The goal of Bayesian inverse reinforcement learning (IRL) is recovering a posterior distribution over reward functions using a set of demonstrations from an expert optimizing for a reward unknown to the learner. The resulting posterior over rewards can then be used to synthesize an apprentice policy that performs well on the same or a similar task. A key challenge in Bayesian IRL is bridging the computational gap between the hypothesis space of possible rewards and the likelihood, often defined in terms of Q values: vanilla Bayesian IRL needs to solve the costly forward planning problem - going from rewards to the Q values - at every step of the algorithm, which may need to be done thousands of times. We propose to solve this by a simple change: instead of focusing on primarily sampling in the space of rewards, we can focus on primarily working in the space of Q-values, since the computation required to go from Q-values to reward is radically cheaper. Furthermore, this reversion of the computation makes it easy to compute the gradient allowing efficient sampling using Hamiltonian Monte Carlo. We propose ValueWalk - a new Markov chain Monte Carlo method based on this insight - and illustrate its advantages on several tasks.

Every day, new organizations announce how AI is revolutionizing the industry with disruptive results . As more and more business decisions are based on AI and advanced data analytics it is critical to provide transparency to the inner workings within that technology. McKinsey Global InstituteHarvard Business Review According to a recent McKinsey Global Institute analysis, the financial services sector is a leading adopter of AI and has the most ambitious AI investment plans. In a related article by the Harvard Business Review, adoption will center on AI technologies like neural-based machine learning and natural language processing because those are the technologies that are beginning to mature and prove their value. Below, we explore a challenge and opportunity that is unique to the rapid adoption of machine learning.

Simply put, machine learning (ML) puts the "intelligence" in artificial intelligence (AI). And two-thirds of respondents in a Deloitte survey say that AI brings substantial value to companies. Yet many businesses, especially smaller ones, haven't figured out how to use ML as a competitive advantage. The hesitation to embrace ML most likely comes from a misunderstanding about it. Like most emerging technologies, ML's adoption rates began slowly and within just a few industries.

Concepts of machine learning and artificial intelligence have become more present and available in most of the industrial processes. As more and more businesses are turning towards the implementation of machine learning and AI, the benefits reaped from these technologies are unparalleled. And the fintech industry is no longer an exception. The implementation of smarter machines and prediction concepts has provided the benefit of generating the best outputs with the least investments done. Get the entire 10-part series on our in-depth study on activist investing in PDF.

Artificial word has created hype in the digital age, and everyone is curious to know what it is actually? And how does that work? The adjective is ambiguous because all the things created by humans considered artificial. AI is the science concept, as a branch of science call this way a bunch of algorithms which do learn from data-experience, and in this way, they are called artificial, but they learn naturally. Many of the models of learning has been taken from nature, the artificial neural networks (ANNs), whose model was first presented by Many Scientist, among them are Newton, Thomas Edison, in the early 40s.

Digital marketing is the buzzword of 2020. In fact, it wouldn't be wrong to say that it has been the buzzword of the online world since 2019. All the hype and all the struggle of marketers makes sense because the more visible a business is on the internet, the more successful it's considered. Speaking of digital marketing, today we are here to talk about how AI and machine learning is transforming digital marketing. Yes, you can easily look for a company to manage SEO in Dubai but you should know how your business can change.

Founded on July 5th, 1994, Amazon.com, Inc. (NASDAQ:AMZN) is quite possibly one of the biggest success stories of the digital boom. Starting from humble beginnings as an online bookseller, Jeff Bezos has created Amazon into a household name and it is beginning to encroach on all aspects of our lives. In just under 25 years, Amazon has grown into the world's largest online marketplace and has spawned many successful side-projects, such as Amazon Alexa, Amazon Kindle and Amazon Prime. No matter what you are looking for, you will find it on Amazon and, given that the company holds almost 50% of the USA's e-commerce market and regularly has Amazon offers, it is easy to see why.

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The rise of artificial intelligence (AI) has been quite remarkable in the past few years. Many experts predict that, like the Internet, AI will bring huge changes to our lives and be part of almost everything we do. Vast sums of money have been put into AI start-ups in China, and many existing tech companies have opened new research labs. But amid the optimistic atmosphere, many also have concerns about the ethical questions that artificial intelligence raises. Get the entire 10-part series on Warren Buffett in PDF.

Artificial intelligence is not a zero-sum, winner takes all game. It is an advancement that will result in a wide variety of companies benefitting, a recent JPMorgan report points out. The significant degree of hype surrounding the larger technical categorization of AI has been blistering, but it is justified, the report claims, pointing to spending levels on AI-focused hardware, software and services forecast to increase by more than 5x over by 2021. Humans shouldn't panic about their future existence being threatened, not yet anyway. Artificial intelligence will be narrowly focused.