Data lakes are emerging as the most common architecture built in data-driven organisations today. A data lake enables an organisation to store unstructured, semi-structured, or fully-structured raw data, and process them for different types of analytics – from dashboards and visualisations to big data processing, real-time analytics, and machine learning. Well designed data lakes ensure that organisations get the most business value from their data assets. This article explores the meaning of data lakes and the business case for building one.
For 30 years, the dynamics of Moore’s Law (an observation that the number of transistors in an integrated circuit would double every two years) held true as microprocessor performance grew at 50 percent per year. But the limits of semiconductor physics mean that CPU performance now only grows by 10 percent per year. However, the demand for computing resource to train artificial intelligence (AI) models has shot up enormously over the past six years (more than 300,000 times according to OpenAI) and is showing no signs of slowing down. Or to put it simply, AI’s compute hunger is outpacing Moore’s Law. So how is the AI industry dealing with this challenge? To address this question, this article explores how the rise of GPU computing and custom-designed AI chips is overcoming the end of Moore’s Law and enabling computationally intense algorithms and AI.
An inspirational speech by Brett King – futurist, fintech entrepreneur and best-selling author of books like “The Augmented” and “Bank 4.0”.
Mobile supercomputing. Artificially-intelligent robots. Self-driving cars. Neuro-technological brain enhancements. Genetic editing. The evidence of dramatic change is all around us and it’s happening at exponential speed. Previous industrial revolutions liberated humankind from animal power, made mass production possible and brought digital capabilities to billions of people. This Fourth Industrial Revolution is, however, fundamentally different. It is characterised by a range of new technologies that are fusing the physical, digital and biological worlds, impacting all disciplines, economies and industries, and even challenging ideas about what it means to be human. To explain what this all means, I have complied a collection of videos that describe the challenges that lie ahead for the workers of the planet. More specifically, the video collection covers the following topics:
– What is the fourth industrial revolution?
– The future of work: will our children be prepared?
– Automation is entering white-collar job, but some are also fighting back by learning how to code.
– What will the future of our jobs look like?
– Will you lose your job to automation?
– The big debate about the future of work.
– The digital future of work: what skills will be needed?
– How AI can save our humanity.
– McKinsey’s Gary Pinkus on the future of work
“Using TensorFlow makes me feel like I’m not smart enough to use TensorFlow” – Rachel Thomas, Professor at the University of San Francisco and co-founder of Fast.ai. If you are wondering what is TensorFlow, it is one of the most popular deep learning framework originally developed by Google (if you would like to know more, here’s the link to an article I wrote about TensorFlow a couple of weeks back: Getting Started with TensorFlow and Deep Learning).
Using the native TensorFlow API for deep learning is no easy task, especially if you are a beginner. The main reason for this is the relatively low-level of abstraction offered by the TensorFlow API, which makes it extremely hard, if not impossible, to express complex ideas. However, things changed for the better in early 2017, when Francois Chollet, author of Keras and AI researcher at Google, revealed that work was already underway to develop a TensorFlow implementation of the Keras API specification. Keras is an extremely user friendly high-level API for building and training deep learning models; and making TensorFlow accessible natively via the Keras API means that developers new to machine learning can now get started with TensorFlow and deep learning very quickly, without sacrificing the flexibility and performance of TensorFlow. As a matter of fact, when TensorFlow 1.4 was released (we are awaiting the release of version 2.0 anytime now), it included an early implementation of the Keras API (tf.keras), which made it possible to build, train and test a pretty complex deep learning model (such as a classifier for handwritten digits) in just six to ten lines of Python code!
So where does Uber fit into all of this? It turns out that Uber’s Artificial Intelligence team was also working on a similar initiative (i.e. simplifying the development and use of deep learning models), codenamed Ludwig, over the past two years, but at an even higher level of abstraction – i.e. deep learning using TensorFlow without actually having to write a single line of code! And more importantly, Uber announced this week that they are open sourcing Ludwig! At a first glance, Ludwig looks pretty impressive as a toolkit for deep learning, and I look forward to trying it out (I will report back once I have run some of my own tests). But in the meantime, here is a list of some of the coolest features of Ludwig.
We’ve long seen robots in industry, handling repetitive tasks and heavy machinery. They were once confined to safety cages in manufacturing facilities, programmed to perform one task perfectly, over and over again. Their purpose was to make high volumes of goods more quickly and cheaply.
But rapid technological advancements are giving rise to a new generation of smarter, flexible, more mobile and more human-like robots. Some can perform diverse tasks in unstructured environments and work with and alongside people. Some can fly, others can navigate terrestrial routes.
There is no doubt that the new generation of robots and humanoids are getting better and better at mimicking human behaviour. But one must not be deceived into thinking that they have human intelligence. Nevertheless, in this article, I present a collection of YouTube video clips that serve as a reminder of just how far we have come in developing robotic technology.
The global population is set to reach 9.7 billion by 2050; and as a result, the challenge of producing enough food to meet human demand has never been greater. Feeding an ever growing population also requires efficient ways to maintain crops and animals. In more developed parts of the world, farmers are finding these efficiencies with the aid of modern technology. For example, a dairy farmer today can mount AI-powered cameras in the barns to monitor the cows. Over time, the cameras learn to recognise the cows individually (facial recognition!) and then continuously checks them for signs of disease or other issues. The AI also flags any changes in the eating habits or weight gains, so that the farmer can tend to the cows on demand, saving them time, money and energy.
Many researchers believe that by the year 2026, technology like the one above will be used in almost every aspect of farming. But what will that future really look like, and how will it change our farming communities? In this article, I present a collection of YouTube videos that capture some of the recent technological innovations in farming – that perhaps offer a glimpse of what the future of our farming industry may look like.
Most people are aware of the factory robots that are involved with assembling, packing, or handling items before they reach retail stores. But over the past few years, due to advances in Artificial Intelligence and Deep Learning, robots have evolved dramatically and can take on new applications outside of the factory with far greater variability in their environments. The retail industry in particular provides a challenging setting for robots, but we are already starting to see a surge in robotic applications in retail. The introduction of robots in retail, whether in the store or behind the scenes, promises to transform the retail industry forever.
In an impressive documentary titled “Ready for the Retail Robots?”, BBC Click takes its viewers through an immersive experience of how some retail robots are starting to change the way human employees work, shoppers purchase goods in stores, and sports broadcasters capture the best footage of a fast-paced basketball game for live streaming. I will highly recommend watching this documentary to get glimpse of what what the retail industry may look like in not so distant future.
Scientists have been working on artificial intelligence since the middle of the last century. Their goal: to develop machines that learn and think like humans. In this article, I share with you a video documentary of the key learnings and technological milestones they have reached.
Artificial Intelligence for healthcare is not about replacing humans with robots. But it is about taking healthcare to the next level, by providing new tools to medical practitioners that enable them to offer better healthcare to patients, perform complex operations with relative ease, and more importantly, make world-class healthcare services affordable and available to every segment of the society. To support this viewpoint, I present to you in this article a collection of fascinating videos on how machine learning and artificial intelligence is bringing a paradigm shift to healthcare, powered by increasing availability of healthcare data and rapid progress of analytics techniques.