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Deep Learning News
Analyticsindiamag
16h
164
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This New Logic Gate Network Reduces Inference Speed to Only 4 Nanoseconds
- Researchers at Stanford University have introduced convolutional differentiable logic gate networks (LGNs) with logic gate tree kernels.
- The research combines concepts from machine vision with differentiable logic gate networks, allowing for the training of deeper LGNs and improving training efficiency.
- The proposed architecture, 'LogicTreeNet', decreases model size and improves accuracy compared to the state of the art.
- The model achieves inference speeds of only 4 nanoseconds and improves accuracy on MNIST and CIFAR-10 datasets.
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Nvidia
1d
33
Image Credit: Nvidia
Japan Develops Next-Generation Drug Design, Healthcare Robotics and Digital Health Platforms
- Japan is using AI tools to provide high-quality healthcare to its population of around 30% people who are 65 or older. Breakthrough technology deployments by the country’s healthcare leaders including in AI-accelerated drug discovery, genomic medicine, healthcare imaging, and robotics are highlighted at the NVIDIA AI Summit Japan. AI scanners and scopes give radiologists and surgeons real-time superpowers. Japanese surgical AI companies are investigating the use of Holoscan to power applications that could detect anatomical structures like organs in real-time. Fujifilm has launched NURA, a group of health screening centers with AI-augmented medical examinations designed to help doctors test for cancer and chronic diseases. Developed using NVIDIA DGX systems, the tool incorporates large language models that create text summaries of medical images.
- AI tools trained on country-specific data and local compute infrastructure are supercharging the abilities of Japan’s clinicians and researchers amid an expected shortage of nearly 500,000 healthcare workers by next year.
- Powered by NVIDIA AI computing platforms like the Tokyo-1 NVIDIA DGX supercomputer, these applications were developed using domain-specific platforms.
- NVIDIA is supporting Japan’s pharmaceutical market with NVIDIA BioNeMo, an end-to-end platform that enables drug discovery researchers to develop and deploy AI models for generating biological intelligence from biomolecular data.
- Tokyo-based Astellas Pharma uses BioNeMo biomolecular AI models to accelerate biologics research. Astellas has accelerated chemical molecule generation by more than 30x. The company plans to use BioNeMo NIM microservices to further advance its work.
- Genomics researchers across Japan have adopted the NVIDIA Parabricks software suite to accelerate secondary analysis of DNA and RNA data. The University of Tokyo Human Genome Center uses it to find gene variants unique to Japan’s population.
- Fujifilm has developed an AI application in collaboration with NVIDIA to help surgeons perform surgery more efficiently by converting CT images into 3D simulations to support surgery.
- Olympus recently collaborated with NVIDIA and NTT to demonstrate how cloud-connected endoscopes can efficiently run image processing and AI applications in real-time.
- NVIDIA is also supporting real-time AI-powered robotic systems for radiology and surgery in Japan with Holoscan, a sensor processing platform that streamlines AI model and application development for real-time insights.
- Fujifilm has launched NURA, a group of health screening centers with AI-augmented medical examinations designed to help doctors test for cancer and chronic diseases with faster examinations and lower radiation doses for CT scans.
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Medium
1d
104
Image Credit: Medium
Transformers (Cross Attention)
- Cross Attention, a multi-head attention block, receives inputs from both the encoder and decoder, unlike other multi-head attention blocks. Cross Attention allows finding the relationship between two sequences, which is the most important component of the decoder architecture of Transformers.
- The Cross Attention mechanism helps to capture the relationship between each word of the input sequence and each word of the output sequence. It involves finding the relationship between two different sequences by multi-head attention.
- Cross Attention is used in situations where two different types of sequences need to be compared. For instance, machine translation, question-answering systems, and multimodality applications such as image captioning, text-to-image, text-to-speech.
- Query vectors come from the output sequence while Key and Value vectors come from the input sequence. The calculation for attention scores in Cross Attention is same as in Self Attention. Both mechanisms are similar except Cross Attention deals with two sequences instead of one.
- The difference between the self attention and cross attention lies in the input aspect(the input for self-attention is the embeddings of a sequence while Cross Attention simultaneously requires two sequences in the input) and output aspect (the number of word vectors in the contextual embeddings obtained from Cross Attention is equal to the number of words in the output sequence).
- The structure of Cross Attention is conceptually very similar to that of self-attention, except for its input and output differences, and with the addition of the other sequence in the processing.
- Cross Attention is used in situations where two different types of sequences need to be compared. For instance, machine translation, question-answering systems, and multimodality applications such as image captioning, text-to-image, text-to-speech.
- The Cross Attention mechanism is frequently used in situations that requiresimultaneously check for the relationships between two sequences.
- Cross Attention mechanism of the Decoder architecture of transformers is essential and a high-level concept that needs to be understood for building NLP applications, and for understanding the working of transformer applications.
- Understanding Cross Attention requires understanding of Self Attention, the decoding architecture of the transformer, and the use case scenarios to finally grasp the concept.
