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Data Science News
Medium
2w
66
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Mentoring the Next Generation of Data Scientists: Interview Tips
- Before an interview, research and understand the company's values, mission, and expectations.
- Be authentic and admit when you don't know the answer to a question.
- Prepare for the interview but also prioritize rest and relaxation the night before.
- Embrace rejection and see it as a misalignment leading you to better opportunities.
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Medium
2w
357
The Emergence of the Human Digital Twin and Exploring a Transformative Future
- This blog article will delve deep into the concept of the digital twin of a human being.
- Advances in sensors, biomedical technology, artificial intelligence, neuroscience, and virtual reality are converging to enable faithful digital representations of individuals.
- Translating the digital twin concept into the realm of the human body, it might begin as a high-fidelity anatomical model constructed from medical imaging data.
- Emerging neurotechnology offers a pathway to model how the brain processes information.
- Digital twins could revolutionize medicine by enabling personalized healthcare.
- As humans push exploration to distant planets, undersea habitats, and harsh industrial environments, digital twins may serve as proxies.
- A fully realized digital twin could live on as a kind of legacy.
- As digital twins become more common, the field of human-computer interaction will undergo a monumental shift.
- In the future, HCI professionals may need expertise in neuroscience, medical ethics, and artificial intelligence as well.
- The path ahead is uncertain, but by engaging thoughtfully, we have the chance to harness the incredible power of digital twins to uplift, rather than diminish, our shared humanity.
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Medium
2w
109
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Building Advanced AI Agents with LangGraph: Enhancing Your LLM Applications
- Artificial intelligence is rapidly advancing towards the use of intelligent agents and multi-agent systems.
- LangGraph is an open-source framework designed to streamline the development of LLM-powered applications.
- LangGraph offers a graph-based approach to define and manage complex workflows.
- LangGraph empowers developers to build complex, multi-agent AI applications with ease.
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Medium
2w
4
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Automated Machine Learning Tools
- Automated Machine Learning or AutoML is a new approach to machine learning development.
- AutoML tools, such as Google Cloud AutoML, H2O.ai, Auto-sklearn, and DataRobot, simplify and accelerate predictive model development.
- AutoML automates end-to-end machine learning workflows, including data preprocessing, feature engineering, and model selection.
- It eliminates the need for manual feature, algorithm, and hyperparameter selection, making it easier for businesses and non-experts to discover insights and implement models.
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Medium
2w
91
Vocabulary Bubble Centers pt 1 | Fotek Erica
- This news article discusses various topics related to vocabulary bubble centers and Fotek Erica.
- The article covers concepts like statistical mechanics, macroscopic and microscopic scales, interacting subsystems, nonlinear partial differential equations, and white noise.
- It also explores the connection between mathematics, science, and sociology, as well as the application of mathematical quantum field theory in different fields such as economics, psychology, biology, and therapy.
- Additionally, the article touches upon topics like numerical analysis, engineering, respiratory tract physiology, and shock wave physics.
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Medium
2w
126
Vector Database and Scalar Database
- Vector databases store vectors, which are represented as arrays of values in n-dimensional space.
- Key operations in vector databases include nearest neighbor search and vector indexing using techniques like Product Quantization (PQ).
- Scalar databases store individual scalar values and are designed to manage structured data in tables with rows and columns.
- Vector databases excel at finding similar vectors based on distance calculations using metrics like cosine similarity.
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Fourweekmba
2w
74
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AI Agents and the Push Toward an Outcome-Based Business Model
- AI agents are driving outcome-based business models by automating tasks and delivering measurable results.
- The core components of the outcome-based business model include AI automation, outcome delivery, results-driven costs, and client confidence.
- This model has broad applicability in industries like finance, healthcare, retail, logistics, and customer support, offering scalability and adaptability.
- The operational and ethical framework involves scalable AI agents, performance metrics, human oversight, transparency, and trust.
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Fourweekmba
2w
239
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2024 AI Investment Trends
- In 2024, AI companies raised billions, led by OpenAI ($6.6B) and xAI ($6B), with investments focused on infrastructure, advanced models, and specialized applications in fintech, education, and cybersecurity.
- Key investments include OpenAI raising $6.6 billion at a $157 billion valuation, xAI securing $6 billion at a $50 billion valuation, and CoreWeave raising $1.1 billion for expanding computational power.
- Industry-specific AI solutions gained traction, with Skild AI raising $300 million for education platforms, Cyera securing $300 million for cybersecurity, and Poolside raising $500 million for fintech advancements.
- Trends in 2024 AI investment include massive infrastructure scaling, growing valuations, a shift towards specialized AI solutions, and cross-sector transformation fueled by AI tools.
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VentureBeat
2w
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Here’s the one thing you should never outsource to an AI model
- Despite its potential to streamline R&D, outsourcing general artificial intelligence (AI) in innovation pipelines could be catastrophically counterproductive, writes software engineer Ashish Pawar in VentureBeat.
- Gen AI, driven by a vast dataset, is only capable of predicting improvements rather than creating a genuinely new product or concept and is therefore limited in ability to generate game-changing output.
- One key danger of a world where AI is the default for R&D is that content is processed in a highly convergent way, leading to template-style homogenization of the market. If every company in a category uses an AI system to design its products, all will generate variations of the basic structure.
