Tag: Machine Learning

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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Collaborative robots in healthcare

CIOs to Enhance the Customer Experience

The amalgamation of artificial intelligence (AI) and wearable technology has transformed how healthcare providers monitor and manage patients’s health through emergency responses, early-stage diagnostics, and medical research.

Therefore, AI-powered wearables are a boon to the digital era as they lower the cost of care delivery, eliminate healthcare providers’ friction, and optimize insurance segmentations. According to research by MIT and Google, these portable medical devices are equipped with large language models (LLMs), machine learning (ML), deep learning (DL), and neural networks that provide personalized digital healthcare solutions catering to each patient’s needs, based on user demographics, health knowledge, and physiological data.

In today’s article, let’s explore the influence of these powerful technologies that have reshaped personalized healthcare solutions.

Integration of AI in Wearable Health Technology

AI has been a transforming force for developing digital health solutions for patients, especially when implemented in wearables. However, 21st-century wearables are not just limited to AI but employ advanced technologies such as deep learning, machine learning, and neural networking to get precise user data and make quick decisions on behalf of medical professionals.

This section will focus on how ML and DL are essential technologies in developing next-generation wearables.

Machine Learning Algorithms to Analyze Data

Machine learning (ML) algorithms are one of the most valuable technologies that analyze the extensive data gathered from AI wearable devices and empower healthcare professionals to identify patterns, predict necessary outcomes, and make suitable decisions on patient care.

For instance, certain wearables use ML algorithms, especially for chronic diseases such as mental health issues, cardiovascular issues, and diabetes, by measuring heart rate, oxygen rate, and blood glucose meters. By detecting these data patterns, physicians can provide early intervention, take a closer look at patients’s vitals, and make decisions.

Recognizing Human Activity with Deep Learning Algorithms

Deep learning (DL) algorithms are implemented in wearables as multi-layered artificial neural networks (ANN) to identify intricate patterns and find relationships within massive datasets. To develop a high-performance computing platform for wearables, numerous DL frameworks are created to recognize human activities such as ECG data, muscle and bone movement, symptoms of epilepsy, and early signs of sleep apnea. The DL framework in the wearables learns the symptoms and signs automatically to provide quick solutions.

However, the only limitation of the DL algorithms in wearable technology is the need for constant training and standardized data collection and analysis to ensure high-quality data.

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

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Cloud Computing Chronicles

Future of QA Engineering

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

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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Democratized Generative AI

Generative AI Applications and Services

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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Information Security and the C-suite

Mental Health Apps for 2023

In the fast-growing digital world, deep learning (DL) and big data are highly used methods for data scientists. Numerous companies, such as Yahoo, Amazon, and Google, have maintained data in Exabytes, which helps generate large amounts of data with the help of big data analytics and deep learning tools and techniques.

Earlier data scientists used traditional data processing techniques, which came with numerous challenges in processing large data sets. However, with technological advancements in recent years, data scientists can utilize big data analytics, a sophisticated algorithm based on machine learning and deep learning techniques that process data in real-time and provide high accuracy and efficiency in business processes.

In recent times, it has been witnessed that DL methods are extensively used in healthcare, finance, and IT for speech recognition, learning methods in language processing, and image classification, especially when incorporated into various hybrid learning and training mechanisms for processing data with high speed.

Today’s exclusive AI Tech Park article aims to discuss integrating deep learning methods into big data analytics, analyze various applications of deep learning in big data analytics, and discuss the future of big data and deep learning.

Efficient Deep Learning Algorithms in Big Data Analytics

Deep learning is a subset of machine learning (ML), and it is considered the trendiest topic as DL is adopted in almost every field where big data is involved.

Every year, IT companies generate trillions of GBs of data, which makes extracting useful information a challenging task for them. Therefore, the answer to such a problem is deep learning, which automatically learns the hidden structure and patterns in the raw data using ML techniques.

Some deep learning models and algorithms show great potential in unleashing the complexity of patterns within big data analytics. In this section, we will take a glance at the effective ways data scientists can utilize deep learning techniques to implement big data analytics:

Preparing the Data

The initial step to implementing deep learning in big data analytics is data preparation. The quality of data used in training data learning models must be accurate to the model prepared by data scientists and IT professionals. Therefore, it is essential to ensure that the data is well structured and clean and should work as a problem solver.

