US Business News

6 Ways AI Will Aid in Your Skill Development and Upskilling

By: Valliappan Narayanan, Senior Engineering Manager at Walmart

With the average lifespan of contemporary skills at just five years, how do we stay ahead in a constantly evolving workforce? The prominence of continuous learning and skill development has never been more critical, especially with the unpredictable trajectory of Artificial Intelligence (AI). According to an IBM study, a staggering 40% of workers will need to reskill within the next three years due to the pervasive influence of AI. Skill development is emerging as a burgeoning market sector, yet in this AI-dominated era, predicting the longevity and relevance of a particular skill is a challenge. OpenAI’s report accentuates that AI has already begun reshaping numerous job roles. Consequently, individuals must prioritize refining skills pertinent to both their current and prospective professions.

Employers, recognizing the shifting dynamics, are increasingly valuing skill enhancement. Notably, industry giants like Amazon have set a benchmark, wherein new recruits are expected to surpass the skill set of their existing workforce. Leveraging AI can supercharge one’s skill development process. AI’s adeptness in tailoring instructions, offering pinpointed assistance, and rendering timely feedback significantly bolsters outcomes, including improved knowledge retention, augmented skill proficiency, and optimized team performance.

  • The average lifespan of contemporary skills is only about five years, emphasizing the need for ongoing learning.
  • The World Economic Forum predicts a 15% increase in demand for skilled labor by 2025, potentially leading to a shortage of skills.
  • Burning Glass Technologies’ report reveals that individuals with sought-after skills can earn around 25% more than their less-skilled peers.
  • Research from the Society for Human Resource Management shows that 60% of trained employees achieve promotions within a year.
  • The National Association of Colleges and Employers survey indicates that 72% of employers prefer candidates who have undergone training or development.
  • In today’s dynamic job market, skill enhancement is not just advantageous but essential.

How does one stay relevant in such a rapidly changing environment?

Identify skill gaps: Often, individuals remain unaware of their deficiencies, aspirations, and potential due to the demands of daily life. AI provides a solution by offering deep insights. Harnessing vast data from myriad users and their respective career trajectories, AI models undergo rigorous training. These insights enable the creation of personalized plans and learning pathways tailored to an individual’s current proficiency level. AI can meticulously monitor employee performance, pinpointing areas that might benefit from additional training or development. By identifying these skill gaps, AI ensures that productivity isn’t compromised, and employees can realize their maximum potential. Moreover, AI’s capability to spot emerging skills positions it as a vital tool for futureproofing. It empowers employees to strategize their career trajectory effectively, ensuring they continually evolve their skill set to stay ahead in a dynamic professional landscape.

Create Learning Paths and Insights: “The learning journey is tailored to your unique pace, preferences, and goals. AI doesn’t just show you the path; it molds the path to fit you perfectly.” By ensuring that learners engage only with the most pertinent content, their learning becomes both effective and time-efficient. Using insights garnered from prior learners and their progress, AI crafts the most optimized learning journey for each individual. These bespoke plans consider various elements, from the learner’s current proficiency and learning pace to their unique style and end goals. This customization fosters deeper learner engagement, fueling their drive to succeed.

AI’s ability to generate tailored content is transforming education. It’s like having an expert tutor creating materials just for you, enhancing the entire learning experience.” – Mark Taylor, EdTech Entrepreneur

Moreover, AI’s capability to provide instantaneous feedback and assess performance is a game-changer. It equips learners to fine-tune their strategies continually, leveraging AI-derived insights for peak learning outcomes. Beyond merely tracking progress, AI also pinpoints areas requiring extra attention. Such insights facilitate timely adjustments to the learning pathway, ensuring that each learner remains aligned with their aspirations, thereby maximizing the impact of their educational endeavors.

Generate content: Content development, with its meticulous and intricate nature, often proves to be a protracted and labor-intensive endeavor. While an array of manual tools exists to aid this process, curating an engaging course remains a herculean task. Enter AI, which harnesses expansive text corpora to seamlessly generate diverse content types, from FAQs to micro-courses, tailored for swift and effective learning.

AI is becoming a partner in creating engaging learning materials. It’s like unraveling the secrets of knowledge, as you enjoy the process, just like opening a mystery. A predominant AI application in this domain is the synthesis of Q&A segments that follow a course, providing learners with immediate avenues to clarify and consolidate their understanding. Moreover, AI can craft flashcards derived from the foundational course material, offering learners a concise and focused revision tool. In essence, AI not only streamlines the content development process but also enhances its pedagogical efficacy.

Multilingual capabilities: Additionally, AI’s prowess in handling multiple languages unlocks a universe of opportunities, effortlessly bridging linguistic divides. This ensures that users can delve into educational materials in the comfort of their native language. With the removal of these language constraints, learners gain unhindered access to a vast repository of knowledge, sidestepping the traditional hurdles of manual content translation. Such capabilities are particularly beneficial for global or geographically dispersed teams, as they can seamlessly engage with learning content in their mother tongue, fostering inclusivity and enhancing comprehension across diverse demographics.

Feedback and Engaging: AI stands at the forefront of enhancing the learning experience by continually assimilating feedback and leveraging insights from past user interactions. This dynamic approach determines which strategies most effectively foster skill development. With its capacity to provide instantaneous feedback and modulate the learning tempo in response to a learner’s progress, AI ensures a vibrant and immersive educational journey. Users can even refine the AI’s recommendations by specifying criteria, including user ratings, course difficulty, the mode of instruction, duration, and beyond.

Several platforms incorporate chatbots, which function akin to learning co-pilots or tutors, assisting users by addressing questions during their educational voyage. AI’s analytical capabilities offer deep dives into learner behavior and performance metrics. Such data becomes instrumental in elevating the overall learning experience. Moreover, AI monitors learner engagement with content, pinpointing both lackluster materials and highlighting areas for content refinement.

“AI’s real-time feedback mechanism transforms learning into a dynamic experience. It paces itself according to the learner’s progress, ensuring a personalized and immersive educational journey.”