- The process of generating breakthrough innovations is undermined because AI is not designed to embrace ambiguity or complexity, which flourishes in human researchers' proven ability to learn from unexpected or ambiguous findings.
- The more a company becomes reliant on AI, the less capable of innovation it may become, as human skills and engagement disappear from the workplace.
- Pawar argues that rather than turning to AI for answers, companies should focus on steering the development potential of AI with human empathy, vision and a long-term commitment to investing in human creativity.
- Ashish Pawar is a software engineer.
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Medium
2w
139
Data Science Without Maths?_ Debunking the Biggest Myths
- To make a career in data science, advanced knowledge of calculus, linear algebra, and statistics is not necessary.
- Data science tools and automation do not eliminate the need for math in data science.
- Data science is a combination of coding, business knowledge, creativity, and math.
- A beginner in data science should focus on basic math concepts and gradually cover complex topics.
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Medium
2w
56
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What is knowledge representation ?
- Knowledge representation in AI is concerned with representing and manipulating knowledge symbolically.
- Types of knowledge in AI include factual, procedural, meta-knowledge, heuristic knowledge, and structural knowledge.
- The AI knowledge cycle involves perception, learning, knowledge representation, reasoning, planning, and execution.
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Medium
2w
34
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OpenCV for Computer Vision
- OpenCV is an open-source computer vision library that offers a versatile toolbox for tasks like object detection and filtering.
- It supports various programming languages, including Python, and allows developers to quickly prototype and deploy computer vision applications.
- OpenCV provides comprehensive functionality for image and video processing, making it popular in research and production.
- The library is known for its efficiency, optimized for speed, and can integrate with other libraries like NumPy.
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Analyticsindiamag
2w
78
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Fine-Tuning is Dead, Long Live Reinforcement Fine-Tuning
- OpenAI has launched reinforcement fine-tuning (RFT) for its o1 models, marking the end of traditional fine-tuning. With RFT, models do not just copy, they reason and learn from feedback to handle domain-specific tasks with minimal data. Early adopters have made significant achievements with RFT, from identifying genetic mutations that cause rare diseases to training legal models for high-stakes applications such as law and insurance. However, the approach may struggle in subjective domains or creative applications where there is no definite consensus. The RFT alpha program is open to select organizations, integrating domain-specific knowledge to advance mathematics, research and agent-based decision-making.
- OpenAI's RFT enables organizations to train models using reinforcement learning to handle domain-specific tasks with minimal data, sometimes as few as 12 examples. RFT improves reasoning and accuracy in expert-level tasks by using reference answers to evaluate and refine model outputs.
- Justin Reese, a computational biologist, highlighted RFT's transformative potential in healthcare, particularly for rare diseases affecting millions. "The ability to combine domain expertise with systematic reasoning over biomedical data is game-changing," he said.
- OpenAI aims to refine RFT based on feedback from early participants and is set to release it publicly in 2025. Beyond its initial applications, OpenAI envisions RFT models advancing fields like mathematics, research, and agent-based decision-making.
- OpenAI has also released o1, the full version, and a new $200 ChatGPT Pro model, which includes unlimited access to o1, o1-mini, and GPT-4o along with the advanced voice mode. The ChatGPT Pro plan offers all the features of the Plus plan and a new o1 Pro model, which uses more compute for the best answers to the hardest problems. OpenAI has also announced new developer-centric features, including structured outputs, function calling, developer messages, and API image understanding.
- Early adopters have achieved remarkable results with RFT, from identifying genetic mutations that cause rare diseases to training legal models for high-stakes applications such as law and insurance.
- OpenAI is using reinforced learning to train AI models to reason and think through problems, representing an advancement beyond traditional fine-tuning. In contrast to traditional fine-tuning, RFT allows the model to explore various solutions rather than relying on fixed labels, which enables it to focus on improving its reasoning capabilities.
- Interestingly, with RFT, significant performance improvements can be achieved with just a few dozen examples because the model learns from feedback rather than needing to see all possible scenarios. However, the performance of RFT depends heavily on the quality of the training data and the design of the task.
- OpenAI has also announced that its RFT alpha program is now open to select organizations to integrate domain-specific knowledge with the new approach.
- OpenAI aims to refine RFT based on feedback from early adopters and plans to release it publicly in 2025. Beyond its initial applications, OpenAI envisions RFT models advancing fields like mathematics, research, and agent-based decision-making.
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Medium
2w
261
Day 18: Introduction to Convolutional Neural Networks (CNNs)
- CNNs are specially designed to handle images, breaking them down into smaller pieces and extracting important features.
- CNNs use three main techniques to understand images: breaking them down, extracting features, and combining summaries.
- CNNs excel at image recognition and are used for various applications.
- Tomorrow, we'll explore Recurrent Neural Networks (RNNs) and LSTMs.
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Medium
2w
279
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What is face detection and how does it work?
- Privacy is essential to protect personal data and assets from misuse.
- Different individuals have varying levels of privacy needs.
- Face detection technology like Apple's Face ID provides secure authentication.
- FaceLock uses vectors and specific points to match patterns for unlocking.
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