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) …
Any sufficiently advanced technology is indistinguishable from magic. In the world of artificial intelligence & machine learning (AI & ML), black- and white-box categorization of models and algorithms refers to their interpretability. That is, given a model trained to map data inputs to outputs (e.g. And just as the software testing dichotomy is high-level behavior vs low-level logic, only white-box AI methods can be readily interpreted to see the logic behind models' predictions. In recent years with machine learning taking over new industries and applications, where the number of users far outnumber experts that grok the models and algorithms, the conversation around interpretability has become an important one.
And for in-house teams, labeling data can be the proverbial bottleneck, limiting a company's ability to quickly train and validate machine learning models. By its very definition, artificial intelligence refers to computer systems that can learn, reason, and act for themselves, but where does this intelligence come from? For decades, the collaborative intelligence of humans and machines has produced some of the world's leading technologies. And while there's nothing glamorous about the data being used to train today's AI applications, the role of data annotation in AI is nonetheless fascinating. Imagine reviewing hours of video footage – sorting through thousands of driving scenes, to label all of the vehicles that come into frame, and you've got data annotation.
Today, with open source machine learning software libraries such as TensorFlow, Keras or PyTorch we can create neural network, even with a high structural complexity, with just a few lines of code. Having said that, the Math behind neural networks is still a mystery to some of us and having the Math knowledge behind neural networks and deep learning can help us understand what's happening inside a neural network. It is also helpful in architecture selection, fine-tuning of Deep Learning models, hyperparameters tuning and optimization. I ignored understanding the Math behind neural networks and Deep Learning for a long time as I didn't have good knowledge of algebra or differential calculus. Few days ago, I decided to to start from scratch and derive the methodology and Math behind neural networks and Deep Learning, to know how and why they work.
At the G7 meeting in Montreal last year, Justin Trudeau told WIRED he would look into why more than 100 African artificial intelligence researchers had been barred from visiting that city to attend their field's most important annual event, the Neural Information Processing Systems conference, or NeurIPS. Now the same thing has happened again. More than a dozen AI researchers from African countries have been refused visas to attend this year's NeurIPS, to be held next month in Vancouver. This means an event that shapes the course of a technology with huge economic and social importance will have little input from a major portion of the world. The conference brings together thousands of researchers from top academic institutions and companies, for hundreds of talks, workshops, and side meetings at which new ideas and theories are hashed out.
Gaby Ecanow loves listening to music, but never considered writing her own until taking 6.S191 (Introduction to Deep Learning). By her second class, the second-year MIT student had composed an original Irish folk song with the help of a recurrent neural network, and was considering how to adapt the model to create her own Louis the Child-inspired dance beats. "It was cool," she says. "It didn't sound at all like a machine had made it." This year, 6.S191 kicked off as usual, with students spilling into the aisles of Stata Center's Kirsch Auditorium during Independent Activities Period (IAP).
Growing up in Seattle, I was exposed to tech at a pretty young age. Most of my friends' parents worked for Microsoft. I spent a lot of my free time working on little coding projects, and even started my own business developing Video game mods in high school. Movies like 2001: A Space Odyssey captured my imagination, and gave me a sense that AI was going to be an important part of the future, even if it seemed distant at the time. But I really wanted to get involved.
"What's the problem you're trying to solve?" Clayton Christensen, the late Harvard business professor, was famous for posing this aphoristic question to aspiring entrepreneurs. By asking it, he was teaching those in earshot an important lesson: Innovation, alone, isn't the end goal. To succeed, ideas and products must address fundamental human problems. This is especially true in healthcare, where artificial intelligence is fueling the hopes of an industry desperate for better solutions. But here's the problem: Tech companies too often set out to create AI innovations they can sell, rather than trying to understand the problems doctors and patients need solved.
Artificial intelligence is not one thing. Artificial intelligence is not an algorithm. An algorithm is a set method for completing a task. Typically, we talk about algorithms that are implemented by a computer and written in computer code. But algorithms can also be written in math, like the quadratic formula or the equation to calculate area of a circle; or they can be written in natural language, like a chocolate chip cookie recipe or instructions for assembling a desk.
In the previous article, we studied Artificial Intelligence, its functions, and its python implementations. In this article, we will be studying Machine Learning. One thing that I believe is that if we are able to correlate anything with us or our life, there are greater chances of understanding the concept. So I will try to explain everything by relating it to humans.
FirstWord MedTech's Digital Ten is a fortnightly round-up of the 10 most read and noteworthy headlines related to digital health, including industry deals, alliances, collaborations, innovations and R&D news. Insulet, the company behind the Omnipod tubeless wearable insulin delivery system, is partnering with Abbott to integrate the latter's Freestyle Libre continuous glucose monitoring (CGM) sensor with its new-generation Omnipod Horizon automated insulin delivery (AID) system onto a digital platform. The companies will make their respective technologies compatible so they can be paired and share CGM and insulin dosing data on a digital platform. Abbott has similar partnerships with Novo Nordisk and Sanofi, in which the CGM tech will be developed to share data with the drug companies' connected insulin pens. Abbott also counts Bigfoot Biomedical and Tandem Diabetes Care among its insulin delivery partners.