Did You Just Make Something Automated?

?Author and Generator: Kelly C. Dreger, Ed.D.

Non-Automated Explanation: On 07/05/2022, I decided to generate new article content using https://articlegenerator.org/ to see what would happen. Have I ever created an automatic article from a search engine before this time? No. Business-based automation has become VERY trendy, so I decided to check it out. Does this beat personable, original writing by real people? My opinion is no. I get much more enjoyment and satisfaction from making my own articles from scratch.

If you just want people to click on something in a site, no matter what it is, then yes, generating articles from an article-generating search engine would be the more efficient way to do it. It would be about views and sharing information that you may or may not credit yourself for finding. Sometimes the generated articles will give credit to someone, but not all the time. Why? Because these are public domain, free articles. If you want to put them on a blog and do what you want to with them, that is alright as long as it is legal and ethical to do so. But is this really good to do? Depends on who you ask.

This article of articles is mine though, and without me it would not have been possible because I have the idea. The non-automated part is the real me. I can cite this document as a publication, but I would not label this as my finest work for obvious reasons.

Now to the fun part: The articles below are the top three articles I was able to generate by using the keyword “reinforcement” at the time of searching. I do not include photos, just the text generated and saved to the text file from the site. So credit goes to me for the idea, but the actual content is generated from https://articlegenerator.org/

DO YOU PREFER REAL ME OR AUTOMATED ME? Read the automated articles here:

ARTICLE 1

Author: Automated Me

Everything you need to know about model-free and model-based reinforcement learning

Reinforcement learning is one of the exciting branches of artificial intelligence. It plays an important role in game-playing AI systems, modern robots,?chip-design systems, and other applications. There are many different types of?reinforcement learning algorithms, but two main categories are “model-based” and “model-free” RL. They are both inspired by our understanding of learning in humans and animals. Nearly every book on reinforcement learning contains a chapter that explains the differences between model-free and model-based reinforcement learning. But seldom are the biological and evolutionary precedents discussed in books about reinforcement learning algorithms for computers.

Subscribe to our newsletter now for a weekly recap of our favorite AI stories in your inbox.

I found a very interesting explanation of model-free and model-based RL in?The Birth of Intelligence, a book that explores the evolution of intelligence. In a conversation with?TechTalks, Daeyeol Lee, neuroscientist and author of?The Birth of Intelligence, discussed different modes of reinforcement learning in humans and animals, AI and natural intelligence, and future directions of research. American psychologist Edward Thorndike proposed the “law of effect,” which became the basis for model-free reinforcement learning In the late nineteenth century, psychologist Edward Thorndike proposed the “law of effect,” which states that actions with positive effects in a particular situation become more likely to occur again in that situation, and responses that produce negative effects become less likely to occur in the future. Thorndike explored the law of effect with an experiment in which he placed a cat inside a puzzle box and measured the time it took for the cat to escape it. To escape, the cat had to manipulate a series of gadgets such as strings and levers. Thorndike observed that as the cat interacted with the puzzle box, it learned the behavioral responses that could help it escape. Over time, the cat became faster and faster at escaping the box. Thorndike concluded that the cat learned from the reward and punishments that its actions provided.

The law of effect later paved the way for?behaviorism, a branch of psychology that tries to explain human and animal behavior in terms of stimuli and responses. The law of effect is also the basis for model-free reinforcement learning. In model-free reinforcement learning, an agent perceives the world, takes an action, and measures the reward. The agent usually starts by taking random actions and gradually repeats those that are associated with more rewards. “You basically look at the state of the world, a snapshot of what the world looks like, and then you take an action. Afterward, you increase or decrease the probability of taking the same action in the given situation depending on its outcome,” Lee said. “That’s basically what model-free reinforcement learning is. The simplest thing you can imagine.” In model-free reinforcement learning, there’s no direct knowledge or model of the world. The RL agent must directly experience every outcome of each action through trial and error. American psychologist Edward C. Tolman proposed the idea of “latent learning,” which became the basis of model-based reinforcement learning Thorndike’s law of effect was prevalent until the 1930s, when Edward Tolman, another psychologist, discovered an important insight while exploring how fast rats could learn to navigate mazes. During his experiments, Tolman realized that animals could learn things about their environment without reinforcement. For example, when a rat is let loose in a maze, it will freely explore the tunnels and gradually learn the structure of the environment. If the same rat is later reintroduced to the same environment and is provided with a reinforcement signal, such as finding food or searching for the exit, it can reach its goal much quicker than animals who did not have the opportunity to explore the maze. Tolman called this “latent learning.” Latent learning enables animals and humans to develop a mental representation of their world and simulate hypothetical scenarios in their minds and predict the outcome.

