust each other and prioritize their obligations. These behaviors can significantly impact team productivity and contribute to the overall success of the organization.

A positive change in a team's culture depends on specific actions, not amorphous principles. Purposeful, mindful, punctual, and reliable team members can eliminate friction and lag time that can drain productivity. To adjust the team's culture, members must embrace the inherent value of productivity and delineate their specific productivity principles. Cooperation is essential for high-performing teams, and team members must cultivate an active mindset to weigh work requirements and respond to urgent requests.

Urgency rules, everywhere, are crucial for effective communication. Limiting emails to essential information and using direct conversations can help reduce noise and improve productivity. A team must develop its productivity culture by following guidelines such as "primum non nocere," "lead from the front," "remember that you are always on show," and "create projects for the team to rally around."

To cut costs and boost productivity, take a long and careful look around your organization and identify productivity impediments. By implementing these strategies, you can create a more productive and efficient team.

 Software integrations manifest in Infrastructure Engineering as well. As cloud engineers provision resources and configure them for their data scientists and application engineering teams, they must be mindful of interoperability. One specific example is used to discuss the changes to the practices of Cloud IaC in this article and it pertains to System for Cross-Domain Identity Management aka SCIM.

Identity and access solutions have always looked for bringing together users recognized by different systems that do not share anything. A user in AWS public cloud might not be recognized in Azure for instance. SCIM is a protocol that standardizes how identity information is exchanged between one entity and another. By virtue of being an open standard for people to access cloud-based resources, it eases onboarding and interoperability. That includes how identity data is exchanged, how the communication is fostered across different platforms and how identity providers and IAM systems can work together even they are heterogeneous. Having manually add users to each application and a few other Role-based access control chores become tedious in organizations that have many employees, partners and stakeholders. SCIM makes it easy to grant access to new hires to the appropriate applications and revoke access from those leaving with seamless synchronization across a variety of platforms. SCIM leverages JSON data format and database create-update-delete operations on resources which improves programmability. An SCIM endpoint is created usually within Azure AD which can communicate with on-premises Active Directory. Using a predefined schema and automatic synchronization of identity resources, it improves consistency and security across disparate systems. The automatic user provisioning, standardized API and user attributes management simplifies identity management by bringing together a single source of truth.

As a protocol, IAM solution developers are already aware of Security Assertion and Markup Language that has worked for a while. While SAML works with SSO to integrate login across security domains and features similar to SCIM for a unified experience, SCIM lays the foundation for SAML to work in a new target system. SAML leverages XML for assertions and gives you a token to use with your browser session, SCIM is for provisioning identities across multiple applications. SCIM and SSO work together but like SAML, SSO is for authentication not provisioning.

Bulk recognition of identities is possible with SCIM which saves time and cost and spans proprietary and third-party technologies even though certain IAM products like Active Directory have also tried to embrace open standards with their OpenLDAP protocol. While OpenLDAP used to be for legacy systems and on-premises, SCIM is cloud-first and cloud-friendly where different identity providers can participatge.

 As Infrastructure engineering deploys AI at scale, organizations can take a better approach with AI and LLMS. This list here discusses those actionable items that have gained widespread nod in the community.

1. Observability becomes more critical and empowering as LLMs grow and mature. The types of teams working in the Generative AI have become more diverse. While data scientists are savvy about continuous monitoring, many others are not. If they perform prompt engineering and retrieval augmented generation (RAG) they will need to know what’s going on behind the scenes. They learn quickly with their prompt engineering, then might do more experimentation with language models, then proceed with RAG, before tuning its models to a specific domain knowledge and grounding outputs with searches. Observability helps with every stage of the life cycle.

2. Simpler use cases are called for before sophisticated ones with multi-agents. This aligns with an organization’s human resources and skills experiences. And since the landscape changes quickly, developing “evergreen” skills in evaluation and implementation remain essential. Leveraging context windows for RAG is an example. As context windows get larger, will RAG remain relevant? Organizations could do well to have not only a goal but a path to the AI maturity for their production deployments.

3. Gaining customer confidence in the AI models output is crucial. Customers neither trust the AI model to learn enough to make a decision based on its output nor the data on which the AI models train and test. Fetching some telemetry, accessing certain logs, and presenting them along with the AI output garners some trust and confidence. Techniques like factual grounding help here.

4. The most powerful use cases for LLMs are often the simplest as in the case of summarizing driving productivity gains. Summarization is popular because it is an easy way to build trust in LLMs with its straightforward evaluation. They are also powerful because they add instant value in many use cases. Multimodality is also important to consider here. Building a multimodal model from the ground up helps summaries to be drawn from text, image, and a variety of sources.

5. Tooling must keep up with the AI maturity roadmap. As production-oriented systems are developed, controls and human evaluation are necessary at each stage of the game. Organizations know that usage and maturity must evolve without disruption.

6. Balancing compute time, compute cost, and model quality requires cutting edge observability. Organizations must drive quality and performance of their observability capabilities because there are a number of integrations that need to work collaboratively and effectively. Even networking performance monitoring can become imperative.

7. Prioritizing the metrics that matter is more important than ever. With changes happening quickly, metrics serve as guardrails to ensure that new approaches do not chew up time and effort for the promise of productivity and introduce new risks. Establishing clear service level objectives works in this case.

 This is the summary of the book titled “How to age healthy and strong at 50 and beyond” written by Isaac Finnegan and published by Majestic Franchise Unlimited LLC in Oct 2024. Reaching the age of 50 is often considered to be the peak of a lifetime but the author debunks this myth. Taking us on a journey through physical fitness, nutrition, mental health, preventive healthcare, lifestyle and mind body connection in distinct sections of his book, he gets us started on a transformation where aging translates to empowerment.

Regular exercise, a healthy diet, and routine medical checkups are essential for men as they get older so that they can lower the risk of various health conditions that otherwise come with aging. Suppressing emotions and toughing them out does not cut it anymore. Relaxation, meditations, social connections and seeking support are preferred instead. When this energy manifests into personal and professional lives, it can lead to greater satisfaction and fulfillment.

