Tag: Natural Language Processing

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

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/

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Generative AI for SMBs and SMEs

Mental Health Apps for 2023

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

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

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

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

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

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

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

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

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

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

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Diversity and Inclusivity in AI

Guide to the Digital Twin Technology

The emergence of artificial intelligence (AI) has continually reshaped a range of sectors across the business world.

However, the convenience of AI needs to be balanced against the environmental consequences and the unplanned actions that often arise from the unnecessary usage of hardware, energy, and model training. With the knowledge of digital technologies and a robust foundation to support sustainable development, chief information officers (CIOs) should consider implementing AI initiatives.

According to a survey by Gartner, it is evident that environmental issues are a top priority, and tech companies need to focus on eliminating these issues. Consequently, the CIOs are under pressure from executives, stakeholders, and regulators to initiate and reinforce sustainability programs for IT.

Thus, the combination of adopting AI and environmental sustainability requires proactive strategies that will transform your business. This article describes a framework for the adoption of green algorithms that CIOs can implement in IT organizations to support sustainable development.

AI Supporting Environmental Sustainability

For tracking a sustainable environment within an IT organization, the CIOs have to deliver mandates and requirements to track and trace their businesses’ sustainability KPIs, such as energy consumption or the percentage of carbon footprint. However, the importance of these KPIs and the effectiveness of CIOs rest in how well the research matter is integrated into their digital foundation or digital dividend into the digitized metrics of the organization.

Let’s consider an example of modern networks that are implemented in data centers that allow you and your team to monitor, manage, and minimize energy consumption. It is always advisable to use optical networks because they are more energy efficient and resilient than copper cables, as copper cables are rare earth metals and are mined and refined to transform them into strong cables. Thus, the production of fiber networks uses few raw materials and fewer plants when compared to copper cables.

There are findings that IT companies that have implemented modern networking strategies have witnessed a reduction in their environmental footprint by four times compared to those that have not.

A Five-Step Framework for Adopting Green Algorithms

The green algorithms come into play when there is a lot of complexity, cost, and carbon involved in implementing AI in IT organizations. The green algorithms can be seamlessly integrated with a range of methodologies, from natural language processing (NPL) for analyzing stakeholders’ sentiments to machine learning (ML) to enable predictive maintenance.

However, to implement green algorithms effectively, a collaborative initiative with CIOs and IT project managers is required to develop a structured approach to encourage the development of energy efficiency and environmentally responsible AI solutions that will be the backbone of modern project management.

To Know More, Read Full Article @ https://ai-techpark.com/the-convergence-of-ai-and-sustainability-in-the-it-industry/

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Ethics in the Era of Generative AI

Democratized Generative AI

We live in an ecosystem where we desire a personalized experience, from music to web series, and the products and services we purchase are often recommended to us based on the data that is collected by these websites or applications.

This ability lets us understand our needs and wants for a better living experience.

Similarly, in the healthcare industry, we can monitor our health and get personalized treatment with the help of artificial intelligence (AI), Natural language processing (NLP), and machine learning (ML) models and algorithms, which tech and healthcare visionaries refer to as AI in healthcare.

AI in healthcare is a promising collaboration, as it challenges the traditional way patients are treated by doctors and healthcare specialists to bring a futuristic clinical and administrative solution. Using modern-age technology, doctors, researchers, and other healthcare providers improve healthcare delivery in areas like preventive care, disease diagnosis and prediction, treatment plans, as well as care delivery and administrative work.

AI in healthcare is further helping recruiting companies contribute to consumer health swiftly. Nowadays, the increasing use of AI in consumer wearables and other medical devices is providing value in monitoring and identifying early-stage heart diseases. This AI-powered integration of sensors and devices helps healthcare service providers observe and detect life-threatening diseases at an early stage.

Nevertheless, healthcare areas are plentiful. However, this article will focus on how AI has been implemented and what the future of healthcare policies looks like for the industry.

