Anand Rao is a principal in PwC’s Advisory practice, is the Innovation Lead for the US firm’s Analytics Group and is the co-lead for the Global Project Blue, Future of Insurance research. With his PhD and research career in Artificial Intelligence and his subsequent experience in management consulting he brings business domain knowledge, statistical, and computational analytics to generate unique insights into the practice of ‘data science’.
Prior to joining management consulting, Anand was the Chief Research Scientist at the Australian Artificial Intelligence Institute. He has held Board positions at start-ups and currently serves as a Board member for not-for-profit industry associations. He is a frequent speaker on behavioral economics, analytics, and technology topics in academic and trade forums.
Top 10 artificial intelligence (AI) technology trends for 2018
Learn about the artificial intelligence advances that will have the most impact.
Robotic process automation underpins artificial intelligence
How robotic process automation technology can make artificial intelligence even smarter in the enterprise.
Demystifying machine learning part 4: Image and video applications
Some firms are using machine learning to process large amounts of unstructured data, but it’s not widespread—yet.
AI everywhere & nowhere part 3 – AI is AAAI (Assisted-Augmented-Autonomous Intelligence)
Changing the definition of artificial intelligence to mean assisted, augmented, autonomous intelligence.
AI everywhere & nowhere part 2 – AI is UI (Ubiquitous Intelligence)
Artificial intelligence must be ubiquitous to truly impact business and consumer life in a meaningful way.
AI everywhere & nowhere part 1 – Tempered excitement
Artificial intelligence has a lot of promise – but it also has a ways to go before it can be deployed widely to full advantage.
Demystifying machine learning: Part 3 – Exploring deep learning
What exactly is “deep learning” and what accounts for its rapid rise in popularity and media coverage?
Demystifying machine learning part 2: Supervised, unsupervised, and reinforcement learning
What do all technology innovators have in common? These three guiding principles.
The 5 Dimensions of the So-Called Data Scientist
What is “data science”? Is it really a new emerging discipline as some claim it to be; or is it the emperor in new clothes – data mining, statistics, business intelligence or analytics re-branded? Moreover, is it possible that one person can fulfil the role of a data scientist? Rather than answering this question directly, let’s review some of the skills required for someone to be a “data scientist.” First and foremost, a “data scientist” is a business or domain expert: Someone who has to have the ability to articulate how information, insights, and analytics can help business leadership answer key questions – and even determine which questions need answering – and make appropriate decisions. The data scientist will need a thorough understanding of the business across the value chain (from marketing, sales, distribution, operations, pricing, products, finance, risk, etc.) to do this well. Second, a “data scientist” is a statistics expert: Someone who has to have the ability to determine the most appropriate statistical techniques for addressing different classes of problems, apply the relevant techniques, and translate the results and generate insights in such a way that the businesses can understand the value. This will be predicated on a …