Common health concerns like high blood pressure, high cholesterol, prostrate issues, Osteoporosis and reduced bone density, depression and anxiety and others require men to get professional help as early as possible. Factors that affect midlife include testosterone levels, reduced muscle mass, mood changes, improper diet and exercise, excessive smoking and alcohol consumption and avoidance of screening tests.

Cardiovascular exercise, strength training, flexibility and balance, breathing control and mindful movement, rest and recovery must be prioritized. Similarly, nutrition needs must be understood. A good intake of proteins, vitamins, and minerals as well as fiber are necessary. Hydration, also, cannot be overstated. All these serve to fuel the body for you to thrive in your later years. In the event of chronic conditions or when battling preventive diseases, getting input from nutritionists is important. Boosting your immune system naturally after checking with a doctor is also good practice. To put these in practice, try regular exercises, sleep adequately, stay hydrated, manage stress and avoid harmful habits.

Exercise and relaxation techniques work great for mental and emotional well-being as well. Men need to prioritize their health and take self-care. Seeking support from friends, family or mental health professional can also be beneficial for men dealing with stress. Stress is one of the silent killers and inadequately coping with it leaves a tell-tale sign in old age. Therapy, medication, and lifestyle changes must be undertaken to cope with it. Rehabilitation from bad habits is also available for help. Surrounding ourselves with people who care about us and are there to listen can make the journey less lonely.

Sexual health and intimacy are one aspect of this. Hormonal shifts, lifestyle choices and unhealthy conditions contribute to dissatisfaction. It is never too late to make positive changes and regular medical checkups can help. Taking a comprehensive approach to sexual health including communicating with your partner, can help maintain satisfaction and fulfilment. Intimacy goes beyond personal closeness and encompasses emotional, mental, and physical connection with one’s partner.

Incorporating mindfulness and meditation techniques can significantly boost the benefits from all efforts, in addition to maintaining a positive outlook, and achieving clarity and emotional stability. Making them part of your life makes you happier and healthier.

A number of alternative wellness practices are also available that attempt to address the underlying issue through a combination of one or more of the above and addressing mind, body, and spirit as a whole. Similarly, preventive care and screening from medical professionals helps to constantly monitor our current state and progress. Getting 7-9 hours of quality sleep is similarly important. Support groups and community resources are plenty and create a safe space for men to share their experiences and learn from others. Books, websites, and apps also make it easy to imbibe the best practices. Finally, we must take charge of our own health.

 This is a summary of the book “The Freedom Frameworks: infinite possibilities to achieve career independence on your own terms” written and self-published by Jack Cohen in 2024. The author argues that you are your own asset and that skills, experience and relationships are key to professional success. He draws on his vast business experience to propose freedom Frameworks for you to thrive in your career. The goal and the path are for us to articulate and we can only invest in ourselves. Setting quarterly priorities and meeting them, learning to read people and team dynamics, facilitating collaboration by establishing a common context, viewing experiences as objectively as possible, learning from mistakes, and being honest with oneself are some of the other suggestions in his book.

To pursue your dreams and cultivate freedom, face your demons, which often arise in the form of resistance. Identify these fears and work with them to develop the courage and confidence to progress. Clarify your dreams by asking yourself who you want to become and on what timeline. Reframe resistance as helpful, like facing defeat to improve your tennis game. Defining who you want to be and planning your path forward is crucial. Visualize your intended future and take notes on your current reality. Bridge the gap by outlining specific steps to move toward your desired future. Invest in your most valuable asset: yourself. Economic independence provides the freedom to make career choices without immediate financial pressure, relying on your most valuable asset: yourself. Invest in building skills, gaining experiences, forming relationships, and getting things done better. Connect the dots between past and present, applying new knowledge to improve your skills and earning power.

To turn your value into greater earning power, be reliable, responsible, and accountable. Develop skills, experiences, and relationships to become more capable, adaptable, and interesting. Balance depth with breadth and push yourself outside your comfort zone. Surround yourself with supportive people, such as volunteering for industry groups or seeking mentors for long-term growth. Set quarterly priorities and protect them from distraction. Use the Red, Blue, and Black methods to prioritize activities for 90-day goals. Reassess and readjust allocations quarterly to make the most of finite resources. Set due dates and do dates for different projects and establish "border protection" to maintain uninterrupted focus. Learn how people behave to improve communication and team dynamics. Understand different behavioral styles, such as control, power, influence, and authority, and understand how people make decisions. People do not care how much you know until they know just how much you care about them. By understanding how people make choices, you can better understand and manage your time effectively.

Team success requires a common context and accountable team members. The "issues clearing model" helps address conflicts constructively by stating the facts of the situation, how the facts make the speaker feel, what judgments or conclusions they have drawn, and what the speaker would like to have happened next. Viewing experiences as neutral as possible is essential, as the story we tell ourselves determines our feelings.

Create curiosity and ask better questions to improve decision-making. Use the "Shark Theory" to avoid analysis paralysis and take small steps forward to reach the tipping point. Learn from mistakes, embrace your journey, and be yourself. Focus on overdoing rather than overthinking and compare where you are now to where you used to be.

Embrace your development journey, even when it's slow or feel lost, and tap into your grit to see your goals through. Trust in yourself and don't be afraid to stand out from the crowd. Be honest with yourself, mitigate your weaknesses, and be caring yet direct with others. Manage any liabilities in your temperament through self-awareness and practical strategies.

 

This is a summary of Generative AI Trends surveyed from various vendors across industry sectors, as made available publicly from their respective websites.  For those unfamiliar with the term, it refers to machine learning algorithms that generate new content including but not restricted to text, images, audio, and video. For those in technology, this includes all AI products that can generate test cases, datasets, and code. This form of AI transforms work across different industries.