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Arstechnica
2d
323
Image Credit: Arstechnica
How a stubborn computer scientist accidentally launched the deep learning boom
- In 2008, neural networks were considered a backwater in the field of artificial intelligence.
- Researchers had shifted their focus to support vector machines and other approaches.
- Meanwhile, a team at Princeton led by Prof. Fei-Fei Li was working on a project that would revolutionize neural networks.
- Their work would eventually lead to the deep learning boom.
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Marktechpost
20h
11
FinSafeNet: Advancing Digital Banking Security with Deep Learning for Fraud Detection and Real-Time Transaction Protection
- With rapid technological advances and increased internet use in business, cybersecurity has become a major global concern, especially in digital banking and payments.
- Researchers have developed FinSafeNet, a deep-learning model for secure digital banking, which achieved 97.8% accuracy in fraud detection on the Paysim database.
- FinSafeNet incorporates advanced features such as Bi-LSTM, CNN, dual attention mechanism, and optimized feature selection using the Improved Snow-Lion Optimization Algorithm (I-SLOA).
- The model offers potential for real-time deployment in diverse banking environments, and future blockchain integration could further reinforce transaction security against cyber threats.
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Medium
23h
298
Image Credit: Medium
Groundbreaking AI Innovations That Are Transforming Business Processes Forever
- AI agents are transforming the way businesses operate and offering unprecedented efficiency to business processes.
- AI is not just a tool but a strategic partner, offering smart solutions to relieve stress and reclaim precious time.
- AI excels in data-pooling and predictive analysis, helping businesses make use of pattern recognition and interpret patterns to make routine processes smoother.
- AI enhances human input by automating mundane tasks, freeing up time and allowing for greater focus on creative problem-solving.
- AI is a versatile tool for customer service, providing tireless assistance to human reps and answering questions with boundless patience.
- AI offers insights and acts like a trusted advisor when working alongside managers in various fields.
- AI has the ability to learn and adapt continuously from every interaction, extending its role from a static tool to a dynamic ally.
- A reliable and tailored strategy, combined with modular AI platforms like scheduling AI, maximizes personal productivity and achieves success.
- AI has become more than just a buzzword and has transformed into a linchpin of modern business strategies for tech giants and other entrepreneurial ventures.
- AI has shifted the landscape by reducing operational bottlenecks, increasing efficiency and innovation more than expected.
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Medium
1d
317
Image Credit: Medium
Paradise of Dice and the Circle of Hello
- Paradise depicts the gamblers' table of life, where we all throw odds into the hands of fate.
- Hell stands as a reminder of hidden strength, where we learn to strive, challenge ourselves, and promise that we will.
- Hell transforms into Hello, a circle of warmth, where the test becomes the doorway to new relationships.
- The cycle of Paradise, Hell, and Hello represents a beautiful dance full of courage, effort, and love.
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Medium
1d
230
Image Credit: Medium
5 Shocking Ways AI Agents Are Transforming Business Operations Forever
- AI agents are becoming integral to business operations, helping companies make smarter decisions, optimize processes, and engage customers in new ways.
- AI can automate repetitive tasks, such as sorting through vast amounts of customer data, allowing human teams to focus on the more creative aspects of the work.
- AI agents are capable of answering customer inquiries 24/7 with unparalleled accuracy, anticipating needs based on prior interactions and preferences.
- AI can predict customer behavior patterns, future trends, and even craft personalized marketing campaigns.
- Focusing on key Artificial Intelligence applications delivered immediate value without overwhelming team or resources. By starting small and scaling strategically, many pitfalls can be avoided.
- Security was a primary concern, but by working with trusted AI providers, customer data remained protected.
- AI agents don’t replace jobs; it transforms them. The workforce can be shifted from mundane tasks to more strategic and creative roles, enhancing job satisfaction.
- AI agents are practical tools reshaping business operations today, crafting a new business era.
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Medium
1d
366
9 PyTorch Layers You Must learn it
- torch.nn.Linear: Applies a linear transformation to incoming data by multiplying the input with a weight matrix and adding a bias.
- Convolutional Layer: Applies convolutional filters to input data to extract spatial or temporal patterns.
- Recurrent Layers: Used to handle sequential data by keeping information over time and learning temporal dependencies.
- Embedding Layer: Converts input indices into dense vectors of fixed size, commonly used for representing discrete items.
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Medium
1d
84
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Narcissistic and Evil Mothers
- The author reflects on the painful experiences of growing up with a narcissistic and evil mother.
- The author describes being manipulated and controlled by their mother, who would argue and take away gifts when they went out with friends, only to give them back later.
- The author recounts a shocking incident where their mother accused them of planning to rape her during an argument.
- The author expresses feelings of being a monster due to the toxic environment created by their parents and the emotional abuse endured throughout their childhood.
