Machine Learning has made great advances in pharma and biotech efficiency. AI is also helping us more efficiently diagnose diseases, develop drugs, personalize treatments, and even edit genes. But this is just the beginning. The more we digitize and unify our medical data, the more we can use AI to help us find valuable patterns — patterns we can use to make accurate, cost-effective decisions in complex analytical processes. Machine Learning is particularly helpful in areas where the diagnostic information a doctor examines is already digitized. This post summarizes the top 4 applications of AI in medicine today.
How AI works, what you can do with it, and how to get started - Almost every business application of AI today is about learning to produce certain outputs from certain inputs. AI is powerful because it turns data into insights. But AI is less efficient at learning than people are, yes, way less efficient, so it needs a lot of data in order to learn. If you have lots of data, you should think about AI
What's different about machine learning projects? How do you reduce risks and build a good solution quickly? In standard software development, you simply answer the question: What do you want to implement? And then you, well, implement. But in machine learning projects, you first need to explore what’s possible – with the data you have. So the first question is: What can you implement? Here’s what we learned works to keep machine learning project on track from start to finish.
Machine Learning and AI are used interchangeably. Usually both terms are used to mean supervised learning. A big part of the confusion is that – depending on who you talk to – Machine Learning and AI mean different things to different users. Machine Learning is the field of Artificial Intelligence concerned with learning from data on its own. Especially in business contexts, you can use both terms to refer to machines that learn from data on their own.
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?
In the last 10 years, there’s no field where AI has been more consistently applied than in digital marketing. That’s because, compared to other industries, internet companies: collected bigger, more structured, datasets, employed more data engineers and have a more tech-focused culture. But even though the big tech giants are using machine learning a lot to optimise marketing, many organisations are still just getting started. If you are wondering how best to use machine learning / AI in marketing, here’s an overview of the top applications today.
Financial products are mostly mathematical bets: statistical equations that – on average – should return a profit. In the simpler and more repetetive parts of this game, we can substitute Machine Learning for human brainpower and automate a lot of decisions. There is a lot to be gained for finance businesses in applying AI. And - in fact - plenty are already doing so successfully. To give you an idea - here’s a summary of the top applications of AI in Finance.
This article presents the easiest way to turn your machine learning application from a simple Python program into a scalable pipeline that runs on a cluster. You will learn how to use luigi to manage tasks, how to easily create Command Line Interface for python script with click, how to run the pipeline in multiple Docker containers, how to deploy a small cluster on your machine, and how to run your application on the cluster
Trading is a gruesomely competitive world. And with AI being painted as the new wonder weapon for everything, it’s understandable that there’s a huge amount of interest in discovering how to use AI for trading. AI does play an important role in trading – but maybe not in the way you’d expect. Unfortunately, AI can’t be used to power a superhuman trading machine that steals human traders’ lunches in every market. Then, how AI helps traders make better decisions & improve high-frequency trading?
For each use case, find out whether the necessary data is being captured. Specifically, check whether the different datasets you need can be merged. The more machine learning projects you’ve already implemented, the better you can pinpoint the right questions to ask. Build on your previous experience: What are the common pitfalls in projects like this one? Which datasets are the most important to have, and which ones are optional? If you haven’t implemented a similar use case before, talk to a team that has.