Tag: Generative Ai

Leslie, can you please introduce yourself and share your experience as a CEO and Founder at TurinTech?

As you say, I’m the CEO and co-founder at TurinTech AI. Before TurinTech came into being, I worked for a range of financial institutions, including Credit Suisse and Bank of America. I met the other co-founders of TurinTech while completing my Ph.D. in Computer Science at University College London. I have a special interest in graph theory, quantitative research, and efficient similarity search techniques.

While in our respective financial jobs, we became frustrated with the manual machine learning development and code optimization processes in place. There was a real gap in the market for something better. So, in 2018, we founded TurinTech to develop our very own AI code optimization platform.

When I became CEO, I had to carry out a lot of non-technical and non-research-based work alongside the scientific work I’m accustomed to. Much of the job comes down to managing people and expectations, meaning I have to take on a variety of different areas. For instance, as well as overseeing the research side of things, I also have to understand the different management roles, know the financials, and be across all of our clients and stakeholders.

One thing I have learned in particular as a CEO is to run the company as horizontally as possible. This means creating an environment where people feel comfortable coming to me with any concerns or recommendations they have. This is really valuable for helping to guide my decisions, as I can use all the intel I am receiving from the ground up.

To set the stage, could you provide a brief overview of what code optimization means in the context of AI and its significance in modern businesses?

Code optimization refers to the process of refining and improving the underlying source code to make AI and software systems run more efficiently and effectively. It’s a critical aspect of enhancing code performance for scalability, profitability, and sustainability.

The significance of code optimization in modern businesses cannot be overstated. As businesses increasingly rely on AI, and more recently, on compute-intensive Generative AI, for various applications — ranging from data analysis to customer service — the performance of these AI systems becomes paramount.

Code optimization directly contributes to this performance by speeding up execution time and minimizing compute costs, which are crucial for business competitiveness and innovation.

For example, recent TurinTech research found that code optimization can lead to substantial improvements in execution times for machine learning codebases — up to around 20% in some cases. This not only boosts the efficiency of AI operations but also brings considerable cost savings. In the research, optimized code in an Azure-based cloud environment resulted in about a 30% cost reduction per hour for the utilized virtual machine size.

To Know More, Read Full Interview @ https://ai-techpark.com/ai-tech-interview-with-leslie-kanthan/ 

Related Articles -

Generative AI Applications and Services

Smart Cities With Digital Twins

Trending Category - IOT Wearables & Devices

Bernard, kindly brief us about Generative AI and its impact on various industries such as retail, healthcare, finance, education, manufacturing, marketing, entertainment, sports, coding, and more?

Generative AI (GenAI) is revolutionizing multiple sectors by enabling the creation of new, original content and insights. In retail, it’s personalizing shopping experiences; in healthcare, it’s accelerating drug discovery and patient care customization. Finance is seeing more accurate predictive models, while education benefits from tailored learning materials. Manufacturing, marketing, entertainment, sports, and coding are all experiencing unprecedented innovation and efficiency improvements, showcasing GenAI’s versatility and transformative potential.

Your latest book, “Generative AI in Practice,” is set to release soon. Could you share some key insights from the book, including how readers can implement GenAI, its differences from traditional AI, and the generative AI tools highlighted in the appendix?

In “Generative AI in Practice,” I explore how GenAI differs fundamentally from traditional AI by its ability to generate novel content and solutions. The book offers practical guidance on implementing GenAI, highlighting various tools and platforms in the appendix that can kickstart innovation in any organization. It’s designed to demystify GenAI and make it accessible to a broader audience.

With your extensive experience advising organizations like Amazon, Google, Microsoft, and others, what role do you see GenAI playing in transforming business strategies and performance?

It’s clear that Generative AI (GenAI) is poised to become a pivotal element in reshaping business strategies and boosting performance across industries. By leveraging GenAI, companies can gain a significant competitive advantage through the acceleration of innovation, the automation of complex and creative tasks, and the generation of actionable insights. This transformative technology enables businesses to refine their decision-making processes and enhance customer engagement in ways previously unimaginable. As we move forward, the integration of GenAI into core business operations will not only optimize efficiency but also open up new avenues for growth and value creation, marking a new era in the corporate landscape.

Why is Generative AI considered the most powerful technology humans have ever had access to, and what makes it stand out compared to other advancements in the tech industry?

