At the end of each year, eWEEK posts observations from IT thought leaders about what they think we should all expect in the coming year—new products, innovative services, trends to look for, and so on. In this article, we’re focusing on key trends involving AI, machine learning and deep learning IT software and services.
Florian Douetteau, CEO and co-founder of Dataiku:
Inclusive engineering will begin to make its way into the mainstream to support diversity. In order to ensure diversity is baked into their AI plans, companies must also commit the time and resources to practice inclusive engineering. This includes, but certainly isn’t limited to, doing whatever it takes to collect and use diverse datasets. This will help companies to create an experience that welcomes more people to the field — looking at everything from education to hiring practices.
Christine Boles, VP, IoT Group and GM, Industrial Solutions Division, Intel:
The pandemic has greatly accelerated the need for companies to complete their Industry 4.0 transformations with solutions that allow them to have more flexibility, visibility and efficiency in their operations. We’ll see an acceleration of adoption of solutions that help address that need, ranging from AI including machine learning, machine vision and advanced analytics. As the economy bounces back, we’ll continue to see investment in the foundational OT infrastructure with more IT capabilities to allow the broad ecosystem of players to deploy these solutions and will see Industry 4.0 adoption significantly ramp up in 2021.
Marianna Tessel, CTO of Intuit:
AI-first apps changing customer service: As AI continues to mature and becomes ubiquitous across every aspect of our lives, Intuit is betting that AI will be integral to the way apps are developed and used, and will revolutionize the way apps are designed – no longer an afterthought, but “AI-first” apps.
Ethics ruling the AI space: AI is as good and safe as the team building/behind it. As companies increasingly work under a microscope, held responsible by their consumers, there will be a continued focus on ethical AI.
AI driving small business recovery: Fifty percent of small businesses fail in their first five years. And most of them fail because of cash flow problems. Due to the pandemic, now more than ever small businesses have fewer resources, and they are managing a ton of uncertainty – they don’t know if they can buy more inventory, if they can manage their cash flow accurately, or if they can hire more people. Plus, tax season is right around the corner, adding to the anxiety.
Joe Jensen, VP, IoT Group and GM, Retail/Banking/Hospitality/Education, Intel:
In 2021, shifting to Education-as-a-Service will become a priority, and I expect advances in education policy and investment to drive this concept forward. It will be critical to shift funding and allocations to schools to advance this service model, to ensure that affordable and high-quality education is accessible to all students. Longer-term, Education As-A-Service will become the standard for education across the globe.
Criminals will weaponize AI in new ways for fraud: The past decade has given rise to an entire cybercrime ecosystem on the dark web. Increasingly, cybercriminals have gained access to new and emerging technologies to automate their attacks on a massive scale. The dark web has also become a virtual watercooler for cybercriminals to share tips and tricks for scanning for vulnerabilities and perpetrating fraud. The evolution and sophistication of cybercrime will continue in 2021 as criminals leverage artificial intelligence and bots more than ever before.
Robert Prigge, CEO of Jumio:
Just as organizations have adopted artificial intelligence to shore up the attack surface and thwart fraud, fraudsters are using artificial intelligence to carry out attacks at scale. In 2021 we will essentially witness an AI arms race, as companies attempt to stay ahead of the attack curve while criminals aim to overtake it. We anticipate this at unprecedented levels across several key areas:
Machine Learning: Bad actors will deploy machine learning (ML) to accelerate attacks on networks and systems, using AI to pinpoint vulnerabilities. As companies continue to digitally transform, spurred by the COVID-19 pandemic, we will witness more fraudsters rapidly leveraging ML to identify and exploit security gaps.
Attacks on AI: Yes, AI systems can be hacked. Attacks on AI systems are different from traditional attacks and exploit inherent limitations in the underlying AI algorithms that cannot be fixed. The end goal is to manipulate an AI system to alter its behavior – which could have widespread and damaging repercussions, as AI is now a core component in critical systems across all industries. Imagine if someone changed how data is classified and where it is stored at-scale. We expect more attacks on AI systems in 2021.
AI Spear-Phishing Attacks: AI will be used to increase the precision of phishing attacks in 2021. AI-powered spear-phishing email campaigns are hyper-targeted with a specific audience in mind. Scouting information from social media and tailoring attacks to a specific victim can increase the click-through rate by as much as 40 times and all of this can be automated through sophisticated AI technology. In 2021, cybercriminals will continue to model phishing attacks after human behavior, replicating specific language or tone, to drive higher levels of ROI on attack investments.
Deepfake Videos: Deepfake technology uses AI to combine existing imagery to replace someone’s likeness, closely replicating both their face and voice. Increasingly in 2020, deepfake technology was leveraged for fraud. As more companies adopt biometric verification solutions in 2021, deepfakes will be a highly coveted technology for fraudsters to gain access to consumer accounts. Conversely, technology capable of identifying deepfakes will be of equal importance to organizations leveraging digital identity verification solutions. Organizations must be sure any solution they implement has the sophistication in place to stop these growing attacks, which will be highly utilized by fraudsters in 2021.
