This lecture gives an overview of the main categories of machine learning, including supervised, un-supervised, and semi-supervised techniques, depending on the availability of expert labels. We also discuss the different methods to handle discrete versus continuous labels.
End of the Beginning: What’s Next for Artificial Intelligence
AI began in the 1950s. What followed was the AI winter, progress was over-estimated. In the 2000s a lot of progress was made with machine learning.
Today’s AI is machine learning, which are programs that learn from experience. It’s data that is writing code.
Automation will replace human jobs, but more jobs will be created as well as more opportunities. AI will help to free us from mundane tasks and to solve problems.
Quantum Computing Leaves the Lab
Dr. Robert Sutor, vice president of IBM quantum computing, strategy, and ecosystem at IBM Research, speaks with Tonya Hall at ZDNet about how quantum computing is changing and becoming more accessible.
How Facebook’s Libra Currency Will Work
Facebook unveiled plans for a new, global financial system with a broad group of partners from Visa to Uber on board to create a cryptocurrency it expects will one day trade much like the U.S. dollar and inject a new source of revenue.
Machine Learning: Living in the Age of AI
This video examines ways in which people are interacting with AI today. Hobbyists and teenagers are now developing technology powered by machine learning. AI is impacting schoolchildren and farmers and senior citizens. The video also looks at implications the rapidly accelerating technology can have, including the good and bad of AI as well as ethical dilemmas posed by AI.
Blockchain in the Global Supply Chain
Blockchain technology has the potential to revolutionize trade. This short video outlines how HSBC completed a trade transaction across two independently built blockchain platforms – merging the financial with the physical logistics of trade.
P4P Marketing and Advertising
Data science, artificial intelligence and pay-for-performance advertising.
Raytheon Artificial Intelligence Tools
Raytheon is developing AI to help manage battlefield data, as well as predict when planes need maintenance and to aid responses to humanitarian crises.
What is Quantum Computing?
A brief discussion about quantum computing and artificial intelligence.
The Coming Software Apocalypse

The problem is that programmers are having a hard time keeping up with their own creations. Since the 1980s, the way programmers work and the tools they use have changed remarkably little. There is a small but growing chorus that worries the status quo is unsustainable. “Even very good programmers are struggling to make sense of the systems that they are working with,” says Chris Granger, a software developer who worked as a lead at Microsoft on Visual Studio, an IDE that costs $1,199 a year and is used by nearly a third of all professional programmers. He told me that while he was at Microsoft, he arranged an end-to-end study of Visual Studio, the only one that had ever been done. For a month and a half, he watched behind a one-way mirror as people wrote code. “How do they use tools? How do they think?” he said. “How do they sit at the computer, do they touch the mouse, do they not touch the mouse? All these things that we have dogma around that we haven’t actually tested empirically.”
The findings surprised him. “Visual Studio is one of the single largest pieces of software in the world,” he said. “It’s over 55 million lines of code. And one of the things that I found out in this study is more than 98 percent of it is completely irrelevant. All this work had been put into this thing, but it missed the fundamental problems that people faced. And the biggest one that I took away from it was that basically people are playing computer inside their head.” Programmers were like chess players trying to play with a blindfold on—so much of their mental energy is spent just trying to picture where the pieces are that there’s hardly any left over to think about the game itself.
TLA+
TLA+, which stands for “Temporal Logic of Actions,” is similar in spirit to model-based design: It’s a language for writing down the requirements—TLA+ calls them “specifications”—of computer programs. These specifications can then be completely verified by a computer. That is, before you write any code, you write a concise outline of your program’s logic, along with the constraints you need it to satisfy (say, if you were programming an ATM, a constraint might be that you can never withdraw the same money twice from your checking account). TLA+ then exhaustively checks that your logic does, in fact, satisfy those constraints. If not, it will show you exactly how they could be violated.
The language was invented by Leslie Lamport, a Turing Award–winning computer scientist. With a big white beard and scruffy white hair, and kind eyes behind large glasses, Lamport looks like he might be one of the friendlier professors at the American Hogwarts. Now at Microsoft Research, he is known as one of the pioneers of the theory of “distributed systems,” which describes any computer system made of multiple parts that communicate with each other. Lamport’s work laid the foundation for many of the systems that power the modern web.
For Lamport, a major reason today’s software is so full of bugs is that programmers jump straight into writing code. “Architects draw detailed plans before a brick is laid or a nail is hammered,” he wrote in an article. “But few programmers write even a rough sketch of what their programs will do before they start coding.” Programmers are drawn to the nitty-gritty of coding because code is what makes programs go; spending time on anything else can seem like a distraction. And there is a patient joy, a meditative kind of satisfaction, to be had from puzzling out the micro-mechanics of code. But code, Lamport argues, was never meant to be a medium for thought. “It really does constrain your ability to think when you’re thinking in terms of a programming language,” he says. Code makes you miss the forest for the trees: It draws your attention to the working of individual pieces, rather than to the bigger picture of how your program fits together, or what it’s supposed to do—and whether it actually does what you think. This is why Lamport created TLA+. As with model-based design, TLA+ draws your focus to the high-level structure of a system, its essential logic, rather than to the code that implements it.
