AI & Machine Learning

Deep learning is a technique that, as many other AI related models, is inspired by our natural brain. In particular, Neural Networks, which are the underlying architecture of Deep Learning, are loosely analogous to biological neurons, albeit greatly simplified, and the connections between nodes can be thought of as in some way reflecting connections between neurons. Thanks to a growing availability of computing processing power, neural networks have increased the number of hidden layers they can handle – thus the phrasing “deep” to describe the development of large artificial neural networks capable of processing an incredible amount of information.

Let’s focus on something really scary: over half of enterprise AI projects fail. They’re strategic, often board-driven, expensive and highly visible, and yet most of them flop. AI initiatives that never go into production cost people their jobs and reputations. When something goes wrong after the model is deployed, sometimes there are nasty headlines and the need for crisis management.AI projects fail for many reasons but these common data training mistakes can significantly improve the odds of a project’s success when avoided or corrected.

While the data science and AI disciplines go through exciting and exhilarating advances, it’s important to keep the user’s expectations and experiences in perspective. This is very critical since a sizeable segment of target AI users are fast getting alienated with deepening disconnect and distrust. What’s needed is an acknowledgment of this divide and a conscious effort to address it by adapting the above visual framework along with the constituent 4 key aspects, while implementing machine learning solutions.

AI related technologies are set to transform all industries in a massive way. In fact, most stakeholders believe that AI technologies will have almost the same landscape changing effects that electricity had on the entire human civilization over 100 years ago. The integration of AI into the healthcare sector is just one of the ways we’re going to feel this effect in just a few short years. However, since the healthcare and medical industry is a unique one, AI integration comes with its own set of challenges and opportunities. These will be the focus of this article.

As AI becomes more deeply embedded in the next generation of software, developers and testers will need to incorporate AI technologies to ensure quality. While it may be a frightening prospect to imagine how a program could train itself to test your apps, it is as inevitable as speech recognition and natural language processing appeared to be a few years ago. AI in software testing is already being applied in a variety of ways. Here are three areas in which AI is making the most immediate impact.

Machine Learning and AI are used interchangeably. Usually both terms are used to mean supervised learning. A big part of the confusion is that – depending on who you talk to – Machine Learning and AI mean different things to different users. Machine Learning is the field of Artificial Intelligence concerned with learning from data on its own. Especially in business contexts, you can use both terms to refer to machines that learn from data on their own.

In a consumer centric market, it is becoming increasingly important for businesses to keep up with their changing needs. It could be a market trend or a simple drift in the kind of solutions that the consumers are looking for. That’s where predictive analytics comes into play.

How AI works, what you can do with it, and how to get started – Almost every business application of AI today is about learning to produce certain outputs from certain inputs. AI is powerful because it turns data into insights. But AI is less efficient at learning than people are, yes, way less efficient, so it needs a lot of data in order to learn. If you have lots of data, you should think about AI

Deep learning continues to be the hottest thing in data science. Deep learning frameworks are changing rapidly. Just five years ago, none of the leaders other than Theano were even around. I wanted to find evidence for which frameworks merit attention, so I developed this power ranking. I used 11 data sources across 7 distinct categories to gauge framework usage, interest, and popularity. Without further ado, here are the Deep Learning Framework Power Scores

AI-based products are designed to do a great number of things today: solve complex problems associated with care and claims in a fraction of the time, automate operations and improve efficiency, and enable greater, more personalized customer service — just to name a few potential benefits. Every solution a vendor tries to sell you can sound compelling on the surface. But literally, the million-dollar question is whether there is real, tangible value for your organization.

What’s different about machine learning projects? How do you reduce risks and build a good solution quickly? In standard software development, you simply answer the question: What do you want to implement? And then you, well, implement. But in machine learning projects, you first need to explore what’s possible – with the data you have. So the first question is: What can you implement? Here’s what we learned works to keep machine learning project on track from start to finish.