Summary:<\/em><\/strong>\u00a0 Here are our 5 predictions for data science, machine learning, and AI for 2019.\u00a0 We also take a look back at last year\u2019s predictions to see how we did.<\/em><\/p>\n \u00a0<\/p>\n We\u2019ve been watching and reporting on these trends all year and we scoured the web and some of our professional contacts to find out what others are thinking.\u00a0<\/p>\n \u00a0<\/p>\n Here\u2019s a Quick Look at Last Year\u2019s Predictions and How We Did.<\/strong><\/span><\/p>\n Clearly a win.\u00a0 No code data scie<\/u><\/em><\/a>nce<\/u><\/em> is on the rise as is end-to-end integration in advanced analytic platforms.<\/p>\n Yep.\u00a0 And there\u2019s more talk about Data Engineers<\/u><\/em><\/a> now than Data Scientists.\u00a0 Data Engineers are the folks who are supposed make those data science models work in the real world.<\/p>\n It\u2019s true.\u00a0 See the popularity of Data Viz and Visual Analytics as bridge technologies to let non-data scientists extract more value from sophisticated data science tools.<\/p>\n Both Microsoft and Google rolled out automated deep learning platforms<\/u><\/em><\/a> in 2018.\u00a0 These started with transfer learning but are headed toward full AutoDL.\u00a0 There are also a number of startup integrated AutoDL platforms.\u00a0 We reviewed OneClick.AI<\/u><\/em><\/a> earlier this year with both a full AutoML and AutoDL platform.\u00a0 Gartner recently nominated <\/a>DimensionalMechanics<\/u><\/em><\/a> as one of its \u201c5 Cool Companies\u201d with an AutoDL platform.<\/p>\n With the exception of chatbots which are becoming ever present, applications of real AI in business are narrow.\u00a0 They\u2019re coming but there not there yet.\u00a0 The most comprehensive studies I\u2019ve seen<\/u><\/em><\/a> show that among large companies only 1\/5th<\/sup> to 1\/3rd<\/sup> are implementing AI \u2018at scale\u2019, meaning they are putting on a full court press.\u00a0 Among smaller companies the percentage is much much smaller.\u00a0 And we\u2019re not really sure if they mean \u2018true\u2019 AI<\/u><\/em><\/a>.<\/p>\n Just take a look at the news and the stream of top social media executives being called on the carpet by our government and the EU.\u00a0 Begins to look like a perp walk.\u00a0 Government regulation is coming if not at the federal level, then in a more chaotic state-by-state balkanization like the California privacy rules about to go into effect or the Australian mandatory anti-encryption requirements.<\/p>\n So we\u2019re calling that 6 for 6 wins on last year.\u00a0 While many of last year\u2019s predictions could serve as well for next year we\u2019ll try to be a little more specific.\u00a0<\/p>\n \u00a0<\/p>\n Here\u2019s What We See for 2019.<\/strong><\/span><\/p>\n Prediction 1: Data Becomes More Important Than Algorithms<\/strong><\/span><\/p>\n It\u2019s been more than a year since we had any major new breakthroughs in either deep learning or classical machine learning.\u00a0 There\u2019s been some incremental improvement like using Temporal Convolutional Nets (TCNs)<\/u><\/em><\/a> instead of RNNs to reduce latency in NLP but no huge new innovations.\u00a0 The top performing algorithms are pretty well known or can easily be discovered using Automated Machine Learning.<\/p>\n We\u2019re well into the period when having more and better data is the key to success as companies make their journey of digital transformation.\u00a0 As a practical matter this opens up opportunities for competition in providing data-related solutions that\u2019s moving in several directions at once.<\/p>\n One axis is that getting accurately labeled training data for either image or text is still extremely expensive and time consuming.\u00a0 Companies like Figure Eight which specialize in labeling data are promoting smart cost effective strategies like Active Learning<\/u><\/em><\/a> that let you determine the optimum tradeoff between labeled data and model accuracy.\u00a0 That involves multiple iterations of adding either human-labeled or machine-labeled data followed by retraining to find the best tradeoff.<\/p>\n Another axis is access to third party data.