In the Healthcare Industry, there are plenty of health records and medical devices that demand to be properly vetted. Generative AI can create realistic data sets for various scenarios that can help with testing software products associated with these healthcare assets. When analyzing medical images, discovering new drugs, and making personalized drugs, LLM-as-a-judge can also come in helpful to test. This leads to better quality and safety.

In the design and manufacturing industry, there is a need to create new designs for new requirements based on existing designs and patterns and AI models can learn and mimic humans in doing that. So, efficiency and innovation are both boosted, and Generative AI can simulate various production scenarios and identify potential flaws or issues before they arise, which makes production both effective and efficient.

In the field of customer experience across retail industries, copilots and agents have become immensely popular because of their ability to be human like in their responses while providing relevant information. While they are getting better at conversations, they also have the ability to factor in numerous steps delegated to software agents that can help them better frame a response to the customer. For example, one of the steps can be a calculator agent that converts one form of measurement to another for better correlation. Both the domain and the language can also be varied for different chatbots.

In the fields of software testing and security testing, Generative AI is a game changer and has garnered a lot of attention to AI safety and security. Issues and reports can now be filed faster than ever, which makes integrations less painful and more collaborative than ever before. It is also helping to reduce manual testing. Scripted automation and data-driven testing helped tremendously to do that, but with generative AI there is indeed a revolution. Creating new tests and adapting to situations with minimal human intervention is now possible autonomously. Unlike traditional methods where the software engineer was training the testing bots, Generative AI uses models to learn the patterns without human intervention. This is referred to as Autonomous test case generation.

In the field of monitoring with alerts and notifications for both active and passive observance of metrics and measurements of diverse systems in different industries, Generative AI is proving to be a blessing in reducing both the noise and improving the quality of alerts all while processing large amounts of machine data that are generally difficult to comprehend without investigative querying. The expertise to solve problems and troubleshoot software issues across a fast evolving and complex landscape of technology products and services has always demanded more from CloudOps and DevOps Engineers and AI models and Generative Models are providing the best of anomaly detection, outlier detections, false positives detections and responding to human investigations in a chat like interface which is a welcome addition to the tools these engineers use to keep all systems up and available for mission critical purposes. 

 The previous articles talked about infrastructure but business and infrastructure go together. While describing generative AI and infrastructure to support AI models, cloud engineers who participate in new and upcoming business initiatives can bring their AI chops to contribute to proposals.

As with all aspects of writing, Generative AI can be helpful to write business proposals. AI has a transformative power in business and it only helps to remain competitive. The McKinsey Global Survey highlights the rapid adoption of generative AI, with one-third of organizations using it regularly. This article explains how to integrate generative AI into proposal writing.

Generative AI reasons like a human to generate content by mimicking human language patterns. The same models can be extended to music, images, designs and more. Specific actions can be automated with agents and made part of the flow so that the models can leverage that to enhance content. When a human drafts an article in solo mode, it might be usually 500 words per hour but with generative AI, this can be up to 2500 words per hour. It saves time in knowledge management, leading to higher proposal-win rates.

The way to unlock efficiency in bid writing, for example, is to leverage Generative AI to access and organize crucial information because models can learn the salience of various topics in the domain and predict what comes after say, a given topic. Brainstorming and thinking is still the forte of humans and for the near future. By combining both, writers can dedicate more time for creative approaches to their proposals while leveraging run-off-the-mill language and content for specific topics. This results in a more compelling proposal. Early adopters can outshine competitors and capture new opportunities. Emphasis must be on educating leadership about generative AI to harness its full potential and create a culture of acceptance within the organization.

As with any emerging technology, safety and security must be constantly assessed. If there is reason to believe that a suggestion in the generated content might not be true, a review will catch that. It is therefore necessary to watch out for hallucinations in the generated content as much as it is time-saving to leverage the points cited in the generated content that would have taken a while by itself. Care must also be taken to remove bias and make the data more inclusive for the AI models. This will improve the outcome of the research and the proposal. There are AI Standards to adhere to for fairness, reliability and safety, privacy and security, inclusiveness, transparency, and accountability. Vendors often use the term “Responsible AI” to demonstrate compliance to these principles.

The right tool for proposal writing also makes a difference. It should align with your purpose and be future-ready so that the full return on investment on AI can be unlocked. Since organizational needs evolve, continuously enhancing and refining the expectations from the tool also helps.

Proposal writing is an industry in itself. AI enhances stages for creative ideation, evidenced ideation, contextualized ideation, story-boarding, narrative structure creation, evidenced-winning prose evaluation, case study insertion, statistics insertion, “Tell me how” evidencing, incorporation of win themes, issues, and requirements, scoring criteria analysis, mega-extraction and mega-transformations and embedded semantic research. All of these can be used with slight modifications for bids & proposals, marketing, sales materials, thought leadership, internal communications, and public relations.

#codingexercise: CodingExercise-01-19-2025.docx

 There are N points (numbered from 0 to N−1) on a plane. Each point is colored either red ('R') or green ('G'). The K-th point is located at coordinates (X[K], Y[K]) and its color is colors[K]. No point lies on coordinates (0, 0).

We want to draw a circle centered on coordinates (0, 0), such that the number of red points and green points inside the circle is equal. What is the maximum number of points that can lie inside such a circle? Note that it is always possible to draw a circle with no points inside.

Write a function that, given two arrays of integers X, Y and a string colors, returns an integer specifying the maximum number of points inside a circle containing an equal number of red points and green points.