To Know More, Read Full Article @ https://ai-techpark.com/deep-learning-in-big-data-analytics/

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Generative AI in Virtual Classrooms

Information Security and the C-suite

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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Future-proof Marketing Strategies With AI

Democratized Generative AI

In a highly regulated business environment, it is a challenging task for IT organizations to manage data-related risks and compliance issues. Despite investing in the data value chain, C-suites often do not recognize the value of a robust data governance framework, eventually leading to a lack of data governance in organizations.

Therefore, a well-defined data governance framework is needed to help in risk management and ensure that the organization can fulfill the demands of compliance with regulations, along with the state and legal requirements on data management.

To create a well-designed data governance framework, an IT organization needs a governance team that includes the Chief Data Officer (CDO), the data management team, and other IT executives. Together, they work to create policies and standards for governance, implementing, and enforcing the data governance framework in their organization.

However, to keep pace with this digital transformation, this article can be an ideal one-stop shop for CDOs, as they can follow these four principles for creating a valued data governance framework and grasp the future of data governance frameworks.

The Rise of the Chief Data Officer (CDO)

Data has become an invaluable asset; therefore, organizations need a C-level executive to set the company’s wide data strategy to remain competitive.

In this regard, the responsibility and role of the chief data officers (CDOs) were established in 2002. However, it has grown remarkably in recent years, and organizations are still trying to figure out the best integration of this position into the existing structure.

A CDO is responsible for managing an organization’s data strategy by ensuring data quality and driving business processes through data analytics and governance; furthermore, they are responsible for data repositories, pipelines, and tools related to data privacy and security to make sure that the data governance framework is implemented properly.

The Four Principles of Data Governance Frameworks

The foundation of a robust data governance framework stands on four essential principles that help CDOs deeply understand the effectiveness of data management and the use of data across different departments in the organization. These principles are pillars that ensure that the data is accurate, protected, and can be used in compliance with regulations and laws.

C-suites should accept the changes and train themselves through external entities, such as academic institutions, technology vendors, and consulting firms, which will aid them in bringing new perspectives and specialized knowledge while developing a data governance framework.

To Know More, Read Full Article @ https://ai-techpark.com/chief-data-officer-in-data-governance/

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Guide to the Digital Twin Technology

AI and RPA in Hyper-automation

The ever-evolving digital landscape presents a constant challenge in the face of cyber threats. While traditional security methods offer a foundation, their limitations often become apparent. AI & Cybersecurity emerges as a powerful new tool, promising to enhance existing defenses and even predict future attacks. However, embracing AI necessitates careful consideration of ethical implications and fostering harmonious collaboration between humans and algorithms. Only through such mindful implementation can we build a truly resilient and secure digital future.

The digital frontier has become a battleground teeming with unseen adversaries. Cybercriminals, wielding an arsenal of ever-evolving malware and exploits, pose a constant threat to critical infrastructure and sensitive data. Traditional security methodologies, built upon rigid rule sets and static configurations, struggle to keep pace with the agility and cunning of these digital attackers. But on the horizon, a new solution emerges: Artificial intelligence (AI).

The Evolution of AI in Cybersecurity

AI-powered solutions are rapidly transforming the cybersecurity landscape, not merely enhancing existing defenses, but fundamentally reshaping the way we understand and combat cyber threats. At the forefront of this revolution lie cognitive fraud detection systems, leveraging machine learning algorithms to scrutinize vast datasets of financial transactions, network activity, and user behavior. These systems, adept at identifying irregular patterns and subtle anomalies, operate at speeds that surpass human analysis, uncovering fraudulent activity in real-time before it can inflict damage.

Gone are the days of rule-based systems, easily circumvented by attackers. AI-powered algorithms, in perpetual self-improvement, evolve alongside the threats. They learn from prior attacks, adapting their detection models to encompass novel fraud tactics and emerging trends. This approach significantly surpasses the static limitations of conventional methods, reducing false positives and ensuring a more resilient, adaptive defense.

The future of cybersecurity is intricately intertwined with the evolution of AI. By embracing the transformative potential of these algorithms, while remaining mindful of their limitations and fostering a human-centric approach, we can forge a future where the digital frontier is not a battleground, but a safe and secure terrain for innovation and progress. The algorithmic sentinel stands watch, a powerful ally in the ongoing quest for a more secure digital world.

To Know More, Read Full Article @ https://ai-techpark.com/evolution-of-ai-in-cybersecurity/

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AI in Medical Imaging: Transforming Healthcare

Guide to the Digital Twin Technology