I no longer read the Economist. I just listen to it – thanks to the Economist audio edition! It almost feels like I am listening to a radio station with some of the finest handpicked playlists, but without any of the usual baloney. It is also convenient, and saves me time. And that’s exactly why Voice is going to be the next big thing. In not so distant future, Google searching or texting by typing will be an obsolete concept. Instead, we will be talking and listening to intelligent devices and apps. What will then become of the businesses built on making us “read and see” stuff displayed on every possible corners of our screen? Who will be the winners and losers? And will we sacrifice some of our privacy for convenience and time? If you would like to explore these thoughts, listen to this fascinating and inspirational talk by Gary Vaynerchuk.
“Success in creating effective A.I.,” said the late Stephen Hawking, “could be the biggest event in the history of our civilization. Or the worst. We just don’t know.” Are we creating the instruments of our own destruction or exciting tools for our future survival? Once we teach a machine to learn on its own – as the programmers behind AlphaGo have done, to wondrous results – where do we draw moral and computational lines? In this video, leading specialists in A.I., neuroscience, and philosophy tackle the very questions that may define the future of humanity.
In the recent years, Artificial Intelligence (AI) has been a subject of intense media speculation. Discussions related to machine learning, deep learning, and AI come up regularly in countless news articles, internet blogs and TV shows. We are being promised a future of intelligent robots, self-driving cars, transformative healthcare technology, and virtual assistants – a future sometimes painted in a grim light, where human jobs would be scarce and most economic activity would be largely handled by smart robots and better-than-human AI agents. Speculation or not, what is at stake here is our future, and it is important that we are able to recognise the signal in the noise, to tell apart world-changing developments from what are merely over-hyped media guesswork. To that end, this article is an attempt to provide a non-technical explanation of deep learning, an immensely rich and hugely successful sub-field of machine learning.
According to a recent study of GitHub, the de facto online code repository, Python came up as the number one programming language for machine learning and is also the third most commonly used language on GitHub overall. The same study also identified Tensorflow followed by scikit-learn as being the most active and popular machine learning projects on GitHub. Tensorflow, which was developed by the Google Brain Team, is an open-source programming library widely used for machine learning; and scikit-learn is the native Python library for machine learning.
The results of this study are not surprising to those of us who have done some amount of machine learning related development work. But if you are new to Python, Tensorflow and scikit-learn, and would like to invest some of your time and energy to learn how to use these tools in your own machine learning projects or to even beef up your CV, which book should you consider reading? Here’s my “one book” recommendation.
Data science skills are likely to be more readily available within the next decade or so. If you are not convinced, just have a look at the list of engineering degrees offered by most universities today – you will almost certainly find at least one curriculum related to data science. But while we wait for the next generation of engineers and data scientists to finish their university degrees and gap years, in the meantime, shortage of data science skills in the workforce will continue to pose a major challenge for organisations in realising the potential of their data. So the obvious question is: why don’t we use machine learning techniques to create our own data scientists? It turns out that some data scientists have already been working on this idea in the recent years. Their objective: develop automated machine learning methods and processes that can make machine learning (ML) more readily available for non-ML experts, to improve efficiency of ML and to accelerate research on ML.
You have probably heard of machine learning, but are you sure you know what it is? If you are struggling to make sense of it, you are not alone. There is a lot of buzz that makes it hard to tell what’s science and what’s fiction, what’s rebranding and what’s new. This article is an attempt to provide a simple non-technical explanation of machine learning and why it matters.