Examples:

1. Given X = [4, 0, 2, −2], Y = [4, 1, 2, −3] and colors = "RGRR", your function should return 2. The circle contains points (0, 1) and (2, 2), but not points (−2, −3) and (4, 4).

class Solution {

    public int solution(int[] X, int[] Y, String colors) {

        // find the maximum

        double max = Double.MIN_VALUE;

        int count = 0;

        for (int i = 0; i < X.length; i++)

        {

            double dist = X[i] * X[i] + Y[i] * Y[i];

            if (dist > max)

            {

                max = dist;

            }

        }

        for (double i = Math.sqrt(max) + 1; i > 0; i -= 0.1)

        {

            int r = 0;

            int g = 0;

            for (int j = 0; j < colors.length(); j++)

            {

                if (Math.sqrt(X[j] * X[j] + Y[j] * Y[j]) > i)

                {

                    continue;

                }

                if (colors.substring(j, j+1).equals("R")) {

                    r++;

                }

                else {

                    g++;

                }

            }

            if ( r == g && r > 0) {

                int min = r * 2;

                if (min > count)

                {

                    count = min;

                }

            }

        }

        return count;

    }

}

Compilation successful.

Example test: ([4, 0, 2, -2], [4, 1, 2, -3], 'RGRR')

OK

Example test: ([1, 1, -1, -1], [1, -1, 1, -1], 'RGRG')

OK

Example test: ([1, 0, 0], [0, 1, -1], 'GGR')

OK

Example test: ([5, -5, 5], [1, -1, -3], 'GRG')

OK

Example test: ([3000, -3000, 4100, -4100, -3000], [5000, -5000, 4100, -4100, 5000], 'RRGRG')

OK

 Infrastructure development with a collaborative design-forward culture:

There is business value in design even before there is business value in implementation. With the pressure from customers for better experiences and their expectations for instant gratification, organizations know the right investment is in the design, especially as a competitive differentiator. But the price for good design has always been more co-ordination and intention. With the ever expanding and evolving landscape of digital tools and data, divisions run deeper than before. Fortunately, newer technologies specifically generative AI can be brought to transform how design is done. By implementing core practices of clear accountability, cross-functional alignment, inclusion of diverse perspectives and regularly shared work, organizations can tap into new behaviors and actions that elevate design

Design boosts innovation, customer experience and top-line performance. Lack of clarity, collaboration and cross-team participation are the main limitations. Leading design teams emphasize clear accountability, cross-functional alignment, inclusion of diverse perspectives and regularly shared work. Design can also provide feedback to business and product strategy. More repeatable and inclusive design processes yield more thoughtful, customer-inspired work. Better creativity, innovation and top-line performance compound over time. For example. There can be up to 80% savings in the time it takes to generate reports. The saying is go faster and go further together.

The limitations to better design are also clear in their negatives that can be quantified as duplicate and recreate work while customer input is often left unused. Systems fracture because people will tend to avoid friction and save time. Ad hock demands tend to drift design from solid foundations. Vicious development cycles eat time.

No one will disagree to better understand a problem before kicking off a project. Many will praise or appreciate those who incorporate feedback. Many meetings are more productive when there is a clear owner and driver. Being more inclusive of others has always helped gain more understanding of requirements. Defining clear outcomes and regularly updating progress is a hallmark of those who design well. Articulation of a clear standard for design quality and leveraging a development process that is collaborative are some of the others. Leaders who are focused on organizational structure do suffer an impedance to adopting design first strategy but they could give latitude to teams and individuals to find the best way to achieve goals and run priorities and initiatives. Care must be taken to avoid creating a race to satisfy business metrics without the diligence to relieve the pain point they are solving.

Inclusivity is harder to notice. With newer technologies like Artificial Intelligence, employees are continuously upskilling themselves, so certain situations cannot be anticipated. For example, engineering leaders working with AI tend to forget that they must liaison with legal department at design time itself. The trouble with independent research and outsourced learning is that they may never be adopted. Cross-team collaboration must be actively sought for and participated in because the payoff is improved cross-functional understanding, culture-building and innovation – leading to better end product. Some teams just use existing rituals to gather quick thoughts on design ideas. Others favor offline review and more documentation prior to meetings. Sharing as a value by stressing openness, as a habit by maintaining a routine, as an opportunity to see the customer come through the work and as an avoided risk by reducing back and forth brings a culture that leads to a single source of truth. Designing must involve others but not create different versions.

#Codingexercise: https://1drv.ms/w/c/d609fb70e39b65c8/Echlm-Nw-wkggNYXMwEAAAABrVDdrKy8p5xOR2KWZOh3Yw?e=YjxxZA

 One of the fundamentals in parallel processing in computer science involves the separation of tasks per worker to reduce contention. When you treat the worker as an autonomous drone with minimal co-ordination with other members of its fleet, an independent task might appear something like installing a set of solar panels in an industry with 239 GW estimate in 2023 for the global solar powered renewable energy. That estimate was a 45% increase over the previous year. As industry expands, drones are employed for their speed. Drones  aid in every stage of a plant’s lifecycle from planning to maintenance. They can assist in topographic surveys, during planning, monitor construction progress, conduct commissioning inspections, and perform routine asset inspections for operations and maintenance. Drone data collection is not only comprehensive and expedited but also accurate.

During planning for solar panels, drones can conduct aerial surveys to assess topography, suitability, and potential obstacles, create accurate 3D maps to aid in designing and optimizing solar farm layouts, and analyze shading patterns to optimize panel placement and maximize energy production. During construction, drones provide visual updates on construction progress, and track and manage inventory of equipment, tools, and materials on-site. During maintenance, drones can perform close-up inspections of solar panels to identify defects, damage, or dirt buildup, monitor equipment for wear and tear, detect hot spots in panels with thermal imaging, identify and manage vegetation growth that might reduce the efficiency of solar panels and enhance security by patrolling the perimeter and alerting to unauthorized access.

When drones become autonomous, these activities go to the next level. The dependency on human pilots has always been a limitation on the frequency of flights. On the other hand, autonomous drones boost efficiency, shorten fault detection times, and optimize outcomes during O&M site visits. Finally, they help to increase the power output yield of solar farms. The sophistication of the drones in terms of hardware and software increases from remote-controlled drones to autonomous drones. Field engineers might suggest selection of an appropriate drone as well as the position of docking stations, payload such as thermal camera and capabilities. A drone data platform that seamlessly facilitates data capture, ensures safe flight operations with minimal human intervention,  prioritize data security and meet compliance requirements becomes essential at this stage. Finally, this platform must also support integration with third-party data processing and analytics applications and reporting stacks that publish various charts and graphs. As usual, a separation between data processing and data analytics helps just as much as a unified layer for programmability and user interaction with API, SDK, UI and CLI. While the platform can be sold separately as a product, leveraging a cloud-based SaaS service reduces the cost on the edge.

