Autore: martechcubejohn

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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With the dawn of the COVID-19 pandemic, mental health has become an area of concern, as more than 1 billion humans every year seek help from clinicians and therapists to cure problems such as depression, anxiety, and suicidal thoughts. This inevitable growing pressure has stretched healthcare and therapeutic institutes to choose smarter technologies such as artificial intelligence (AI) and machine learning (ML) to interact with patients and improve their mental health.

According to new studies found in the Journal of the American Medical Association (JAMA), advanced AI and LLM models can enhance mental health therapies on a larger scale by analyzing millions of text conversations from counseling sessions and predicting patients’ problems with clinical outcomes.

Hence, for a more accurate diagnosis, AI in mental wellness has the potential to lead to a positive transformation in the healthcare sector.

Today’s exclusive AI Tech Park article explores the transformative potential of AI in mental healthcare.

Decoding Mental Health Therapies With AI

In contrast to physical health specialties such as radiology, cardiology, or oncology, the use of AI in mental healthcare has been comparatively modest; where the diagnosis of chronic conditions can be measured by laboratory tests, mental illness requires a complex and higher degree of pathophysiology, which includes a major understanding of genetic, epigenetic, and environmental and social determinants of a patient’s health. To gain more accurate data, mental healthcare professionals need to build a strong and emotional rapport with the patient while being observant of the patient’s behavior and emotions. However, mental health clinical data is quite subjective, as data comes in the form of patient statements and clinician notes, which affect the quality of the data and directly influence AI and ML model training.

Despite these limitations, AI technologies have the potential to refine the field of mental healthcare with their powerful pattern recognition technologies, streamlining clinical workflow, and improving diagnostic accuracy by providing AI-driven clinical decision-making.

The Dilemma of Ethical Considerations

As the world moves towards digitization, it is quite noteworthy that the mental healthcare sector is gradually adopting AI and ML technologies by understanding the technicalities, adhering to rules and regulations, and comprehending the safety and trustworthiness of AI.

However, it is often witnessed that these technologies come with drawbacks of varying accuracy in finding the correct psychiatric applications; such uncertainty triggers dilemmas in choosing the right technology as it can hamper patients’ health and mental well-being.

In this section, we will highlight a few points where mental healthcare professionals, AI professionals, and data engineers could collaborate to eliminate ethical issues and develop trustworthy and safe AI and ML models for patients.

Overall, the promising development of AI in healthcare has unlocked numerous channels, from cobots helping surgeons perform intricate surgeries to aiding pharmaceutical companies and pharmaceutical scientists to develop and discover new drugs without any challenges.

To Know More, Read Full Article @ https://ai-techpark.com/mental-healthcare-with-artificial-intelligence/ 

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Neda, please share some key milestones and experiences from your professional journey that have shaped your perspective and approach as Chief Product Officer at Stibo Systems.

I have the honor of mentoring a couple of young leaders, and they ask this question a lot. The answer I always give is that I approach each day as an opportunity to learn, so it’s difficult to pinpoint a specific milestone. However, there have been some crucible moments where I made radical decisions, and I think those moments have influenced my journey. One of them was my shift towards computer science. Despite having a background in linguistics and initially aspiring to be a teacher, I took a complete shift and decided to explore computer science. Programming was initially intimidating for me, and I had always tried to avoid math throughout my student life. I saw myself more as an art and literature person, and that gutsy shift turned out to be a great decision in the long term. The decision was made by me, but it wouldn’t have turned into a success without support from my mentors and leaders – it’s super important to have champions around you to guide you, especially early in your career.

Another significant moment was when I accepted a consulting job that involved phasing out legacy systems. This required negotiating with users who would lose functionalities they had been using for years. These conversations were often challenging, and I was tempted to quit. However, I made the decision to stay and tackle the problem with a more compassionate approach towards the application users. It was during this time that I truly understood the nature of change management in the product development process. People find it difficult to let go of their routines and what has made them successful. The more successful users are with their apps, the less likely they are to embrace change. However, sometimes solutions become outdated and need to be replaced – plain and simple. The challenge is how to build a changing product while ensuring that users come along. This story applies to Stibo Systems. We have been around for over 100 years and have managed to transform our business. Stibo Systems is a perfect example of how to build lasting products, be open to change and transformation and make sure you aren’t leaving any customers behind.

Could you provide an overview of Stibo Systems’ mission and how it aligns with the concept of “better data, better business, better world”?

Our heritage extends far back, but we are a cutting-edge technology company. We specialize in delivering data management products that empower companies to make informed decisions, resulting in remarkable outcomes. This approach not only contributes to our sustainable growth but also supports our profitability, allowing us to reinvest and expand.

Our mission statement encapsulates our business ethos – one with a strong sense of conscientiousness. Our primary focus revolves around doing what’s right for our customers, employees and the environment. Customer satisfaction is at the forefront of our priorities, evident in our high software license renewal rates, a testament to our commitment to delivering top-notch products and services.

Moreover, we hold a unique position in the market as one of the few major companies headquartered in Europe. Europe is facing increasing pressure to embrace sustainable practices, and we are actively engaged in leading this transformation.

