If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Recommender systems fundamentally address the question – What do people want? Although it is an extensive question, in the context of a consumer application like e-commerce, the answer could be to serve the best products in terms of price and quality for a consumer. For a news aggregator website, it could be to show reliable and relevant content. In a case where a user would have to look through thousands or millions of items to find what they are looking for, a recommendation engine is indispensable. The engine filters over 3,000 titles at a time using 1,300 recommendation clusters based on user preferences. It is so accurate that personalised recommendations from the engine drive 80% of Netflix viewer activity. However, building and evaluating a recommender system is very different compared to a single ML model regarding design decisions, engineering, and metrics. In this article, we will focus on testing a recommendation system. The second and third require a lot of user-item interaction data. If that is not available, one might start with the first type of recommender system.
New and continuously improving treatment options such as thrombolysis and thrombectomy have revolutionized acute stroke treatment in recent years. Following modern rhythms, the next revolution might well be the strategic use of the steadily increasing amounts of patient-related data for generating models enabling individualized outcome predictions. Milestones have already been achieved in several health care domains, as big data and artificial intelligence have entered everyday life. The aim of this review is to synoptically illustrate and discuss how artificial intelligence approaches may help to compute single-patient predictions in stroke outcome research in the acute, subacute and chronic stage. We will present approaches considering demographic, clinical and electrophysiological data, as well as data originating from various imaging modalities and combinations thereof. We will outline their advantages, disadvantages, their potential pitfalls and the promises they hold with a special focus on a clinical audience.
A machine learning (ML) project requires collaboration across multiple roles in a business. We'll introduce the high level steps of what the end-to-end ML lifecycle looks like and how different roles can collaborate to complete the ML project. Machine learning is a powerful tool to help solve different problems in your business. The article "Building your first machine learning model" gives you basic ideas of what it takes to build a machine learning model. In this article, we'll talk about what the end-to-end machine learning project lifecycle looks like in a real business.
Technology has risen to prominence in recent years, both at work and at home. The fields of artificial intelligence (AI) and machine learning (ML) are advancing at a rapid pace right now. Almost everyone's everyday life will be impacted by AI in some way. Siri, Google Maps, Netflix, and social media (Facebook/Snapchat) are just a few examples. Artificial Intelligence and Machine Learning (ML) are two buzzwords that are frequently used interchangeably.
Pascal Bornet is an expert in AI and Automation, best-selling author, keynote speaker, and CDO at Aera Technology. Decision intelligence is a new field that helps support, augment and automate business decisions by linking data with decisions and outcomes. It uses a combination of methods (e.g., decision mapping and decision theories) and technologies (e.g., machine learning and automation) to improve the way decisions are made in companies. Decision intelligence includes continually evaluating decision outcomes and optimizing them through a feedback system. The term "decision intelligence" was popularized in Lorien Pratt's 2019 book, Link: How Decision Intelligence Connects Data, Actions, and Outcomes for a Better World, after Google launched its decision intelligence department in 2018.
Machine learning is playing an ever-increasing role in biomedical research. Scientists at the Technical University of Munich (TUM) have now developed a new method of using molecular data to extract subtypes of illnesses. In the future, this method can help to support the study of larger patient groups. Nowadays doctors define and diagnose most diseases on the basis of symptoms. However, that does not necessarily mean that the illnesses of patients with similar symptoms will have identical causes or demonstrate the same molecular changes.
Brain tumors are one of the most challenging diseases for clinical researchers, as it causes severe harm to patients. The brain is a central organ in the human body, and minor damage to this organ could affect the correct functioning of the human body. Brain tumors can lead to irreversible and dysfunctional damage to patients, including memory and vision loss. For these reasons, medical studies have, for a long time, focused on the study of the brain and its diseases, including brain tumors. Computer studies have contributed to medical research by offering machine learning algorithms to classify medical analysis records as brain tumors or normal clinical conditions.
In our previous post, we talked about how red AI means adding computational power to "buy" more accurate models in machine learning, and especially in deep learning. We also talked about the increased interest in green AI, in which we not only measure the quality of a model based on accuracy but also how big and complex it is. We covered different ways of measuring model efficiency and showed ways to visualize this and select models based on it. Maybe you also attended the webinar? If not, take a look at the recording where we also cover a few of the points we'll describe in this blog post.
Here I manually saved the column names, which are numerical and categorical, and also saved the target column. From the info function, there seem to be missing values, and we can see that location and sex should be categorical, so we have to do some data type conversion later on. Let's first visualize our target class. We see location and species seemingly for their respective locations and species (loc2 & species C, loc3 & species A). We also see there are slightly more female (1) birds than the male counterpart.
It's easy to feel overwhelmed by the amount of tools and skills required to become a data scientist. While it can take years to master everything, there are clear steps you can take to get started towards your goal. As with any big goal, keep in mind that it might not be possible to get there overnight: much like climbing a mountain or running a marathon, becoming a data scientist will require patience, grit, and practice. But if you're motivated by the prospect of working with data for a living, let this guide serve as the map for the journey ahead. Programming is an important part of working as a data scientist.