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 Orr Danon, CEO of Hailo:
With growing demand and wider adoption due to affordability and accessibility, 2021 will bring broader deployment of AI at the edge solutions across various industries. AI and edge computing will play a crucial role for businesses in alleviating the growing strain of managing large amounts of data since processing data right at the edge versus needing to transfer it to the cloud, creating more powerful, versatile, responsive and secure solutions.
AI at the edge will make it more efficient to process large amounts of data and complete time and resource demanding tasks for industries that rely on dozens of cameras, such as smart retail and industry 4.0. Edge processing will help process video streams coming from multiple cameras in real time. Camera-attached processing in addition to collective, multiple stream processing will remove the need for huge, in-store servers while also reducing communication costs. Overall, various industries will benefit from edge processing due to its reduction in network bandwidth costs and improvement on issues related to privacy, latency, and efficiency. 2021 will be a transformative year for enterprises leveraging computer vision for automation, with fast and smart adopters of edge AI becoming industry leaders for years to come.
From Rob Brainin, CEO, Genuity Science:
Drug targeting will be as important as the clinical trial phase of drug development: COVID has changed the trajectory of drug development; the industry has shown they can do it faster. But choosing the right targets for drugs at the start of the process will only help the clinical trial process. Why? Better targets at the start could save millions of dollars downstream. If you get it wrong at the start, it is wrong throughout the process.
We will find root causes of—and cures for—diseases. Single-cell biology and AI have helped unlock a key strategy to finding the root causes of diseases including aortic aneurysms. This approach opens doors to more discoveries in underlying causes of disease, including Multiple Sclerosis, NASH (a growing disease involving cirrhosis of the liver), and Parkinson’s, to name a few.
AI will take its lumps in other industries. It is vital healthcare not to get tarred with the same brush. Although AI is expanding our understanding of biology at the cellular level, there will be failures along the way in other sectors (video deep fakes, maligned bots, not really autonomous cars, etc.). But AI is helping uncover live-saving discoveries (i.e., the aortic aneurysm paper discussed above) and improved disease treatment research, data-collection analysis and more. By classifying human disease based on cellular data—like what is done with cancer—we can leverage this for other diseases. This approach is “disease-agnostic,” and can help advance our understanding of human biology.
From Steve Shwartz, AI researcher, investor, statistician, author of the upcoming book, “Evil Robots, Killer Computers, and Other Myths: The Truth About AI and the Future of Humanity” (Feb 9, 2021):
On whether we will achieve artificial general intelligence (AGI): The technology behind narrow AI systems cannot progress to artificial general intelligence and evil robots. There are several ideas for how we might get to AGI but these are all vague ideas. Since the late 1950s, AI researchers have had many ideas for how to create AGI. None have panned out. There is absolutely no evidence today’s ideas will fare any better.
Both the optimism and fear of achieving AGI are grounded in the success of narrow AI systems. This optimism for narrow AI has naturally, but incorrectly, spilled over to optimism about prospects for AGI. As Oren Etzioni, the CEO of the Allen Institute for AI, said, “It reminds me of the metaphor of a kid who climbs up to the top of the tree and points at the moon, saying, ‘I’m on my way to the moon.’”
You probably do not expect time travel to happen in your lifetime. You probably expect it to remain in the realm of science fiction for hundreds of years, if not forever. You probably feel the same way about warp speed, putting people into hibernation, invisibility, teleportation, uploading a person’s mind into a computer, and reversing aging. You should put artificial general intelligence and evil robots in this same category.
From Pat Calhoun, CEO and founder at Espressive:
The employee experience will become synonymous with the digital experience. Not only are employees expecting their work lives to be as easy as their personal lives, intelligent automation has become an imperative for IT and HR to maximize employee productivity and contain costs.
There will be a much stronger alignment between the CIO and Chief Human Resources Officer (CHRO) in 2021. According to a recent Gartner study, the Future of Work moved from the #5 to the #1 position in terms of top initiatives for HR due to work from home mandates. As a result, a core focus for HR leaders will be enabling automation and AI adoption, and they need to be closely aligned with the CIO to make that happen.
