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) …
Despite recent blows to the footwear industry, there's ample reason to be hopeful with cutting-edge technology. "We're living in amazing times where new innovations coming out of research in these fields is capturing people's imaginations. And those innovations will be realities in the not-too-distant future," said Acharya. "Today, we're creating machine-learning algorithms to help retailers incorporate all these sources of data and solve new business challenges." He pointed to returns forecasting -- and how data science can help -- as one example.
How do you see the current landscape for digital and data science in pharma? The timescale under which we operate today is not the fastest. I bet two years from now the same conversations will probably be going on, just with different faces trying to make the same impact in different companies. You have to ask yourself how much of what we're doing right now is truly impactful vs trying to marginally improve an already inefficient process. What's been even scarier is that we're looking at digital being the utopian cure for everything when I actually think it's the reverse – it's becoming something like an anti-bacterial agent that's developing its own resistance and pitfalls, and I think the companies that are going to win in this space with customers are the ones working on the antidote and scale.
Artificial intelligence, machine learning, and deep learning are buzzworthy terms in the world of business, ranging across channels from customer service to finance and beyond. Because big data is big news, companies want to implement AI to improve their businesses -- but some are making the mistake of trying to layer in artificial intelligence without the basis in analytics and data that allows AI to make a true impact. Other companies -- those with a strong foundation in analytics, possessing a store of data to work from -- can utilize AI with great success. According to the Harvard Business Review, "companies with strong basic analytics -- such as sales data and market trends -- make breakthroughs in complex and critical areas after layering in artificial intelligence." AI innovations like those aren't possible without the right data and specialized data science staff who know how to use it.
For a very long time, women working in the fields of science, technology, engineering and math were unwelcome and underappreciated. Take for example the story of Katherine Johnson and her colleagues, who made remarkable contributions to the early years of NASA's space program. The world had not even heard of her name until two years ago, when the movie, Hidden Figures, hit the screens. Sadly, it is still a man's world in the STEM fields, and women struggle every day to find a strong foothold in it. The disparity between the number of men and women with successful careers in STEM is unfortunately large.
Along with problems inherent in ML systems themselves, there's the broader problem of creating such systems in the first place, systems that are hungry for training data. In most cases, the ML models needs data that has been labeled with "ground truth" information. The model learns from these labeled examples, such that it can then predict the labels for future data. Companies like Facebook, Amazon, and Google have vast quantities of data available to work with, much of it helpfully labeled by their users. But most companies looking to employ ML techniques to solve problems have nothing like this kind of data available to them.
Here's an interesting interview with Josh Dillon, who works on Tensorflow. In this video, he discusses working on the Distribution API, which is based on probabilistic programming. Watch this video to find out what exactly probabilistic programming is, where the use of Distributions and Bijectors comes into play, & how you can get started. Subscribe to our channel to stay up to date with Google Developers.
Welcome to Part 3 of Applied Deep Learning series. Part 1 was a hands-on introduction to Artificial Neural Networks, covering both the theory and application with a lot of code examples and visualization. In Part 2 we applied deep learning to real-world datasets, covering the 3 most commonly encountered problems as case studies: binary classification, multiclass classification and regression. Now we will start diving into specific deep learning architectures, starting with the simplest: Autoencoders. The code for this article is available here as a Jupyter notebook, feel free to download and try it out yourself.
A few weeks ago, a dejected CTO told me it took his team three weeks to build a machine learning model. I told him a model in just three weeks sounded great, and he agreed. Because eleven months later, the model was still sitting on a shelf. That gap between great AI prototypes and AI in operation is starting to be a common theme as AI and machine learning make contact with the real world. The reason is … Actually, there are a lot of reasons and we can look at a bunch of them, but the reason underneath all the other reasons is that data doesn't sit still, and never will.
As a data scientist who has been in the profession for several years now, I am often approached for career advice or guidance in course selection related to machine learning by students and career switchers on LinkedIn and Quora. Some questions revolve around educational paths and program selection, but many questions focus on what sort of algorithms or models are common in data science today. With a glut of algorithms from which to choose, it's hard to know where to start. Courses may include algorithms that aren't typically used in industry today, and courses may exclude very useful methods that aren't trending at the moment. Software-based programs may exclude important statistical concepts, and mathematically-based programs may skip over some of the key topics in algorithm design.