Como evitamos otro invierno de la ia
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¿Como evitamos otro invierno de Inteligencia Artificial? En IBM Cognitive Systems creemos que para evitar un nuevo invierno de la IA no solo basta con las disponibilidad de la tecnología de procesamiento, la abundancia de los datos gracias al fenómeno de Big Data y el furor de la academía por las redes neuronales. Creemos que las organizaciones deben generar valor con estas tecnologías. Con eso en mente en esta sesión comentamos como estos 4 pilares se vuelven una realidad con nuestra oferta para Inteligencia Artificial Corporativa: - Diálogo y Colaboración entre LoB, Ciencia de Datos y IT. - Código Abierto. - Acceso y democratización para todos accedad a las tecnologías de IA para ayudar a construir evidencia para la toma de decisiones. - Escala para acompañar desde un esfuerzo individual hasta el de una corporación multinacional sin sobresaltos en un entorno de nube híbrida.
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Como evitamos otro invierno de la ia
- 2. Group Name / DOC ID / Month XX, 2018 / © 2018 IBM Corporation ¿Cómo evitamos otro invierno de la IA? — @mRcSqZd Technical Leader IBM Cognitive Systems
- 4. Extract Data Prepare Data Build models Train Models Evaluate Deploy Use models Monetize $$$ Monitor Your typical artificial intelligence workflow is a team sport Biz Analyst Dev OpsData Engineer App DeveloperDev OpsData Scientist IT Supports and Services the complete workflow Business Value
- 5. Watson Machine Learning Community Edition TensorFlow TensorFlow Probability TensorBoard TensorFlow Serving IBM Enhanced Caffe Caffe2 OpenBLAS HDF5 Curated, tested and pre-compiled binary software distribution that enables enterprises to quickly and easily deploy deep learning for their data science and analytics development Including all of the following frameworks: Keras & Keras-team cuDF & cuML
- 6. the wisdom of the cr owd
- 8. NON TECHIES ... CAN upload training data from on-site video or cameras create detection categories to match the safety use case using business domain expertise label data using acquired business domain expertise build model & expose it as a RESTful API endpoint for hybrid cloud consumption use the model
- 10. IBM hybrid open source based AI stack Intelligent Storage IBM Watson Studio Auto AI Tools SnapML IBM Watson OpenScale Business KPIs Production metrics Explainability fairness Continuous evolution IBM Watson Machine Learning IBM Watson ML Accelerator Data preparation Data exploration Model development IBM Watson Knowledge Catalog Data profiling Quality & lineage Data governance ORGANIZE FOR AI BUILD AI Model deployment Model management Model retraining OPERATE AI DEPLOY, RUN & MANAGE AI IBM Spectrum Discover DISCOVER FOR AI IBM Spectrum Scale STORAGE FOR AI
- 11. BI and Data Warehousing Self-Service Analytics New Business Models insight-driven transformationmodernizationcost reduction renovate innovate most are here value we help you get here insight-driven transformationmodernizationcost reduction renovation operations analytics self-service analytics new business models innovation Cloud Private Cloud Private for Data AI
- 12. IBM Cognitive Systems @mRcSqZd IBM Cognitive Systems developer portal https://ibm.biz/poweraideveloper IBM Systems Worldwide Client Experience Centers http://ibm.biz/cecc-portal IBM Power Systems Enterprise AI Solutions http://www.redbooks.ibm.com/Redbooks.nsf/Redpiec eAbstracts/redp5556.html
Editor's Notes
- https://discourse.forosaluddigital.cl/t/los-veranos-y-los-inviernos-de-la-inteligencia-artificial-esto-ya-paso-otras-2-veces/400
- Aunque hay varias versiones de la historia de la IA. Algunos coinciden en que ya han habido dos inviernos en la historia de la IA. Caracterizados por la falta de interés en la disciplina, inversión e innovación. 1974 – 1980 1988 – 2011 Lejos de querer hablar de veranos e inviernos. Lo que observé es que en general los inviernos se producen por exceso de promesas y falta de inversión. Entonces no alcanza solamente con que la academia descubra nuevos algoritmos, o que existan ahora los datos para entrenarlos gracias a el fenómeno de Big Data o que todos tengamos accesos a la capacidad de procesamiento… En IBM nos pusimos a pensar cómo hacemos para no quedarmos sólo en eso en empezamos a accionar sobre lo que podría ocasionar otro invierno. Y la conclusión fue… tenemos que poner foco en generar valor para las empresas rápidamente, que éste afecte el bottom line y eso generará un circulo virtuoso que propociará inversión. Y para que eso ocurra además de los algoritmos, big data y la capacidad de procesamiento necesitamos estas 4