Generative AI not only stands out as perhaps the most potent technology available today due to its capacity for creativity and innovation, surpassing prior tech advancements by enabling machines to understand, innovate, and create alongside humans, but it also offers a pathway to artificial general intelligence (AGI). This potential to achieve AGI, where machines could perform any intellectual task that a human can, marks a significant leap forward. It represents not just an evolution in specific capabilities, but a foundational shift towards creating systems that can learn, adapt, and potentially think with the breadth and depth of human intelligence. This aspect of generative AI not only differentiates it from other technological advancements but also underscores its transformative potential for the future of humanity.

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

Related Articles -

Cloud Computing Chronicles

Transforming Business Intelligence Through AI

Trending Category - Mobile Fitness/Health Apps/ Fitness wearables

Marketing organizations are increasingly adopting artificial intelligence to help analyze data, uncover insights, and deliver efficiency gains, all in the pursuit of optimizing their campaigns. The era of AI augmentation to assist marketing professionals will continue to gain momentum for at least the next decade. As AI becomes more pervasive, this shift will inevitably reshape the makeup and focus for marketing teams everywhere.

Humans will retain control of the marketing strategy and vision, but the operational role of machines will increase each year. By 2025, it is projected that 70% of lower-level administrative duties will largely disappear as artificial intelligence tools become more deeply entwined in the operations of marketing departments. Similarly, many analytical positions will become redundant, with smart chatbots expected to assume up to 60% of daily responsibilities.

However, the jobs forecast is not all doom and gloom because the demand for data scientists will explode. The ability to aggregate and analyze massive amounts of data will become one of the most sought-after skillsets for the rest of this decade. By 2028, the number of data science positions is expected to grow by 30%, remaining immune to economic pressures. These roles will be less susceptible to budget cuts, highlighting the critical importance of data analysis in the evolving marketing landscape.

Effects of the AI Rollout on Marketing Functions

As generative AI design tools are increasingly adopted, one thorny issue involves copyright protection. Many new AI solutions scrape visual content without being subjected to any legal or financial consequences. In the year ahead, a lot of energy and effort will be focused on finding a solution to the copyright problem by clarifying ownership and setting out boundaries for AI image creation. This development will drive precious cost and time savings by allowing marketing teams to embrace AI design tools more confidently, without the fear of falling into legal traps.

In addition, AI will become more pivotal as marketing teams struggle to scale efforts for customer personalization. The gathered intelligence from improved segmentation will enable marketing executives to generate more customized experiences. In addition, the technology will optimize targeted advertising and marketing strategies to achieve higher engagement and conversion levels.

By the end of 2024, most customer emails will be AI-generated. Brands will increasingly use generative AI engines to produce first drafts of copy for humans to review and approve. However, marketing teams will have to train large language models (LLMs) to fully automate customer content as a way of differentiating their brands. By 2026, this practice will be commonplace, enabling teams to shift their focus to campaign management and optimization.

To Know More, Read Full Article @ https://ai-techpark.com/future-of-marketing-with-ai-augmentation/ 

Related Articles -

Future of QA Engineering

Mental Healthcare with Artificial Intelligence

Trending Category - IOT Smart Cloud

The intersection of artificial intelligence (AI) and digital advertising to create truly engaging experiences across global audiences and cultures is reaching an inflection point. Companies everywhere are leveraging powerful trends in AI, machine learning and apps for performance marketing.

Today’s AI and machine learning technologies are allowing apps to understand speech, images, and user behavior more naturally. As a result, apps with AI capabilities are smarter and more helpful, and companies are using these technologies to create tailored experiences for customers, regardless of language or background. AI is leveling the playing field by making advanced data tools accessible to anyone, not just data scientists.

Kochava has incorporated AI and machine learning across our diverse solutions portfolio for years, such as within our advanced attribution and fraud prevention products. We have also adopted advanced technologies, like large language models (LLMs) to develop new tools.

Many organizations are instituting internal restructuring with a focus on enhancing the developer experience. The aim is to leverage the full potential of AI for smart applications, providing universal access to advanced tech tools, while adapting to changes in app store policies. Engineering teams are spearheading the development of self-service platforms managed by product teams. The primary objective is to optimize developers’ workflows, speeding up the delivery of business value, and reducing stress. These changes improve the developer experience which can help companies retain top talent.

From an overall organizational structure perspective, in pursuit of a more efficient and effective approach, Kochava is focused on enhancing developer experiences, leveraging AI for intelligent applications, democratizing access to advanced technologies, and adapting to regulatory changes in app marketplaces.