Joe Jensen, VP, IoT Group and GM, Retail, Banking, Hospitality & Education, Intel:
Micro-fulfillment centers have enabled smaller retailers to keep up with online retail giants during the pandemic. Over the next year, we will see the "warehouse-ization" of retail--with retailers shifting focus to fulfilling orders, whether they be groceries or consumer goods, at micro-fulfillment locations. This will provide a savings and operational boon especially for smaller retailers, for enabling decreased rents and customer foot traffic.
In the long term, retailers will continue to reply on seamless, convenient solutions like dark stores to cost effectively serve delivery customers. To be a "winner" in the changing retail space, retailers must transform production methods in creative ways to meet customer expectations.
CEO Jeff Catlin and Chief Scientist Paul Barba, Lexalytics:
Data Annotation will become the next big "side hustle” in 2021. It's already a common way to make an extra buck or two, but there's been a race to the bottom in pricing, where annotations are largely sourced well below minimum wage in industrialized nations. However, as AI sees successes in industries requiring expertise, like health care or law, the demand for specialist knowledge will see the development of infrastructure for matching more lucrative annotation contracts to professionals.
There will be more consolidation in the ML platform space. As AI became the “it” technology over the last few years, a bunch of AI infrastructure companies popped up and began peddling AI platforms to ease the task of building models for companies looking to leverage AI. While it sounds good on the surface, there is no identified business task being solved here, it’s simply more efficient use of technology, and that’s hard to sell. It’s likely that the VC’s who backed these plays will begin severing the cash lifelines in 2021.
AI platforms will consolidate, but AI services will pick up the slack here. Companies are becoming more accepting of 3rd party expertise in machine learning, and this is driving an increase in consulting services for ML. This trend will continue and accelerate in 2021.
Alex Quach, VP and GM, Wireline and Core Network, Intel:
Virtualization in the core network will hit a tipping point growing from 50%, to more than 80% of core network workloads to be virtualized in 2024, and we also expect the majority of the leading 5G Operators to start 5G SA Core Deployments in 2021.
Ryohei Fujimaki, Founder and CEO of dotData:
AI Automation will accelerate digital transformation initiatives: While the first wave of digital transformation focused on the digitization of products and services, the second wave - and what we will begin to see much more of in the coming year - will focus on using AI to optimize organizational efficiencies, generate deeper data-driven insights, and automate intelligent business decision-making. The wave of AI-enabled digital transformation will expand from “early adopters'' as financial services, insurance, and manufacturing to all other industries and AI and machine learning will be embedded into multiple business functions, across key business areas to not only drive efficiencies but also to create new products and services. One of the key reasons that this is happening now is the availability of AI and ML automation platforms that make it possible for organizations to implement AI quickly and easily without investing in a data science team. These AutoML 2.0 platforms automate up to 100 percent of the AI/ML development workflow, to speed up the painfully slow AI deployment and accelerate digital transformation initiatives.
More BIs doing AI: The COVID-19 pandemic has slowed down AI investments during 2020 for most enterprises. Although AI is still one of critical technology areas, enterprises need an efficient way to scale their AI practices and implement AI in business to accelerate ROI in AI investment. As organizations face increased pressure to optimize their workflows, more and more businesses will begin asking BI teams to develop and manage AI/ML models. This drive to empower a new class of BI-based “AI developers” will be driven by two critical factors: first, enabling BI teams with tools like AutoML 2.0 platforms is more sustainable and more scalable than hiring dedicated data scientists. second, because BI teams are closer to the business use-cases than data scientists, the life-cycle from “requirement” to working model will be accelerated. New AutoML 2.0 platforms that help automate 100% of the AI/ML development process will allow businesses to build faster, more useful models.
The evolution of no-code AI: Going beyond drag and drop visual programming tools to true no-code full cycle AI automation: As the need for additional AI applications grows, businesses will need to invest in technologies that help them accelerate and democratize the data science process. This has given rise to what some call no-code AI. Many of these no-code platforms are workflow-driven, visual drag-and-drop tools (a.k.a. visual programming) that claim to help make AI easier for non-technical people. The problem is that although simple workflows are easy to build and conceptualize, the reality is that most AI/ML models require large, very complex, and sophisticated workflows that quickly become unwieldy and create a whole new set of challenges of their own. In fact, the vast majority of the work that data scientists must perform is often associated with the tasks that precede the selection and optimization of ML models such as feature engineering--the heart of data science. This means that organizations will need to look for new, more sophisticated AutoML 2.0 platforms that enable true no-code end-to-end automation, from automatically creating and evaluating thousands of features (AI-based feature engineering) to the operationalization of ML and AI models--and all the steps in between.