- The genesis of IBM PowerAI (now known as Watson Machine Learning Community Edition - WML CE) was to make it simple for data scientists to be more productive, more quickly, by greatly simplifying the tasks necessary to get up and running. WML CE is an enterprise software distribution that combines popular open source deep learning frameworks, efficient AI development tools, and accelerated IBM Power Systems servers to take your deep learning projects to the next level. For a fee, IBM offers formal support for WML CE components as long as their versions are consistent with the release configuration (NOTE that WML CE is a no charge offering but we do offer support for a fee). If you choose to use a different version of any of the components, no formal support will be available. However, in keeping with industry norms, specific questions can be posted on the WML CE space on DeveloperWorks Answers: https://developer.ibm.com/answers/topics/powerai/. This forum is monitored by the IBM technical team and technical support is provided on a best effort basis. There a several ways for you to get WML CE. Order it. WML CE is available as a no charge orderable part number from IBM (called PowerAI until 2H2019). Download it from here: http://ibm.biz/download-powerai Get the Docker container from here: https://hub.docker.com/r/ibmcom/powerai/ As of WML CE (PowerAI) 1.5.4, the following frameworks are included in WML CE: (Make sure to check the Knowledge Center for the latest versions as they change rapidly https://www.ibm.com/support/knowledgecenter/SS5SF7_1.5.4/navigation/pai_software_pkgs.html): DDL 1.2.0 - Distributed Deep Learning (with support for up to 4 nodes in WML CE) TensorFlow 1.12.0 Tensorflow Probability 0.5.0 - TensorFlow Probability is a library for probabilistic reasoning and statistical analysis. TensorBoard 1.12.0 - a suite of visualization tools for TensorFlow TensorFlow Keras – NOTE that Keras is supported as part of the TensorFlow core library and as such we can support Keras through TensorFlow IBM enhanced Caffe 1.0.0 BVLC Caffe 1.0.0 - The Berkeley Vision and Learning Center (BVLC) Caffe2 1.0rc1 – in technology preview PyTorch 1.0rc1 Snap ML 1.0.0 Spectrum MPI 10.2 Bazel 0.15.0 OpenBLAS 0.3.3 HDF5 1.10.1 Protobuf 3.6.1 ONNX 1.3.0 – in technology preview
- Now that you have rungs to climb up the AI ladder … how do you really change things for an enterprise? As the top rung of the AI ladder shows, the goal is to scale insights with AI everywhere. Some companies go at this with the end result being that only a few in the organization have been able to participate in the AI journey (due to the way AI talent is typically distributed today in an organization). Companies that will separate themselves from their peers and become market leaders are those that get everyone to participate in the AI renaissance. I call this the “Wisdom of the Crowd”. When everyone can participate in AI, it means that ideas can come from anywhere in the enterprise … for example, the line of business (LOB) can leverage their expert knowledge of the business and use self-service tools to builds 1000s of models; after all, tomorrow’s business landscape will be characterized by 1000s of algorithms within a single company as opposed to the one, two, or dozen maintained today. In fact, behind closed doors, companies will brag about their algorithms. <CLICK> A thousand free-form self-service ideas become validated when the LOB and the technical side of the business choose 100 or so to build out in a fail-fast methodology. <CLICK> Data science teams take the prototype models and work with operations to get them more accurate and drive them to production. Then the whole process goes around this loop again… in fact, it never stops. It’s an idea wheel. In order to benefit from the “Wisdom of the Crowd” you need a collaborative environment in which all can participate. AI needs to be taken from the hands of the privileged few and democratized for the many, as all rolls share, communicate, build, and collaborate in a cloud-based microservices environment designed from the ground up to climb the AI ladder. So now the question becomes how do you get everyone in the organization to participate in an AI transformation?