— Prof. Alex Johnson, Learning Science Specialist

Furthermore, AI’s prowess extends to critiquing learner submissions, furnishing feedback that aids in honing their skills and comprehension. Such insights also spotlight regions where learners might require augmented support. With the integration of support chatbots, organizations can deliver prompt, round-the-clock responses to learner queries. This not only ensures swift resolutions and precise information based on AI-curated content but also alleviates the pressure on Learning & Development teams, heralding a new era of efficient and enhanced learning experiences.

Gamification: By integrating gamification, certain AI tools elevate the educational landscape, weaving in elements such as stars, points, and rewards to enhance learner engagement. This not only instills a feeling of accomplishment but also ignites an intrinsic motivation, compelling learners to revisit and persevere in their studies. The gamified framework creates a constructive feedback cycle, embedding a philosophy of perpetual growth and an insatiable quest for knowledge.

Here’s how gamification plays a pivotal role in sustaining learner enthusiasm:

Points and Badges: Serving as milestones, they document learner progression, acting as tangible tokens of their achievements. These rewards amplify their dedication and commitment.

Challenges and Quests: Crafting a narrative of purpose and goal-oriented tasks, they infuse a directional clarity, ensuring learners remain steadfast in their pursuit and realize their objectives.

Leaderboards: By juxtaposing learner achievements, leaderboards kindle a healthy competitive spirit, urging learners to continually refine their skills and excel.

Virtual Economies: By bestowing virtual assets or currencies upon learners as recognition of their efforts, these economies cultivate a sentiment of proprietorship and accomplishment.

Social Connectivity: By fostering interaction amongst peers, social features craft a communal learning ambiance. This interconnectedness promotes mutual growth, as learners derive insights from one another’s experiences and perspectives.

In essence, gamification, when woven with learning, not only makes the process enjoyable but also enhances retention, persistence, and collaboration, transforming the very essence of the educational journey.

In conclusion, as the contemporary professional landscape undergoes seismic shifts, propelled by the rapid evolution of technology and AI’s growing influence, the need for continuous learning and skill enhancement emerges as paramount. AI presents a revolutionary approach to addressing this need, offering personalized learning paths, dynamic content generation, multilingual support, real-time feedback mechanisms, and the appeal of gamification. The future of skill development and upskilling undoubtedly lies in harnessing the potential of AI. By intertwining AI’s capabilities with the pursuit of knowledge, we not only ensure that individuals remain competitive in their respective fields but also lay the foundation for a workforce that is adaptable, resilient, and ever-evolving. As we stand on the precipice of this transformative era, one must ponder: How can I embrace the power of AI-driven learning to future-proof my skills, effectively using AI as a catalyst for my own growth? Even in the age of automation and digitization, the unyielding human desire for growth and self-improvement remains unwavering.

Navigating the Future: LiDAR Technology in the Age of AI and Machine Learning

By: Navin Kamuni

Light Detection and Ranging, or LiDAR for short, is a cutting-edge remote sensing technique that measures distances remarkably accurately by using pulsed laser light. It was first created in the 1960s and has basically changed how we map and comprehend the world. With a wide range of uses, including autonomous car navigation and geographical mapping, LiDAR technology is now indispensable in numerous industries, providing in-depth understanding of both the built and natural environments.

An unparalleled period of technical synergy has been brought about by the combination of LiDAR with AI and ML. This combination gives machines hitherto unheard-of skills to sense, understand, and engage with their environment. We are witnessing the birth of systems that map and interpret their environments intelligently, thanks to the combination of precise LiDAR data collecting and the analytical and adaptive learning strengths of AI and ML. This will undeniably lead to creative applications in a variety of fields. This technological breakthrough is changing how we engage with the outside world, opening doors for new ideas, and raising the bar for human success.

LiDAR – A Primer

LiDAR works by directing laser pulses in the direction of a target and timing how long it takes for the pulses to return to the sensor. The exceptional accuracy of distance calculations is made possible by this method, which makes use of the constant speed of light. With the help of several pulses and the reflections they get, LiDAR creates a “point cloud” of data that forms an intricate three-dimensional map of the region. Due to its capacity to outperform older approaches in terms of precision and detail, LiDAR has become an indispensable instrument in meteorology and detailed topographical mapping.

The broad range of applications for LiDAR can be attributed to its adaptability. High-resolution mapping and safe navigation by identifying barriers are critical functions of autonomous car technology. LiDAR helps with biomass assessment and forest structure mapping in forestry. In addition to its use in planetary exploration, LiDAR technology has produced important discoveries in archaeology by revealing structures hidden beneath thick vegetation. NASA’s Mars rovers have mapped the Martian surface in great detail thanks to this technology. LiDAR’s vital significance in expanding our knowledge of Earth and space is highlighted by these various uses.

LiDAR Meets AI and Machine Learning

In terms of data processing and analysis, the combination of LiDAR with AI and ML constitutes a revolutionary step. Large-scale, intricate spatial data generated by LiDAR creates complicated datasets that are difficult for conventional processing techniques to handle well. AI systems are excellent at managing this complexity, sifting through the many layers of LiDAR data to derive insightful information. More accurate and nuanced interpretations than ever before are made possible by these algorithms’ quick processing and analysis of the point clouds to identify patterns and spatial linkages. This capacity is especially helpful in situations like autonomous car navigation and urban planning, when making decisions quickly and accurately based on geographical data is essential.

The essential AI field of machine learning expands on LiDAR’s potential. ML models are trained on big datasets to identify different patterns and abnormalities in the LiDAR data. For example, in autonomous vehicles, machine learning models trained on LiDAR data are able to distinguish between stationary objects, other vehicles, and pedestrians, allowing the vehicle to navigate with confidence. These models can detect changes in landscape features in environmental studies, which helps with resource management and monitoring. Combining LiDAR and ML allows systems to become more intelligent and autonomous since they can continuously learn from fresh data and develop more intelligent, effective, and adaptive applications. This convergence is pushing the limits of what machines can do and creating a new standard in technology.

