AI & Machine Learning

What does the human expert lack? Why can the world’s best experts be beaten using subject naive methods? A human can’t weigh historical observations fairly, they put too much weight on experience A and not enough on experience B. A human is also limited to their own personal experience and wisdom, where a computer can learn from more data than a human can see in a lifetime (medical imaging being a great one). Lastly, a computer can overtake a human expert’s ability to experiment, do feature discovery, and validate new ideas. 

Some steps are hard to take on your own. Schools aren’t good at teaching data prep, ML devops, or networking. Most people learn those things on the job or from a mentor if they’re lucky. Many people never learn them at all. But how do you bridge that gap in the general case? How do you get a job without experience when you need a job to get experience? So to help everyone at the same time, I’ve put together a progression that you can follow from any starting point to actually become a machine learning engineer.

John McCarthy coined the term Artificial Intelligence in the 1950s, being one of the founding fathers of Artificial Intelligence along with Marvin Minsky. Also in 1958, Frank Rosenblatt built a prototype neuronal network, which he called the Perceptron. In addition, the key ideas of the Deep Learning neural networks for computer vision were already known in 1989; also the fundamental algorithms of Deep Learning for time series such as LSTM were already developed in 1997, to give some examples. So, why now this Artificial Intelligence boom?

AIOps is an emerging technology that combines the usage of artificial intelligence with operations to help solve critical issues that can bring your business to its knees. AIOps solutions help organizations cut through the alerting noise and gain critical control by proactively managing the health and performance of their enterprise IT services. By applying data science and computational techniques, AIOps tools can accurately predict a range of incidents across infrastructure, common IT management tools, and enterprise processes – and resolve them autonomously. 

In order to evolve certain levels of business process automation, the software robot, or bot, was formed. This was a simplistic technology aimed at delivering automation. However, over the next 10 years, this resulted in companies having all these automated processes that weren’t intelligent and that couldn’t evolve themselves. The next evolution required to make BPA work better was intelligent bots, which is where we are today, with robotic process automation (RPA).

Creating an AI Project always involves answering the same questions: What is the value you’re adding? What data do you need? Who are the customers? What costs and revenue are expected? This post is part of an ongoing series aiming to educate Data Scientists in the area of customer-centric thinking and business acumen. We’re encouraging Data Scientists to get rid of the “Let’s implement this paper and see what happens”-attitude towards a “What value can we generate”-attitude. We’re entering the decade of AI implementation and need champions to productionalize Machine Learning models.

Apache Spark is now becoming the big-data platform of choice for enterprises. It is a powerful open source engine that provides real-time stream processing, interactive processing, graph processing, in-memory processing as well as batch processing with very fast speed, ease of use and standard interface. Sooner or later, your company or your clients will be using Spark to develop sophisticated models that would enable you to discover new opportunities or avoid risk. Spark is not hard to learn, if you already know Python and SQL. learn how to build a binary classification application using PySpark and MLlib Pipelines API. 

Recently, Information Age spoke to Falon Fatemi, founder of Node and she has a radical prediction about the future of AI, unsupervised deep learning is coming, she says. Right now, data scientists and humans are trying to create rules for what we can comprehend and then articulate to a machine, enabling machine learning. But in

Robotic Process Automation is rapidly gaining popularity in the current era. It has become one of the most intensive technologies in the digital world. With the adoption by various industries,  RPA has experienced robust growth. Thus, 2019 is coming up with the latest RPA trends which we’re going to benefit businesses, enterprises, market, and individuals as

The reality is that no matter how advanced it appears; artificial intelligence will always need to be managed to some degree. As we enter an age where intelligent machines can more efficiently do the jobs of many humans, businesses face a key challenge – how to prepare their workplace, and just as importantly their workforce,

Today’s consumers have relentless expectations. They expect a personalized one-to-one experience, but many brands struggle with how to bring all their disparate data sources together to engage their customers. Artificial intelligence (AI) can bring companies closer to truly understanding their customers so they can deliver personalized, contextual, and timely experiences. Data is the foundation of AI.