Tag: Machine Learning

In the last few years, artificial intelligence for IT operations (AIOps) and observability have been hot topics in the IT operations sector. Organizations are looking for improvements in development and operation processes as these technologies have become more accessible, with various benefits and challenges. With the power of artificial intelligence (AI), machine learning (ML), and natural language processing, IT professionals such as engineers, DevOps, SRE (Site Reliability Engineering) teams, and CIOs can detect and resolve incidents, drive operations, and optimize system performance.

Today, we will understand how AIOps and observability have benefited most enterprises and why they are important for your business.

The Challenges and Solutions of Observability and AIOps

AIOps and observability have been critical tools in modern IT operations that have changed the traditional way of managing data. However, IT professionals need help with certain challenges and limitations that can bottleneck the use of these tools properly. Let’s explore some key challenges and their solutions:

Complexity of Implementation

Implementing observability and AIOps involves a lot of complexity, as these technologies require investment in infrastructure and expertise to implement and maintain. Moreover, a shift in mindset from traditional IT operations, where monitoring and responding to issues are done manually, is also crucial.

Solution: The only way to overcome these challenges is by investing in proper training and infrastructure that supports AIOps and observability, along with continuous organizational improvement and learning. The IT teams should also embrace new technologies and methods to stay updated and competitive in the AI industry.

AIOps’s Limitation

Even though AIOps is a powerful tool, it has certain limitations as it can partially replace human expertise. On the other hand, ML can recognize trends and patterns, but it struggles with the underlying cause of an issue.

Solution: To solve these complex issues, human expertise is still needed, as small organizations may not require the complexity of AIOps. The IT teams have to intervene to identify patterns and trends with the help of the ML algorithm.

Organizations today are under pressure to keep their IT solutions and infrastructure up and running with minimal downtime. While it is a tough job and has become harder to achieve with modern architecture, AIOPs and observability coming together can help your company enjoy cost-effective solutions to data and IT issues.

To Know More, Read Full Article @ https://ai-techpark.com/observability-and-aiops/

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Can you provide an overview of AvaSure’s current use of AI technology in your organization?

Absolutely. AvaSure’s TeleSitter® solution enables acute virtual care and remote safety monitoring. Our platform enables virtual team care by combining remote patient sitters, virtual nurses, and other providers in a single enterprise technology solution to enhance clinical care, improve safety, and boost productivity.

Recently we unveiled artificial intelligence (AI) capabilities to our virtual care platform. AI augmentation will enable health system partners to enhance efficiency and time-savings while also improving the quality of care they deliver. Our initial applications will enhance a virtual safety attendant’s capacity for reducing elopement – which is when a hospital patient leaves a facility without any caregiver’s knowledge – and preventing falls.

How do you ensure the ethical use of AI in your organization, particularly in terms of privacy and security of patient data?

To reiterate, we’re not recording any of the data. We’re just building models that don’t include patient-identifiable information. What’s important is that we have a large enough sample size for computer vision that the models perform on different demographics and we are able to fill in race, age groups, gender, and similar variables.

How do you see AI technology evolving in the healthcare industry in the next few years, and what role does AvaSure plan to play in this evolution?

Given the structural staffing shortages, the aging population, the need for the healthcare system to care for more patients more efficiently, there’s going to be an even bigger demand for healthcare. At the same time, we don’t have enough nurses and physicians. AI can play a key role in helping to leverage experts most effectively and where needed by automating tasks and augmenting the expertise of clinicians.

I think computer vision is going to be a very powerful tool in the clinical environment in terms of reducing harm and minimizing errors. Have you ever been in a hospital where you’ve had the nurse come into your room to take your blood pressure every four hours while you’re trying to sleep? Something like that can be automated so that it’s less disruptive to a patient.

In terms of AI adoption, what advice would you give to other healthcare organizations looking to incorporate AI into their operations?

I have several pieces of advice to offer: Use AI to augment people, not replace them. Keep a human in the loop so trust can be established. Most importantly, partner with a company that has deep experience gained from thousands of implementations and who is coming at it from a clinical expertise perspective, and not from an IT perspective. This means a partner that has its own clinical resources, understands your environment, can safely and effectively drive adoption and change management, and can ensure compliance. At AvaSure, 15% of our staff are experienced nurses.

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

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In the fast-moving business world, IT professionals and enthusiasts cannot ignore the use of machine learning (ML) in their companies. Machine learning tends to give a better insight into improving business performance, like understanding trends and patterns that human eyes generally miss out on. Thus, Machine learning (ML) and artificial intelligence (AI) aren’t just words; rather, they have the potential to change the industry positively. Through this article, we will focus on the importance of implementing machine learning and its use cases in different industries that will benefit you in the present and future.

