The cryptocurrency craze has spawned a wide variety of use cases and an interesting amount of data. As many of the blockchains that power these cryptocurrencies are open and public by default, there are vast amounts of data being generated across different blockchains. Since each data point creates economic value, there have been a variety of projects and actors looking into the technology, from tax regulators to those looking to predict future cryptocurrency prices.
I was one of the first to write about new cryptocurrencies and the use of Bitcoin in remittance payments for TechCrunch and VentureBeat. I have a familiarity with the subject, and I also am a data science lover who has helped Springboard work on its machine learning bootcamp with job guarantee, so I’m always looking for ways to combine both the vast data generated by cryptocurrencies and the machine learning projects you can build on them with real-world implications. …
This is an excerpt from the Springboard guide to AI/ML jobs prepared for our research for the Springboard AI/Machine Learning Career Track. Use it to understand the difference between the two fields, especially when it comes to getting a job in the field and in hiring for those different positions.
Let’s begin by taking a quick look at a company that’s making lawyers’ lives easier and making them infinitely better at their jobs. Everlaw developed technology that looks through caselaw during discovery to find documents that are relevant and important to the case. …
Data science is the underlying force that is driving recent advances in artificial intelligence (AI), and machine learning (ML). This has lead to the enormous growth of ML libraries and made established programming languages like Python more popular than ever before.
It makes sense to put them all together (even though they’re not interchangeable) because there’s significant overlap. In some ways we can say that data science is about producing insights, while AI is about producing actions, and ML is focused on making predictions.
To better understand the inner workings of data science in AI and ML, you will have to dive right into the machine learning engineering stack listed below to understand how it’s used. …
My name is Roger, and as part of our work developing the first online course to offer a machine learning job guarantee, I’ve compiled this list of Twitter influencers that would be helpful to follow for research and learning purposes.
1. Kai-Fu Lee
The former president of Google China, Kai-Fu Lee has a lot of insight into the development of artificial intelligence in both China and the United States, the two booming superpowers in AI.
2. Google AI
The developers of everything from DeepMind and much more: you’ll be able to get the latest AI news from Google.
If you’ve been at machine learning long enough, you know that there is a “no free lunch” principle — there’s no one-size-fits-all algorithm that will help you solve every problem and tackle every dataset.
I work for Springboard — we’ve put a lot of research into machine learning training and resources. At Springboard, we offer the first online course with a machine learning job guarantee.
What helps a lot when confronted with a new problem is to have a primer for what algorithm might be the best fit for certain situations. Here, we talk about different problems and data types and discuss what might be the most effective algorithm to try for each one, along with a resource that can help you implement that particular model. …
The current partial government shutdown has affected many important science and engineering websites. Here’s a list, and if you have any more important websites that have been shut down you want me to highlight, tweet me it at Roger Huang.
Purpose: First off, most of the National Institute of Standards and Technology websites are not available. This affects websites such as the math section which offered tutorials and certain mathematical tools.
History is filled with writers who didn’t just create characters in their novels, but who also created characters, ideas and challenged norms out of their own identity.
We know of Orwellian ideas about the future — but perhaps they might be more accurately described as Blairite. George Orwell was the pen name of Eric Arthur Blair: Orwell the “anti-Communist” contrasting quite nicely with Eric Arthur Blair, the socialist militiaman.
The Brontë family were three sisters who reached the pinnacle of English literature with Jane Eyre, Wuthering Heights, and The Tenant of Wildfell Hall. They originally wrote under male pseudonyms to “gain legitimacy” in a male-dominated age: they were known as Currer, Ellis and Acton Bell. Lest we think this an anachronism, J.K …
In 1938, after Kristallnacht, the night Jewish-owned stores and synagogues had their windows smashed in by the SA, the psychologist Michael Müller-Claudius interviewed 41 randomly selected Nazi Party members. 26 of the 41 were extremely upset and indignant that the events happened. Only 2 advocated for more racially-based persecution. The rest were non-committal.
Afterward, as the Holocaust and WW2 were raging, the same psychologist interviewed 61 randomly selected long-term members of the Nazi Party, members who had joined the Party or the Hitler Youth before the Nazi seizure of power. 3 of the 61 applauded the idea of exterminating all Jews. An equal amount of members fully rejected anti-Semitism. Twelve of them advocated for a future Jewish state. But the vast majority of the respondents neither embraced nor denounced anti-Semitism. …
Love often works in mysterious ways.
Take, for example, the story of Lord Byron and Ada Lovelace.
Lord Byron was the pinnacle of Romanticism. He is often described as one of the greatest English poets. His love burned through pages and ink, and it burned through people too.
He would father one legitimate child among a litter of others — driving his wife to bitter cynicism. Lord Byron would set sail for Greece five months after his one legitimate daughter was born.
A few years later, he would die of fever in Greece at the tender age of 36. After having fought for the Greeks in their war of independence, he was forever immortalized as a loveful folk hero. Animated by love for Hellenic principles, he had given his life for a country halfway around the world. …
I’m sure you’ve heard of the incredible artificial intelligence applications out there — from programs that can beat the world’s best Go players to self-driving cars.
The problem is that most people get caught up on the AI hype, mixing technical discussions with philosophical ones.
If you’re looking to cut through the AI hype and work with practically implemented data models, train towards a data engineer or machine learning engineer position.
Don’t look for interesting AI applications within AI articles. Look for them in data engineering or machine learning tutorials.
These are the steps I took to build this fun little scraper I built to analyze gender diversity in different coding bootcamps. It’s the path I took to do research for Springboard’s new AI/ML online bootcamp with job guarantee. …