"The field of Machine Learning seeks to answer these questions: How can we build computer systems that automatically improve with experience, and what are the fundamental laws that govern all learning processes?"
– from The Discipline of Machine Learning by Tom Mitchell. CMU-ML-06-108, 2006.
With increased regulatory pressures, data silo proliferation and cognitive drain on analysts, AI-powered platforms become a key enabler to extract insights from data. Today, we announced that Sinequa is featured in a new IDC Technology Spotlight report: Financial Services Organizations: Extracting Powerful Insights with AI-Powered Platforms. The report, written by Steven D'Alfonso, research director, IDC Financial Insights, and David Schubmehl, research director, Cognitive/AI Systems, highlights the importance of AI-powered platforms in their ability to extract insights from data as well as the need for financial services organizations (FSOs) to improve their capabilities to derive insights from the data they possess. According to the report, collecting and maintaining increased amounts of data related to their clients and portfolios can provide major opportunities to improve the customer experience and increase revenue while reducing risk. But at the same time, too much data can be a cognitive drain on analysts and knowledge workers.
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In this project, we are going to use a pre-trained VGG16 model which looks as follows. Keep in mind that we are not going to use fully connected (blue) and softmax layers (yellow). They act as a classifier which we don't need here. We are going to use only feature extractors i.e convolutional (black) and max pooling (red) layers. Let's take a look at how specific features look at the selected VGG16 layers trained on ImageNet dataset.
The Holy Grail for data scientists is the ability to obtain labeled data sets for the purpose of training a supervised machine learning algorithm. An algorithm's ability to "learn" is based on training it using a labeled training set -- having known response variable values that correspond to a number of predictor variable values. There are a number of common and maybe not-so-common methods for labeling a data set. In this article, we'll run down a short list of such methods and then you can choose the best for your specific circumstances. Sometimes, labeled datasets are readily available as a byproduct of on-going business operations.
In pursuit of automation-driven efficiencies, the rapidly evolving artificial intelligence (AI) tools and techniques (such as neural networks, machine-learning, predictive analytics, speech recognition, natural-language processing and more) are now routinely used across nations: its governments, industries, organizations and academia (NGIOA) for navigation, translation, behavior modeling, robotic control, risk management, security, decision making and many other applications. As AI is becoming democratized, these evolving intelligent algorithms are now rapidly becoming prevalent in most, if not all, aspects of human and machine decision-making. While Decision Utilities like intelligent algorithms have been in use for many years, there are rising concerns about the general lack of algorithmic understanding, usage practices, the rapidly penetrating bias in automated decisions, and the lack of transparency and accountability. As a result, ensuring integrity, transparency and trust in algorithmic decision-making is becoming a complex challenge for the creators of algorithms with huge implications for the future of society. Irrespective of cyberspace, geospace or space (CGS), since technology revolutions are driven not just by accidental discovery but also by societal needs, the question we all individually and collectively need to first and foremost evaluate is whether there really is a need for decision-making algorithms--and if yes, where and why.
The Technical University of Munich (Technische Universität München TUM) has launched the Munich Center for Machine Learning (MCML). MCML is funded by Germany's Federal Ministry of Research. Artificial intelligence and machine learning are crucial technologies for today's and tomorrow's digital economy. TUM says that MCML will be connecting key areas of expertise from computer science, data science, and statistics. AI or artificial intelligence includes software technologies that make machines and other devices think like humans.
In most of the practical use cases, data scientists are satisfied by machine learning models that simply make predictions. Given unseen observations, a model performs the prediction of certain outcome . The performance of such a model is usually assessed by comparing the predicted value with the ground truth, whenever available. There is however, another measure that might be of interest: uncertainty. How uncertain is a model in predicting a particular sample?
Two out of three Indians count on agriculture as their primary livelihood yet the sector contributes just one-sixth of the country's national income. For long, policy mandarins and economists have bemoaned this skew and the urgent need to boost farm productivity but little has moved the needle in Indian farming in recent decades except in pockets. Like in every other sector, artificial intelligence and machine learning techniques, combined with on the ground automated sensing using internet of things devices, is being deployed in agriculture, too. The start-ups are paving the way for tech to to help the Indian farmer to tackle one the biggest challenges before farming: uncertainty. "Uncertainty is the poison in the blood of Indian farming. Farming is difficult and stressful, driving farmers out of farming and sometimes even to suicide. Technology companies in the agri-tech space are helping to make farming into a more stable and desirable industry," says Kahn, a Harvard MBA with over a decade in the Indian agriculture space Today a handful of startups working on AI-backed solutions are paving the way ahead for bringing in the tech to help the Indian farmer to tackle one of the biggest challenges: uncertainty.
As AI and machine learning have infused themselves over the last half decade into nearly every corner of our lives, there has been a growing interest in how the biases of these models may be silently impacting society. Much of this focus has been on the issues of biased training data and a homogeneous workforce that lacks sufficient diversity of experience to recognize bias. However, lost in this conversation is the far bigger driving force: the lack of economic incentive to minimize bias in the technologies that increasingly power our lives. The digital world is an incredibly biased place. Geographically, linguistically, demographically, economically and culturally, the technological revolution has skewed heavily towards a small number of very economically privileged slices of society.
The purpose of this project is to introduce a shortcut to developers and researcher for finding useful resources about Deep Learning. There are different motivations for this open source project. There other similar repositories similar to this repository and are very comprehensive and useful and to be honest they made me ponder if there is a necessity for this repository! The point of this repository is that the resources are being targeted. The organization of the resources is such that the user can easily find the things he/she is looking for.