Top artificial intelligence trends for 2020
Top AI trends for 2020 are increased automation to extend traditional RPA, deeper explainable AI with more natural language capacity, and better chips for AI on the edge.
The AI trends 2020 landscape will be dominated by increasing automation, more explainable AI and natural language capabilities, better AI chips for AI on the edge, and more pairing of human workers with bots and other AI tools.
AI trends 2020: increased automation
In 2020, more organizations across many vertical industries will start automating their back-end processes with robotic process automation (RPA), or, if they are already using automation, increase the number of processes to automate.
RPA is 'one of the areas where we are seeing the greatest amount of growth', said Mark Broome, chief data officer at Project Management Institute (PMI), a global nonprofit professional membership association for the project management profession.
Citing a PMI report from summer 2019 that compiled survey data from 551 project managers, Broome said that now, some 21% of surveyed organizations have been affected by RPA. About 62% of those organizations expect RPA will have a moderate or high impact over the next few years.
RPA is an older technology, organizations have used RPA for decades. It's starting to take off now, Broome said, partially because many enterprises are becoming aware of the technology.
'It takes a long time for technologies to take hold, and it takes a while for people to even get trained on the technology', he said.
Moreover, RPA is becoming more sophisticated, Broome said. Intelligent RPA or simply intelligent process automation (IPA), RPA infused with machine learning, is becoming popular, with major vendors such as Automation Anywhere and UiPath often touting their intelligent RPA products. With APIs and built-in capabilities, IPA enables users to more quickly and easily scale up their automation use cases or carry out more sophisticated tasks, such as automatically detecting objects on a screen, using technologies like optical character recognition (OCR) and natural language processing (NLP).
Sheldon Fernandez, CEO of DarwinAI, an AI vendor focused on explainable AI, agreed that RPA platforms are becoming more sophisticated. More enterprises will start using RPA and IPA over the next few years, he said, but it will happen slowly.
AI trends 2020: push toward explainable AI
Even as AI and RPA become more sophisticated, there will be a bigger move toward more explainable AI.
'You will see quite a bit of attention and technical work being done in the area of explainability across a number of verticals', Fernandez said.
Users can expect two sets of effort behind explainable AI. First, vendors will make AI models more explainable for data scientists and technical users. Eventually, they will make models explainable to business users.
Likely, technology vendors will move more to address problems of data bias as well, and to maintain more ethical AI practices.
'As we head into 2020, we're seeing a debate emerge around the ethics and morality of AI that will grow into a highly contested topic in the coming year, as organizations seek new ways to remove bias in AI and establish ethical protocols in AI-driven decision-making', predicted Phani Nagarjuna, chief analytics officer at Sutherland, a process transformation vendor.
AI trends 2020: natural language
Furthermore, BI, analytics and AI platforms will likely get more natural language querying capabilities in 2020.
NLP technology also will continue to evolve, predicted Sid Reddy, chief scientist and senior vice president at virtual assistant vendor Conversica.
'Human language is complex, with hundreds of thousands of words, as well as constantly changing syntax, semantics and pragmatics and significant ambiguity that make understanding a challenge', Reddy said.
'As part of the evolution of AI, NLP and deep learning will become very effective partners in processing and understanding language, as well as more clearly understanding its nuance and intent', he continued.
Among the tech giants involved in AI, AWS for example, revealed Amazon Kendra in November 2019, an AI-driven search tool that will enable enterprise users to automatically index and search their business data. In 2020, enterprises can expect similar tools to be built into applications or sold as stand-alone products.
More enterprises will deploy chatbots and conversational agents in 2020 as well, as the technology becomes cheaper, easier to deploy and more advanced. Organizations won't fully replace contact center employees with bots, however. Instead, they will pair human employees more effectively with bot workers, using bots to answer easy questions, while routing more difficult ones to their human counterparts.
'There will be an increased emphasis in 2020 on human-machine collaboration', Fernandez said.
AI trends 2020: better AI chips and AI at the edge
To power all the enhanced machine learning and deep learning applications, better hardware is required. In 2020, enterprises can expect hardware that's specific to AI workloads, according to Fernandez.
In the last few years, a number of vendors, including Intel and Google, released AI-specific chips and tensor processing units (TPUs). That will continue in 2020, as startups begin to enter the hardware space. Founded in 2016, the startup Cerebras, for example, unveiled a giant AI chip that made the news. The chip, the largest ever made, Cerebras claimed, is the size of a dinner plate and designed to power massive AI workloads. The vendor shipped some last year, with more expected to ship this year.
While Cerebras may have created the largest chip in the world, 2020 will likely introduce smaller pieces of hardware as well, as more companies move to do AI at the edge.
Max Versace, CEO and co-founder of neural network vendor Neurala, which specializes in AI technology for manufacturers, predicted that in 2020, many manufacturers will move toward the edge, and away from the cloud.
'With AI and data becoming centralized, manufacturers are forced to pay massive fees to top cloud providers to access data that is keeping systems up and running', he said. 'As a result, new routes to training AI that can be deployed and refined at the edge will become more prevalent'.
Author: Mark Labbe