Category Archives: Technology

Deep learning

Deep learning (deep machine learning, or deep structured learning, or hierarchical learning, or sometimes DL) is a branch ofmachine learning based on a set of algorithms that attempt to model high-level abstractions in data by using multiple processing layers with complex structures or otherwise, composed of multiple non-linear transformations.[1][2][3][4][5]

Deep learning is part of a broader family of machine learning methods based on learning representations of data. An observation (e.g., an image) can be represented in many ways such as a vector of intensity values per pixel, or in a more abstract way as a set of edges, regions of particular shape, etc.. Some representations make it easier to learn tasks (e.g., face recognition or facial expression recognition[6]) from examples. One of the promises of deep learning is replacing handcrafted features with efficient algorithms for unsupervised or semi-supervised feature learning and hierarchical feature extraction.[7]

Research in this area attempts to make better representations and create models to learn these representations from large-scale unlabeled data. Some of the representations are inspired by advances in neuroscience and are loosely based on interpretation of information processing and communication patterns in a nervous system, such as neural coding which attempts to define a relationship between various stimuli and associated neuronal responses in the brain.[8]

Various deep learning architectures such as deep neural networks, convolutional deep neural networks, deep belief networks andrecurrent neural networks have been applied to fields like computer vision, automatic speech recognition, natural language processing, audio recognition and bioinformatics where they have been shown to produce state-of-the-art results on various tasks.

Alternatively, deep learning has been characterized as a buzzword, or a rebranding of neural networks.[9][10]

Artificial Intelligence – Depth First Search(DFS)

Depth-first search (DFS) is an algorithm for traversing or searching tree or graph data structures. One starts at the root (selecting some arbitrary node as the root in the case of a graph) and explores as far as possible along each branch before backtracking.

A version of depth-first search was investigated in the 19th century by French mathematician Charles Pierre Trémaux[1] as a strategy for solving mazes.

Algorithmic Thoughts - Artificial Intelligence | Machine Learning | Neuroscience | Computer Vision

Okay! So this is my first blog post!

I will start by talking about the most basic solution to search problems, which are an integral part of artificial intelligence.

What the hell are search problems?

In simple language, search problems consist of a graph, a starting node and a goal(also a node). Our aim while solving a search problem is to get a path from the starting node to the goal.

Consider the diagram below, we want to get to the node G starting from the node S.

Which path will we get on solving the search problem? How do we get the path? This is where algorithms come into picture and answer all our questions! We will look at Depth First Search which can be seen as a brute force method of solving a search problem.

Creating the search tree

So how do we simplify this problem? If we…

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Charles Joseph Minard

Charles Joseph Minard (French: [minaʁ]; 27 March 1781 – 24 October 1870) was a French civil engineer recognized for his significant contribution in the field of information graphics in civil engineering and statistics. Minard was among other things noted for his representation of numerical data on geographic maps.

Systems thinking

Systems thinking involves the use of various techniques to study systems of many kinds. In nature, examples of the objects of systems thinking include ecosystems – in which various elements (such as air, water, movement, plants, and animals) interact. In organizations, systems consist of people, structures, and processes that operate together to make an organization “healthy” or “unhealthy”. Systems Engineering is the discipline that utilizes systems thinking to design, build, operate and maintain complex engineered systems.

SCHOOLS OF THOUGHT

The Circular Economy concept has deep-rooted origins and cannot be traced back to one single date or author. The generic concept has been refined and developed by the following schools of thought:

Regenerative design (representative: John T. Lyle).
Performance economy (representative: Walter Stahel).
Cradle to Cradle (representatives: Michael Braungart and William McDonough)
Blue Economy (representative: Gunter Pauli)
Permaculture (representatives: Bill Millison and David Holmgren)
Biomimicry (representative: Janine Benyus)
Industrial Ecology (this is more than a school of thought, it is an academic discipline that has been taught from the 1990s)

220px-Waste_hierarchy.svg

The evaluation of processes that protect the environment alongside resource and energy consumption to most favourable to least favourable actions.[1] The hierarchy establishes preferred program priorities based on sustainability.[1] To be sustainable, waste management cannot be solved only with technical end-of-pipe solutions and an integrated approach is necessary.[2]

The waste management hierarchy indicates an order of preference for action to reduce and manage waste, and is usually presented diagrammatically in the form of a pyramid.[3] The hierarchy captures the progression of a material or product through successive stages of waste management, and represents the latter part of the life-cycle for each product.[3]

The aim of the waste hierarchy is to extract the maximum practical benefits from products and to generate the minimum amount of waste. The proper application of the waste hierarchy can have several benefits. It can help prevent emissions of greenhouse gases, reduces pollutants, save energy, conserves resources, create jobs and stimulate the development of green technologies.[4]

All products and services have environmental impacts, from the extraction of raw materials for production to manufacture, distribution, use and disposal. Following the waste hierarchy will generally lead to the most resource-efficient and environmentally sound choice but in some cases refining decisions within the hierarchy or departing from it can lead to better environmental outcomes.[5]

Life cycle thinking and assessment can be used to support decision-making in the area of waste management and to identify the best environmental options. It can help policy makers understand the benefits and trade-offs they have to face when making decisions on waste management strategies. Life-cycle assessment provides an approach to ensure that the best outcome for the environment can be identified and put in place.[5] It involves looking at all stages of a product’s life to find where improvements can be made to reduce environmental impacts and improve the use or reuse of resources.[5] A key goal is to avoid actions that shift negative impacts from one stage to another. Life cycle thinking can be applied to the five stages of the waste management hierarchy.

For example, life-cycle analysis has shown that it is often better for the environment to replace an old washing machine, despite the waste generated, than to continue to use an older machine which is less energy-efficient. This is because a washing machine’s greatest environmental impact is during its use phase. Buying an energy-efficient machine and using low- temperature detergent reduce environmental impacts.[5]

The European Union Waste Framework Directive has introduced the concept of life-cycle thinking into waste policies.[5] This duality approach gives a broader view of all environmental aspects and ensures any action has an overall benefit compared to other options. The actions to deal with waste along the hierarchy should be compatible with other environmental initiatives.

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Webcam Collections

Explore.org: A selection of high-quality live cameras

Bird webcams from Avibase

Bird webcams from the RSPB

WildEarth: a selection of wildlife and safari cameras. Backup site

Animal Planet Live: a collection of live cameras of captive animals 

Safari cameras from Africam.com

EarthCam collection

Mangolink: Collection organised by animal groups

World Land Trust cameras

Zoos and Aquariums

Edinburgh zoo (4)

Vancouver Aquarium (5)

Monterey Bay Aquarium (8)

San Diego zoo (4)

Houston zoo (9)

SeaWorld and Busch Gardens

Chester zoo (4)

Woodland Park zoo (3)

Dublin zoo (4)