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Automation and financial services: debunking the myths

#artificialintelligence

In an age of self-driving cars, 'robot surgery' and computers capable of trouncing human players in hugely-complex games such as Chess or Go, it seems obvious to many that the automation of Wall Street, the City of London, Frankfurt and other financial centres must be imminent.




Nvidia steps up its transition to an AI company

#artificialintelligence

Nvidia reported earnings that beat expectations and showed that the company's focus on artificial intelligence is still paying off. For the past decade, Nvidia has been rising above graphics chips for gamers, expanding to parallel processing in data centers and lately to artificial intelligence processing for deep learning neural networks and self-driving cars. The company reported earnings per share of $1.33 (up 60 percent from a year ago) on revenue of $2.6 billion (up 32 percent), beating Wall Street's expectations. The company's stock price is up more than 100 percent in the past year on the popularity of artificial intelligence. But it slumped during the day on Thursday, along with the broader market.


The $1tn question: how far can the new iPhone 8 take Apple?

The Guardian

Apple's stock market value is heading towards a new milestone and its latest product launch on 12 September could push the tech giant closer to becoming the first ever $1tn (£760bn) company. At the end of last week, the company's market capitalisation hovered around $830bn, continuing a 10-year run that has generally headed upwards since a low of $69bn in January 2009, during the financial crisis. Tuesday's event, with the iPhone 8 the star attraction, will strive to meet investors' – and customers' – vaulting expectations. But what will Apple tempt users with to justify Wall Street's faith in its future profits? An Apple spokesman declined to discuss what will be revealed at the event in the company's $5bn, spaceship-shaped Cupertino headquarters.


3 Growth Stocks That Could Soar More Than Nvidia -- The Motley Fool

#artificialintelligence

NVIDIA's (NASDAQ:NVDA) graphic cards have long been favorites among hardcore gamers, but who would've thought the chipmaker's stock would explode the way it has in recent times? The share price has more than tripled in just the past year, turning NVIDIA into a near eight-bagger in just five years. It's more an artificial intelligence computing company today, having made huge headway in two of the hottest technology fields of our times: AI and self-driving cars. For investors looking to find the "next NVIDIA," the trick is to find a company that is sitting on a big growth opportunity, or is already tapping into a soon-to-heat-up trend, but that is still flying under Wall Street's radar. These are stocks with the potential to soar.


Notes on a New Philosophy of Empirical Science

arXiv.org Machine Learning

This book presents a methodology and philosophy of empirical science based on large scale lossless data compression. In this view a theory is scientific if it can be used to build a data compression program, and it is valuable if it can compress a standard benchmark database to a small size, taking into account the length of the compressor itself. This methodology therefore includes an Occam principle as well as a solution to the problem of demarcation. Because of the fundamental difficulty of lossless compression, this type of research must be empirical in nature: compression can only be achieved by discovering and characterizing empirical regularities in the data. Because of this, the philosophy provides a way to reformulate fields such as computer vision and computational linguistics as empirical sciences: the former by attempting to compress databases of natural images, the latter by attempting to compress large text databases. The book argues that the rigor and objectivity of the compression principle should set the stage for systematic progress in these fields. The argument is especially strong in the context of computer vision, which is plagued by chronic problems of evaluation. The book also considers the field of machine learning. Here the traditional approach requires that the models proposed to solve learning problems be extremely simple, in order to avoid overfitting. However, the world may contain intrinsically complex phenomena, which would require complex models to understand. The compression philosophy can justify complex models because of the large quantity of data being modeled (if the target database is 100 Gb, it is easy to justify a 10 Mb model). The complex models and abstractions learned on the basis of the raw data (images, language, etc) can then be reused to solve any specific learning problem, such as face recognition or machine translation.