This is also the basis of model-based reinforcement learning. “In model-based reinforcement learning, you develop a model of the world. In terms of computer science, it’s a transition probability, how the world goes from one state to another state depending on what kind of action you produce in it,” Lee said. “When you’re in a given situation where you’ve already learned the model of the environment previously, you’ll do a mental simulation. You’ll basically search through the model you’ve acquired in your brain and try to see what kind of outcome would occur if you take a particular series of actions. And when you find the path of actions that will get you to the goal that you want, you’ll start taking those actions physically.” The main benefit of model-based reinforcement learning is that it obviates the need for the agent to undergo trial-and-error in its environment. For example, if you hear about an accident that has blocked the road you usually take to work, model-based RL will allow you to do a mental simulation of alternative routes and change your path. With model-free reinforcement learning, the new information would not be of any use to you. You would proceed as usual until you reached the accident scene, and then you would start updating your value function and start exploring other actions.

Model-based reinforcement learning has especially been successful in developing?AI systems that can master board games?such as chess and Go, where the environment is deterministic. In some cases, creating a decent model of the environment is either not possible or too difficult. And model-based reinforcement learning can potentially be very time-consuming, which can prove to be dangerous or even fatal in time-sensitive situations. “Computationally, model-based reinforcement learning is a lot more elaborate. You have to acquire the model, do the mental simulation, and you have to find the trajectory in your neural processes and then take the action,” Lee said. Lee added, however, that model-based reinforcement learning does not necessarily have to be more complicated than model-free RL. “What determines the complexity of model-free RL is all the possible combinations of stimulus set and action set,” he said. “As you have more and more states of the world or sensor representation, the pairs that you’re going to have to learn between states and actions are going to increase. Therefore, even though the idea is simple, if there are many states and those states are mapped to different actions, you’ll need a lot of memory.” On the contrary, in model-based reinforcement learning, the complexity will depend on the model you build. If the environment is really complicated but can be modeled with a relatively simple model that can be acquired quickly, then the simulation would be much simpler and cost-efficient. “And if the environment tends to change relatively frequently, then rather than trying to relearn the stimulus-action pair associations whenever the world changes, you can have a much more efficient outcome if you’re using model-based reinforcement learning,” Lee said. Daeyeol Lee, professor of neuroscience at Johns Hopkins School of Medicine Basically, neither model-based nor model-free reinforcement learning is a perfect solution. And wherever you see a reinforcement learning system tackling a complicated problem, there’s a likely chance that it is using both model-based and model-free RL—and possibly more forms of learning.