There is still another improvement possible over this with the formation of dynamic squadrons, consensus protocol and distributed processing with hash stores. While there are existing applications that serve to improve IoT data streaming at the edges and cloud processing via stream stores and analytics with the simplicity of SQL based querying and programmability, a cloud service that installs and operates a deployment stamp with a solution accelerator and as a citizen resource of a public cloud helps bring the best practices of storage engineering, data engineering and enabling businesses to be more focused.

 

The preceding articles on security and vulnerability management mentioned that organizations treat the defense-in-depth approach as the preferred path to stronger security. They also engage in feedback from security researchers via programs like AI Red Teaming and Bug Bounty program to make a positive impact to their customers. AI safety and security are primary concerns for the emerging GenAI applications. The following section outlines some of the best practices that are merely advisory and not a mandate in any way.

As these GenAI applications become popular as productivity tools, the speed of AI releases and adoption acceleration must be matched with improvements to existing SecOps techniques. The security-first processes to detect and respond to AI risks and threats effectively include visibility, zero critical risks, democratization, and prevention techniques. Out of these the risks refer to data poisoning that alters training data to make predictions erroneous, model theft where proprietary AI models suffer from copyright infringement, adversarial attacks by crafting inputs that make model hallucinate, model inversion attacks by sending queries that cause data exfiltration and supply chain vulnerabilities for exploiting weaknesses in the supply chain.

The best practices leverage the new SecOps techniques and mitigate the risks with:

1.      Achieving full visibility by removing shadow AI which refers to both unauthorized and unaccounted for AI. AI bill-of-materials will help here as much as setting up relevant networking to ensure access for only allow-listed GenAI providers and software. Employees must also be trained with a security-first mindset.

2.      Protecting both the training and inference data by discovering and classifying the data according to its security criticality, encrypting data at rest and in transit, performing sanitizations or masking sensitive information, configuring data loss prevention policies, and generating a full purview of the data including origin and lineage.

3.      Securing access to GenAI models by setting up authentication and rate limiting for API usage, restricting access to model weights, and allowing only required users to kickstart model training and deployment pipelines.

4.      Using LLM-built-in guardrails such as content filtering to automatically removing or flagging inappropriate or harmful content, abuse detection mechanisms to uncover and mitigate general model misuse, and temperature settings to change AI output randomness to the desired predictability.

5.      Detecting and removing AI risks and attack paths by continuously scanning for and identifying vulnerabilities in AI models, verifying all systems and components that have the most recent patches to close known vulnerabilities, scanning for malicious models, assessing for AI misconfigurations, effective permissions, network resources, exposed secrets, and sensitive data to detect attack paths, regularly auditing access controls to guarantee authorizations and least-privilege principles, and providing context around AI risks so that we can proactively remove attack paths to models via remediation guidance.

6.      Monitoring against anomalies by using detection and analytics at both input and output, detecting suspicious behavior in pipelines, keeping track of unexpected spikes in latency and other system metrics, and supporting regular security audits and assessments.

7.      Setting up incident response by including processes for isolation, backup, traffic control, and rollback, integrating with SecOps tools, and availability of an AI focused incident response plan.

In this way, existing SecOps practices that leverage well-known STRIDE threat modeling and Assets, Activity Matrix and Actions chart with enhancements and techniques specific to GenAI.

References:

Across Industry

Row-level security

Metrics

 

 This is a summary of the book titled “Your AI Survival Guide” written by Sal Rashidi and published by Wiley in 2024. Sal argues that organizations cannot afford to be Laggards and Late majority sections of people adopting AI even if they are non-technical because that is here to stay and unless they want to be eliminated in business. So, the only choices are the Early Majority who adopt technology once it has demonstrated its advantages, early adopters who are more on the forefront, and innovators who pioneer the use of AI in their respective fields. Each group plays a crucial role in the adoption of lifecycle of technology which usually spans the duration until something better replaces it, so there is no wrong pick, but the author’s book lays out everything that helps you uncover your “why” to building your team and making your AI responsible. With applications already ranging from agriculture to HR, the time to be proactive is Now. His playbook involves assessing which AI strategy fits you and your team, selecting relevant use cases, planning how to launch your AI project, choosing the right tools and partners to go live, ensuring the team is gritty, ambitious, and resilient and incorporating human oversight onto AI decision making.

To successfully implement AI within a company, it is essential to balance established protocols with the need to adapt to changing times. To achieve this, consider the reasons for deploying AI, develop an AI strategy, and start small and scale quickly. Choose a qualified AI consultant or development firm that fits your budget and goals. Set a realistic pace for your project. Conduct an AI readiness assessment to determine the best AI strategy for your company. Score yourself on various categories, such as market strategy, business understanding, workforce acumen, company culture, role of technology, and data availability.

Select relevant use cases that align with your chosen AI strategy and measure the criticality and complexity of each use case. For criticality, measure how the use case will affect sales, growth, operations, culture, public perception, and deployment challenges. For complexity, measure how the use case will affect resources for other projects, change management, and ownership. Plan how to launch your AI project well to ensure success and adaptability.

To launch an AI project successfully, outline your vision, business value, and key performance indicators (KPIs). Prioritize project management by defining roles, deliverables, and tracking progress. Align goals, methods, and expectations, and establish performance benchmarks. Outline a plan for post-launch support, including ongoing maintenance, enterprise integration, and security measures. Establish a risk mitigation process for handling unintended consequences. Choose the right AI tool according to your needs and expertise, ranging from low-cost to high-cost, requiring technical expertise. Research options, assess risks and rewards, and collaborate with experts to create standard operating procedures. Ensure your team is gritty, ambitious, and resilient by familiarizing yourself with AI archetypes. To integrate AI successfully, focus on change management, create a manifesto, align company leadership, plan transitions, communicate changes regularly, celebrate small wins, emphasize iteration over perfection, and monitor progress through monthly retrospectives.