Reimagining the Future

The software and applications industry is one that evolves particularly quickly. The app market now represents a multibillion-dollar sector exhibiting no signs of slowing. This rapid growth and constant change presents abundant opportunities for developers to build innovative new applications while pursuing their passions. For app developers, monitoring trends provides inspiration for maintaining engaging, innovative user experiences.

As AI integration increases, standards will develop to ensure AI can automatically interface between applications. It will utilize transactional and external data to provide insights. Applications will shift from set features to AI-driven predictions and recommendations tailored for each user. This advances data-driven decision making and transforms the experience for customers, users, teams, and developers.

To Know More, Read Full Article @ https://ai-techpark.com/generative-ai-marketing-trends/ 

Related Articles -

Chief Data Officer in the Data Governance

Power of Hybrid Cloud Computing

Trending Category - IOT Wearables & Devices

In the last few years, we have witnessed that the digital landscape’s boundary between reality and fiction has become increasingly blurred thanks to the advent of deepfake technology. While the intention of developing deep fake technology was purely for entertainment and other legitimate applications, in recent times it has become infamous for spreading misinformation. This technology can also manipulate the cybersecurity domain by confusing or influencing users, exploiting their trust, and bypassing traditional security measures.

Numerous cybersecurity experts have raised questions about deep fake technology playing a multifaceted role and risking national security and prohibited information sources.

Today’s exclusive AITech Park article will explore the nature, risks, real-life impacts, and measures needed to counter these advanced threats.

Decoding DeepFakes

At its core, deep fakes are a part of artificial intelligence (AI) and machine learning (ML) that leverages sophisticated AI algorithms to superimpose or replace elements within audio, video, or images and develop hyper-realistic simulations of individuals saying or doing things they never did.

As the availability of personal information rises online, cybercriminals are investing in technology to exploit deep fake technology, especially with the introduction of social engineering techniques for phishing attacks, as it can mimic the voices and mannerisms of trusted individuals. Cyber attackers orchestrate complicated schemes to mislead unsuspecting targets into revealing sensitive information or transferring funds.

The Progression of Deep Fakes

Deepfakes have opened a new portal for cyber attackers, ranging from suave spear-phishing to the manipulation of biometric security systems. Spear phishing is a common form of deep fake phishing that develops near-perfect impersonation of trusted figures, making a gigantic leap by replicating writing style, tonality, or mincing exact email design. This realistic initiation of visuals and voice can tend to pose an alarming threat to organizations and stakeholders, raising serious concerns about privacy, security, and the integrity of digital content.

For instance, there are cases registered where cyber attackers impersonate business associates, vendors, suppliers, business partners, or C-level executives and make payment requests, demand bank information, or ask for invoices and billing addresses to be updated to steal sensitive data or money. Another example is business email compromise (BEC), which is a costlier form of cybercrime, as these scams are possibly conducted for financially damaging organizations or individuals.

In this era of digitization, we can say that we are navigating the uncharted territory of generative AI (GenAI), where we need to understand the importance of collaboration, stay vigilant, and take measures to combat the threat of deepfakes. The question here shouldn’t be whether we can completely eradicate the threat but how we acclimate our strategies, systems, and policies to mitigate deepfake threats effectively.

To Know More, Read Full Article @ https://ai-techpark.com/the-rise-of-deep-fake-technology/ 

Related Articles -

Future of QA Engineering

Top 5 Data Science Certifications

Trending Category - AI Identity and access management

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/ 

Related Articles -

Top 5 Data Science Certifications

Intersection of AI And IoT

Trending Category - Mental Health Diagnostics/ Meditation Apps

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/ 

Related Articles -

Data Privacy With CPOs

Spatial Computing Future of Tech

Trending Category - IOT Wearables & Devices

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.

To Know More, Read Full Article @ https://ai-techpark.com/the-future-of-ai-powered-coding/ 

Related Articles -

Democratized Generative AI

Digital Technology to Drive Environmental Sustainability

Trending Categories - IOT Smart Cloud

Murali, Could you begin by providing us with an introduction and detailing your career trajectory as the Senior Vice President, Engineering at Skillsoft?

I joined Skillsoft in 2016 as the VP of engineering after a career spanning over two decades at IBM, where I led the build out of large-scale enterprise solutions and innovative software products. 2016 was an exciting time to join Skillsoft as the learning industry was undergoing major disruption. To stay competitive, Skillsoft was in the process of building an innovative, AI-driven learning platform called Percipio. With the support of a new leadership team, we were able to build the platform from the ground up and bring it to market within a year.  