Research in neuroscience shows that humans and animals have multiple forms of learning, and the brain constantly switches between these modes depending on the certainty it has on them at any given moment. “If the model-free RL is working really well and it is accurately predicting the reward all the time, that means there’s less uncertainty with model-free and you’re going to use it more,” Lee said. “And on the contrary, if you have a really accurate model of the world and you can do the mental simulations of what’s going to happen every moment of time, then you’re more likely to use model-based RL.” In recent years, there has been growing interest in creating AI systems that combine multiple modes of reinforcement learning. Recent?research by scientists at UC San Diego?shows that combining model-free and model-based reinforcement learning achieves superior performance in control tasks. “If you look at a complicated algorithm like AlphaGo, it has elements of both model-free and model-based RL,” Lee said. “It learns the state values based on board configurations, and that is basically model-free RL, because you’re trying values depending on where all the stones are. But it also does forward search, which is model-based.” But despite remarkable achievements, progress in reinforcement learning is still slow. As soon as RL models are faced with complex and unpredictable environments, their performance starts to degrade. For example, creating a?reinforcement learning system that played Dota 2?at championship level required tens of thousands of hours of training, a feat that is physically impossible for humans. Other tasks such as?robotic hand manipulation?also require huge amounts of training and trial-and-error. Part of the reason reinforcement learning still struggles with efficiency is the gap remaining in our knowledge of learning in humans and animals. And we have much more than just model-free and model-based reinforcement learning, Lee believes. “I think our brain is a pandemonium of learning algorithms that have evolved to handle many different situations,” he said.

In addition to constantly switching between these modes of learning, the brain manages to maintain and update them all the time, even when they are not actively involved in decision-making. “When you have multiple learning algorithms, they become useless if you turn some of them off. Even if you’re relying on one algorithm—say model-free RL—the other algorithms must continue to run. I still have to update my world model rather than keep it frozen because if I don’t, several hours later, when I realize that I need to switch to the model-based RL, it will be obsolete,” Lee said. Some interesting work in AI research shows how this might work. A?recent technique?inspired by psychologist Daniel Kahneman’s System 1 and System 2 thinking shows that maintaining different learning modules and updating them in parallel helps improve the efficiency and accuracy of AI systems. Another thing that we still have to figure out is how to apply the right inductive biases in our AI systems to make sure they learn the right things in a cost-efficient way. Billions of years of evolution have provided humans and animals with the inductive biases needed to learn efficiently and with as little data as possible. “The information that we get from the environment is very sparse. And using that information, we have to generalize. The reason is that the brain has inductive biases and has biases that can generalize from a small set of examples. That is the product of evolution, and a lot of neuroscientists are getting more interested in this,” Lee said. However, while inductive biases might be easy to understand for an object recognition task, they become a lot more complicated for abstract problems such as building social relationships. “The idea of inductive bias is quite universal and applies not just to perception and object recognition but to all kinds of problems that an intelligent being has to deal with,” Lee said. “And I think that is in a way orthogonal to the model-based and model-free distinction because it’s about how to build an efficient model of the complex structure based on a few observations. There’s a lot more that we need to understand.”

ARTICLE 2

Author: Automated Me

Manchester United Need Reinforcement In The Forward Department

ROTTERDAM, NETHERLANDS - APRIL 17: head coach Erik ten Hag of Ajax prior to the TOTO KNVB Cup Final match between PSV and Ajax at Stadion Feijenoord on April 17, 2022 in Rotterdam, Netherlands

(Photo by Geert van Erven/BSR Agency/Getty Images) Getty Images

As Manchester United finalise their first summer dealings in Tyrell Malacia and Frenkie de Jong, as well as waiting for a final decision on Christian Eriksen, Erik Ten Hag will be focussed on bringing a forward to bolster the club’s attack. The Red Devils have been public in their approach for AFC Ajax’s Antony, but a deal looks to be dragging by the wayside the Dutch club demanding up to ￡70 million for the Brazil international. Villareal’s Arnaut Danjuma has also been mentioned inside Manchester United for a summer transfer, but West Ham currently lead the way in prizing him from Spain in a deal worth circa ￡35 million. With only Cristiano Ronaldo leading the line as things stand next season, Ten Hag is desperately light in the centre forward department. The Dutch manager has communicated to the board that he would prefer a versatile forward that can play in all three forward positions, rather than an out-and-out number nine.

LONDON, ENGLAND - MAY 22: Marcus Rashford of Manchester United during the Premier League match between Crystal Palace and Manchester United at Selhurst Park on May 22, 2022 in London, United Kingdom.