AI projects require human oversight to ensure ethical, transparent, and trustworthy systems. Principles for responsible AI include transparency, accountability, fairness, privacy, inclusiveness, and diversity. AI is expected to transform various sectors, generating $9.5 to $15.4 trillion annually. Legal professionals can use AI to review contracts, HR benefits from AI-powered chatbots, and sales teams can leverage AI for automated follow-up emails and personalized pitches. AI will drive trends and raise new challenges for businesses, such as automating complex tasks, scaling personalized marketing, and disrupting management consulting. However, AI opportunities come with risks such as cyber threats, privacy and bias concerns, and a growing skills gap. To seize AI opportunities while mitigating risks, businesses must learn how AI applies to their industry, assess their capabilities, identify high-potential use cases, build a capable team, create a change management plan, and keep a human in the loop to catch errors and address ethical issues.

 
ETA at waypoints using time-series algorithms:

Problem statement: Given the NURBS method for trajectory generation for UAV swarms as described in previous article, the UAV trajectory was independent of in-flight parameters and both the position and velocity profile of planned trajectory could be obtained using the global locations and expected time of arrival at the waypoints. While a single drone can adhere to the planned trajectory, the internal dynamics of the UAV swarm and their effect on the ETA are harder to quantify. A closer tracking of the ETA at waypoints and trajectory deviations is needed for the UAV swarm.

   

Solution:

Consider a closed loop trajectory of UAV swarm. The effects of UAV swarm dynamics are easier to observe along waypoints in the loop because the NURBS trajectory assumes a constant velocity profile. Uncertainty in external variables such as unmodeled wind-field or uncertainty from internal friction between the drone units, can lead to different arrival times. Uncertainty affecting cruise velocity can be modeled using Gaussian independent random variables with covariance but a time-series algorithm does not need any attributes other than the historical collection of ETAs at the waypoints to be able to predict the next ETA. It only looks at scalar value regardless of the type or factors playing into the arrival time of the swarm while weights can be used to normalize the irregularity of distances between waypoints on the trajectory between start to finish. The historical data is utilized to predict an estimation on the arrival time as if the arrival were a scatter plot along the timeline. Unlike other data mining algorithms that involve additional attributes of the event, this approach uses a single auto-regressive method on the continuous data to make a short-term prediction. The regression is automatically trained as the data accrues so there is no need to parameterize or quantify uncertainties. 

Central to the step of fitting the linear regression, is the notion of covariance stationarity which suggests: 

·        The mean is not dependent on t 

·        The standard deviation is not dependent on t 

·        The covariance (Yt, Yt-j) exists and is finite and does not depend on t 

·        This last factor is called jth order autocovariance 

·        The jth order autocorrelation is described as autocovariance divided by the square of standard deviation 

 

The autocovariance measures the direction of the linear dependence between Yt and Yt-j. 
while the autocorrelation measures both the direction and the strength of the linear dependence between the Yt and Yt-j. 
An autoregressive process is defined as one in which the time dependence in the process decays to zero as the random variables in the process get farther and farther apart. It has the following properties: 
E(Yt) = mean 
Var(Yt) = sigma squared 
Cov(Yt, Yt-1) = sigma squared . phi 
Cor(Yt, Yt-1) = phi 

To fit the linear regression for a restricted data set, we determine the values of the random variable from the length p transformations of the time series data set. 

For a given time-series data set, a corresponding nine data sets for length p transformations are created. The p varies from zero to eight for the nine data sets. Each of these transformed datasets is centered and standardized before modeling; that is for each variable we subtract the mean value and divide by the standard deviation. Then we divided the data set into a training set used as input to the learning method and a holdout set to evaluate the model. The holdout set contains the cases corresponding to the last five observations in the sequence. 

 Waypoint Selection

A previous article1 introduced waypoints and trajectory smoothing for UAV swarms. This section focuses on waypoint selection.

The fight path management we propose is about the example of flying a fleet of drones around skyscrapers. The sample space can be considered a grid that must be navigated from one end to another and all intermediary spaces can be thought of as waypoints to occupy on the way to the other end and allowing the fleet to organize themselves around these intermediary points. By treating sub grids within grids as potential candidates to select from, a path can be forged with a sequence of sub grids to forge to the other end and the fleet organizes itself around each sub grid. The sub grids are pre-determined, invariant and uniform in size in each epoch.

Searching for the optimum intermediary point for the flight of the drones translates to the selection of waypoints by way of centroids of the sub grids. Each viable waypoint acts as a vector for various features such as potential gain towards eventual destination, safety, signal strength and wind effects. All information about adjacencies of sub grids as viable paths is known beforehand. Treating sub grids as nodes in a graph, and using depth first traversal for topological sort, it is possible to discover paths between start to finish. The approach outlined here uses a gradient descent method to determine the local optima given the waypoints as vectors. A quadratic form representing the waypoints as vectors is assumed to denote their initial matrix.

The solution to the quadratic form representing the embeddings is found by arriving at the minima represented by Ax = b using conjugate gradient method.     

We are given input matrix A, b, a starting value x, a number of iterations i-max and an error tolerance epsilon < 1     

This method proceeds this way:      

set I to 0      

set residual to b - Ax      

set search-direction to residual.     

And delta-new to the dot-product of residual-transposed.residual.     

Initialize delta-0 to delta-new     

while I < I-max and delta > epsilon^2 delta-0 do:      

    q = dot-product(A, search-direction)     

    alpha = delta-new / (search-direction-transposed. q)      

    x = x + alpha.search-direction     

    If I is divisible by 50      

        r = b - Ax      

    else      

        r = r - alpha.q      

    delta-old = delta-new     

    delta-new = dot-product(residual-transposed,residual)     

     Beta = delta-new/delta-old     

     Search-direction = residual + Beta. Search-direction     

     I = I + 1      

The Jacobi iteration gives eigen values and eigen vectors.