Revolutionizing Industries with LiDAR and AI

Autonomous Vehicles: The integration of LiDAR and AI technologies is transforming autonomous vehicle development. LiDAR sensors on vehicles produce three-dimensional maps of the environment by emitting thousands of laser pulses per second. The maps provide a range of key data points, from the location of nearby vehicles to the detection of pedestrians and road obstructions. The AI algorithms play a vital role here, processing the rich LiDAR data in real-time to allow vehicles to make split-second decisions. As a result, autonomous driving is more unswerving and closer to becoming widespread as it increases a vehicle’s ability to navigate complex environments safely and efficiently.

Smart Cities: The convergence of LiDAR and AI are leading to create smarter, more sustainable cities in the field of urban planning and management. LiDAR is being used by city planners to build intricate 3D representations of metropolitan regions. When AI algorithms are used to examine these models, they offer invaluable information on infrastructure upkeep, traffic flow optimization, and urban development. By identifying regions that are inclined to congestion, LiDAR data may be analyzed using AI to provide better traffic control solutions. Additionally, AI helps with civic infrastructure maintenance by identifying structural flaws in buildings and roadways, enabling preemptive upkeep. In addition to streamlining city administration, this addition helps create more eco-friendly and productive metropolitan areas.

Agriculture and Environmental Monitoring: The use of AI and LiDAR in agriculture is undergoing a transformation in the field. LiDAR technology is utilized by farmers to generate comprehensive topographical maps of their fields, which are essential for assessing soil properties and organizing crop rotations. By analyzing LiDAR data to forecast crop yields, evaluate soil quality, and even identify early symptoms of plant illnesses, AI systems further improve this. Farmers are able to increase crop yield and resource efficiency with this focused approach. LiDAR is essential for environmental monitoring because it makes ecosystems easier to map and comprehend. By assessing tree heights and canopy density—which are essential for estimating biomass and analyzing wildlife habitat—it aids in forest management. When combined with AI, LiDAR data can monitor ecological changes over time and provide important insights.

Cutting-Edge Developments and Challenges

Cutting-edge developments, especially in the miniaturization of LiDAR systems, have caused the landscape of LiDAR technology to change dramatically. Because of this development, these systems are now much reduced and less costly, which opens up a variety of uses for them, including consumer electronics and small-scale drones. At the same time, processing and interpreting data has become much faster and more accurate thanks to the combination of LiDAR with cutting-edge AI algorithms.

These algorithms are now more capable than ever of quickly sorting through the dense point clouds produced by LiDAR to extract precise information, enabling real-time applications like immediate environmental assessments and autonomous vehicle navigation. Even with these developments, there are still significant obstacles facing the technology. Handling the massive amounts of data generated by high-resolution LiDAR systems is the most important task, requiring a significant amount of computer power and effective data handling techniques. Furthermore, maintaining the correctness and dependability of AI-driven choices endures to be a significant problem, especially in situations that are volatile. It is imperative that these problems are resolved in order to successfully and safely deploy LiDAR technology in a variety of real-world applications.

The Road Ahead for LiDAR and AI

As we move forward, the convergence of AI and LiDAR promises to push limits and provide new opportunities across various sectors. This combination has the potential to completely revolutionize patient care in the medical field. LiDAR-enabled devices provide accurate, non-invasive monitoring and diagnostics, enhancing treatment precision and patient comfort. The application of LiDAR and AI to scan and analyze extraterrestrial landscapes in unparalleled detail would greatly improve space exploration efforts in searching for resources and livable habitats.

Furthermore, the field of augmented reality (AR) is about to undertake a paradigm shift, since LiDAR-enhanced AR offers highly engaging and immersive experiences that smoothly blend the digital and physical realms. But when we accept these developments, it becomes essential to thoughtfully consider the ethical consequences. This necessitates protecting privacy in the face of ever-more-advanced data collecting capabilities and guaranteeing the ethical use of AI-driven systems to prevent biases and blunders in decision-making. The secret to harnessing the full potential of LiDAR and AI in creating a better future is striking a balance between innovation and ethical responsibility.


An important turning point in the history of technology is the combination of LiDAR, AI, and ML. This signals the beginning of a new era in which machines are able to not only see their surroundings, but also comprehend them and engage with them intelligently. This cooperative relationship is not only an improvement; rather, it signifies a fundamental shift in the capabilities of automated systems, enabling them to function with an unprecedented degree of accuracy and independence. 

This convergence is set to change a wide range of sectors, affecting both daily living and global economies, impacting urban planning and environmental conservation to transforming the automobile industry with self-driving cars. It paves the way for groundbreaking applications in uncharted territory, promising to improve not only the present but also the efficient, secure, and interconnected future. This technology has the potential to alter not only how humans interact with robots but also how we see innovation and progress in the twenty-first century and beyond as it develops and becomes more integrated into society.

About the Author – Navin Kamuni: An AI Visionary and Technology Innovator

Navigating the Future: LiDAR Technology in the Age of AI and Machine Learning

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Navin Kamuni, a prominent figure in contact center technologies and AI innovation. His career, spanning over two decades, showcases his significant role in advancing technology in these sectors. His proficiency in Artificial Intelligence (AI) and Machine Learning (ML) has been pivotal in his journey.

Kamuni’s expertise goes beyond professional achievements; he is a respected mentor and advisor in the tech community. His book, “Connect.AI: The New Era of Digital Experiences,” reflects his in-depth understanding of AI’s impact on digital customer experiences, establishing him as a thought leader. His contributions through articles, research papers, and active participation in conferences and seminars have solidified his status as an influential voice in technology.

His involvement with platforms like IEEE and highlights his dedication to sharing knowledge and inspiring others. Kamuni’s LinkedIn profile offers an insight into his professional journey, achievements, and ongoing projects, inviting connections to explore his extensive experience and contributions to technology and AI.

In summary, Navin Kamuni’s selection as a judge for the prestigious CXE (Customer Experience) – International Business Excellence Awards 2024 is a testament to his visionary leadership and influential work at the nexus of technology and AI, continually shaping the industry’s landscape.

Global AI Council: Redefining the Future with Ethical AI Education

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In a landmark move for the tech world, the Global AI Council has emerged as a beacon of innovation and ethical responsibility in artificial intelligence (AI). This pioneering organization is reimagining the landscape of AI education, making it a universal right, and championing ethical practices in AI.

