It's easy to shrug off trends as temporary ways of approaching business. The best approach is to stick to what you know works, right? Wrong, if you ask Reggie Bradford, SVP of product development at Oracle. Bradford spoke Monday at the multinational tech corporation's OpenWorld conference in San Francisco about what he called "megatrends." Megatrends, per Bradford's PowerPoint, "are global, sustained, and macroeconomic forces of development that impact business, economy, society, culture, and personal lives, thereby defining our world and its increasing pace of change."

While the number of headlines about machine learning might lead one to think that we just discovered something profoundly new, the reality is that the technology is nearly as old as computing. It's no coincidence that Alan Turing, one of the most influential computer scientists of all time, started his 1950 treatise on computing with the question "Can machines think?" From our science fiction to our research labs, we have long questioned whether the creation of artificial versions of ourselves will somehow help us uncover the origin of our own consciousness, and more broadly, our role on earth. Unfortunately, the learning curve on AI is really damn steep. By tracing a bit of history, we should hopefully be able to get to the bottom of wtf machine learning really is.

S. Somasegar is a venture partner at Madrona Venture Group and the former head of Microsoft's Developer Division. More posts by this contributor: Escaping the trough of disillusionment for virtual and augmented reality The intelligent app ecosystem (is more than just bots!) How to join the network Daniel Li is an investor with Madrona Venture Group. More posts by this contributor: The new paradigm for human-bot communication The intelligent app ecosystem (is more than just bots!) How to join the network You can hardly talk to a technology executive or developer today without talking about artificial intelligence, machine learning or bots. While everyone agrees on the importance of machine learning to their company and industry, few companies have adequate expertise to do what they wanted the technology to do. Here are some insights into what we can expect in the coming years around ML and AI.

Finding the ideal position for interventional needles – as used in biopsies – is a difficult and time-consuming process that can now be performed automatically, using a robotic arm to place a needle guide for the doctor at the optimal insertion point. With robotic assistance, doctors need five minutes to position the needle, as opposed to 30 minutes with conventional techniques. An ultrasound shows a shadow on the liver – but is it a tumour? Often, the only way to conclusively answer this question is to perform a biopsy, to send the suspected tissue to a laboratory for testing. However, placing the biopsy needle with precision is far from easy.

Want to watch this again later? Need to report the video? This feature is not available right now. Keynote - Bring ML to every corner of your Organization, Danny Lange, Head of Machine Learning, Uber at Applied AI Conference 2016 by BootstrapLabs Danny Lange recaps the history of Artificial Intelligence and Machine Learning, as well as explains the roles it currently plays in companies everywhere. Dr. Lange demonstrates the application of Machine Learning models in Uber and how it affects service.

Artificial intelligence (AI), deep learning, and neural networks represent incredibly exciting and powerful machine learning-based techniques used to solve many real-world problems. For a primer on machine learning, you may want to read this five-part series that I wrote. While human-like deductive reasoning, inference, and decision-making by a computer is still a long time away, there have been remarkable gains in the application of AI techniques and associated algorithms. The concepts discussed here are extremely technical, complex, and based on mathematics, statistics, probability theory, physics, signal processing, machine learning, computer science, psychology, linguistics, and neuroscience. That said, this article is not meant to provide such a technical treatment, but rather to explain these concepts at a level that can be understood by most non-practitioners, and can also serve as a reference or review for technical folks as well.

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In the last few posts, where we discussed Logistic regression, we had a fair bit of discussions on classification problems. Classification problems are the most prevalent ones we encounter in the real world machine learning setting and it is important to deal with various facets of this problem.In the next few posts, we will decipher some of the popular algorithms used within the classification context. The first of those algorithms which we are discussing is called the Random Forest. It is one of the most popular and powerful algorithms, which is currently used in the classification setting. In addition to deciphering the dynamics of Random Forest, we will also be looking at a practical applications powered by Random Forest algorithm.

This week's top Machine Learning stories, including AI agents that both encrypt and explain themselves, and more! Machine Learning is one of the most exciting fields in the world. Every week we discover something new, something amazing, something revolutionary. It's incredible, but it can also be overwhelming. That's why we created This Week in Machine Learning!

One of the major announcements to come out of Microsoft's Build 2016 developer conference today was the bet the company is making on bots. Microsoft believes that bots are the new apps. Yesterday, they invited developers to build bots for Cortana, the company's virtual assistant. Cortana, for you non-gamers, is a virtual persona character from Microsoft's blockbuster first-person shooter Halo franchise. Microsoft is betting on the notion of "conversational computing," which is why the company is putting its voice recognizing virtual assistant front and center.