 Monitoring, Telemetry and Observability are important aspects of infrastructure. The public cloud becomes the gold standard in demonstrating both active and passive monitoring. With a vast landscape of platforms, products, services, solutions, frameworks and dynamic clouds, modern IT infrastructure has enormous complexity to overcome to set up monitoring. Yet, they are seldom explained. In this article, we list five such challenges.

The first is the most obvious by nature of a diverse landscape and this is complexity. Contemporary environments for many teams and organizations are dynamic, complex, ephemeral and distributed. Tools for monitoring must keep up with these. To set up monitoring for a big picture that spans hybrid stacks and environments, one must grapple with disconnected data, alerts and reports and engage in continuously updating tagging schemas to maintain context. So, the solution to addressing complexity, unified observability and security with automated contextualization is a key solution. A comprehensive solution can indeed monitor containers, hosting frameworks like Kubernetes, and cloud resources. Topology and dependency mapping enable this flexible and streamlined observability.

The second challenge is the sprawl of tools and technologies for monitoring that are often also disconnected. Do-it-yourself and open-source solutions for monitoring were partly to blame for this. Leveraging built-in solutions from the cloud eases the overall efficiency and effort involved. This challenge has often resulted in a patchwork view, blind spots and duplicated efforts and redundant monitoring. This implies that a solution would comprise of a single, integrated full-stack platform that reduces licensing costs, increases visibility to support compliance, and empowers proactive issue remediation and robust security.

The third challenge is the sheer size of MELT (Metrics, Logs and Traces) data. With the ever-increasing volume, variety and velocity of data generated, IT Teams are tasked with finding ways to ingest, store, analyze and interpret the information often grappling with numerous and disconnected ways to do each. This results in critical issue being buried under a ton of data or overlooked due to unavailability or inadequate context which results in delayed decision making and potential for errors whose cost and impact to business are both huge and indeterministic. The right modern monitoring tool acts as a single source of truth, enriching data with context and not shying away from using AI to reason vast volumes of data. It would also have sufficient processing to emit only quality alerts and reduce triage efforts.

The fourth challenge is troubleshooting and time to resolution because teams suffering from glitches and outages do not have the luxury to root cause incidents as they must struggle to restore operations and business. As users struggle with frustrations, poor experiences, insufficient information, and the risks of not meeting Service Level Agreements, there is decreased productivity, low team morale and difficulty in retaining the most valuable employees in addition to fines that can be incurred from missed SLAs. A true monitoring solution will come with programmability features that can make triaging and resolving easier. AI can also be used to find patterns and anomalies so that there can be some proactive measures on approaching thresholds rather than being reactive after incidents.

The fifth challenge is the areas of the technological landscape that either do not participate in monitoring or do so insufficiently. In fact, data breaches and hacks that can result from incomplete monitoring have devastating financial consequences, fines and legal fees besides damaged market reputation that erodes stakeholders’ and customers’ trust. A single-entry point for comprehensive monitoring across entire infrastructure is a favored solution to meet this challenge. By visualizing the dependencies and relationships among application components and providing real-time, end-to-end observability with no manual configuration, gaps, or blind spots, a monitoring solution renders a complete picture.

Reference: Previous articles.

#Codingexercise: https://1drv.ms/w/c/d609fb70e39b65c8/Echlm-Nw-wkggNYXMwEAAAABrVDdrKy8p5xOR2KWZOh3Yw?e=hNUMeP

 This is a summary of the book titled “The Equality Machine: harnessing digital technology for a brighter, more inclusive future” written by Orly Lobel and published by Public Affairs in 2022. The author proposes “An Equality Machine” in his drive to use the common grounds of humanity to bridge two disparate and often at opposite ends of the spectrum of people impacted by technology: 1. those who fear new technologies due to their potential to exacerbate existing inequities and 2. those who envision a technological utopia without anticipating risks. The goal of this proposal is to create a better future in which humanity uses “technology for good”. It’s common knowledge that advances in technology such as artificial intelligence and chatbots are recognized both for their potential to empower as well as their drawbacks in meeting equity and fairness. Careful auditing can help algorithms from displaying the same bias as humans do. Making the data more transparent helps to value the labor involved. Feminizing agents and chatbots can normalize existing inequities. New technologies also help to discover gaps in representation and protect people from crime and disease. With their interactions to these technologies, humans are cognizant of their shift in interactions with others and with bots. Makers of chatbots and new technological inventions can explore assumptions that disrupt stereotypes.

The rise of intelligent machines has prompted a need for upholding values of equity and fairness. Technological change has been polarized, with insiders focusing on disruption and embracing new technologies, while outsiders, such as people of color, women, and those from rural areas, worry about exclusion and inequities. To improve machine fairness, humanity must strike a balance between naive optimism and fearful pessimism. Machine learning algorithms can often ascertain identity markers from other data, but this does not address the root causes of inequities. To prevent algorithmic models from reflecting human biases, organizations must be proactive about auditing the output of their AI models as well as their data inputs. Human resources can run hypothetical job candidates through their AI models to test for biases and choose more inclusive data sets. AI decision-making can offer advantages, such as easier dissecting and correcting machine bias than flawed human decision-making. Additionally, predictive algorithmic models can help companies screen a larger pool of applicants for more nuanced qualities, such as high performance and long-term retention. It would be prudent to strike a balance between machine screening and human review.

