Two researchers at Ruhr-Universität Bochum (RUB) have come up with a new theory of consciousness. They have long been exploring the nature of consciousness, the question of how and where the brain generates consciousness, and whether animals also have consciousness. The new concept describes consciousness as a state that is tied to complex cognitive operations – and not as a passive basic state that automatically prevails when we are awake. Professor Armin Zlomuzica from the Behavioral and Clinical Neuroscience research group at RUB and Professor Ekrem Dere, formerly at Université Paris-Sorbonne, now at RUB, describe their theory in the journal Behavioural Brain Research. The printed version will be published on 15 February 2022, the online article has been available since November 2021.
This paper describes a generative theory of bugs. It claims that all bugs of a procedural skill can be derived by a highly constrained form of problem solving acting on incomplete procedures. These procedures are characterized by formal deletion operations that model incomplete learning and forgetting. The problem solver and the deletion operator have been constrained to make it impossible to derive “star-bugs”—algorithms that are so absurd that expert diagnosticians agree that the alogorithm will never be observed as a bug. Hence, the theory not only generates the observed bugs, it fails to generate star-bugs.
It could be a great question, but has the question been formulated correctly? And is "conspiracy theory" still the correct term for belief in phenomenon such as "pizzagate?" Once again, these could be great questions. Let us put them aside for one moment and turn our attention to an experiment carried out by researchers at UCLA. Professors used artificial intelligence machine learning to compare the characteristics of an actual known and proven conspiracy which tool place on earth in real time, and a conspiracy theory which has been repeatedly debunked and known to be not true.
In recent years, machine learning has become ubiquitous in industry and production environments. Both academic and industry institutions had previously focused on training and producing models, but the focus has shifted to productionizing the trained models. Now we hear more and more machine learning practitioners really trying to find the right model deployment options. In most scenarios, deployment means shipping the trained models to some system that makes predictions based on unseen real-time or batch data, and serving those predictions to some end user, again in real-time or in batches. This is easier said than done.
The process of considering a trained Machine learning model and making its predictions available to the desired targets is what you call deployment. Model deployment wherein a machine learning model is integrated into an existing production environment to take inputs and deliver outputs is definitely not as easy as it sounds. This is, no doubt, one of the most crucial yet a cumbersome task to deal with. Most organizations spend a lot of time dealing with this. The efforts taken might not yield fruitful results as well, thus making the entire process way more tedious than expected.