My 2 month summer internship at Skymind (the company behind the open source deeplearning library DL4J) comes to an end and this is a post to summarize what I have been working on: Building a deep reinforcement learning library for DL4J: … (drums roll) … RL4J! This post begins by an introduction to reinforcement learning and is then followed by a detailed explanation of DQN (Deep Q-Network) for pixel inputs and is concluded by an RL4J example. I will assume from the reader some familiarity with neural networks. But first, lets talk about the core concepts of reinforcement learning. A "simple aspect of science" may be defined as one which, through good fortune, I happen to understand. Reinforcement Learning is an exciting area of machine learning. It is basically the learning of an efficient strategy in a given environment. Informally, this is very similar to Pavlovian conditioning: you assign a reward for a given behavior and over time, the agents learn to reproduce that behavior in order to receive more rewards. It is an iterative trial and error process. Formally, an environment is defined as a Markov Decision Process (MDP). Note: It is usually more convenient to use the set of Action \(A_s\) which is the set of available move from a given state, than the complete set A. \(A_s\) is simply the elements \(a\) in \(A\) such that \(P(s' s, a) 0\).

A protester holds a placard reading'Let's be realistic, ask for the obvious, 32h sharing of work time, Basic income, more jobs thanks to energy transition' during a demonstration called by youth organizations and students' unions on March 9, 2016, in Paris, during a nationwide day of protest against proposed labour reforms. In June of this year, Swiss voters saw an initiative on their ballots calling for an "unconditional basic income" that would "allow the whole population to lead a decent life and participate in public life." Put on the ballot by a petition drive after it was rejected in parliament, the initiative was rejected by 77 percent of Swiss voters, with 23 percent approving. The initiative lost badly, but even having a national vote on a universal basic income (UBI) shows how far the idea has come. Although people have advocated some type of universal basic livelihood or support for centuries, usually tied to concerns about poverty, recent advocacy is closely linked to fears about extensive job losses due to technology, especially artificial intelligence (AI) and robotization of work.

When you picture AI, what do you see? When you think about a real-world application of AI, what comes to mind? When you think about the technical details of AI, what approach do you name? I'm willing to bet it's deep learning. In reality AI comes in many shapes and forms.

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Prof. Bostrom has written a book that I believe will become a classic within that subarea of Artificial Intelligence (AI) concerned with the existential dangers that could threaten humanity as the result of the development of artificial forms of intelligence. What fascinated me is that Bostrom has approached the existential danger of AI from a perspective that, although I am an AI professor, I had never really examined in any detail. When I was a graduate student in the early 80s, studying for my PhD in AI, I came upon comments made in the 1960s (by AI leaders such as Marvin Minsky and John McCarthy) in which they mused that, if an artificially intelligent entity could improve its own design, then that improved version could generate an even better design, and so on, resulting in a kind of "chain-reaction explosion" of ever-increasing intelligence, until this entity would have achieved "superintelligence". This chain-reaction problem is the one that Bostrom focusses on. He sees three main paths to superintelligence: 1. The AI path -- In this path, all current (and future) AI technologies, such as machine learning, Bayesian networks, artificial neural networks, evolutionary programming, etc. are applied to bring about a superintelligence.

Sometimes, behind-the-scenes tech upgrades can make a big difference. New York's Governor Cuomo reports that an overhaul of the state DMV's face recognition software in January has led to more than 100 arrests and 900 open investigations so far. The new system checks 128 points on a face instead of 64, dramatically increasing the chances that it'll match a photo against the DMV's database. Combine that with new comparison modes (like black-and-white and overlays) and it's easier to catch identity thieves and fraudsters, such as one man who tried to get a license with a stolen identity in order to evade a suspension. New York isn't alone in using face recognition in the US, let alone the world.

Financial companies such as Bank of New York Mellon, BBVA and American Express have become early adopters of a new generation of artificial intelligence and robotic-process-automation technology that automate human tasks. Their efforts resemble the higher-profile work of Google, Ford and others with self-driving cars and BAE Systems with drones. Specifically, the banks are creating tech to do chores previously performed by people in operations, wealth management, algorithmic trading, risk management and other areas. Though it holds great promise in improving efficiency and cutting costs, many worry that low-level jobs will be irretrievably lost in the process. McKinsey predicted in 2013 that AI and robotic banking will displace 110 million full-time workers around the world by 2025.

But over the next few years, AI in the cloud promises to democratize intelligence, potentially making every company and every employee smarter, faster and more productive. Machine learning algorithms can analyse billions of signals to determine which customers are most likely to purchase a particular product or automatically escalate and route customer service calls to the most appropriate agent. From online to in-store, the shopping experience is being transformed by AI. More than 90% of shopping is still done in brick-and-mortar stores today. But soon every customer who walks into a store will be able to interact with a chatbot knowing all of their shopping history, preferences and other relevant information to make recommendations, offer special discounts and handle customer service issues.

We've covered a lot of low-code/no-code development tools, but they've mostly been relegated to simple app creation, not complicated, cutting-edge technology like machine learning (ML) -- until now. That's because predictive analytics specialist Dataiku has updated its Dataiku Data Science Studio (DSS) platform to Version 3.1, which it said "unleashes visual machine learning." Dataiku has introduced five new back-ends for visual ML development with no need for those hard-to-find, expensive developer types well versed in writing programming code. Much like the numerous mobile development back-ends that take care of tedious chores such as external integrations, database access and so on, those new ML backstops help create predictive ML models. "Dataiku DSS 3.1 introduces new visual machine learning engines that allow users to create incredibly powerful predictive applications within a code-free interface," the company said in a statement this week.

A new technique using artificial intelligence to read satellite images could aid efforts to eradicate global poverty by indicating where help is needed most, a team of U.S. researchers say. The method would assist governments and charities trying to fight poverty but lacking precise and reliable information on where poor people are living and what they need, the researchers based at Stanford University in California said. Eradicating extreme poverty, measured as people living on less than 1.25 a day, by 2030 is among the sustainable development goals adopted by United Nations member states last year. A team of computer scientists and satellite experts created a self-updating world map to locate poverty, said Marshall Burke, assistant professor in Stanford's Department of Earth System Science. It uses a computer algorithm that recognizes signs of poverty through a process called machine learning, a type of artificial intelligence, he said.