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

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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 business world, when major companies decide to combine, it’s a big deal. These moves shake up the norm and can turn not only the organizations, but the entire industry on its head. But as the dust settles on the agreement, a new challenge looms large on the horizon: how to bring together two different sets of data into one without jeopardizing customer experience.

As a developer of a customer data platform (CDP), I’ve observed first-hand the challenges and opportunities that arise during these transitions where data is involved. In this article, I’ll share insights on why effective data integration is critical in M&A scenarios and outline best practices to ensure a smooth, efficient, and value-generating process.

The Dance of Data: A Merger’s Make-or-Break Moment

Mergers bring together not just the businesses themselves on paper, but also diverse customer groups and distinct corporate cultures. Combining these elements successfully requires well-orchestrated data integration. It’s this integration that allows businesses to grasp the complete landscape of a newly combined customer base. Understanding this landscape is essential—it empowers them to serve customers more effectively and unlocks the potential for strategic cross-selling opportunities.

As Bill Gates once wrote, “The most meaningful way to differentiate your company from your competition, the best way to put distance between you and the crowd, is to do an outstanding job with information. How you gather, manage, and use information will determine whether you win or lose.” That’s never more true than in the world of M&A, where data integration is the key to accessing operational synergies, amplifying strategies, and deepening customer engagement.

When Amazon bought Whole Foods for $13.7 billion back in 2017, it wasn’t just about absorbing a national grocery chain. It was a masterclass in merging worlds. Amazon, with its tech dominance and data expertise, brought Whole Foods into the future. They tuned into customer preferences with precision, streamlined store operations, and expanded Whole Foods’ customer base.

Once the merger was complete, the grocery chain began using data for targeted promotions and discounts to Amazon Prime members. It also shifted to a centralized model to better manage local and national products, and stores adopted a just-in-time approach for stocking perishable food, streamlining inventory, and ensuring freshness.

This example highlights the potential for data integration to accelerate business wins and tap into new audiences. But to make the most of the opportunity, there are several important steps involved.

Finally, by pinpointing potential risks, from compliance issues to data security, you’re not just planning for a smooth merger—you’re building a resilient, long-term data infrastructure. This is the path to successful data integration, one where clear goals, the right tools, impeccable data, open communication, and empowered people come together to create a whole that’s greater than the sum of its parts.

Data integration in the context of M&A is more than a technical challenge; it’s a strategic initiative that can significantly influence the merged entity’s future trajectory. A methodical, goal-oriented approach that prioritizes data quality, stakeholder engagement, and the use of sophisticated integration tools will serve as a foundation for success.

To Know More, Read Full Article @ https://ai-techpark.com/a-guide-to-mastering-ma-data-integration/ 

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The term robot was invented by the Robotics Institute of America in the late 1940s as a machine that executes mechanical functions just like human beings but lacks the same “sensitivity” as a human.

Initially, robots were designed and employed to work like humans, especially in the manufacturing industry, FMCG industry, and automotive industry, but later, robotics engineers observed that they were capable of working in other application areas due to their ability to multitask efficiently and faster. Hence, robotics engineers introduced medical robotics into the healthcare sector, causing a drastic change in the outlook for treatment and diagnostics.

With advances in technologies, the utilization of robotic devices has upscaled from spearheading lab tests to automating medical procedures to collaborative robotics (cobots), helping healthcare workers in surgeries and improving treatment outcomes with negligible error encounters.

This exclusive AI Tech Park article focuses on the application of cobots in healthcare while keeping ethical considerations in mind. In addition, we will also focus on the ongoing research and development of robotics in healthcare.

Application of Collaborative Robots in Healthcare

In this section, we will highlight the application areas where collaboration between robots and healthcare practitioners can improve healthcare outcomes, such as helping with surgeries or offering personalized patient care. The below subtopics explore the different aspects of collaborative robots with humans.

Cobots in Rehabilitation and Physical Therapy

The rehab cobots are developed to help impaired and injured patients recover from accidents so that they can lead normal lives. One will find a variety of rehab robots that are intended to help patients with numerous medical conditions, including cerebral palsy, stroke, and injuries to bones or muscles. One such example is Orthoses, a robotic exoskeleton system that assists paralyzed patients in limb movement. This system works on “pre-set user-defined commands” that are fed into the robot to read the user’s mind and act accordingly.

Some studies show that children with autism spectrum disorder (ASD) have a positive reaction to therapies when interacting with cobots, as they have various AI functions such as playing games and recognizing facial expressions that keep patients motivated and entertained throughout the therapy journey. For instance, the Keepon robot developed by Hideki Kozima is a unique robot that studies autistic behavioral changes in children and monitors their overall health.  

Cobots in Precision Surgery

With the advancement of technology, cobots have been assisting surgeons in critical operations. These surgery cobots are categorized under Active Surgical Systems, which pre-program electronics and can work autonomously; Master-Slave Systems, which are totally under the control of surgeons; and Semi-Active Systems, which allow surgeon-driven electronics with pre-programmed electronics. The most commonly used cobot system (Master-Slave System) in the healthcare industry is the DaVinci system, a robotic arm that mimics the surgeon’s hand movement into smaller and more precise actions for less invasive and complex surgeon procedures.  

