AI & Machine Learning

Machine learning is not new, but there is a new paradigm to do it, and maybe it’s the future of the field. Inside of the data fabric, we have new concepts like ontology, semantics, layers, knowledge-graph, etc; but all of those can improve the way we think about and do machine learning. In this paradigm, we discover hidden insights in the data fabric by using algorithms that are able to find those insights without being specifically programmed for that.

Machine learning (ML) has a surprise, too. One of the biggest misconceptions about ML deployment within organizations is comprehending the difficulty and the value. Integrating ML into your business workflows can be broken down into five activities. Optimizing an ML algorithm takes much less relative effort, but collecting data, building infrastructure, and integration each take much more work. The differences between expectations and reality are profound. Not every problem has an ML-powered solution, but many do, and even those that do not will benefit from this journey.

AI-based platforms are quantifying and getting value from you by gathering your contacts, your ideas, your skills, your free time, your shopping habits and even your very genes. The tradeoffs here are not so clear. Companies win big using your data. So, on top of worrying about whether or not AI will steal your job, you must also worry about AI stealing your time, information, ideas, relationships, and more. AI is breaking us down into our piece parts, and using them up.

We must recognize that artificial intelligence is a fluid term, whose definition changes with time. Therefore, we need to define what is the current context of AI. Can we consider anything that uses a machine learning algorithm as artificial intelligence? Should AI be limited to systems that employ neural networks and deep learning algorithms? Or maybe we should evaluate AI based on the cognitive behavior a system manifests, regardless of the underlying technology? If yes, what is the minimum level of cognitive accomplishment for a system to be considered AI?

It is important to know which activation functions to use within your neural network. Be aware of the fact that you can use different activation functions at different layers. Most often the sigmoid function is used but often other functions can work much better. In this post you will learn the most common Activation Functions within Deep Learning and when you should use them. You will also discover why you mostly need to use non-linear activation functions.

Generally, AR and VR are utilized in industry sectors such as finance, healthcare, construction, and retail. But, can augmented reality, along with robotics and virtual reality work together? Yes! Together, augmented reality, robotics, and virtual reality can be the ‘Three Amigos.’ VR and AR can offer an immersive medium to operate robots. With the help of low-latency networks, people can utilize robots remotely using intuitive AR and VR controls. Augmented reality, robotics, and virtual reality can be used together in various industry sectors such as manufacturing, healthcare, and private space research. 

This blog post is an overview of quantum machine learning written by the author of the paper Bayesian deep learning on a quantum computer. In it, we explore the application of machine learning in the quantum computing space. The authors of this paper hope that the results of the experiment help influence the future development of

This article is one in a series related to the 10th Global Peter Drucker Forum, with the theme management. the human dimension, that took place on November 29 & 30, 2018 in Vienna, Austria #GPDF18 Lately, we have received quite a number of requests asking us to explain further why artificial intelligence (AI) and robots are unlikely to

In a recent survey by O’Reilly, respondents reported “company culture” to be the biggest bottleneck for artificial intelligence adoption in their organizations. In spite of the buzz around AI, most people in organizations don’t yet recognize the need for AI. In my view, executives face three common obstacles in every organization: Where do you get started with AI? Will it

New technologies don’t just change the world, they change our language too. The automobile era gave us over a thousand new words and phrases such as limousine, drive through, hot rod, tailgate, and muscle car. Computers gave us chips, CPUs, beta testers, operating systems, programs, and bugs. The Internet has already contributed hundreds of new

The potential threat of technological unemployment is one of the most hotly debated economic issues of our times: in boardrooms and trade union offices but also increasingly amongst policy-makers. The catch-all term ‘digital’ may have been added to numerous political concepts in recent years but beyond such branding there has been very little debate of