Autore: martechcubejohn

Hello Bill, we’re delighted to have you with us, could you provide an overview of your professional journey leading up to your current role as Chief Revenue Officer at BlueCloud?   

I come from a family of entrepreneurs, finding ways to help support the family business from an early age. My first job was cleaning cars at my parent’s car rental company in Buffalo, NY. We worked hard and constantly discussed business, outcomes, and the variables that could be controlled to help drive the company KPIs in the right direction. It was a central part of my life. As I progressed through my academic journey, my focus was on financial and accounting management. However, my practical experiences led me away from the traditional paths of corporate and public accounting and towards a career in sales within the financial services sector. Over the years, I gained extensive exposure to businesses of all sizes, from small enterprises to corporate giants like General Electric. This diverse background equipped me with a comprehensive understanding of financial operations, laying the groundwork for my transition into business intelligence and analytics. Embracing emerging technologies, I navigated through various roles spanning sales, customer success, and solution engineering across multiple organizations. Despite experiencing success in different environments, I continually sought challenges that would leverage my financial expertise and keep me at the forefront of technological innovation. My journey eventually led me to ThoughtSpot, where I spearheaded market expansion efforts and rose through the ranks to manage multiple regions. However, it was my alignment with BlueCloud’s vision and values that ultimately drew me to my current role. Here, I’ve found the perfect combination of my diverse skill set and passion for driving business outcomes through transformative technologies.

In your extensive experience, what specific challenges do IT companies and consulting firms encounter when adapting to the rapidly evolving digital landscape?  

I’ve observed that one of the primary challenges is the necessity for clearly defined business values and a willingness to embrace change. This dynamic closely mirrors the fundamentals of a standard sales process. Just as in sales, it’s crucial to identify and understand the pain points driving the need for change. While it may seem tempting to stick with legacy technology, the risks associated with maintaining outdated systems can be just as significant, if not more so, than keeping pace with the evolving technological landscape. At its core, navigating this landscape requires effective change management and risk mitigation strategies. Moreover, it involves bridging the gap between technical solutions and non-technical stakeholders within organizations. For IT companies and consulting firms like ours, this often entails dedicating time and resources to ensure that stakeholders comprehend how technology integration aligns with and supports their overarching business objectives. Ultimately, the conversation must revolve around the delivery of tangible business outcomes and value rather than merely implementing cutting-edge technology for its own sake. If we fail to address this fundamental aspect, we risk providing solutions that lack meaningful impact and fail to meet the client’s objectives. Therefore, our challenge lies in consistently facilitating discussions that center on the alignment of technology with specific business needs and desired outcomes.

To Know More, Read Full Interview @ https://ai-techpark.com/aitech-interview-with-bill-tenant-cro-at-bluecloud/ 

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Despite remarkable progress in pharmaceuticals, more than 7,000 diseases persist without efficacious treatments. Many medical conditions remain underfunded and overlooked, leading to low success rates in new drug discovery endeavors.

The journey from identifying a potential molecule to developing a market-ready medicine is an extensive, laborious, and expensive process. However, quantum computing (QC) offers the potential to revolutionize this journey by addressing complex challenges within the healthcare supply chain and even creating new medications from scratch. Nevertheless, the integration of QC into drug research remains a gradual process.

Today, we delve into the transformative impact of QC on drug research and its promising prospects in the realm of healthcare.

Enhancing Drug Research Efficiency with Quantum Computing

Drug discovery entails intricate processes that blend computational simulations with laboratory experimentation. QC introduces novel discovery approaches, enabling the selection of candidate molecules with desired properties without the need for exhaustive screening procedures. Leveraging artificial intelligence (AI) and machine learning (ML) alongside QC's unique computational principles accelerates drug development, particularly for diseases such as cancer and Alzheimer's, where traditional methods have fallen short.

Democratizing Drug Development with Quantum Computing

QC not only promises to streamline drug development processes but also democratize access to them. Cloud-based QC services provide researchers, ranging from startups to established pharmaceutical firms, with access to quantum computing resources. This accessibility reduces barriers to entry in the pharmaceutical industry, empowering a wider range of stakeholders to participate in drug development endeavors.

Future Trends of Quantum Computing in Drug Discovery

The future of QC in the pharmaceutical industry is rapidly evolving, especially with the emergence of hybrid quantum-classical systems. These systems combine quantum and classical computing techniques to address complex challenges more efficiently. Collaborative ecosystems between pharmaceutical companies, technology firms, and academic institutions are also on the rise, particularly in the realm of QC-enabled drug discovery. Such collaborations aim to leverage quantum algorithms to enhance ML capabilities in drug design and discovery processes, promising groundbreaking advancements in the field.

