Originally posted by: seb_
IBM Systems Technical University 2018 in Rome is already 3 weeks ago and I still try to sort all the impressions I got. There were a lot of topics that merely felt like hypes before but now really impact day-to-day reality. NVMe over FC, deep learning and even quantum computing are technologies you can already touch and embrace for your business today. At least you should think about how to exploit them in the years to come now.
For deep learning the two things I heard a lot that week were:
- How much time it takes to create good machine learning models
- How few really gifted data scientists there are in the market
Those data scientists, much-vaunted luminaries of insight, spend weeks and months to create a model with just the right features to allow deep learning giving you the answers for your most pressing business questions. They crawl through your plethora of data records and work together with your subject matter experts to find out which data features contribute to the intended results. They are the few enlighted ones being able to control the oracle of AI.
Well, that feels a lot like the dawn of computers in the mid of the 20th century, doesn't it? Fueled by the success of the first real computers in and after world war 2 the first companies started to utilize those big electronic brains. And only a handful of experts were able to control them in the beginning. I don't have to tell you that it didn't stay that way.
Now you just work with it. For me and many others a computer is just a daily-work tool. The internet is just a tool. We use them to get, analyze, transform and distribute the most important material today: data. While digitalization is a long-term trend and the digital transformation of basically all industries is still ongoing, it's already an "old hat" for everybody working with data. Accountants, analysts, controllers, business developers, product managers, planners, dispatchers, brokers, project managers - all those typical "spreadsheet acrobats". They all try to get insights out of data and they all were somehow impacted by developments like analytics, big data, data warehouses, you name it.
The commodification of AI
And now they depend on data scientists? That might be true for this very moment in IT history, but this situation will not last long and it already started to change. Being able to construct purpose-built multi-layered neural networks might be a requirement now, but it won't be in the future. Actually writing code to implement them might be required now for many cases, but again: this won't last. IBM researchers already work on methods to directly convert theoretical deep learning models described in scientific papers into real live models ready to be trained by user data. But there are applications that are much more "down-to-earth". Two of them are:
H2O.ai Driverless AI
IBM offers H2o.ai's Driverless AI solution on top of PowerAI, our machine learning platform. It's called "driverless", because it basically replaces the data scientist in the process and returns the control back to the roles described above - the ones really owning the data. With Driverless AI they have a tool in their hand that enables them to utilize the possibilities of AI themselves and relieves them from the tedious and time-consuming feature engineering. Feature engineering is done to find out which parts of the data should be used for the model to get the best results. Now imagine a tool that resolves that by massive parallel trial and error. An intelligently optimized brute-force approach to feature engineering. Presented by a UI that keeps the coding away from you but shifts your focus back to the data. I tried it in an STU session and was quite impressed.
More info: https://www.ibm.com/us-en/marketplace/driverless-ai
But machine learning and deep learning are not just new tools for "number cruncher" roles. PowerAI Vision enables use cases that were effectively not possible some years back. Oncologists had to examine organic samples from biopsies visually. A task that means hours of searching for anomalies and is not only prone to human error - it is also preventing specialists from working on other important tasks. Completely other example: Our cities are plastered with CCTV cameras. But other from a hopefully discouraging effect, do they stop crimes? Only if humans actually sit in front of the monitors and watch the many incoming video streams ALL THE TIME. Even they will most probably not recognize suspicious behavior of terrorists preparing an attack or thieves scouting a possible target. And most CCTVs are just recording without anybody watching the videos if not requested by authorities after a crime.
PowerAI Vision is IBM's solution for visual recognition. Like H2o.ai Driverless AI it provides an easy-to-use user interface that puts the focus on working with the images and videos. It simplifies the whole process so much that users can really care about WHAT they want "the AI" to do, not HOW it does it. After providing the training data to PowerAI Vision - for example video streams from CCTVs - they only have to label what they want PowerAI Vision to look for by simply drawing frames around that on pictures. The more the better. PowerAI Vision is doing all the rest.
More info: https://www.ibm.com/us-en/marketplace/ibm-powerai-vision
Conclusion and the data take
For many use cases deep learning is now a "down-to-earth" tool available to be used not only by computer scientists "in their ivory towers" but normal users like you and me. Yes, it requires a lot of computing power and IBM is offering a real AI powerhouse with the IBM Power System AC922 to even minimize that time.
And it requires a lot of data to train these models. The backbone of the whole concept. The most important part of the equation. Data that has to be collected, prepared and stored in a clever way. With availability, accessibility, redundancy, security, confidentiality and cost efficiency in mind.
That's where the Storageneers can help you. We are the engineers for your data - helping you to focus on WHAT you want to do with your data and AI rather than HOW to do it.
Ask your IBM sales representative about the EMEA Storage Competence Center (ESCC) and we're ready to help!