advanced analytics

1月 262018
 

Let’s lay down some fundamentals. In business you want to achieve the highest revenues with the best margins and the lowest costs. More specifically, in manufacturing, you want your products to be the highest quality (relative to specification) when you make the item. And you want it shipped to the [...]

How do you take your manufacturing business to the next level? was published on SAS Voices by Tim Clark

1月 062018
 

Deep learning is not synonymous with artificial intelligence (AI) or even machine learning. Artificial Intelligence is a broad field which aims to "automate cognitive processes." Machine learning is a subfield of AI that aims to automatically develop programs (called models) purely from exposure to training data.

Deep Learning and AI

Deep learning is one of many branches of machine learning, where the models are long chains of geometric functions, applied one after the other to form stacks of layers. It is one among many approaches to machine learning but not on equal footing with the others.

What makes deep learning exceptional

Why is deep learning unequaled among machine learning techniques? Well, deep learning has achieved tremendous success in a wide range of tasks that have historically been extremely difficult for computers, especially in the areas of machine perception. This includes extracting useful information from images, videos, sound, and others.

Given sufficient training data (in particular, training data appropriately labelled by humans), it’s possible to extract from perceptual data almost anything that a human could extract. Large corporations and businesses are deriving value from deep learning by enabling human-level speech recognition, smart assistants, human-level image classification, vastly improved machine translation, and more. Google Now, Amazon Alexa, ad targeting used by Google, Baidu and Bing are all powered by deep learning. Think of superhuman Go playing and near-human-level autonomous driving.

In the summer of 2016, an experimental short movie, Sunspring, was directed using a script written by a long short-term memory (LSTM) algorithm a type of deep learning algorithm.

How to build deep learning models

Given all this success recorded using deep learning, it's important to stress that building deep learning models is more of an art than science. To build a deep learning or any machine learning model for that matter one need to consider the following steps:

  • Define the problem: What data does the organisation have? What are we trying to predict? Do we need to collect more data? How can we manually label the data? Make sure to work with domain expert because you can’t interpret what you don’t know!
  • What metrics can we use to reliably measure the success of our goals.
  • Prepare validation process that will be used to evaluate the model.
  • Data exploration and pre-processing: This is where most time will be spent such as normalization, manipulation, joining of multiple data sources and so on.
  • Develop an initial model that does better than a baseline model. This gives some indication of whether machine learning is ideal for the problem.
  • Refine model architecture by tuning hyperparameters and adding regularization. Make changes based on validation data.
  • Avoid overfitting.
  • Once happy with the model, deploy it into production environment. This may be difficult to achieve for many organisations giving that a deep learning score code is large. This is where SAS can help. SAS has developed a scoring mechanism called "astore" which allows deep learning method to be pushed into production with just a click.

Is the deep learning hype justified?

We're still in the middle of deep learning revolution trying to understand the limitations of this algorithm. Due to its unprecedented successes, there has been a lot of hype in the field of deep learning and AI. It’s important for managers, professionals, researchers and industrial decision makers to be able to distill this hype from reality created by the media.

Despite the progress on machine perception, we are still far from human level AI. Our models can only perform local generalization, adapting to new situations that must be similar to past data, whereas human cognition is capable of extreme generalization, quickly adapting to radically novel situations and planning for long-term future situations. To make this concrete, imagine you’ve developed a deep network controlling a human body, and you wanted it to learn to safely navigate a city without getting hit by cars, the net would have to die many thousands of times in various situations until it could infer that cars are dangerous, and develop appropriate avoidance behaviors. Dropped into a new city, the net would have to relearn most of what it knows. On the other hand, humans are able to learn safe behaviors without having to die even once—again, thanks to our power of abstract modeling of hypothetical situations.

Lastly, remember deep learning is a long chain of geometrical functions. To learn its parameters via gradient descent one key technical requirements is that it must be differentiable and continuous which is a significant constraint.

Looking beyond the AI and deep learning hype was published on SAS Users.

10月 042017
 

Stories have been flooding the market lately about artificial intelligence (AI) causing the next world war. Based originally on comments made by Elon Musk (see below), many others are jumping in to share similar fears. China, Russia, soon all countries w strong computer science. Competition for AI superiority at national level [...]

Will AI cause the next major war? was published on SAS Voices by Mary Beth Ainsworth

9月 272017
 

Depending on who you speak with you will get varying definitions and opinions regarding demand sensing and shaping from sensing short-range replenishment based on sales orders to manual blending of point-of-sales (POS) data and shipments.        Most companies think that they are sensing demand when in fact they are [...]

Is demand sensing and shaping a key component of your company’s digital supply chain transformation? was published on SAS Voices by Charlie Chase

7月 182017
 

In part one of this series, Clark Twiddy, Chief Administrative Officer of Twiddy & Company, shared some best practices from the first of three phases of Twiddy’s journey to becoming a data-driven SMB. This post focuses on phases two and three of their journey. Phase two is about action. Now [...]

How to be a data-driven SMB: Part 2 of Twiddy’s Tale was published on SAS Voices by Analise Polsky

7月 072017
 

For colleges and universities, awarding financial aid today requires sophisticated analysis. When higher education leaders ask, “How can we use financial aid to help meet our institutional goals?” they need to consider many scenarios to balance strategic enrollment goals, student need, and institutional finances in order to optimize yield and [...]

Meet student enrollment goals by optimizing your financial aid strategy was published on SAS Voices by Georgia Mariani

6月 262017
 

The role of analytics in combating terrorism Earlier this spring, I found myself walking through a quiet and peaceful grove of spruce trees south of the small hamlet of Foy outside of Bastogne, Belgium.  On travel in Europe, I happened to have some extra time before heading to London.  I [...]

The analytics of evil was published on SAS Voices by Steve Bennett

6月 262017
 

The role of analytics in combating terrorism Earlier this spring, I found myself walking through a quiet and peaceful grove of spruce trees south of the small hamlet of Foy outside of Bastogne, Belgium.  On travel in Europe, I happened to have some extra time before heading to London.  I [...]

The analytics of evil was published on SAS Voices by Steve Bennett

6月 202017
 

Following the UK General Election result where no political party secured a clear majority ahead of the Brexit negotiations, it’s fair to say there’s lots of uncertainty facing the UK government right now. This, and the more outward looking post-Brexit era we're facing, are just two reasons why I believe [...]

Why our future prosperity demands smart borders was published on SAS Voices by Peter Snelling

6月 072017
 

In a world that never stands still, especially in the post-Brexit whirlwind the UK will soon be entering, it’s imperative that government is agile and responsive. More importantly, how can this capability be arrived at without breaking the bank? The UK Government recently released its Transformation Strategy 2017 to 2020. [...]

From government digitalisation to wholesale transformation was published on SAS Voices by David Downing