AI & Machine Learning

Bias in the training data surely plays a role, but I don’t think that it is the primary explanation for the bias. The usual explanation is that the systems are trained on the “wrong” data and merely perpetuate the biases of the past.  If they were trained on unbiased data, the explanation goes, they would achieve less biased results. It appears that the bias comes substantially from how we approach the notion of fairness itself.  We assess fairness as if it were some property that should emerge automatically, rather than a process that must be designed in. 

AI is already being deployed in a range of arenas, from digital assistants and self-driving cars to predictive analytics software providing early detection of diseases or recommending consumer goods based on shopping habits. More executives view adoption of AI and other emerging technologies as a top operational challenge rather than an opportunity. This suggests a consensus among global executives that these technologies are important, but that it is not yet clear whether this will help or hurt their business in the short term.

General intelligence is not simple, or well understood. Whatever the challenges of artificial general intelligence, the chances of us actually achieving it will be greatly improved if we have a better idea of just what we are trying to create. So far, that means better understanding human intelligence. We may not need human-like intelligence for solving specific problems, but it looks like it could be critical for developing artificial general intelligence.

 Companies often expect that AI models once implemented will stay smart and work magically, ever after. Business leaders must adopt a long-term outlook and plan for model upkeep and well-being, to avoid data science disillusionment.  Business leaders often find it disappointing to learn that, a machine learning model, after consuming precious time and dollars of investment still needs humans for routine maintenance. Let’s see why this is the reality today and how companies can plan for it.

If you are a product manager and want to build anything with machine learning, here’s a list of the 4 most important things to keep in mind. They are prioritising engineering over data science, reducing risks by going lean, not getting distracted by the algorithm, and sharing all the business requirements with your developers. Once the engineering team starts building, they have to make a lot of choices. The better they know your priorities, the more they can make the right decisions. 

Comparison between artificial intelligence (AI) and human intelligence has been a heated debate ever since Turing envisioned thinking machines. Humans and machines are different and it is not possible to say that the correspondence is 100%. However, it is evident that there is a strong relationship. Let’s utilize this valuable similarity between human and machine learning to understand and overcome challenges in the way we educate our children. Is it possible for machines to think like humans? How far are we from intelligent machines taking the world? Is the artificial neural network inspired by the brain?

Want to learn more about AI and how to use it in your business? When you give your entire team a basic understanding of AI they can find innovative ways to use it. Many think of machine learning as a complicated black box. It doesn’t have to be. If you’ve always wanted to be part of the discussion and find your own use cases for AI, here is what you need to know: What is machine learning? When can you use machine learning? What are the common misconceptions?

As larger companies begin to adopt more and more AI and data-based technology into their daily practices, it is clear that smaller ones will soon fall behind. Small businesses may be concerned about the commitment of switching practices over to this fairly new and quickly changing technology. But, the pros far outweigh the cons. Small businesses that are looking to get the most bang for their buck when it comes to AI, so to speak, should seek to implement it into these four key areas.

The big question to ask is was we even prepared for AI? Or to be more practical what are we doing to create either a New World Order or a strong Global Governance that would assure a smooth humane implementation of AI, in the best interest of humans and their immediate imminent surroundings that include plants, animals, birds, marine-life and this planet’s natural resources? Governance in AI demands engagement at multiple levels that include a range of research sprints, local and international pilots, governance community building efforts, continuous interaction with AI developers & business owners and other outreach activities.

Surviving in the Deep Learning world means understanding and navigating through the jungle of technical terms. Use this guide as a reference to freshen up your memory when you stumble upon a term that you safely parked in a dusty corner in the back of your mind. This dictionary aims to briefly explain the most important terms of the Deep Learning. It contains short explanations of the terms, accompanied by links to follow-up posts, images, and original papers. The post aims to be equally useful for Deep Learning beginners and practitioners. 

The journey to Reinforcement learning continues… It’s time to analyze the Q-learning and see how it became the new standard in the field of AI with a little help from neural networks. We will learn the Q value from trial and error? We initialize the Q, we choose an action and perform it, we evaluate it by measuring the reward and we update the Q accordingly. In first, randomness will be a key player but as the agent explores the environment, the algorithm will find the best Q value for each state and action.