In conclusion, QC stands poised to revolutionize drug discovery, offering improvements in accuracy and accelerating the overall process. By harnessing the power of quantum bits and algorithms, researchers can address current challenges in drug development and expedite the delivery of novel treatments. As research and innovation in QC continue to advance, its role in transforming the pharmaceutical industry and improving patient outcomes will undoubtedly become increasingly significant.

To Know More, Read Full Article @ https://ai-techpark.com/the-intersection-of-quantum-computing-and-drug-discovery/ 

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The rise of Generative AI (GenAI) has enormous potential for the banking and finance industries. By utilizing GenAI, banks and credit unions speed applications from submission to approval, save time and effort, and deliver a desirable customer experience.

A recent report from the Society for Human Resource Management (SHRM) and The Burning Glass Institute details how GenAI will have an outsized role on the banking and finance industries. The report lists Morgan Stanley, Bank of America and Northwest Mutual as some of the organizations that are most likely to capitalize on the implementation of GenAI. Their study also measures GenAI exposure among several different professional industries; “investment banking and securities dealing and brokerage” measured third highest while “mortgage and nonmortgage loan brokers” ranked highest overall. If SHRM and The Burning Glass Institute are so convinced that GenAI will profoundly alter how financial institutions operate, what will that change look like and why does it matter?

GenAI is distinct from other forms of automation by its ability to automate what is typically considered knowledge work. This represents a sea change in how professional industries, including financial services, will implement automation technology in their workplaces. In fact, financial services are especially dependent on repetitive manual processes requiring specialized knowledge. Processes like loan underwriting and credit card applications require knowledge workers to manually input data and individually connect with customers or members, which takes up the majority of workers’ time and tasks.  GenAI excels in automating repetitive, manual tasks—such as data processing and pattern identification—streamlining operations and freeing up valuable time for knowledge workers.

The applications of GenAI within financial services manifest in both evident and nuanced ways, each offering distinct advantages to forward-thinking institutions. Many industries have begun employing GenAI solutions as chatbots for customer service, and financial services are no exception. GenAI-powered chatbots, operational around the clock, offer an immediate response to customer inquiries, significantly reducing the need for direct intervention by skilled professionals and enhancing service efficiency.  However, these solutions become even more compelling for financial institutions when embedded in the bank or credit union’s broader systems. For example, a loan applicant can interact with a GenAI-enabled chatbot and get a real-time status update on their loan status by providing a few identifying details. In this way, GenAI increases efficiency while also directly improving the customer or member experience.

GenAI technology is novel, and its implementations are sure to evolve further in the coming months and years. However, its potential for financial services is undeniable. In order for banks and credit unions to take full advantage of this nascent technology, financial institutions need to create AI policies, complete digital transitions and start exploring and investing in GenAI use cases now.

To Know More, Read Full Article @ https://ai-techpark.com/how-generative-ai-enhances-credit-unions-and-community-banks/ 

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In the past few years, we have witnessed rapid advancements in the field of quantum computing (QC), which triggers the potential revolutionization in various industries, such as healthcare, supply chain, and manufacturing. This technology can perform complex computations at an unimaginable speed when compared to classical computers, even against quantum threats.

According to the National Institute of Standards and Technology (NIST), the post-quantum cryptography (PQC) standards are expected to be completed by 2024, allowing quantum vendors and experts to keep up with the six QC trends that intersect machine learning (ML) and artificial intelligence (AI).

In today’s exclusive AI Tech Park article, we will delve into the top six quantum computing trends for 2024, providing detailed insight for quantum vendors and experts to harness the transformative power of this cutting-edge technology.

Quantum-Sensing Technologies

The implementation of quantum sensing technologies will enable IT organizations, quantum vendors, and experts to achieve unprecedented levels of sensitivity and precision in measuring and detecting applications. In 2024, businesses will leverage quantum sensor tools and applications for environmental monitoring, medical diagnostics, and mineral exploration to gather actionable insights and make informed decisions based on highly accurate data.

Quantum-Safe Cryptography

With the arrival of quantum computers, traditional cryptographic algorithms will become absolute and vulnerable to quantum attacks. Therefore, organizations will adopt quantum-safe cryptography solutions and technology to protect their sensitive data and communications from quantum threats. The implementation of quantum-safe algorithms, such as quantum key distribution or lattice-based cryptography, will become essential tools for securing digital assets and guaranteeing data privacy in a post-quantum world.

