Tag: Artificial Intelligence

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/ 

Read Related Articles:

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/ 

Read Related Articles: 

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/ 

Read Related Articles:

Information Security and the C-suite

Mental Health Apps for 2023

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/

Read Related Articles:

Future-proof Marketing Strategies With AI

Democratized Generative AI

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/

Read Related Articles:

AI in Medical Imaging: Transforming Healthcare

Guide to the Digital Twin Technology

According to research from VMware Carbon Black, ransomware attacks surged by 148% during the onset of the Covid-19 pandemic, largely due to the rise in remote work. Key trends influencing the continuing upsurge in ransomware attacks include:

Exploitation of IT outsourcing services: Cybercriminals are targeting managed service providers (MSPs), compromising multiple clients through a single breach.

Vulnerable industries under attack: Healthcare, municipalities, and educational facilities are increasingly targeted due to pandemic-related vulnerabilities.

Evolving ransomware strains and defenses: Detection methods are adapting to new ransomware behaviors, employing improved heuristics and canary files, which serve as digital alarms, deliberately placed in a system or to entice hackers or unauthorized users.

Rise of ransomware-as-a-service (RaaS): This model enables widespread attacks, complicating efforts to counteract them. According to an independent survey by Sophos, average ransomware payouts have escalated from $812,380 in 2022 to $1,542,333 in 2023.

Preventing Ransomware Attacks

To effectively tackle the rising threat of ransomware, organizations are increasingly turning to comprehensive strategies that encompass various facets of cybersecurity. One key strategy is employee education, fostering a culture of heightened awareness regarding potential cyber threats. This involves recognizing phishing scams and educating staff to discern and dismiss suspicious links or emails, mitigating the risk of unwittingly providing access to malicious entities.

In tandem with employee education, bolstering the organization’s defenses against ransomware requires the implementation of robust technological measures. Advanced malware detection and filtering systems play a crucial role in fortifying both email and endpoint protection. By deploying these cutting-edge solutions, companies can significantly reduce the chances of malware infiltration. Additionally, the importance of fortified password protocols cannot be overstated in the battle against ransomware. Two-factor authentication and single sign-on systems provide formidable barriers, strengthening password security and rendering unauthorized access substantially more challenging for cybercriminals.

To Know More, Read Full Article @ https://ai-techpark.com/top-trends-in-cybersecurity-ransomware-and-ai-in-2024/

Read Related Articles:

Automated Driving Technologies Work

Ethics in the Era of Generative AI

Currently, the two most dominant technologies in the world are machine learning (ML) and artificial intelligence (AI), as these aid numerous industries in resolving their business decisions. Therefore, to accelerate business-related decisions, IT professionals work on various business situations and develop data for AI and ML platforms.

The ML and AI platforms pick appropriate algorithms, provide answers based on predictions, and recommend solutions for your business; however, for the longest time, stakeholders have been worried about whether to trust AI and ML-based decisions, which has been a valid concern. Therefore, ML models are universally accepted as “black boxes,” as AI professionals could not once explain what happened to the data between the input and output.

However, the revolutionary concept of explainable AI (XAI) has transformed the way ML and AI engineering operate, making the process more convincing for stakeholders and AI professionals to implement these technologies into the business.

Why Is XAI Vital for AI Professionals?

Based on a report by Fair Isaac Corporation (FICO), more than 64% of IT professionals cannot explain how AI and ML models determine predictions and decision-making.

However, the Defense Advanced Research Project Agency (DARPA) resolved the queries of millions of AI professionals by developing “explainable AI” (XAI); the XAI explains the steps, from input to output, of the AI models, making the solutions more transparent and solving the problem of the black box.

Let’s consider an example. It has been noted that conventional ML algorithms can sometimes produce different results, which can make it challenging for IT professionals to understand how the AI system works and arrive at a particular conclusion.

After understanding the XAI framework, IT professionals got a clear and concise explanation of the factors that contribute to a specific output, enabling them to make better decisions by providing more transparency and accuracy into the underlying data and processes driving the organization.

With XAI, AI professionals can deal with numerous techniques that help them choose the correct algorithms and functions in an AI and ML lifecycle and explain the model’s outcome properly.

To Know More, Read Full Article @ https://ai-techpark.com/why-explainable-ai-is-important-for-it-professionals/

Read Related Articles:

What is ACI

Democratized Generative AI

Artificial intelligence (AI) has been a game changer in the business landscape, as this technology can analyze massive amounts of data, make accurate predictions, and automate the business process.

