Tag: Generative Ai

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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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/ 

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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/ 

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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/ 

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Chief Data Officer in the Data Governance

Rise of Low-Code and No-Code

Shaun, could you please introduce yourself and elaborate your role as a CEO of NINJIO?

I’m Shaun McAlmont, CEO of NINJIO Cybersecurity Awareness Training. I came to NINJIO after decades leading organizations in higher education and workforce development, so my specialty is in building solutions that get people to truly learn.

Our vision at NINJIO is to make everyone unhackable, and I lead an inspiring team that approaches cybersecurity awareness training as a real opportunity to reduce organizations’ human-based cyber risk through technology and educational methodologies that really change behavior.

Can you share insights into the most underestimated or lesser-known cyber threats that organisations should be aware of?

The generative AI boom we’re experiencing now is a watershed moment for the threat landscape. I think IT leaders have a grasp of the technology but aren’t fully considering how that technology will be used by hackers to get better at manipulating people in social engineering attacks. Despite the safeguards the owners of large language models are implementing, bad actors can now write more convincing phishing emails at a massive scale. They can deepfake audio messages to bypass existing security protocols. Or they can feed a few pages of publicly available information from a company’s website and a few LinkedIn profiles into an LLM and create an extremely effective spearphishing campaign.

These aren’t necessarily new or lesser-known attack vectors in cybersecurity. But they are completely unprecedented in how well hackers can pull them off now that they’re empowered with generative AI.

With the rise of ransomware attacks, what steps can organisations take to better prepare for and mitigate the risks associated with these threats?

The first and biggest step to mitigating that risk is making sure that everyone in an organization is aware of it and can spot an attack when they see one. It took a ten-minute phone call for a hacking collective to breach MGM in a ransomware attack that the company estimates will cost it over $100 million in lost profits. Every person at an organization with access to a computer needs to be well trained to spot potential threats and be diligent at confirming the validity of their interactions, especially if they don’t personally know the individual with whom they’re supposedly speaking. The organizational cybersecurity culture needs to extend from top to bottom.

Building that overarching cultural change requires constant vigilance, a highly engaging program, and an end-to-end methodological approach that meets learners where they are and connects the theoretical to the real world.

To Know More, Read Full Interview @ https://ai-techpark.com/ai-tech-interview-with-dr-shaun-mcalmont-ceo-at-ninjio/ 

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The potential for deepfakes to sway public opinion and influence the outcome of India’s Lok Sabha is raising red flags throughout the cyber community. While Indians are deciding on which candidate best represents their views, deepfakes, and generative technologies make it easy for manipulators to create and spread realistic videos of a candidate saying or doing something that never actually occurred.

The Deepfake threat in politics

The use of deepfakes in politics is particularly alarming. Imagine a scenario where a political candidate appears to be giving a speech or making statements that have no basis in reality. These AI-generated impersonations, based on a person’s prior videos or audio bites, can create a fabricated reality that could easily sway public opinion. In an environment already riddled with misinformation, the addition of deepfakes takes the challenge to a whole new level.

For instance, the infamous case where Ukrainian President Volodymyr Zelensky appeared to concede defeat to Russia is a stark reminder of the power of deepfakes in influencing public sentiment. Though the deception was identified due to imperfect rendering, there is no way of knowing who believes it to be true even after being disproved, showcasing the potential for significant political disruption.

Deepfakes as a danger in the digital workplace

Employees, often the weakest link in security, are especially vulnerable to deepfake attacks. Employees can easily be tricked into divulging sensitive information by a convincing deepfake of a trusted colleague or superior. The implications for organisational security are profound, highlighting the need for advanced, AI-driven security measures that can detect anomalies in user behaviour and access patterns.

The double-edged sword of AI in cybersecurity

However, it’s important to recognize that AI, the very technology behind deepfakes, also holds immense capabilities to help hackers discover cybersecurity loopholes and breach business networks. While AI may help discover new vulnerabilities for threat actors, it also can be used to discover counter-measures, such as identifying patterns in data that would have otherwise gone unnoticed.

A system can then flag the potential Deepfake content and remove it before it achieves its goal. This can help bridge the global skills gap in cybersecurity, enabling analysts to focus on strategic decision-making rather than sifting through endless data.

Companies must prioritise AI-driven cybersecurity solutions as part of a broader, company-wide approach that intertwines safety with quality across all aspects of their operations. From online behaviour to development processes, a centralised AI- ingested understanding of an organisation’s baseline is crucial. Such technologies can identify breaches in real time, whether perpetrated by external threat actors or employees misled by deepfakes. This proactive stance is essential for maintaining integrity and security in a digital landscape increasingly complicated by AI technologies.

