What Are Neural Networks?
A neural network is a network or circuit of neurons, or in a modern sense, an artificial neural network, composed of artificial neurons or nodes. Thus a neural network is an artificial neural network, for solving artificial intelligence (AI) problems.
It is a network of artificial neurons programmed in software. It tries to simulate the human brain, so it has many layers of “neurons” just like the neurons in our brain. The first layer of neurons will receive inputs like images, video, sound, text, etc. This input data goes through all the layers, as the output of one layer is fed into the next layer.
How Artificial Neural Networks Function
ANNs are statistical models designed to adapt and self-program by using learning algorithms in order to understand and sort out concepts, images, and photographs.. The input layer is analogous to the dendrites in the human brain’s neural network. The hidden layer is comparable to the cell body and sits between the input layer and output layer (which is akin to the synaptic outputs in the brain). The hidden layer is where artificial neurons take in a set of inputs based on synaptic weight, which is the amplitude or strength of a connection between nodes. These weighted inputs generate an output through a transfer function to the output layer.
Neurons are the decision makers. Each neuron has one or more inputs and a single output called an activation function. This output can be used as an input to one or more neurons or as an output for the network as a whole. Some inputs are more important than others and so are weighted accordingly. Neurons themselves will “fire” or change their outputs based on these weighted inputs.
Why Do We Use Neural Networks?
Neural networks’ human-like attributes and ability to complete tasks in infinite permutations and combinations make them uniquely suited to today’s big data-based applications. Because neural networks also have the unique capacity to make sense of ambiguous, contradictory, or incomplete data, they are able to use controlled processes when no exact models are available.
“OKRA’s platform helps healthcare stakeholders and biopharma make better, evidence-based decisions in real-time, and it answers both treatment-related and brand questions for different markets,” emphasizes Loubna Bouarfa, CEO and Founder of Okra Technologies and an appointee to the European Commission’s High-Level Expert Group on AI. “In foster care, we apply neural networks and AI to match children with foster caregivers who will provide maximum stability. We also apply the technologies to offer real-time decision support to social caregivers and the foster family in order to benefit children,” she continues.
Like many AI companies, OKRA leverages its technology to make predictions using multiple, big data sources, including CRM, medical records, and consumer, sales, and brand measurements. Then, Bouarfa explains, “We use state-of-the-art machine learning algorithms, such as deep neural networks, ensemble learning, topic recognition, and a wide range of non-parametric models for predictive insights that improve human lives.”
According to the World Cancer Research Fund, melanoma is the 19th most common cancer worldwide. One in five people on the planet develop skin cancer, and early detection is essential to prevent skin cancer-related death. There’s an app for that: a phone app to perform photo self-checks using a smartphone.
Keeping track of data in any work environment and making good use of it can be a challenge. Rob May is CEO and Co-Founder of Talla, a company that builds “digital workers” that assist employees with daily tasks around information retrieval, access, and upkeep. “We give businesses the ability to adopt AI in a meaningful way and start realizing immediate improvements to employee productivity and knowledge sharing across the organization,” May explains. “If a company stores their product documentation in Talla, its sales reps can instantly access that information while on sales calls. This ability to immediately and easily access accurate, verified, up-to-date information has a direct impact on revenue. By having information delivered to employees when they need it, the process of onboarding and training new reps becomes better, faster, and less expensive.”
Talla’s neural network technology draws on different learning approaches. “We use semantic matching, neural machine translation, active learning, and topic modeling to learn what’s relevant and important to your organization, and we deliver a better experience over time,” he says. May differentiates Talla’s take on AI: “This technology has lifted the hood on AI, allowing users to train knowledge-based content with advanced AI techniques. Talla gives users the power to make their information more discoverable, actionable, and relevant to employees. Content creators can train Talla to identify similar content, answer questions, and identify knowledge gaps.”
While proving the concept in laboratories and games contests, it was also quietly rolled out across many of Google’s services.
It’s first practical use was in image recognition, where it was put to work sorting through the millions of images uploaded to the parts of the internet which Google indexes. It does this in order to more accurately classify them, and in turn give users more accurate search results. Google’s latest breakthrough involving deep learning in the field of image analytics is in image enhancement. This involves restoring or filling in detail missing from images, by extrapolating for data that is present, as well as using what it knows about other similar images.
Another platform, Google Cloud Video Intelligence focuses on opening up video analytics to new audiences. Video stored on Google’s servers can be segmented and analyzed for content and context, allowing automated summaries to be generated, or even security alerts if the AI thinks something suspicious is going on.
Language processing is another area of their services where the tech has been implemented. It’s Google Assistant speech recognition AI uses deep neural networks to learn how to better understand spoken commands and questions. Techniques developed by Google Brain were rolled into this project. More recently, Google’s translation service was also put under the umbrella of Google Brain. The system was rewritten to run on a new platform called Google Neural Machine Translation, moving everything to a deep learning environment.
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