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Machine Learning, Deep Reinforcement Learning, and Limitations in Deep Learning

Artificial intelligence (AI) and its subfield deep learning have begun to train robots to behave like humans in recent years, thanks to some remarkable technological achievements. (data science course Malaysia)

Robots that act like humans are becoming a reality in many industries today, as machines progressively mimic complex cognitive skills including deductive reasoning, conclusions, and informed decision-making.

Machines, on the other hand, are still behind in explaining the reasoning for their decisions or actions. To put it another way, we cannot utilize a machine witness to solve a case in a court of law because it cannot “justify” prior actions. Neural networks and deep learning (DL), which combine unique training chances for machines to learn from layers of knowledge and then apply that knowledge to achieve specific goals, are notable achievements in AI applications.

Deep Learning and Neural Networks (data science course Malaysia)

Deep learning and neural networks are used in fields as diverse as physics, mathematics, statistics, signal processing, machine learning, neuroscience, and many more.

Within the larger subject of artificial intelligence, machine learning (ML), neural networks, and deep learning combined comprise a fast growing core group of technologies. Machines are now capable of solving many complex issues in real time using these strategies, which would ordinarily take a capable human brain a long time to figure out.

Human is using smart machines to make decisions and solve issues on a daily basis. The availability of neural networks and Deep Learning in AI applications has made this possible.

Deep Q-Network (data science course Malaysia)

The Triumph of Deep Reinforcement Learning (DQN)
DQN, is DeepMind’s Deep Reinforcement Learning brainchild. After Nature Magazine publish the first report on DQN in 2015, many reputable research institutions jumped into the topic. Because of the presence of DQN techniques, the entire research community believes that deep neural networks (DNN) can enhance reinforcement learning (RL) for interacting with HD images and others. Many other industry titans, including Google and Facebook, are eagerly awaiting the outcomes of advanced research with DQN.

Market Success of Reinforcement Learning Evolution Strategies
In reinforcement learning, evolution strategies (ES) appear to have made a comeback. The explorative algorithms that do not rely on gradients, scalability of methods, and low hardware requirements – no expensive GPU is required for fast parallel processing – appear to be the reasons for its apparent success.

Frameworks for Deep Learning (data science course Malaysia)

The year 2020 will undoubtedly be remembered as the Year of Deep Learning Frameworks. Although both Google’s Tensorflow and Facebook’s PyTorch have received widespread praise in the natural language processing (NLP) community, Tensorflow is better suited for static graphs. PyTorch, on the other hand, is perfect for dynamic structures. In their respective arenas, both Tensorflow and PyTorch have received positive user feedback and are making larger plans for the future.

Reinforcement Learning Agent beat Human AlphaGo Players

A reinforcement learning agent defeated the world’s best human Go players , according to this Nature paper. Training data from human players was used in the first version of AlphaGo, which was fine-tuned using a combination of self-play and Monte Carlo Tree Search. Following that, in AlphaGo Zero, the machine learned to play without the need for human intervention. In the paper titled Thinking Fast and Slow with Deep Learning and Tree Search, we used the science and technology  in this version of the game. These games inspired so many human players to improve their skills that DeepMind created an AlphaGo Teach programme to help them.

DeepMind’s Next Obstacle

DeepMind began considering multi-player Poker games utilising RL approaches after seeing the recent success and popularity of the AlphaGo family of games. DeepMind is also developing Starcraft 2, a real-time research environment.

Deep Learning’s Role in AI: Misinformation Is a Possibility
Deep Learning is not the AI future, according to industry watchdog KDNugget. Users have unintentionally received the wrong information about the usefulness of DL in artificial intelligence applications because both Google and Facebook have pushed their DL solutions globally. The worldwide publicity of DL techniques, according to KDNuggets, is more hype than substance. In its defence, KDNuggets claims that techniques like decision trees, which XGBoost utilise it, “do not make headlines,” but are as as vital as, if not more important than, deep learning.

Even in the case of AlphaGo, the media focused on Deep Learning (DL), whereas in reality, the Monte Carlo Tree Search approach contributed as much to the game’s success as DL. We accomplished many learning tasks in real life using Neuroevolution’s NEAT rather than back-propagation, as the media claims.

DL-Enabled Data Collection Systems

Deep Learning’s Most Serious Limitation: Machines Cannot Provide Legal Explanations
DL currently has two major flaws: the first is its proclivity to forget previous knowledge, and the second is its inability to question or rationalise the information provided. In the DL world, the machine believes what it is fed during the learning process and does not have the ability to question what it has learned. The last trend is risky because nonsense can be fed to the machine under the guise of “truisms.”

A judge cannot accept or use the NLP remarks submitted in DL petitions as evidence or argument in a court of law. For the reasons just stated, we cannot deem even the most intelligent AI systems “accountable” for their acts. Because of this significant constraint, we will deem many DL-driven AI systems illegal or non-compliant in the future.

DL-Enabled Data Collection Systems Didn’t Met GDPR Requirements

Many DL-enabled AI systems are dealing with the security regulations set forth by various regulatory bodies, as data security will be of paramount importance in the Data Science world of 2021 and beyond.  In 2018, the General Data Protection Regulation (GDPR) came into effect. Some force all data collection agencies operating in the 28 European countries to modify their DL-enabled apps or platforms well before that date, or face harsh penalties.

Source: data science course malaysia , data science in malaysia


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