To Know More, Read Full Article @ https://ai-techpark.com/deepfakes-and-the-quest-for-authenticity-in-a-digital-world/ 

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Empowering Change: Women Leading the Generative AI Revolution in Marketing for International Women’s Day

Shaping the Future: Women Leaders Spearheading Generative AI and Marketing Innovation for International Women’s Day

Celebrating Women Leaders Shaping the Future of Marketing with Gen AI

“Inspire change” sets the tone for this year’s International Women’s Day theme. It’s a fitting reflection of the ever-evolving nature of marketing, where change is the only constant. The last third-party cookie has finally crumbled, privacy laws are tightening, and now, Generative AI is quickly ushering in a new era of innovation and adaptation.

With mounting research demonstrating that gender-diverse teams outperform their peers time and time again, we turned the conversation over to the exceptional women thought leaders who are at the forefront of shaping the narrative surrounding Gen AI and marketing.

Let’s dive into their insights and experiences:

Julie Shainock, Managing Director Travel, Transport & Logistics (TTL) at Microsoft

Shainock is responsible for developing Microsoft’s point of view and future strategy for our WW Travel and Transport Industry. She is focused on leading the airlines, hospitality companies, cruise and freight logistics and rail companies to driving innovation that will enhance the customer and employee journey, while driving increased productivity and cost reduction with the use of Microsoft’s technology and its ecosystem of solution partners.

Generative AI is set to revolutionize the Travel, Transport, and Logistics industries by delivering unprecedented levels of personalization, efficiency, and innovation. It’s not just about automation; it’s about creating intuitive, seamless customer experiences and unlocking new levels of operational efficiency. For organizations to tackle the full potential of GenAI effectively, establishing a clean data foundation and a clear strategic vision for desired outcomes is critical.”

Heather Roth, Director of Digital Strategy, Slalom

Roth has over a decade of experience in digital strategy and analytics, marketing technology, AdTech and marketing transformation for a variety of clients in all key industries, both midmarket and enterprise.

“The promise of Generative AI in marketing has brought forward the importance of data quality and having a strong data strategy. For years, marketers have operated around data owned by publishers, often piecemealed together in different platforms and spreadsheets. The ability to execute on Generative AI tactics is only as good as the data you put into it, which is really driving companies to focus on understanding what data is needed across the business to execute on AI-driven experiences and making investments in owning their data and building out higher quality data inputs. The investment in data maturity has accelerated by years in a matter of months.”

To Know More, Read Full Article @ https://ai-techpark.com/women-leaders-in-marketing-gen-ai/ 

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2023 was a terrific year in the IT industry, but 2024 is set to bring some exciting and groundbreaking developments that will help IT professionals and data scientists develop innovative software and tools to strive in the competitive landscape.

The most recent technological advancement in the data landscape is quite commendable. In 2024, IT enterprises will be heavily impacted, as data is the new oil that can transform any business and reshape the traditional process of analyzing, visualizing, and making data-driven decisions.

As IT enterprises grapple with the data deluge, they often find themselves at an intersection of technological innovation, ethical considerations, and the need for actionable solutions.

In today’s exclusive AI Tech Park article, we will focus on gearing up IT professionals and data scientists to understand the data trends they can expect in 2024.

The Era of the Data Renaissance

The phrase “data is the new oil” was stated in 2006 by British data scientist Clive Humby. The one big difference between data and oil is that oil is a nonrenewable energy, and data can be renewed and reused in an infinite number of ways.

Three decades ago, one of the main challenges that IT enterprises faced was the scarcity of data. However, with time, the main challenge for most IT businesses was having a plethora of data.

With such a volume of data, enterprises struggle with how to use the data, where to implement it, when they need it, and most importantly, how to store it. The traditional database management systems (DMS) failed to tackle the new data sets, which made data professionals realize the importance of cloud storage, which is efficient in handling numerous types of data and quite cost-efficient compared to DMS.

As we stand at the crossroads of a data renaissance, the year 2024 heralds an important role in the data analytic landscape, where data analytics is no longer a tool for data-driven decision-making but a driving force to push greater efficiency, innovation, real-time data insights, responsible AI, reinforce security, and more.

However, IT professionals and data scientists need to address the challenges and considerations of imposing data privacy, skill development, and ethical dilemmas to stay compliant with this evolving regulatory landscape.

Data Democratization

Data democratization has been a growing trend for the past few years, but the increased usage of AI and machine learning (ML) tools has rekindled a new horizon for this trend. With data democratization, every employee in an IT organization will have access to the data to make data-driven decisions for a seamless business process. However, to get full access to data, IT leaders need to provide in-house training on data literacy to familiarize them with the principles and techniques of working with data.

