Hybrid intelligence is on the way, and so are new opportunities for Artificial Intelligence (AI) applications. Hybrid intelligence merges elements of artificial and human intelligence to tackle problems that have not only been deemed impossible by traditional AI, but also those once thought exclusive to humans. Hybrid intelligence looks to effectively expand the reach of AI into unique and exciting fields such as strategic game theory, conversational agents (chatbots), computer vision, and natural language processing.
The Future of AI:
The future of AI is not an easy one to predict. The way we use artificial intelligence (AI) today is very different from how it will be used in the next decade or so. We have seen rapid advancements in AI technologies over the past few years, but we are still far from achieving a truly intelligent machine that can be considered to be like us.
Deep learning (DL)
Deep learning (DL) is a subset of machine learning that has been applied to many applications, including natural language processing, speech recognition, image recognition and predictive analytics. DL is often used when there is a massive amount of data and the task at hand involves complex nonlinear relationships among many input variables.
DL has generated a lot of excitement over the past few years due to its success in developing accurate classifiers for image recognition, speech recognition, natural language processing and other tasks. The most common form of DL is deep neural networks (DNN), which are inspired by the human brain’s structure and function. In this article, I will provide an overview of DL and discuss some areas where it can be applied to solve real-world problems.
Artificial neural networks (ANNs)
Are a form of AI that is inspired by the biological neural networks that make up animal brains. ANNs are made up of artificial neurons, which are connected together to form a network. Each neuron receives input signals, performs a computation based on those inputs and then sends outputs to other neurons in the network. In this way, an ANN can be used to model and analyze complex systems such as images, text, audio and video.
ANNs have been around since the 1960s but their use has accelerated rapidly since the early 2000s. They have been used to create intelligent systems for many applications including speech recognition, image recognition, machine translation and automated control systems such as self-driving cars.
DL algorithms
In DL algorithms, there are two types of layers: convolutional layers and fully connected layers. Convolutional layers contain neurons that are organized into modules called kernels which have weights associated with them. These weights are learned by feeding back information from a previous layer into the current layer (backpropagation). Fully connected layers allow for arbitrary projections onto high-dimensional data sets, making them ideal for tasks such as classification or regression problems where we want our model to predict a continuous value instead of discrete categories
Lot of data and computing power
AI can help us understand the world and our place in it, but it needs to be trained. To train a deep learning algorithm you need a lot of data and computing power. That’s why cloud computing has been so important for deep learning.
Cloud providers have been offering services that allow anyone to upload their data and train an AI model on it. The most famous example is Google’s TensorFlow (TF). TF has become the de facto standard for training deep learning models because of its ease of use, flexibility and support for multiple platforms like Android and iOS.
As we’ve seen with other technologies such as virtualization or containers, cloud providers are adding more features to their services that make it even easier to build AI applications. One example is AWS DeepLens, a camera that allows you to train an image recognition model using computer vision technology from Amazon Rekognition. This product was announced at AWS re:Invent 2018 along with other things like new GPUs and machine learning tools such as SageMaker which allows developers to build, train, deploy and monitor machine learning models without having to worry about infrastructure management or scaling issues related to deep learning models which can take up hundreds of gigabytes of memory per instance.
Deep learning (DL)
Plays a key role in our lives today, helping us with tasks such as translating languages, driving cars, mapping our world and helping doctors identify diseases.
It’s important to realize that AI, machine learning and deep learning are all useful for different purposes. They’re not competing with one another, but work together toward reaching the singular goal of understanding data. At the same time, limitations still exist on each side, and understanding where they lie is vital to realizing their full potential and creating a hybrid intelligence. This is what Fuel6 aims to achieve, and we will continue to provide developers the tools they need to create this new kind of intelligence.