The concept of patient-centricity focuses on AI-based prescription medicine, which offers enhanced personal treatment by empowering patients and providing visual care.

Focus Areas of AI in Healthcare

The introduction of AI in healthcare implements modern healthcare systems that are equipped to cure diseases at a rapid pace with greater accuracy, improving the quality of care through technological advancements.

The integral focus areas for artificial intelligence help in making the modern healthcare process and system more patient-centric, further fostering care delivery, strengthening disease surveillance mechanisms, and enhancing the drug discovery process.

The future of AI in healthcare holds immense potential for helping shape public and private health policies. While prioritizing education and training initiatives and embracing this technology responsibly, custodians in the health tech industry can unlock the full potential for creating innovative and lasting solutions that address the relentless healthcare challenges.

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

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Digital Patient Engagement Platforms

Importance of AI Ethics

The rapid advancement of artificial intelligence (AI) is revolutionising our lives and work, making processes more efficient. Technologies like large-scale machine learning and natural language processing models, such as ChatGPT, are pushing the boundaries of what was once confined to the realm of science fiction. However, a significant challenge remains in bridging the gap between technical brilliance and real-world application.

While AI has made significant progress in virtual environments, the introduction of AI-powered general-purpose robots in the physical world still faces substantial obstacles. Why is this the case, and how can we address these barriers? We explore the topic in more detail below.

Energy efficiency stands out as a primary obstacle. At its core, a robot is essentially a self-propelled computer. Anyone who has used a laptop knows that even the best devices struggle to operate for more than a few hours without recharging. With robots, energy demands are even higher due to internal processes and physical movement. Safety considerations prevent them from relying on tethered connections, necessitating extended battery life.

Unfortunately, current robot mechanics and autonomous systems lack the energy efficiency required for sustained operation. They require frequent and extended charging periods to perform optimally. While the first generation of robots is utilised in industrial settings for manufacturing, they remain constantly tethered to a power source. Although there are general-purpose robots available, like Sanctuary’s Phoenix humanoid, they are still cumbersome and expensive. It will likely take five to ten more iterations before we achieve a model that is truly independent, freely moving, and capable of performing various tasks.

To bridge this gap, we must start with smaller and simpler applications that gradually lead to full AI integration in the physical world. Cobots, which are robots designed for simple tasks, can play a crucial role in this process. Examples include self-driving wheelchairs, robots cleaning building facades, or autonomous technology performing complex, focused tasks like a smoke-diving robot searching for people or a drone fixing power lines. The key is focusing on single-duty performance, not only to enhance energy efficiency but also to achieve the highest standard of work.

Mechanical efficiency is another critical aspect. By improving the way robots move, potentially by utilising artificial muscles and joints to mimic human motion, we can reduce their energy requirements. However, achieving fully functional humanoid technology is still a considerable distance away.

To Know More, Read Full Article @ https://ai-techpark.com/overcoming-barriers-with-ai/ 

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Hadoop for Beginners

Guide to the Digital Twin Technology

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In our previous articles, we have highlighted how machine learning (ML) and artificial intelligence (AI) can revolutionize IT organizations. But there is another very powerful resource that has the potential to change the traditional way of computing, which is called quantum computing (QC). In today’s article, we will highlight how to overcome computing limitations with quantum machine learning (QML) and what tools and techniques this technology can offer. But first, let’s take a quick glimpse of what quantum computing is.

Quantum computing is currently an emerging field that requires the development of computers based on the principles of quantum mechanics. Recently, scientists, technologists, and software engineers have found advancements in QC, which include increasingly stable qubits, successful demonstrations of quantum supremacy, and efficient error correction techniques. By leveraging entangled qubits, quantum computing enables dramatic advances in ML models that are faster and more accurate than before.

