Tag: Machine Learning

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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In today’s digital era, the merge of artificial intelligence (AI) and the Internet of Things (IoT) has kicked off a tech revolution that tends to reshape numerous industries across the globe with a vision that this transformation helps businesses enhance efficiency and drives innovations at an unprecedented pace.

For a better understanding, IoT is all about an extensive network of connected devices with an embedded sensor that collects and transfers large-scale data that is processed according to the behavior and patterns of the user to make the correct decision at the right time.

On the other hand, artificial intelligence (AI) is a technology that imitates human intelligence and behavior in computer systems, further enhanced by learning and experimenting, to develop new behaviors and skills.

These technologies, together, can solve real-world problems and create new products for businesses to enhance the customer-digital experience. This article explores the opportunities and approaches of AI and IoT that will revolutionize market paradigms.

Five Skills to Stay Relevant and Competitive in the Age of IoT and AI

The scope of implementation and importance of IoT and AI determine the need for qualified IT professionals; however, the demand for experts in these technologies requires certain technical and soft skills.

Below, we have reviewed some wanted skills that are required in an IT professional to succeed in IoT and how they can boost their career profile:

Artificial Intelligence and Machine Learning

AI and machine learning (ML) have become key technologies that have reshaped the IT field. To excel, IT engineers and IoT developers need to have a good understanding of ML and AI technologies, as these technologies are the base of developing tools and frameworks that are essential to developing IoT devices and AI applications that are further used in various application areas, like automotive, manufacturing, finance, and healthcare. Learning and understanding where and how to implement ML algorithms with the help of data sensors are used to develop smarter appliances.

The skill of big data management will be useful for predictive analytics, which is based on identifying data patterns. The IT engineers and IoT developers should also have knowledge of popular ML libraries such as Kera and Tensorflow and the ability to program in languages such as R, Python, and C++.

IoT Systems and Networking

The Internet of Things (IoT) is a blooming field in the IT industry that involves connecting physical devices (motion sensors, smart glasses, VR headsets, smart devices, trackers, drones, etc.) to the Internet. To be a master, IT engineers, and IoT developers need to have a better understanding of the IoT concept and technologies to develop robust and seamless connected devices that require a unique user interface (UI). The knowledge about IoT includes networking protocols such as Bluetooth, Zigbee, and Wi-Fi; engineers and developers should also be familiar with IoT platforms such as AWS IoT and Microsoft Azure. IT professionals who have good UI skills in visual design, analytics, wireframing, and prototyping excel at developing satisfactory devices.

To Know More, Read Full Article @ https://ai-techpark.com/exploring-the-intersection-of-ai-and-iot/

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In today’s competitive world, the various sectors are at a crossroads. Companies following traditional approaches are struggling to keep pace with the fast pace of digital transformation.

So, the pace of the ongoing digital transformation demands a shift in paradigm with the help of intelligent applications (I-apps), which imbue the power of artificial intelligence (AI) and machine learning (ML) to automate tasks, get insights, and drive better decision-making that has the potential to reshape the future of various sectors.

Why Are Intelligent Apps Important for Business?

Intelligent apps are capable of continually learning the environmental, behavioral, and emotional patterns of users while completing the tasks assigned to them.  The applications are meant to forecast your requirements and present them as relevant information, ideas, or recommendations using predictive analysis, prescriptive analysis, operational vision, and product insights. Chatbots, virtual assistants, and recommendation engines on e-commerce sites are just a few examples of intelligent applications.

Let’s consider an example of Slack, a popular collaboration application that provides channels for team communication, file sharing, and connections with other productivity apps.

How Do Intelligent Apps Work?

Intelligent applications are not just clever software; they are built on a foundation of powerful technologies that enable their unique capabilities by empowering software developers to create intelligent apps that automate tasks, analyze data, and predict outcomes. Let’s take a quick glimpse into the mechanics behind how this application works:

AI and ML Technologies

Intelligent apps lie in AI and ML, as these technologies enable apps to process vast amounts of data, learn patterns, make predictions, and adapt their behavior intelligently.

Low-code and No-code Platforms

Democratizing app development, these platforms allow software developers with varying skill levels to create intelligent apps without extensive coding knowledge. These platforms provide visual interfaces, pre-built components, and drag-and-drop functionalities, making intelligent AI-powered app development more accessible.

Data Integration

Intelligent applications need seamless integration with various data sources, including databases, cloud platforms, and IoT devices, to make intelligent decisions and provide personalized experiences.

To Know More, Read Full Article @ https://ai-techpark.com/why-intelligent-applications-are-no-longer-an-option-for-business/

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The inherent inertia and inefficiency of regulators in responding to rapidly evolving sectors like AI can be attributed to several factors rooted in their nature, design, and skill sets. First, regulatory bodies are typically structured to be cautious and deliberative, prioritizing stability and risk aversion over rapid adaptation. This approach, while beneficial for maintaining systemic integrity in traditional markets, often results in a lag when faced with fast-paced technological innovations. Additionally, the design of these institutions, often bureaucratic and bound by complex legislative processes, hampers their ability to swiftly enact new policies or adapt existing ones to novel contexts. Lastly, there is often a skill and knowledge gap; regulators may lack the specialized expertise required to understand and effectively govern cutting-edge technologies, leading to a reliance on outdated frameworks or overly cautious approaches that fail to address the unique challenges and opportunities presented by sectors like cryptocurrency.

This pattern of slow and inadequate responses was most recently highlighted by the rise and fall of FTX, a major cryptocurrency exchange. In 2021, FTX quickly grew into one of the world’s largest cryptocurrency exchanges. In 2022 it collapsed in one of the most prolific financial fraud cases in US history. This failure served as a wake-up call. It demonstrated the risks inherent in the crypto market and the consequences of the US government’s slow response in establishing a comprehensive regulatory framework.

Consumers are getting screwed

All these regulations will create negative consequences for consumers if not carefully crafted or if they “inadvertently” favor large companies at the expense of smaller ones or innovation in general. Primarily, a reduction in innovation and diversity, slower access to advanced technologies, and decreased competition are a few of many concerns. The best example of this happening is in Canada. The telecom industry consists of only three players: Rogers Communications Inc., Telus Corporation, and Bell Canada. This became possible as they lobbied and bullied their way into the top to introduce regulations to stifle any competition in mobile phone and internet services.

As a result, Canadians have significantly worse coverage plans, both locally and globally, than Americans do. Mobile phone bills have skyrocketed to eye watering prices, and Canadians are often the last to get many interesting and innovative services. This extreme competition stifling has even resulted in fatal consequences. On July 8th, 2022, Rogers Communications experienced a service outage that knocked 25% of Canada offline. This resulted in many crucial services being knocked offline, including 911 services.

What do we do then?

AI must be decentralized. Period. Full stop. Allowing something as game changing and powerful to be centralized and to follow the bottom line of business corporations is akin to allowing the internet to be controlled by corporations. If this had happened, this would have resulted in a much less free and open internet than the one we have today. Consumers of the internet today have freedom of choice in where they shop from, how often they do it, and what they wish to pay, due to the many services allowed on the internet. If it had been regulated like Microsoft had attempted to do in 1995, it would be akin to shopping in a random strip mall in midwestern America.

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

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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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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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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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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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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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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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Maximize your growth potential with the seasoned experts at SalesmarkGlobal, shaping demand performance with strategic wisdom.