AI & Machine Learning

Convolutional neuronal networks are widely used in computer vision tasks. These networks are composed of an input layer, an output layer, and several hidden layers, some of which are convolutional, hence its name. In this post, we will present a specific case that we will follow step by step to understand the basic concepts of this type of networks. Specifically, together with the reader, we will program a convolutional neural network to solve the same MNIST digit recognition problem.

Data Types are an important concept of statistics, which needs to be understood, to correctly apply statistical measurements to your data and therefore to correctly conclude certain assumptions about it. In this post, discover the different data types that are used throughout statistics. Learn the difference between discrete & continuous data and learn what nominal, ordinal interval and ratio measurement scales are.  Know what statistical measurements you can use at which datatype and which are the right visualization methods. This enables you to create a big part of an exploratory analysis on a given dataset

Using Artificial Intelligence (AI), machine translation allows businesses and brands globally to translate content, both externally, for example, on their website or mobile app,  and internally such as on a company email at scale, in a matter of seconds. This means that technology is able to really “listen” to people at a global enterprise level, at a national level and at a hyper-local level – enabling business and marketing communications to become more accurate, relevant and effective.

A neural network is made up of neurons connected to each other; at the same time, each connection of our neural network is associated with a weight that dictates the importance of this relationship in the neuron when multiplied by the input value. Training our neural network, that is, learning the values of our parameters (weights wij and bj biases) is the most genuine part of Deep Learning and we can see this learning process in a neural network as an iterative process of “going and return” by the layers of neurons. 

We’re accustomed to the idea of machines acting like people. We’re even accustomed to the idea of machines thinking in ways that remind us of humans. The first generation of Robots we are familiar so far with were programmed by Humans. Now we are at a new stage of robotics when programmed robots are replaced by Machine Learned Robots. The goal of MLR is to design an efficient algorithm that statistically gives the right answer “almost always”.

By interpreting the model, we can gain a much deeper understanding and address problems like bias, leakage and trust. Interpretability is the degree to which a human can consistently estimate what a model will predict, how well the human can understand and follow the model’s prediction and finally, how well a human can detect when a model has made a mistake. It goes without saying that AI systems must be secure and safeguarded against adversarial attacks. 

Some of you are probably thinking about integrating an AI-based solution into your organization. Well, the good news is that you do not need to be an expert on AI, but you do need to understand the basics such as the importance of data(many articles are available for non-tech people on Medium). Once this is done, you can proceed to automate key tasks and use data to detect patterns and outcomes. Before jumping into the AI bandwagon, please ask yourself these three questions.

We are interacting with AI algorithms, in many cases without even knowing it.Many believe artificial intelligence has much more to offer. Ironically, one of the things that is preventing AI from realizing its full potential is the cloud, one of the main technologies that helped usher AI into the mainstream. The reason we still don’t see AI everywhere is not that the algorithms or technology are not there. The main reason is cloud dependency.

Part 1 of this article discussed a few simple techniques that helped with initial scalability of machine learning… and hopefully with reducing manual ops. Since then, despite a few production hiccups due the lack of high availability, life has been pretty good. However, traffic soon starts to increase, data piles up, more models need to be trained, etc. Technical and business stakes are getting higher, and the current architecture will go underwater soon. This post focuses on scaling training to a large number of machines.

One thing you should do is build a portfolio of your personal machine learning projects. But, how to do that? I’ve seen hundreds of examples of personal projects that ranged from very good to very bad. So in this post, I’ll tell you how.   If I had to summarize the secret to a great ML project in one sentence, it would be: Build a project with an interesting dataset that took obvious effort to collect and make it as visually impactful as possible.

Artificial intelligence has become a part of our day-to-day lives many times in a single day and most of us aren’t even aware of this.  While there is a buzz on various emerging and hot technology platforms AI has arrived in 2019 where it will be leveraged in real-time within the enterprise and create true ROI as a result. It is true a few on the list like autonomous things, smart spaces, IoT, and even the empowered edge are used in some capacity today but true industry adoption of AI-centered tech initiatives are becoming mainstream in 2019.