Tag: Natural Language Processing

Picture a world where your professional services business operates like a well-oiled machine, effortlessly balancing resources, predicting project outcomes, and communicating with precision. This isn’t a far-off dream—it’s the reality that Artificial Intelligence (AI) is bringing to the professional services industry right now. As the lines between human expertise and technological capabilities blur, AI is emerging as the secret weapon for firms looking to surge ahead in a fiercely competitive market.

By leveraging AI-powered Professional Services Automation (PSA) software, firms can optimize resource allocation, enhance governance, and streamline communication processes. Let’s explore how AI can revolutionize three key areas of professional services operations.

Intelligent Resource Management

One of the most significant challenges faced by professional services firms is effective resource management. AI-powered solutions can provide unprecedented insights and automation in this critical area:

Optimizing Team Utilization

AI algorithms can analyze historical project data, current workloads, and upcoming commitments to balance the workload across team members. This ensures that no individual is overworked while maximizing overall team productivity. By continuously monitoring utilization rates, AI can alert managers when team members are approaching burnout or when there’s capacity for additional projects.

Forecasting and Capacity Planning

Predictive AI models can forecast resource requirements for upcoming projects based on past performance data and project characteristics. This allows firms to anticipate staffing needs, plan for hiring or training, and make informed decisions about taking on new projects. AI can also help identify potential resource conflicts well in advance, giving managers time to reallocate resources or adjust timelines.

Skill Matching and Project Staffing

AI can analyze the skill sets of available resources and match them with the requirements of incoming projects. This ensures that the right people with the right expertise are assigned to each project, improving project outcomes and client satisfaction. Additionally, AI can identify skill gaps within the organization and suggest upskilling opportunities to prepare teams for future high-stakes projects.

Profitability Analysis and Pricing Optimization

By analyzing PSA tool data, AI can uncover patterns in project profitability across different types of engagements, clients, or project phases. This insight allows firms to identify which areas of their business are most profitable and which may be losing money. AI can then suggest optimal pricing strategies for different types of projects or clients, helping firms maintain a healthy balance between competitive pricing and profitability.

To Know More, Read Full Article @ https://ai-techpark.com/revolutionizing-professional-services-with-ai/

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Greetings Robert, Could you please share with us your professional journey and how you came to your current role as Chief Innovator of Monjur?

Thank you for having me. My professional journey has been a combination of law and technology. I started my career as an intellectual property attorney, primarily dealing with software licensing and IT transactions and disputes.  During this time, I noticed inefficiencies in the way we managed legal processes, particularly in customer contracting solutions. This sparked my interest in legal tech. I pursued further studies in AI and machine learning, and eventually transitioned into roles that allowed me to blend my legal expertise with technological innovation. We founded Monjur to redefine legal services.  I am responsible for overseeing our innovation strategy, and today, as Chief Innovator, I work on developing and implementing cutting-edge AI solutions that enhance our legal services.

How has Monjur adopted AI for streamlined case research and analysis, and what impact has it had on your operations?

Monjur has implemented AI in various facets of our legal operations. For case research and analysis, we’ve integrated natural language processing (NLP) models that rapidly sift through vast legal databases to identify relevant case law, statutes, and legal precedents. This has significantly reduced the time our legal professionals spend on research while ensuring that they receive comprehensive and accurate information. The impact has been tremendous, allowing us to provide quicker and more informed legal opinions to our clients. Moreover, AI has improved the accuracy of our legal analyses by flagging critical nuances and trends that might otherwise be overlooked.

Integrating technology for secure document management and transactions is crucial in today’s digital landscape. Can you elaborate on Monjur’s approach to this and any challenges you’ve encountered?

At Monjur, we prioritize secure document management and transactions by leveraging encrypted cloud platforms. Our document management system utilizes multi-factor authentication and end-to-end encryption to protect client data. However, implementing these technologies hasn’t been without challenges. Ensuring compliance with varying data privacy regulations across jurisdictions required us to customize our systems extensively. Additionally, onboarding clients to these new systems involved change management and extensive training to address their concerns regarding security and usability.

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

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Suppose you’ve been working on landing a high-value B2B client for months, writing a proposal that you believe is tailored to their needs. It explains your solution based on the technological features, comes with compelling references, and responds to their challenges. Yet, when the client responds with a simple “thanks, we’ll be in touch,” you’re left wondering: Was I heard? Was the intended message or the value provided by the product clear?

Here the shortcomings of conventional approaches to Natural Language Processing (NLP) in B2B communication manifest themselves…Despite these strengths, NLP tools are not very effective in understanding the nuances of B2B business and language and are rather limited in understanding the essence and intention behind the text. Common technical words in the document, rhetoric differences, and constant dynamics of the field that specialized terms reflect are beyond the capabilities of traditional NLP tools.

This is where Quantum Natural Language Processing (QNLP) takes the spotlight. It combines quantum mechanics with its ability to process language, making it 50% more refined than previous AI systems. It’s like having the ability to comprehend not only the direct meaning of the text but also the tone, humor references, and business-related slang, improving contextual understanding by 70%.