Breaking New Ground in AI Education
Adhering to its principle of ‘AI for Good,’ the Council is revolutionizing access to AI education. By offering free, comprehensive training, it empowers individuals from diverse backgrounds, ensuring that the advancements in AI are not just for a privileged few but a tool for collective progress.

Leadership from the Forefront of AI
An advisory board of distinguished figures like Liz Heller, Rana Gujral, Shira Lazar, Carrie Santos, Daniel Robbins, and Kate Hancock steers this ship. Their unparalleled expertise in various facets of technology and business is pivotal in guiding the Council towards a future where AI is used ethically and for the benefit of all.

Amplifying the AI Narrative
The spokesperson for the Council plays a vital role in shaping global AI discourse. By promoting awareness and driving conversations on ethical AI, they are at the vanguard of transforming how we interact with and perceive AI technologies.

Commitment to High-Quality, Accessible Training
The Council’s dedication to providing top-notch, free AI training is a cornerstone of its mission. This initiative is crucial in closing the knowledge gap between the rapid advancement of AI and the general public’s understanding and utilization of this technology.

A Visionary’s Dream
Kate Hancock, the founder of the Global AI Council, envisions a world where AI is not a distant, complex technology but a tool for empowerment and societal enhancement. Her ambition is to harness AI’s potential to its fullest, ensuring it serves a greater purpose for humanity.

A New Chapter for AI in Society
The Global AI Council is more than an educational institution; it’s a movement towards a future where AI is synonymous with ethical use, inclusivity, and societal betterment.

In its pursuit of transforming AI education and ethics, the Global AI Council has become a catalyst for change. The leadership, including influential figures like Liz Heller, Rana Gujral, Shira Lazar, Carrie Santos, Daniel Robbins, and Kate Hancock, stands at the forefront, guiding the Council toward a future where AI is not only harnessed for innovation but also for the collective good. The Council’s commitment to offering free, high-quality AI training ensures that knowledge is democratized, breaking barriers and making AI accessible to a wider audience. Spearheaded by founder Kate Hancock, the Council is not just an educational institution; it’s a visionary force shaping a new chapter for AI in society. As the spokesperson for ethical AI practices, the Council is amplifying the narrative, fostering awareness, and fostering conversations that will redefine the relationship between humanity and AI technologies. Join this revolutionary journey towards an AI-powered future at

For in-depth insights and to join this revolutionary journey, visit


Technology in Construction Safety, Challenges and Future Trends

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By Ayad Chammas

Twenty one years ago, on an oil and gas job site in the MENA region, a worker was trapped for 30 minutes between the giant blades of a cooling fan rotating at 100 rpm, and the protective cage surrounding the fan. He had been inspecting cables within the cage when a supervisor, unaware of his presence, started the fan and left it to run. Analysis of the incident demonstrated many vulnerabilities in the project inspection/safety arrangements, which were subsequently strengthened. The project was put on hold for 2 weeks during the investigation, and although the worker escaped physically unharmed, he had to leave the job due to PTSD. 

Today, the possibility of such an incident can be avoided by using a device that allows remote inspections in tight inaccessible areas without the need for direct human intervention.  Nevertheless, construction remains a dangerous industry with workers exposed to serious safety hazards – such as falling from heights, injury from unguarded machinery, being struck by heavy objects, electrocutions, inhalation of toxic materials – as well as a wide range of health issues. 

According to the U.S Bureau of Labor Statistics (press release 16 December 2021) there were 4764 construction related fatalities in the US alone in 2020. Although this is a decrease of 10% from 2019, possibly due to the Covid related slow-down in construction, the long term trend shows little improvement over 10 years. Fatalities have persisted at between 4500 and 5500 per year despite the advent of new technologies and innovations in construction safety. In other words a worker dies every 2 hours from a construction related injury in the US. U.S. Bureau of Labor Statistics

Workers’ Compensation payments due to construction injuries annually amount to about $2.5 billion in the US, and one in every ten US construction workers is injured each year according to OHSA.

Therein lies a huge opportunity to save lives and improve worker welfare: It is estimated that construction firms in the US can save between $4 to $6 on the cost of injuries for every $1 invested in safety programs or technologies. Globally, injury rates on construction sites worldwide are 71% higher than the injury rates across all other industries. From my personal  industry experience spanning 4 decades. In this article we will briefly elucidate the emerging technologies and innovations available to solve a variety of HSE problems, to reduce fatalities and improve the wellbeing of all industry participants. At the end of the day, everyone must go home to their families and loved ones.

Technology for a Safe Construction Site 

According to OSHA classification, the “fatal four” causes of construction accidents are falls, electrocution, struck by object, and caught in between two objects. The acceleration of ‘safety tech’ to address these causes, whether by large companies or via innovative construction founding teams, has proven to be impactful whenever construction contractors have taken the decision to invest. Having said that, much of the industry is made up of small and medium sized contractors without the capacity to invest in new technologies. Currently, with much of the construction industry’s investment going into BIM and Digital Twins, the safety sector is being monopolized by a few large digital companies geared towards large scale implementations. Implementation on smaller job sites is often unfeasible. But that is changing with a host of new approaches that are more likely to be ubiquitous in their application.             

 Health monitoring and intervention innovations, often utilizing wearable devices, can provide real time monitoring of worker vital signs with live feedback to a control center. These applications can also help with mental health monitoring and suicide prevention.

Construction Robotics has benefited from the huge advances driven by the space program and manufacturing industry. Robotics technology has become more affordable and accessible. Construction Robotics startups are  working to automate dangerous processes on job sites, to augment worker capabilities, and to facilitate remote monitoring and inspections. 

Startups developing IOT Wearables are working to increase the effective strength of human workers and to eliminate occupational injuries as a result of lifting heavy objects or installation work in confined spaces.  IoT devices can be used to monitor and report on a range of environmental factors that impact health and safety. These include chemical contamination, inhalable dust, radiation, carbon dioxide, humidity, temperature, light, noise etc. 

Predictive Safety startups are using Machine learning and AI based predictive analytics to uncover potentially hazardous situations on job sites and make informed predictions about what may occur including accidents/incidents and near misses.