- As previously mentioned, PowerAI Vision has 3 core capabilities: Label, Train, and Deploy. This slide shows you the home screen of the PowerAI Vision toolset; as you can see, it is designed around these 3 tenets. The rest of this deck will walk through each of the capabilities in order. But before we get into the specifics, let’s look at some of the terminology we will use in this course to make sure that you understand what each of these terms means as they are the key advantages provided by PowerAI Vision. Classification of images is the act of identifying what is inside of an image and putting that image into one of the classifications that have been created as part of the labeling of the images. Object detection is the act of finding various objects within an image. It is the analysis of images and/or video to detect objects that have been labeled previously in similar images. Note that with classification, an image is classified into one group; in object detection, you may find multiple objects within a single image. Auto labeling is the ability to use a partially trained model to assist the user in labelling addition objects in other images. This allows for rapid processing of image data sets so that a novice user can create larger datasets to train more accurate models with. Data augmentation is another way to create larger datasets to use when training a model. With augmentation, images that are already classified can be changed (rotated, flipped, colours changed, blurred, sharpened, etc.) in order to build additional images that can then be used to make your models more accurate. Once the model is created, you need to be able to call the model from your application in order to make an inference. PowerAI Vision creates a REST API endpoint automatically for you when you deploy the model that can easily be integrated into an application; this is particularly useful for web-based microservices applications.
- The most powerful computers on the planet Summit and Sierra, supercomputers at Oak Ridge National Laboratory and Lawrence Livermore National Laboratory, are now ranked the #1 and #2 fastest computers. They are helping us model supernovas, pioneer new materials and explore cancer, genetics and the environment — using technologies available to all businesses. To put that in human terms, approximately 6.3 billion people would all have to make a calculation at the same time, every second, for an entire year, to match what Summit can do in just one second. (Another way to see it: if you want to go toe-to-toe with Summit yourself, settle in. You’ll be making a calculation every single second for the next 6.3 billion years.) Summit by the numbers 200 quadrillion calculations per second 250 petabytes storage capacity 9,216 IBM POWER9 CPUs 25 gigabytes per second between nodes 27,648 NVIDIA Tesla GPUs
- To help climb the AI ladder, IBM has put in place an open source-based stack (you’ll often hear it referred to it as OneAI … the moniker used to represent how across various IBM divisions, our offerings synergize to make the climb faster, higher, and safer; for example. Watson Studio and Watson Machine Learning helps build and deploy models, while Watson Machine Learning Accelerator helps to speed up the training, and Spectrum Scale helps to make access to the data used by the models to train them faster and easier to manage, as well as add enterprise resiliency such as business policies (quota, encryption, and so on), availability, and more. This slide is just a quick reminder of the IBM Watson tools which can be used to build, deploy and manage AI models to be included in digital agents. Watson Knowledge Catalog contains trusted, curated data which can be combined with Business Intelligence (BI) data for more complete and effective AI models. Watson Studio provides a build environment for creating models using popular, open source languages and frameworks. Data science and artificial intelligence (AI) practices have evolved to a point where organizations of all sizes are actively experimenting to inject predictive insight into business. Yet, moving from experimentation to production has remained a challenge. IBM Watson Machine Learning helps data scientists and developers work together to accelerate the process of moving to deployment and integrate AI into their applications. By simplifying, accelerating and governing AI deployments, it enables organizations to harness machine learning and deep learning to deliver business value. Integrated to work with Watson Studio, Watson Machine Learning empowers your cross-functional team to deploy, monitor and optimize models quickly and easily. APIs are generated automatically to help your developers infuse AI into their applications in minutes. Watson Machine Learning’s intuitive dashboards make it simple for your teams to manage models in production, and its seamless workflows enable continuous retraining to maintain and improve model accuracy. Watson OpenScale provides a management platform for your AI models in production. OpenScale addresses many of the trust and transparency issues which were addressed as inhibitors in rolling out AI across business processes and includes tools to help update and evolve AI and ML models.
