Almost everything you hear about artificial intelligence today is thanks to deep learning. This category of algorithms works by using statistics to find patterns in data, and it has proved immensely powerful in mimicking human skills such as our ability to see and hear. To a very narrow extent, it can even emulate our ability to reason. These capabilities power Google's search, Facebook's news feed, and Netflix's recommendation engine--and are transforming industries like health care and education. But though deep learning has singlehandedly thrust AI into the public eye, it represents just a small blip in the history of humanity's quest to replicate our own intelligence.

This week at the World Economic Forum (WEF), an annual gathering that put tech executives at the same table as far-right Brazilian president Jair Bolsonaro, Salesforce CEO Marc Benioff called San Francisco the canary in the coal mine. "San Francisco is kind of a train wreck; we have a real inequality problem," he said. Benioff and Salesforce, which has the largest skyscraper on the San Francisco skyline, led the Prop C campaign, a $300 million business tax aimed at reducing homelessness in San Francisco that is currently held up in court. Benioff also asserted at the gathering in Davos, Switzerland that artificial intelligence is "a new human right" that all people deserve. "Those who have the artificial intelligence will be smarter, will be healthier, will be richer, and of course, you've seen their warfare will be significantly more advanced," he said.

Tensors are the primary data structure used in deep learning, inputs, outputs everything within a neural network are represented using Tensors. The Deep Learning Book says as follows. A tensor is a multidimensional array, ie an nd-array. A number is a zero-dimensional Tensor, a vector is a one-dimensional Tensor and an n-dimensional array is an n-dimensional Tensor. A Tensor is a generalization whereas number, vector etc are specific cases of a Tensor.

Abstract: "Consumer drone developers often face the challenge of achieving safe autonomous navigation under very tight size, weight, power, and cost constraints. In this talk, I will present our recent results towards a minimalist, but complete perception and navigation solution utilizing only a low-cost monocular visual-inertial sensor suite. I will start with an introduction of VINS-Mono, a robust state estimation solution packed with multiple features for easy deployment, such as online spatial and temporal inter-sensor calibration, loop closure, and map reuse. I will then describe efficient monocular dense mapping solutions utilizing efficient map representation, parallel computing, and deep learning techniques for real-time reconstruction of the environment. The perception system is completed by a geometric-based method for estimating full 6-DoF poses of arbitrary rigid dynamic objects using only one camera. With this real-time perception capability, trajectory planning and replanning methods with optimal time allocation are proposed to close the perception-action loop. The performance of the overall system is demonstrated via autonomous navigation in unknown complex environments, as well as aggressive drone racing in a teach-and-repeat setting."

With the rise of artificial intelligence (AI), the demand for machine learning capabilities has increased dramatically. A vast array of industries from finance to health are seeing an uptake of machine learning-based technology. Yet, defining machine learning models remains a complex and resource-intensive endeavour for most businesses and organizations. The challenges can be reduced with the help of a good machine learning framework. Below is a list of some of the best open-source frameworks and libraries that businesses and individuals can use to build machine learning models. Amazon Machine Learning provides tools and wizards for developing machine learning models.

The potential of deep learning and AI are almost limitless, certainly well beyond the scope of our current imagination. Complex machines imbued with the characteristics of human intelligence (e.g., the ability to sense the world through sight, sound, and touch; to reason and plan; to communicate in natural language, and to move and manipulate objects), will influence society in untold ways. This dilemma is not at all new or limited to the field of computing; consider the ethical debates sparked by breakthroughs in gene editing, stem cell research, or genetically modified foods. Like many technologists of my generation, I am a rational optimist by nature. I believe AI can be harnessed in ways that dramatically improve our lives, and that its potential to do good far outweighs its potential to do harm. However, we can't presume that progress will automatically translate to benefits for humankind as a whole.

First, if you are interested in technology and AI and haven't joined the MIT Technology Review email lists, do so now! To follow the hyperlinks go to the full article here. Almost everything you hear about artificial intelligence today is thanks to deep learning. This category of algorithms works by using statistics to find patterns in data, and it has proved immensely powerful in mimicking human skills such as our ability to see and hear. To a very narrow extent, it can even emulate our ability to reason.

After playing benchmark matches back in December, DeepMind's StarCraft playing AI AlphaStar has beaten professional players in a series of games. Blizzard's StarCraft is complex E-sports game with no single winning strategy. It has its own AI in singleplayer mode, but it relies on hand crafted rules, having somewhat more information on the state of the map and its opponents than actual players, and being able to execute commands simultaneously, much faster than humans. Given its complexity, beating humans is considered another, huge milestone in AI research. But all other StarCraft AIs before relied mostly on a series of manually written rules and restrictions. None of them came close to a professional player's level, until now.

My dad taught me when I was young, but I guess he was one of those dads who always let their kid win. To compensate for this lack of skill in one of the world's most popular games, I did what any data science lover would do: build an AI to beat the people I couldn't beat. But I wanted to see how a chess engine would do without reinforcement learning as well as learn how to deploy a deep learning model to the web. FICS has a database of 300 million games, individual moves made, the results, and the rating of the players involved. I downloaded all the games in 2012 where at least one player was above 2000 ELO.

Anyone who uses Facebook can't have failed to notice that in recent years it has become rather good at recognising faces – it is as if the social network has been scrolling through photos of your friends for years and is now as familiar with them as you are. You may find this unsettling or remarkable, but the reality is that this is only the most visible manifestation of the rapid improvements in artificial intelligence under way across the globe – improvements that have, for example, led the US government to announce it is trialling facial recognition systems as a security measure at the White House, and sparked protests – on ethical and privacy grounds – against e-commerce giant Amazon for selling such technology. "The exciting part is that the results produced are free from bias, which improves confidence and relationships between clients and contractors" The artificial intelligence (AI) technologies behind facial recognition are machine learning, in which computers use algorithms to analyse data and learn without assistance, and deep learning – a similar but more advanced system based on "recurrent neural networks" – that replicate some of the ways the brain works, for example making decisions based on an ability to differentiate between pieces of information. This is smart stuff, but it's tech that the construction industry has been somewhat sluggish to seize on and apply to its own processes. But now it seems construction is waking up to the potential of AI.