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Analyticsindiamag
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No Noise, Only Sound – How AI is Changing Our Hearing Aids
- Less than 0.5% of people with auditory impairment have sought treatment in India, according to the WHO. AI allows hearing aids to adjust to real-life environments more effectively, separating background noise from speech and improving clarity even in complex acoustic environments. AI-enabled hearing aids come at higher prices compared to traditional ones that range from INR 12,000 up to INR 4 lakh per device. Advanced hearing aids offer a more natural listening experience, closely mimicking the sound quality of normal hearing. Philips is one of the widely recognised brands in India that offers AI-enabled hearing aids.
- AI can automatically scan the environment and fine-tune the settings in real-time, ensuring optimal hearing without manual intervention. The elderly and those who move frequently between different environments are particularly benefiting from this innovation. The cost of high-quality hearing aids with AI starts around INR 1,00,000. Approximately 20-25% of people are choosing premium hearing aids, most of which come with advanced AI features. Advanced hearing aids offer improved clarity, better communication, and an overall enhanced quality of life.
- Connectivity with AI-enabled features can enrich retirement with seamless access to sound. In India, there are approximately 2,500 to 3,000 distribution points for hearing aids. 70-80% of clinics are already offering AI-enhanced technology in higher-income areas. Philips is fully AI-enabled in its hearing care instruments, enabling seamless communication across various medical equipment. The role of an audiologist remains pivotal in the hearing care sector, but the company has started developing AI-powered audiologists that could analyse the condition, explain in detail, and guide to the right solutions.
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Medium
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Antichrist revealed
- The archetype of the Mother embodies creation’s potential and nurturing essence, emphasized in various religious texts.
- The Father represents conscious will and divine presence, emphasizing unity and singularity.
- The Son symbolizes the bridge between the divine and the material, embodying realized potential.
- The birth of divine potential into the physical realm is highlighted as a transformative process.
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Medium
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Transformer Algorithms Revolutionize AI: From Natural Language to Self-Driving Cars
- Transformers, using the self-attention mechanism, have solved the challenge of understanding long-range dependencies in data. They assign attention scores to different parts of the input sequence to focus on the most relevant elements at each step.
- Transformers have greatly influenced natural language processing. They power chatbots, virtual assistants, and translate language in real-time with context awareness.
- Transformers are also advancing healthcare and biotechnology. AlphaFold, for example, uses transformers to predict protein folding and accelerate drug discovery.
- Scientific research applications include climate modelling, materials science, and astrophysics that would aid in data interpretation and hypothesis formulation.
- Transformers are being used in autonomous systems like self-driving cars to detect objects, optimize routes, and decide in real-time. They are also used in drones, UAVs, and industrial automation.
- Transformers are revolutionizing business operations by optimizing supply chains, detecting financial fraud, and providing personalized marketing leveraging data and analytics.
- Issues plaguing transformers include environmental impact and concerns about AI regulation and ethical guidelines. Efforts are underway to improve efficiency, cross-disciplinary applications, and explainability.
- Transformers offer the potential to address the pressing challenges faced by humanity as they continue to evolve, reshaping and driving the innovative AI landscape.
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Medium
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The Future of Artificial Intelligence Leadership
- The global AI market is projected to reach $1,811.8 billion by 2030.
- Companies in the U.S. are integrating AI into their business strategies, expecting a 40% increase in employee output by 2035.
- Generative AI is offering new possibilities, enabling businesses to edit, write, and gain insights from data with ease.
- Challenges include potential job losses, ethical concerns, and the need for responsible AI practices and skill development.
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Medium
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Can Training On Cellular Automata Push Passed The AI Data Bottleneck?
- 1D cellular automata are simple computational systems that generate patterns through rules governing how cells evolve over time.
- The richness of CA stems from their parameter space. This vast “CA space” provides what seems like an infinite dataset filled with surprising behaviors and complex patterns for AI to explore and learn from.