Software is now helping project managers identify high-risk areas in projects such as Oil and Gas installations, Highway and  Bridge projects, and large Building projects. Project managers can resolve issues and address problematic safety behaviors in real time via monitoring tools that suggest the actions to be taken backed up by analytics.

Technology in Construction Safety, Challenges and Future Trends

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Sensors and inter-device communications are also being used to minimize emergency response times and improve safety for end users. These devices can detect sudden movements or falls and share real time locations with emergency responders. IoT is being used to enable real time workforce reporting by integrating sensors or RFID tags into workers’ helmets or vests thus connecting everyone on the job site by wireless technology.

Safety begins with the qualifications and experience of the workforce. A major factor that influences the number of accidents on any job site is the level of competence of the workers and their familiarity with site conditions. For large accelerated projects, it is often the case that contractors resort to hiring the workforce in large numbers in a short period of time to perform specific activities such as concreting, paving, welding, structural steel installation, scaffolding, cable laying etc. Startups now have developed platforms that can work in concert with the concerned government agencies to set up national construction safety databases to validate workforce hiring choices.  Such Digital Platforms help builders ensure that they hire qualified workers on their projects and at the same time, provide construction companies with lists of skilled workers to choose from. Digital platforms are also used to train and test workers’ competencies remotely prior to hiring them, and without incurring costs for travel, testing and qualification.

Safety has become paperless with safety professionals using digital mobile phone applications to record and resolve minor safety issues on site without interrupting the work and suspending activities except where absolutely necessary. In emergencies, they can issue stop work notices in real time and direct safety evacuations without resorting to manual head counts. Regular safety activities such as automatic sign-on, inductions, evaluations, and worker permits, are now part of a simple digital solution for the job site. 

Managers and employees can now utilize safety applications which include inspections, training, observations, and near-miss reporting without laborious paperwork, thereby improving the engagement of every worker in the safety process via their mobile devices. Such applications include safety onboarding, job hazard analysis, pre-task planning, toolbox talks, audits, checklists, worker certifications and incident reporting.


Based on industry statistics, construction safety has not improved despite the availability of new technologies that could make a big difference. As compared to other sectors in the economy, the construction industry has been a slow adopter, but is now in the initial stages of a safety tech revolution. Technology is out there and is becoming increasingly accessible and affordable, such as construction robotics, IoT wearables and devices, sensors, Digital Platforms, and mobile applications to mention a few. The economics for adoption are justifiable and most importantly these technologies are saving lives every day. Young industry founders are putting in massive amounts of time and effort to democratize safety in the construction industry. It is no longer necessary for construction contractors to incur heavy licensing/technology costs, particularly medium size and small companies, in order to protect workers’ lives. They only need to be open to adopting these new technologies on their job sites. The construction accidents/fatality pandemic could become a thing of the past within this decade.

Ayad Chammas is a Construction and Engineering professional, Directing large infrastructure, oil & gas and renewable energy projects all over the world for the past three decades. He served as a senior executive in multinational infrastructure and Oil and Gas companies. Over the last decade, Ayad has been an active promoter of clean energy, investing in sectors such as EV and PV as well as Data Management and Fintech. He also serves on the board of several startups where he is also an investor and a partner in Nirman Ventures. 

The Vanguard of Security: LENX AI Weapons Detection and Communications Solution

In an era where the dynamics of security and defense are rapidly evolving, the integration of artificial intelligence (AI) into these domains is no longer a futuristic concept but a present-day necessity. Among the pioneers in this revolutionary transition is LENX, a company that has meticulously engineered a solution poised to redefine military and security protocols worldwide. The LENX AI Weapons Detection and Communications Solution emerges as a formidable force, seamlessly blending AI’s analytical prowess with advanced communication systems to address the intricate challenges besieging contemporary security efforts. “Your Safety, Our Duty,” encapsulates the ethos driving LENX’s innovation, symbolizing their unwavering commitment to advancing safety through technology.

At its core, the LENX solution is designed to detect potential threats with an accuracy and speed unattainable by human operatives alone. By harnessing the power of AI, this system can analyze vast arrays of data from multiple sources in real-time, identifying weapons and potential threats with unparalleled precision. This capability is not just about enhancing efficiency; it’s about saving lives by enabling quicker response times and more informed decision-making processes.

However, what sets the LENX solution apart is not merely its detection capabilities but its sophisticated integration with state-of-the-art communication technologies. In high-stakes scenarios where every second counts, effective communication channels are vital. The system ensures that once a threat is identified, information is swiftly relayed to relevant parties—be it military units or law enforcement agencies—facilitating immediate action.

This seamless marriage of detection and communication underpins a proactive approach to security. Rather than reacting to incidents post-occurrence, the LENX solution empowers authorities to anticipate and neutralize threats before they materialize. This paradigm shift towards preemptive action embodies a more intelligent form of warfare and public safety management, one where information superiority translates directly into enhanced security.

The ingenuity behind this solution stems from LENX’s meticulous research and development efforts. The team behind this innovation comprises experts in AI technology, defense strategy, and communications systems—all working synergistically to push the boundaries of what’s possible in security technology. This collaborative spirit extends beyond internal operations; LENX actively engages with stakeholders across various sectors to ensure their solution remains at the cutting edge of technological advancements.

In line with their mission “Your Safety, Our Duty,” LENX has prioritized ethical considerations in deploying their AI solutions. The company adheres strictly to regulations governing AI use in defense and security contexts while advocating for responsible innovation within the industry. This ethical stance underscores their dedication not only to enhancing safety but doing so with integrity.

The social media presence of LENX offers intriguing insights into their journey and achievements in developing this groundbreaking solution. Through platforms like Instagram (@lenxplatform) and LinkedIn (LEN Platform), they share updates on technological advancements, collaborations, and real-world applications of their system—a testament to their transparent approach in engaging with both industry peers and the general public.

Moreover, beyond just offering a product, LENX positions itself as a thought leader in AI-driven security solutions. Their website ( serves as a resource hub for understanding the complexities involved in integrating AI into defense mechanisms comprehensively—highlighting case studies, research findings, and expert analyses that provide deeper insights into their work’s impact.