Quantum Machine Learning

Quantum computing, when intersected with ML, enables businesses to leverage quantum algorithms for pattern recognition, optimization, and predictive analytics. The quantum machine learning (QML) algorithms will unlock new insights from large data sets, accelerate model training processes, and enable more accurate predictions in numerous domains. The quantum vendors and experts can further explore the possibilities of integrating QML into the data and analysis section to make data-driven decisions to streamline innovation and develop a competitive advantage in this digital world.

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Askia, can you share more about your role as Chief Growth Officer at DriveLine.ai and the key responsibilities associated with it?

In my role as Chief Growth Officer, I wear several hats, all focused on one critical goal: driving revenue growth and expansion. Through a multi-pronged approach that leverages strategic partnerships, comprehensive growth strategies, I am responsible for propelling DriveLine to market leadership.

My key responsibilities include the development of strategic partnerships and alliances, implementing comprehensive growth strategies, identifying and leveraging category and industry trends including new market opportunities, and the productization of our audience and location intelligence.

Beyond these key responsibilities, I also contribute to other areas which support our growth including working closely with our product and business development teams, to ensure alignment and collaboration across the organization.

With 17+ years of experience in consumer strategy, how has your journey shaped your approach to driving consumer behavior for brands?

Over the past 17+ years, my approach to consumer strategy has been profoundly reshaped a few times. My journey began in 2000 at KTLA-TV, where I dove headfirst into the bustling world of advertising sales, right as the digital advertising revolution converged with television. This early exposure to the nascent digital landscape, when monetization through consumer interaction was still largely uncharted territory, instilled in me a deep appreciation for innovation and a future-focused approach has become a defining characteristic of my strategic skill set ever since.

With almost two decades of experience navigating the ever-evolving media landscape, I have not only witnessed significant changes, but actively participated in shaping them. Through triumphs and setbacks, I have acquired a deep understanding of consumer behavior and the critical role it plays in successful media campaign outcomes. This valuable knowledge informs my strategic approach, ensuring that every campaign I develop is human-centered, data-driven, results-oriented, and impactful.

Can you elaborate on your future-focused approach to campaign performance and how it is applied across various client types, whether local, regional, national, or global?

Every component of advertising is related to a time period, timing and/or seasonality, making advertising campaigns intrinsically planned for the future. By focusing on the future, I help brands achieve their marketing goals in a sustainable and scalable way. By applying my future-focused approach to campaign performance, I help brands achieve success regardless of their size or location. This means focusing on long-term trends, anticipating future consumer behavior, and proactively adapting to stay ahead of the curve.

To Know More, Read Full Interview @ https://ai-techpark.com/aitech-interview-with-askia-underwood/ 

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As the trajectory of computing power continues its exponential ascent, quantum computing stands at the forefront, poised to tackle challenges that have long confounded traditional computational methods. In the ever-evolving landscape of the 21st century, quantum computing emerges as a dynamic field brimming with promise, offering a plethora of solutions across diverse domains such as climate modeling, energy optimization, drug discovery, and healthcare.

The allure of quantum computing lies in its ability to conduct simulations and optimizations on a scale previously unimaginable, presenting a paradigm shift that beckons computer engineers, scientists, and developers to delve into the realms of quantum physics. Indeed, the fusion of quantum principles with computational prowess heralds a digital revolution, paving the way for transformative innovations and novel approaches to age-old problems.

To facilitate the journey into this exciting frontier, a curated selection of quantum computing certification courses stands ready to guide aspiring learners:

The Complete Quantum Computing Course by StationX:

Tailored for STEM professionals embarking on their quantum odyssey, this foundational course unravels the mysteries of quantum regulations and their pivotal role in bestowing unparalleled computational supremacy. From quantum computing basics to error correction techniques, quantum algorithms, and states manipulation, participants gain insights into applications spanning cybersecurity, pharmaceuticals, and engineering.

Quantum Computing: The Big Picture by Pluralsight:

Delving into the nuances of quantum mechanics, this professional course offers a panoramic view of key concepts such as superposition, entanglement, and the crafting of quantum algorithms. Designed to empower IT engineers, developers, and computer scientists, it sheds light on the transformative potential of quantum computing across diverse domains including IoT, wireless security, network engineering, and augmented reality.

Applied Quantum Computing III: Algorithm and Software by EdX: 
Catering to the discerning palate of IT engineers and computer scientists, this advanced-level offering delves deep into the intricacies of quantum Fourier transform, search algorithms, and their myriad applications. With a focus on optimization, simulation, quantum chemistry, machine learning, and data science, participants are immersed in live sessions and personalized learning experiences, honing their skills in programming, data science, and algorithmic design.