The project involved not only building a new product but changing the culture and operations of our technology team, including the launch of a new tech stack built on the AWS public cloud infrastructure. Over the past years, we have grown the product family and organization to include new products and services, and in the process, took ownership to transform the cloud operations organization.

We managed to modernize how we build, deploy, and support our products in the cloud through continuous integration and deployment to deliver new capabilities to the market at lightning speed while maintaining a highly secure, resilient, and performant learning platform that serves millions of learners.

Over the years, we built a strong culture of innovation within our engineering team, which is one of the most exciting parts of my job today. Every quarter, we do an innovation sprint, where team members organically produce ideas to advance platform capabilities. Our philosophy is to establish a grassroots mindset to produce innovative ideas that solve our customers’ business problems and improve experiences for our learners. Many of our AI and machine learning innovations have come out of this process, helping to make our platform smarter and our learning experiences more personalized.  

Can you provide a brief introduction to CAISY (Conversation AI Simulator) and its role in Skillsoft’s offerings?

CAISY, which is an AI-based conversation simulator that helps learners build business and leadership skills, was born out of one of our innovation sprints. The original idea was implemented on a simple terminal text-based interface using GPT 3.5, though we saw the power of the concept and decided to progress it to be customer-facing. Skillsoft launched CAISY out of beta in September using generative AI and GPT 4, to help learners practice and role model various business conversations. While Skillsoft has extensive learning content on how business, management, and leadership conversations should be handled, learners can now practice and apply these skills in real time. Developments in generative AI allow us to leverage our knowledge and expertise in this area while providing a hands-on environment for our learners, so that they can practice conversational skills in a safe and secure zone before implementing them in the real world.

To Know More, Read Full Interview @ https://ai-techpark.com/ai-tech-interview-with-murali-sastry/ 

Read Related Articles:

Collaborative robots in healthcare

CIOs to Enhance the Customer Experience

Social media has transformed marketing. Platforms like Instagram with its 2 billion subscribers allow businesses to connect directly with customers and build their brands through compelling visual storytelling. However, the highly competitive and fast-paced nature of social media also presents challenges. This is where artificial intelligence (AI) comes in. AI technologies are revolutionizing social media marketing, providing data-driven insights and automation that help brands cut through the noise and thrive on social media.

How Artificial Intelligence Helps in Social Media Marketing

Artificial Intelligence is the next big thing in the world of technology and is poised to set forth the course of digital environments in the coming decades. Here below we will see how artificial intelligence is paving the way ahead:

Understanding Your Audience With AI

One of the foundational principles of marketing is understanding your target audience intimately so you can create relevant and engaging content. AI makes discovering audience interests and behaviors easy. Tools like Facebook Analytics, Sprout Social, and Rafflekey utilize machine learning algorithms to reveal demographic data, top-performing content, post timings, picking up winners, and more. These AI-powered insights help you fine-tune Instagram content to match what your followers respond to. Instagram influencers have massively benefited leveraging AI to create instagram giveaway ideas that helps them in boosting their persona and brand.

AI takes audience analysis even further with sentiment analysis and predictive analytics. Sentiment analysis uses natural language processing to determine how audiences feel about your brand by analyzing emotions like joy, surprise, anger, etc. in user-generated content. Predictive analytics examines past performance data to forecast future outcomes. This helps you stay ahead of trends and optimize social media initiatives for maximum impact.

Generating High-Quality Visual Content With AI

Visual storytelling is central to success on Instagram. But constantly producing fresh, eye-catching photos and videos can be challenging. AI creativity tools expand what’s humanly possible by autonomously generating unique visual content.

For example, tools like Canva, Over, and Recite leverage AI to transform text prompts into stunning social media graphics in just seconds. Adobe’s Sensei AI identifies aesthetically pleasing image compositions and automatically adjusts parameters like lighting, color balance, and cropping. For video, generative AI can craft natural voiceovers for explainer videos based on your script.

These AI creativity enhancements remove friction from design and allow you to produce loads of on-brand, high-quality visual content to feed Instagram’s voracious appetite.

To Know More, Read Full Article @ https://ai-techpark.com/the-role-of-ai-in-social-media-marketing/ 

Read Related Articles:

Chief Data Officer in the Data Governance

Rise of Low-Code and No-Code