As we stand on the cusp of a new age in military strategy and public safety management—one increasingly dominated by digitalization—the contributions of companies like LENX cannot be overstated. Their AI Weapons Detection and Communications Solution represents not just an advancement in technology but a leap forward in how we conceptualize security itself.

Embracing such innovations requires openness from all societal segments—governments need to recognize these technologies’ potential benefits; citizens must trust that such advancements aim at safeguarding rather than intruding upon personal liberties; industry players should collaborate towards ensuring these technologies are accessible yet secure.

The journey toward fully realizing this vision will undoubtedly be complex—fraught with technical challenges and ethical dilemmas alike—but it’s initiatives like those undertaken by LENX that illuminate our path forward.

With each step taken by entities like LENX towards refining such technologies further lies hope—not only for enhanced national defense capabilities but for fostering global peace through proactive deterrence strategies.

Indeed “Your Safety” becomes not just “Our Duty” at LENX but our collective responsibility as stewards of tomorrow’s world—a world where safety doesn’t come at freedom’s expense but rather hand-in-hand with it.


Published By: Aize Perez

Commit’s Dante Martinez sheds light on the benefits of platform engineering with AWS to streamline software delivery for startups

By: Dana Hoffsberg

Software development has become a crucial aspect of successful businesses in today’s fast-paced digital world. To meet the ever-increasing demands of businesses and customers, startups must be able to deploy new software quickly and efficiently. According to Dante Martinez, Commit’s Vice President of Solutions Architecture for North America, the best way to achieve this is by utilizing a platform engineering framework in conjunction with Amazon Web Services (AWS).

“Building and delivering software in the fast-paced world of startups can be filled with obstacles and complexities,” said Martinez. “As competition intensifies, optimizing your developer experience and streamlining software delivery becomes essential. That’s where the platform engineering framework, the AWS platform, and Commit’s expertise come together for our customers to innovate efficiently.”

Martinez explains that platform engineering is a game-changing approach to software delivery. By offering a suite of reusable tools and interfaces, it simplifies the process, improves efficiency, and ensures high-quality results.

“Startups frequently have a very limited budget and yet have to iterate with feature builds to find the perfect product market fit. Since Commit has a portfolio of more than a thousand projects, we have identified the proper frameworks that enable startups to experiment while limiting their cash burn rates.”

One of the key benefits of platform engineering is the simplification of software delivery which boosts developer efficiency and productivity. Automation and workflows that utilize AWS services and dynamic configuration capabilities can reduce the burden and stress on developers by automating repetitive tasks, allowing them to focus on developing new features that enhance software functionality. Startups particularly can leverage the advantages of traveling golden paths for architecture, application, and data decisions, which are critical to speed of innovation. 

According to Martinez, Internal Developer Portals (IDPs) represent the most advanced stage of platform engineering. These developer-centric platforms provide the tools, processes, and capabilities to deliver a high quality platform engineering experience for developers. The IDP becomes the interface for developers to interact with the platform. Developers experiment, plan, develop, test, and deploy, all from within the IDP. A common set of tooling and process means your teams collaborate better and share a protocol for delivery of software.

Startups particularly benefit from platform engineering as it helps them to avoid the accumulation of technical debt, which can be a true drain on resources and budget. By implementing best practices and automated solutions on day one, startups can focus on writing high-quality code and delivering best in class software. 

 Martinez suggests that a customized framework utilizing open-source tools and AWS native services can provide startups with platform engineering capabilities and advantages. This framework would save costs and enhance the competitive edge of startups. 

In conclusion, platform engineering in conjunction with AWS capabilities revolutionizes software delivery by simplifying the process, enhancing efficiency, and ensuring quality results. It offers customizable automation, developer-centric IDPs, and the ability to avoid technical debt, making it an essential tool for startups in the fast-paced digital landscape.

Published by: Martin De Juan

GPT-4 vs GPT-3.5 Differences

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As artificial intelligence continues its rapid evolution, the transition from GPT-3.5 to GPT-4 represents a monumental step forward. This detailed exploration highlights the key differences and advancements between these two AI giants, showcasing GPT-4’s groundbreaking impact across a spectrum of industries.

Building the Foundation: GPT-3.5’s Contribution to AI Development

GPT-3.5 served as a pivotal milestone in AI progression. Its enhanced abilities in understanding context, refining text generation, and interacting in more human-like ways laid the groundwork for the more advanced GPT-4. It represented a significant step in AI’s journey towards more sophisticated natural language processing, setting the stage for a more profound leap.

GPT-4: Revolutionizing AI-Language Models

GPT-4 emerges as a trailblazer in AI, distinguished by its exceptional understanding of context, enhanced multi-modal information processing, and an ability to interpret complex language nuances. This model represents not just an improvement, but a transformative shift in the capabilities of AI language models.

Key Features and Innovations Across Industries

  • Revolutionary Multi-modal Capabilities: GPT-4’s proficiency in handling both text and image inputs allows for groundbreaking applications, significantly enhancing user experiences in diverse fields such as e-commerce, education, healthcare, and media.
  • Exceptional Token Processing Power: GPT-4’s advanced token capacity enables it to digest and analyze long-form content, ranging from detailed scientific papers to comprehensive legal documents, thereby offering valuable insights and aiding in decision-making processes.
  • Parametric Superiority: The dramatic increase in GPT-4’s parameter count not only enhances its language processing abilities but also its understanding of complex concepts, making it an invaluable tool for tasks that require deep, nuanced comprehension.

