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.]
Prince Kohli, CTO, Automation Anywhere:
Bots will help businesses scale to meet increased customer service requests: “Many businesses across the world will turn to RPA [robotic process automation] to scale their customer support and front office efforts due to its simplicity and on-demand nature, allowing humans to focus on higher-value tasks and driving efficiency while creating a more meaningful form of engagement with their customers. This will have a resonance in sectors such as travel which have been devastated by COVID-19 and also in others as they look to grow again in the most efficient manner.”
RPA bots and personal health devices mark a dramatic milestone in the future of health: “RPA, combined with advancements in health monitoring devices such as the Apple Watch, will be one of the most important technological tandems ever created. Through most of human existence on Earth, we’ve had a very limited understanding of our body’s vital stats outside a hospital. Now, these IoT devices can measure our vitals 24/7 and are continuing to add new measurement capabilities. While humans would be unable to parse the deluge of data and see trends across data streams, bots can do this with ease—providing unparalleled healthcare monitoring, alerting, and actionable capabilities that we are just now beginning to realize.”
Kimberly Nevala, AI Strategic Advisor, SAS:
The Analytics “Core” Gets Reinforced: The pandemic upended expected business trajectories and exposed the weaknesses in machine learning systems dependent on large amounts of representative historical data, including well-bounded and reasonably predictable patterns. As a result, organizations will bolster investments in traditional analytics teams and techniques better suited to rapid data discovery and hypothesizing.
Sarah Gates, Analytics Strategist, SAS:
The Year of ModelOps: Pressures created by COVID-19 have raised organizational awareness of and need for ModelOps—the holistic approach used to rapidly move mathematical models through the analytics lifecycle, delivering value and insights faster. For organizations wanting to accelerate their digital transformation and to rev up agility and competitiveness, ModelOps is the magic fairy dust that will make it possible.
Bill Scudder, GM of AIoT Solutions, Aspen Technology, Inc.:
In 2021, industrial organizations will pivot to a business-first mindset. An increased emphasis will be on applying AI technology to domain-specific industrial challenges with a focus on business outcomes. While exploring and identifying industrial AI-enabled use cases may be intriguing, the starting point of any organizational strategy is never the technology. It will begin with identifying the business problems, corporate objectives, and strategic goals. However, a lack of in-house data science skills is one of the top barriers to AI adoption.
We will see more organizations lower the barriers to AI adoption by deploying targeted, embedded industrial AI applications. This will be the key to overcoming a lack of skills and drastically reduce the need for many data scientists. Through the adoption of industrial AI, next-generation asset optimization solutions can be implemented without data science experts.
Dr. Taniya Mishra, Founder and CEO, SureStart:
We’ll see more of a focus on people and communities in AI. The reawakened questions around diversity, equity, and inclusion in all arenas of business, including AI and technology, is an emergent trend that is not going away. Specifically, AI companies that ignore DEI (Diversity, Equity, Inclusion) are taking on business risks that will affect their bottom line. If AI does not work for all of its intended markets or users, businesses will leave money on the table.
Discussion has ensued on addressing AI’s diversity and ethics problem through technical evaluations of data and algorithmic bias, which are certainly important. But to really address this issue—and in light of conversations around racial equity and justice—I believe we’ll see more of a focus on the people, communities, and teams building AI.
AI is no longer a tech-only discipline; it requires a multi-disciplinary approach to understand the human impact that AI will have. So companies that are thoughtfully addressing this issue will begin to build teams composed not only of tech talent, but also with ethicists, sociologists, and anthropologists who have the training and perspective to think about the implications of technology beyond the product techs and specs.
The way the AI industry uses data will have to change to restore faith in science and tech. For years, we’ve seen a movement toward innovation driven enterprises (IDE). But we’re moving toward a new focus on the DDE: the data driven enterprise. This is especially true with AI companies, as AI systems require massive amounts of data to train, test and validate algorithms.
But with massive amounts of data comes massive amounts of responsibility when it comes to ethically collecting, storing, and using such data. The market will be driven by these ethical challenges, as consumers increasingly want to be involved in decision-making on how their data will be used, and by whom. The access control to personal data is going to lie with users, not with developers of tech or owners of tech corporations.
Dr. Rana el Kaliouby, Co-Founder and CEO, Affectiva:
We’ll see new use cases of Emotion AI to improve online collaboration and communication in light of the pandemic. In the COVID-19 pandemic we are relying more than ever on video conferencing to connect us virtually—working remotely, learning from home, and in our social lives. But there’s a big problem: these technologies are emotion blind. When we communicate in person, we convey so much more than the words we say: we express ourselves through nonverbal cues from our faces, voices and body language. But technology is not designed to capture the nuances of how we interact with those around us.
AI may be the answer to preserving our humanity in virtual environments. Specifically, Emotion AI—software that can understand nuanced human emotions and complex cognitive states based on facial and vocal expressions – can address some of technology’s shortcomings in light of the pandemic, and we’ll see companies using it for new use cases, such as:
Video conferencing and virtual events: Emotion AI can provide insight on how people are emotionally engaging in a virtual event or meeting. This provides presenters with valuable audience feedback, gives participants a sense of shared experience, and can help companies take a pulse on collective engagement during this stressful time.
Online learning: Emotion AI can give feedback on how students are engaging with online educational materials and lectures, flagging if they’re confused, stressed or bored. This becomes especially important during the pandemic as so many students are learning online and suffering from “Zoom fatigue.”
Telehealth: Emotion AI can create more meaningful discussions and trust between patients and healthcare providers as telehealth appointments are replacing in-person visits. And, a data-driven analysis of a patient’s emotional wellbeing provides a quantitative measure of mental health that goes beyond self-reporting on a rating scale of 1-10.