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As a IT Recruiter and Talent Partner, my passion lies in bridging exceptional analytical talent and top-tier cybersecurity professionals with innovative and forward-thinking organizations.
#chatgpt #llms #promptengineering #generativeai #largelanguagemodels #openai AI Prompt Engineer - Recruiter Role Template Job Title: AI Prompt Engineer Description: Are you passionate about the intersection of language and technology? As an AI Prompt Engineer, you'll be at the forefront of the AI and NLP landscape, acting as a crucial link between human intent and machine understanding. Your role involves shaping user interactions with AI systems by crafting and refining prompts or queries that guide AI models. You'll leverage your expertise in linguistics and data science to design prompts that extract accurate and relevant responses, ensuring alignment with user expectations and industry standards. Key Responsibilities: Design and refine prompts to extract accurate and relevant responses from AI models. Deep understanding of language nuances, context, and domain-specific vocabulary. Collaborate with domain experts to create prompts supporting decision-making and responsible AI interactions. Fine-tune prompt strategies to reduce biases and advance human-AI dialogue. Play a pivotal role in optimizing AI models for various industries and applications. Requirements: Background in linguistics, data science, or related field. Strong understanding of NLP concepts and techniques. Ability to collaborate with cross-functional teams, including NLP engineers and machine learning engineers. Stay updated on the latest advancements in AI and NLP. AI Prompt Engineer - NLP Engineer Role Template Job Title: Natural Language Processing Engineer - Prompt Engineering Specialist Description: Are you a linguistic architect with a passion for AI communication? Join our team as an NLP Engineer specializing in prompt engineering. In this role, you'll work closely with domain experts to craft input instructions that guide AI models like GPT-4, ensuring accurate and contextually relevant outputs. Your expertise will be crucial in fine-tuning prompt strategies, reducing biases, and advancing human-AI dialogue, contributing to the seamless integration of NLP across industries. Key Responsibilities: Craft input prompts to guide AI models for accurate and contextually relevant outputs. Collaborate with domain experts to create prompts supporting decision-making and responsible AI interactions. Fine-tune prompt strategies to reduce biases and advance human-AI dialogue. Play a pivotal role in shaping how AI responds to users across industries. Requirements: Strong background in NLP concepts and techniques. Experience collaborating with cross-functional teams, including prompt engineers and machine learning engineers. Commitment to staying updated on the latest advancements in AI and NLP. AI Prompt Engineer - Machine Learning Engineer Role Template
Role of an AI Prompt Engineer
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Sharing Gen AI insights in healthcare | LLM squad product owner at Roche | Building Gradehive AI to help instructors save time with coding assignments | Opinions are only mine
Recently, A Gen AI leader from Google remarked that role of AI engineers like us is becoming similar to that of IT. These roles are shifting towards implementation rather than an traditional ML R&D and deployment There is some truth in that. Most Gen AI applications, such as Chatbots and RAG, are just one API call away. The role of AI engineers seems to have transitioned to developing thin wrappers. Even for data intensive tasks like complete model fine tuning, there are APIs and open-source libraries that have completely simplified the technical complexity. However, I still think there there are responsibilities which can be intellectually stimulating to an AI engineer. 1️⃣ Validation and monitoring: For critical applications, such as in healthcare, accurately identifying errors and biases in language model-generated text is essential. This requires AI engineers to continually develop and apply novel methods for quantifying these inaccuracies, ensuring the models' performance is constantly monitored and enhanced in real-world deployments. 2️⃣ NLP: In situations where deploying a LLM is an overkill due to costs, privacy concerns, or the need for reproducibility- such as sentiment classification. In such cases, AI engineers have the opportunity to create a labeled dataset using an LLM, or alternatively, employ traditional methods by having experts label the dataset before training a smaller, domain-specific model. 3️⃣ Non-text LLMs: : Problems involving sequential data other than text, like DNA or protein sequences, represent a unique class of challenges. Developing models for such data not only requires domain expertise but also a significant amount of R&D. What once required a team of 5 NLP data scientists can now be accomplished by a single AI engineer with a few API calls in an afternoon. As Gen AI systems become more sophisticated, we will continue to see radical transition in our responsibilities.