The Usefulness of ML in Different Industries

Machine learning is a game-changer, and let’s see here how different industries have made the best use of it:

Predictive Analytics for Recommendations

Predictive analytics are generally used to identify opportunities before an event occurs. For example, identifying the customers that have spent the most time on your e-commerce website will result in profit for your company in the long run. These insights are only possible through predictive analytics, which allows your company to optimize market spending and focus on acquiring customers that will generate profit.

 Automate Decision-making

Automated and intelligent decision-making solutions and tools can be used by you to make quick decisions for efficient teamwork. For instance, some industries require strict adherence to compliance, which can only be applied by decision-management tools that help in maintaining records of legal protocols. These tools can make quick decisions if the business fails to obey any compliance rules.

 Creating a Data-Driven Culture

Creating a data-driven culture helps in getting numbers and insights that are generated through data. A data-driven organization not only empowers your teams but also improves your decision-making efficiency and effectiveness. One such example of a data-driven culture is DBS Bank, which has embraced AI and data analytics to provide customers with personalized recommendations. This is helping the customers and the bank authorities make better financial decisions and also improving customer loyalty. By embracing a data-driven culture, DBS Bank has also invested in training employees in data analytics and big data.

Machine learning is an important tool for making automated decisions in various business processes. These models help you identify errors and make unbiased and informed decisions. By analyzing data through customer interaction, preference, and behavior, ML algorithms can help identify the correct patterns and trends, which will help your company in the long run.

To Know More, Read Full Article @ https://ai-techpark.com/ml-helps-make-decisions/ 

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Artificial intelligence (AI) has improved the outcomes for hundreds of thousands of businesses by automating and speeding up their processes. Yet, it has also helped the criminals too, making it easier for them to commit fraud and steal money.

Nowhere has this been more keenly felt than in the banking and finance industry, where the technology has been successfully deployed in the fight against fraud, tackling everything from credit card fraud to money laundering. But one of the key areas where it is proving most effective is in detecting business lending fraud.

There’s no doubt that business lending fraud has been on the rise in recent years, increasing at an average of 14.5% year-over-year for small and mid-sized businesses in 2022, as per a LexisNexis report. But that’s just the tip of the iceberg, with many of these types of fraud going undetected or unreported.

The problem was exacerbated during the Covid-19 pandemic as businesses became increasingly stretched, with employees forced to work remotely. As a result, they have become obvious targets for scammers looking to exploit them.

Types of business lending fraud

As technology continues to evolve, so the criminals’ methods have too. There are four key areas where they are now focusing their efforts: application fraud, impersonating another business, providing incorrect information and hiding data.

Application fraud is fast becoming one of the most prevalent forms of deception. It involves a business or individual using their own details to apply for a financial product such as a loan, but when they complete the application they use false information or counterfeit documents, often to try and get a larger amount of money.

Another common tactic among fraudsters is impersonation. By using fake documentation to trick the lender into believing that they are another business, they can dupe them into lending them big sums of money.

Knowingly providing the wrong information is fraud too. This typically includes but is not limited to, the submission of misstated management information and fudged bank statements, which are hard to verify without the correct records.

But perhaps the hardest fraud to uncover of all is hiding data. By withholding key information that can be used to determine a lending decision, scammers can secure a bigger loan.

Given the complexity of these kinds of fraud and the fact that they can be committed by individuals and companies themselves or others who have stolen their identity by posing as them, it makes it even harder to identify and prevent them from happening in the first place. And so deceptive are they that the victim may never know they have been targeted or only find out when they are turned down for a loan after the fraud was perpetrated without them being aware of it.

To Know More, Read Full Article @ https://ai-techpark.com/the-rise-of-business-lending-fraud-and-ai/ 

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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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In the rapidly evolving world of technology, data analytics continues to shape industries and drive innovation. As we delve deeper into 2023, it is crucial to examine the emerging trends that are revolutionizing data analytics and the vital role diversity and inclusivity play in the field of artificial intelligence (AI). This article explores the latest data analytics trends and highlights the importance of fostering diversity and inclusivity to create a more equitable and effective AI landscape.

Data Analytics Trends in 2023:

Augmented Analytics: Augmented analytics leverages machine learning algorithms and AI techniques to automate data preparation, analysis, and visualization, empowering businesses to make data-driven decisions quickly and efficiently. With advanced capabilities, augmented analytics simplifies complex data processes and democratizes access to insights, enabling a wider range of users to harness the power of data.