As the virtual support agent market matures, there will be an increasing focus not only on reduction of incident volume, but on full automation of service requests as well. Enterprises will more boldly look for opportunities where they can deploy full auto-resolution of help desk issues.
From Sean Knapp, founder and CEO of data engineering company Ascend.io:
2021: The defining year for chief data officers. In years past, chief data officers, and therefore the teams they direct and influence, have approached their position from a very technical and tactical mindset – operating primarily as a cost center for the enterprise as they lay a foundation for the future. However, as data becomes more intertwined with the fundamental success of the business, CDOs must refocus their efforts on strategy and transformation of how the business interacts with and benefits from data, rather than the technologies employed along the way. I predict that 2021 will be the defining year for the chief data officer, where we will see the role take shape and truly establish whether they will go the route of a cost or profit center.
Metadata is big data. As digital transformation initiatives accelerated significantly in 2020, massive volumes of structured, semi-structured, and unstructured data have become scattered throughout the enterprise. However, Gartner predicts that through 2022, only 20% of the organizations that invest in information governance will succeed in scaling that governance. To achieve full governance and audibility, businesses are turning to metadata to provide deeper context into where data came from, the entire series of code that ran on it, and where it went. With this continuing surge of data and increasing governance requirements, organizations are realizing that the ability to track and automate the management of metadata is the new growing challenge. In the coming year, I anticipate that with the sheer volume of metadata continuing to increase, enterprises and vendors alike will be looking for new, scalable ways to solve the metadata challenge, and increasingly lean on AI to make sense of it all.
With conflicting team priorities, ‘data mutinies’ will be on the rise. Data teams today are on a collision course with conflicting priorities. For infrastructure teams, building for scale, security, and cost are of the utmost importance while engineering teams prioritize flexibility, development speed, and maintainability. Meanwhile, data scientists and analysts are focused on the availability and discoverability of data, and the connectivity of tools. As enterprises scale their efforts and their teams to build new data products, the interconnectedness and resulting complexity can be paralyzing for these groups. If organizations continue to cater to one group’s needs amidst these conflicting priorities, we can anticipate a rise of “data mutinies” in 2021 – in which internal users create their own engineering organizations with a mandate to move quickly and free themselves from these conflicts.
From Dr. Barbara Rembiesa, CEO, the International Association of IT Asset Managers (IAITAM):
Remote Work is Never Going Away; It’s Time for Tighter Controls. Many organizations have discovered that they are able to maintain business continuity in a work-from-home environment while saving money on overhead costs. Ad hoc ITAM processes to maintain business continuity and survive the government-imposed shutdowns worked in 2020 but will not be good enough for 2021. Regaining mature ITAM programs with strong tracking and security policies and procedures will be a major necessity in 2021.
“Snitch Software” Will Go Wider. “Snitch software” is a newer trend that has just started sneaking into auditing practices. In a recent example from a court case between a vendor and a consumer, the vendor placed Piracy Detection and Reporting Security Software (PDRSS) into their product to track when unlicensed software is used from an IP address. Eventually, the vendor audited the consumer and explained that their software was being used incorrectly, but the consumer argued that had not been proven. This essentially led to the vendor explaining their implemented PDRSS, which led to a privacy and permissions dispute. We are likely to see cases with audit rates likely to increase as organizations seek to financially recover from the pandemic. Knowing this, organizations should be prepared for snitch software to gain momentum and for software makers to put them into more products. For companies to avoid fines and embarrassment, IT Asset Managers in 2021 will need to focus on ensuring that license use is current and proper.
From Joe Petro, CTO, Nuance Communications:
In 2021, we will continue to see AI come down from the hype cycle, and the promise, claims, and aspirations of AI solutions will increasingly need to be backed up by demonstrable progress and measurable outcomes. As a result, we will see organizations shift to focus more on specific problem-solving and creation of solutions that deliver outcomes that translate into tangible ROI — not gimmicks or building technology for technology’s sake.