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Medium
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What You Should Know About Getting Into Data Science
- To get into data science, it is important to have a strong foundation in mathematics and statistics, as well as programming knowledge.
- Data engineering skills, including knowledge of databases and technologies like Apache Spark or Hadoop, are crucial for managing and processing large amounts of data.
- Data science plays a significant role in the creation of artificial intelligence systems, utilizing machine learning and deep learning for advanced technology applications.
- In addition to technical skills, communication, critical thinking, and data visualization abilities are important for a data scientist.
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Medium
2d
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The Role of AI in Software Engineering
- AI brings automation, predictive insights, and adaptive solutions to traditional software engineering methods.
- AI enhances the software development lifecycle by analyzing code, automating testing, and processing requirements.
- AI optimizes DevOps pipelines, aids in anomaly detection, and assists architects in designing scalable software.
- Challenges include bias in AI systems, talent gap, ethical concerns, and the need for interpretability and quantum algorithms.
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Medium
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Classifier-free guidance for LLMs performance enhancing
- Classifier-free guidance for LLM text generation using conditional and unconditional score estimates has been developed as a simpler version of classifier guidance.
- CFG is used to update predicted scores of generated LLM text in a direction of some predefined class without applying gradient-based updates.
- An alternative implementation approach to CFG for LLM text generation without severe degradation of generated sample quality has been suggested.
- The original CFG approach may cause unexpected artefacts and degradation of LLM text quality, but the artefacts depend on multiple factors such as the model and prompts.
- The suggested alternative implementation has been shown to prevent the degradation of generated LLM sample quality in both manual and automatic tests.
- Examples of artefacts and degradation of generated LLM sample quality have been demonstrated through tests for different CFG coefficients on a GPT2 model.
- The problem arises from the logarithm component in the original CFG implementation, which treats probabilities unequally and can cause low-probability tokens to receive high scores after applying CFG.
- The suggested alternative implementation removes the logarithm component and aligns the text-CFG with diffusion-models CFG that only operate with model predicted scores without gradients.
- The suggested alternative implementation introduces minimal changes to the HuggingFace Transformers' UnbatchedClassifierFreeGuidanceLogitsProcessor function.
- The suggested alternative implementation has improved text quality in manual tests and has not deteriorated performance on automatic tests compared to the original implementation.
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Medium
2d
3
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Joyce Shen’s picks: musings and readings in AI/ML, December 23, 2024
- Musings and Readings: AI/ML news highlights
- 1. Research papers: 'Towards Friendly AI', 'Jet: A Modern Transformer-Based Normalizing Flow', 'Large Language Models and Code Security'
- 2. Discussion on enterprise AI product management and value creation with Nadiem von Heydebrand at Mindfuel
- 3. Deals: Seed financing raised by Triton Anchor, NeuroKaire, Chargezoom, Anatomy Financial, Starboard, Indapta Therapeutics, Slip Robotics, Salt AI, Basis, Hamming.ai, Simulation Theory, Kurrent, Sotelix Endoscopy, Portal Biotechnologies
- 4. Sunairio secures $6.4 million financing round for leveraging high-resolution climate data for energy asset risk simulation
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Medium
2d
197
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AI Engineers Future
- Companies are adopting AI-driven solutions to keep up with current trends.
- AI chatbots are being implemented on company websites for internal and client interaction.
- The demand for AI engineers who possess skills in Data Science, AWS, and Python is high.
- The future of the IT industry is focused on AI, making AI engineers essential.
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Amazon
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Using transcription confidence scores to improve slot filling in Amazon Lex
- Transcription confidence scores are used to help ensure reliable slot filling when building voice-enabled chatbots with Amazon Lex. These scores provide a measure of confidence in Amazon Lex's conversion of speech to text for slot values.
- Transcription confidence scores can be used to validate if a spoken slot value was correctly understood, decide whether to ask for confirmation or re-prompt, and branch conversation flows based on recognition confidence.
- Progressive confirmation, adaptive re-prompting, and branching logic are ways to leverage confidence scores for better slot handling.
