In order for a machine learning system to operate at its peak capacity and offer the best insights, it needs premium raw data directly from the client base. However, that data often remains inaccessible until the system itself is up and running. The algorithms inside a machine learning platform which analyse, automate and provide predictions? Or the invaluable data which drives the learning curve? Although confusing at first glance, the answer may be simpler than imagined.
"Well, if droids could think, there'd be none of us here, would there?" - Obi-Wan Kenobi Fully autonomous robots with humanlike capabilities might yet be some way away, still the realm largely of science fiction, but lawmakers, legal experts and manufacturers are already engaged in debates about the ethical challenges involved in their production and use, and their legal status, their "legal personality": ultimately, whether it's these machines or human beings who should bear responsibility for their actions. There are questions about whether and how much self-learning machines should be taking independent decisions about moral equivalence involving ethical choices which have traditionally been the preserve of humans. At the extreme, for example, can it be right for a machine to decide to kill an enemy combatant that it has identified without resort to human agency? Or is the robot morally no different from a "brainless" weapon? Is there an inherent difference morally between a "sexbot" and a standard, brainless sex toy?
The self-taught and low-cost car park, created by Cambridge Consultants, recognises cars and how those cars appear in parking spaces. The system aptly named Goldeneye, can do this both in the day and night as well as a variety of lighting and weather conditions, including the recent severe snow in the UK, without expensive physical infrastructure. Goldeneye uses a machine vision and deep learning solution developed entirely at Cambridge Consultants, along with the existing security camera and networking infrastructure on-site, to consistently monitor the availability of parking bays. Goldeneye uses 12 cameras to oversee 430 parking spaces and with digital signs at the entrance to the site, the system alerts a 500-strong workforce and visitors to where they can quickly find a parking space. Traditional parking monitoring solutions use sensors for each individual parking space, which can be expensive to maintain and often the business case to justify a large investment in bay sensors does not exist.
AI-Driven Data Could Be the Music Industry's Best Marketing Instrument The music industry is using artificial intelligence (AI) to transform its marketing model by applying it to consumer data sorted via machine learning, offering improved insights to harmonize artists with the industry and fans while keeping profits to a maximum. One area of AI development is audience engagement metrics that gauge how audiences respond to new music genres, trends, artists, and songs. Industry professionals can use this data to increase visibility for their signed artists and reach more fans. Music labels also can target audiences and track patterns to inform business decisions and stimulate revenue. Furthermore, data filtering can be a marketing advantage that spurs subscriber growth and better design of outreach initiatives and content to fuel competition.
Chqbook is a Gurgaon based financial technology start-up that allows customers to explore, compare, book and get personal finance products such as home loans, personal loans, and credit cards. Chqbook is a marketplace for financial products -- that brings suppliers (banks and NBFC's), distributors, and customers onto a single platform -- both online and offline. The startup currently offers 23 options from the country's leading banks & NBFC's for home loans. It also offers customers a choice of 16 institutions for personal loans and has over 35 credit cards. Chqbook is currently operational in 14 cities for home loans and personal loans through its 400 plus verified experts on its platform.
This learning path will introduce you to neural networks, TensorFlow, and Google Cloud Machine Learning Engine. Even if you don't have any previous experience with machine learning, that's okay, because these courses cover the basic concepts. The first course explains the fundamentals of neural networks and how to implement them using TensorFlow. Then it shows you how to train and deploy a model using Cloud ML Engine. The second course explains how to build convolutional neural networks, which are very effective at performing object detection in images, among other tasks.
The report also provides a professional and in-depth analysis on the global market while formulating industry insights into its current state of affairs. The report offers details on the pricing structure and channels of distribution of equipment suppliers in the global industry. Factors such as low penetration, rapid advances in technology, and high fragmentation are inducing high competition in the market. Most of the market is still untapped and there are considerable growth opportunities for new players entering this industry. However, owing to high fragmentation the competition is expected to intensify in the coming years.
A machine learning method called "deep learning," which has been widely used in face recognition and other image- and speech-recognition applications, has shown promise in helping astronomers analyze images of galaxies and understand how they form and evolve. In a new study, accepted for publication in Astrophysical Journal and available online, researchers used computer simulations of galaxy formation to train a deep learning algorithm, which then proved surprisingly good at analyzing images of galaxies from the Hubble Space Telescope. The researchers used output from the simulations to generate mock images of simulated galaxies as they would look in observations by the Hubble Space Telescope. The mock images were used to train the deep learning system to recognize three key phases of galaxy evolution previously identified in the simulations. The researchers then gave the system a large set of actual Hubble images to classify.
The vast majority of videos removed from YouTube toward the end of last year for violating the site's content guidelines had first been detected by machines instead of humans, the Google-owned company said on Monday. YouTube said it took down 8.28 million videos during the fourth quarter of 2017, and about 80 percent of those videos had initially been flagged by artificially intelligent computer systems. The new data highlighted the significant role machines -- not just users, government agencies and other organizations -- are taking in policing the service as it faces increased scrutiny over the spread of conspiracy videos, fake news and violent content from extremist organizations. Those videos are sometimes promoted by YouTube's recommendation system and unknowingly financed by advertisers, whose ads are placed next to them through an automated system. This was the first time that YouTube had publicly disclosed the number of videos it removed in a quarter, making it hard to judge how aggressive the platform has previously been in removing content, or the extent to which computers played a part in making those decisions.
PAW Business is the leading cross-vendor conference covering the commercial deployment of machine learning and predictive analytics. PAW Financial covers the deployment of machine learning and predictive analytics for financial services. The PAW Healthcare program will feature sessions and case studies across Healthcare Business Operations and Clinical applications so you can witness how predictive analytics is employed at leading enterprises and resulting in improved outcomes, lower costs, and higher patient satisfaction. PAW Manufacturing focuses on real-world examples of deployed predictive analytics. Attend and hear how some of the world's largest and most forward-thinking manufacturers are tapping the powering predictive modeling to improve business outcomes.