As reported on AI Trends:
By AI Trends Staff
[Ed. Note: We have heard from a range of AI practitioners for their predictions on AI Trends in 2021. Here are predictions from a selection of those writing.]
From Dr. Lance Eliot, author of AI Trends Insider on Autonomy column in AI Trends, and global expert on AI:
The Machine Learning (ML) juggernaut continues to grow. In 2021, interest in all things entailing ML will get even hotter, which is hard to imagine since this area of AI is already red hot. Turns out that the pandemic of 2020 actually dampened many Machine Learning projects that had not yet gotten off-the-ground. Once the post-pandemic era of 2021 takes hold, expect all-hands-on-deck gangbusters for ML and Deep Learning efforts. This includes endeavors that had gone into sleep mode for 2020 and also will encompass enthralling new projects that leap into the AI unstoppable stream.
AI Ethics begins to make its mark on the AI sphere. There has been a lot of talk about AI Ethics and the importance of ensuring that AI systems are unbiased, transparent, explainable, and so on. Sadly, it is mostly up until now just talk. There are enough AI Ethics sets of principles that you can wallpaper your house with them, but very few of those vital guidelines seem to have received genuine traction. In 2021, there will be an increasing hue and cry about the continued unearthing of AI For Bad, and a realization that the only path to AI For Good involves turning the “talk” about AI Ethics into the “walk” for AI Ethics (actual implementation and adherence).
AI-based autonomous cars emerge onto public roadways, cautiously and without their babysitter. Yes, 2021 will witness AI-based autonomous cars on our roadways, which you might at first exhort that they have been there for the last several years, but it has ostensibly been with a human handler or so-called safety driver sitting at the ready to take over the vehicle. Do not conflate this toe-in-the-water with any overnight tsunami of self-driving cars on all highways and byways. You can bet your bottom dollar that these experiments, which is what they are rightfully called, will hit some bad patches and get themselves into trouble. If handled well, things will continue. If poorly dealt with, expect a cold freeze to descend upon those driverless car dreams.
From Guy Kirkwood, the Chief Evangelist at UiPath:
Digital transformation will—at last—start to become transformational. At this point, “digital transformation” has become a buzzword that all enterprises have learned to recognize, yet the vast majority (80% according to IDC) of these efforts are still too tactical in nature. Robotic process automation (RPA), for example, may be considered a transformational tool, but on its own it’s not. In order for organizations to see true transformation in 2021, they’ll need to leverage more advanced platforms that combine core automation and AI features—such as text analytics, document understanding, and process mining. It’s also critical that these platforms have low-code capabilities that enable citizen developers to build and deploy enterprise grade automations that drive value back to their organizations. Without that, it will continue to be challenging for companies to deliver enterprise-wide digital transformation—which is fueled by the ability to easily deploy automation, even to the most complex processes.
From Adzmel Adznan, Partner & Operating Manager, Piva Capital:
The Rise of Industrial Robots. The public health crisis of COVID-19 has created a dire urgency for worker safety in the industrial and manufacturing sectors and on many operations across the assembly line. In order to reduce human exposure, ensure factory workers remain healthy and COVID-free across the globe, and continue to increase productivity, the appetite to find viable AI and robotics solutions will soar to new heights. In 2021, more companies will be open to adopt these types of technologies to optimize their assembly lines, protect their employees, and operate in a leaner, cleaner and greener manner.
Industry 4.0 and COVID-19 Leads to More ‘Glocalization.’ With COVID-19 shutting down international borders and business travel, expect to see a continued rise in “glocalization” as more countries understand the implications of the Fourth Industrial Revolution and take proactive steps in ensuring they keep an advantage over their neighbors. Due to this, there will be a renewed focus on discovering the technology solutions that can improve the resiliency and efficiency of critical supply chains in localized regions. Optimizing and strengthening these supply chains will present viable opportunities for emerging countries to advance their economies and obtain a competitive edge.
From Nick Elprin, CEO, Domino Data Lab:
IT empowered to end “Shadow IT” Rapidly evolving, data science has until now lacked both appropriate governance and a centralized platform within enterprises—leading to “shadow IT” practices that include data scientists downloading unapproved tools and data science packages, while using unofficial infrastructure for storage and specialized compute. Use of these rogue systems create significant risk from a security and IT perspective. Now, as we see data science becoming increasingly pervasive and critical to every business function, in 2021 we will see more organizations treat models as digital assets, motivating IT to take the reins and provide the infrastructure to support data science at scale.
Increased pressure to ensure explainability and transparency around the use of algorithms and predictive models. While increased AI engineering capabilities provide greater structure and sophistication about how we bring models into production, rapidly evolving privacy standards first seen with GDPR and now California’s CCPA, will require in 2021 that attention equally be paid to making AI models more transparent and secure. This will require a very heavy lift involving ModelOps, DevOps, MRM, xAI and ethical AI requiring both an evolution of technology and processes.
COVID-19 to accelerate model monitoring solutions COVID-19 has had an enormous impact nearly every facet of business operations and, coincident with this, massive data drift, a change in model input leading to performance degradation is occurring. Model monitoring will be critical in 2021.
Jon Hirschtick, President of the SaaS Division at PTC:
Supply Chain to get more attention. Smart companies must revamp their supply chains in case of a severe second wave and to be more nimble in light of new market demands and conditions. We see changes affecting both procurement (supply) and the production (manufacturing) of products. Firms are pulling back from overseas (China) due to Covid and shifting to domestic production due to the need for more predictable, shorter delivery times. What this means: Instead of a Just-in-Time supply chain that delivers materials just when the customer needs it, companies will look to implement a Just-in-Case supply chain to serve as a backup in case of a future disruption. A Just-in-Case approach can reduce variability in timelines and production. This could include the possibility of firms buying and storing more raw materials.
Talent acquisition and retention/training will be important—and challenging. Remote work gives employees many more options to jump to new opportunities. How are firms attracting and retaining the best talent? What are the expectations that these folks have (Cloud, intuitive/latest tech) to allow them to do the job they were hired to do versus admin tasks? What this means: Companies will need to find new ways to identify potential employees, develop new onboarding processes to get them to feel part of the team and its mission, and support and nurture them remotely.
Source: AI Trends
Author: Allison Proffitt
Date: 2020 12 29
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