- Training on CA offers a unique opportunity to build an AI that excels at a deeply creative and challenging task: programming.
- The CA could be trained to build universal computers in a computationally universal CA.
- The AI could be rewarded for designing not only universal computers but also simpler, task-specific machines that are computationally less powerful but more efficient for particular programs.
- The training incentivizes leaner, more elegant designs, promoting highly creative and effective problem-solving, encouraging the AI to discover non-obvious solutions.
- Beyond the rule number, other parameters can be manipulated to push a CA program into new realms of complexity and exploration, expanding the space of possibilities.
- More complex CA systems could teach AI how to build technology in increasingly intricate environments, providing a pathway to greater adaptability and creativity.
- Using cellular automata as a training ground for AI might be a key step toward creating systems that can program with the adaptability and insight of a human.
- While the computational complexity of exploring CA space could eventually become limiting, there is a vast horizon of potential to explore before reaching that point.
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Medium
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"XBANKING’s Commitment to Decentralized Liquidity Pools"
- Liquidity pools are reserves of token pairs used for seamless transactions in DeFi, eliminating the need for centralized order books.
- XBANKING offers non-custodial liquidity pools that give users full control over their assets, ensuring trust and security.
- XBANKING provides dynamic rewards and profit sharing to liquidity providers, maximizing their profits.
- Compared to traditional liquidity providers, XBANKING combines user-centric design, risk management, and profit-sharing, making it a preferred choice.
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Medium
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Step-by-step guide to implementing Google Cloud Contact Center AI (CCAI):
- Step 2: Set up Google Cloud Platform (GCP) (1-2 days)
- Step 3: Configure CCAI Components (2-4 days)
- Step 4: Integrate with Existing Systems (2-4 days)
- Step 5: Train and Test AI Models (2-4 weeks)
- Step 6: Deploy and Configure Agents (1-2 days)
- Step 7: Monitor and Optimize (Ongoing)
- Step 8: Train Agents and Staff (1-2 days)
- Timeline: 6-12 weeks
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Medium
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Building Conversational AI: A step by step guide to develop a Chatbot with Dialogflow and Node Js.
- Step 1: Go to the Dialogflow console and create an agent
- Step 2: Create intents for the chatbot
- Step 3: Create entities for the chatbot
- Step 4: Set up fulfillment with a webhook
- Step 5: Create a Node.js server to handle requests
- Step 6: Deploy the Node.js server to a hosting platform
- Step 7: Test the chatbot using the Dialogflow console
- Step 8: Continuously refine and improve the chatbot
- Timeline: 2-3 hours
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Medium
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The Future of Spatial Computing
- Spatial computing is a technology that blends the real and digital worlds, enhancing our surroundings with data and imagery to improve navigation, education, and safety.
- The industry for spatial computing is expected to grow from $97.9 billion in 2023 to $280.5 billion by 2028, with a yearly growth rate of 23.4%.
- Tools like Apple's ARKit and Google's ARCore enable the integration of augmented reality into everyday life, allowing for guided experiences and the overlay of digital information onto the physical world.
- Spatial computing has limitless applications, from interactive art exhibits in public spaces to immersive educational experiences.
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Medium
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Imagine a world where healthcare is accessible, efficient, and personalized.
- Chatbots are revolutionizing healthcare by using NLP and ML to provide personalized support and streamline workflows.
- Benefits include 24/7 access, personalized care, efficient data management, and cost savings.
- Real-world applications include appointment booking, symptom checking, medication management, and mental health support.
- The future of chatbots in healthcare includes integration with wearables, telemedicine expansion, and predictive analytics.
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Medium
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How Canva Handles Billions of Events to Track Content Usage
- Canva allows designers, artists, photographers, etc., to share their work and earn royalties when used.
- The content usage counting service tracks usage data to calculate monthly payouts.
- Initially, the system processed data in three separate stages, causing issues with performance and scalability.
- To overcome these challenges, Canva moved the deduplication and aggregation stages to an OLAP database, Snowflake.
