Tag: Neural Networks

There is growing concern over deepfakes, which are videos and audios that are highly realistic yet fake across various industries, but perhaps more pertinent in the B2B context. These synthetic media can mislead society and create negative impacts on reputation and financial aspects. However, it is evident that social media platforms have an essential role in addressing the fake problem and enhancing the credibility of online interactions as enterprises operate in this challenging environment. This article looks at the rise of deep fakes and also explores how popular social media companies are responding to this problem.

Understanding Deepfakes

Deepfakes are a form of synthetic media that apply artificial intelligence and machine learning to generate hyper-realistic fake audiovisual data. This technology relies on neural networks, and particularly on generative adversarial networks (GANs), to create realistic modifications of existing media.

The first step involves the accumulation of massive data sets that include images, videos, and even voice clips of the targeted person. These datasets enable AI to capture the details of the person’s gestures, voice, and even their tone. For example, GANs are composed of two neural networks, including a generator and a discriminator. The generator thus generates fake content, and the discriminator compares it with real media. This process is carried out in a cycle where the generator generates outputs until the results are as real as the original content being emulated.

Deepfakes can accommodate a range of manipulations based on simple swaps of facial images in videos to advanced ways of forgery where a person looks and acts like doing something they never did. It can also be applied where someone’s voice is changed to say sentences he has never said. This level of realism presents some problems in differentiating between real media and fakes, which could perpetuate skepticism and distrust of digital media.

Social media platforms are at the forefront of the fight against deepfakes, serving as essential gatekeepers to maintain the integrity of online communication. As the sophistication of deepfake technology rapidly evolves, these platforms face the growing challenge of detecting and mitigating manipulated content before it spreads. Their role is critical, not just in protecting users from deception but also in preserving trust across digital spaces where businesses interact with clients, stakeholders, and the public.

For companies, the stakes are equally high. Deepfakes can significantly damage brand reputation and sow confusion, eroding the trust that is central to B2B relationships. Businesses must be vigilant, ensuring they remain informed about the latest developments in deepfake technology and taking proactive steps to defend against its potential harms. By adopting a strategy that includes close collaboration with social media platforms, regular updates to security protocols, and internal training on identifying manipulated content, companies can safeguard their reputation and maintain the trust of their audience.

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

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Trending Category - Clinical Intelligence/Clinical Efficiency

The amalgamation of artificial intelligence (AI) and wearable technology has transformed how healthcare providers monitor and manage patients’s health through emergency responses, early-stage diagnostics, and medical research.

Therefore, AI-powered wearables are a boon to the digital era as they lower the cost of care delivery, eliminate healthcare providers’ friction, and optimize insurance segmentations. According to research by MIT and Google, these portable medical devices are equipped with large language models (LLMs), machine learning (ML), deep learning (DL), and neural networks that provide personalized digital healthcare solutions catering to each patient’s needs, based on user demographics, health knowledge, and physiological data.

In today’s article, let’s explore the influence of these powerful technologies that have reshaped personalized healthcare solutions.

Integration of AI in Wearable Health Technology

AI has been a transforming force for developing digital health solutions for patients, especially when implemented in wearables. However, 21st-century wearables are not just limited to AI but employ advanced technologies such as deep learning, machine learning, and neural networking to get precise user data and make quick decisions on behalf of medical professionals.

This section will focus on how ML and DL are essential technologies in developing next-generation wearables.

Machine Learning Algorithms to Analyze Data

Machine learning (ML) algorithms are one of the most valuable technologies that analyze the extensive data gathered from AI wearable devices and empower healthcare professionals to identify patterns, predict necessary outcomes, and make suitable decisions on patient care.

For instance, certain wearables use ML algorithms, especially for chronic diseases such as mental health issues, cardiovascular issues, and diabetes, by measuring heart rate, oxygen rate, and blood glucose meters. By detecting these data patterns, physicians can provide early intervention, take a closer look at patients’s vitals, and make decisions.

Recognizing Human Activity with Deep Learning Algorithms

Deep learning (DL) algorithms are implemented in wearables as multi-layered artificial neural networks (ANN) to identify intricate patterns and find relationships within massive datasets. To develop a high-performance computing platform for wearables, numerous DL frameworks are created to recognize human activities such as ECG data, muscle and bone movement, symptoms of epilepsy, and early signs of sleep apnea. The DL framework in the wearables learns the symptoms and signs automatically to provide quick solutions.

However, the only limitation of the DL algorithms in wearable technology is the need for constant training and standardized data collection and analysis to ensure high-quality data.

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

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In the fast-growing digital world, deep learning (DL) and big data are highly used methods for data scientists. Numerous companies, such as Yahoo, Amazon, and Google, have maintained data in Exabytes, which helps generate large amounts of data with the help of big data analytics and deep learning tools and techniques.

Earlier data scientists used traditional data processing techniques, which came with numerous challenges in processing large data sets. However, with technological advancements in recent years, data scientists can utilize big data analytics, a sophisticated algorithm based on machine learning and deep learning techniques that process data in real-time and provide high accuracy and efficiency in business processes.

In recent times, it has been witnessed that DL methods are extensively used in healthcare, finance, and IT for speech recognition, learning methods in language processing, and image classification, especially when incorporated into various hybrid learning and training mechanisms for processing data with high speed.

Today’s exclusive AI Tech Park article aims to discuss integrating deep learning methods into big data analytics, analyze various applications of deep learning in big data analytics, and discuss the future of big data and deep learning.

Efficient Deep Learning Algorithms in Big Data Analytics

Deep learning is a subset of machine learning (ML), and it is considered the trendiest topic as DL is adopted in almost every field where big data is involved.

Every year, IT companies generate trillions of GBs of data, which makes extracting useful information a challenging task for them. Therefore, the answer to such a problem is deep learning, which automatically learns the hidden structure and patterns in the raw data using ML techniques.

Some deep learning models and algorithms show great potential in unleashing the complexity of patterns within big data analytics. In this section, we will take a glance at the effective ways data scientists can utilize deep learning techniques to implement big data analytics:

Preparing the Data

The initial step to implementing deep learning in big data analytics is data preparation. The quality of data used in training data learning models must be accurate to the model prepared by data scientists and IT professionals. Therefore, it is essential to ensure that the data is well structured and clean and should work as a problem solver.

To Know More, Read Full Article @ https://ai-techpark.com/deep-learning-in-big-data-analytics/

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