In 2021 we will see the rise of AutoML 2.0 platforms that take no-code to the next level and finally begin to deliver on the promise of one-click no-code development with an environment that automates 100% of the workflow.
The rise of real-time intelligence: Real-time intelligence will increasingly become a factor next year. Along with the inevitable shift from physical to digital, more organizations begin to see the benefits of access to real-time information. The ability of real-time predictions will gain widespread interest. In addition and to predictions, the ability to understand and uncover hidden and actionable insights from real-time and streaming data sources will become essential for real-time intelligent decision making. The combination of easy to use AutoML 2.0 platforms and real-time predictions and insights will allow businesses to access real-time intelligence and take continuous actions.
AI and ML will go beyond predictions: While predictions are one of the most valuable outcomes, AI and ML must produce actionable insights beyond predictions, that businesses can consume. AutoML 2.0 automates hypothesis generations (a.k.a. feature engineering) and explores thousands or even millions of hypothesis patterns that were never possible with the traditional manual process. AutoML 2.0 platforms that provide for automated discovery and engineering of data features will be used to provide more clarity, transparency and insights as businesses realize that data features are not just suited for predictive analytics, but can also provide invaluable insights into past trends, events and information that adds value to the business by allowing businesses to discover the unknown unknowns, trends and data patterns that are important but that no one had suspected would be true.
Integrating AI into business faster through agile AI: AI and ML produce business values only when they are integrated into businesses. However, many enterprise AI projects have been struggling to monetize AI/ML and many AI/ML projects cannot get out of the data science lab. MLOps is one of the important trends to simplify the AI and ML production. While MLOps is an important aspect of developing and deploying AI/ML, it’s not the end-all-be-all of successful enterprise AI. 2021 will show a rise in interest and adoption of platforms that can also automate the first half of the enterprise AI workflow--data engineering and feature engineering. By being able to automate 100% of the life-cycle, businesses will be able to start moving away from long, time-consuming waterfall approaches to AI development and begin to adopt more agile processes that rely on speed of execution and rapid feedback.
Cristina Rodriguez, VP and GM, Wireless Access Network Division, Intel:
Reggie Yativ, COO and CRO, Agora Inc.:
Technology like AI, AR, VR and more will usher in the next-generation of hot apps and platforms. AI, AR, VR, transcription, metaverse applications, and many more technology pieces will create use cases much more powerful and scalable than anyone can imagine or anticipate.
Sameer Sharma, GM, Smart Cities and Intelligent Transportation, Intel:
2021 will be a breakout year for smart and resilient cities, infrastructure and transportation. In the short-term, we’ll see a sharp increase of mid-size cities adopting smart cities technology, which will lead to the democratization of technology outside of the usual tech hubs. Longer-term, smart cities infrastructure will be adopted in more rural areas, as consumers start to see the benefits for quality of life.
To reach this future, we’ll also see a ramp up in technology investments, from the Edge (AI) to the (5G) network to the cloud. As cities continue to recover from the pandemic, technology will be a key driver in ensuring progress and adoption of new business models, leading to economic growth.
Torsten George, cybersecurity evangelist, Centrify:
AI Will Help Optimize Governance Modeling: In Identity Governance and Administration (IGA), the establishment of broad responsibilities, assignment to groups, etc. typically results in particular privileges being assigned to identities. AI can be used to see if those privileges are being used or not, and how they are being used. Then it can help make recommendations to help adjust those assignments based on usage, and in 2021 will likely lead to more accurate access modeling for who should get access to what assets and why.
AI Can Help Stop Viruses Before They Mutate: No, this isn’t about COVID-19 but rather about computer viruses. For decades, anti-virus software solutions have all been signature-based, whereby they identify the unique signature of the virus and put it into their code, hoping the virus doesn’t change between software updates. AI can be used to address this issue. Complex algorithms can be developed that establish particular patterns, so they are no longer signature bound. The chances to capture these viruses while mutating is much higher than with traditional tools, which will become increasingly important in 2021 as threat actors up their efforts to wreak havoc during ongoing uncertain times.
Stacey Shulman, VP, IoT Group and GM, Health, Life Sciences and Emerging Technologies, Intel:
One of the things that is currently holding the health care industry back is standardizing medical records and data sharing across organizations. Collaboration in the medical industry for the purpose of solving illness and health issues can be critical, especially when it comes to public health crises and tracking population health, as we have seen with the pandemic this year.
In 2021, we will see improvement in the delivery models for information sharing, as emerging technologies such AI and federated learning become more ubiquitous in healthcare. In addition to powering innovations like telehealth, these technologies will accelerate and streamline the collaboration process, making it easier for healthcare professionals to deliver quality care to their patients as well as stay up to date on new treatment options.
eWEEK is running a series of prediction articles throughout the month of December.