- These patterns help create more robust slot filling experiences, reduce errors in capturing critical information, improve containment rates for self-service, and enable smarter conversation flows.
- An AWS CloudFormation template is included to demonstrate these patterns.
- This approach optimizes the conversation flow based on the quality of the input captured and prevents erroneous or redundant slot capturing, leading to an improved user experience while increasing the self-service containment rates.
- Audio transcription confidence scores are available only in the English (GB) (en_GB) and English (US) (en_US) languages and are supported only for 8 kHz audio input.
- To test the solution, you can examine a conversation with words that might not be clearly understood. Assign the Amazon Lex bot to an Amazon Connect workflow and make a call.
- Optimizing the user experience is at the forefront of any Amazon Lex conversational designer’s priority list, and so is capturing information accurately. This new feature empowers designers to have choices around confirmation routines that drive a more natural dialog between the customer and the bot.
- The authors of this article include Alex Buckhurst, Kai Loreck, Neel Kapadia, and Anand Jumnani, all of whom have careers focused on Amazon Web Services and customer-centric design.
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Amazon
2d
15
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Improving Retrieval Augmented Generation accuracy with GraphRAG
- Customers looking to enhance generative AI accuracy can use vector-based retrieval systems and the Retrieval Augmented Generation (RAG) architectural pattern.
- Lettria demonstrated that integrating graph-based structures into RAG workflows improves answer precision by up to 35% compared to vector-only retrieval methods.
- Human questions are complex and graphs represent data in a machine-readable format that preserves the rich relationships between entities, leading to a more accurate answer to complex queries.
- Graphs maintain the natural structure of the data, allowing for a more precise mapping between questions and answers.
- Lettria conducted extensive benchmarks using GraphRAG, resulting in answers that were 80% correct, compared to 50.83% with traditional RAG.
- Amazon Web Services (AWS) offers tools and services to build and deploy generative AI applications, including Amazon Neptune, a fully managed graph database service.
- Implementing GraphRAG with AWS requires domain definition, graph database storage, and developing skills in graph modeling, graph queries, prompt engineering, or LLM workflow maintenance.
- Lettria provides an accessible and scalable solution to integrate GraphRAG into applications, including simplified ingestion and processing of complex datasets.
- Managed GraphRAG implementations through Lettria and Amazon Bedrock offer improved question-answering performance, scalability, and flexibility.
- By incorporating graphs into RAG workflows, organizations can achieve up to 35% improvement in accuracy, leading to more informed decision-making.
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Medium
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197
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Why AI Matters: Shaping the Future
- Increased Efficiency and Productivity: AI enables automation of repetitive work, increasing efficiency and reducing costs. It allows workers to focus on higher-level tasks that require creativity and complex decision-making.
- Improvement in Health Care and Medicine: AI revolutionizes healthcare by enabling early detection of diseases through scanning medical data. It also speeds up drug discovery and individualizes medicine for patients.
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TechBullion
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11
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AI and Machine Learning Reshape Modern Data Center Operations
- Data centers are undergoing a radical transformation driven by AI and machine learning innovations.
- AI-driven predictive maintenance systems reduce downtime and extend the lifespan of hardware components.
- AI-powered resource optimization optimizes computing resources while minimizing energy consumption and costs.
- AI-driven energy management systems in data centers reduce energy waste and lower operational costs.
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Medium
2d
142
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How AI-Driven Chatbots are Shaping New Trends in Emotional Marketing
- AI-driven chatbots with advanced algorithms and NLP can deliver tailored interactions that resonate emotionally with consumers.
- They offer immediate support, addressing consumer inquiries promptly and enhancing satisfaction.
- AI-driven chatbots reshape the landscape of consumer-brand interactions in emotional marketing.
- They foster emotional connections through personalized interactions and sentiment analysis, enhancing customer satisfaction and brand loyalty.
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Medium
2d
384
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PrimeArc-10: A Next-Gen Prime-Skipping Benchmark for Advanced AI Reasoning — Blaze Ubah
- OpenAI introduces PrimeArc-10, a non-linear prime-skipping challenge in ten structured levels.