Technology can help stakeholders work towards a future of financial equity by enabling access to vast amounts of data, identifying and correcting disparities, and reducing biases. Research shows that algorithms created to reduce bias in the fintech industry were 40% less discriminatory than humans. Research also shows that companies are more likely to penalize women for initiating salary negotiations even though men might be praised for assertiveness. AI and societal shifts towards greater data transparency are empowering workers with a better understanding of their labor market value. Some governments have passed legislation banning employers from asking prospective employees to disclose their past salaries. New digital resources, such as Payscale, are bringing greater transparency to the salary negotiation process. Feminizing AI assistants and chatbots can normalize existing inequities, but companies must reflect on the preference to depict subservient robots as female. This reinforces gender as a binary construct and promotes outmoded views of women's roles in society.

Researchers are using new technologies to detect patterns in representation gaps and address systemic inequities. Natural language processing (NLP) methods are being used to analyze large amounts of information, revealing unequal power dynamics and opportunities. AI can be used to assess whether people with different identity markers are getting equitable representation in media forms. Machine learning and AI analytics can help detect gaps in representation and biases in various media industries and inspire more empowering narratives. Technology can also help protect people from harmful influences by enabling organizations to share data and develop data hubs. AI and health data can also help stakeholders accelerate drug discovery and collaborate to prevent the global spread of viruses. However, democratizing AI use in medical research contexts is crucial to ensure improved health outcomes for everyone, not just the rich.

Algorithms and embodied robots are transforming human connection and social bonds. Algorithmic biases can exacerbate existing class, racial, and social divides, while the growing prevalence of robots with sexual capacities is transforming intimacy and emotional connection. Some argue that framing robots solely as AI-empowered sex dolls is oversimplification, while others worry about the potential for violence against women.

Roboticists can challenge stereotypes by creating robots that challenge assumptions. Embodied robots can support humans in various functions, such as care labor, reception work, and space exploration. However, some critics worry about privacy risks, consent, and misuse of data.

Robots can surprise those they interact with by disrupting expectations. NASA uses feminine-looking robots like Valkyrie to support in-space exploration, while masculine-looking robots like Tank act as "roboceptionists." These robots demonstrate the choice roboticists face when designing robots that cater to existing biases or inspire imaginative new possibilities.

 This is a summary of the book titled “Future Ready: Your organization’s guide to rethinking climate, resilience and sustainability” written by Alastair MacGregor and Tom Lewis and published by Wiley in 2023. This book is about integrating sustainability and resilience into infrastructure and building projects. They introduce climate science for practitioners and decision makers who grapple with evolving environmental challenges. They recognize the target audience of the book are already aware they need to make changes. Those in the construction industry can support sustainability by adopting innovative technologies and sustainable materials. Those in transportation must prioritize decarbonization. Infrastructure designers must meet urbanization challenges. The authors recommend that they are best served by putting systems for measuring, say emissions, setting targets and implementing strategies to meet those targets. Achieving net zero will involve integrating technologies and understanding carbon markets. Leaders can generate support for these activities. Nature-based solutions provide a holistic and cost-effective approach to addressing challenges of sustainability and resilience.

Climate change has highlighted the need for infrastructure designers and urban planners to incorporate sustainability and resilience in their decision-making. The aftermath of Superstorm Sandy exposed vulnerabilities in current systems, prompting policymakers, urban planners, and industry professionals to shift their approaches to risk management and sustainability. The Future Ready framework, developed by WSP, consists of four lenses for considering decisions: climate, society, technology, and resources. The construction industry can support sustainability by adopting innovative technologies and sustainable materials. Buildings contribute nearly 50% of global greenhouse gas emissions and consume significant natural resources. Decision-makers must rethink building practices, focusing on sustainability, resilience, and long-term value. Innovative projects integrating modern technologies and sustainable materials, such as LEED and BREEAM, can provide potential paths forward.

Transportation innovations have advanced global trade and daily life, but they also contribute to 27% of US GHG emissions, exacerbating climate change and increasing infrastructure risks. To build a resilient, sustainable, and equitable future, decision-makers must prioritize decarbonization. The adoption of electric vehicles and hydrogen fuel cell vehicles will play a crucial role in reducing emissions, but adoption rates and charging infrastructure currently fall short of climate targets. Infrastructure designers are embracing innovations to meet climate and urbanization challenges, such as clean water infrastructure, green infrastructure, electrification, green hydrogen, and high-voltage direct current networks. Leaders in urban areas can enhance resilience with technological innovations, community-based solutions, and sustainable development. Cities contribute significantly to global GHG emissions and face high climate risks due to their dense populations, so they must adopt comprehensive, integrated approaches to climate resilience that address both mitigation and adaptation. Community-based solutions are essential in resilience efforts, as demonstrated by Staten Island's Living Breakwaters project.

Organizations must establish systems for measuring emissions, set targets, and implement strategies to achieve net-zero emissions. This involves accurately measuring GHG emissions, setting science-based targets, and implementing comprehensive strategies. Scientists set a deadline of 2050 to achieve net-zero emissions, but sooner it is better. Tools like wedge diagrams, gap analyses, backcasting, and life cycle assessment can help manage pathways to net zero. Integrating technologies and understanding carbon markets is crucial for achieving net-zero goals. Energy efficiency measures, industry-specific technologies, and carbon removal offsets can help reduce emissions. Carbon markets use compliance-based and voluntary schemes to encourage reductions of emissions and investments in renewables. Assessment systems and standardized reporting frameworks are necessary to manage climate-related risks. Scenario analysis tools like the American Society of Civil Engineers' Future World Vision can help decision-makers visualize potential climate risks and develop adaptive strategies. Innovation must continue to reach global net-zero targets.

Leaders can support climate-related initiatives by achieving early successes, educating stakeholders, and fostering engagement. They should focus on measurable strategies and address issues such as employee retention and regulatory uncertainties. External engagement is crucial for public sector organizations, involving diverse stakeholders. Nature-based solutions (NbS) offer a cost-effective, adaptive, and sustainable approach to environmental challenges, enhancing infrastructure resilience and community well-being. Examples include the restoration of oyster reefs in Florida, which serve as natural barriers against storm waves, protect infrastructure, and improve water quality. Community engagement is essential for tailoring solutions to local needs and addressing climate challenges.

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