Sector-Specific Advancements

  • E-Commerce and Marketing: GPT-4 is transforming the online retail landscape by generating rich, detailed product descriptions, and personalized shopping experiences. Its ability to create engaging narratives and marketing strategies is unparalleled.
  • Healthcare and Medical Research: In the medical field, GPT-4’s analytical capabilities facilitate breakthroughs in diagnosis and treatment planning, offering support in interpreting patient data and staying on top of all the latest research findings.
  • Finance and Economic Analysis: The model’s enhanced data processing abilities provide financial experts with deeper market insights, aiding in the development of more accurate predictive models and investment strategies.
  • Legal Industry: GPT-4 is revolutionizing legal research, quickly and accurately processing vast amounts of legal texts, which assists lawyers and legal professionals in case preparation and strategy development.
  • Creative Arts and Content Creation: In creative sectors, GPT-4 acts as a collaborator, assisting in the creation of diverse content ranging from literature to digital media. Its ability to understand and generate narrative structures pushes the boundaries of AI-assisted creativity.
GPT-4 vs GPT-3.5 Differences

Photo Credit: Grayling and Wraith

Embracing a Future Powered by GPT-4

The advancement from GPT-3.5 to GPT-4 is not just a step forward in AI technology; it’s a leap into a future where the boundaries between human and machine intelligence are increasingly blurred. GPT-4’s sophisticated language understanding and versatile applications across various fields underscore its role not just as a tool, but as a partner in innovation and creativity. This evolution is critical as we explore and unlock the vast potential of AI in improving and transforming our lives and work. With GPT-4, we stand on the cusp of an exciting era where the possibilities of AI are limitless.

To delve deeper into the nuances of GPT-4 and its comparison with GPT-3.5, refer to our full article 

Published by: Aly Cinco

KLOK partners with Mobilen Communcations INC to Redefine Mobile Communications Security

Revolutionizing the world of mobile communications, KLŌK is a fresh player in the market with an unwavering commitment to securing wireless networks. This ground-breaking company collaborates with the reputable Mobilen Communications INC to provide consumers with state-of-the-art WiFi devices safeguarded by top-tier network security protocols.

Seamlessly blending the expertise of cybersecurity specialists with telecommunications professionals, the KLŌK team embodies an exceptional blend of proficiencies to ensure your mobile communication remains strictly confidential.

KLŌK’s pioneering mission is underpinned by one simple principle – their dedication to securing wireless networks. Developing cutting-edge devices that not only uphold this resolve but elevate it is what sets KLŌK apart from its competition. They are driven by the belief that confidentiality and safety in mobile communications should never be compromised. Hence, they are devotedly engaging with innovative solutions to ensure that their devices serve as a bastion against all digital threats. Government entities and businesses that prefer top-notch security and privacy in their mobile communications may find KLŌK to be the go-to choice in the market.

A unique aspect of KLŌK’s method of operation rests in its zero-trust philosophy. In network security parlance, ‘zero trust’ signifies a belief that no device, user or system should be inherently trusted by default, even if they’re already operating within the network. This extends to routers and access points engaged by users for network connection, especially those shared among different users.

This approach ensures that every single access attempt is verified every time it is made. This increases the level of security immeasurably and significantly reduces the risk of internal threats. By maintaining a cautious vigilance and not placing unbridled trust in any component, KLŌK guarantees an unrivaled level of security that remains ever-vigilant.

Powering KLŌK’s promise of exceptional security is a special collaboration with Mobilen Communications Inc., which offers secure SIM Cards equipped with Post Quantum Encryption. This partnership embodies the unique philosophy of KLŌK, showing that its commitment to security is not only verbal but also evident in its actions. Mobilen’s secure SIM cards ensure that the network is protected by utilizing post-quantum cryptography. 

In the words of the company, “Mobilen is proud to partner with KLŌK to deliver its post-quantum encrypted network and secure all data in transit.” This level of detail illustrates the collaborative effort of both organizations and highlights KLŌK’s unwavering commitment to network security.

New to the market but a powerhouse in potential, KLŌK forms a promising prospect for anyone seeking a secure, reliable, and cutting-edge option in the world of mobile communications. Its partnership with Mobile Communications amplifies this, providing a greater level of assurance and trust in its brand and offerings.

The journey of KLŌK and its dedication to privacy and advancement of network security offers exciting prospects for its customers and for the telecommunications industry at large. As we move into an era where mobile communications have become a bedrock of our daily lives, the protection offered by companies like KLŌK, supported by strong partnerships with firms like Mobilen, serves as a beacon of reassurance in a world where data privacy and security can often feel uncertain.

To discover more about KLŌK’s offerings and their forward-thinking vision, visit their official website at Their resolve to safeguard your network is clear and remains a vital spark in the burgeoning field of mobile communications and network security.


Published By: Aize Perez

Tech Startup Innovation: High School Student’s Algorithm Revolutionizes Oil Transportation Industry

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In a fusion of youthful innovation and business acumen, high school student and start-up entrepreneur Warren Zhang has made his way in the oil transportation industry. His company, Ideal Thermal Technology Ltd., founded in 2022, has developed an algorithm that revolutionized the heating system in crude transportation, significantly reducing greenhouse gas emissions and for sure, reducing the fuel cost for shipowners and operators.

Zhang, balancing his school studies with his passion for mathematical modeling and computational simulation, has utilized his computational fluid dynamics (CFD) and machine learning skills to address a longstanding industry problem: the inefficient heating of oil in tankers. The traditional methods, often based more on engineering experience than precise calculations, have led to excessive energy consumption and quality degradation of the oil.

The algorithm, a culmination of various modules like the boiler, oil tank, and pipeline, integrated through advanced mathematical modeling and machine learning, offers a tailored heating approach. It adjusts heating based on external parameters, optimizing energy usage, and reducing fuel consumption by up to 7%. It is a complex process using theoretical analysis, MATLAB, CFD, database, VBA coding, machine learning and user interface design. This innovation is not only a technical breakthrough but also a testament to Zhang’s business prowess, as evidenced by his successful sale of the technology to Synergy Group in 2023, the leading oil tanker management company in the world.

The commercial success of Zhang’s algorithm reflects a shift in the oil transportation sector, traditionally slow to adopt new technologies, towards embracing innovative solutions. Ideal Thermal Technology’s journey, from a high school project to a key player in a global industry, highlights the potential of young entrepreneurs to innovate and transform established markets with sustainable and efficient solutions.

Moreover, this case exemplifies the growing trend of young innovators taking the lead in addressing global challenges. Zhang’s work aligns with the increasing emphasis on environmental responsibility and energy efficiency in traditional industries. His dual role as a student and entrepreneur showcases the potential of the younger generation to drive significant technological advancements while pursuing sustainable practices.