(Photo by Jacques Feeney/Offside/Offside via Getty Images) Offside via Getty Images

Marcus Rashford is being tipped for a run through the middle in pre-season in order to see if he has the capabalities to play there in a Ten Hag system, but with the departures of Juan Mata and Jesse Lingard, as well as Anthony Martial likely to be moved on, Man United need to bring in at least one forward reinforcement. Jadon Sancho, Anthony Elanga and Rashford are the three wingers currently in the building that will be battling out for places, while Ronaldo remains alone leading the team through the middle. The likes of Tahith Chong, Facundo Pellistri and Amad Diallo are poised to go back out on loan next season for further experience before coming back into the building to either be sold on or properly integrated into the first team.

AMSTERDAM, NETHERLANDS - AUGUST 08: Antony of Ajax celebrates scoring his teams fifth goal of the game during the pre-season friendly match between Ajax Amsterdam and RKC Waalwijk at Johan Cruijff Arena on August 08, 2020 in Amsterdam, Netherlands.

(Photo by Dean Mouhtaropoulos/Getty Images) Getty Images

While ￡70 million is likely to be too much out of this summer’s budget, Manchester United have made it clear that Antony is a player they admire and would like to bring to Old Trafford. The Brazilian has showcased his ability in the Eredivisie over the course of the past two seasons, creating 42 goal involvements in 78 appearances. For a player that had no prior experience to European football, Antony demonstrated his ability to understand the game quickly and adapt accordingly. With other positions being made a priority above that of a forward this summer, it is likely going to roll into the end of the summer transfer window to conclude a suitable deal for Antony or another top target for the Red Devils. There is plenty of time to go, but Ten Hag will be kncoking the door down each passing day as he begins to undertake his pre season training with Manchester United.

?ARTICLE 3

Author: Automated Me

Two in one out? Nottingham Forest continues summer reinforcement

The sands continue to shift in the first-team squad at Nottingham Forest this summer, with the promoted outfit gearing up for its first season in the Premier League since 1998 but will be made to do it without fan-favorite Brice Samba. Samba bid farewell to Forest in an emotional video as he sets off back to France with upstarts Racing Club de Lens this summer after spending three seasons at the City Ground as the club’s first-choice goalkeeper after arriving from SM Caen in 2019. But Forest has already replaced him expertly with a loan deal for Manchester United’s number two shot-stopper Dean Henderson and is continuing to add reinforcements in all areas of the pitch under Steve Cooper as they look to put building blocks in place to survive their return to top-flight football. Additionally, Union Berlin striker Taiwo Awoniyi and Troyes defender Giulian Biancone have also officially joined Forest for a combined ~￡26million, and the club looks set to potentially return to the Bundesliga for two more recruits as well. A deal for 1.FSV Mainz 05’s Moussa Niakhaté is set to be finalized today after the former French youth international is at the club to complete his medical today according to David Ornstein, while Sky Sports’ Florian Plettenberg has reported that Forest is also on the heels of Bayern Munich’s Omar Richards. 2GABK8H Muenchen, ALLIANZARENA, Germany. 28th July, 2021. Omar RICHARDS (# 3, M). Individual action with ball, action, football, FC Bayern Munich (M) – Borussia Monchengladbach (MG) 0: 2, preparatory game for the 2021-2022 season, on July 28th, 2021 in Muenchen, ALLIANZARENA, Germany.

Credit: dpa/Alamy Live News

Richards arrived in Bavaria last season after spending four seasons with Championship outfit Reading, with the academy product making twelve appearances under Julian Nagelsmann at the Allianz Arena but could be set to depart the club already after the report states that Bayern is happy to let him leave. Any deal for the Lewisham native would cost between ￡8.5million and ￡10million for the 24-year-old, and given his experience both in England and Germany, would likely see him become the first-choice left-back under Cooper for the upcoming 2022-23 Premier League campaign.

For more on Nottingham Forest, read below Forest receives boost as starlet Brennan Johnson set for a new deal