The AI industry continued to thrive this year as companies sought ways to support business continuity through rapidly-changing situations. For those already invested, many are now doubling-down after reaping the benefits.
As we wrap up the year, it’s time to look ahead at what to expect from the AI industry in 2022.
Tackling bias
Our ‘Ethics & Society’ category got more use than most others this year, and with good reason. AI cannot thrive when it’s not trusted.
Biases are present in algorithms that are already causing harm. They’ve been the subject of many headlines, including a number of ours, and must be addressed for the public to have confidence in wider adoption.
Explainable AI (XAI) is a partial solution to the problem. XAI is artificial intelligence in which the results of the solution can be understood by humans.
Robert Penman, Associate Analyst at GlobalData, comments:
“2022 will see the further rollout of XAI, enabling companies to identify potential discrimination in their systems’ algorithms. It is essential that companies correct their models to mitigate bias in data. Organisations that drag their feet will face increasing scrutiny as AI continues to permeate our society, and people demand greater transparency. For example, in the Netherlands, the government’s use of AI to identify welfare fraud was found to violate European human rights.
Reducing human bias present in training datasets is a huge challenge in XAI implementation. Even tech giant Amazon had to scrap its in-development hiring tool because it was claimed to be biased against women.
Further, companies will be desperate to improve their XAI capabilities—the potential to avoid a PR disaster is reason enough.”
To that end, expect a large number of acquisitions of startups specialising in synthetic data training in 2022.
Smoother integration
Many companies don’t know how to get started on their AI journeys. Around 30 percent of enterprises plan to incorporate AI into their company within the next few years, but 91 percent foresee significant barriers and roadblocks.
If the confusion and anxiety that surrounds AI can be tackled, it will lead to much greater adoption.
Dr Max Versace, PhD, CEO and Co-Founder of Neurala, explains:
“Similar to what happened with the introduction of WordPress for websites in early 2000, platforms that resemble a ‘WordPress for AI’ will simplify building and maintaining AI models.
In manufacturing for example, AI platforms will provide integration hooks, hardware flexibility, ease of use by non-experts, the ability to work with little data, and, crucially, a low-cost entry point to make this technology viable for a broad set of customers.”
AutoML platforms will thrive in 2022 and beyond.
From the cloud to the edge
The migration of AI from the cloud to the edge will accelerate in 2022.
Edge processing has a plethora of benefits over relying on cloud servers including speed, reliability, privacy, and lower costs.
Versace commented:
“Increasingly, companies are realising that the way to build a truly efficient AI algorithm is to train it on their own unique data, which might vary substantially over time. To do that effectively, the intelligence needs to directly interface with the sensors producing the data.
From there, AI should run at a compute edge, and interface with cloud infrastructure only occasionally for backups and/or increased functionality. No critical process – for example, in a manufacturing plant – should exclusively rely on cloud AI, exposing the manufacturing floor to connectivity/latency issues that could disrupt production.”
Expect more companies to realise the benefits of migrating from cloud to edge AI in 2022.
Doing more with less
Among the early concerns about the AI industry is that it would be dominated by “big tech” due to the gargantuan amount of data they’ve collected.
However, innovative methods are now allowing algorithms to be trained with less information. Training using smaller but more unique datasets for each deployment could prove to be more effective.
We predict more startups will prove the world doesn’t have to rely on big tech in 2022.
Human-powered AI
While XAI systems will provide results which can be understood by humans, the decisions made by AIs will be more useful because they’ll be human-powered.
Varun Ganapathi, PhD, Co-Founder and CTO at AKASA, said:
“For AI to truly be useful and effective, a human has to be present to help push the work to the finish line. Without guidance, AI can’t be expected to succeed and achieve optimal productivity. This is a trend that will only continue to increase.
Ultimately, people will have machines report to them. In this world, humans will be the managers of staff – both other humans and AIs – that will need to be taught and trained to be able to do the tasks they’re needed to do.
Just like people, AI needs to constantly be learning to improve performance.”
Greater human input also helps to build wider trust in AI. Involving humans helps to counter narratives about AI replacing jobs and concerns that decisions about people’s lives could be made without human qualities such as empathy and compassion.
Expect human input to lead to more useful AI decisions in 2022.
Avoiding captivity
The telecoms industry is currently pursuing an innovation called Open RAN which aims to help operators avoid being locked to specific vendors and help smaller competitors disrupt the relative monopoly held by a small number companies.
Enterprises are looking to avoid being held in captivity by any AI vendor.
Doug Gilbert, CIO and Chief Digital Officer at Sutherland, explains:
“Early adopters of rudimentary enterprise AI embedded in ERP / CRM platforms are starting to feel trapped. In 2022, we’ll see organisations take steps to avoid AI lock-in. And for good reason. AI is extraordinarily complex.
When embedded in, say, an ERP system, control, transparency, and innovation is handed over to the vendor not the enterprise. AI shouldn’t be treated as a product or feature: it’s a set of capabilities. AI is also evolving rapidly, with new AI capabilities and continuously improved methods of training algorithms.
To get the most powerful results from AI, more enterprises will move toward a model of combining different AI capabilities to solve unique problems or achieve an outcome. That means they’ll be looking to spin up more advanced and customizable options and either deprioritising AI features in their enterprise platforms or winding down those expensive but basic AI features altogether.”
In 2022 and beyond, we predict enterprises will favour AI solutions that avoid lock-in.
Chatbots get smart
Hands up if you’ve ever screamed (internally or externally) that you just want to speak to a human when dealing with a chatbot—I certainly have, more often than I’d care to admit.
“Today’s chatbots have proven beneficial but have very limited capabilities. Natural language processing will start to be overtaken by neural voice software that provides near real time natural language understanding (NLU),” commented Gilbert.
“With the ability to achieve comprehensive understanding of more complex sentence structures, even emotional states, break down conversations into meaningful content, quickly perform keyword detection and named entity recognition, NLU will dramatically improve the accuracy and the experience of conversational AI.”
In theory, this will have two results:
Augmenting human assistance in real-time, such as suggesting responses based on behaviour or based on skill level.
Change how a customer or client perceives they’re being treated with NLU delivering a more natural and positive experience.
In 2022, chatbots will get much closer to offering a human-like experience.
It’s not about size, it’s about the quality
A robust AI system requires two things: a functioning model and underlying data to train that model. Collecting huge amounts of data is a waste of time if it’s not of high quality and labeled correctly.
Gabriel Straub, Chief Data Scientist at Ocado Technology, said:
“Andrew Ng has been speaking about data-centric AI, about how improving the quality of your data can often lead to better outcomes than improving your algorithms (at least for the same amount of effort.)
So, how do you do this in practice? How do you make sure that you manage the quality of data at least as carefully as the quantity of data you collect?
There are two things that will make a big difference: 1) making sure that data consumers are always at the heart of your data thinking and 2) ensuring that data governance is a function that enables you to unlock the value in your data, safely, rather than one that focuses on locking down data.”
Expect the AI industry to make the quality of data a priority in 2022.
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