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👩💻 Top 8 In-demand Gen AI skills
DTP #35: How Gen AI can Transform Supply Chains
Mukundan Sivaraj
January 26, 2024
Research from Upwork found a shift in search activity from hiring organizations: they were moving from singular generative AI tools to generative AI applications and services.
This means that leaders and hiring managers are advancing their comprehension of generative AI and its varied applications.
They discuss which generative AI skills are most in demand by companies:
💼 AI in Business
How Gen AI can Transform Supply Chains
Image generated by Midjourney
Generative AI is positioned as a transformative force for supply chains, according to Deloitte's Subit Mathew. In a recent chat with the Future of Supply Chain podcast, he emphasized that while human creativity will steer future supply chains, AI and intelligent technologies will power them, significantly enhancing efficiency.
Subit suggests companies focus on improving efficiency, user experience, and introducing new processes and innovation through AI.
The deployment of AI in supply chains requires careful consideration of governance, prioritization, value quantification, and technology selection.
SAP's ERP platform is highlighted as a valuable tool when combined with large language models for achieving remarkable results in AI applications.
The integration of AI across supply chain layers is expected to bring about hyper-predictive and efficient processes, transforming the flow of goods.
AI-powered demand sensing algorithms, illustrated by ZF Friedrichshafen, demonstrate a 92% reduction in forecast turnaround time and improved flexibility in supply chain control.
TensorFlow
TensorFlow is an open-source machine learning framework developed by the Google Brain team. It is widely used for building and training machine learning models, particularly neural networks.
It supports both research and practical applications, making it a popular choice among developers and researchers in the AI community.
Expertise here is highly sought after in numerous sectors and professional positions. Applicable job titles encompass machine learning engineer, deep learning engineer, AI research scientist, NLP engineer, data scientists, AI product manager, AI consultant, AI systems architect, AI ethics and compliance analyst, etc.
Read: TensorFlow guide
Natural language processing (NLP)
Natural Language Processing (NLP) is a subfield of artificial intelligence (AI) that focuses on the interaction between computers and human languages. It involves the development of algorithms and models that enable machines to understand, interpret, and generate human language in a way that is both meaningful and contextually relevant.
Roles where learning NLP can be particularly useful include NLP engineer, NLP consultant, AI Ethics and Compliance analyst, etc.
Image processing
Image processing involves manipulation and analysis of visual information, typically in digital images. It encompasses a range of techniques to enhance, modify, or extract information from images, making it a crucial aspect of computer vision and artificial intelligence.
Learning image processing is beneficial for roles such as Computer Vision Engineer, Image Processing Engineer, Remote Sensing Specialist, Image Analyst, Robotics Engineer, Forensic Image Analyst.
Model tuning
Model tuning, also known as hyperparameter tuning, involves the optimization of machine learning model parameters to enhance its performance on a specific task or dataset. It is a critical step in the machine learning workflow as it aims to find the best set of hyperparameters that result in a model with improved accuracy, precision, recall, or other performance metrics.
Learning model tuning is useful in various roles, including, Machine Learning Engineer, Data Scientist, Research Scientist (AI/ML), Predictive Analytics Specialist, Predictive Analytics Specialist, etc.
Read: Model tuning refresher
PyTorch
PyTorch is an open-source machine learning library developed by Facebook. It is widely used for deep learning and artificial intelligence applications. PyTorch provides a dynamic computational graph, making it popular for research and experimentation.
Relevant roles include Deep Learning Engineer, Machine Learning Engineer, Data Scientist, Software Developer (ML Applications).
Stable Diffusion
Stable Diffusion, introduced in 2022, is a generative AI model designed to produce distinctive photorealistic images by responding to textual and visual prompts. In addition to crafting images, the model extends its capabilities to the creation of videos and animations. Rooted in diffusion technology and leveraging latent space, this model offers a versatile approach to content generation.
ChatGPT
ChatGPT, developed by OpenAI based on the GPT (Generative Pre-trained Transformer) architecture, specifically GPT-3.5. It is designed to understand and generate human-like text based on the input it receives.
Learning how to use ChatGPT can be beneficial for individuals in roles such as Language Model Researcher, AI Ethics and Compliance Analyst, Chatbot Developer.
AI content creation
The demand for professionals adept at effectively instructing generative AI engines has risen due to companies increasingly utilizing AI technology for content creation, including blog posts, social media content, graphics, articles, and videos.
💻 Platform Highlight
RagaAI: Automated AI testing platform. Secured $4.7m in seed funding.
Helix by HL: Building an AI assistant for advisors and investors. Recently raised $6m in seed capital.
OctaiPipe: Edge AI platform for industrial IoT. Raised £3.5M in Pre-Series A funding.
🌐 AI Weekly News Roundup
Amid the integration of AI in workplaces, a shift towards valuing soft skills is observed. Research by Professor Peter Cardon suggests a growing emphasis on ethics and interpersonal communication, with integrity topping the list of sought-after qualities. AI integration demands a workforce adept at navigating ethical considerations and fostering genuine human connections.
Courses can help enhance AI knowledge and skills, along with reading articles, and teaching oneself Python. Brainstorming ways AI can benefit one's work, following AI/Tech companies for innovations, and networking with AI experts for insights are effective ways forward.
As employers prioritize AI skills, candidates can enhance their proficiency through free online courses from companies like Microsoft and Google. Research indicates a demand for AI skills, with employers willing to pay a premium. Overcoming barriers in knowledge acquisition is crucial, and initiatives like Amazon's AI Ready aim to provide accessible training.
Reddit reviews a data scientist's resume - A post
“For folks working on AI & law" – A thread
🤖 Prompt of the week
Act as a data scientist. I need to create a numpy array. This numpy array should have the shape of (x,y,z). Initialize the numpy array with random values. See you next week,
Mukundan
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