Enterprise AI For Dummies. Zachary Jarvinen
reduced false negative by 9.4 percent and false positives by 5.7 percent.
Network intrusion detection and prevention
Hackers and cyber-crime date back to the 1970s, but things have changed a lot since Matthew Broderick hacked into a government computer to impress a girl and almost annihilated the planet in War Games. (Spoiler alert: Regarding playing the game, the computer comes to its conclusion through the use of reinforcement learning, the same AI technique used to train computers to beat human experts at chess and Go, and to train robots to walk.)
Back in the real world, it took the proliferation of the Internet and the dawn of e-commerce to provide the incentive for electronic malfeasance on a global scale. Conventional network intrusion detection systems (NIDS) and network intrusion prevention systems (NIPS) detect and prevent network attacks.
However, these systems have a significant usability issue in the triggering of false positives, marking legitimate traffic or behavior as an attack and requiring human intervention to respond to the anomaly and mark it as safe. A 2018 SANS survey found that in the face of a false positive rate of 50 percent, many security teams have taken to tuning the security settings to reduce the number of alerts. The problem with this practice is the potential to increase the number of false negatives, identifying a breach as harmless traffic — and it only takes one breach to cause a world of hurt.
As early as the mid-1990s, designers began exploring AI techniques, including unsupervised machine learning and artificial neural networks, to improve protection while reducing the need for human intervention. AI offers these capabilities:
AI leverages supervised learning and, especially, artificial neural networks to build a massive library of markers of hostile code, and then it scans incoming data for matches.
AI uses machine learning and security analytics, including user and entity behavior analytics, to detect external and internal risks earlier and more accurately than a traditional rules-based approach.
Some systems use natural-language processing to repel text-based attempts to trick users into replying with sensitive information via email and messaging by pretending to be from a legitimate source, such as a tech support agent, bank, or government agency, also known as phishing attacks.
Fraud protection and prevention
Anyone who has seen It’s a Wonderful Life remembers how nervous everyone became when the bank examiner showed up at Bailey Building and Loan. He walked in with a briefcase and spent hours going over financial statements. These days, he might show up with a laptop and a scanner and let the algorithms process the paperwork and identify anomalies and violations of federal and state regulations.
As a field that is driven by well-defined practices, structured data, and pro forma documents and reports, the finance sector is well aligned for automation, and in light of the financial crises of 2000 and 2008, the emergence of practical AI is timely. The “2018 AFCE Global Study on Occupational Fraud and Abuse” reported a loss of $7 billion due to fraud in 2018 alone.
Recent approaches to fraud prevention use a range of AI tools, including data mining, supervised and unsupervised machine learning, behavioral analysis, link analysis, regression, decision trees, neural networks, and Bayesian networks.
Over the past five decades, AI has evolved from offering lightweight consumer applications or add-ons to supplementing and performing mission-critical and even life-or-death functions. The next sections introduce some of the ways AI is changing the landscape for the enterprise.
Benefits of AI for Your Enterprise
In addition, when an algorithm produces results with high accuracy and predictability, mundane processes and routine decisions can be automated, thus reducing the need for human intervention in the paper chase of the typical enterprise and freeing workers to focus on tasks that increase revenue and customer satisfaction.
This section provides a bite-sized overview of industries that can derive specific benefits from implementing AI. Later chapters explore use cases for each in depth.
Healthcare
It’s hard to find an industry more bogged down in data than healthcare. With the advent of the electronic health record, doctors often spend more time on paperwork and computers than with their patients.
In a 2016 American Medical Association study, doctors spent 27 percent of their time on “direct clinical face time with patients” and 49 percent at their desk and on the computer. Even worse, while in the examination room, only 53 percent of that time was spent interacting with the patient and 37 percent was spent on the computer.
A 2017 American College of Healthcare study found that doctors spend the same amount of time focused on the computer as they do on the patients.
A 2017 Summer Student Research and Clinical Assistantship study found that during an 11-hour workday, doctors spent 6 of those hours entering data into the electronic health records system.
The good news is that AI is changing that equation. Healthcare is a data-rich environment, which makes it a prime target for AI:
Natural-language processing can extract targeted information from unstructured text such as faxes and clinical notes to improve end-to-end workflow, from content ingestion to classification, routing documents to the appropriate backend systems, spotting exceptions, validating edge cases, and creating action items.
Data mining can accelerate medical diagnosis. In a 2017 American Academy of Neurology study, AI diagnosed a glioblastoma tumor specimen with actionable recommendations within 10 minutes, while human analysis took an estimated 160 hours of person-time.
Artificial neural networks can successfully triage X-rays. In a 2019 Radiology Journal study, the team trained an artificial neural network model with 470,300 adult chest X-rays and then tested the model with 15,887 chest X-rays. The model was highly accurate, and the average reporting delay was reduced from 11.2 to 2.7 days for critical imaging findings and from 7.6 to 4.1 days for urgent imaging findings compared with historical data.
Speech analytics can identify, from how someone speaks, a traumatic brain injury, depression, post-traumatic stress disorder (PTSD), or even heart disease.
Manufacturing
If any system is ripe for transferring the tedious work to intelligent agents, it’s a system of thousands of moving parts that must be monitored and maintained to optimize performance. By combining remote sensors and the Internet of Things with AI to adjust performance and workflows within