World War two brought together scientists from many disciplines. Their discussion took the form of an invention called the Turning Test, which set the bar for an intelligent machine. The term'Artificial Intelligence' was coined for a summer conference at Dartmouth University, organised by a young computer scientist named John McCarthy. Shakey, the first general-purpose robot was able to make decisions on its own actions by reasoning about its surroundings. After the AI winter when the world went into rough weather on tech grounds, people started realising the commercial value of artificial intelligence and started investing in it.

You might think that's something like Kubeflow but Kubeflow is more of a pipelining and orchestration system that's not really agnostic to the languages and frameworks that run on it.

The earth formed about 4.5 billion years ago, and it is believed that life began to emerge about 800 million years later. Humans evolved from apes around three million years ago, with modern humans emerging only about 200,000 years ago. The evolution of computers is generally described in generations. The first generation (1940–1956) used vacuum tubes and could take up the space of an entire room. The second generation (1956–1963) replaced vacuum tubes with transistors, making computers smaller and faster. The third generation (1964–1971) introduced the integrated circuit, making computers even smaller and faster.

It seems like only yesterday when everyone was talking about the impact of Big Data on the insurance industry. Talk about Big Data now and you will almost seem old-fashioned – it's all about artificial intelligence and InsurTech. Artificial intelligence (AI) will soon be everywhere. It is making decisions about what we buy, when we buy it and how much it costs. It is controlling interactions between customers and suppliers.

When you think of the rapid evolution of technology, the first thing that comes to mind is likely self-driving cars or artificial intelligence, not the real estate industry. But just because the real estate industry is not at the forefront of the technological revolution, it doesn't mean there aren't exciting new developments happening in the sector – and some of them can benefit you as a real estate investor. Nearly every industry has benefited from the advent of "big data," but what does that really mean for real estate? Together, these factors mean we're now able to access and analyze higher volumes of data more quickly. As a result, real estate data companies can now deliver more insightful information to the investment community faster, allowing investors to make better decisions.

Standing on the Shore*: How AI is Disrupting the World's largest Industries Artificial Intelligence is undergoing a massive acceleration driven by rapid growth in available data and rapid evolution of algorithms. Intel's acquisition this Tuesday of our portfolio company Nervana Systems validates that our companies' platforms, driving this acceleration, are disrupting the world's largest industries. Our thesis is that a) increasingly powerful and inexpensive hardware that is machine learning/deep learning-friendly (lots of multipliers and fast memory; e.g., Nervana Systems), b) a flourishing of learning approaches running on that hardware, and c) large, novel data sets, now inexpensive to acquire and refresh, to train and drive those novel learning algorithms, is fueling a transformation of major global industries right in front of everyone's eyes. Mission-critical decisions can now be made in the face of huge amounts of even chaotic data, and life-or-death actions can be implemented in the real-world, at large scale, with dramatically less cap-ex and op-ex than ever before, thanks to the speed, clarity, and efficacy of commercially practical AI. We've been investing in this thesis for the better part of a decade along with a small number of like-minded folks**, and we expect that it will fundamentally disrupt every industry vertical.

When millions of people across the world tuned in to watch DeepMind's machine beat the human Go world champion Lee Sedol, they also witnessed a historic victory for open-source. DeepMind used a scientific computing framework called Torch extensively in the development and execution of AlphaGo's neural networks. Torch was first released back in 2002 under a BSD open-source license with algorithms that are still commonly used by data scientists such as multi-layer perceptrons, support vector machines and K-nearest neighbours. Torch also supported ensembles -- a popular technique that combines the output of multiple algorithms, usually with a weighted average. It's not just open-source software that contributed to the growth of machine learning.

I have been building technology start-ups since 2003. Throughout the years I observed a trend towards the commoditization of machine learning algorithms and the data wrangling tools to deploy these techniques in the real world. The team at Seldon had been hand-crafting recommendation algorithms for a number of years. We adopted Hadoop back in 2011 in order to scale our data processing capabilities beyond programmatic and relational databases. Hadoop had a sister called project Apache Mahout that bundled a variety of machine learning algorithms.