Tag: Machine Learning

AI and ML are changing the way we live and work. Many people think they’re reserved for tech giants, however. But increasingly we’re seeing SMEs harness the power of these tools. And the benefits are clear: artificial intelligence and machine learning can improve operations, boost customer satisfaction and help companies to outpace the competition – all of which are essential if you want your business to not only survive but thrive.

Interested in knowing more? Here we look at the benefits of AI and ML in the business world as well as the perceived challenges.

Customer service

We’ve probably all communicated with a chatbot online when searching our favourite store’s website. In fact, many might not even realise that when you use the chat box function, you’re not actually speaking to a human. This is one of the best examples of how technology can be used to help a business as it offers 24/7 customer support without breaks or vacations, and it’s likely to save money over time too. Even better, these chat boxes provide instant responses to customers, whatever time of the day, meaning customers are better served and we know that is crucial for customer retention and loyalty. That’s not to say there isn’t a place for human customer service agents, instead, your human team can be deployed to other areas of the business and can tackle more complex issues.

Administration

Data entry, accounts, general admin – these are all essential tasks for business owners to complete but it’s not always easy to find the time to dedicate to them. That’s where AI and ML come in. In fact, AI can automate data entry making it faster and error-free. It can even take care of administrative tasks, report generation, and appointment scheduling meaning you and your team can focus on business-critical tasks. With more time freed up, you’ll likely be able to respond quicker around the business and can put more time into your overall strategy.

Decision-making

Another benefit of AI is that it provides deeper insights and can analyse large amounts of data much quicker than a human could. This makes it even easier to predict market demand, understand specific customer preferences and optimise resource allocation.

The challenges in implementing AI

Despite the benefits of AI and ML, we can’t ignore the challenges surrounding it. This includes the difficulties in managing vast data storage, recruiting skilled AI professionals, and not to mention the rapid changes in the AI landscape. Indeed, implementing AI isn’t a one-stop approach. Instead, companies need to continuously innovate to ensure they can keep pace with competitors.

To Know More, Read Full Article @ https://ai-techpark.com/transforming-smes-for-success/

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Diversity and Inclusivity in AI

Things You Should Know About Quantum Computing
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In the ever-evolving landscape of theology, three revolutionary forces have gained momentum and have a promising future in reshaping industries. These three are quantum computing (QC), artificial intelligence (AI), and blockchain technology, which have already left a mark in various domains. Thus, by combining these three technologies organizations can benefit researchers by improving scalability, efficiency, and security when implemented in the real world.

So, in this article, we will explore the future of quantum computing, AI, and blockchain technology by exploring the potential and powerful synergies, challenges, and opportunities.

Quantum Computing

As discussed in our previous articles, quantum computing has the potential to address the traditional computing methods that the modern technological industry needs, for example, in manufacturing, finance, astronomy, and many more. QCs are capable of performing complex calculations at a much faster magnitude than traditional computers. For example, quantum computing can be used to optimize the supply chain, enhance financial risk management, improve drug discovery, and optimize e-commerce logistics.

Revolutionizing with AI

Artificial intelligence has made a remarkable contribution to our industry by enabling machines to perform tasks that were previously conducted by humans. AI has a bright future for making daily work autonomous, self-improvement, unstructured data, and understanding complex equations. With quantum computing and quantum machine learning algorithms, we can process and analyze massive datasets with efficiency, empowering AI systems to predict accurately and make correct decisions.

Blockchain Technology

On the other hand, blockchain technology is a distributed ledger that enables transparent and secured transactions without the need for banks or financial institutions by introducing decentralized cryptocurrencies like Bitcoin. Blockchain technology comes with the concept of “proof-of-work” used in many blockchains, which requires computation tasks. By adding new blocks, tampering with the blockchain becomes even more difficult. Blockchain technology can be used in other areas as well, like smart contracts, supply chain monitoring, the delivery of secure medical records, and voting systems.

Overall, blockchain technology offers a secure and immune way to manage and store data related to the Quantum machine learning system. By harnessing the power of blockchain technology, financial organizations can ensure that the data is safe and up-to-date. As quantum computing is continuously advancing, blockchain technology has become an important tool for securing QML systems.

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

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AI in Medical Imaging: Transforming Healthcare

AI and RPA in Hyper-automation

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Quantum computing (QC) and machine learning (ML) are the two most hot technologies that are being adopted in the IT field. QC has the power of quantum physics to perform computation by providing an unprecedented level of scalability and accuracy; on the other hand, ML has deep learning capabilities and intelligent automation as leverage to scale out large data sets. Thus, the combination of these two applications, i.e., QC and ML, can create new opportunities that could solve complex problems with greater accuracy and efficiency than the traditional way of computing could.

