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.