Natural Language Processing (NLP): NLP techniques enable machines to understand, interpret, and respond to human language in a way that resembles human conversation. In 2023, NLP is predicted to witness significant advancements, making it easier for organizations to extract valuable insights from unstructured data sources like text documents, emails, social media, and customer reviews. NLP-driven sentiment analysis and text mining will become integral parts of data analytics, offering profound insights into customer behavior, market trends, and brand reputation.

Edge Analytics: The proliferation of Internet of Things (IoT) devices has led to an exponential increase in data generated at the edge of networks. Edge analytics allows organizations to analyze data in real-time at the point of collection, reducing latency and enhancing decision-making capabilities. In 2023, edge analytics will play a vital role in enabling data-driven insights in various domains, such as healthcare, manufacturing, transportation, and smart cities.

Diversity and Inclusivity in AI:

While embracing these cutting-edge data analytics trends, it is imperative to foster diversity and inclusivity in the field of AI. Diversity in AI teams, including gender, race, cultural backgrounds, and perspectives, is crucial to building unbiased and ethical AI systems. Here's why:

Avoiding Bias: AI algorithms are only as unbiased as the data they are trained on. Without diverse representation, AI systems can inadvertently perpetuate biases and discrimination, leading to unfair outcomes. A diverse team can identify and mitigate such biases, ensuring that AI systems are developed with fairness and inclusivity in mind.

Addressing Real-World Challenges: AI solutions should address real-world challenges faced by diverse populations. By including individuals from diverse backgrounds in AI development, the specific needs, concerns, and experiences of different communities can be better understood and incorporated into AI models, resulting in solutions that are more responsive and beneficial to all.

Conclusion:

As we navigate the data-driven era of 2023, embracing the emerging data analytics trends while prioritizing diversity and inclusivity in AI is essential. Augmented analytics, NLP, edge analytics, and AutoML are transforming the way organizations harness

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Artificial Intelligence (AI) has emerged as a game-changer in countless industries, and its impact on content creation is no exception. The integration of AI and cloud computing frameworks has unlocked unprecedented possibilities, enabling businesses and individuals to leverage AI-generated content to enhance creativity, efficiency, and productivity. In this article, we will delve into the exciting developments and applications of AI-generated content in 2023, all made possible by the power of cloud computing frameworks.

The Evolution of Cloud Computing Frameworks:

Cloud computing frameworks have experienced rapid growth and evolution, providing scalable and flexible infrastructure to support AI applications. By leveraging the cloud, AI algorithms can access vast amounts of data, computational resources, and advanced machine learning models, enabling them to generate highly sophisticated and contextually relevant content.

AI-Generated Content in Various Industries:

a. Marketing and Advertising: AI-generated content has revolutionized marketing and advertising campaigns. Marketers can now create personalized and hyper-targeted content by utilizing AI algorithms to analyze customer data, generate persuasive ad copy, and design captivating visuals.

b. Journalism and News Reporting: With the aid of AI, news organizations can automate the process of gathering, analyzing, and generating news articles. AI algorithms can sift through massive amounts of data, extract key insights, and present unbiased and fact-checked news stories in real-time.

c. Entertainment and Media: The entertainment industry has embraced AI-generated content for various purposes, including scriptwriting, character development, and even music composition. AI models can analyze vast libraries of existing content and generate new, original works tailored to specific genres or styles.

d. E-commerce and Retail: AI-powered recommendation systems have become indispensable for e-commerce platforms. By analyzing user behavior and preferences, AI algorithms can generate personalized product recommendations, enhancing the shopping experience and increasing customer satisfaction.

Enhancing Creativity and Collaboration:

AI-generated content acts as a powerful tool for creative professionals, enabling them to streamline their workflows and push the boundaries of their creativity. By automating repetitive tasks, such as image editing or video processing, creatives can focus more on ideation and experimentation. Moreover, cloud-based collaboration platforms facilitate seamless teamwork, allowing individuals from different locations to work together on AI-generated content projects.

Conclusion:

In the year 2023, the convergence of AI and cloud computing frameworks has transformed content creation across industries. AI-generated content has empowered businesses and individuals with unparalleled capabilities, enhancing creativity, efficiency, and productivity. As we navigate the ever-evolving landscape of AI-generated content, it is imperative to address ethical concerns, prioritize human oversight, and ensure the responsible and accountable use of these technologies.

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