In this article, we will dive into how to implement quantum machine learning (QML) and what the best practices are for AI technologists.

Success Story- Quantum Machine Learning in Automotive Industry

The BMW Group is among the first automotive firms to take an interest in quantum computing. In 2021, BMW Group issued the Quantum Computing Challenge in association with AWS to crowdsource innovations around specific use cases, believing that quantum computing could benefit businesses by solving complex computing problems.

The objective was to determine if the image-trained machine learning system presently in use to detect fractures in produced elements might be improved. To properly train the model, high-resolution photographs of the manufactured components were required. In addition, the organization required a lot of them because those kinds of defects are quite uncommon. There is potential for improvement because obtaining and storing these photos requires time and memory.

BMW Group gave a statement that, “In light of the required human expertise to hand-tune algorithms, machine learning (ML) techniques promise a more general and scalable approach to quality control. Quantum computing may one day break through classical computational bottlenecks, providing faster and more efficient training with higher accuracy.”

After implementing the QML solution, the BMW Group has witnessed 97% accuracy by enhancing the classical algorithm by orchestrating quantum processing unit (QPU) calculations at a crucial part of the analysis. The Quantum model was trained on 40% of the whole dataset. In contrast, the Benchmark model was trained on 70%, which implies that the classical approach is more efficient and manages to provide accurate predictions without unnecessary inputs.

Future Implementation of Quantum Machine Learning

Quantum machine learning (QML) algorithms have the potential to solve maximum problems in a much faster time than the classical algorithm. According to IBM researcher Kristan Temme, there is strong evidence that QML is emerging at a significant speed in all industries. He quotes, “At this point, I’d say it’s a bit difficult to exactly pinpoint a given application that would be of value.”

There are also proven examples where QML has been an advantageous technology over classical computing.

To Know More, Read Full Article @ https://ai-techpark.com/best-practices-for-quantum-machine-learning/ 

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AI and Blockchain Revolution

AI and RPA in Hyper-automation

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The business world is increasingly turning to artificial intelligence (AI) systems and machine learning (ML) algorithms to automate complex and simple decision-making processes. Thus, to break through the paradigm in the field of IT operations, IT professionals and top managers started opting for AIOps platforms, tools, and software, as they promised to streamline, optimize, and automate numerous tasks quickly and efficiently. However, there are a few shortcomings, like algorithmic bias, that have been a major concern for IT professionals and other employees in the company.

Key Technologies in Addressing Algorithmic Biases

With the use of cutting-edge AIOps technologies, IT professionals can understand and explore the algorithmic biases in the system. Thus, here are a few key technologies that will help you detect such issues:

Time Series Analysis

When having abundant data, time series analysis emerges as a crucial tool in AIOps as it records data over time by tracking users’ behavior, network activity, and system performance. Algorithms should represent temporal dependencies, trends, and seasonality to detect biases effectively. AIOps uses a time series analysis method that includes autoregressive models, moving averages, and recurrent neural networks to examine the time-stamped data for deviation and identify abnormalities quickly.

Unsupervised Learning Techniques

Unsurprised learning is an essential component of AIOps for detecting algorithm biases and unwanted labeled data, which is necessary for traditional supervised learning but with limited knowledge. To discover issues, techniques like clustering and dimensionality reduction are crucial in revealing hidden structures within data.

Machine Learning and Deep Learning

The use of ML and deep learning techniques helps in regulating the different established standards, which enables the AIOps system to learn patterns and relationships from complicated and massive data and also enables it to detect analogous biases.

While not all scenarios involving algorithmic bias are concerning, they can have major negative effects when the stakes are high. We have seen that algorithmic prejudice poses a severe threat to human privacy, with lives, livelihoods, and reputations at stake, as well as concerns about data integrity, consent, and security. Integrated AIOps ensure that IT professionals and managers avoid bias and unfairness in their AI and ML models by considering any subjective elements associated with people, locations, products, etc. in their training data and models.

To Know More, Read Full Article @ https://ai-techpark.com/algorithmic-biases-solutions/ 

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Ethics in the Era of Generative AI

Generative AI for SMBs and SMEs

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Data science is a rapidly evolving field. How does Tredence stay ahead of the curve and ensure its solutions incorporate the latest advancements and best practices in the industry?