Usefulness of Utilizing Quantum Computing in Machine Learning

Quantum computing has the power to revolutionize ML by allowing natural language processing (NLP), predictive analytics, and deep learning tasks to be completed properly and with greater accuracy than the traditional style of computing methods. Here is how using QC will benefit technologists and software engineers when applied properly in their company:

Automating Cybersecurity Solutions

As cybersecurity is constantly evolving, companies are seeking ways to automate their security solutions. One of the most promising approaches is QML, as it is a type of AI that uses quantum computing to identify patterns and anomalies in large-scale datasets. This allows the companies to identify and respond to threats faster and reduce the cost of manual processes.

Accelerate Big Data Analysis

Quantum computing has gained traction in recent years as a potentially revolutionary technology that can be accurate in computing tasks and improve the speed of completing tasks. However, researchers are nowadays investigating the potential of QML for big data analysis. For example, a team of researchers from the University of California recently developed a QML algorithm that can analyze large-scale datasets more quickly and accurately than traditional ML algorithms.

The potential of QML algorithms is immense, and training them properly can be a major challenge for IT professionals and technologists. Researchers are finding new ways to address these problems related to the training of quantum machine learning algorithms.

To Know More, Read Full Article @ https://ai-techpark.com/overcoming-limitations-with-quantum-ml/ 

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Safeguarding Business Assets

Cloud Computing Frameworks

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Kindly brief us about yourself and your role as the Co-CEO at Sinequa.

I’m a serial entrepreneur, business developer and investor inspired by technology that improves the way we work. I’m passionate about human-augmented technologies like AI and machine learning that elevate human productivity and intelligence, rather than replace humans. In 2010, I co-founded Seal Software, a contract analytics company that was the first to use an AI-powered platform to add intelligence, automation, and visualization capabilities to contract data management. During my tenure, I oversaw the company’s fiscal growth and stability, which led to the acquisition of Seal by DocuSign in May 2020. I later served as President and Chief Revenue Officer of Time is Ltd., a provider of a productivity analytics SaaS platform. I joined Sinequa’s board of directors in March 2021, providing strategic planning and oversight during a time of rapid European expansion. With Sinequa’s fast growth, my role also expanded. So, in January 2023, I joined Alexander Bilger – who has successfully served as Sinequa president and CEO since 2005, in a shared leadership role as Co-CEO with the aim to further accelerate Sinequa’s ambitious global growth. Today there is so much innovation happening around the confluence of AI and enterprise search. I can’t imagine a more exciting space right now, and especially with Sinequa as a leading innovator.

In your opinion, how important is it to augment AI and ML in a way that they can be utilized to their fullest potential and not be a substitute for human skills?

We are experiencing a revolution in what can be done with AI, but it’s not going to make humans obsolete. Humans innately seek ways to make their lives easier and therefore tend to trust automation if it simplifies something. But AI isn’t perfect; for all its capabilities, it still makes mistakes. The more complex and nuanced the situation, the more likely AI is to fail, and those are often the situations that are the most critical. So it is important that we don’t rely on AI to automate everything, but use it to augment human ability, and rely on humans to ensure that the right information is being used to drive the right outcomes.

How important is it to leverage the power of AI in order to boost business performance?

I’m confident that AI is going to very quickly become a key differentiator in everything we do. Being able to use AI effectively will be a competitive advantage; not using AI will be a weakness. Perhaps you’ve heard the saying, “AI isn’t going to replace your job. But someone using AI will.” That is a new era that we are entering, and the same holds true for businesses. Those who find how to apply AI in new and creative ways to improve their business – even in the most mundane of areas – are going to create competitive advantages. I believe it’s going to be less and less about the technology and capability of the AI itself, but rather in how the AI is applied. ChatGPT is just the beginning.

Please brief our audience about the emerging trends of the new generation and how you plan to fulfill the dynamic needs of the AI-ML infrastructure.

To Know More, Visit @ https://ai-techpark.com/aitech-interview-with-ulf-zetterberg/ 

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