10 Predictions for AI and Machine Learning in 2020

In 2019 artificial intelligence and machine learning continued its upward trajectory in the market, promising to change the future as we know it. To help support data management processes and decision making, artificial and augmented intelligence is being infused into products and services.

Machine learning sits in the center of all AI conversations, as combining machine learning with AI and cognitive technologies can make it even more effective in processing large volumes of information. Both technologies can lead to automation of tasks inside and outside the enterprise-another subject that promises to make waves in the future.  Here, executives of leading companies offer 10 predictions for what's ahead in 2020.

The Rise of the AI-enabled Business Analyst; AI is No Longer for the Precious Few ML Experts and Data Scientists: “Businesses have been working to break through the logjam of AI projects that have been back-burned in the face of machine learning skills shortages. However, we’re seeing the real world reach of AI expand with more companies looking at ways to foster collaboration, gain economies of scale and accelerate their AI paths from concept to production with maturing tools. AI is no longer for the small minority of machine learning experts and data scientists. With data at their core, business analysts are also eager for a slice of the pie. With AI and ML tools at their disposal, the skills of business analysts are expanding towards data science to explore insights from more diverse and richer data sets through the use of machine learning. Technology and automated machine learning techniques will begin shifting the use of data and AI to a greater proportion of a company’s business analysts. The demand for these skills are also starting to shape higher-ed curriculums to contend with this new wave of expectations.” - Per Nyberg, chief commercial officer, Stradigi AI

ML gets operationalized: “Companies adopt best practices to operationalize machine learning and go-live in production for mission-critical processes. Silos will be broken and multi-disciplinary will teams emerge with data engineers, application developers, data scientists, and subject-matter experts. Companies will kill the data lake process and start focusing on applications. New tools to track data science workflow will become the standard (e.g., MLFLow) and new comprehensive data platforms kill the Lambda Architecture.”- Monte Zweben, CEO, Splice Machine

AI with Focus: “There will be a shift to “narrow” AI that focuses on a single problem within an industry. Broad AI providers that promise to do “everything AI” will diminish as more narrowed and expert-level solutions will be offered. The new offerings will produce tangible value for companies as others scramble to keep up.” - Vance, director of AI, data science and emerging technology, TD Ameritrade.

Object Storage will be Key to Processing AI and ML Workloads: “As data volumes continue to explode, one of the key challenges is how to get the full strategic value of this data. In 2020, we will see a growing number of organizations capitalizing on object storage to create structured/tagged data from unstructured data, allowing metadata to be used to make sense of the tsunami of data generated by AI and ML workloads. While traditional file storage defines data with limited metadata tags (file name, date created, date last modified, etc.) and organizes it into different folders, object storage defines data with unconstrained types of metadata and locates it all from a single API, searchable and easy to analyze.” - Jon Toor, CMO, Cloudian

AI is becomes a standard technique: “Between random forests, linear regression, and other search patterns, AI has become a standard technique. AI, like standard numeric techniques, is best done with compute close to data. This means the techniques of “big data” (separating compute and data) are a poor choice just like they were for a majority of analytics. Running AI as code on a compute grid, or within your database, does not allow the kinds of optimizations that an AI framework, or an AI-centric query system can provide. In 5 years, we’ll wonder why custom code lasted so long in the AI space.” - Brian Bulkowski, CTO, Yellowbrick Data

AI/ML will overcome challenges related to stream processing: “To achieve low latency stream processing and high throughput at scale and in real-time, AI models and applications must be iterative and responsive to change. 44% of IT decision-makers find it extremely difficult to manage advances in technology speed, a recent Hazelcast survey found. The marriage of stream processing and AI will enable companies dealing with a massive volume of real-time events to generate immediate value from their data, opening up more opportunities for innovation. It will also allow developers to more quickly identify anomalies, respond to events or publish events in a data repository for storage and historical analyses, ultimately impacting business outcomes.” - John DesJardins, VP of solution architecture & CTO, Hazelcast

The next wave of digital transformation will be led by AI modeling and natural language processing: “As of 2019, AI modelling and language processing technologies are robust but not packaged accessibly enough to everyone that could make use of it. When everyone – from business analysts to data scientists begin to have full accessibility, then real improvements will rapidly accelerate. It’s not about coding – the future is now about how abilities are packaged to transfer skills and enable people to get moving faster” - Alan Jacobson, chief data and analytics officer, Alteryx

AI will go from identifying trends to making intelligent decisions: “AI will begin to drive real-world productivity across all aspects of business in 2020. As companies start using AI to gain deeper insights and understand trends, the technology will lead to more prescriptive actions and further automation of tasks. As AI continues to improve, we will see AI taking automatic actions that are “intelligent.” As humans become more familiar with this newfound “intelligence,” they will remove themselves from the equation, and businesses will benefit from greater productivity gains. For example, right now AI can predict when a printer needs a new toner cartridge, but taking a step further, AI can order the toner before it runs out, creating a seamless experience.” - Dave Wright, chief innovation officer, ServiceNow

AI can tackle climate change: “Climate change may be the biggest challenge of our time. And it will continue to be a significant topic of discussion in the year ahead. A challenge this big and literally Earth-changing calls on humanity to use every tool at our disposal to address it. Artificial intelligence can play a critical role on this front. AI can contribute to everything from CO2 removal to creating more energy-efficient buildings and optimizing energy production. It can also enable better climate predictions, better illustrate the effects of extreme weather and track the sources of pollution.” - Asheesh Mehra, CEO, AntWorks

AI Knowledge Graphs will Debunk Fake News:“Knowledge Graphs in combination with deep learning will be used to identify photos and video that have been altered by superimposing existing images and videos onto source images. Machine learning knowledge graphs will also unveil the origin of digital information that has been published by a foreign source. Media outlets and social networks will use AI Knowledge Graphs as a tool to determine whether to publish information or remove it.” - Dr. Jans Aasman, CEO of Franz, Inc.

Photo by Charles on Unsplash