machine learning
AI and Tax Season – Why AI and Data Does Not Solve Every Problem & Why Systems and Good Architecture Matter More
By Ikhlaq Sidhu | April 22, 2019 A few weeks back, I had the opportunity to visit a tax department for one of the world’s leading global economies. My colleague Alexander Fred-Ojala and I were there to discuss how artificial intelligence and data technologies might fit into their strategic plans. It was a great discussion, and a…
Data-X Poster Session shows breadth of AI projects
This semester students had an opportunity to apply the latest data science tools and algorithms to real-life industry problems in our course Applied Data Science with Venture Applications, also known as Data-X (INDENG 135 / 290). For the first time, students were able to showcase their projects in a poster session event on December 4th at…
Humu is using machine learning to create a happier workplace
Wayne Crosby, co-founder and head of product & engineering at Humu, visited the Sutardja Center’s Silicon Valley Innovation Leadership Week executive program to talk about the next industry being revolutionized by artificial intelligence and machine learning: human resources. In Crosby’s view, the HR industry (which he estimates to be about a $400 billion market worldwide)…
Alums partner with Mayo Clinic to develop ML algorithms to detect heart disease
SCET alums at Eko, a healthcare device company, have recently partnered with the Mayo Clinic to develop technology that uses machine-learning to help physicians better detect easily-missed heart diseases in patients. According to AI in Healthcare: Through the collaboration, the companies hope to create and commercialize a machine-learning based algorithm, which along with a smart digital…
Artificial Intelligence and the Rumsfeld Test
“But there are also unknown unknowns – the ones we don’t know we don’t know.” – United States Secretary of Defense Donald Rumsfeld, February 12, 2002 An artificial intelligence strategy is the corporate equivalent of your spleen: everyone has one, but not everyone understands quite what it will accomplish. There are bold plans afoot everywhere in…
Beyond the Molecule and Beyond the Device: Machine Learning and the Future of Healthcare
“The best minds of my generation are thinking about how to make people click ads. That sucks.” – Jeffrey Hammerbacher, Facebook Hammerbacher’s Lament Are we fated to suffer Hammerbacher’s Lament, with our best minds, and our best data, solely serving the gods of commerce? What of serving mankind’s existential needs in an area as fundamental…
Data-X: An Experimental Course Model that is Working
I’d like to start by congratulating all the teams that participated in our first Data-X course this spring. We just watched the final presentations, and it has been a great experience. Three months ago, we were just introducing the basic frameworks. And now, by the end of the semester, the projects have included running code…
Is Your Transformation Working?
As we discussed in earlier posts, firms like Amazon, Apple, Google, and many others are shining examples of companies that have successfully transformed and adapted over the past decade. In fact, most firms today realize they need to adapt in similar ways or face disruptive pressures themselves. But here is the problem: While most…
Keeping it Personal: Machine Learning Meets EgoVid
According to IBM, the cognitive computing market size presents a $2 trillion dollar opportunity over the next decade. One of the biggest components of this metric falls into the category of machine learning. While many have been using it as an overarching term to refer to a myriad of technological advancements and applications, visionary…