If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
I tried some experiments using Colab a couple of weeks back.It is good they are giving out GPU for free. But I had to deal with a lot frustrations using it. Every new session your storage resets and a fresh clean FS is allocated to you and you loose everything you stored in the last session.I came to know about Google drive option and even purchased a 100GB quota, but only to know that access to drive is EXTREMELY slow. I have came across some methods online to overcome this slowness, still not tried them though. So have temporarily paused my experiments and searching for some other options.
Adversarial Machine Learning (AML) is emerging as a major field aimed at the protection of automated ML systems against security threats. The majority of work in this area has built upon a game-theoretic framework by modelling a conflict between an attacker and a defender. After reviewing game-theoretic approaches to AML, we discuss the benefits that a Bayesian Adversarial Risk Analysis perspective brings when defending ML based systems. A research agenda is included.
The Covid-19 virus since its spread to other countries from January and becoming a pandemic has created a sense of panic among everyone. The very reason for this virus to become a pandemic is it being asymptomatic. Which means that you might get Covid-19 and not show any symptoms. In this whole confusion of who has this virus and who doesn't, developers are trying to do their bit by creating new apps and software to help the government in any possible way. The easiest would be to help detect the virus as early as possible.
Note: the original article has been split into two since I think the two points were only vaguely related, I will leave it as is here, since I'd rather not re-post stuff and I think the audience on LW might see the "link" between the two separate ideas presented here. Let's begin with a gentle introduction in to the field of AI risk - possibly unrelated to the broader topic, but it's what motivated me to write about the matter; it's also a worthwhile perspective to start the discussion from. I hope for this article to be part musing on what we should assume machine learning can do and why we'd make those assumptions, part reference guide for "when not to be amazed that a neural network can do something". I've often had a bone to pick against "AI risk" or, as I've referred to it, "AI alarmism". When evaluating AI risk, there are multiple views on the location of the threat and the perceived warning signs. I would call one of these viewpoints the "Bostromian position", which seems to be mainly promoted by MIRI, philosophers like Nick Bostrom and on forums such as AI Alignment.
Understanding intelligence is one of the major scientific challenges of our time; however, the science of intelligence is very much in its infancy. By working closely with scientists and leading thinkers from multi-disciplines, the founders of the AGI Sentinel Initiative (AGISI) believe that we can help humanity to better understand intelligence. If human beings have a better understanding of intelligence, it will not only help to build artificial intelligent machines, but it will also help to improve individuals' situational awareness, decision making, and values, and ultimately greatly improve people's knowledge of each other and our world, and thereby improve the quality of life for society overall.
Or is it just another hyped innovation? It comes with no surprise how AI today becomes a catchall term that is said out loud in the job market. The US and China are in nip and tuck in the AI race for supremacy. Although China aims to be the technology leader by 2030, the economy is still at a struggle phase with a slowdown and trade war with the US. Emerging trends in artificial intelligence (AI) significantly points toward having a geopolitical disruption in the foreseeable future.
JP Morgan is backing the use of machine learning for the future of foreign exchange algorithmic trading, after applying the technology to its FX algos earlier this year. The investment bank launched Deep Neural Network for Algo Execution (DNA) as a tool to bolster its FX algorithms in April, using machine learning to bundle its existing algos into a single execution strategy. "DNA is an optimisation feature that leverages simulated data from various types of market conditions to select the best order placement and execution style designed to minimise market impact," said Chi Nzelu, head of macro eCommerce at JP Morgan. "It then uses reinforcement learning – a subset of machine learning – to assess the performance of individual order placement choices." JP Morgan added that in recent years, algo trading strategies such as time-weighted average price (TWAP) and volume-weighted average price (VWAP) have multiplied, forcing clients to choose from a suite of algos with various execution methods.
The digital revolution in true sense has taken the world by storm. Ever since its introduction, the service industry has shifted its focus completely towards offering personalized experiences to the customers. The travel insurance industry is also sailing in the same boat and has been using new-age technology to offer the best products and services to its customers. But if you go by the experts, artificial intelligence is the key to offering personalised travel insurance services in future. Artificial intelligence (AI) is a widely used digital tool in the insurance industry today.