In conclusion, the imperative of familiarizing oneself with quantum computing in the digital age cannot be overstated. These meticulously curated certification courses offer not merely a gateway, but a pathway to mastery, equipping computer scientists, engineers, and programmers with the requisite knowledge and skills to harness the transformative potential of quantum computing and chart a course towards innovation and excellence.

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The dawn of the digital age brought forth a range of technological advancements, reshaping industries and redefining norms. In the realm of software engineering, generative AI coding assistants, including tools like GitHub Copilot and Tabnine, epitomise this wave. Drawing from the impact of foundational models like OpenAI’s GPT and Anthopic’s Claude, these tools interpret natural language inputs to suggest and generate code snippets, amplifying developer productivity. Notably, GitHub Copilot now underpins a staggering 46% of coding tasks, enhancing coding speed by an impressive 55%.

A study from McKinsey emphasised that software development stands as one of the best ways to achieve organisational efficiency with generative AI. Yet, the overarching question remains: How can generative AI go beyond mere code generation to elevate the software development life cycle?

Code better, not just faster

According to a recent survey from Stack Overflow, 70% of developers are either harnessing AI tools or gearing up to integrate them in the imminent future. Yet, while tools like GitHub Copilot and Replit’s Ghostwriter are predominantly centred on development and testing, there are several ways that generative AI could be used to enhance developer’s workflows.

Among the various stages of the Software Development Life Cycle, code optimisation is one that is often overlooked. Yet, when embedded within the Continuous Integration and Continuous Deployment processes, it becomes the point wherein code is developed to peak performance. It’s the point at which code isn’t just moulded to function but to excel, to minimise latency and to amplify user experiences.

However, the benchmarks for code performance are continuously being changed, particularly in a landscape dominated by AI. But what exactly is driving this?

Cost of compute and profitability: Software is eating the world. Even the allure of modern vehicles often lies in digital features like parking assistance and IoT connectivity. Yet, the attraction of generative AI coding assistants comes at a price. A16Z’s report underscores this, with cloud spending often taking 75-80% of revenue for software firms. Clearly, efficient code is not merely a technical goal but a financial necessity, as it can significantly cut cloud costs and boost profit margins for organisations.

Speed, Scale and Customer Experience: In the business world where milliseconds matter, code optimisation is the linchpin. From high-frequency trading to autonomous vehicle decision-making, performance is king. However, the advent of Generative AI and LLMs brings a new dimension to the speed challenge. Despite their benefits, the extensive processing times associated with LLMs can pose a significant hurdle for real-time and edge applications, particularly as the number of users and applications continues to grow.

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Digital twin (DT) is a rapidly growing concept that has gained traction as it can improve product designs, optimize performance at an industrial level, and create proactive maintenance services. This upgrading technology has started taking shape on an entirely new and different scale as it has become the pillar for futuristic smart cities.

In the scenario of smart cities, digital twins work as virtual replicas of the city’s assets, such as buildings, road lighting systems, energy and grid capabilities, and mobility solutions. However, it is not enough to develop a third-dimensional (3D) model of these sources. Therefore, the digital twin of smart cities pairs the 3D information with spatial modeling (for building the environment), simulations and mathematical models (for workable electric and mechanical systems), and other components that use real-time data feeds from the Internet of Things (IoT) platforms.

In this exclusive AITech Park, we will explore how digital twins will help smart cities evolve in 2024.

Twinning With the New Age Smart Cities

With the introduction of digital twins in the construction field, this technology has the potential to unlock data that was traditionally trapped in silos.

When constructing a new building, the digital twin is developed from the initial phases of the project by the architects, engineers, and construction (AEC) teams to work together to define each other’s performance goals and get the desired outcomes. Now, as the project progresses, the data is continuously collected and fed into the model using any digital twin solution. When the infrastructure is handed over to the owner, the virtual twin collects operations data that will fine-tune performance and manage maintenance in the long term.

As the digital twin mostly revolves around data supplies, it’s the physical twin that helps in performing predictions and simulations in response to real-world conditions. For instance, in the construction industry, the physical twin can be used to align a building’s solar facade that follows the path of the sun and modifies airflow to minimize the spread of germs.

Therefore, it is evident that DT allows the AEC teams to connect better throughout the entire assignment lifecycle, from design to decommissioning. Further, integrating static data aids in specifying the segment and creating maintenance schedules based on the dynamic data of occupancy rates and environmental conditions.

When DT is combined with building information modeling (BIM), the AEC team is well connected to data, which processes dynamic, real-time, bidirectional information management, bringing out the full potential of integrated workflows and information sharing with clients.

