TechRepublic's Karen Roby talks with an AI expert who believes we need to rethink our approach and focus more on cost benefit tradeoffs and resourcing. The following is an edited transcript of the interview. We're talking with Arijit Sengupta, he's an AI expert with over 20 years of education and experience working in artificial intelligence and even wrote a book called AI is a Waste Of Money. So Arijit, you obviously think we need to re-evaluate our approach to AI… explain! Arijit Sengupta: The answer is to go back to the fundamentals.

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If you've been thinking about building your own deep learning computer for a while but haven't quite got'round to it, here's another reminder. Not only is it cheaper to do so, but the subsequent build can also be faster at training neural networks than renting GPUs on cloud platforms. When you start trying small side projects like, say, building little autonomous drones or crafting a bot to spit out random snippets of poetry, you begin to realise how much compute power is really needed to get interesting results. So you can either fork out money to rent hardware via cloud services like AWS or Google Compute Platform or build your own server. Jeff Chen, an AI engineer and entrepreneur, drew up a handy shopping list for all the different parts needed to craft your own deep learning rig.

This class provides a practical introduction to deep learning, including theoretical motivations and how to implement it in practice. As part of the course we will cover multilayer perceptrons, backpropagation, automatic differentiation, and stochastic gradient descent. Moreover, we introduce convolutional networks for image processing, starting from the simple LeNet to more recent architectures such as ResNet for highly accurate models. Secondly, we discuss sequence models and recurrent networks, such as LSTMs, GRU, and the attention mechanism. Throughout the course we emphasize efficient implementation, optimization and scalability, e.g. to multiple GPUs and to multiple machines. The goal of the course is to provide both a good understanding and good ability to build modern nonparametric estimators.

Imagine you're taking an online business class -- the kind where you watch video lectures and then answer questions at the end. But this isn't a normal class, and you're not just watching the lectures: They're watching you back. Every time the facial recognition system decides that you look bored, distracted, or tuned out, it makes a note. And after each lecture, it only asks you about content from those moments. This isn't a hypothetical system; it's a real one deployed by a company called Nestor.

Noemi Derzsy is a Senior Inventive Scientist at AT&T Labs Research within the Data Science and AI Research organization, doing lots of science with lots of data. Previously, she was a Data Science Fellow at Insight Data Science NYC and a postdoctoral research associate at Social Cognitive Networks Academic Research Center at Rensselaer Polytechnic Institute. Holding a PhD in Physics and research background in Network Science and Computer Science, her interests revolve around the study of complex systems and complex networks through real-world data. She is also a NASA Datanaut and the co-organizer of Women in Machine Learning and Data Science NYC meetup group. In her free time she develops online technical courses as course instructor for Pearson and DataCamp.

Machine-learned diagnosis models have shown promise as medical aides but are trained under a closed-set assumption, i.e. that models will only encounter conditions on which they have been trained. However, it is practically infeasible to obtain sufficient training data for every human condition, and once deployed such models will invariably face previously unseen conditions. We frame machine-learned diagnosis as an open-set learning problem, and study how state-of-the-art approaches compare. Further, we extend our study to a setting where training data is distributed across several healthcare sites that do not allow data pooling, and experiment with different strategies of building open-set diagnostic ensembles. Across both settings, we observe consistent gains from explicitly modeling unseen conditions, but find the optimal training strategy to vary across settings.

Novel applications of artificial intelligence can endanger people in new ways. As AI is integrated into new parts of our lives, we must keep safety and security in the development and maintenance of AI systems top of mind. Last year at Data & Society, we convened experts from a number of fields, including cybersecurity, machine learning, computer science, political science, national security, activism, and advocacy, to conceptualize the greatest opportunities and challenges to building safe and secure socio-technical systems. Among our most significant findings was a rift between what it means to make something "safe" and make something "secure." Safety and security have different valences for different communities.

What are the competencies of a good leader? Great leaders can achieve unprecedented results and pave a company's path to the highest achievements. However, leadership alone cannot do much. Leadership has to be translated and broadcasted to stakeholders via efficient and thoroughly defined communication. Therefore effective leadership is all about communicating effectively.

A registered model is a logical container for one or more files that make up your model. For example, if you have a model that is stored in multiple files, you can register them as a single model in your Azure Machine Learning workspace. After registration, you can then download or deploy the registered model and receive all the files that were registered.