This work introduces a method to tune a sequence-based generative model for molecular de novo design that through augmented episodic likelihood can learn to generate structures with certain specified desirable properties. We demonstrate how this model can execute a range of tasks such as generating analogues to a query structure and generating compounds predicted to be active against a biological target. As a proof of principle, the model is first trained to generate molecules that do not contain sulphur. As a second example, the model is trained to generate analogues to the drug Celecoxib, a technique that could be used for scaffold hopping or library expansion starting from a single molecule. Finally, when tuning the model towards generating compounds predicted to be active against the dopamine receptor type 2, the model generates structures of which more than 95% are predicted to be active, including experimentally confirmed actives that have not been included in either the generative model nor the activity prediction model.

To learn how humans and AI systems can best live together, we may need to kill a whole lot of Zerg. DeepMind, the AI-focused unit of Alphabet, and the games company Blizzard Entertainment are releasing a set of tools that will let will programmers unleash all sorts of AI algorithms inside the space-themed game StarCraft. The game is more challenging than most of those tackled by AI programs to date. Not only is StarCraft extremely complex, it also requires planning far ahead and trying to second-guess what your opponent is up to. This means developing AI programs capable of matching humans ought to help researchers explore new facets of humanlike intelligence with machines.

This is part 5 of a series on Game AI for Non-Human Behavior. Here's what you might have missed! Part 1: Defining "Game AI" and "Non-Human Behavior" Part 2: Making Decisions, Predators and Prey Part 3: Weird Inspirations from Nature Part 4: Modes of Hunting Part 5 will be a deep dive into sensory input, and resulting behaviors. Source: Atari In 2015 there was an article published in nature that used Atari 2600 games to explore Reinforcement Learning in AI. Reinforcement Learning is the process of allowing AI to explore different options and learn behavior through a reward system, as opposed to Supervised Learning where AI performing sub-optimal behavior is explicitly corrected.

Regardless of whether the learner is a human or machine, the basic learning process is similar. Machine learning algorithms are divided into categories according to their purpose. There are lots of overlaps in which ML algorithms are applied to a particular problem. As a result, for the same problem, there could be many different ML models possible. So, coming out with the best ML model is an art that requires a lot of patience and trial and error.

Then this paragraph is self-explanatory. Deep Learning methods don't work easily with reinforcement learning like they do in supervised/unsupervised learning. Most DL applications have involved huge training datasets with accurate samples and labels. Or in unsupervised learning, the target cost function is still quite quite convenient to work with. But in RL, there's a catch -- as you know, RL involves rewards which could be delayed many time steps into the future (for example it takes several moves to knock the opponent's queen in chess, and each of those moves doesn't return the same immediate reward as the final move, EVEN IF one of those moves might be more important than the final move). The rewards could also be noisy -- for instance, sometimes the points for a particular move are slightly random and not easily predictable!

Summary: Reinforcement Learning (RL) is likely to be the next big push in artificial intelligence. But the concept of modeling in RL is very different from our statistical techniques and deep learning. In this two part series we'll take a look at the basics of RL models, how they're built and used. In the next part, we'll address some of the complexities that make development a challenge. Now that we have pretty much conquered speech, text, and image processing with deep neural nets, it's time to turn our attention to what comes next. It's likely that the next most important area of development for AI will be reinforcement learning (RL).

Going to school for a formal degree program for isn't always possible or desirable. For those considering an autodidactic alternative, this is for you. There's too much to learn, and the field is advancing rapidly. Master foundational concepts and then focus on projects in a specific domain of interest -- whether it's natural language understanding, computer vision, deep reinforcement learning, robotics, or whatever else. Motivation is far more important than micro-optimizing a learning strategy for some long-term academic or career goal.

In the blog "From Autonomous to Smart: Importance of Artificial Intelligence," we laid out the artificial intelligence (AI) challenges in creating "smart" edge devices: We also talked about how Moore's Law isn't going to bail us out of these challenges; that the growth of Internet of Things (IOT) data and the complexity of the problems that we are trying to address at the edge (think "smart" cars) is growing much faster than Moore's Law can accommodate. So we are going to use this blog to deep dive into the category of artificial intelligence called reinforcement learning. We are going to see how reinforcement learning might help us to address these challenges; to work smarter at the edge when brute force technology advances will not suffice. With the rapid increases in computing power, it's easy to get seduced into thinking that raw computing power can solve problems like smart edge devices (e.g., cars, trains, airplanes, wind turbines, jet engines, medical devices). Look at the dramatic increase in the number of possible moves between checkers and chess even though the board layout is exactly the same.

Machine Learning refers to a lot of things in a vast field which is rapidly growing. Arthur Samuel (1901-1990), an American pioneer in the field of computer gaming and artificial intelligence, coined the term "machine learning" in 1959. He defined it as a "field of study that gives computers the ability to learn without being explicitly programmed". Today, many say that machine learning is the future, although I'd say is already part of lots of things we have today. Machine learning, as a type of artificial intelligence (AI), enables computers to learn without being explicitly programmed, and to improve their functions when exposed to new data. By analyzing patterns in this data, the machine learning algorithms are self-adjusting based on a set of design rules.

In recent years, we've seen a resurgence in AI, or artificial intelligence, and machine learning. Machine learning has led to some amazing results, like being able to analyze medical images and predict diseases on-par with human experts. Google's AlphaGo program was able to beat a world champion in the strategy game go using deep reinforcement learning. Machine learning is even being used to program self driving cars, which is going to change the automotive industry forever. Imagine a world with drastically reduced car accidents, simply by removing the element of human error.