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By: Sandeep Singh
AI, short for Artificial Intelligence, is like giving computers a bit of a brain boost, allowing them to think, learn, and even make decisions in ways similar to us humans. Think of it as teaching machines to be a tad more clever and a lot less clunky. Why the fuss about it? Well, AI has the potential to revolutionize everything from how we order our coffee to tackling big problems like climate change or medical diagnoses. It’s kind of like having a super-smart buddy in your pocket, helping to make life easier and solving some of the world’s trickiest puzzles.
For industry professional Sandeep Singh, the Head of Applied AI/Computer Vision (Chief Data Scientist) at Beans.ai, the subsets of AI – like machine learning (ML) and deep learning – are integral to innovation.
“With ML, computers are trained to learn from data,” Singh explains. “Instead of being explicitly programmed to perform a task, they use large amounts of data and algorithms to learn how to perform that task themselves. Think of it as teaching computers to recognize patterns and make decisions based on that.”
Deep learning, on the other hand, is a subset of machine learning that uses neural networks with many layers (hence “deep”). It’s inspired by the structure and function of the brain, specifically the interconnections between neurons. These deep neural networks can process vast amounts of data and automatically extract features, making them especially powerful for tasks like image and speech recognition. If machine learning is like teaching computers to be better at tasks through examples, deep learning is giving them a set of virtual “neurons” to process and refine information in more intricate ways.
“This is where we specialize at Beans.ai,” Singh shares. “We’re a location intelligence company focused on creating hyper-accurate data for secondary addresses. We have a vast database that includes mapping for over 12 million apartment units along with over 50 million points of interest.”
He continues that the company leverages this detailed information to enhance the efficiency of deliveries and various other operations. The Beans.ai platform provides precise mapping locations and other pertinent details that help various stakeholders, from delivery companies like UberEats, Instacart, and FedEx, to telecom carriers and government agencies, optimize their processes and improve user experience.
Singh was the creator of the company’s support chat bot, BeansBot. He meticulously approached the creation of BeansBot by first understanding its business goals and customers’ needs. Recognizing the chatbot’s vital roles in answering queries, troubleshooting, and complaint resolution, he selected a reputable chatbot builder that employed the latest gen AI LLM techniques. To ensure a user-friendly experience, Singh mapped out diverse conversation pathways and collected data from sources like support tickets, social media, and surveys for chatbot training.
For the technical underpinnings, Singh utilized the Bard LLM from Google AI, renowned for its vast training dataset, enabling BeansBot to mimic human-like text generation. He fine-tuned Bard using transfer learning on specific customer interactions. Additionally, Singh implemented reinforcement learning, rewarding the bot for favorable outcomes and used natural language processing to enhance its language understanding. Collaborating closely with the development team, Singh ensured the seamless integration of these advanced AI techniques, continually tested, and refined BeansBot’s performance.
Singh has over 18 years of experience in software development, and has spent half that time specializing in machine learning, deep learning, and computer vision. As a renowned speaker on the subject of AI, and winner of the Top 5 Generative AI Scientists Award, he has the credentials and experience to provide authentic insights into AI innovation.
“When I lead workshops, give talks, or participate in discussion panels, I’m often asked about Generative AI,” Singh says. “That’s probably because tools like Chat GPT and image generation programs are becoming so popular. And I think that’s something to remember: AI is a tool. It’s not out to take jobs or replace humans. It’s a tool to make our lives and jobs easier.”
Singh serves as a mentor at communities like deeplearning.ai and Full Stack Deep Learning, and strongly believes in lighting the way for the next generation. “Everything I’ve learned, I wish to pass on to emerging engineers, software developers, and AI professionals,” Singh affirms. “Much of my mentoring and teaching is for the tech landscape audience and those in AI/ML/deep learning and computer vision.”
He’s been a major part of the AI fraternity in Silicon Valley for the past decade, and holds a masters from Georgia Tech with a specialization in machine learning; thus he’s had ample time to watch AI evolve. “I personally specialize in deep learning frameworks like TensorFlow, PyTorch, and Keras,” he says. “I’m also familiar with cloud platforms like AWS, Azure, and GCP. Technology always ebbs and flows. There’s always something new to learn, and I do have a passion for staying on top of new programs.”
Singh is a fervent proponent of democratizing deep learning. As a Technical Mentor at DeepLearning.AI he guides students through the intricacies of deep learning; he says that sharing his insights at conferences and contributing to open-source initiatives is vitally important to him in his journey to educate and inspire.
“I’m dedicated to advancing the field,” Singh states. This, along with his rich experience and his love for education, makes him a crucial asset in the AI realm, poised to drive significant advancements and economic growth. Singh’s proficiency in leading AI frameworks, his adeptness with cloud solutions, a history of notable contributions to AI, and his knack for mentoring budding data professionals make him a respected name in the world of AI and all its subsets.
Learn more: https://www.beans.ai/
Connect: https://www.linkedin.com/in/san-deeplearning-ai/
Medium: https://medium.com/@sandeepsign