To Know More, Read Full Article @ https://ai-techpark.com/collaborative-robots-in-healthcare/ 

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Digital Patient Engagement Platforms

To start, Daniel, could you please provide a brief introduction to yourself and your work at Kognic?

 I’m an experienced machine-learning expert and passionate about making AI useful for safety critical applications. As CEO and Co-Founder of Kognic, I lead a team of data scientists, developers and industry experts. The Kognic Platform empowers industries from autonomous vehicles to robotics – Embodied AI as it is called – to accelerate their AI product development and ensure AI systems are trusted and safe.

Prior to founding Kognic, I worked as a Team Lead for Collection & Analysis at Recorded Future, gaining extensive experience in delivering machine learning solutions at a global scale and I’m also a visiting scholar at both MIT and UC Berkeley.

Could you share any real-world examples or scenarios where AI alignment played a critical role in decision-making or Embodied AI system behaviour?

One great example within the automotive industry and the development of autonomous vehicles, starts with a simple question: ‘what is a road?’

The answer can actually vary significantly, depending on where you are in the world, the topography of the area you are in and what kind of driving habits you lean towards. For these factors and much more, aligning and agreeing on what is a road is far easier said than done.

So then, how can an AI product or autonomous vehicle make not only the correct decision but one that aligns with human expectations? To solve this, our platform allows for human feedback to be efficiently captured and used to train the dataset used by the AI model.

Doing so is no easy task, there’s huge amounts of complex data an autonomous vehicle is dealing with, from multi-sensor inputs from a camera, LiDAR, and radar data in large-scale sequences, highlighting not only the importance of alignment but the challenge it poses when dealing with data.

Teaching machines to align with human values and intentions is known to be a complex task. What are some of the key techniques or methodologies you employ at Kognic to tackle this challenge?

Two key areas of focus for us are machine accelerated human feedback and the refinement and fine-tuning of data sets.

First, without human feedback we cannot align AI systems, our dataset management platform and its core annotation engine make it easy and fast for users to express opinions about this data while also enabling easy definition of expectations.

The second key challenge is making sense of the vast swathes of data we require to train AI systems. Our dataset refinement tools help AI product teams to surface both frequent and rare things in their datasets. The best way to make rapid progress in steering an AI product is to focus on that which impacts model performance. In fact, most teams find tons of frames in their dataset that they hadn’t expected with objects they don’t need to worry about – blurry images at distances that do not impact the model. Fine-tuning is essential to gaining leverage on model performance.  

To Know More, Read Full Article @ https://ai-techpark.com/aitech-interview-with-daniel-langkilde/ 

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Trends in Big Data for 2023

Generative AI for SMBs and SMEs

In recent years, artificial intelligence (AI) in the pharmaceutical industry has gained significant traction, especially in the drug discovery field, as this technology can identify and develop new medications, helping AI researchers and pharmaceutical scientists eliminate the traditional and labor-intensive techniques of trial-and-error experimentation and high-throughput screening.

The successful application of AI techniques and their subsets, such as machine learning (ML) and natural language processing (NLP), also offers the potential to accelerate and improve the conventional method of accurate data analysis for large data sets. AI and ML-based methods such as deep learning (DL) predict the efficacy of drug compounds to understand the accrual and target audience of drug use.

For example, today’s virtual chemical databases contain characterized and identified compounds. With the support of AI technologies along with high-performance quantum computing and hybrid cloud technologies, pharmaceutical scientists can accelerate drug discovery through existing data and the experimentation and testing of hypothesized drugs, which leads to knowledge generation and the creation of new hypotheses.

The Role of ML and DL in Envisioning Drug Effectiveness and Toxicity

In this section, we will understand the role of the two most important technologies, i.e., machine learning and deep learning, which have helped both AI researchers and pharmaceutical scientists develop and discover new drugs without any challenges:

Machine learning in drug discovery

Drug discovery is an intricate and lengthy process that requires the utmost attention to identify potential drug candidates that can effectively treat various acute and chronic drugs, which can transform the pharmaceutical industry by speeding up the prediction of toxicity and efficacy of potential drug compounds, improving precision, and decreasing costs. Based on the large set of data, ML algorithms can identify trends and patterns that may not be visible to pharma scientists, which enables the proposal of new bioactive compounds that offer minimum side effects in a faster process. This significant contribution prevents the toxicity of potential drug compounds by addressing whether the drug interacts with the drug candidates and how the novel drug pairs with other drugs.

Deep learning in drug discovery

Deep learning (DL) is a specialized form of machine learning that uses artificial neural networks to learn and examine data. The DL models in the pharmaceutical industry have different algorithms and multiple layers of neural networks that read unstructured and raw data, eliminating the laborious work of AI engineers and pharma scientists. The DL model can handle complex data through images, texts, and sequences, especially during “screen polymers for gene delivery in silico.” These data were further used to train and evaluate several state-of-the-art ML algorithms for developing structured “PBAE polymers in a machine-readable format.”

To Know More, Read Full Article @ https://ai-techpark.com/ai-in-drug-discovery-and-material-science/ 

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