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Artificial Intelligence Development is often mistaken as a one-size-fits-all job. The widely held belief is that AI developers and AI engineers are the same thing, a belief that overlooks the distinct roles and responsibilities of each. I can attest to their differences, and I'm here to debunk that myth and shed light on the unique characteristics and value each brings to the table. #aidevelopervsengineer #aidevelopervsarchitect #aidevelopervsscientist #aidevelopervsresearcher #aidevelopervsspecialist https://lnkd.in/dE84w4WK
hireaideveloper.com
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🔥 ChatGPT to Microservice Ultra: Embracing AI 🔥 As non-AI engineers, every day we read articles on Medium and LinkedIn about AI advancements. And it makes us anxious. Should I read system design, DSA, or become an AI/ML engineer? Is it too late? 🔹The AI Anxiety🔹 AI development is not just another buzzword; it's a crucial skill. Just as we've adapted to various technologies over time (remember when Docker and Kube were new), we need to adopt to AI frameworks. The best way to not be anxious about AI is to accept it with open arms. I recommend diving into the world of Large Language Models (LLMs), Neural Networks, Transformers, and reading some AI research papers. Read and build something every day. 🔹What is Semantic Kernel?🔹 This weekend, I started exploring Semantic Kernel from Microsoft. It is open-source! Semantic Kernel acts as an AI orchestrator within your Python applications, enabling a multitude of actions such as WebSearch, ComputerVision, Text-to-Video, and Text-to-Music, depending on the integrated model. 🔹Key Features:🔹 - Multiple Agents: Define various Python functions as agents, each with unique tasks. - LLM Integration: Work with Local or Remote LLMs like OpenAI or HuggingFace. - Query Enrichment : Enhance your queries - Application Integration: Seamlessly integrate with your existing apps. I've attached a diagram for a clearer understanding, and you can also explore my GitHub for basic Semantic Kernel code: https://lnkd.in/gqNmRhnV 🔹Final Thoughts 🔹 It seems inevitable that we will soon be in an AI Agent economy, if we aren't already. And learning and developing on top of Orchestrator and Agent frameworks is going to be a crucial dev skill. Happy building! Please do share your experiences in the comments and connect with me for more such content 🙏🏾🙏🏾 #ai #aiadoption #loveforlearning #generatieveai
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This weekend I asked myself how much AI can help in developing a project from scratch using only the code generated by entering prompts. I have to say I was really surprised... What I ended up building is an API with a couple of services that allow creating a user, logging in, and assigning roles to those users. Want to see the result??? Here you can check out the API that ChatGPT (3.5) created for me. It took quite a few prompts, text changes, and many iterations to reach this result. Where do I see a problem? As I needed more abstraction, the AI started to struggle, as it couldn't grasp the context, and began to generate code that wasn't compatible with the existing code... Nevertheless, I must say the result is astonishing. Another issue... The use of 3.5 makes it rely on some deprecated features... I understand this issue could be resolved with GPT-4... (I'll try it soon...) And what about the differences between ChatGPT and Gemini? At the project's inception, I said "I can compare the two major AIs we have and see how the results differ." I used the same prompts for both, and I have to say OpenAI is FAR ahead of Google at the moment. #AI #Coding #Development #OpenAI #Gemini #Comparison https://lnkd.in/dwXFWCrE
GitHub - crespoantonio/ai-api-poc
github.com
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New Post: Exploring the Concept of Prompt Engineering - https://lnkd.in/eE9R5MYn engineering is one of the fastest-growing job roles in the tech sector today. As AI tools like ChatGPT, OpenAI, Bard, and others become increasingly popular, the demand for prompt engineers is rising. But what exactly is prompt engineering and why is it so important? Prompt engineering is a term used to describe the development of technologies to help people interact with AI-driven tools. It is a relatively new field, but it is quickly becoming an essential part of many businesses. Prompt engineers are responsible for creating tools that enable people to interact with AI-driven systems in a natural and […] https://lnkd.in/eyN2yBrW
Exploring the Concept of Prompt Engineering
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Strategic Automation Consultant/Chief Content Evangelist/Speaker/IBM Champion 2020/2021/2022/2023/2024 IBM BA Partner
This AI Research from Google Reveals How Encoding Graph Data Elevates Language Model Performance on Complex Tasks #ai #artificalintelligence #leadership #innovation #ChatGPT #futureofwork #GenerativeAI #businessautomation #digitaltransformation #processautomation #ibm IBM #chatbot #startup #marketing #strategy #business #publicsector #technology #metaverse #airegulation #llm
This AI Research from Google Reveals How Encoding Graph Data Elevates Language Model Performance on Complex Tasks
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Developer at Microsoft, A11Y, ChatGPT, LLM, C#, MVC, SQL, SSIS, Azure, Applied Mathematician - UCSD, UPRM, Fraud Detection & Benford's Law Expert, Excel VBA, Data Science, Selenium, Matlab, Wikipedia Editor, Ecommerce