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Medium
2d
214
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Some Specific Research topics focusing on Self-attention and Transformers:
- Develop methods to reduce the computational complexity of transformers for handling large datasets.
- Explore the use of self-attention mechanisms in models that integrate text, image, and audio data.
- Study the advancements in transformer-based models like BERT, GPT, and their applications in various NLP tasks.
- Apply self-attention to graph neural networks for tasks like node classification and graph generation.
- Develop self-attention mechanisms that provide better interpretability and transparency in model predictions.
- Use self-attention mechanisms to improve the analysis and forecasting of time series data.
- Investigate the application of transformers in reinforcement learning environments to enhance decision-making processes.
- Apply self-attention mechanisms to detect anomalies in various types of data, such as network traffic or financial transactions.
- Study the role of cross-attention mechanisms in improving the performance of multi-task learning models.
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12 Likes
Medium
2d
328
Winners speak differently then everyone else
- 7th Level and Jeremy Miner have upcoming projects that will provide value to clients and those interested in joining 7th Level.
- David Duncan plays a vital role in the workflow, ensuring structure and stretching abilities.
- New recruits will be welcomed tomorrow, and Jeremy's next podcast guest is David Price, an entrepreneurship powerhouse.
- Setting the stage for 2025, there is a clear course planned to reach an $80 million goal.
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19 Likes
Medium
3d
87
### The Revolution of Artistic Expression: How Generative AI Models Are Changing Creation
- Generative AI models can produce content that resembles human-created work in multiple domains such as images, stories, music, etc.
- DALL-E 2 and Stable Diffusion are the two most remarkable AI models that have made possibilities to craft the desired result in less time with little effort.
- Visual art, literature, and music are three most transformative applications where AI models have been applied.
- These AI models empower artists to iterate ideas quickly without spending days sketching or painting.
- With the assistance of these AI models, writers can overcome creative blocks, brainstorm plot twists, or develop dialogue that matches a character's voice.
- AI models such as MuseNet and Magenta can create musical pieces that sound as if they were crafted by human musicians.
- Generative AI offers potential to democratize creativity by enabling anyone with a computer and an idea to create professional-grade artwork or stories.
- AI fosters a new era of collaborative art where human and machine work together, leading to results that are greater than the sum of their parts.
- Generative AI can complement traditional art forms, expanding the possibilities for artistic expression.
- Platforms and AI developers are working to establish clearer guidelines on data usage and content ownership in response to ethical and copyright issues.
- As generative AI continues to evolve, future prospects and innovations emerging in the interactive media and educational tool domains.
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Medium
3d
19
Image Credit: Medium
Unlocking the Future of Generative AI
- Generative AI uses sophisticated algorithms to generate original content across many mediums, transforming how we produce and consume information.
- Generative AI is on track to create 10% of all generated data by 2025, according to Gartner’s 2022 report with an even larger share in consumer applications.
- Recent advancements in generative AI have cast a spotlight on its versatility and growth across industries.
- Addressing challenges like authenticity of AI-created content involves developing more transparent AI models and enforcing better regulatory standards.
- Generative AI, with its skillful assistance, paves the way for enriched innovation without losing the human touch.
- Generative AI can enhance projects by expediting brainstorming and providing fresh ideas while maintaining creative control.
- Issues related to content authenticity, privacy, and bias in AI decisions due to incomplete data representations are common ethical concerns regarding generative AI.
- While AI advances creativity, the unique human perspective and emotional depth remain irreplaceable.
- Incremental technological advancements are helping mitigate the environmental costs of using generative AI.
- Experimentation is key to seamless integration of generative AI into workflow, along with pilot projects and exploration of AI tools.
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Medium
3d
296
Image Credit: Medium
10 Shocking AI Innovations Transforming Smart Homes
- Artificial Intelligence (AI) is transforming smart homes through innovation and creativity. AI creates intelligent, comfortable, and proactive homes that offer residents ease and satisfaction.
- AI seamlessly blends into life, allowing homes to think and adapt. The introduction of smart speakers and central hubs facilitates automation and provide efficient services such as heating, security systems activation, lighting adjustment, and more.
- AI enables homeowners to save energy costs and accurately manage energy consumption through efficient lighting and temperature controls. Smart homes also enhance security and safety by installing motion sensor cameras, smart locks, and sending alerts to the homeowner's phone.
- With AI-powered personalization, smart homes offer a comforting ambiance that adapts depending on the time of day and the user's mood, along with personalized sound adjustment that can transform the home into a world-class theater.
- Adapting to privacy concerns, smart home technology experts guide users on building secure ecosystems without compromising on convenience. Moreover, user-friendly guides and backup solutions ensure that the technology is reliable, with manual overrides used in case of a temporary internet shutdown.
- Smart homes are not just a collection of gadgets, but rather interactive lifestyles that shift focus from routine drudgeries to meaningful moments while making every interaction a little more magical. Smart home technology transforms houses into responsive environments, capable of self-learning, adapting, and evolving with each AI upgrade.
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