- PrimeArc-10 resists rote memorization and encourages true number-theoretic insight.
- The benchmark tests prime recognition, skipping logic, and scalable numeric reasoning.
- The results can be compared with the answer array in a JSON file.
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Medium
3d
341
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Enhancing Intent Analysis: Adding ML Capabilities with BERT and Vector Search
- A three-pronged approach combining BERT embeddings, vector search, and pattern recognition was used to enhance the intent analysis.
- BERT was utilized to provide the system with the ability to understand context.
- Efficient storage and search of BERT embeddings were implemented.
- Intelligent pattern recognition was incorporated to work with the embeddings.
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Hackernoon
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Become a Problem Solving Machine With 'AI Thinking'—No Neural Implants Necessary!
- AI and ML have become essential tools across industries to automate repetitive tasks, predict trends, and help us make more informed decisions. When starting AI and ML, clearly define the problem and ensure you have relevant data. Match specific needs with suitable AI techniques, and leverage existing tools like AutoML to simplify your work. Training and testing are critical for creating a model that is robust and accurate in real-world scenarios. Developing an AI solution is an iterative process rather than a one-time effort. Finally, it is crucial to recognize that not every situation requires the application of AI.
- Starting with a simple, practical understanding of your problem will help you clarify how AI could assist. By focusing on the desired outcomes for your support ticket management, you’ll better understand whether AI is the right fit and what kind of model might be needed. Data is the fuel that powers AI. The more relevant, clean data you have, the better the AI can perform. For support ticket management, this data might come from ticket logs, customer emails, chat transcripts, or feedback forms.
- Once you’ve clearly defined the problem and gathered relevant data, it’s time to identify the best AI approach for the task. Common approaches include prediction tasks, classification tasks, pattern recognition and automation and decision making among others. By leveraging these AI techniques, you can streamline processes, improve ticket handling accuracy, and gain insights that help anticipate and address customer needs. You don’t have to build AI models from scratch! Many robust AI and ML tools are available to help you get started without needing extensive expertise.
- Training and testing are central to creating a reliable AI model. This process ensures that your model not only learns from past data but also generalizes well to new, unseen data. Developing an AI solution is an iterative process rather than a one-time effort. By continuously experimenting, learning from failures, and making incremental improvements, you can enhance the effectiveness of your AI system.
- While AI offers powerful solutions for many complex problems, it’s crucial to recognize that not every situation requires its application. Sometimes, traditional programming or rule-based systems may be more effective and efficient. By carefully assessing whether AI is necessary for your tasks, you can avoid over-engineering solutions.
- In conclusion, adopting an AI mindset involves understanding your problem, identifying available data, and leveraging the right tools for effective solutions—without needing to master complex algorithms. By focusing on problem-solving, iterating your approach, and knowing when traditional methods are more suitable, you’ll be prepared to enhance processes and improve outcomes.
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Medium
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349
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What is GenAI?
- Generative AI is a subset of artificial intelligence that can produce original content like text, audio, images, video, or software code as a response to a user’s request or prompt.
- To create a foundation model that can support multiple gen AI applications, generative AI involves three phases - training, tuning, and retuning.
- Various methods can be used for tuning the generative AI like fine-tuning or reinforcement learning with human feedback (RLHF).
- Generative AI models rely on various large pre-trained machine learning models like foundation models (FMs) and large language models (LLMs) that are trained to develop deep patterns and relationships in data.
- Generative AI produces various types of content like text, images, video, audio, software code, and design and art.
- Generative AI offers several benefits like dynamic personalization, improved decision-making, constant availability, etc.
- Generative AI also has some limitations like security concerns, cost, limited creativity, etc.
- It possesses a black box problem, and enhancing interpretability and transparency is necessary to gain trust and adoption.
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Medium
3d
254
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Can I learn AI without coding?
- You can learn AI concepts and theory without coding through courses, books, and videos.
- No-code AI tools allow building AI models without coding using drag-and-drop interfaces.
- Many AI-powered everyday tools are designed for users with no coding experience.
- Courses and learning platforms like Coursera, edX, and Udemy offer AI courses that require little or no coding.
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