👉Ask ChatGPT: What use there is for rational functions in ML - Part 1 of 2 ChatGPT 🤖 Rational functions, which are ratios 👉 of two polynomials, have various applications in machine learning (ML). Here are some of their uses: ### 1. Function Approximation and Regression Rational functions can be used for function approximation and regression tasks. They provide a flexible way to model complex relationships between variables. - **Nonlinear Regression**: Rational functions can model nonlinear relationships more flexibly than polynomial functions alone. This is particularly useful in cases where the data exhibits complex patterns that simpler models cannot capture. ```python from scipy.optimize import curve_fit import numpy as np def rational_function(x, a, b, c, d): return (a * x + b) / (c * x + d) x_data = np.linspace(0, 10, 100) y_data = rational_function(x_data, 2, 3, 4, 5) + 0.1 * np.random.normal(size=x_data.size) params, _ = curve_fit(rational_function, x_data, y_data) ``` ### 2. Neural Network Activation Functions Rational functions can be used as activation functions in neural networks. Traditional activation functions like ReLU, Sigmoid, and Tanh have limitations, and rational functions can offer more flexibility and smoothness in the activation landscape. - **Rational Activation Functions**: These functions can be tailored to specific needs and can potentially lead to better convergence properties during training. ```python import torch import torch.nn as nn class RationalActivation(nn.Module): def __init__(self): super(RationalActivation, self).__init__() self.a = nn.Parameter(torch.tensor(1.0)) self.b = nn.Parameter(torch.tensor(0.0)) self.c = nn.Parameter(torch.tensor(1.0)) self.d = nn.Parameter(torch.tensor(1.0)) def forward(self, x): return (self.a * x + self.b) / (self.c * x + self.d) # Example of using RationalActivation in a neural network model = nn.Sequential( nn.Linear(10, 20), RationalActivation(), nn.Linear(20, 1) ) ``` ### 3. Data Transformation and Preprocessing Rational functions can be used for transforming data in ways that make it more suitable for machine learning algorithms. - **Normalization and Scaling**: Certain datasets may benefit from transformations that rational functions can provide, especially when the relationships between variables are inherently rational in nature. ```python from sklearn.preprocessing import FunctionTransformer def rational_transform(x): a, b, c, d = 1, 0, 1, 1 # example parameters return (a * x + b) / (c * x + d) transformer = FunctionTransformer(rational_transform) transformed_data = transformer.transform(x_data.reshape(-1, 1)) ``` --continued--
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Experience in BFSI, FMCG, Sales, Analytics Consulting | Top B-School MBA | Building Products & Processes at Haleon | Analytics Manager at Haleon aka GlaxoSmithKline CH | Former Senior Consultant at Fractal Analytics
Today I asked ChatGPT : What do you mean by AI Skills? ChatGPT answer is below: "AI skills" typically refer to the abilities and knowledge required to work with or develop artificial intelligence (AI) systems. These skills can vary widely depending on the specific role or task within the AI domain. Here are some key AI skills: 1. **Programming Languages:** - Proficiency in programming languages such as Python, Java, C++, or others commonly used in AI development. 2. **Machine Learning:** - Understanding of machine learning algorithms, including supervised and unsupervised learning, classification, regression, clustering, and deep learning. 3. **Data Science:** - Skills in data analysis, data preprocessing, and feature engineering. 4. **Mathematics and Statistics:** - Strong foundation in mathematical concepts such as linear algebra, calculus, probability, and statistics. 5. **Deep Learning Frameworks:** - Experience with popular deep learning frameworks like TensorFlow or PyTorch. 6. **Natural Language Processing (NLP):** - Knowledge of techniques and tools for processing and understanding human language. 7. **Computer Vision:** - Familiarity with computer vision concepts and tools for image and video processing. 8. **Reinforcement Learning:** - Understanding of reinforcement learning algorithms and applications. 9. **Problem Solving:** - Strong analytical and problem-solving skills to tackle complex issues related to AI development. 10. **Algorithmic Thinking:** - Ability to design and implement efficient algorithms. 11. **Software Development:** - General software development skills for building robust and scalable AI applications. 12. **Cloud Computing:** - Familiarity with cloud platforms and services for deploying and scaling AI solutions. 13. **AI Ethics:** - Awareness of ethical considerations and responsible AI practices. 14. **Version Control:** - Proficiency in using version control systems like Git for collaborative development. 15. **Communication Skills:** - Ability to effectively communicate complex AI concepts to both technical and non-technical stakeholders. These skills are crucial for roles such as AI engineers, data scientists, machine learning engineers, AI researchers, and other professionals working in the field of artificial intelligence. As the field continues to evolve, staying updated with the latest technologies and methodologies is also an essential aspect of AI skills. #ArtificialIntelligence #MachineLearning #DataScience #Python #TensorFlow #PyTorch #NLP #ComputerVision #AIEngineering #TechInnovation
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🌟 Exploring AI’s Next Frontier! 🧠💡 it’s been more than one year since the sensation of chatGPT, but still feeling excited about the progress in open-source technologies that echo the human brain's complex reasoning capabilities. Stanford University’s RAPTOR project exemplifies this, offering a novel approach to data retrieval that mimics our own cognitive processes. This system structures data hierarchically, allowing deeper and more intuitive data interactions that enhance AI's reasoning abilities. Similarly, Microsoft’s Visualization-of-Thought (VoT) significantly advances spatial reasoning in AI, enabling models to interpret and navigate physical and conceptual spaces with unprecedented accuracy. These breakthroughs are not just technological feats; they are steps towards more intelligent, accessible, and versatile AI systems. What potential applications do you see in your field? How do you think such technologies could reshape our future? #AI #OpenSource #Innovation #Stanford #Microsoft https://lnkd.in/gJg8yUNY
RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval
arxiv.org
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