The IoT Needs Artificial Intelligence
The Internet of Things (IoT) is creating a lot of data. From health information to environmental conditions to warehouse and logistics data, the sheer amount of data produced on a regular basis by IoT devices is far more than any human being can process or make use of in a productive way. Fortunately, there is a solution: artificial intelligence in the form of big data. The IoT needs artificial intelligence.
Artificial intelligence systems can learn, over time, the patterns and trends that are most important. It can identify when specific events occur which require human intervention. It can sense security breaches and stop them before they become crises. Simply put, in order for the IoT to grow to its full potential, it needs artificial intelligence.
The cyber security of IoT devices is another big reason why IoT needs artificial intelligence. IoT devices are often created with little to no regard for security, yet they process and transmit a significant amount of personal data, making them a big target for hackers. As hackers become more sophisticated and hacks become more plentiful, artificial intelligence may be the only way to keep up.
Artificial intelligence systems can spot patterns and anomalies in ways that humans can’t, helping cyber security teams stem the tide of cyber attacks that might otherwise steal personal data. In fact, as hackers employ artificial intelligence systems themselves to develop increasingly sophisticated attacks, using artificial intelligence to defeat cyber attacks may be the only way to protect vulnerable systems and the data they contain.
IoT devices can simplify the lives of people, bringing benefits that increase our health and well-being. It’s important to ensure that artificial intelligence is deployed in productive ways to enable people to continue to enjoy the benefits of IoT without the data security risks.
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Artificial Intelligence system can predict school violence
Researchers evaluated 103 teenage students in 74 traditional schools throughout the US who had a major or minor behavioural change or aggression toward themselves or others.
cientists have developed an artificial intelligence system that can help predict which students are at higher risk of perpetrating school violence. The researchers found that machine learning – the science of getting computers to learn over time without human intervention – is as accurate as a team of child and adolescent psychiatrists, including a forensic psychiatrist, in determining risk for school violence. “Previous violent behaviour, impulsivity, school problems and negative attitudes were correlated with risk to others,” said Drew Barzman, a child forensic psychiatrist at Cincinnati Children’s Hospital Medical Center in the US.
“Our risk assessments were focused on predicting any type of physical aggression at school. We did not gather outcome data to assess whether machine learning could actually help prevent school violence. That is our next goal,” said Barzman, lead author of the study published in the journal Psychiatric Quarterly. Researchers evaluated 103 teenage students in 74 traditional schools throughout the US who had a major or minor behavioural change or aggression toward themselves or others. The students were recruited from psychiatry outpatient clinics, inpatient units and emergency departments. The team performed school risk evaluations with participants. Audio recordings from the evaluations were transcribed and manually annotated.
The students, as it turned out, were relatively equally divided between moderate- to high-risk, and low-risk, according to two scales that the team developed and validated in previous research. There were significant differences in total scores between the high-risk and low-risk groups. The machine learning algorithm that the researchers developed achieved an accuracy rate of 91.02 per cent, considered excellent, when using interview content to predict risk of school violence. The rate increased to 91.45 per cent when demographic and socioeconomic data were added.
“The machine learning algorithm, based only on the participant’s interview, was almost as accurate in assessing risk levels as a full assessment by our research team, including gathering information from parents and the school, a review of records when available, and scoring on the two scales we developed,” said Yizhao Ni, a computational scientist at Cincinnati. “Our ultimate goal, should research support it, is to spread the use of the machine learning technology to schools in the future to augment structures, professional judgment to more efficiently and effectively prevent school violence,” said Barzman.
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Talent is a major concern for organizational leaders, given that labor costs and services together now account for a whopping 53 percent of total marketing budgets (Gartner). But even as companies struggle to find humans to manage their newly implemented technologies, many CMOs have yet to define exactly how Artificial Intelligence and Intelligent Automation are meant to transform their business models and processes in the first place.
We’ve talked at length in previous columns about the benefits of finding that synergy between talent and martech, and bridging the talent gap that’s holding up more widespread adoption of machine learning. In this column, we’ll peer through the other side of the looking glass, at the technology profoundly impacting modern marketing employees and how to deploy it at scale.
Understanding the role of AI in IA
We’ve tended to think of technology, particularly software, in terms of automation until very recently, but taking a legacy approach to new tech can only result in lackluster performance.
Automation is a task-driven imperative with a sole purpose: to get machines to do the repetitive tasks we creative, free-thinking humans don’t want or really need to do. Automation performs calculations for us. It improves communication. It runs machinery, assembles product, steers vehicles and more.
Advances in machine learning, natural language processing and other areas of computing are being spurred on by a veritable explosion of data, more than human teams could ever activate. Intelligent automation combines the efficiency-finding, time-saving benefits of straight automation with elements of AI — gathering input, analyzing data, and even making decisions.