QNLP is particularly rich for B2B professionals. This simply means that Through QNLP, companies and businesses can gain a deeper understanding of what the customer needs and what competitors are thinking, which in turn can re-invent the analysis of contracts to create specific marketing strategies.

Demystifying QNLP for B2B professionals

B2B communication is all the more complex. Specificities in the contracts’ text, specific terminals, and constant changes in the industry lexicon represent the primary difficulty for traditional NLP. Many of these tools are based on simple keyword matches and statistical comparisons, which are capable of failing to account for the context and intention behind B2B communication.

This is where the progress made in artificial intelligence can be seen as a ray of hope. Emerging techniques like Quantum Natural Language Processing (QNLP) may bring significant shifts in the analysis of B2B communication. Now let’s get deeper into the features of QNLP and see how it can possibly revolutionize the B2B market.

Unveiling the Quantum Advantage

QNLP uses quantum concepts, which makes it more enhanced than other traditional means of language processing. Here’s a simplified explanation:

Superposition: Think of a coin that is being rotated in the air with one side facing up; it has heads and tails at the same time until it falls. In the same way, QNLP can represent a word in different states at once, meaning that it is capable of capturing all the possible meanings of a certain word in a certain context.

Entanglement: Imagine two coins linked in such a way that when one flips heads, the other is guaranteed to be tails. By applying entanglement, QNLP can grasp interactions as well as dependencies between words, taking into account not only isolated terms but also their interconnection and impact on the content of B2B communication.

To Know More, Read Full Article @ https://ai-techpark.com/qnlp-enhancing-b2b-communication/ 

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In this digital age, data is the new oil, especially with the emergence of augmented analytics as a game-changing tool that has the potential to transform how businesses harness this vast technological resource for strategic advantages. Earlier, the whole data analysis process was tedious and manual, as each project would have taken weeks or months to get executed. At the same time, other teams had to eagerly wait to get the correct information and further make decisions and actions that would benefit the business’s future.

Therefore, to pace up the business process, the data science team required a better solution to make faster decisions with deeper insights. That’s where an organization needs to depend on tools such as augmented analytics. Augmented analytics combines artificial intelligence (AI), machine learning (ML), and natural language processing (NLP) to enhance the data analytics processes, making them more accessible, faster, and less prone to human error.

Organizations using augmented analytics report up to a 40% reduction in data preparation time and a 30% increase in insight generation speed. Furthermore, augmented analytics automates data preparation, insight generation, and visualization, enabling users to gain valuable insights from data without extensive technical expertise.

Yellowfin

Yellowfin specializes in dashboards and data visualization that have inbuilt ML algorithms that provide automated answers in the form of an easy guide for all the best practices in visualizations and narratives. It has a broad spectrum of data sources, including cloud and on-premises databases such as spreadsheets, which enables easy data integration for analysis. The platform comes pre-built with a variety of dashboards for data scientists that can embed interactive content into third-party platforms, such as a web page or company website, allowing users of all expertise levels to streamline their business processes and report creation and sharing. However, when compared to other augmented analytics tools, Yellowfin had issues updating the data in their dashboard on every single update, which poses a challenge for SMEs and SMBs while managing costs and eventually impacts overall business performance.

Sisense

Sisense is one of the most user-friendly augmented analytics tools available for businesses that are dealing with complex data in any size or format. The software allows data scientists to integrate data and discover insights through a single interface without scripting or coding, allowing them to prepare and model data. Eventually allows chief data officers (CDOs) to make an AI-driven analytics decision-making process. However, the software is extremely difficult to use, with complicated data models and an average support response time. In terms of pricing, Sisense functions on a subscription pricing model and offers a one-month trial period for interested buyers; however, the exact pricing details are not disclosed.

To Know More, Read Full Article @ https://ai-techpark.com/top-5-best-augmented-analytics-tools-of-2024/ 

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With the rapid growth in the digital world, organizations are implementing Automated Machine Learning (AutoML) that helps data scientists and MLOps teams automate the training, tuning, and deployment of machine learning (ML) models. This technology will save time and resources for the data scientists and MLOps teams, which will accelerate research on ML and solve specific problems related to ML models.

For instance, some AutoML tools focus on optimizing ML models for a given dataset, while others focus on finding the best model for specific tasks, such as picking the appropriate ML algorithm for a given situation, preprocessing the data, and optimizing the model’s hyperparameters, aiding different industries to predict customer behavior, detect fraud, and improve supply chain efficiency.

Therefore, AutoML is a powerful mechanism that makes ML models more accessible and efficient; however, to create a model, execute stratified cross-validation, and evaluate classification metrics, data scientists and MLOps teams need the right set of AutoML tools or platforms.

In today’s AI TechPark article, we will introduce you to the top four AutoML tools and platforms that simplify using ML algorithms.

Auto-SKLearn

Auto-SKLearn is an AutoML toolkit that is available as an open-source software library that can automate the process of developing and selecting the correct ML models using the Python programming language. The software package includes attributes that are used in engineering methods such as One-Hot, digital feature standardization, and PCA. It improvises the model and operates SKLearn estimators to process classification and regression problems. Furthermore, Auto-SKLearn builds a pipeline and utilizes Bayes search to optimize that channel, adding two components for hyper-parameter tuning using Bayesian reasoning: The tools also have an inbuilt meta-learning feature that is used to format optimizers using Bayes and assess the auto-collection structure of the arrangement during the optimization process.

