There is an impending digital tsunami — or D-Quake — that can drive enterprises to destruction if they don’t continually update their business model canvas for competitive advantage. They should be dynamically adopting innovations led or influenced by Andrew Ng and other top leaders.
The most important attribute for successful executives and enterprises is to analyze exponential trends as they will influence your work, strategy and success. History provides the lessons: Think of the Internet, email, mobile, social media, crowd sourcing.
Massive open online classes (MOOCs) will reduce training costs by over 80 per cent. Coursera is a good example having raised funding of $85 million with more than 600 courses from 100-plus notable universities and nearly eight million students. A sample course for your enterprise team: Mobile Cloud Computing with Android.
Robots and off-shoring are changing labour roles, Google is testing self-driving cars and Amazon is testing drones for package delivery.
There are the new wearable / embedded devices with more than 10 sensors and the ubiquitous Internet of Things forming a planetary nervous system. Virtual reality systems (Oculus Rift/Facebook, Sony, Microsoft), glasses (Google), Amazon’s dynamic perspective providing 3-D-like capabilities in their new smart phones are already here. Dramatically impacting the data center is the Facebook founded Open Compute Project (OCP) with 150 plus members, custom servers, commoditized networking through Wedge and FBOSS.
Big data will be smart data with machine learning, deep learning where computers grow brain-like capabilities. Examples are the NELL and NEIL projects at Carnegie Mellon where computers are learning the Web with NELL understanding the web in 10 years. There is fully automated deep learning beating physicists in particle discovery such as Higgs boson when analyzing data from particle accelerators/colliders.
At the root of much of this is Andrew Ng, whose efforts are recognized by magazines, fellowships and awards. His work with Google can be found in the Android speech recognition system, and his work with robotics is evidenced in ROS, the open-source robotics software platform.
Andrew’s work as a director of the AI lab at Stanford, leading Google in its brain project, head of research at Chinese search engine Baidu and co-founder of Coursera and MOOCs impacts you through open education, speech recognition systems, artificial intelligence applications, robotics, machine learning systems and much more. All of his work significantly impacts the enterprise.
Here are partial extracts from our interview. The full podcast is in the link provided here.
Q: In the work that you do what are the practical applications for 2015 or 2016 and how do they impact things like business/government/media/education and society?
“….I think because of the work of the major tech companies using deep learning, many of us are already using deep learning algorithms … I think machine learning touches so many aspects of our lives, and I think what some of these organizations have done is create deep learning through the power engineered throughout their organizations in order to apply the deep learning algorithm to many different problems. I like to tell people that most of us use machine learning algorithms dozens of times a day without knowing it …”
Q: What are some controversies in your field?
“… Deep learning is very exciting and one of the confusions in the discipline is that the term ‘deep learning’ encompasses really two ideas. The first idea is called Supervised Learning in which if you have a lot of labeled data, these algorithms are fantastic at soaking up the labels to make accurate predictions … But there’s a second, not really unrelated body of ideas that also goes by the term deep learning that is very different, which is: ‘can you get a piece of software to watch YouTube or read text on the Internet or listen to audio for hours on end and without you telling it anything or tagging or labeling any data and have it figure it out for itself?’ … I think the second unsupervised learning, learning from unlabeled or untagged data is maybe most human-like. I think most humans learn primarily from unlabeled data and I think that this unsupervised learning idea has tremendous potential for letting us make a lot of progress in machine perception …”
Q: What will computers and robots look like by 2020?
“… It’s only in the last two or three years that there have been far more robotics startups than in the previous 3 or 5 or 10 years, but I really don’t know where it’s going to go. It’s really exciting work and people are producing low cost robots for manipulation which is fantastic for researchers … I think if we want to make progress towards truly intelligent robots we have to be careful not to overhype the science. In order to make a little bit of progress towards AI or towards truly intelligent perception, I think there is tremendous potential in deep learning algorithms, especially the uncertified versions of learning algorithms …”
Q: Is there a value to other market segments such as business, government or society?
“… In the broadest sense I think an education gives you super powers. With an education you can learn to write software, teach other people, learn to cook healthier food for your children, with an education you will live a longer life….At a societal level I think that we can accelerate the progress of civilization. Governments are looking to MOOCs as a way to level up their populations’ skill set….The world today changes so fast that all of us need regular infusions of knowledge in order to stay current. Even though we work with universities and this is a project that the five of us had launched out of university, the biggest impact MOOCs is having today is not on college students, it is on continuing adult education. I think many businesses, either individuals, the working professionals or often management are coming or sending others to come to Coursera to take MOOCs in order to continue to develop their employees….”
Q: From your extensive experiences, speaking, travels and work, can you share any stories — perhaps something amusing, surprising, unexpected or amazing?
“… A few months ago I was at a party at LinkedIn here in Silicon Valley and I met one of the students who had taken one of my Machine Learning MOOCs … He said one of the features he most liked was the ability to play video at 2x speed because it allowed him to blaze through the video and if he missed something he could just do an instant replay. But he said to me, ‘I’ve listened to about 20 hours of video of you talking and all of these videos are of you talking at 2x speed. But now that I meet you in person, I’m really surprised that in person you talk….so….slow’….”
Big Data Opens the Door for Prescriptive Analytics
Making customer-level decisions that balance risk and profit just keeps getting harder. And when you think you have it right, turning them into actions can be even trickier. You also need to consider the factors that make smart decisions difficult. Big data. Regulations. Customers who want an offer, fast, or else you’re going to lose them. No doubt some of these challenges sound familiar. And this is where prescriptive analytics represents the next step in the analytic journey.