AI & Machine Learning

The concept and science behind artificial neural networks have existed for many decades. But it has only been in the past few years that the promises of neural networks have turned to reality and helped the AI industry emerge from an extended winter. While neural networks have helped the AI take great leaps, they are also often misunderstood. Here’s everything you need to know about neural networks. Artificial neural networks are inspired from their biological counterparts.

Adopting robotic process automation in HR is vital for organizations who hope to retain their employees. When HR no longer spends all of its time on files, reports, and so on, it actually has the ability to reach out to employees, provide ongoing training programs for employees who need and want them, pay attention to those employees who are truly making an effort in their positions, and boost the overall morale of the organization. Here are six ways in which HR automation can play an important role in your organization.

AI will make superhuman capabilities available that we will harness to take our understanding of the universe around us and the evolution of human society to the next level. Businesses that utilise AI will outperform others because they will be able to accomplish things that those without AI cannot. At a strategic level, businesses need AI to help make sense of this new world where the amount of data being generated is overwhelming and we don’t have the capabilities to process it manually.

What makes artificial intelligence different than earlier technologies is that the system learns as data is fed into it. What’s most important for business leaders to know is that AI is no longer some kind of “gee whiz” technology, but increasingly key to competing effectively in today’s marketplace. As the technology continues to evolve from complex integrated systems to a modular ecosystem, even small and medium enterprises will find that they need to adopt these capabilities or fall behind.

There is an intensively competitive market for artificial intelligence and machine learning specialists.  Many companies first attempt to hire Ph.D.-level data scientists with expertise in AI algorithms and feature engineering. Some analysts have even equated “AI talent” with such researchers. However, AI talent goes far beyond machine learning Ph.D’s.  Equally important and less understood are the set of talent issues emerging around AI product development and engineering. Most firms have not filled these roles, and their AI projects are suffering as a result.

The fabric in the data fabric is built from a knowledge-graph, to create a knowledge-graph you need semantics and ontologies to find a useful way of linking your data that uniquely identifies and connects data with common business terms. The knowledge graph consists in integrated collections of data and information that also contains huge numbers of links between different data. The data here can represent concepts, objects, things, people and actually whatever you have in mind. The graph fills in the relationships, the connections between the concepts.

Now we are facing this new character in the stage of evolution that is Artificial Intelligence. Where do we have to put this card in the puzzle of human history? Artificial Intelligence is not a tool at all. It’s more like a synthetic partner in our lives. It’s something able to use cognitive capabilities in order to perform certain tasks faster than we can. So it’s not a tool; it’s an artificial extension of our brain. AI, with its role of boosting human capabilities, can actually be the key for salvation from self-extinction. And this has nothing to share with technology.

Does your company need a chatbot? Do you have money to invest in good AI? There is nothing that can annoy customers more than dealing with a bot who can do literally nothing besides direct them to a person who can. We’ve all had experiences of dealing with chatbots that were efficient and smart—and those that made us want to smash our computer screen. Does your company have money to invest in quality AI? If not, it might be worth skipping the chatbot option.

It’s possible to run deep learning algorithms on the data fabric by deploying graph neural nets models for the graph data we have, if we can connect the knowledge-graph with the Spektral (or other) library. Besides standard graph inference tasks such as node or graph classification, graph-based deep learning methods have also been applied to a wide range of disciplines, such as modeling social influence, recommendation systems, chemistry, physics, disease or drug prediction, natural language processing (NLP), computer vision, traffic forecasting, program induction and solving graph-based NP problems.

In this article, you will learn that a proper sample can be statistically significant to represent the whole population. This may help us in machine learning because a small dataset can make us train models more quickly than a larger one, carrying the same amount of information. However, everything is strongly related to the significance level we choose. For certain kinds of problems, it can be useful to raise the confidence level or discard those variables that don’t show a suitable p-value.

The technology support with AutoML and deep-learning is there now, where this is a 24hr curiosity for an engineer instead of a 7 figure high-risk 12–16-month science project. As these types of problems become low hanging fruit, we will see more job disruption. At first, the jobs will be augmented/validated, and then eventually they will be automated (except appraisals that are predicted to have issues e.g. on a ski resort, etc…). In the end, there is no reason why this wouldn’t be completely automated.