However, AI and ethics problems have been in the picture for the past few years and are gradually increasing as AI becomes more pervasive. Therefore, the need of the hour is for chief information officers (CIOs) to be more vigilant and cognizant of ethical issues and find ways to eliminate or reduce bias.

Before proceeding further, let us understand the source challenge of AI. It has been witnessed that the data sets that AI algorithms consume to make informed decisions are considered to be biased around race and gender when applied to the healthcare industry, or the BFSI industry. Therefore, the CIOs and their teams need to focus on the data inputs, ensuring that the data sets are accurate, free from bias, and fair for all.

Thus, to make sure that the data IT professionals use and implement in the software meet all the requirements to build trustworthy systems and adopt a process-driven approach to ensure non-bais AI systems

This article aims to provide an overview of AI ethics, the impact of AI on CIOs, and their role in the business landscape.

Understanding the AI Life Cycle From an Ethical Perspective

Identify the Ethical Guidelines

The foundation of ethical AI responsibility is to develop a robust AI lifecycle. CIOs can establish ethical guidelines that merge with the internal standards applicable to developing AI systems and further ensure legal compliance from the outset. AI professionals and companies misidentify the applicable laws, regulations, and on-duty standards that guide the development process.

Conducting Assessments

Before commencing any AI development, companies should conduct a thorough assessment to identify biases, potential risks, and ethical implications associated with developing AI systems. IT professionals should actively participate in evaluating how AI systems can impact individuals’ autonomy, fairness, privacy, and transparency, while also keeping in mind human rights laws. The assessments result in a combined guide to strategically develop an AI lifecycle and a guide to mitigate AI challenges.

Data Collection and Pre-Processing Practice

To develop responsible and ethical AI, AI developers and CIOs must carefully check the data collection practices and ensure that the data is representative, unbiased, and diverse with minimal risk and no discriminatory outcomes. The preprocessing steps should focus on identifying and eliminating the biases that can be found while feeding the data into the system to ensure fairness when AI is making decisions.

To Know More, Read Full Article @ https://ai-techpark.com/the-impact-of-artificial-intelligence-ethics-on-c-suites/

Read Related Articles:

Generative AI for SMBs and SMEs

Mental Health Apps for 2023

We believe that the journey of developing software is as tough as quality assurance (QA) engineers want to release high-quality software products that meet customer expectations and run smoothly when implemented into their systems. Thus, in such cases, quality assurance (QA) and software testing are a must, as they play a crucial role in developing good software.

Manual testing has limitations and many repetitive tasks that cannot be automated because they require human intelligence, judgment, and supervision.

As a result, QA engineers have always been inclined toward using automation tools to help them with testing. These AI tools can help them understand problems such as finding bugs faster, and more consistently, improving testing quality, and saving time by automating routine tasks.

This article discusses the role of AI in the future of QA engineering. It also discusses the role of AI in creating and executing test cases, why QA engineers should trust AI, and how AI can be used as a job transformer.

The Role of AI in Creating and Executing Test Cases

Before the introduction of AI (artificial intelligence), automation testing and quality assurance were slow processes with a mix of manual and automatic processes.

Earlier software was tested using a collection of manual methodologies, and the QA team tested the software repetitively until and unless they achieved consistency, making the whole method time-consuming and expensive.

As software becomes more complex, the number of tests is naturally growing, making it more and more difficult to maintain the test suite and ensure sufficient code coverage.

AI has revolutionized QA testing by automating repetitive tasks such as test case generation, test data management, and defect detection, which increases accuracy, efficiency, and test coverage.

Apart from finding bugs quickly, the QA engineers use AI by using machine learning (ML) models to identify problems with the tested software. The ML models can analyze the data from past tests to understand and identify the patterns of the programs so that the software can be easily used in the real world.

AI as a Job Transformer for QA Professionals

Even though we are aware that AI has the potential to replace human roles, industrialists have emphasized that AI will bring revolutionary changes and transform the roles of QA testers and quality engineers.

Preliminary and heavy tasks like gathering initial ideas, research, and analysis can be handled by AI. AI assistance can be helpful in the formulation of strategies and the execution of these strategies by constructing a proper foundation.

The emergence of AI has brought speed to the process of software testing, which traditionally would take hours to complete. AI goes beyond saving mere minutes; it can also identify and manage risks based on set definitions and prior information.

To Know More, Read Full Article @ https://ai-techpark.com/ai-in-software-testing/

Read Related Articles:

Revolutionize Clinical Trials through AI

AI Impact on E-commerce

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/

Read Related Articles:

Diversity and Inclusivity in AI

Guide to the Digital Twin Technology