Google AutoML Cloud

The Google Cloud AutoML suite is designed to make it easiest for data scientists and MLops teams to apply ML-specific tasks such as image and speech recognition, natural language processing, and language translation in business. The platform accelerates the process of building custom AI solutions with a variety of open-source tools and proprietary technology that Google has evolved over the last decade. AutoML supports homegrown TensorFlow and offers partially pre-trained features for designing custom solutions using smaller data sets.

To Know More, Read Full Article @ https://ai-techpark.com/automl-platforms-for-2024/ 

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As we have stepped into the realm of 2024, the artificial intelligence and data landscape is growing up for further transformation, which will drive technological advancements and marketing trends and understand enterprises’ needs. The introduction of ChatGPT in 2022 has produced different types of primary and secondary effects on semantic technology, which is helping IT organizations understand the language and its underlying structure.

For instance, the semantic web and natural language processing (NLP) are both forms of semantic technology, as each has different supportive rules in the data management process.

In this article, we will focus on the top four trends of 2024 that will change the IT landscape in the coming years.

Reshaping Customer Engagement With Large Language Models

The interest in large language models (LLMs) technology came to light after the release of ChatGPT in 2022. The current stage of LLMs is marked by the ability to understand and generate human-like text across different subjects and applications. The models are built by using advanced deep-learning (DL) techniques and a vast amount of trained data to provide better customer engagement, operational efficiency, and resource management.

However, it is important to acknowledge that while these LLM models have a lot of unprecedented potential, ethical considerations such as data privacy and data bias must be addressed proactively.

Importance of Knowledge Graphs for Complex Data

The introduction of knowledge graphs (KGs) has become increasingly essential for managing complex data sets as they understand the relationship between different types of information and segregate it accordingly. The merging of LLMs and KGs will improve the abilities and understanding of artificial intelligence (AI) systems. This combination will help in preparing structured presentations that can be used to build more context-aware AI systems, eventually revolutionizing the way we interact with computers and access important information.

As KGs become increasingly digital, IT professionals must address the issues of security and compliance by implementing global data protection regulations and robust security strategies to eliminate the concerns.  

Large language models (LLMs) and semantic technologies are turbocharging the world of AI. Take ChatGPT for example, it's revolutionized communication and made significant strides in language translation.

But this is just the beginning. As AI advances, LLMs will become even more powerful, and knowledge graphs will emerge as the go-to platform for data experts. Imagine search engines and research fueled by these innovations, all while Web3 ushers in a new era for the internet.

To Know More, Read Full Article @ https://ai-techpark.com/top-four-semantic-technology-trends-of-2024/ 

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Social media has transformed marketing. Platforms like Instagram with its 2 billion subscribers allow businesses to connect directly with customers and build their brands through compelling visual storytelling. However, the highly competitive and fast-paced nature of social media also presents challenges. This is where artificial intelligence (AI) comes in. AI technologies are revolutionizing social media marketing, providing data-driven insights and automation that help brands cut through the noise and thrive on social media.

How Artificial Intelligence Helps in Social Media Marketing

Artificial Intelligence is the next big thing in the world of technology and is poised to set forth the course of digital environments in the coming decades. Here below we will see how artificial intelligence is paving the way ahead:

Understanding Your Audience With AI

One of the foundational principles of marketing is understanding your target audience intimately so you can create relevant and engaging content. AI makes discovering audience interests and behaviors easy. Tools like Facebook Analytics, Sprout Social, and Rafflekey utilize machine learning algorithms to reveal demographic data, top-performing content, post timings, picking up winners, and more. These AI-powered insights help you fine-tune Instagram content to match what your followers respond to. Instagram influencers have massively benefited leveraging AI to create instagram giveaway ideas that helps them in boosting their persona and brand.

AI takes audience analysis even further with sentiment analysis and predictive analytics. Sentiment analysis uses natural language processing to determine how audiences feel about your brand by analyzing emotions like joy, surprise, anger, etc. in user-generated content. Predictive analytics examines past performance data to forecast future outcomes. This helps you stay ahead of trends and optimize social media initiatives for maximum impact.

Generating High-Quality Visual Content With AI

Visual storytelling is central to success on Instagram. But constantly producing fresh, eye-catching photos and videos can be challenging. AI creativity tools expand what’s humanly possible by autonomously generating unique visual content.

For example, tools like Canva, Over, and Recite leverage AI to transform text prompts into stunning social media graphics in just seconds. Adobe’s Sensei AI identifies aesthetically pleasing image compositions and automatically adjusts parameters like lighting, color balance, and cropping. For video, generative AI can craft natural voiceovers for explainer videos based on your script.

These AI creativity enhancements remove friction from design and allow you to produce loads of on-brand, high-quality visual content to feed Instagram’s voracious appetite.

To Know More, Read Full Article @ https://ai-techpark.com/the-role-of-ai-in-social-media-marketing/ 

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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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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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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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