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What is the difference between Artificial Intelligence, Machine Learning, and Deep Learning?

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Are you confused by buzzwords like Artificial Intelligence (AI) and DeepLearning? Have you ever wondered if DeepLearning is better than Machine Learning (ML) or why AI and ML are used interchangeably? Keep reading to decode these tech-jargons. As technologies rise in the Hype-cycle towards their "Peak of Inflated Expectations", they tend to loose their precise technical definitions. This is because non-partitioners introduce a more colloquial interpretation, which can confuse the space.


Why We Should Be Careful When Developing AI

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Artificial intelligence offers a lot of advantages for organisations by creating better and more efficient organisations, improving customer services with conversational AI and reducing a wide variety of risks in different industries. Although we are only at the start of the AI revolution, we can already see that artificial intelligence will have a profound effect on our lives, both positively and negatively. The financial impact of AI on the global economy is estimated to reach US$15.7 trillion by 2030, with 40% of jobs expected to be lost due to artificial intelligence, and global venture capital investment in AI is growing to greater than US$27 billion in 2018. Such estimates of AI potential relate to a broad understanding of its nature and applicability. AI will eventually consist of entirely novel and unrecognisable forms of intelligence, and we can see the first signals of this in the rapid developments of AI. In 2017, Google's Deepmind developed AlphaGo Zero, an AI agent that learned the abstract strategy board game Go with a far more expansive range of moves than chess. Within three days, by playing thousands of games against itself, and without the requirement of large volumes of data (which would normally be required in developing AI), the AI agent beat the original AlphaGo, an algorithm that had beaten 18-time world champion Lee Sedol.


Meet The Father Of AlphaGo: AlphaGo Zero

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Google's New AlphaGo Breakthrough Could Take Algorithms Where No Humans Have Gone, 'It's able to create knowledge itself'. "The time when humans can have a meaningful conversation with an AI has always seemed far off and the stuff of science fiction. But for Go players, that day is here."- While it sounds like some sort of soda, AlphaGo Zero may represent as much of a breakthrough as its predecessor, since it could presage the development of algorithms with skills that humans do not have and others go far and say that this Stunning AI Breakthrough Takes Us One Step Closer to the Singularity, I think is still early to tell how this can take us and Intelligence is not solved yet. "It's like an alien civilization inventing its own mathematics which allows it to do things like time travel…Although we're still far from'The Singularity,' we're definitely heading in that direction."


Pursuit of the AI Singularity Is Modern Alchemy

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Students who study calculus are experiencing the fruits of the genius of Isaac Newton (1643–1727). The same is true for introductory courses in physics and differential equations. All are due to Newton, who built the beautiful palace of mathematical physics. The wings of quantum mechanics and relativity were added in the early twentieth century. But Newton had other interests as well.


Deep Reinforcement Learning Hands-On: Apply modern RL methods, with deep Q-networks, value iteration, policy gradients, TRPO, AlphaGo Zero and more: Maxim Lapan: 9781788834247: Amazon.com: Books

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When I started learning RL three years ago, it was really hard to get practical information about the methods and ways that they could be implemented. Sparse blog posts about individual methods and theoretical papers, without code examples, were the only source of knowledge. To get something to experiment with, lots of time and effort was needed, fighting with weird bugs and misunderstanding mystic math in papers. With the rising popularity of RL, the situation has improved slightly, but, still, there is a lack of structured overview of the modern deep RL methods with a unified code base. This book fills the gap between theory and practice, providing a structured overview of recent RL methods, using clear examples written in uniform style.


A Simple, yet Technical Glossary of Terminology in AI

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Agent: A bot used in AI-related tasks. Algorithm: Process that follows a set of rules, a problem solver -- especially used by computers. AlphaGo: Computer program that plays the board game Go. Recognized as the first computer Go program to beat a professional human Go player [5]. Artificial Intelligence (AI): Science and engineering of making computers behave in ways that, until recently, we thought required human intelligence [1].


Pharma's AlphaGo Moment: For First Time AI Has Designed and Validated a New Drug in Days

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This is Pharma's AlphaGo moment when the potential for AI to radically transform the normal operating procedures and business models of the entire industry becomes tangibly obvious to the public. In the case of the AI industry, this happened in 2015, when AI company DeepMind succeeded in developing the first AI capable of beating a human Go champion in Go. This study by Insilico Medicine may be an analogous game-changing moment for Pharma. While it typically takes 2-3 years to go from initial drug discovery to preclinical validation, Insilico Medicine has done this in less than 2 months end-to-end. This is 15 times faster than Pharma companies capable of conducting the most efficient R&D processes. In a landmark study published in Nature Biotechnology on September 2, 2019, Insilico Medicine showed that they generated and validated a novel small molecule in just 46 days, and designed the drug from scratch based on specified molecular properties in just 21 days.


Facebook Reveals 'ELF' OpenGo Bot Code After Being 'Inspired' By DeepMind

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Mike Schroepfer, Facebook CTO speaks during the second day of the Web Summit in Lisbon, Portugal on November 8, 2016. Facebook and Google DeepMind's race to create an artificial intelligence (AI) that could beat professional players at the ancient Chinese board game of Go has been no secret. Mastering Go -- one of the most complex games on the planet for computers to excel at due to the sheer number of possible moves -- has been the dream of AI researchers for decades but only recently did big tech firms like Facebook and Google start throwing millions of dollars at the cause in the hope that it will lead to further breakthroughs. DeepMind arguably won the race in March 2016 when its AlphaGo AI agent beat Lee Sedol, one of the best Go players ever, by four games to one. But Facebook's "ELF" OpenGo bot isn't far behind now -- it too has defeated world champion and professional Go players.


AI Learning to land a Rocket(Lunar Lander) Reinforcement Learning

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Reinforcement learning is one of the most discussed, followed and contemplated topics in artificial intelligence (AI) as it has the potential to transform most businesses. At the core of reinforcement learning is the concept that optimal behaviour or action is reinforced by a positive reward. Similar to toddlers learning how to walk who adjust actions based on the outcomes they experience such as taking a smaller step if the previous broad step made them fall. Machines and AI agents use reinforcement learning algorithms to determine the ideal behaviour based upon feedback from the environment. An example of the reinforcement Learning in Action is AlphaGo Zero which was in the headlines in 2017.


Elon Musk: SpaceX founder's dire AI warning revealed – 'Famous last words!'

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The Tesla and SpaceX founder has notoriously urged for Artificial Intelligence to be respected and potentially regulated. Speaking at MIT in 2014, he called AI humanity's "biggest existential threat" and compared it to "summoning the demon". Four years on and Musk, who is usually far from a technological pessimist, reignited those fears.