Even in a simple development environment, machines and algorithms are still powered by human intelligence.

Just when you thought it was safe to go back to normal -- are you ready for round two? "There are big changes ahead," says Forrester VP Brandon Purcell. "There are a lot of changes that have been brought about by what happened over the last 2 years. The pace of change is very rapid. There are pretty big things happening." Purcell spoke with InformationWeek about the predictions for AI in 2022 and beyond.

The rush is on to implement in a battle for competitive advantage. However, in the haste to implement, some organizations are stumbling because their initiative lacks a solid foundation. "People want to solve problems with AI just because it's AI and not because it's the best solution," said Scott Zoldi, chief analytics officer at analytics decisioning platform provider FICO. "It has to be soup to nuts. How are we going to develop AI from a governed perspective of having a governance process that talks about the data, the success criteria and the risks from both a project perspective and an ethical perspective?"

All neural networks are susceptible to "adversarial attacks," where an attacker provides an example intended to fool the neural network. Any system that uses a neural network can be exploited. Luckily, there are known techniques that can mitigate or even prevent adversarial attacks completely. The field of adversarial machine learning is growing rapidly as companies realize the dangers of adversarial attacks. We will look at a brief case study of face recognition systems and their potential vulnerabilities.

On one hand, organizations recognize the potential value of machine learning to scale operations, gain faster and deeper insights, respond to quickly changing conditions, and more. On the other hand, it's hard to get started on something that is novel to your organization. You may not have the talent in-house, and you don't have any experience. What's more, even for those organizations that have run successful pilots, many have struggled to move those pilots into production for a variety of reasons. It feels like many organizations are stuck.

Expectations are high for artificial intelligence's ability to prime businesses for post-pandemic resiliency, and rightfully so. Research firm IDC predicts that global spending on AI will double over the next four years, growing to more than $110 billion in 2024. Research from Accenture also shows that companies that successfully scale AI achieve nearly 3x the return on investment and a 30% premium on key financial valuation metrics. While the hype around this technology is not new, the COVID-19 pandemic sharpened the contrast between those who have professionalized their AI capabilities to scale across the enterprise, and those who have yet to tap into the full value of their AI investments. In an attempt to recover and achieve sustainable growth beyond 2021, it will be crucial for companies to embrace evolving AI capabilities by transforming into an intelligent enterprise that embeds analytics into the core of its operations.

The graph represents a network of 1,425 Twitter users whose tweets in the requested range contained "iiot ai", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Friday, 02 April 2021 at 10:24 UTC. The requested start date was Friday, 02 April 2021 at 00:01 UTC and the maximum number of tweets (going backward in time) was 7,500. The tweets in the network were tweeted over the 2-day, 9-hour, 52-minute period from Tuesday, 30 March 2021 at 00:38 UTC to Thursday, 01 April 2021 at 10:30 UTC. Additional tweets that were mentioned in this data set were also collected from prior time periods.

The disruption of the COVID-19 pandemic highlights how critical it is to continually seek new approaches to survive, corner competition, and empower business futures ahead of major market disruptions. Given the modern demands of technology to deliver on a wide swath of services, products and solutions, data has come to play a major role in monetization. It's no surprise that so many have begun to realize their success as a business depends upon their ability to improve collection and uses of datasets. Data intelligence, or the use of data to glean useful information, allows a business to both increase revenue and their position in the market. But the continual multiplication of data and its sources are making an already substantial challenge even more laborious.

Even the most sophisticated and finely tuned AI models couldn't predict the long-lasting magnitude of COVID-19. Its disruption on our personal and professional lives is hard to quantify. Last March, it was almost impossible to foresee how this past year would unfold -- including the tragic moments and devastation for so many around the world. AI is just one of many technology capabilities leaned on to help companies survive the pandemic. However, as we enter year two, many of these new ways of doing business have shown their long-term value.

Algorithms are the heartbeat of applications, but they may not be perceived as entirely benign by their intended beneficiaries. Most educated people know that an algorithm is simply any stepwise computational procedure. Most computer programs are algorithms of one sort of another. Embedded in operational applications, algorithms make decisions, take actions, and deliver results continuously, reliably, and invisibly. But on the odd occasion that an algorithm stings -- encroaching on customer privacy, refusing them a home loan, or perhaps targeting them with a barrage of objectionable solicitation -- stakeholders' understandable reaction may be to swat back in anger, and possibly with legal action.