"The field of Machine Learning seeks to answer these questions: How can we build computer systems that automatically improve with experience, and what are the fundamental laws that govern all learning processes?"
– from The Discipline of Machine Learning by Tom Mitchell. CMU-ML-06-108, 2006.
Amita Kapoor is an Associate Professor in the Department of Electronics, SRCASW, University of Delhi and has been actively teaching neural networks and artificial intelligence for the last 20 years. She completed her masters in Electronics in 1996 and Ph.D. in 2011, during Ph.D. she was awarded a prestigious DAAD fellowship to pursue a part of her research work in Karlsruhe Institute of Technology, Karlsruhe, Germany. She was awarded the Best Presentation Award at the Photonics 2008 international conference. She is an active member of ACM, AAAI, IEEE, and INNS. She has co-authored four books including the best-selling book "Deep learning with TensorFlow2 and Keras" with Packt Publications.
"Among the machine learning strategy consultations you've done, which kinds of product team were the most challenging to work with?" After consulting on hundreds of machine learning projects, I've learned to pay attention to early warning signs that the client is in danger of shooting themselves in the foot. There's a lot of hype and nonsense about AI out there, so when teams come to me straight from the latest As-Seen-On-TV session ("…but if you call now, we'll throw in 50 free deep neural networks which you can compose into an unholy ensemble!") I know I'm going to have to undo all kinds of unrealistic expectations. Science fiction is a terrible teacher… but an ace of sales.
When we imagine the future of AI, we may think of the fiction we see in cinema: highly advanced robots that can mimic humans so well as to be indistinguishable from them. It is true that the ability to quickly learn, process, and analyze information to make decisions is a key feature of artificial intelligence. But what most of us have come to know as AI actually belongs to a subdiscipline called machine learning. Artificial intelligence has become a catch-all term for several algorithmic fields of mathematics and computer science. There are some key differences between them that are important to understand to maximize their advancement potential.
In March 1950, an RAF wing commander and trained accountant called Charles Reep turned his eye for numbers to football. Reep, who had become interested in the sport in the 1930s and was fascinated by Herbert Chapman's pioneering Arsenal team, had returned from the Second World War to find that the tactical revolution he'd witnessed before had stalled. Finally, at half-time during a drab Division Three game between Swindon Town and Bristol City during which he watched countless attacks amount to nothing, Reep's patience ran out. He grabbed a notebook and a pencil and began furiously jotting down what happened on the pitch – he started counting the number of passes and shots, in one of the first systematic attempts to use data to analyse football. Seven decades later, the data revolution has reached the grassroots – fans are fluent in xG and net spend, and the top teams pluck statistics PhD students straight from university in the search for an edge.
This course focuses on using state-of-the-art Natural Language processing techniques to solve the problem of question generation in edtech. If we pick up any middle school textbook, at the end of every chapter we see assessment questions like MCQs, True/False questions, Fill-in-the-blanks, Match the following, etc. In this course, we will see how we can take any text content and generate these assessment questions using NLP techniques. This course will be a very practical use case of NLP where we put basic algorithms like word vectors (word2vec, Glove, etc) to recent advancements like BERT, openAI GPT-2, and T5 transformers to real-world use. We will use NLP libraries like Spacy, NLTK, AllenNLP, HuggingFace transformers, etc.
Research being carried out by a research team around Professor Ohbyung Kwon at Kyung Hee University and Dr Christine (Eunyoung) Sung at Jake Jabs College of Business and Entrepreneurship, Montana State University, involves examining consumers' evaluations of fashion products designed using generative adversarial networks (GANs), an Artificial Intelligence (AI) technology. They analyse consumers' buying behaviour and offer practical advice for businesses that are considering using GANs to develop products for the retail fashion market. Artificial Intelligence (AI) technology is changing the retail landscape. Generative AI is being used to produce creative outputs; tasks that have traditionally been considered exclusive to humans. In particular, generative adversarial networks (GANs), an Artificial Intelligence technology, powerful machine learning models that can generate realistic images, videos, and voice outputs, are successfully performing creative tasks previously considered unique to humans.
Has anyone ever worked on a machine learning model for "queues"? Suppose there is a bakery: the bakery has has "n" people working, "m" people in line" and "q" orders that they are currently working on. The bakery is interested in making a machine learning model that predicts how long a customer will have to wait before the customer's order is ready and how long will the next customer have to wait before they can place an order. Has anyone ever come across a machine learning model which can predict waiting and processing times? I have seen examples online where people try fitting exponential distributions to historical waiting times and see how well they fit, as well as trying different m/m/k combinations... but has anyone ever come across an instance where machine learning algorithms (e.g.