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Medium
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How to Effectively Train a Vision Transformer for Video-Based Person Re-Identification
- Video-based Person ReID task involves grouping images into tracklets, allowing the model to take advantage of motion patterns and contextual information across frames.
- The two most commonly used metrics in person ReID are Cumulative Matching Characteristics (CMC) curve and mean Average Precision (mAP).
- Pre-training ImageNet-21k is generally the best choice for ViT models.
- Overlapping patch embedding method captures fine-grained details and spatial continuity, thus it enhances feature representation.
- Two effective loss functions in ReID tasks are Cross-Entropy Loss and Triplet Loss, and their combination improves model performance.
- Training with only the first 10 blocks of the Vision Transformer (ViT) base architecture is often adopted to balance computational efficiency and model performance.
- Dividing video sequences into smaller chunks helps in capturing temporal dynamics while reducing computational complexity.
- Using overlapping patches, combining loss functions, and chunking help in achieving state-of-the-art results in video ReID tasks.
- In the next article, the author will provide a more comprehensive guide on fine-tuning and optimizing these approaches for real-world applications.
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Dev
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Insertion Sort
- Insertion Sort is an algorithm that builds the final sorted array one item at a time.
- It is simple and adaptable, making it efficient for small datasets.
- Shell Sort is another sorting algorithm that sorts elements at a specific interval and gradually reduces the interval until performing a final pass using insertion sort.
- Shell Sort performs better than Insertion Sort but is unstable.
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Dev
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Selection Sorts
- Selection Sort is a comparison-based algorithm that swaps the minimum element in place to achieve a time complexity of O(n^2) and space complexity of O(1).
- Steps to perform Selection Sort: Start with the first element as the minimum. Compare this element to the rest of the elements to find the smallest. Swap the smallest element with the first element. Move to the next element and repeat until the list is sorted.
- Heap Sort is a comparison-based sorting algorithm that uses a binary heap data structure. It is efficient and consistent.
- Steps to perform Heap Sort: Build a max heap from the input data. The largest element is swapped with the last element of the heap and removed from the heap. Heapify the root of the heap. Repeat the process until all elements are sorted.
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TechBullion
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5 ML-Solutions in HRTech: Boosting Hiring with NLP, Scoring Models, and AI Assistants
- Artificial Intelligence (AI) has the potential to transform traditional practices into dynamic, efficient processes in HRTech, with solutions like Natural Language Processing (NLP), scoring models, and AI assistants.
- According to a 2024 Gartner study, AI is already securing its place in the HR landscape as 38% of HR leaders are actively investigating or integrating AI solutions to meet organizational goals.
- AI can ensure that organizations have the best talent to drive success in an ever-changing business environment by enabling HR teams to implement time-efficient, resource-conscious, and inclusive hiring strategies.
- AI can address the deluge of resumes and applications by automating and significantly enhancing the job fitness process through smart talent matching and NLP.
- Predictive analytics and scoring models allow HR departments to anticipate which candidates are likely to thrive within their organization by analyzing patterns and deriving insights from historical data.
- AI-enhanced communication tools, such as chatbots, offer real-time, personalized interactions that keep candidates engaged throughout the recruitment process.
- Through algorithmic data analyses of hiring patterns, job description language, and candidate selection processes, AI can reveal hidden or unintentional biases, significantly benefiting workforce engagement, boost morale, and foster a healthier company culture.
- AI is revolutionizing onboarding, helping efficiently embed newcomers into the organizational fabric through personalized experiences that create customized continuous learning paths.
- To fully harness the potential of AI, HR leaders can take a strategic, measured approach to integration by clearly defining goals, piloting solutions in smaller teams, ensuring transparency in AI algorithms, regularly auditing systems, and integrating feedback mechanisms.
- Despite AI’s advancements, the irreplaceable role of human intuition and discretion in recruitment cannot be understated, ensuring that AI applications are not only data-driven but also empathetic and aligned with organizational values.
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