The beauty of starting a company in this day and age

Although founding a company is always a challenge, there has never been a better time than now for starting a business. The acquisition announced last month of WhatsApp by Facebook for $19 billion illustrates this opportunity. It is not the size of the deal; there have been huge deals that have made founders incredibly wealthy for decades. What is exciting is how WhatsApp achieved this huge exit.

What is amazing now is that you can build a $19 billion business quickly without a huge investment because of cloud computing. When you look at Microsoft and Google (and even Facebook), it took them thousands of engineers to build their businesses. WhatsApp has just 32 software engineers, which means that each one supported about 14 million users.

WhatsApp logo
Continue reading “The beauty of starting a company in this day and age”

Bayes Theorem Part 5: How it can help find the missing Malaysian Airlines jet

Over the last few weeks, I have been writing about and its applications. I just read a post on Nate Silver’s new blog, FiveThirtyEight. I recommend you read the post not only because it is interesting but to understand the breadth of applications of Bayes Rule.

Pushing the edge of machine learning

As part of a MOOC I just finished (Northwestern’s Content Strategy course, in which there was an interview with IBM’s SVP of the Watson Group, Mike Rhodin) that did a great job of showcasing the potential and future of machine learning. The Watson Group is probably most famous for developing the computer that won on Jeopardy! and it is now deploying that technology to push the boundaries of machine learning.

During the interview, Rhodin explained how Watson can read and understand text. As it can understand text, Watson can learn by reading or obtaining additional information that either will confirm or question a hypothesis. In the latter case, it can then seek out additional information to reach the most likely answer, including looking at historical research and results. It will then give a recommendation and confidence level based on all available information, with the supporting evidence and why the evidence is important. Watson will then analyze whether its recommendations were correct, learn from its mistakes, and effectively get smarter (e.g., why when it played Jeopardy! it got stronger as it completed a column).

Watson wins Jeopardy

Rhodin discussed how Watson is currently helping doctors make more accurate diagnoses. The doctor will tell Watson a patient’s symptoms (Watson can understand spoken English), then Watson will compare these symptoms with what it has read and what is in its knowledge base. It will then narrow down the possible causes and present that information to the doctor. Keep in mind that there is so much research being published daily (and even more historical research) that no doctor can stay on top of all of it. Once Watson has narrowed it down to a few possible causes, it will present these to the doctor with the evidence it generated. The doctor can either do their own research or it may trigger a memory of an article they read in the past. Watson has thus helped the doctor reach a diagnose faster, which often helps recovery rates and reduces treatment costs.

Continue reading “Pushing the edge of machine learning”

%d bloggers like this: