A Gentle Introduction to Object Recognition With Deep Learning

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The model is significantly faster to train and to make predictions, yet still requires a set of candidate regions to be proposed along with each input image. Python and C (Caffe) source code for Fast R-CNN as described in the paper was made available in a GitHub repository. The model architecture was further improved for both speed of training and detection by Shaoqing Ren, et al. at Microsoft Research in the 2016 paper titled "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks." The architecture was the basis for the first-place results achieved on both the ILSVRC-2015 and MS COCO-2015 object recognition and detection competition tasks. The architecture was designed to both propose and refine region proposals as part of the training process, referred to as a Region Proposal Network, or RPN. These regions are then used in concert with a Fast R-CNN model in a single model design. These improvements both reduce the number of region proposals and accelerate the test-time operation of the model to near real-time with then state-of-the-art performance.


Why You Need To Activate Intelligence in Your Business

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AI and automation are changing the business environment across industries, delivering new opportunities through intelligent, automated solutions. Some companies are ahead of the curve, while others are stagnating in adopting the technology. Operators and enterprises are aware of the benefits of AI and automation, but the questions that always remain are, "What does it bring to my business? How will it solve my problems?" Artificial intelligence (AI) is a constellation of technologies that describes the processes of intelligent automation, like machine learning, natural language processing (NLP), cognitive computing, and deep learning.


Data Analytics Performance Gap Ruins CX in Banking

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The mission of building one-to-one communication and engagement is not a new concept. Back in 1993, Don Peppers and Martha Rogers, Ph.D., proposed that organizations could use technology to gather information about, and to communicate directly with, individuals to form a personal bond. The book, The One to One Future: Building Relationships One Customer at a Time, stated that technology had made it possible and affordable to track individual consumers, to understand each person's individual journey, and to provide contextual offers at the optimal time of need. Six years later, internationally recognized best-selling author Seth Godin published Permission Marketing. He built a logical case for creating incentives for consumers to accept advertising voluntarily.


Create deep learning models with Flowpoints

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For those who like their dessert first: here's the finished model, and here's the colab for this example. A rather empty user-interface should show up on your screen. In the sidebar, click the Library-dropdown, and select TensorFlow. Now the code for our model will use TensorFlow instead of PyTorch. Next, click on the Theme-dropdown and select "orange".


AlphaGalileo Item Display

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In a recent pilot study, researchers from the National University of Singapore (NUS) have shown that a powerful artificial intelligence (AI) platform known as CURATE.AI could potentially be used to customise training regimens for individuals to personalise learning and improve cognitive performance. Using performance data from a given person, CURATE.AI creates an individualised profile that enables cognitive training to be tailored to the individual's learning habits and competencies so as to enhance training effectiveness. Such dynamic AI-guided personalisation overcomes the current limited improvement produced by using traditional training methods which often involve repetitive behavioural exercises. The results of the study provide evidence that the CURATE.AI platform has the potential to enhance learning, and paves the way for promising applications for personalised digital therapy, including the prevention of cognitive decline. The research, led by Professor Dean Ho and Assistant Professor Christopher L. Asplund from the N.1 Institute for Health (N.1) of NUS, which was formerly the Singapore Institute for Neurotechnology (SINAPSE), was published in the journal Advanced Therapeutics on 22 May 2019.


Three Ways To Leverage AI And Keep Pace With The Future Of Digital Marketing

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From personal assistants to legal counsel on parking fines, artificial intelligence (AI) and machine learning (ML) have established their potential as disruptive technology that will alter industries. With each passing day, further discoveries enable AI to become more sophisticated and viable in our world. Naturally, like all things digital, AI has had a profound impact on digital marketing as well. From Google's RankBrain search engine algorithm to Amazon's personalized recommendations, it is powering the world's leading organizations and changing the face of the modern digital marketing landscape. Currently, I work as senior vice president of marketing at CUJO AI, an AI-driven network security and intelligence company.


Swedish Distillery Creates First Whisky Designed By AI

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Would you drink a whisky designed and created by artificial intelligence? This fall, this hypothetical question becomes a reality, as popular award-winning Swedish whisky distillery Mackmyra releases the first ever whisky, a single malt, designed with machine learning. Working in collaboration with Microsoft and Fourkind, a Finnish technology consultancy specializing in AI spearhead projects, the distillery has made the claim that this is the first ever machine-learning designed complex consumer product recipe. I for one, welcome the chance to try a whisky created by our robot overlords. The distillery's machine learning models running off of Microsoft's Azure Cloud Computing platform and AI cognitive services will be fed raw data related to whisky production (including malting, fermentation, distillation, and maturation), Mackmyra's historical recipes, sales numbers, and customer preferences.


Take Your Business Use Cases to the Next Level with AI & ML - insideBIGDATA

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When we think of artificial intelligence (AI) and machine learning (ML), we tend to think of a technology that is new and at the cutting edge. In reality, AI and ML have been around since the 1950s and 1960s. The concept of the technology hasn't changed; what's evolved is the technology that makes AI and ML easier to use and applicable to more industries. The companies that are further along in their innovation journeys, those identified as digerati and digital experimenters, have already mastered the foundational technologies. AI and ML are becoming a tool that smart companies are using to innovate on the foundation they have already put in place.


Google's AI boosts accuracy of lung cancer diagnosis, study shows - STAT

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One of lung cancer's most lethal attributes is its ability to trick radiologists. Some nodules appear threatening but turn out to be false positives. Others escape notice entirely, and then spiral without symptoms into metastatic disease. On Monday, however, Google unveiled an artificial intelligence system that -- in early testing -- demonstrated a remarkable talent for seeing through lung cancer's disguises. A study published in Nature Medicine reported that the algorithm, trained on 42,000 patient CT scans taken during a National Institutes of Health clinical trial, outperformed six radiologists in determining whether patients had cancer.


U.S. Senators propose legislation to fund national AI strategy

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U.S. Senators Rob Portman (R-OH), Martin Heinrich (D-NM), and Brian Schatz (D-HI) today proposed the Artificial Intelligence Initiative Act, legislation to pump $2.2 billion into federal research and development and create a national AI strategy. The $2.2 billion would be doled out over the course of the next 5 years to federal agencies like the Department of Energy, Department of Commerce's National Institute of Standards and Technology (NIST), and others. The legislation would establish a National AI Coordination Office to lead federal AI efforts, require the National Science Foundation (NSF) to study the effects of AI on society and education, and allocate $40 million a year to NIST to create AI evaluation standards. The bill would also include $20 million a year from 2020-2024 to fund the creation of 5 multidisciplinary AI research centers, with one focused solely on K-12 education. Plans to open national AI centers in the bill closely resembles plans from the 20-year AI research program proposed by the Computing Consortium.