As DT is integrated with artificial intelligence (AI) and machine learning (ML), this technology will evolve from being a conceptual tool to becoming more competent and autonomous as software capabilities expand. The application areas for digital twins will continue to reach new heights in the coming years and will change the way AEC teams create, use, and optimize physical spaces and multiple processes.

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Generative AI technology like ChatGPT has brought the world one step closer to the futuristic society envisioned by forward-thinking science fiction writers. But will this future be a utopian or dystopian one? Time will tell. In the meantime, businesses must understand and leverage AI’s burgeoning influence over the zeitgeist to build favorable public sentiment about their brand’s reputation. It has become a make-it-or-break-it moment for corporations in the battle against the spread of AI-led misinformation.  

AI Angst

Opinions run hot, cold, and everywhere in between when it comes to AI’s possibilities and ramifications. A recent survey by the Pew Research Center found that 52% of Americans are expressing greater concern rather than excitement regarding the increasing dependence on AI. This sentiment has risen by 14% since 2022. The current era represents a Cognitive Industrial Revolution teeming with potential, including AI’s provocative ability to sway public opinion.

Businesses and their communication teams must fortify their message with an if-you-can’t-beat-them, join-them approach, leveraging AI’s influence over public opinion and using that to their advantage. By being open and transparent, companies can direct the narrative and strengthen their brand’s image by becoming thought leaders in their industry—with more communication, not less.

Better Communication Through Thought Leadership

Businesses are urged to become thought leaders, effectively communicating their brand message through credible third-party channels such as the media and influencers. The influencer marketing sector is booming and is expected to reach a value of $24 billion by year-end. AI is being embraced by 63% of companies for campaign executions, with 55% utilizing it to pinpoint influencers. Moreover, 33% of the total market capitalization of the S&P 500, attributed to goodwill, is impacted by public relations (PR) strategies and tactics.  Brands must proactively shape and manage their narratives to influence their target audiences. Failure to do so relinquishes control of these narratives to others—rendering marketing, and sales, less effective.

However, implementing this shift necessitates moving away from stale approaches in public relations and public perception. Traditional methods in these areas have been neglected, with only a minority understanding the strategic guiding of public opinion. As AI-generated content becomes more prevalent, the importance of compelling storytelling at the beginning of the customer journey or at the start or top of the PR>Marketing>Sales funnel cannot be overstated.

Mastering the Codified Body of Knowledge of Public Opinion

Companies benefit from becoming better acquainted with the established principles and empirical data that mold public perception. Delving into case studies, contemporary theories, rules of engagement, and the evolution of public relations offers valuable insights into the intricate dynamics that shape public opinion. Armed with this knowledge, organizations can develop strategies, and tailor messages that strike a chord with audiences, enhancing the prospects of broader acceptance and dissemination.

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As we have stepped into an era of rapid technological transformation and competition, the business environment needs to prioritize workers’ safety and well-being to safeguard their employees from workplace hazards. According to a recent statement by the International Labour Organization (ILO), it is reported that approximately 2.3 million individuals globally lose their lives to work-related incidents or illnesses annually, and here AI comes as a savior that enhances occupational safety and health (OHS) practices.

The continuous advancement of technology unlocked a new channel of employee welfare, which paved the way for a future where employees feel protected and prosperous. AI ensures workforce measures, identifies potential hazards, and creates a more secure work environment for employees.

The implementation of occupational health and safety software can aid employers in elevating their company’s safety, as AI and data professionals design these digital solutions in collaboration with occupational healthcare practitioners, safety professionals, and paramedics to document, monitor, analyze, and manage employees’ health care.

Today’s exclusive AITech Park article will discuss the top six occupational safety and health software programs that will change workplace dynamics forever.

The Top Five Occupational Health and Safety (OHS) Software of 2024

For a better understanding, let’s have a quick look at some of the best occupational health and safety software that will be a guide for employers to streamline their business processes.

EcoOnline Platform

The EcoOnline Platform is a centralized cloud-based solution for workplace safety, chemical safety, and compliance that includes the guidance of safety managers and chemical managers to understand OHS practices. This software is developed for companies to adopt health and safety procedures so that all workers may take part in them and make safe decisions. It contains numerous modules for SDS management, chemical safety reporting, risk assessments, accident management, work permits, training, and much more.

Intenseye Software

Intenseye is the world’s #1 environmental health and safety (EHS) platform, powered by cutting-edge AI. Using existing cameras within facilities, Intenseye captures safety risks and provides real-time notifications, risk and trend reports, visualizations, and tailored mitigation strategies to modernize overall safety management and ensure the frontline remains injury-free. The software encourages EHS teams to focus on priority tasks, establish robust safety measures, and achieve time and resource savings, ultimately boosting productivity.

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