To Know More, Read Full Article @ https://ai-techpark.com/top-4-data-trends-it-professionals-need-in-2024/ 

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Ethics in the Era of Generative AI

In the rapidly evolving landscape of artificial intelligence (AI), media companies and other businesses alike continue to find themselves entangled in a web of lawsuits and public criticism, shining a spotlight on the issue of ethical transparency. Journalism has long been plagued by issues around deception — consumers often wonder what’s sensationalism and what’s not. However, with the latest casualty in the ongoing Sports Illustrated debacle, whose reputation greatly suffered after being accused of employing non-existent authors for AI-generated articles, a new fear among consumers was unlocked. Can consumers trust even the most renowned organizations to leverage AI effectively?

To further illustrate AI’s negative implications, early last year Gannett faced similar scrutiny when its AI experiment took an unexpected turn. Previously, the newspaper chain used AI  to write high school sports dispatches, however, the technology proved to be more harmful than helpful after it made several major mistakes in articles. The newspaper laid off part of its workforce, which was likely in hopes AI could replace human workers.

Meaningful Change Starts at The Top

It’s clear the future of AI will face a negative outlook without meaningful change. This change begins at the corporate level where organizations play a key role in shaping ethical practices around AI usage and trickles down to the employees who leverage it. As with most facets of business, change begins at the top of the organization.

In the case of AI, companies must not only prioritize the responsible integration of AI but also foster a culture that values ethical considerations (AI and any other endeavor), accountability, and transparency. By committing to these principles, leadership, and C-level executives set the tone for a transformative shift that acknowledges both the positive and negative impact of AI technologies.

To avoid any potential mishaps, workforce training should be set in place and revisited at a regular cadence to empower employees with the knowledge and skills necessary to combat the ethical complexities of AI.

However, change doesn’t stop at leadership; it also relates to the employees who use AI tools. Employees should be equipped with the knowledge and skills necessary to navigate ethical considerations. This includes understanding the limitations and biases as well as learning from the mistakes of others who’ve experienced negative implications using AI technologies, such as the organizations previously aforementioned.

To Know More, Read Full Article @ https://ai-techpark.com/how-to-reconcile-lawsuits-and-public-backlash/

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Explainability is crucial in AI applications. How does Kyndi ensure that the answers provided by its platform are explainable and transparent to users?

Explainability is a key Kyndi differentiator and enterprise users generally view this capability as critical to their brand as well as necessary to meet regulatory requirements in certain industries like the pharmaceutical and financial services sectors.

Kyndi uniquely allows users to see the specific sentences that feed the resulting generated summary produced by GenAI. Additionally, we further enable them to click on each source link to get to the specific passage rather than just linking to the entire document, so they can read additional context directly. Since users can see the sources of every generated summary, they can gain trust in both the answers and the organization to provide relevant information. This capability directly contrasts with ChatGPT and other GenAI solutions, which do not provide any sources or have the ability to utilize only relevant information to generate summaries. While some vendors may technically provide visibility into the sources, there will be so many to consider that it would render the information impractical to use.

Generative AI and next-generation search are evolving rapidly. What trends do you foresee in this space over the next few years?

The key trend in the short term is that many organizations were initially swept up in the hype of GenAI and then witnessed issues such as inaccuracy via hallucinations, the difficulty in interpreting and incorporating domain-specific information, explainability, and security challenges with proprietary information.

The emerging trend that organizations are starting to understand is that the only way to enable trustworthy GenAI is to implement an elegant solution that combines LLMs, vector databases, semantic data models, and GenAI technologies seamlessly to deliver direct and accurate answers users can trust and use right away. As organizations realize that it is possible to leverage their trusted enterprise content today, they will deploy GenAI solutions sooner and with more confidence rather than continuing their wait-and-see stance.

How do you think Kyndi is positioned to adapt and thrive in the ever-changing landscape of AI and search technology?

Kyndi seems to be in the right place at the right time. ChatGPT has shown the world what is possible and opened a lot of eyes to new ways of doing business. But that doesn’t mean that all solutions are enterprise ready as OpenAI openly admits that it is inaccurate too often to be usable by organizations. Kyndi has been working on this problem for 8 years and has a production-ready solution that addresses the problems of hallucinations, adding domain-specific information, explainability, and security today.

In fact, Kyndi is one of a few vendors offering an end-to-end complete solution that integrates language embeddings, LLM, vector databases, semantic data models, and GenAI on the same platform, allowing enterprises to get to production 9x faster than other alternative approaches. As organizations compare Kyndi to other options, they are seeing that the possibilities suggested by the release of ChatGPT are actually achievable right now.

To Know More, Read Full Interview @ https://ai-techpark.com/aitech-interview-with-ryan-welsh-ceo-of-kyndi/

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