\u00a0 Service companies like DymstData have entered the field as clearing houses for many hundreds of types of append data<\/u><\/em><\/a>.\u00a0 They\u2019re also taking on the onerous role of making sure sensitive PII is protected and that their users can enforce role based accessed to certain sensitive information particularly important in financial and health services.<\/p>\n The third axis is automatically tracking and recording the provenance of data used in models.\u00a0 Particularly when streaming data from many sources is being integrated and changes over time, knowing where it originated and how it has been used is critical to both accuracy and to compliance.\u00a0 Tibco and some other analytic platforms are incorporating this feature.<\/p>\n Look for service offerings around data to expand significantly this year.<\/p>\n \u00a0<\/p>\n Prediction 2: Everything Gets Easier as AI\/ML Moves Off Analytic Platforms Onto Industry or Process Specific Applications.<\/strong><\/span><\/p>\n A quick look around the world of AI\/ML startups shows that competition is moving to industry or process specific applications.\u00a0 These applications or mini-platforms are focused on solving industry specific problems in businesses as diverse as marketing, B2B sales, healthcare, fintech, and roughly a dozen other defined groupings.\u00a0 For a quick view take a look at CB Insights annual AI 100 winners<\/u><\/em><\/a> in the nearby chart and the way they are grouped by industry or process.<\/p>\n Some call this the commoditization of AI\/ML but more accurately you might think of this as the specialization of AI\/ML.\u00a0<\/p>\n If you\u2019ve been around long enough to remember the transition from Reengineering to ERPs in the late 90s this is very much the same thing.\u00a0 Initially Reengineering called on companies to improve processes with complex custom developed IT solutions because standardized solutions didn\u2019t yet exist.\u00a0 That rapidly opened the door to the rise of the major integrated ERPs from Oracle, PeopleSoft, SAP and others, and also for specialized applications like CRMs.\u00a0 Our industry is undergoing this same change right now.<\/p>\n These new vendors all strive to provide broad solutions in their particular niches but inevitably end up with less than grand ERP-scale platforms.\u00a0 Watch for many waves of consolidation among developers in each of these industry groups.\u00a0<\/p>\n Watch also for accelerating rates of AI\/ML adoption in mid-size and smaller companies who no longer have to have large data science teams or to rely exclusively on custom developed models.<\/p>\n \u00a0<\/p>\n Prediction 3: Rise of the Data Engineer and Data Analyst<\/strong><\/span><\/p>\n It\u2019s not that the world has fallen out of love with data scientists.\u00a0 Far from it.\u00a0 But when you have a shortage of a skill then the market moves to ease that pain by filling it in different ways.<\/p>\n One way is through the industry and process specific smart applications we discussed above that don\u2019t require great squads of in house data scientists.<\/p>\n The second is what\u2019s going on in all the major analytic platforms and the dozens of Automated Machine Learning (AML) platforms that are rapidly emerging.\u00a0 That is to be more efficient in data science meaning that fewer data scientists can do the work of many.<\/p>\n Since the volume of models does not decrease, in fact increases, this moves the work load to data engineers<\/u><\/em><\/a> who have two primary functions.\u00a0<\/p>\n First, to be able to create the required infrastructure required for data science like data lakes and Spark instances.\u00a0<\/p>\n The second is to be the one who takes the models and ensures they are implemented in operational systems and tracked for accuracy and refresh.\u00a0<\/p>\n Some data engineers are also responsible for DataOps<\/u><\/em><\/a>, ensuring a clean and preprocessed data stream.<\/p>\n The other evolution of analytic platforms is the growth of Visual Analytics and Data Visualization tools.\u00a0 These are now mostly fully integrated alongside the data science toolset and allow data analysts and LOB managers to extract more value and even guide efforts in analytics.