At Tredence, we constantly innovate to stay ahead in the rapidly evolving data science field. We have established an AI Center of Excellence, fueling our innovation flywheel with cutting-edge advancements.

We’ve built a Knowledge Management System that processes varied enterprise documents and includes a domain-specific Q&A system, akin to ChatGPT. We’ve developed a co-pilot integrated data science workbench, powered by GenAI algorithms and Composite AI, significantly improving our analysts’ productivity.

We’re also democratizing data insights for business users through our GenAI solution that converts Natural Language Queries into SQL queries, providing easy-to-understand insights. These are being implemented across our client environments, significantly adding value to their businesses.

How does Tredence leverage data science to address specific challenges faced by businesses and industries?

Tredence, as a specialized AI and technology firm, delivers bespoke solutions tailored to businesses’ unique needs, leveraging cutting-edge data science concepts and methodologies. Our accelerator-led approach significantly enhances time to value, surpassing traditional consulting and technology companies by more than 50%. Tredence offers a comprehensive suite of services that cover the entire AI/ML value chain, supporting businesses at every stage of their data science journey.

Our Data Science services empower clients to seamlessly progress from ideation to actionable insights, enabling ML-driven data analytics and automation at scale and velocity. Tredence’s solutioning services span critical domains such as Pricing & Promotion, Supply Chain Management, Marketing Science, People Science, Product Innovation, Digital Analytics, Fraud Mitigation, Loyalty Science, and Customer Lifecycle Management.

Focusing on advanced data science frameworks, Tredence excels in developing sophisticated Forecasting, NLP models, Optimization Engines, Recommender systems, Image and video processing algorithms, Generative AI Systems, Data drift detection, and Model explainability techniques. This comprehensive approach enables businesses to harness the full potential of data science, facilitating well-informed decision-making and driving operational efficiency and growth across various business functions. By incorporating these data science concepts into their solutions, Tredence empowers businesses to gain a competitive advantage and capitalize on data-driven insights.

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

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Diversity and Inclusivity in AI

AI in Medical Imaging: Transforming Healthcare

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AI and AIOps have been transforming the future of the workplace and IT operations, which accelerates digital transformations. The AIOps stands out as it uses machine learning (ML) and big data tracking, such as root cause analysis, event correlations, and outlier detection. According to the survey, large organizations have been solely relying on AIOps to track their performance. Thus, it is an exciting time for implementing AIOps that can help software engineers, DevOps teams, and other IT professionals to serve quality software and improve the effectiveness of IT operations for their companies.

Adoption of AIOps

Most companies are in the early stages of adopting AIOps to analyze applications and machine learning to automate and improve their IT operations. AIOps have been adopted amongst diverse industries, and more enterprises are adopting it to digitally transform their businesses and simplify complex ecosystems with the help of interconnected apps, services, and devices. AIOps have the potential to tackle complexities that are often unnoticed by IT professionals or other departments in a company. Therefore, AIOps solutions enhance operational efficiency and prevent downtime, which makes work easier.

Numerous opportunities can change the way AIOps has been incorporated into the company. To do so, businesses and IT professionals should be aware of appropriate trends and best practices to embrace AIOps technologies. Let’s take a closer look at these topics:

Best Practices of AIOps

To get the most out of AIOps, DevOps engineers and other IT professionals can implement the following practices:

Suitable Data Management

DevOps engineers must be aware that ill-managed data often gives undesired output and affects decision-making. Thus, for a suitable outcome, you should ensure that the gathered data is properly sorted, clean, and classified for seamless data monitoring and browse data through a large database for your enterprise.

Right Data Security

The security of user data is essential for your company, as it is under the guidance of data protection regulation agencies that can impose fines if the data is misused. The DevOps and IT engineers can ensure that the data is properly safeguarded and used within their control to avoid data breaches.

Appropriate Use of Available AI APIs

AIOps’s main aim is to improve the productivity of IT operations with the help of artificial intelligence. Therefore, the IT teams should look for great AI-enabled APIs that improve the tasks they have to accomplish.

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

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Importance of AI Ethics

AI and RPA in Hyper-automation

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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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Event-driven Architecture In Hyper-automation

AI and RPA in Hyper-automation

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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AI in Medical Imaging: Transforming Healthcare

Cloud Computing Frameworks

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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Best API Security Practices for C-Suiters

Digital Patient Engagement Platforms

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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