- To recap: the goal of traversing the phases of this framework is to not just put your data to work, it’s to transform the org into a data-driven organization – one that is led by data in everything it does. What do I mean when I say “where an organization starts to be led by data?” It’s not that machines are taking over the business. Not at all. It’s that the entire organization participates and operates with the ability to access and use data to make better decisions … it’s democratized. For our discussion, we labelled the phases of the initiatives we will get our clients to subscribe to, to transform their business (we do this through teaching them … if you get to this point, you’re in great shape and you’ll not even have mentioned an IBM product yet). Specifically, we will map ‘operations’, ‘business intelligence’, ‘self-service analytics’, and ‘new business models’ to the data acumen framework for this example. Notice a couple of things with respect to the slope of the new curve when you partner with IBM, as it works its way across the different phases of data acumen. First, value is no longer limited to budgetary dollars in the initial renovation phase … it moves you away from the typical (and unfortunate) notion that renovation is just about cost cutting. It’s now about infusing automation and compute density into the infrastructure in order to save costs in the long run, which will accelerate projects in the phases to the right (be prepared for some short-sightedness here, but it’s your job to expand the client’s aperture). We now know from the last slide why most clients get so little long term value in this renovation phase … cuttings costs without an information architecture (IA) doesn’t yield a lot of long term value. With this approach, there will still be an obvious jump in reward (costs are saved, you see the curve make a quick jump) but the curve overall has been lifted and the slope different. This creates enormous momentum to move you further to the right on the data acumen curve. And notice the “AI” has gotten bigger – because there will be more of it! There is no AI without IA! <CLICK> Here is just an example of some of the ways IBM Systems can help in the cost savings side of renovation: storage compression is a great way to save costs and it’s instant … and wont impact the CPU utilization rate on your database servers. There is an incredible consolidation play for Oracle onto LinuxONE (more on this later but we’ve seen clients consolidate over 200 cores of x86 running Oracle onto just 17 LinuxONE cores – this represents enormous savings for the client; as a test go and look at the fully loaded costs of an Oracle Exadata appliance, then figure out the software in relation to the hardware … it’s 95% software costs and that’s why Oracle keeps adding more and more low performing Intel cores to their appliance – it drives up software costs. I would expect Oracle to shudder at the thought of LinuxONE running their stuff for this very reason). <CLICK> As I said earlier, cost savings lead to modernization and you can see that modernization can also deliver costs savings. Those costs might be density of compute, performance, it could be a more agile way to drive changes into production (NoSQL databases and their correlation to agile), and more. Analytic environments soar here on IBM Systems. <CLICK> The reason why I stated that my clients are in the middle is because they’ve done some cost savings (although it’s not paying off like it should because they simply looked at ‘low hanging fruit’; for example, did they see a strong governance strategy as a cost savings event – it is). In order to democratize Big Data you have to make it self-service – as opposed to waiting on IT to deliver these resource for exploration. Self-service analytics and being a true insights-driven company is about participation – by the entire organization. This is where cloud comes in. IBM is all about cloud: hybrid, public, and private and the Red Hat acquisition bolsters this – beyond any IBM acquisition in the company’s history – to dominate this pillar. <CLICK> Finally, the new business models, where you realize AI. It’s about supporting the most prevalent open source frameworks and then giving clients a way to get them up and running (and supported) fast, so they can focus on the business. It’s about making data science a team sport (Watson Studio, H20 Driverless AI, PowerAI Vision, among others). Shortening the loop of updating models (zML), or the right storage to further boost AI (Spectrum Scale – as you will find out storage matters in an AI architecture more than you ever thought). <CLICK> Speaking of storage, in 2Q19 IBM Storage announced 5 key routes to market and you can see how they easily fit on this proposed framework.