\u00a0 They don\u2019t replace data scientists.\u00a0 It reinforces the team aspect that advanced analytics is becoming.<\/p>\n \u00a0<\/p>\n Prediction 4: Neuromorphic Chips:\u00a0 AI Comes to the Edge for IoT<\/strong><\/span><\/p>\n Two different technologies are reaching semi-maturity at the same time to solve a long standing problem.\u00a0 That problem is latency.<\/p>\n Consider for example that when you want to use your mobile device to automatically translate those text or image foreign words into English, or vice versa that your device actually sends that signal all the way back to the app in the cloud where the translation occurs, then all the way back to your device.\u00a0<\/p>\n Google and others providing instant translation services have been moving from RNN to specialized CNN structures called Temporal Convolutional Nets<\/u><\/em><\/a> because RNNs don\u2019t adapt well to MPP but CNNs do.\u00a0 That switch reduced latency, but the signal still had to make the full round trip.<\/p>\n The first of two technologies to solve this problem is 5G networks.\u00a0 You may be aware that 5G is faster but its real benefit is the density of traffic it can carry.\u00a0 This really opens the door to letting pretty much everything in your life communicate on the internet.\u00a0 How many of those will prove worthwhile remains to be seen.<\/p>\n However, both the major chip makers and several startups are now releasing spiking neuromorphic chips specially optimized to do CNN and RNN style models on the chip (without the round trip signal).\u00a0 Some of these are also optimized for very low power consumption.<\/p>\n Together these features are ideal for moving deep learning onto chips that can exist at the true edge of a network.\u00a0 Watch IoT and other streaming data apps explode with these new capabilities starting this year.<\/p>\n \u00a0<\/p>\n Prediction 5:\u00a0 Different AI Frameworks Learn to Speak to Each Other<\/strong><\/span><\/p>\n Now that text, speech, image, and video models are going mainstream we\u2019ve hit an unexpected barrier.\u00a0 Models built on one framework (Caffe2, PyTorch, Apache MXNet, Microsoft Cognitive Toolkit, and TensorFlow) can\u2019t easily be ported to a different framework.\u00a0 We can translate speech, but we did it with a veritable Tower of Babel.<\/p>\n Fortunately pain points like this drive innovation.\u00a0 AWS, Facebook and Microsoft have collaborated to build Open Neural Network Exchange (ONNX), making models interoperable on different frameworks.<\/p>\n ONNX is shaping up to be a key technology this coming year as the number of models being shared among developers, apps, and devices becomes larger and larger.<\/p>\n Well that\u2019s it for this year.\u00a0 Tune in next year and we\u2019ll see how we did.<\/p>\n \u00a0<\/p>\n Other articles by Bill Vorhies.<\/u><\/em><\/a><\/p>\n \u00a0<\/p>\n About the author:\u00a0 Bill is Editorial Director for Data Science Central.\u00a0 Bill is also President & Chief Data Scientist at Data-Magnum and has practiced as a data scientist since 2001.\u00a0\u00a0\u00a0 He can be reached at:<\/p>\n Bill@DataScienceCentral.com<\/a> or<\/span> Bill@Data-Magnum.com<\/a><\/p>\n<\/div>\n Go to Source<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<\/a>It\u2019s that time of year again when we do a look back in order to offer a look forward.\u00a0 What trends will speed up, what things will actually happen, and what things won\u2019t in the coming year for data science, machine learning, and AI.<\/p>\n
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<\/a>These new applications are focused on embedding the AI\/ML so that the user\u2019s organization does not need support from a large in-house group of data scientists and can rely on these developers to continue to provide updates and improvements.<\/p>\n
<\/a>The second solution is the introduction of new and better neuromorphic chips (aka spiking neural networks<\/u><\/em><\/a>).\u00a0 We hope that these totally new architectures for neural nets may be the gateway to achieving Artificial General Intelligence (AGI).\u00a0 That\u2019s still quite a way off.\u00a0<\/p>\n