Take support requests, for example. Automation can make this traditionally labor-intensive task more manageable by automatically completing predictable, repeatable tasks according to a defined set of rules: autoresponding, flagging for specific departments, applying a priority, designating a status or notifying administrators.
Artificial intelligence adds a critical layer of understanding, a typically human trait. Natural language processing can enable the software to “read” support tickets and make decisions about prioritization, or even respond intelligently to resolve a ticket.
Intelligent automation improves scalability exponentially, while driving customer satisfaction, employee performance and business results. Recent research from KPMG shows that digital-first companies like Amazon have a distinct advantage when it comes to IA, but that shouldn’t hold your organization back. All companies have the opportunity today to evaluate (or re-evaluate) technologies through the lens of business model opportunities and constraints.
Deploying Intelligent Automation in martech
The long, bumpy road of manual marketing processes is receding in the rear-view now, as we hurtle at breakneck speed onto a wide-open freeway of AI-enabled possibility. Here’s what automation in various marketing practices and channels looks like today, and what we’ll see in the not-so-far-off-future.
Automation is the very foundation of email marketing, enabling us to manage more contacts and send a greater volume of email than we’re likely to achieve on our own. We can schedule emails to send at a particular time, create and manage templates and send customized messaging to buckets of subscribers based on the insights we’ve gathered about them.
More intelligent automation will take that personalization to its most granular level — to the individual. Our tech will analyze each subscriber’s email habits to determine the optimal time to send our emails to each person. We’ll be able to target promotions based on each individual’s search and transaction history, as gathered from any number of sources. AI-powered IA will even perform A/B tests and make decisions about subject lines, visuals and calls to action.
Artificial intelligence isn’t new to ad targeting, but GDPR may have an impact on how it develops in the future. Despite that, we’re already finding that CLV (customer lifetime value) is old news. Networks and ad exchanges are now measuring CFV (customer future value), based on predictive analytics that then drives placement recommendations.
Automation gave rise to the programmatic ad industry by automating the process of buying and selling. Now, artificial intelligence is being added to the mix to analyze both first- and third-party data, then make campaign optimization decisions to improve performance. IA will be critical in coming years in managing the massive amount of data available from multiple inputs and across various devices and networks.
It’s going to take a great deal of content to fuel all of this personalization and targeted outreach. As marketing communications become ever-more granular, humans simply won’t be able to keep up with the messaging required to fuel the machines. Imagine the hit your ROI would take if half a page a copy resulted in only two email opens, as it only applied to two prospects?
Perhaps the most controversial aspect of IA on the horizon is the very real prospect that machines will create content. We’re comfortable using automation to distribute, promote and measure content performance. But can humans let go of the wheel and let our technology actually do the creative work of writing an email or a landing page? The answer is yes. According to BrightEdge, over 60 percent of marketers intend to use artificial intelligence (AI) to develop content marketing strategies. Machine learning will be able to create, test, optimize and recreate content personalized for each interaction, based not only on more data points than we could ever hope to wade through, but also on real-time cues from users.
Social media marketing
Outside of the massive potential of social ad targeting, IA will power deeper and more meaningful brand-consumer relationships than ever before. It will be able to analyze content interaction and write social content based on those insights, to share out the most enticing, engaging snippets from each piece. It will determine not only when users are online, but when they’re most likely to be receptive to brand messaging.
IA will quickly and more accurately find and engage influencers, capitalize on real-time interactions and optimize content for sharing. As social listening is increasingly automated, our martech will make smarter decisions about which interactions and input should be distributed internally, enabling brands to make more informed business decisions.
Search engine optimization
While some aspects of SEO are currently automated, it’s still a fairly labor-intensive process requiring human creativity and ongoing management. IA will build on currently automated processes by making recommendations for optimizations and eventually, going ahead and making those optimizations. Imagine meta descriptions that optimize as rankings shift, or image alt text that is written based on the algorithm’s understanding of every other similar image on the internet.
SEO audits are so cumbersome today that many companies never bother with them. But soon they will happen regularly as a background process. New site builds will apply optimizations based on real-time data, rather than best practices and months-old recommendations. Google and other search rankings are dynamic and constantly shifting. In the near future, intelligent SEO software will analyze masses of data, make optimization decisions and measure results, too.
Human + artificial intelligence = Better together
Having the right talent was the single most important factor for driving organizational growth, according to 35.3 percent of CMOs in Deloitte’s 2018 CMO Survey. Technology and data were the primary concerns for 20.8 percent of respondents. It’s fair to say that all three are weighing heavily on CMOs’ minds.