Warren Zhang’s journey from a high school STEM enthusiast to the founder of a groundbreaking tech startup exemplifies the transformative impact of youthful innovation in traditional industries. Besides offering a sustainable solution, his algorithm signals a broader shift towards environmental consciousness. His achievements signify the promising future of technological innovation led by young minds.

Cracking the Code of Machine Learning for Time Series Analysis: Opportunities and Difficulties

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By: Navin Kamuni

Amidst the deluge of data in the modern era, time series analysis has become indispensable in a wide range of fields, from climatic forecasting to stock market trend prediction. Fundamentally, time series analysis is the process of interpreting, modeling, and forecasting data points that are gathered one after the other over a period of time. This field has undergone a revolution with the introduction of machine learning (ML), which provides hitherto unseen insights and predictive power. However, incorporating machine learning into time series analysis is a difficult undertaking that presents a number of unique, intriguing, and complicated challenges.

Machine learning appears to be a natural ally for time series analysis because of its capacity to learn from and make predictions based on data. When these fields work together, deeper insights into temporal data patterns that were previously hidden or too complex to understand could be unlocked. However, due to the very characteristics of time series data, including their sequential structure, potential non-stationarity, and the complexities of temporal correlations, getting these insights is fraught with difficulties.

There are numerous difficulties. Every step of applying machine learning to this domain, from guaranteeing the quantity and quality of data to capturing the minute temporal dependencies that characterize time series, calls for specific methods and careful thought. Another layer of complexity is added by the dynamism of time series data, where patterns can change and evolve over time. Models that held true a day ago might not hold true today, requiring a continuous evolution of approaches and techniques.

It’s important to recognize the difficulty of the task at hand and the creativity needed to traverse this challenging terrain as we set out on this exploration. Applying machine learning to time series analysis presents both opportunities and challenges. It’s not just a technical undertaking; it’s a journey into a realm where data tells the story of time and where the patterns of the past write the future.

Challenge 1: Data Quality and Quantity

  • Handling Missing Values: In contrast to other types of data, time series data frequently has gaps or missing values because of a variety of problems, such as broken sensors or poor data transmission. It is imperative to develop robust imputation techniques.
  • Volume of Data: Processing and storage become difficult due to the sheer amount of data, particularly in high-frequency time series (such as stock market data). Effective ML application requires effective data handling techniques.

Challenge 2: Temporal Dependencies and Seasonality

  • Seizing Seasonality: Seasonality is present in a lot of time series. For ML models to produce reliable predictions, these cyclical variations must be taken into account.
  • Dealing with Stationarity The process of machine learning modeling is made more difficult by non-stationary data, whose statistical characteristics vary over time. It is frequently necessary to use techniques like transformation or differencing.

Challenge 3: Feature Engineering and Selection

Time-based Features: Using date and time components (holidays, day of the week, etc.) can have a big impact on model performance beyond just basic features.

High-Dimensional Feature Space: Time series data may produce a feature space that is highly dimensional, which raises the computational and complexity requirements for machine learning models.

Challenge 4: Model Complexity and Interpretability

Finding a Balance Between Complexity and Performance: While highly complex models may perform well, they may also require more computational power and be harder to understand.

Explaining Predictions: Transparency and explainability of model predictions are just as crucial in crucial domains like healthcare as accuracy.

Challenge 5: Overfitting and Model Evaluation

  • Evaluation Metrics: Selecting relevant metrics that accurately capture the time series forecasting model’s performance is crucial.
  • Dynamic Environments: Time series data must be able to adapt and not just fit historical data because these environments, such as stock markets, are dynamic and subject to rapid change.

Challenge 6: Real-time Analysis, Scalability, and Deployment

  • Stream Processing: ML models need to efficiently process and analyze data streams in scenarios such as IoT and real-time monitoring.
  • Deployment Challenges: Efficient infrastructure and ongoing performance degradation monitoring are necessary when deploying machine learning models in a production setting, especially for real-time applications.


The process of integrating machine learning with time series analysis involves a number of unique obstacles that call for specialized answers. More reliable, understandable, and effective machine learning models that are skilled at handling the subtleties of time series data will be the field’s future. There is a limitless potential for innovative applications and advancements in this field as science and technology develop.


  • Data Quality and Quantity: Kaggle Competitions: Machine Learning Time Series Forecasting Problems (
  • Temporal Dependencies and Seasonality: Temporal Fusion Transformer (TFT): Google Research Blog on Temporal Fusion Transformers (
  • Feature Engineering and Selection: Time-Series Forecasting in Data Center Telemetry: Challenges and Approaches to Time-Series Forecasting in Data Center Telemetry: A Survey (
  • Model Complexity and Interpretability: Healthcare Time Series Analysis: Time Series in Healthcare: Challenges and Solutions (


Cracking the Code of Machine Learning for Time Series Analysis: Opportunities and Difficulties

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About the Author – Navin Kamuni:
An AI Visionary and Technology

Navin Kamuni stands as a notable figure in the realm of contact center technologies and AI innovation. With a career spanning over two decades, he has been instrumental in driving technological advancements in the industry. His expertise extends to the intricate domains of Artificial Intelligence (AI) and Machine Learning (ML), areas where he has demonstrated considerable proficiency and forward-thinking.

Beyond his professional achievements, Kamuni is a revered mentor and guide within the tech community. As an author, Kamuni has ventured into the literary world with his book, “Connect.AI: The New Era of Digital Experiences.” The book encapsulates his deep understanding of AI’s role in transforming digital customer experiences, positioning him as a thought leader in the field.

Kamuni’s contributions extend to writing insightful articles and research papers, and his participation in conferences and seminars has made him a respected voice in the tech community. His commitment to sharing knowledge and inspiring others is evident in his active engagement with various platforms, including IEEE and

For those interested in learning more about Navin Kamuni’s professional journey, achievements, and current projects, his LinkedIn profile offers a comprehensive overview. Connect with him on LinkedIn to explore his extensive experience, publications, and contributions to the field of technology and AI.

In summary, Navin Kamuni is a visionary leader whose work at the intersection of technology and AI continues to inspire and influence the industry’s landscape.